diff --git a/.github/workflows/build_documentation.yml b/.github/workflows/build_documentation.yml index b7d1f895..167b7d61 100644 --- a/.github/workflows/build_documentation.yml +++ b/.github/workflows/build_documentation.yml @@ -16,5 +16,6 @@ jobs: package_name: timm repo_owner: rwightman path_to_docs: pytorch-image-models/hfdocs/source + version_tag_suffix: "" secrets: token: ${{ secrets.HUGGINGFACE_PUSH }} \ No newline at end of file diff --git a/.github/workflows/build_pr_documentation.yml b/.github/workflows/build_pr_documentation.yml index 4b1b5c9d..2b44619f 100644 --- a/.github/workflows/build_pr_documentation.yml +++ b/.github/workflows/build_pr_documentation.yml @@ -17,3 +17,4 @@ jobs: package_name: timm repo_owner: rwightman path_to_docs: pytorch-image-models/hfdocs/source + version_tag_suffix: "" diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 5690c88c..70352d0a 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -40,9 +40,10 @@ jobs: - name: Install torch on ubuntu if: startsWith(matrix.os, 'ubuntu') run: | - pip install --no-cache-dir torch==${{ matrix.torch }}+cpu torchvision==${{ matrix.torchvision }}+cpu -f https://download.pytorch.org/whl/torch_stable.html + sudo sed -i 's/azure\.//' /etc/apt/sources.list sudo apt update sudo apt install -y google-perftools + pip install --no-cache-dir torch==${{ matrix.torch }}+cpu torchvision==${{ matrix.torchvision }}+cpu -f https://download.pytorch.org/whl/torch_stable.html - name: Install requirements run: | pip install -r requirements.txt diff --git a/.gitignore b/.gitignore index e5142b32..9f8f33d9 100644 --- a/.gitignore +++ b/.gitignore @@ -106,6 +106,16 @@ output/ *.tar *.pth *.pt +*.torch *.gz Untitled.ipynb Testing notebook.ipynb + +# Root dir exclusions +/*.csv +/*.yaml +/*.json +/*.jpg +/*.png +/*.zip +/*.tar.* \ No newline at end of file diff --git a/README.md b/README.md index bb6485c0..ee07c368 100644 --- a/README.md +++ b/README.md @@ -21,12 +21,36 @@ And a big thanks to all GitHub sponsors who helped with some of my costs before ## What's New -### πŸ€— Survey: Feedback Appreciated πŸ€— - -For a few months now, `timm` has been part of the Hugging Face ecosystem. Yearly, we survey users of our tools to see what we could do better, what we need to continue doing, or what we need to stop doing. - -If you have a couple of minutes and want to participate in shaping the future of the ecosystem, please share your thoughts: -[**hf.co/oss-survey**](https://hf.co/oss-survey) πŸ™ +* ❗Updates after Oct 10, 2022 are available in 0.8.x pre-releases (`pip install --pre timm`) or cloning main❗ +* Stable releases are 0.6.x and available by normal pip install or clone from [0.6.x](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) branch. + +### Jan 11, 2023 +* Update ConvNeXt ImageNet-12k pretrain series w/ two new fine-tuned weights (and pre FT `.in12k` tags) + * `convnext_nano.in12k_ft_in1k` - 82.3 @ 224, 82.9 @ 288 (previously released) + * `convnext_tiny.in12k_ft_in1k` - 84.2 @ 224, 84.5 @ 288 + * `convnext_small.in12k_ft_in1k` - 85.2 @ 224, 85.3 @ 288 + +### Jan 6, 2023 +* Finally got around to adding `--model-kwargs` and `--opt-kwargs` to scripts to pass through rare args directly to model classes from cmd line + * `train.py /imagenet --model resnet50 --amp --model-kwargs output_stride=16 act_layer=silu` + * `train.py /imagenet --model vit_base_patch16_clip_224 --img-size 240 --amp --model-kwargs img_size=240 patch_size=12` +* Cleanup some popular models to better support arg passthrough / merge with model configs, more to go. + +### Jan 5, 2023 +* ConvNeXt-V2 models and weights added to existing `convnext.py` + * Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808) + * Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC) + +### Dec 23, 2022 πŸŽ„β˜ƒ +* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013) + * NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP +* Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit) +* More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use) +* More ImageNet-12k (subset of 22k) pretrain models popping up: + * `efficientnet_b5.in12k_ft_in1k` - 85.9 @ 448x448 + * `vit_medium_patch16_gap_384.in12k_ft_in1k` - 85.5 @ 384x384 + * `vit_medium_patch16_gap_256.in12k_ft_in1k` - 84.5 @ 256x256 + * `convnext_nano.in12k_ft_in1k` - 82.9 @ 288x288 ### Dec 8, 2022 * Add 'EVA l' to `vision_transformer.py`, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some) @@ -325,46 +349,6 @@ More models, more fixes * TinyNet models added by [rsomani95](https://github.com/rsomani95) * LCNet added via MobileNetV3 architecture -### Nov 22, 2021 -* A number of updated weights anew new model defs - * `eca_halonext26ts` - 79.5 @ 256 - * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288 - * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth)) - * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288 - * `sebotnet33ts_256` (new) - 81.2 @ 224 - * `lamhalobotnet50ts_256` - 81.5 @ 256 - * `halonet50ts` - 81.7 @ 256 - * `halo2botnet50ts_256` - 82.0 @ 256 - * `resnet101` - 82.0 @ 224, 82.8 @ 288 - * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288 - * `resnet152` - 82.8 @ 224, 83.5 @ 288 - * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320 - * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320 -* `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris) -* Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare) - * models updated for tracing compatibility (almost full support with some distlled transformer exceptions) - -### Oct 19, 2021 -* ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights) -* BCE loss and Repeated Augmentation support for RSB paper -* 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl): - * Halo (https://arxiv.org/abs/2103.12731) - * Bottleneck Transformer (https://arxiv.org/abs/2101.11605) - * LambdaNetworks (https://arxiv.org/abs/2102.08602) -* A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added -* freeze/unfreeze helpers by [Alexander Soare](https://github.com/alexander-soare) - -### Aug 18, 2021 -* Optimizer bonanza! - * Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ `timm bits` [branch](https://github.com/rwightman/pytorch-image-models/tree/bits_and_tpu/timm/bits)) - * Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA) - * Some cleanup on all optimizers and factory. No more `.data`, a bit more consistency, unit tests for all! - * SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself). -* EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights. -* Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested. - ## Introduction Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results. @@ -385,6 +369,7 @@ A full version of the list below with source links can be found in the [document * CoaT (Co-Scale Conv-Attentional Image Transformers) - https://arxiv.org/abs/2104.06399 * CoAtNet (Convolution and Attention) - https://arxiv.org/abs/2106.04803 * ConvNeXt - https://arxiv.org/abs/2201.03545 +* ConvNeXt-V2 - http://arxiv.org/abs/2301.00808 * ConViT (Soft Convolutional Inductive Biases Vision Transformers)- https://arxiv.org/abs/2103.10697 * CspNet (Cross-Stage Partial Networks) - https://arxiv.org/abs/1911.11929 * DeiT - https://arxiv.org/abs/2012.12877 @@ -407,6 +392,7 @@ A full version of the list below with source links can be found in the [document * Single-Path NAS - https://arxiv.org/abs/1904.02877 * TinyNet - https://arxiv.org/abs/2010.14819 * EVA - https://arxiv.org/abs/2211.07636 +* FlexiViT - https://arxiv.org/abs/2212.08013 * GCViT (Global Context Vision Transformer) - https://arxiv.org/abs/2206.09959 * GhostNet - https://arxiv.org/abs/1911.11907 * gMLP - https://arxiv.org/abs/2105.08050 diff --git a/benchmark.py b/benchmark.py index 58435ff8..2cce3e2c 100755 --- a/benchmark.py +++ b/benchmark.py @@ -22,7 +22,7 @@ from timm.data import resolve_data_config from timm.layers import set_fast_norm from timm.models import create_model, is_model, list_models from timm.optim import create_optimizer_v2 -from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry +from timm.utils import setup_default_logging, set_jit_fuser, decay_batch_step, check_batch_size_retry, ParseKwargs has_apex = False try: @@ -108,12 +108,15 @@ parser.add_argument('--grad-checkpointing', action='store_true', default=False, help='Enable gradient checkpointing through model blocks/stages') parser.add_argument('--amp', action='store_true', default=False, help='use PyTorch Native AMP for mixed precision training. Overrides --precision arg.') +parser.add_argument('--amp-dtype', default='float16', type=str, + help='lower precision AMP dtype (default: float16). Overrides --precision arg if args.amp True.') parser.add_argument('--precision', default='float32', type=str, help='Numeric precision. One of (amp, float32, float16, bfloat16, tf32)') parser.add_argument('--fuser', default='', type=str, help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") parser.add_argument('--fast-norm', default=False, action='store_true', help='enable experimental fast-norm') +parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs) # codegen (model compilation) options scripting_group = parser.add_mutually_exclusive_group() @@ -124,7 +127,6 @@ scripting_group.add_argument('--torchcompile', nargs='?', type=str, default=None scripting_group.add_argument('--aot-autograd', default=False, action='store_true', help="Enable AOT Autograd optimization.") - # train optimizer parameters parser.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', help='Optimizer (default: "sgd"') @@ -168,19 +170,21 @@ def count_params(model: nn.Module): def resolve_precision(precision: str): - assert precision in ('amp', 'float16', 'bfloat16', 'float32') - use_amp = False + assert precision in ('amp', 'amp_bfloat16', 'float16', 'bfloat16', 'float32') + amp_dtype = None # amp disabled model_dtype = torch.float32 data_dtype = torch.float32 if precision == 'amp': - use_amp = True + amp_dtype = torch.float16 + elif precision == 'amp_bfloat16': + amp_dtype = torch.bfloat16 elif precision == 'float16': model_dtype = torch.float16 data_dtype = torch.float16 elif precision == 'bfloat16': model_dtype = torch.bfloat16 data_dtype = torch.bfloat16 - return use_amp, model_dtype, data_dtype + return amp_dtype, model_dtype, data_dtype def profile_deepspeed(model, input_size=(3, 224, 224), batch_size=1, detailed=False): @@ -228,9 +232,12 @@ class BenchmarkRunner: self.model_name = model_name self.detail = detail self.device = device - self.use_amp, self.model_dtype, self.data_dtype = resolve_precision(precision) + self.amp_dtype, self.model_dtype, self.data_dtype = resolve_precision(precision) self.channels_last = kwargs.pop('channels_last', False) - self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=torch.float16) if self.use_amp else suppress + if self.amp_dtype is not None: + self.amp_autocast = partial(torch.cuda.amp.autocast, dtype=self.amp_dtype) + else: + self.amp_autocast = suppress if fuser: set_jit_fuser(fuser) @@ -243,6 +250,7 @@ class BenchmarkRunner: drop_rate=kwargs.pop('drop', 0.), drop_path_rate=kwargs.pop('drop_path', None), drop_block_rate=kwargs.pop('drop_block', None), + **kwargs.pop('model_kwargs', {}), ) self.model.to( device=self.device, @@ -560,7 +568,7 @@ def _try_run( def benchmark(args): if args.amp: _logger.warning("Overriding precision to 'amp' since --amp flag set.") - args.precision = 'amp' + args.precision = 'amp' if args.amp_dtype == 'float16' else '_'.join(['amp', args.amp_dtype]) _logger.info(f'Benchmarking in {args.precision} precision. ' f'{"NHWC" if args.channels_last else "NCHW"} layout. ' f'torchscript {"enabled" if args.torchscript else "disabled"}') diff --git a/docs/archived_changes.md b/docs/archived_changes.md index 9c2b62b6..35f84bc4 100644 --- a/docs/archived_changes.md +++ b/docs/archived_changes.md @@ -1,5 +1,45 @@ # Archived Changes +### Nov 22, 2021 +* A number of updated weights anew new model defs + * `eca_halonext26ts` - 79.5 @ 256 + * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288 + * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth)) + * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288 + * `sebotnet33ts_256` (new) - 81.2 @ 224 + * `lamhalobotnet50ts_256` - 81.5 @ 256 + * `halonet50ts` - 81.7 @ 256 + * `halo2botnet50ts_256` - 82.0 @ 256 + * `resnet101` - 82.0 @ 224, 82.8 @ 288 + * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288 + * `resnet152` - 82.8 @ 224, 83.5 @ 288 + * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320 + * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320 +* `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris) +* Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare) + * models updated for tracing compatibility (almost full support with some distlled transformer exceptions) + +### Oct 19, 2021 +* ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights) +* BCE loss and Repeated Augmentation support for RSB paper +* 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) +* Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl): + * Halo (https://arxiv.org/abs/2103.12731) + * Bottleneck Transformer (https://arxiv.org/abs/2101.11605) + * LambdaNetworks (https://arxiv.org/abs/2102.08602) +* A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) +* ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added +* freeze/unfreeze helpers by [Alexander Soare](https://github.com/alexander-soare) + +### Aug 18, 2021 +* Optimizer bonanza! + * Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ `timm bits` [branch](https://github.com/rwightman/pytorch-image-models/tree/bits_and_tpu/timm/bits)) + * Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA) + * Some cleanup on all optimizers and factory. No more `.data`, a bit more consistency, unit tests for all! + * SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself). +* EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights. +* Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested. + ### July 12, 2021 * Add XCiT models from [official facebook impl](https://github.com/facebookresearch/xcit). Contributed by [Alexander Soare](https://github.com/alexander-soare) diff --git a/docs/changes.md b/docs/changes.md index 800dc443..edf88c62 100644 --- a/docs/changes.md +++ b/docs/changes.md @@ -1,4 +1,183 @@ # Recent Changes +### Jan 5, 2023 +* ConvNeXt-V2 models and weights added to existing `convnext.py` + * Paper: [ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders](http://arxiv.org/abs/2301.00808) + * Reference impl: https://github.com/facebookresearch/ConvNeXt-V2 (NOTE: weights currently CC-BY-NC) + +### Dec 23, 2022 πŸŽ„β˜ƒ +* Add FlexiViT models and weights from https://github.com/google-research/big_vision (check out paper at https://arxiv.org/abs/2212.08013) + * NOTE currently resizing is static on model creation, on-the-fly dynamic / train patch size sampling is a WIP +* Many more models updated to multi-weight and downloadable via HF hub now (convnext, efficientnet, mobilenet, vision_transformer*, beit) +* More model pretrained tag and adjustments, some model names changed (working on deprecation translations, consider main branch DEV branch right now, use 0.6.x for stable use) +* More ImageNet-12k (subset of 22k) pretrain models popping up: + * `efficientnet_b5.in12k_ft_in1k` - 85.9 @ 448x448 + * `vit_medium_patch16_gap_384.in12k_ft_in1k` - 85.5 @ 384x384 + * `vit_medium_patch16_gap_256.in12k_ft_in1k` - 84.5 @ 256x256 + * `convnext_nano.in12k_ft_in1k` - 82.9 @ 288x288 + +### Dec 8, 2022 +* Add 'EVA l' to `vision_transformer.py`, MAE style ViT-L/14 MIM pretrain w/ EVA-CLIP targets, FT on ImageNet-1k (w/ ImageNet-22k intermediate for some) + * original source: https://github.com/baaivision/EVA + +| model | top1 | param_count | gmac | macts | hub | +|:------------------------------------------|-----:|------------:|------:|------:|:----------------------------------------| +| eva_large_patch14_336.in22k_ft_in22k_in1k | 89.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | +| eva_large_patch14_336.in22k_ft_in1k | 88.7 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/BAAI/EVA) | +| eva_large_patch14_196.in22k_ft_in22k_in1k | 88.6 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | +| eva_large_patch14_196.in22k_ft_in1k | 87.9 | 304.1 | 61.6 | 63.5 | [link](https://huggingface.co/BAAI/EVA) | + +### Dec 6, 2022 +* Add 'EVA g', BEiT style ViT-g/14 model weights w/ both MIM pretrain and CLIP pretrain to `beit.py`. + * original source: https://github.com/baaivision/EVA + * paper: https://arxiv.org/abs/2211.07636 + +| model | top1 | param_count | gmac | macts | hub | +|:-----------------------------------------|-------:|--------------:|-------:|--------:|:----------------------------------------| +| eva_giant_patch14_560.m30m_ft_in22k_in1k | 89.8 | 1014.4 | 1906.8 | 2577.2 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_336.m30m_ft_in22k_in1k | 89.6 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_336.clip_ft_in1k | 89.4 | 1013 | 620.6 | 550.7 | [link](https://huggingface.co/BAAI/EVA) | +| eva_giant_patch14_224.clip_ft_in1k | 89.1 | 1012.6 | 267.2 | 192.6 | [link](https://huggingface.co/BAAI/EVA) | + +### Dec 5, 2022 + +* Pre-release (`0.8.0dev0`) of multi-weight support (`model_arch.pretrained_tag`). Install with `pip install --pre timm` + * vision_transformer, maxvit, convnext are the first three model impl w/ support + * model names are changing with this (previous _21k, etc. fn will merge), still sorting out deprecation handling + * bugs are likely, but I need feedback so please try it out + * if stability is needed, please use 0.6.x pypi releases or clone from [0.6.x branch](https://github.com/rwightman/pytorch-image-models/tree/0.6.x) +* Support for PyTorch 2.0 compile is added in train/validate/inference/benchmark, use `--torchcompile` argument +* Inference script allows more control over output, select k for top-class index + prob json, csv or parquet output +* Add a full set of fine-tuned CLIP image tower weights from both LAION-2B and original OpenAI CLIP models + +| model | top1 | param_count | gmac | macts | hub | +|:-------------------------------------------------|-------:|--------------:|-------:|--------:|:-------------------------------------------------------------------------------------| +| vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k | 88.6 | 632.5 | 391 | 407.5 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k) | +| vit_large_patch14_clip_336.openai_ft_in12k_in1k | 88.3 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k) | +| vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k | 88.2 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k) | +| vit_large_patch14_clip_336.laion2b_ft_in12k_in1k | 88.2 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k) | +| vit_large_patch14_clip_224.openai_ft_in12k_in1k | 88.2 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k) | +| vit_large_patch14_clip_224.laion2b_ft_in12k_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in12k_in1k) | +| vit_large_patch14_clip_224.openai_ft_in1k | 87.9 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.openai_ft_in1k) | +| vit_large_patch14_clip_336.laion2b_ft_in1k | 87.9 | 304.5 | 191.1 | 270.2 | [link](https://huggingface.co/timm/vit_large_patch14_clip_336.laion2b_ft_in1k) | +| vit_huge_patch14_clip_224.laion2b_ft_in1k | 87.6 | 632 | 167.4 | 139.4 | [link](https://huggingface.co/timm/vit_huge_patch14_clip_224.laion2b_ft_in1k) | +| vit_large_patch14_clip_224.laion2b_ft_in1k | 87.3 | 304.2 | 81.1 | 88.8 | [link](https://huggingface.co/timm/vit_large_patch14_clip_224.laion2b_ft_in1k) | +| vit_base_patch16_clip_384.laion2b_ft_in12k_in1k | 87.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k) | +| vit_base_patch16_clip_384.openai_ft_in12k_in1k | 87 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in12k_in1k) | +| vit_base_patch16_clip_384.laion2b_ft_in1k | 86.6 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.laion2b_ft_in1k) | +| vit_base_patch16_clip_384.openai_ft_in1k | 86.2 | 86.9 | 55.5 | 101.6 | [link](https://huggingface.co/timm/vit_base_patch16_clip_384.openai_ft_in1k) | +| vit_base_patch16_clip_224.laion2b_ft_in12k_in1k | 86.2 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in12k_in1k) | +| vit_base_patch16_clip_224.openai_ft_in12k_in1k | 85.9 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in12k_in1k) | +| vit_base_patch32_clip_448.laion2b_ft_in12k_in1k | 85.8 | 88.3 | 17.9 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k) | +| vit_base_patch16_clip_224.laion2b_ft_in1k | 85.5 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.laion2b_ft_in1k) | +| vit_base_patch32_clip_384.laion2b_ft_in12k_in1k | 85.4 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k) | +| vit_base_patch16_clip_224.openai_ft_in1k | 85.3 | 86.6 | 17.6 | 23.9 | [link](https://huggingface.co/timm/vit_base_patch16_clip_224.openai_ft_in1k) | +| vit_base_patch32_clip_384.openai_ft_in12k_in1k | 85.2 | 88.3 | 13.1 | 16.5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_384.openai_ft_in12k_in1k) | +| vit_base_patch32_clip_224.laion2b_ft_in12k_in1k | 83.3 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in12k_in1k) | +| vit_base_patch32_clip_224.laion2b_ft_in1k | 82.6 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.laion2b_ft_in1k) | +| vit_base_patch32_clip_224.openai_ft_in1k | 81.9 | 88.2 | 4.4 | 5 | [link](https://huggingface.co/timm/vit_base_patch32_clip_224.openai_ft_in1k) | + +* Port of MaxViT Tensorflow Weights from official impl at https://github.com/google-research/maxvit + * There was larger than expected drops for the upscaled 384/512 in21k fine-tune weights, possible detail missing, but the 21k FT did seem sensitive to small preprocessing + +| model | top1 | param_count | gmac | macts | hub | +|:-----------------------------------|-------:|--------------:|-------:|--------:|:-----------------------------------------------------------------------| +| maxvit_xlarge_tf_512.in21k_ft_in1k | 88.5 | 475.8 | 534.1 | 1413.2 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_512.in21k_ft_in1k) | +| maxvit_xlarge_tf_384.in21k_ft_in1k | 88.3 | 475.3 | 292.8 | 668.8 | [link](https://huggingface.co/timm/maxvit_xlarge_tf_384.in21k_ft_in1k) | +| maxvit_base_tf_512.in21k_ft_in1k | 88.2 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in21k_ft_in1k) | +| maxvit_large_tf_512.in21k_ft_in1k | 88 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in21k_ft_in1k) | +| maxvit_large_tf_384.in21k_ft_in1k | 88 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in21k_ft_in1k) | +| maxvit_base_tf_384.in21k_ft_in1k | 87.9 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in21k_ft_in1k) | +| maxvit_base_tf_512.in1k | 86.6 | 119.9 | 138 | 704 | [link](https://huggingface.co/timm/maxvit_base_tf_512.in1k) | +| maxvit_large_tf_512.in1k | 86.5 | 212.3 | 244.8 | 942.2 | [link](https://huggingface.co/timm/maxvit_large_tf_512.in1k) | +| maxvit_base_tf_384.in1k | 86.3 | 119.6 | 73.8 | 332.9 | [link](https://huggingface.co/timm/maxvit_base_tf_384.in1k) | +| maxvit_large_tf_384.in1k | 86.2 | 212 | 132.6 | 445.8 | [link](https://huggingface.co/timm/maxvit_large_tf_384.in1k) | +| maxvit_small_tf_512.in1k | 86.1 | 69.1 | 67.3 | 383.8 | [link](https://huggingface.co/timm/maxvit_small_tf_512.in1k) | +| maxvit_tiny_tf_512.in1k | 85.7 | 31 | 33.5 | 257.6 | [link](https://huggingface.co/timm/maxvit_tiny_tf_512.in1k) | +| maxvit_small_tf_384.in1k | 85.5 | 69 | 35.9 | 183.6 | [link](https://huggingface.co/timm/maxvit_small_tf_384.in1k) | +| maxvit_tiny_tf_384.in1k | 85.1 | 31 | 17.5 | 123.4 | [link](https://huggingface.co/timm/maxvit_tiny_tf_384.in1k) | +| maxvit_large_tf_224.in1k | 84.9 | 211.8 | 43.7 | 127.4 | [link](https://huggingface.co/timm/maxvit_large_tf_224.in1k) | +| maxvit_base_tf_224.in1k | 84.9 | 119.5 | 24 | 95 | [link](https://huggingface.co/timm/maxvit_base_tf_224.in1k) | +| maxvit_small_tf_224.in1k | 84.4 | 68.9 | 11.7 | 53.2 | [link](https://huggingface.co/timm/maxvit_small_tf_224.in1k) | +| maxvit_tiny_tf_224.in1k | 83.4 | 30.9 | 5.6 | 35.8 | [link](https://huggingface.co/timm/maxvit_tiny_tf_224.in1k) | + +### Oct 15, 2022 +* Train and validation script enhancements +* Non-GPU (ie CPU) device support +* SLURM compatibility for train script +* HF datasets support (via ReaderHfds) +* TFDS/WDS dataloading improvements (sample padding/wrap for distributed use fixed wrt sample count estimate) +* in_chans !=3 support for scripts / loader +* Adan optimizer +* Can enable per-step LR scheduling via args +* Dataset 'parsers' renamed to 'readers', more descriptive of purpose +* AMP args changed, APEX via `--amp-impl apex`, bfloat16 supportedf via `--amp-dtype bfloat16` +* main branch switched to 0.7.x version, 0.6x forked for stable release of weight only adds +* master -> main branch rename + +### Oct 10, 2022 +* More weights in `maxxvit` series, incl first ConvNeXt block based `coatnext` and `maxxvit` experiments: + * `coatnext_nano_rw_224` - 82.0 @ 224 (G) -- (uses ConvNeXt conv block, no BatchNorm) + * `maxxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.7 @ 320 (G) (uses ConvNeXt conv block, no BN) + * `maxvit_rmlp_small_rw_224` - 84.5 @ 224, 85.1 @ 320 (G) + * `maxxvit_rmlp_small_rw_256` - 84.6 @ 256, 84.9 @ 288 (G) -- could be trained better, hparams need tuning (uses ConvNeXt block, no BN) + * `coatnet_rmlp_2_rw_224` - 84.6 @ 224, 85 @ 320 (T) + * NOTE: official MaxVit weights (in1k) have been released at https://github.com/google-research/maxvit -- some extra work is needed to port and adapt since my impl was created independently of theirs and has a few small differences + the whole TF same padding fun. + +### Sept 23, 2022 +* LAION-2B CLIP image towers supported as pretrained backbones for fine-tune or features (no classifier) + * vit_base_patch32_224_clip_laion2b + * vit_large_patch14_224_clip_laion2b + * vit_huge_patch14_224_clip_laion2b + * vit_giant_patch14_224_clip_laion2b + +### Sept 7, 2022 +* Hugging Face [`timm` docs](https://huggingface.co/docs/hub/timm) home now exists, look for more here in the future +* Add BEiT-v2 weights for base and large 224x224 models from https://github.com/microsoft/unilm/tree/master/beit2 +* Add more weights in `maxxvit` series incl a `pico` (7.5M params, 1.9 GMACs), two `tiny` variants: + * `maxvit_rmlp_pico_rw_256` - 80.5 @ 256, 81.3 @ 320 (T) + * `maxvit_tiny_rw_224` - 83.5 @ 224 (G) + * `maxvit_rmlp_tiny_rw_256` - 84.2 @ 256, 84.8 @ 320 (T) + +### Aug 29, 2022 +* MaxVit window size scales with img_size by default. Add new RelPosMlp MaxViT weight that leverages this: + * `maxvit_rmlp_nano_rw_256` - 83.0 @ 256, 83.6 @ 320 (T) + +### Aug 26, 2022 +* CoAtNet (https://arxiv.org/abs/2106.04803) and MaxVit (https://arxiv.org/abs/2204.01697) `timm` original models + * both found in [`maxxvit.py`](https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/maxxvit.py) model def, contains numerous experiments outside scope of original papers + * an unfinished Tensorflow version from MaxVit authors can be found https://github.com/google-research/maxvit +* Initial CoAtNet and MaxVit timm pretrained weights (working on more): + * `coatnet_nano_rw_224` - 81.7 @ 224 (T) + * `coatnet_rmlp_nano_rw_224` - 82.0 @ 224, 82.8 @ 320 (T) + * `coatnet_0_rw_224` - 82.4 (T) -- NOTE timm '0' coatnets have 2 more 3rd stage blocks + * `coatnet_bn_0_rw_224` - 82.4 (T) + * `maxvit_nano_rw_256` - 82.9 @ 256 (T) + * `coatnet_rmlp_1_rw_224` - 83.4 @ 224, 84 @ 320 (T) + * `coatnet_1_rw_224` - 83.6 @ 224 (G) + * (T) = TPU trained with `bits_and_tpu` branch training code, (G) = GPU trained +* GCVit (weights adapted from https://github.com/NVlabs/GCVit, code 100% `timm` re-write for license purposes) +* MViT-V2 (multi-scale vit, adapted from https://github.com/facebookresearch/mvit) +* EfficientFormer (adapted from https://github.com/snap-research/EfficientFormer) +* PyramidVisionTransformer-V2 (adapted from https://github.com/whai362/PVT) +* 'Fast Norm' support for LayerNorm and GroupNorm that avoids float32 upcast w/ AMP (uses APEX LN if available for further boost) + + +### Aug 15, 2022 +* ConvNeXt atto weights added + * `convnext_atto` - 75.7 @ 224, 77.0 @ 288 + * `convnext_atto_ols` - 75.9 @ 224, 77.2 @ 288 + +### Aug 5, 2022 +* More custom ConvNeXt smaller model defs with weights + * `convnext_femto` - 77.5 @ 224, 78.7 @ 288 + * `convnext_femto_ols` - 77.9 @ 224, 78.9 @ 288 + * `convnext_pico` - 79.5 @ 224, 80.4 @ 288 + * `convnext_pico_ols` - 79.5 @ 224, 80.5 @ 288 + * `convnext_nano_ols` - 80.9 @ 224, 81.6 @ 288 +* Updated EdgeNeXt to improve ONNX export, add new base variant and weights from original (https://github.com/mmaaz60/EdgeNeXt) + +### July 28, 2022 +* Add freshly minted DeiT-III Medium (width=512, depth=12, num_heads=8) model weights. Thanks [Hugo Touvron](https://github.com/TouvronHugo)! ### July 27, 2022 * All runtime benchmark and validation result csv files are up-to-date! @@ -133,42 +312,3 @@ More models, more fixes * TinyNet models added by [rsomani95](https://github.com/rsomani95) * LCNet added via MobileNetV3 architecture -### Nov 22, 2021 -* A number of updated weights anew new model defs - * `eca_halonext26ts` - 79.5 @ 256 - * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288 - * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth)) - * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288 - * `sebotnet33ts_256` (new) - 81.2 @ 224 - * `lamhalobotnet50ts_256` - 81.5 @ 256 - * `halonet50ts` - 81.7 @ 256 - * `halo2botnet50ts_256` - 82.0 @ 256 - * `resnet101` - 82.0 @ 224, 82.8 @ 288 - * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288 - * `resnet152` - 82.8 @ 224, 83.5 @ 288 - * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320 - * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320 -* `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris) -* Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare) - * models updated for tracing compatibility (almost full support with some distlled transformer exceptions) - -### Oct 19, 2021 -* ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights) -* BCE loss and Repeated Augmentation support for RSB paper -* 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl): - * Halo (https://arxiv.org/abs/2103.12731) - * Bottleneck Transformer (https://arxiv.org/abs/2101.11605) - * LambdaNetworks (https://arxiv.org/abs/2102.08602) -* A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added -* freeze/unfreeze helpers by [Alexander Soare](https://github.com/alexander-soare) - -### Aug 18, 2021 -* Optimizer bonanza! - * Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ `timm bits` [branch](https://github.com/rwightman/pytorch-image-models/tree/bits_and_tpu/timm/bits)) - * Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA) - * Some cleanup on all optimizers and factory. No more `.data`, a bit more consistency, unit tests for all! - * SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself). -* EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights. -* Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested. diff --git a/hfdocs/README.md b/hfdocs/README.md new file mode 100644 index 00000000..a0178812 --- /dev/null +++ b/hfdocs/README.md @@ -0,0 +1,14 @@ +# Hugging Face Timm Docs + +## Getting Started + +``` +pip install git+https://github.com/huggingface/doc-builder.git@main#egg=hf-doc-builder +pip install watchdog black +``` + +## Preview the Docs Locally + +``` +doc-builder preview timm hfdocs/source +``` diff --git a/hfdocs/source/_toctree.yml b/hfdocs/source/_toctree.yml index 3fa994b6..9af48fdc 100644 --- a/hfdocs/source/_toctree.yml +++ b/hfdocs/source/_toctree.yml @@ -1,149 +1,160 @@ - sections: - local: index - title: Pytorch Image Models (timm) + title: Home + - local: quickstart + title: Quickstart + - local: installation + title: Installation + title: Get started +- sections: + - local: feature_extraction + title: Using Pretrained Models as Feature Extractors + - local: training_script + title: Training With The Official Training Script + - local: hf_hub + title: Share and Load Models from the πŸ€— Hugging Face Hub + title: Tutorials +- sections: - local: models title: Model Summaries - local: results title: Results - - local: scripts - title: Scripts - - local: training_hparam_examples - title: Training Examples - - local: feature_extraction - title: Feature Extraction - - local: changes - title: Recent Changes - - local: archived_changes - title: Archived Changes - - local: model_pages - title: Model Pages - isExpanded: false - sections: - - local: models/adversarial-inception-v3 - title: Adversarial Inception v3 - - local: models/advprop - title: AdvProp (EfficientNet) - - local: models/big-transfer - title: Big Transfer (BiT) - - local: models/csp-darknet - title: CSP-DarkNet - - local: models/csp-resnet - title: CSP-ResNet - - local: models/csp-resnext - title: CSP-ResNeXt - - local: models/densenet - title: DenseNet - - local: models/dla - title: Deep Layer Aggregation - - local: models/dpn - title: Dual Path Network (DPN) - - local: models/ecaresnet - title: ECA-ResNet - - local: models/efficientnet - title: EfficientNet - - local: models/efficientnet-pruned - title: EfficientNet (Knapsack Pruned) - - local: models/ensemble-adversarial - title: Ensemble Adversarial Inception ResNet v2 - - local: models/ese-vovnet - title: ESE-VoVNet - - local: models/fbnet - title: FBNet - - local: models/gloun-inception-v3 - title: (Gluon) Inception v3 - - local: models/gloun-resnet - title: (Gluon) ResNet - - local: models/gloun-resnext - title: (Gluon) ResNeXt - - local: models/gloun-senet - title: (Gluon) SENet - - local: models/gloun-seresnext - title: (Gluon) SE-ResNeXt - - local: models/gloun-xception - title: (Gluon) Xception - - local: models/hrnet - title: HRNet - - local: models/ig-resnext - title: Instagram ResNeXt WSL - - local: models/inception-resnet-v2 - title: Inception ResNet v2 - - local: models/inception-v3 - title: Inception v3 - - local: models/inception-v4 - title: Inception v4 - - local: models/legacy-se-resnet - title: (Legacy) SE-ResNet - - local: models/legacy-se-resnext - title: (Legacy) SE-ResNeXt - - local: models/legacy-senet - title: (Legacy) SENet - - local: models/mixnet - title: MixNet - - local: models/mnasnet - title: MnasNet - - local: models/mobilenet-v2 - title: MobileNet v2 - - local: models/mobilenet-v3 - title: MobileNet v3 - - local: models/nasnet - title: NASNet - - local: models/noisy-student - title: Noisy Student (EfficientNet) - - local: models/pnasnet - title: PNASNet - - local: models/regnetx - title: RegNetX - - local: models/regnety - title: RegNetY - - local: models/res2net - title: Res2Net - - local: models/res2next - title: Res2NeXt - - local: models/resnest - title: ResNeSt - - local: models/resnet - title: ResNet - - local: models/resnet-d - title: ResNet-D - - local: models/resnext - title: ResNeXt - - local: models/rexnet - title: RexNet - - local: models/se-resnet - title: SE-ResNet - - local: models/selecsls - title: SelecSLS - - local: models/seresnext - title: SE-ResNeXt - - local: models/skresnet - title: SK-ResNet - - local: models/skresnext - title: SK-ResNeXt - - local: models/spnasnet - title: SPNASNet - - local: models/ssl-resnet - title: SSL ResNet - - local: models/swsl-resnet - title: SWSL ResNet - - local: models/swsl-resnext - title: SWSL ResNeXt - - local: models/tf-efficientnet - title: (Tensorflow) EfficientNet - - local: models/tf-efficientnet-condconv - title: (Tensorflow) EfficientNet CondConv - - local: models/tf-efficientnet-lite - title: (Tensorflow) EfficientNet Lite - - local: models/tf-inception-v3 - title: (Tensorflow) Inception v3 - - local: models/tf-mixnet - title: (Tensorflow) MixNet - - local: models/tf-mobilenet-v3 - title: (Tensorflow) MobileNet v3 - - local: models/tresnet - title: TResNet - - local: models/wide-resnet - title: Wide ResNet - - local: models/xception - title: Xception - title: Get started + - local: models/adversarial-inception-v3 + title: Adversarial Inception v3 + - local: models/advprop + title: AdvProp (EfficientNet) + - local: models/big-transfer + title: Big Transfer (BiT) + - local: models/csp-darknet + title: CSP-DarkNet + - local: models/csp-resnet + title: CSP-ResNet + - local: models/csp-resnext + title: CSP-ResNeXt + - local: models/densenet + title: DenseNet + - local: models/dla + title: Deep Layer Aggregation + - local: models/dpn + title: Dual Path Network (DPN) + - local: models/ecaresnet + title: ECA-ResNet + - local: models/efficientnet + title: EfficientNet + - local: models/efficientnet-pruned + title: EfficientNet (Knapsack Pruned) + - local: models/ensemble-adversarial + title: Ensemble Adversarial Inception ResNet v2 + - local: models/ese-vovnet + title: ESE-VoVNet + - local: models/fbnet + title: FBNet + - local: models/gloun-inception-v3 + title: (Gluon) Inception v3 + - local: models/gloun-resnet + title: (Gluon) ResNet + - local: models/gloun-resnext + title: (Gluon) ResNeXt + - local: models/gloun-senet + title: (Gluon) SENet + - local: models/gloun-seresnext + title: (Gluon) SE-ResNeXt + - local: models/gloun-xception + title: (Gluon) Xception + - local: models/hrnet + title: HRNet + - local: models/ig-resnext + title: Instagram ResNeXt WSL + - local: models/inception-resnet-v2 + title: Inception ResNet v2 + - local: models/inception-v3 + title: Inception v3 + - local: models/inception-v4 + title: Inception v4 + - local: models/legacy-se-resnet + title: (Legacy) SE-ResNet + - local: models/legacy-se-resnext + title: (Legacy) SE-ResNeXt + - local: models/legacy-senet + title: (Legacy) SENet + - local: models/mixnet + title: MixNet + - local: models/mnasnet + title: MnasNet + - local: models/mobilenet-v2 + title: MobileNet v2 + - local: models/mobilenet-v3 + title: MobileNet v3 + - local: models/nasnet + title: NASNet + - local: models/noisy-student + title: Noisy Student (EfficientNet) + - local: models/pnasnet + title: PNASNet + - local: models/regnetx + title: RegNetX + - local: models/regnety + title: RegNetY + - local: models/res2net + title: Res2Net + - local: models/res2next + title: Res2NeXt + - local: models/resnest + title: ResNeSt + - local: models/resnet + title: ResNet + - local: models/resnet-d + title: ResNet-D + - local: models/resnext + title: ResNeXt + - local: models/rexnet + title: RexNet + - local: models/se-resnet + title: SE-ResNet + - local: models/selecsls + title: SelecSLS + - local: models/seresnext + title: SE-ResNeXt + - local: models/skresnet + title: SK-ResNet + - local: models/skresnext + title: SK-ResNeXt + - local: models/spnasnet + title: SPNASNet + - local: models/ssl-resnet + title: SSL ResNet + - local: models/swsl-resnet + title: SWSL ResNet + - local: models/swsl-resnext + title: SWSL ResNeXt + - local: models/tf-efficientnet + title: (Tensorflow) EfficientNet + - local: models/tf-efficientnet-condconv + title: (Tensorflow) EfficientNet CondConv + - local: models/tf-efficientnet-lite + title: (Tensorflow) EfficientNet Lite + - local: models/tf-inception-v3 + title: (Tensorflow) Inception v3 + - local: models/tf-mixnet + title: (Tensorflow) MixNet + - local: models/tf-mobilenet-v3 + title: (Tensorflow) MobileNet v3 + - local: models/tresnet + title: TResNet + - local: models/wide-resnet + title: Wide ResNet + - local: models/xception + title: Xception + title: Model Pages + isExpanded: false +- sections: + - local: reference/models + title: Models + - local: reference/data + title: Data + - local: reference/optimizers + title: Optimizers + - local: reference/schedulers + title: Learning Rate Schedulers + title: Reference diff --git a/hfdocs/source/archived_changes.mdx b/hfdocs/source/archived_changes.mdx deleted file mode 100644 index 25778562..00000000 --- a/hfdocs/source/archived_changes.mdx +++ /dev/null @@ -1,418 +0,0 @@ -# Archived Changes - -### July 12, 2021 - -* Add XCiT models from [official facebook impl](https://github.com/facebookresearch/xcit). Contributed by [Alexander Soare](https://github.com/alexander-soare) - -### July 5-9, 2021 - -* Add `efficientnetv2_rw_t` weights, a custom 'tiny' 13.6M param variant that is a bit better than (non NoisyStudent) B3 models. Both faster and better accuracy (at same or lower res) - * top-1 82.34 @ 288x288 and 82.54 @ 320x320 -* Add [SAM pretrained](https://arxiv.org/abs/2106.01548) in1k weight for ViT B/16 (`vit_base_patch16_sam_224`) and B/32 (`vit_base_patch32_sam_224`) models. -* Add 'Aggregating Nested Transformer' (NesT) w/ weights converted from official [Flax impl](https://github.com/google-research/nested-transformer). Contributed by [Alexander Soare](https://github.com/alexander-soare). - * `jx_nest_base` - 83.534, `jx_nest_small` - 83.120, `jx_nest_tiny` - 81.426 - -### June 23, 2021 - -* Reproduce gMLP model training, `gmlp_s16_224` trained to 79.6 top-1, matching [paper](https://arxiv.org/abs/2105.08050). Hparams for this and other recent MLP training [here](https://gist.github.com/rwightman/d6c264a9001f9167e06c209f630b2cc6) - -### June 20, 2021 - -* Release Vision Transformer 'AugReg' weights from [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://arxiv.org/abs/2106.10270) - * .npz weight loading support added, can load any of the 50K+ weights from the [AugReg series](https://console.cloud.google.com/storage/browser/vit_models/augreg) - * See [example notebook](https://colab.research.google.com/github/google-research/vision_transformer/blob/master/vit_jax_augreg.ipynb) from [official impl](https://github.com/google-research/vision_transformer/) for navigating the augreg weights - * Replaced all default weights w/ best AugReg variant (if possible). All AugReg 21k classifiers work. - * Highlights: `vit_large_patch16_384` (87.1 top-1), `vit_large_r50_s32_384` (86.2 top-1), `vit_base_patch16_384` (86.0 top-1) - * `vit_deit_*` renamed to just `deit_*` - * Remove my old small model, replace with DeiT compatible small w/ AugReg weights -* Add 1st training of my `gmixer_24_224` MLP /w GLU, 78.1 top-1 w/ 25M params. -* Add weights from official ResMLP release (https://github.com/facebookresearch/deit) -* Add `eca_nfnet_l2` weights from my 'lightweight' series. 84.7 top-1 at 384x384. -* Add distilled BiT 50x1 student and 152x2 Teacher weights from [Knowledge distillation: A good teacher is patient and consistent](https://arxiv.org/abs/2106.05237) -* NFNets and ResNetV2-BiT models work w/ Pytorch XLA now - * weight standardization uses F.batch_norm instead of std_mean (std_mean wasn't lowered) - * eps values adjusted, will be slight differences but should be quite close -* Improve test coverage and classifier interface of non-conv (vision transformer and mlp) models -* Cleanup a few classifier / flatten details for models w/ conv classifiers or early global pool -* Please report any regressions, this PR touched quite a few models. - -### June 8, 2021 - -* Add first ResMLP weights, trained in PyTorch XLA on TPU-VM w/ my XLA branch. 24 block variant, 79.2 top-1. -* Add ResNet51-Q model w/ pretrained weights at 82.36 top-1. - * NFNet inspired block layout with quad layer stem and no maxpool - * Same param count (35.7M) and throughput as ResNetRS-50 but +1.5 top-1 @ 224x224 and +2.5 top-1 at 288x288 - -### May 25, 2021 - -* Add LeViT, Visformer, Convit (PR by Aman Arora), Twins (PR by paper authors) transformer models -* Cleanup input_size/img_size override handling and testing for all vision transformer models -* Add `efficientnetv2_rw_m` model and weights (started training before official code). 84.8 top-1, 53M params. - -### May 14, 2021 - -* Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl. - * 1k trained variants: `tf_efficientnetv2_s/m/l` - * 21k trained variants: `tf_efficientnetv2_s/m/l_in21k` - * 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_in21ft1k` - * v2 models w/ v1 scaling: `tf_efficientnetv2_b0` through `b3` - * Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s` - * Some blank `efficientnetv2_*` models in-place for future native PyTorch training - -### May 5, 2021 - -* Add MLP-Mixer models and port pretrained weights from [Google JAX impl](https://github.com/google-research/vision_transformer/tree/linen) -* Add CaiT models and pretrained weights from [FB](https://github.com/facebookresearch/deit) -* Add ResNet-RS models and weights from [TF](https://github.com/tensorflow/tpu/tree/master/models/official/resnet/resnet_rs). Thanks [Aman Arora](https://github.com/amaarora) -* Add CoaT models and weights. Thanks [Mohammed Rizin](https://github.com/morizin) -* Add new ImageNet-21k weights & finetuned weights for TResNet, MobileNet-V3, ViT models. Thanks [mrT](https://github.com/mrT23) -* Add GhostNet models and weights. Thanks [Kai Han](https://github.com/iamhankai) -* Update ByoaNet attention modles - * Improve SA module inits - * Hack together experimental stand-alone Swin based attn module and `swinnet` - * Consistent '26t' model defs for experiments. -* Add improved Efficientnet-V2S (prelim model def) weights. 83.8 top-1. -* WandB logging support - -### April 13, 2021 - -* Add Swin Transformer models and weights from https://github.com/microsoft/Swin-Transformer - -### April 12, 2021 - -* Add ECA-NFNet-L1 (slimmed down F1 w/ SiLU, 41M params) trained with this code. 84% top-1 @ 320x320. Trained at 256x256. -* Add EfficientNet-V2S model (unverified model definition) weights. 83.3 top-1 @ 288x288. Only trained single res 224. Working on progressive training. -* Add ByoaNet model definition (Bring-your-own-attention) w/ SelfAttention block and corresponding SA/SA-like modules and model defs - * Lambda Networks - https://arxiv.org/abs/2102.08602 - * Bottleneck Transformers - https://arxiv.org/abs/2101.11605 - * Halo Nets - https://arxiv.org/abs/2103.12731 -* Adabelief optimizer contributed by Juntang Zhuang - -### April 1, 2021 - -* Add snazzy `benchmark.py` script for bulk `timm` model benchmarking of train and/or inference -* Add Pooling-based Vision Transformer (PiT) models (from https://github.com/naver-ai/pit) - * Merged distilled variant into main for torchscript compatibility - * Some `timm` cleanup/style tweaks and weights have hub download support -* Cleanup Vision Transformer (ViT) models - * Merge distilled (DeiT) model into main so that torchscript can work - * Support updated weight init (defaults to old still) that closer matches original JAX impl (possibly better training from scratch) - * Separate hybrid model defs into different file and add several new model defs to fiddle with, support patch_size != 1 for hybrids - * Fix fine-tuning num_class changes (PiT and ViT) and pos_embed resizing (Vit) with distilled variants - * nn.Sequential for block stack (does not break downstream compat) -* TnT (Transformer-in-Transformer) models contributed by author (from https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/TNT) -* Add RegNetY-160 weights from DeiT teacher model -* Add new NFNet-L0 w/ SE attn (rename `nfnet_l0b`->`nfnet_l0`) weights 82.75 top-1 @ 288x288 -* Some fixes/improvements for TFDS dataset wrapper - -### March 7, 2021 - -* First 0.4.x PyPi release w/ NFNets (& related), ByoB (GPU-Efficient, RepVGG, etc). -* Change feature extraction for pre-activation nets (NFNets, ResNetV2) to return features before activation. - -### Feb 18, 2021 - -* Add pretrained weights and model variants for NFNet-F* models from [DeepMind Haiku impl](https://github.com/deepmind/deepmind-research/tree/master/nfnets). - * Models are prefixed with `dm_`. They require SAME padding conv, skipinit enabled, and activation gains applied in act fn. - * These models are big, expect to run out of GPU memory. With the GELU activiation + other options, they are roughly 1/2 the inference speed of my SiLU PyTorch optimized `s` variants. - * Original model results are based on pre-processing that is not the same as all other models so you'll see different results in the results csv (once updated). - * Matching the original pre-processing as closely as possible I get these results: - * `dm_nfnet_f6` - 86.352 - * `dm_nfnet_f5` - 86.100 - * `dm_nfnet_f4` - 85.834 - * `dm_nfnet_f3` - 85.676 - * `dm_nfnet_f2` - 85.178 - * `dm_nfnet_f1` - 84.696 - * `dm_nfnet_f0` - 83.464 - -### Feb 16, 2021 - -* Add Adaptive Gradient Clipping (AGC) as per https://arxiv.org/abs/2102.06171. Integrated w/ PyTorch gradient clipping via mode arg that defaults to prev 'norm' mode. For backward arg compat, clip-grad arg must be specified to enable when using train.py. - * AGC w/ default clipping factor `--clip-grad .01 --clip-mode agc` - * PyTorch global norm of 1.0 (old behaviour, always norm), `--clip-grad 1.0` - * PyTorch value clipping of 10, `--clip-grad 10. --clip-mode value` - * AGC performance is definitely sensitive to the clipping factor. More experimentation needed to determine good values for smaller batch sizes and optimizers besides those in paper. So far I've found .001-.005 is necessary for stable RMSProp training w/ NFNet/NF-ResNet. - -### Feb 12, 2021 - -* Update Normalization-Free nets to include new NFNet-F (https://arxiv.org/abs/2102.06171) model defs - -### Feb 10, 2021 - -* More model archs, incl a flexible ByobNet backbone ('Bring-your-own-blocks') - * GPU-Efficient-Networks (https://github.com/idstcv/GPU-Efficient-Networks), impl in `byobnet.py` - * RepVGG (https://github.com/DingXiaoH/RepVGG), impl in `byobnet.py` - * classic VGG (from torchvision, impl in `vgg`) -* Refinements to normalizer layer arg handling and normalizer+act layer handling in some models -* Default AMP mode changed to native PyTorch AMP instead of APEX. Issues not being fixed with APEX. Native works with `--channels-last` and `--torchscript` model training, APEX does not. -* Fix a few bugs introduced since last pypi release - -### Feb 8, 2021 - -* Add several ResNet weights with ECA attention. 26t & 50t trained @ 256, test @ 320. 269d train @ 256, fine-tune @320, test @ 352. - * `ecaresnet26t` - 79.88 top-1 @ 320x320, 79.08 @ 256x256 - * `ecaresnet50t` - 82.35 top-1 @ 320x320, 81.52 @ 256x256 - * `ecaresnet269d` - 84.93 top-1 @ 352x352, 84.87 @ 320x320 -* Remove separate tiered (`t`) vs tiered_narrow (`tn`) ResNet model defs, all `tn` changed to `t` and `t` models removed (`seresnext26t_32x4d` only model w/ weights that was removed). -* Support model default_cfgs with separate train vs test resolution `test_input_size` and remove extra `_320` suffix ResNet model defs that were just for test. - -### Jan 30, 2021 - -* Add initial "Normalization Free" NF-RegNet-B* and NF-ResNet model definitions based on [paper](https://arxiv.org/abs/2101.08692) - -### Jan 25, 2021 - -* Add ResNetV2 Big Transfer (BiT) models w/ ImageNet-1k and 21k weights from https://github.com/google-research/big_transfer -* Add official R50+ViT-B/16 hybrid models + weights from https://github.com/google-research/vision_transformer -* ImageNet-21k ViT weights are added w/ model defs and representation layer (pre logits) support - * NOTE: ImageNet-21k classifier heads were zero'd in original weights, they are only useful for transfer learning -* Add model defs and weights for DeiT Vision Transformer models from https://github.com/facebookresearch/deit -* Refactor dataset classes into ImageDataset/IterableImageDataset + dataset specific parser classes -* Add Tensorflow-Datasets (TFDS) wrapper to allow use of TFDS image classification sets with train script - * Ex: `train.py /data/tfds --dataset tfds/oxford_iiit_pet --val-split test --model resnet50 -b 256 --amp --num-classes 37 --opt adamw --lr 3e-4 --weight-decay .001 --pretrained -j 2` -* Add improved .tar dataset parser that reads images from .tar, folder of .tar files, or .tar within .tar - * Run validation on full ImageNet-21k directly from tar w/ BiT model: `validate.py /data/fall11_whole.tar --model resnetv2_50x1_bitm_in21k --amp` -* Models in this update should be stable w/ possible exception of ViT/BiT, possibility of some regressions with train/val scripts and dataset handling - -### Jan 3, 2021 - -* Add SE-ResNet-152D weights - * 256x256 val, 0.94 crop top-1 - 83.75 - * 320x320 val, 1.0 crop - 84.36 -* Update results files - -### Dec 18, 2020 - -* Add ResNet-101D, ResNet-152D, and ResNet-200D weights trained @ 256x256 - * 256x256 val, 0.94 crop (top-1) - 101D (82.33), 152D (83.08), 200D (83.25) - * 288x288 val, 1.0 crop - 101D (82.64), 152D (83.48), 200D (83.76) - * 320x320 val, 1.0 crop - 101D (83.00), 152D (83.66), 200D (84.01) - -### Dec 7, 2020 - -* Simplify EMA module (ModelEmaV2), compatible with fully torchscripted models -* Misc fixes for SiLU ONNX export, default_cfg missing from Feature extraction models, Linear layer w/ AMP + torchscript -* PyPi release @ 0.3.2 (needed by EfficientDet) - - -### Oct 30, 2020 - -* Test with PyTorch 1.7 and fix a small top-n metric view vs reshape issue. -* Convert newly added 224x224 Vision Transformer weights from official JAX repo. 81.8 top-1 for B/16, 83.1 L/16. -* Support PyTorch 1.7 optimized, native SiLU (aka Swish) activation. Add mapping to 'silu' name, custom swish will eventually be deprecated. -* Fix regression for loading pretrained classifier via direct model entrypoint functions. Didn't impact create_model() factory usage. -* PyPi release @ 0.3.0 version! - -### Oct 26, 2020 - -* Update Vision Transformer models to be compatible with official code release at https://github.com/google-research/vision_transformer -* Add Vision Transformer weights (ImageNet-21k pretrain) for 384x384 base and large models converted from official jax impl - * ViT-B/16 - 84.2 - * ViT-B/32 - 81.7 - * ViT-L/16 - 85.2 - * ViT-L/32 - 81.5 - -### Oct 21, 2020 - -* Weights added for Vision Transformer (ViT) models. 77.86 top-1 for 'small' and 79.35 for 'base'. Thanks to [Christof](https://www.kaggle.com/christofhenkel) for training the base model w/ lots of GPUs. - -### Oct 13, 2020 - -* Initial impl of Vision Transformer models. Both patch and hybrid (CNN backbone) variants. Currently trying to train... -* Adafactor and AdaHessian (FP32 only, no AMP) optimizers -* EdgeTPU-M (`efficientnet_em`) model trained in PyTorch, 79.3 top-1 -* Pip release, doc updates pending a few more changes... - -### Sept 18, 2020 - -* New ResNet 'D' weights. 72.7 (top-1) ResNet-18-D, 77.1 ResNet-34-D, 80.5 ResNet-50-D -* Added a few untrained defs for other ResNet models (66D, 101D, 152D, 200/200D) - -### Sept 3, 2020 - -* New weights - * Wide-ResNet50 - 81.5 top-1 (vs 78.5 torchvision) - * SEResNeXt50-32x4d - 81.3 top-1 (vs 79.1 cadene) -* Support for native Torch AMP and channels_last memory format added to train/validate scripts (`--channels-last`, `--native-amp` vs `--apex-amp`) -* Models tested with channels_last on latest NGC 20.08 container. AdaptiveAvgPool in attn layers changed to mean((2,3)) to work around bug with NHWC kernel. - -### Aug 12, 2020 - -* New/updated weights from training experiments - * EfficientNet-B3 - 82.1 top-1 (vs 81.6 for official with AA and 81.9 for AdvProp) - * RegNetY-3.2GF - 82.0 top-1 (78.9 from official ver) - * CSPResNet50 - 79.6 top-1 (76.6 from official ver) -* Add CutMix integrated w/ Mixup. See [pull request](https://github.com/rwightman/pytorch-image-models/pull/218) for some usage examples -* Some fixes for using pretrained weights with `in_chans` != 3 on several models. - -### Aug 5, 2020 - -Universal feature extraction, new models, new weights, new test sets. -* All models support the `features_only=True` argument for `create_model` call to return a network that extracts feature maps from the deepest layer at each stride. -* New models - * CSPResNet, CSPResNeXt, CSPDarkNet, DarkNet - * ReXNet - * (Modified Aligned) Xception41/65/71 (a proper port of TF models) -* New trained weights - * SEResNet50 - 80.3 top-1 - * CSPDarkNet53 - 80.1 top-1 - * CSPResNeXt50 - 80.0 top-1 - * DPN68b - 79.2 top-1 - * EfficientNet-Lite0 (non-TF ver) - 75.5 (submitted by [@hal-314](https://github.com/hal-314)) -* Add 'real' labels for ImageNet and ImageNet-Renditions test set, see [`results/README.md`](results/README.md) -* Test set ranking/top-n diff script by [@KushajveerSingh](https://github.com/KushajveerSingh) -* Train script and loader/transform tweaks to punch through more aug arguments -* README and documentation overhaul. See initial (WIP) documentation at https://rwightman.github.io/pytorch-image-models/ -* adamp and sgdp optimizers added by [@hellbell](https://github.com/hellbell) - -### June 11, 2020 - -Bunch of changes: -* DenseNet models updated with memory efficient addition from torchvision (fixed a bug), blur pooling and deep stem additions -* VoVNet V1 and V2 models added, 39 V2 variant (ese_vovnet_39b) trained to 79.3 top-1 -* Activation factory added along with new activations: - * select act at model creation time for more flexibility in using activations compatible with scripting or tracing (ONNX export) - * hard_mish (experimental) added with memory-efficient grad, along with ME hard_swish - * context mgr for setting exportable/scriptable/no_jit states -* Norm + Activation combo layers added with initial trial support in DenseNet and VoVNet along with impl of EvoNorm and InplaceAbn wrapper that fit the interface -* Torchscript works for all but two of the model types as long as using Pytorch 1.5+, tests added for this -* Some import cleanup and classifier reset changes, all models will have classifier reset to nn.Identity on reset_classifer(0) call -* Prep for 0.1.28 pip release - -### May 12, 2020 - -* Add ResNeSt models (code adapted from https://github.com/zhanghang1989/ResNeSt, paper https://arxiv.org/abs/2004.08955)) - -### May 3, 2020 - -* Pruned EfficientNet B1, B2, and B3 (https://arxiv.org/abs/2002.08258) contributed by [Yonathan Aflalo](https://github.com/yoniaflalo) - -### May 1, 2020 - -* Merged a number of execellent contributions in the ResNet model family over the past month - * BlurPool2D and resnetblur models initiated by [Chris Ha](https://github.com/VRandme), I trained resnetblur50 to 79.3. - * TResNet models and SpaceToDepth, AntiAliasDownsampleLayer layers by [mrT23](https://github.com/mrT23) - * ecaresnet (50d, 101d, light) models and two pruned variants using pruning as per (https://arxiv.org/abs/2002.08258) by [Yonathan Aflalo](https://github.com/yoniaflalo) -* 200 pretrained models in total now with updated results csv in results folder - -### April 5, 2020 - -* Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite - * 3.5M param MobileNet-V2 100 @ 73% - * 4.5M param MobileNet-V2 110d @ 75% - * 6.1M param MobileNet-V2 140 @ 76.5% - * 5.8M param MobileNet-V2 120d @ 77.3% - -### March 18, 2020 - -* Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite) -* Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams) - -### April 5, 2020 - -* Add some newly trained MobileNet-V2 models trained with latest h-params, rand augment. They compare quite favourably to EfficientNet-Lite - * 3.5M param MobileNet-V2 100 @ 73% - * 4.5M param MobileNet-V2 110d @ 75% - * 6.1M param MobileNet-V2 140 @ 76.5% - * 5.8M param MobileNet-V2 120d @ 77.3% - -### March 18, 2020 - -* Add EfficientNet-Lite models w/ weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/lite) -* Add RandAugment trained ResNeXt-50 32x4d weights with 79.8 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams) - -### Feb 29, 2020 - -* New MobileNet-V3 Large weights trained from stratch with this code to 75.77% top-1 -* IMPORTANT CHANGE - default weight init changed for all MobilenetV3 / EfficientNet / related models - * overall results similar to a bit better training from scratch on a few smaller models tried - * performance early in training seems consistently improved but less difference by end - * set `fix_group_fanout=False` in `_init_weight_goog` fn if you need to reproducte past behaviour -* Experimental LR noise feature added applies a random perturbation to LR each epoch in specified range of training - -### Feb 18, 2020 - -* Big refactor of model layers and addition of several attention mechanisms. Several additions motivated by 'Compounding the Performance Improvements...' (https://arxiv.org/abs/2001.06268): - * Move layer/module impl into `layers` subfolder/module of `models` and organize in a more granular fashion - * ResNet downsample paths now properly support dilation (output stride != 32) for avg_pool ('D' variant) and 3x3 (SENets) networks - * Add Selective Kernel Nets on top of ResNet base, pretrained weights - * skresnet18 - 73% top-1 - * skresnet34 - 76.9% top-1 - * skresnext50_32x4d (equiv to SKNet50) - 80.2% top-1 - * ECA and CECA (circular padding) attention layer contributed by [Chris Ha](https://github.com/VRandme) - * CBAM attention experiment (not the best results so far, may remove) - * Attention factory to allow dynamically selecting one of SE, ECA, CBAM in the `.se` position for all ResNets - * Add DropBlock and DropPath (formerly DropConnect for EfficientNet/MobileNetv3) support to all ResNet variants -* Full dataset results updated that incl NoisyStudent weights and 2 of the 3 SK weights - -### Feb 12, 2020 - -* Add EfficientNet-L2 and B0-B7 NoisyStudent weights ported from [Tensorflow TPU](https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet) - -### Feb 6, 2020 - -* Add RandAugment trained EfficientNet-ES (EdgeTPU-Small) weights with 78.1 top-1. Trained by [Andrew Lavin](https://github.com/andravin) (see Training section for hparams) - -### Feb 1/2, 2020 - -* Port new EfficientNet-B8 (RandAugment) weights, these are different than the B8 AdvProp, different input normalization. -* Update results csv files on all models for ImageNet validation and three other test sets -* Push PyPi package update - -### Jan 31, 2020 - -* Update ResNet50 weights with a new 79.038 result from further JSD / AugMix experiments. Full command line for reproduction in training section below. - -### Jan 11/12, 2020 - -* Master may be a bit unstable wrt to training, these changes have been tested but not all combos -* Implementations of AugMix added to existing RA and AA. Including numerous supporting pieces like JSD loss (Jensen-Shannon divergence + CE), and AugMixDataset -* SplitBatchNorm adaptation layer added for implementing Auxiliary BN as per AdvProp paper -* ResNet-50 AugMix trained model w/ 79% top-1 added -* `seresnext26tn_32x4d` - 77.99 top-1, 93.75 top-5 added to tiered experiment, higher img/s than 't' and 'd' - -### Jan 3, 2020 - -* Add RandAugment trained EfficientNet-B0 weight with 77.7 top-1. Trained by [Michael Klachko](https://github.com/michaelklachko) with this code and recent hparams (see Training section) -* Add `avg_checkpoints.py` script for post training weight averaging and update all scripts with header docstrings and shebangs. - -### Dec 30, 2019 - -* Merge [Dushyant Mehta's](https://github.com/mehtadushy) PR for SelecSLS (Selective Short and Long Range Skip Connections) networks. Good GPU memory consumption and throughput. Original: https://github.com/mehtadushy/SelecSLS-Pytorch - -### Dec 28, 2019 - -* Add new model weights and training hparams (see Training Hparams section) - * `efficientnet_b3` - 81.5 top-1, 95.7 top-5 at default res/crop, 81.9, 95.8 at 320x320 1.0 crop-pct - * trained with RandAugment, ended up with an interesting but less than perfect result (see training section) - * `seresnext26d_32x4d`- 77.6 top-1, 93.6 top-5 - * deep stem (32, 32, 64), avgpool downsample - * stem/dowsample from bag-of-tricks paper - * `seresnext26t_32x4d`- 78.0 top-1, 93.7 top-5 - * deep tiered stem (24, 48, 64), avgpool downsample (a modified 'D' variant) - * stem sizing mods from Jeremy Howard and fastai devs discussing ResNet architecture experiments - -### Dec 23, 2019 - -* Add RandAugment trained MixNet-XL weights with 80.48 top-1. -* `--dist-bn` argument added to train.py, will distribute BN stats between nodes after each train epoch, before eval - -### Dec 4, 2019 - -* Added weights from the first training from scratch of an EfficientNet (B2) with my new RandAugment implementation. Much better than my previous B2 and very close to the official AdvProp ones (80.4 top-1, 95.08 top-5). - -### Nov 29, 2019 - -* Brought EfficientNet and MobileNetV3 up to date with my https://github.com/rwightman/gen-efficientnet-pytorch code. Torchscript and ONNX export compat excluded. - * AdvProp weights added - * Official TF MobileNetv3 weights added -* EfficientNet and MobileNetV3 hook based 'feature extraction' classes added. Will serve as basis for using models as backbones in obj detection/segmentation tasks. Lots more to be done here... -* HRNet classification models and weights added from https://github.com/HRNet/HRNet-Image-Classification -* Consistency in global pooling, `reset_classifer`, and `forward_features` across models - * `forward_features` always returns unpooled feature maps now -* Reasonable chance I broke something... let me know - -### Nov 22, 2019 - -* Add ImageNet training RandAugment implementation alongside AutoAugment. PyTorch Transform compatible format, using PIL. Currently training two EfficientNet models from scratch with promising results... will update. -* `drop-connect` cmd line arg finally added to `train.py`, no need to hack model fns. Works for efficientnet/mobilenetv3 based models, ignored otherwise. \ No newline at end of file diff --git a/hfdocs/source/changes.mdx b/hfdocs/source/changes.mdx deleted file mode 100644 index 93dc9fac..00000000 --- a/hfdocs/source/changes.mdx +++ /dev/null @@ -1,187 +0,0 @@ -# Recent Changes - -### July 27, 2022 - -* All runtime benchmark and validation result csv files are up-to-date! -* A few more weights & model defs added: - * `darknetaa53` - 79.8 @ 256, 80.5 @ 288 - * `convnext_nano` - 80.8 @ 224, 81.5 @ 288 - * `cs3sedarknet_l` - 81.2 @ 256, 81.8 @ 288 - * `cs3darknet_x` - 81.8 @ 256, 82.2 @ 288 - * `cs3sedarknet_x` - 82.2 @ 256, 82.7 @ 288 - * `cs3edgenet_x` - 82.2 @ 256, 82.7 @ 288 - * `cs3se_edgenet_x` - 82.8 @ 256, 83.5 @ 320 -* `cs3*` weights above all trained on TPU w/ `bits_and_tpu` branch. Thanks to TRC program! -* Add output_stride=8 and 16 support to ConvNeXt (dilation) -* deit3 models not being able to resize pos_emb fixed -* Version 0.6.7 PyPi release (/w above bug fixes and new weighs since 0.6.5) - -### July 8, 2022 - -More models, more fixes - -* Official research models (w/ weights) added: - * EdgeNeXt from (https://github.com/mmaaz60/EdgeNeXt) - * MobileViT-V2 from (https://github.com/apple/ml-cvnets) - * DeiT III (Revenge of the ViT) from (https://github.com/facebookresearch/deit) -* My own models: - * Small `ResNet` defs added by request with 1 block repeats for both basic and bottleneck (resnet10 and resnet14) - * `CspNet` refactored with dataclass config, simplified CrossStage3 (`cs3`) option. These are closer to YOLO-v5+ backbone defs. - * More relative position vit fiddling. Two `srelpos` (shared relative position) models trained, and a medium w/ class token. - * Add an alternate downsample mode to EdgeNeXt and train a `small` model. Better than original small, but not their new USI trained weights. -* My own model weight results (all ImageNet-1k training) - * `resnet10t` - 66.5 @ 176, 68.3 @ 224 - * `resnet14t` - 71.3 @ 176, 72.3 @ 224 - * `resnetaa50` - 80.6 @ 224 , 81.6 @ 288 - * `darknet53` - 80.0 @ 256, 80.5 @ 288 - * `cs3darknet_m` - 77.0 @ 256, 77.6 @ 288 - * `cs3darknet_focus_m` - 76.7 @ 256, 77.3 @ 288 - * `cs3darknet_l` - 80.4 @ 256, 80.9 @ 288 - * `cs3darknet_focus_l` - 80.3 @ 256, 80.9 @ 288 - * `vit_srelpos_small_patch16_224` - 81.1 @ 224, 82.1 @ 320 - * `vit_srelpos_medium_patch16_224` - 82.3 @ 224, 83.1 @ 320 - * `vit_relpos_small_patch16_cls_224` - 82.6 @ 224, 83.6 @ 320 - * `edgnext_small_rw` - 79.6 @ 224, 80.4 @ 320 -* `cs3`, `darknet`, and `vit_*relpos` weights above all trained on TPU thanks to TRC program! Rest trained on overheating GPUs. -* Hugging Face Hub support fixes verified, demo notebook TBA -* Pretrained weights / configs can be loaded externally (ie from local disk) w/ support for head adaptation. -* Add support to change image extensions scanned by `timm` datasets/parsers. See (https://github.com/rwightman/pytorch-image-models/pull/1274#issuecomment-1178303103) -* Default ConvNeXt LayerNorm impl to use `F.layer_norm(x.permute(0, 2, 3, 1), ...).permute(0, 3, 1, 2)` via `LayerNorm2d` in all cases. - * a bit slower than previous custom impl on some hardware (ie Ampere w/ CL), but overall fewer regressions across wider HW / PyTorch version ranges. - * previous impl exists as `LayerNormExp2d` in `models/layers/norm.py` -* Numerous bug fixes -* Currently testing for imminent PyPi 0.6.x release -* LeViT pretraining of larger models still a WIP, they don't train well / easily without distillation. Time to add distill support (finally)? -* ImageNet-22k weight training + finetune ongoing, work on multi-weight support (slowly) chugging along (there are a LOT of weights, sigh) ... - -### May 13, 2022 - -* Official Swin-V2 models and weights added from (https://github.com/microsoft/Swin-Transformer). Cleaned up to support torchscript. -* Some refactoring for existing `timm` Swin-V2-CR impl, will likely do a bit more to bring parts closer to official and decide whether to merge some aspects. -* More Vision Transformer relative position / residual post-norm experiments (all trained on TPU thanks to TRC program) - * `vit_relpos_small_patch16_224` - 81.5 @ 224, 82.5 @ 320 -- rel pos, layer scale, no class token, avg pool - * `vit_relpos_medium_patch16_rpn_224` - 82.3 @ 224, 83.1 @ 320 -- rel pos + res-post-norm, no class token, avg pool - * `vit_relpos_medium_patch16_224` - 82.5 @ 224, 83.3 @ 320 -- rel pos, layer scale, no class token, avg pool - * `vit_relpos_base_patch16_gapcls_224` - 82.8 @ 224, 83.9 @ 320 -- rel pos, layer scale, class token, avg pool (by mistake) -* Bring 512 dim, 8-head 'medium' ViT model variant back to life (after using in a pre DeiT 'small' model for first ViT impl back in 2020) -* Add ViT relative position support for switching btw existing impl and some additions in official Swin-V2 impl for future trials -* Sequencer2D impl (https://arxiv.org/abs/2205.01972), added via PR from author (https://github.com/okojoalg) - -### May 2, 2022 - -* Vision Transformer experiments adding Relative Position (Swin-V2 log-coord) (`vision_transformer_relpos.py`) and Residual Post-Norm branches (from Swin-V2) (`vision_transformer*.py`) - * `vit_relpos_base_patch32_plus_rpn_256` - 79.5 @ 256, 80.6 @ 320 -- rel pos + extended width + res-post-norm, no class token, avg pool - * `vit_relpos_base_patch16_224` - 82.5 @ 224, 83.6 @ 320 -- rel pos, layer scale, no class token, avg pool - * `vit_base_patch16_rpn_224` - 82.3 @ 224 -- rel pos + res-post-norm, no class token, avg pool -* Vision Transformer refactor to remove representation layer that was only used in initial vit and rarely used since with newer pretrain (ie `How to Train Your ViT`) -* `vit_*` models support removal of class token, use of global average pool, use of fc_norm (ala beit, mae). - -### April 22, 2022 - -* `timm` models are now officially supported in [fast.ai](https://www.fast.ai/)! Just in time for the new Practical Deep Learning course. `timmdocs` documentation link updated to [timm.fast.ai](http://timm.fast.ai/). -* Two more model weights added in the TPU trained [series](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights). Some In22k pretrain still in progress. - * `seresnext101d_32x8d` - 83.69 @ 224, 84.35 @ 288 - * `seresnextaa101d_32x8d` (anti-aliased w/ AvgPool2d) - 83.85 @ 224, 84.57 @ 288 - -### March 23, 2022 - -* Add `ParallelBlock` and `LayerScale` option to base vit models to support model configs in [Three things everyone should know about ViT](https://arxiv.org/abs/2203.09795) -* `convnext_tiny_hnf` (head norm first) weights trained with (close to) A2 recipe, 82.2% top-1, could do better with more epochs. - -### March 21, 2022 - -* Merge `norm_norm_norm`. **IMPORTANT** this update for a coming 0.6.x release will likely de-stabilize the master branch for a while. Branch [`0.5.x`](https://github.com/rwightman/pytorch-image-models/tree/0.5.x) or a previous 0.5.x release can be used if stability is required. -* Significant weights update (all TPU trained) as described in this [release](https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-tpu-weights) - * `regnety_040` - 82.3 @ 224, 82.96 @ 288 - * `regnety_064` - 83.0 @ 224, 83.65 @ 288 - * `regnety_080` - 83.17 @ 224, 83.86 @ 288 - * `regnetv_040` - 82.44 @ 224, 83.18 @ 288 (timm pre-act) - * `regnetv_064` - 83.1 @ 224, 83.71 @ 288 (timm pre-act) - * `regnetz_040` - 83.67 @ 256, 84.25 @ 320 - * `regnetz_040h` - 83.77 @ 256, 84.5 @ 320 (w/ extra fc in head) - * `resnetv2_50d_gn` - 80.8 @ 224, 81.96 @ 288 (pre-act GroupNorm) - * `resnetv2_50d_evos` 80.77 @ 224, 82.04 @ 288 (pre-act EvoNormS) - * `regnetz_c16_evos` - 81.9 @ 256, 82.64 @ 320 (EvoNormS) - * `regnetz_d8_evos` - 83.42 @ 256, 84.04 @ 320 (EvoNormS) - * `xception41p` - 82 @ 299 (timm pre-act) - * `xception65` - 83.17 @ 299 - * `xception65p` - 83.14 @ 299 (timm pre-act) - * `resnext101_64x4d` - 82.46 @ 224, 83.16 @ 288 - * `seresnext101_32x8d` - 83.57 @ 224, 84.270 @ 288 - * `resnetrs200` - 83.85 @ 256, 84.44 @ 320 -* HuggingFace hub support fixed w/ initial groundwork for allowing alternative 'config sources' for pretrained model definitions and weights (generic local file / remote url support soon) -* SwinTransformer-V2 implementation added. Submitted by [Christoph Reich](https://github.com/ChristophReich1996). Training experiments and model changes by myself are ongoing so expect compat breaks. -* Swin-S3 (AutoFormerV2) models / weights added from https://github.com/microsoft/Cream/tree/main/AutoFormerV2 -* MobileViT models w/ weights adapted from https://github.com/apple/ml-cvnets -* PoolFormer models w/ weights adapted from https://github.com/sail-sg/poolformer -* VOLO models w/ weights adapted from https://github.com/sail-sg/volo -* Significant work experimenting with non-BatchNorm norm layers such as EvoNorm, FilterResponseNorm, GroupNorm, etc -* Enhance support for alternate norm + act ('NormAct') layers added to a number of models, esp EfficientNet/MobileNetV3, RegNet, and aligned Xception -* Grouped conv support added to EfficientNet family -* Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler -* Gradient checkpointing support added to many models -* `forward_head(x, pre_logits=False)` fn added to all models to allow separate calls of `forward_features` + `forward_head` -* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `foward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head` - -### Feb 2, 2022 - -* [Chris Hughes](https://github.com/Chris-hughes10) posted an exhaustive run through of `timm` on his blog yesterday. Well worth a read. [Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055) -* I'm currently prepping to merge the `norm_norm_norm` branch back to master (ver 0.6.x) in next week or so. - * The changes are more extensive than usual and may destabilize and break some model API use (aiming for full backwards compat). So, beware `pip install git+https://github.com/rwightman/pytorch-image-models` installs! - * `0.5.x` releases and a `0.5.x` branch will remain stable with a cherry pick or two until dust clears. Recommend sticking to pypi install for a bit if you want stable. - -### Jan 14, 2022 - -* Version 0.5.4 w/ release to be pushed to pypi. It's been a while since last pypi update and riskier changes will be merged to main branch soon.... -* Add ConvNeXT models /w weights from official impl (https://github.com/facebookresearch/ConvNeXt), a few perf tweaks, compatible with timm features -* Tried training a few small (~1.8-3M param) / mobile optimized models, a few are good so far, more on the way... - * `mnasnet_small` - 65.6 top-1 - * `mobilenetv2_050` - 65.9 - * `lcnet_100/075/050` - 72.1 / 68.8 / 63.1 - * `semnasnet_075` - 73 - * `fbnetv3_b/d/g` - 79.1 / 79.7 / 82.0 -* TinyNet models added by [rsomani95](https://github.com/rsomani95) -* LCNet added via MobileNetV3 architecture - -### Nov 22, 2021 - -* A number of updated weights anew new model defs - * `eca_halonext26ts` - 79.5 @ 256 - * `resnet50_gn` (new) - 80.1 @ 224, 81.3 @ 288 - * `resnet50` - 80.7 @ 224, 80.9 @ 288 (trained at 176, not replacing current a1 weights as default since these don't scale as well to higher res, [weights](https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/resnet50_a1h2_176-001a1197.pth)) - * `resnext50_32x4d` - 81.1 @ 224, 82.0 @ 288 - * `sebotnet33ts_256` (new) - 81.2 @ 224 - * `lamhalobotnet50ts_256` - 81.5 @ 256 - * `halonet50ts` - 81.7 @ 256 - * `halo2botnet50ts_256` - 82.0 @ 256 - * `resnet101` - 82.0 @ 224, 82.8 @ 288 - * `resnetv2_101` (new) - 82.1 @ 224, 83.0 @ 288 - * `resnet152` - 82.8 @ 224, 83.5 @ 288 - * `regnetz_d8` (new) - 83.5 @ 256, 84.0 @ 320 - * `regnetz_e8` (new) - 84.5 @ 256, 85.0 @ 320 -* `vit_base_patch8_224` (85.8 top-1) & `in21k` variant weights added thanks [Martins Bruveris](https://github.com/martinsbruveris) -* Groundwork in for FX feature extraction thanks to [Alexander Soare](https://github.com/alexander-soare) - * models updated for tracing compatibility (almost full support with some distlled transformer exceptions) - -### Oct 19, 2021 - -* ResNet strikes back (https://arxiv.org/abs/2110.00476) weights added, plus any extra training components used. Model weights and some more details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-rsb-weights) -* BCE loss and Repeated Augmentation support for RSB paper -* 4 series of ResNet based attention model experiments being added (implemented across byobnet.py/byoanet.py). These include all sorts of attention, from channel attn like SE, ECA to 2D QKV self-attention layers such as Halo, Bottlneck, Lambda. Details here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* Working implementations of the following 2D self-attention modules (likely to be differences from paper or eventual official impl): - * Halo (https://arxiv.org/abs/2103.12731) - * Bottleneck Transformer (https://arxiv.org/abs/2101.11605) - * LambdaNetworks (https://arxiv.org/abs/2102.08602) -* A RegNetZ series of models with some attention experiments (being added to). These do not follow the paper (https://arxiv.org/abs/2103.06877) in any way other than block architecture, details of official models are not available. See more here (https://github.com/rwightman/pytorch-image-models/releases/tag/v0.1-attn-weights) -* ConvMixer (https://openreview.net/forum?id=TVHS5Y4dNvM), CrossVit (https://arxiv.org/abs/2103.14899), and BeiT (https://arxiv.org/abs/2106.08254) architectures + weights added -* freeze/unfreeze helpers by [Alexander Soare](https://github.com/alexander-soare) - -### Aug 18, 2021 - -* Optimizer bonanza! - * Add LAMB and LARS optimizers, incl trust ratio clipping options. Tweaked to work properly in PyTorch XLA (tested on TPUs w/ `timm bits` [branch](https://github.com/rwightman/pytorch-image-models/tree/bits_and_tpu/timm/bits)) - * Add MADGRAD from FB research w/ a few tweaks (decoupled decay option, step handling that works with PyTorch XLA) - * Some cleanup on all optimizers and factory. No more `.data`, a bit more consistency, unit tests for all! - * SGDP and AdamP still won't work with PyTorch XLA but others should (have yet to test Adabelief, Adafactor, Adahessian myself). -* EfficientNet-V2 XL TF ported weights added, but they don't validate well in PyTorch (L is better). The pre-processing for the V2 TF training is a bit diff and the fine-tuned 21k -> 1k weights are very sensitive and less robust than the 1k weights. -* Added PyTorch trained EfficientNet-V2 'Tiny' w/ GlobalContext attn weights. Only .1-.2 top-1 better than the SE so more of a curiosity for those interested. diff --git a/hfdocs/source/hf_hub.mdx b/hfdocs/source/hf_hub.mdx new file mode 100644 index 00000000..f4ac7fa9 --- /dev/null +++ b/hfdocs/source/hf_hub.mdx @@ -0,0 +1,54 @@ +# Sharing and Loading Models From the Hugging Face Hub + +The `timm` library has a built-in integration with the Hugging Face Hub, making it easy to share and load models from the πŸ€— Hub. + +In this short guide, we'll see how to: + 1. Share a `timm` model on the Hub + 2. How to load that model back from the Hub + +## Authenticating + +First, you'll need to make sure you have the `huggingface_hub` package installed. + +```bash +pip install huggingface_hub +``` + +Then, you'll need to authenticate yourself. You can do this by running the following command: + +```bash +huggingface-cli login +``` + +Or, if you're using a notebook, you can use the `notebook_login` helper: + +```py +>>> from huggingface_hub import notebook_login +>>> notebook_login() +``` + +## Sharing a Model + +```py +>>> import timm +>>> model = timm.create_model('resnet18', pretrained=True, num_classes=4) +``` + +Here is where you would normally train or fine-tune the model. We'll skip that for the sake of this tutorial. + +Let's pretend we've now fine-tuned the model. The next step would be to push it to the Hub! We can do this with the `timm.models.hub.push_to_hf_hub` function. + +```py +>>> model_cfg = dict(labels=['a', 'b', 'c', 'd']) +>>> timm.models.hub.push_to_hf_hub(model, 'resnet18-random', model_config=model_cfg) +``` + +Running the above would push the model to `/resnet18-random` on the Hub. You can now share this model with your friends, or use it in your own code! + +## Loading a Model + +Loading a model from the Hub is as simple as calling `timm.create_model` with the `pretrained` argument set to the name of the model you want to load. In this case, we'll use [`nateraw/resnet18-random`](https://huggingface.co/nateraw/resnet18-random), which is the model we just pushed to the Hub. + +```py +>>> model_reloaded = timm.create_model('hf_hub:nateraw/resnet18-random', pretrained=True) +``` diff --git a/hfdocs/source/index.mdx b/hfdocs/source/index.mdx index 3733ae1e..cffa5693 100644 --- a/hfdocs/source/index.mdx +++ b/hfdocs/source/index.mdx @@ -1,89 +1,22 @@ -# Getting Started +# timm -## Welcome + -Welcome to the `timm` documentation, a lean set of docs that covers the basics of `timm`. +`timm` is a library containing SOTA computer vision models, layers, utilities, optimizers, schedulers, data-loaders, augmentations, and training/evaluation scripts. -For a more comprehensive set of docs (currently under development), please visit [timmdocs](http://timm.fast.ai) by [Aman Arora](https://github.com/amaarora). +It comes packaged with >700 pretrained models, and is designed to be flexible and easy to use. -## Install +Read the [quick start guide](quickstart) to get up and running with the `timm` library. You will learn how to load, discover, and use pretrained models included in the library. -The library can be installed with pip: - -``` -pip install timm -``` - -I update the PyPi (pip) packages when I'm confident there are no significant model regressions from previous releases. If you want to pip install the bleeding edge from GitHub, use: -``` -pip install git+https://github.com/rwightman/pytorch-image-models.git -``` - -### Conda Environment - - - -- All development and testing has been done in Conda Python 3 environments on Linux x86-64 systems, specifically 3.7, 3.8, 3.9, 3.10 - -- Little to no care has been taken to be Python 2.x friendly and will not support it. If you run into any challenges running on Windows, or other OS, I'm definitely open to looking into those issues so long as it's in a reproducible (read Conda) environment. - -- PyTorch versions 1.9, 1.10, 1.11 have been tested with the latest versions of this code. - - - -I've tried to keep the dependencies minimal, the setup is as per the PyTorch default install instructions for Conda: - -```bash -conda create -n torch-env -conda activate torch-env -conda install pytorch torchvision cudatoolkit=11.3 -c pytorch -conda install pyyaml -``` - -## Load a Pretrained Model - -Pretrained models can be loaded using `timm.create_model` - -```py ->>> import timm - ->>> m = timm.create_model('mobilenetv3_large_100', pretrained=True) ->>> m.eval() -``` - -## List Models with Pretrained Weights - -```py ->>> import timm ->>> from pprint import pprint ->>> model_names = timm.list_models(pretrained=True) ->>> pprint(model_names) -[ - 'adv_inception_v3', - 'cspdarknet53', - 'cspresnext50', - 'densenet121', - 'densenet161', - 'densenet169', - 'densenet201', - 'densenetblur121d', - 'dla34', - 'dla46_c', -] -``` - -## List Model Architectures by Wildcard - -```py ->>> import timm ->>> from pprint import pprint ->>> model_names = timm.list_models('*resne*t*') ->>> pprint(model_names) -[ - 'cspresnet50', - 'cspresnet50d', - 'cspresnet50w', - 'cspresnext50', - ... -] -``` +
+
+
Tutorials
+

Learn the basics and become familiar with timm. Start here if you are using timm for the first time!

+ +
Reference
+

Technical descriptions of how timm classes and methods work.

+ +
+
diff --git a/hfdocs/source/installation.mdx b/hfdocs/source/installation.mdx new file mode 100644 index 00000000..3ff210f3 --- /dev/null +++ b/hfdocs/source/installation.mdx @@ -0,0 +1,74 @@ +# Installation + +Before you start, you'll need to setup your environment and install the appropriate packages. `timm` is tested on **Python 3+**. + +## Virtual Environment + +You should install `timm` in a [virtual environment](https://docs.python.org/3/library/venv.html) to keep things tidy and avoid dependency conflicts. + +1. Create and navigate to your project directory: + + ```bash + mkdir ~/my-project + cd ~/my-project + ``` + +2. Start a virtual environment inside your directory: + + ```bash + python -m venv .env + ``` + +3. Activate and deactivate the virtual environment with the following commands: + + ```bash + # Activate the virtual environment + source .env/bin/activate + + # Deactivate the virtual environment + source .env/bin/deactivate + ``` +` +Once you've created your virtual environment, you can install `timm` in it. + +## Using pip + +The most straightforward way to install `timm` is with pip: + +```bash +pip install timm +``` + +Alternatively, you can install `timm` from GitHub directly to get the latest, bleeding-edge version: + +```bash +pip install git+https://github.com/rwightman/pytorch-image-models.git +``` + +Run the following command to check if `timm` has been properly installed: + +```bash +python -c "from timm import list_models; print(list_models(pretrained=True)[:5])" +``` + +This command lists the first five pretrained models available in `timm` (which are sorted alphebetically). You should see the following output: + +```python +['adv_inception_v3', 'bat_resnext26ts', 'beit_base_patch16_224', 'beit_base_patch16_224_in22k', 'beit_base_patch16_384'] +``` + +## From Source + +Building `timm` from source lets you make changes to the code base. To install from the source, clone the repository and install with the following commands: + +```bash +git clone https://github.com/rwightman/pytorch-image-models.git +cd timm +pip install -e . +``` + +Again, you can check if `timm` was properly installed with the following command: + +```bash +python -c "from timm import list_models; print(list_models(pretrained=True)[:5])" +``` diff --git a/hfdocs/source/model_pages.mdx b/hfdocs/source/model_pages.mdx deleted file mode 100644 index a78663f1..00000000 --- a/hfdocs/source/model_pages.mdx +++ /dev/null @@ -1,5 +0,0 @@ -# Available Models - -`timm` comes bundled with a number of model architectures and corresponding pretrained models. - -In these pages, you will find the models available in the `timm` library, as well as information on how to use them. \ No newline at end of file diff --git a/hfdocs/source/quickstart.mdx b/hfdocs/source/quickstart.mdx new file mode 100644 index 00000000..20771024 --- /dev/null +++ b/hfdocs/source/quickstart.mdx @@ -0,0 +1,228 @@ +# Quickstart + +This quickstart is intended for developers who are ready to dive into the code and see an example of how to integrate `timm` into their model training workflow. + +First, you'll need to install `timm`. For more information on installation, see [Installation](installation). + +```bash +pip install timm +``` + +## Load a Pretrained Model + +Pretrained models can be loaded using [`create_model`]. + +Here, we load the pretrained `mobilenetv3_large_100` model. + +```py +>>> import timm + +>>> m = timm.create_model('mobilenetv3_large_100', pretrained=True) +>>> m.eval() +``` + + + Note: The returned PyTorch model is set to train mode by default, so you must call .eval() on it if you plan to use it for inference. + + +## List Models with Pretrained Weights + +To list models packaged with `timm`, you can use [`list_models`]. If you specify `pretrained=True`, this function will only return model names that have associated pretrained weights available. + +```py +>>> import timm +>>> from pprint import pprint +>>> model_names = timm.list_models(pretrained=True) +>>> pprint(model_names) +[ + 'adv_inception_v3', + 'cspdarknet53', + 'cspresnext50', + 'densenet121', + 'densenet161', + 'densenet169', + 'densenet201', + 'densenetblur121d', + 'dla34', + 'dla46_c', +] +``` + +You can also list models with a specific pattern in their name. + +```py +>>> import timm +>>> from pprint import pprint +>>> model_names = timm.list_models('*resne*t*') +>>> pprint(model_names) +[ + 'cspresnet50', + 'cspresnet50d', + 'cspresnet50w', + 'cspresnext50', + ... +] +``` + +## Fine-Tune a Pretrained Model + +You can finetune any of the pre-trained models just by changing the classifier (the last layer). + +```py +>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True, num_classes=NUM_FINETUNE_CLASSES) +``` + +To fine-tune on your own dataset, you have to write a PyTorch training loop or adapt `timm`'s [training script](training_script) to use your dataset. + +## Use a Pretrained Model for Feature Extraction + +Without modifying the network, one can call model.forward_features(input) on any model instead of the usual model(input). This will bypass the head classifier and global pooling for networks. + +For a more in depth guide to using `timm` for feature extraction, see [Feature Extraction](feature_extraction). + +```py +>>> import timm +>>> import torch +>>> x = torch.randn(1, 3, 224, 224) +>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True) +>>> features = model.forward_features(x) +>>> print(features.shape) +torch.Size([1, 960, 7, 7]) +``` + +## Image Augmentation + +To transform images into valid inputs for a model, you can use [`timm.data.create_transform`], providing the desired `input_size` that the model expects. + +This will return a generic transform that uses reasonable defaults. + +```py +>>> timm.data.create_transform((3, 224, 224)) +Compose( + Resize(size=256, interpolation=bilinear, max_size=None, antialias=None) + CenterCrop(size=(224, 224)) + ToTensor() + Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) +) +``` + +Pretrained models have specific transforms that were applied to images fed into them while training. If you use the wrong transform on your image, the model won't understand what it's seeing! + +To figure out which transformations were used for a given pretrained model, we can start by taking a look at its `pretrained_cfg` + +```py +>>> model.pretrained_cfg +{'url': 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth', + 'num_classes': 1000, + 'input_size': (3, 224, 224), + 'pool_size': (7, 7), + 'crop_pct': 0.875, + 'interpolation': 'bicubic', + 'mean': (0.485, 0.456, 0.406), + 'std': (0.229, 0.224, 0.225), + 'first_conv': 'conv_stem', + 'classifier': 'classifier', + 'architecture': 'mobilenetv3_large_100'} +``` + +We can then resolve only the data related configuration by using [`timm.data.resolve_data_config`]. + +```py +>>> timm.data.resolve_data_config(model.pretrained_cfg) +{'input_size': (3, 224, 224), + 'interpolation': 'bicubic', + 'mean': (0.485, 0.456, 0.406), + 'std': (0.229, 0.224, 0.225), + 'crop_pct': 0.875} +``` + +We can pass this data config to [`timm.data.create_transform`] to initialize the model's associated transform. + +```py +>>> data_cfg = timm.data.resolve_data_config(model.pretrained_cfg) +>>> transform = timm.data.create_transform(**data_cfg) +>>> transform +Compose( + Resize(size=256, interpolation=bicubic, max_size=None, antialias=None) + CenterCrop(size=(224, 224)) + ToTensor() + Normalize(mean=tensor([0.4850, 0.4560, 0.4060]), std=tensor([0.2290, 0.2240, 0.2250])) +) +``` + + + Note: Here, the pretrained model's config happens to be the same as the generic config we made earlier. This is not always the case. So, it's safer to use the data config to create the transform as we did here instead of using the generic transform. + + +## Using Pretrained Models for Inference + +Here, we will put together the above sections and use a pretrained model for inference. + +First we'll need an image to do inference on. Here we load a picture of a leaf from the web: + +```py +>>> import requests +>>> from PIL import Image +>>> from io import BytesIO +>>> url = 'https://datasets-server.huggingface.co/assets/imagenet-1k/--/default/test/12/image/image.jpg' +>>> image = Image.open(requests.get(url, stream=True).raw) +>>> image +``` + +Here's the image we loaded: + +An Image from a link + +Now, we'll create our model and transforms again. This time, we make sure to set our model in evaluation mode. + +```py +>>> model = timm.create_model('mobilenetv3_large_100', pretrained=True).eval() +>>> transform = timm.data.create_transform( + **timm.data.resolve_data_config(model.pretrained_cfg) +) +``` + +We can prepare this image for the model by passing it to the transform. + +```py +>>> image_tensor = transform(image) +>>> image_tensor.shape +torch.Size([3, 224, 224]) +``` + +Now we can pass that image to the model to get the predictions. We use `unsqueeze(0)` in this case, as the model is expecting a batch dimension. + +```py +>>> output = model(image_tensor.unsqueeze(0)) +>>> output.shape +torch.Size([1, 1000]) +``` + +To get the predicted probabilities, we apply softmax to the output. This leaves us with a tensor of shape `(num_classes,)`. + +```py +>>> probabilities = torch.nn.functional.softmax(output[0], dim=0) +>>> probabilities.shape +torch.Size([1000]) +``` + +Now we'll find the top 5 predicted class indexes and values using `torch.topk`. + +```py +>>> values, indices = torch.topk(probabilities, 5) +>>> indices +tensor([162, 166, 161, 164, 167]) +``` + +If we check the imagenet labels for the top index, we can see what the model predicted... + +```py +>>> IMAGENET_1k_URL = 'https://storage.googleapis.com/bit_models/ilsvrc2012_wordnet_lemmas.txt' +>>> IMAGENET_1k_LABELS = requests.get(IMAGENET_1k_URL).text.strip().split('\n') +>>> [{'label': IMAGENET_1k_LABELS[idx], 'value': val.item()} for val, idx in zip(values, indices)] +[{'label': 'beagle', 'value': 0.8486220836639404}, + {'label': 'Walker_hound, Walker_foxhound', 'value': 0.03753996267914772}, + {'label': 'basset, basset_hound', 'value': 0.024628572165966034}, + {'label': 'bluetick', 'value': 0.010317106731235981}, + {'label': 'English_foxhound', 'value': 0.006958036217838526}] +``` \ No newline at end of file diff --git a/hfdocs/source/reference/data.mdx b/hfdocs/source/reference/data.mdx new file mode 100644 index 00000000..b5048739 --- /dev/null +++ b/hfdocs/source/reference/data.mdx @@ -0,0 +1,9 @@ +# Data + +[[autodoc]] timm.data.create_dataset + +[[autodoc]] timm.data.create_loader + +[[autodoc]] timm.data.create_transform + +[[autodoc]] timm.data.resolve_data_config \ No newline at end of file diff --git a/hfdocs/source/reference/models.mdx b/hfdocs/source/reference/models.mdx new file mode 100644 index 00000000..31bb3c27 --- /dev/null +++ b/hfdocs/source/reference/models.mdx @@ -0,0 +1,5 @@ +# Models + +[[autodoc]] timm.create_model + +[[autodoc]] timm.list_models diff --git a/hfdocs/source/reference/optimizers.mdx b/hfdocs/source/reference/optimizers.mdx new file mode 100644 index 00000000..637e7f0a --- /dev/null +++ b/hfdocs/source/reference/optimizers.mdx @@ -0,0 +1,27 @@ +# Optimization + +This page contains the API reference documentation for learning rate optimizers included in `timm`. + +## Optimizers + +### Factory functions + +[[autodoc]] timm.optim.optim_factory.create_optimizer +[[autodoc]] timm.optim.optim_factory.create_optimizer_v2 + +### Optimizer Classes + +[[autodoc]] timm.optim.adabelief.AdaBelief +[[autodoc]] timm.optim.adafactor.Adafactor +[[autodoc]] timm.optim.adahessian.Adahessian +[[autodoc]] timm.optim.adamp.AdamP +[[autodoc]] timm.optim.adamw.AdamW +[[autodoc]] timm.optim.lamb.Lamb +[[autodoc]] timm.optim.lars.Lars +[[autodoc]] timm.optim.lookahead.Lookahead +[[autodoc]] timm.optim.madgrad.MADGRAD +[[autodoc]] timm.optim.nadam.Nadam +[[autodoc]] timm.optim.nvnovograd.NvNovoGrad +[[autodoc]] timm.optim.radam.RAdam +[[autodoc]] timm.optim.rmsprop_tf.RMSpropTF +[[autodoc]] timm.optim.sgdp.SGDP diff --git a/hfdocs/source/reference/schedulers.mdx b/hfdocs/source/reference/schedulers.mdx new file mode 100644 index 00000000..c44577d6 --- /dev/null +++ b/hfdocs/source/reference/schedulers.mdx @@ -0,0 +1,19 @@ +# Learning Rate Schedulers + +This page contains the API reference documentation for learning rate schedulers included in `timm`. + +## Schedulers + +### Factory functions + +[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler +[[autodoc]] timm.scheduler.scheduler_factory.create_scheduler_v2 + +### Scheduler Classes + +[[autodoc]] timm.scheduler.cosine_lr.CosineLRScheduler +[[autodoc]] timm.scheduler.multistep_lr.MultiStepLRScheduler +[[autodoc]] timm.scheduler.plateau_lr.PlateauLRScheduler +[[autodoc]] timm.scheduler.poly_lr.PolyLRScheduler +[[autodoc]] timm.scheduler.step_lr.StepLRScheduler +[[autodoc]] timm.scheduler.tanh_lr.TanhLRScheduler diff --git a/hfdocs/source/scripts.mdx b/hfdocs/source/scripts.mdx deleted file mode 100644 index 46404d81..00000000 --- a/hfdocs/source/scripts.mdx +++ /dev/null @@ -1,35 +0,0 @@ -# Scripts -A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release. - -The training and validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples). I have added significant functionality over time, including CUDA specific performance enhancements based on -[NVIDIA's APEX Examples](https://github.com/NVIDIA/apex/tree/master/examples). - -## Training Script - -The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a `train` and `validation` folder. - -To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value: - -```bash -./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4 -``` - - - It is recommended to use PyTorch 1.9+ w/ PyTorch native AMP and DDP instead of APEX AMP. --amp defaults to native AMP as of timm ver 0.4.3. --apex-amp will force use of APEX components if they are installed. - - -## Validation / Inference Scripts - -Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script. - -To validate with the model's pretrained weights (if they exist): - -```bash -python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained -``` - -To run inference from a checkpoint: - -```bash -python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar -``` \ No newline at end of file diff --git a/hfdocs/source/training_hparam_examples.mdx b/hfdocs/source/training_script.mdx similarity index 63% rename from hfdocs/source/training_hparam_examples.mdx rename to hfdocs/source/training_script.mdx index e582cfc9..3eb772a3 100644 --- a/hfdocs/source/training_hparam_examples.mdx +++ b/hfdocs/source/training_script.mdx @@ -1,6 +1,44 @@ -# Training Examples +# Scripts -## EfficientNet-B2 with RandAugment - 80.4 top-1, 95.1 top-5 +A train, validation, inference, and checkpoint cleaning script included in the github root folder. Scripts are not currently packaged in the pip release. + +The training and validation scripts evolved from early versions of the [PyTorch Imagenet Examples](https://github.com/pytorch/examples). I have added significant functionality over time, including CUDA specific performance enhancements based on +[NVIDIA's APEX Examples](https://github.com/NVIDIA/apex/tree/master/examples). + +## Training Script + +The variety of training args is large and not all combinations of options (or even options) have been fully tested. For the training dataset folder, specify the folder to the base that contains a `train` and `validation` folder. + +To train an SE-ResNet34 on ImageNet, locally distributed, 4 GPUs, one process per GPU w/ cosine schedule, random-erasing prob of 50% and per-pixel random value: + +```bash +./distributed_train.sh 4 /data/imagenet --model seresnet34 --sched cosine --epochs 150 --warmup-epochs 5 --lr 0.4 --reprob 0.5 --remode pixel --batch-size 256 --amp -j 4 +``` + + + It is recommended to use PyTorch 1.9+ w/ PyTorch native AMP and DDP instead of APEX AMP. --amp defaults to native AMP as of timm ver 0.4.3. --apex-amp will force use of APEX components if they are installed. + + + +## Validation / Inference Scripts + +Validation and inference scripts are similar in usage. One outputs metrics on a validation set and the other outputs topk class ids in a csv. Specify the folder containing validation images, not the base as in training script. + +To validate with the model's pretrained weights (if they exist): + +```bash +python validate.py /imagenet/validation/ --model seresnext26_32x4d --pretrained +``` + +To run inference from a checkpoint: + +```bash +python inference.py /imagenet/validation/ --model mobilenetv3_large_100 --checkpoint ./output/train/model_best.pth.tar +``` + +## Training Examples + +### EfficientNet-B2 with RandAugment - 80.4 top-1, 95.1 top-5 These params are for dual Titan RTX cards with NVIDIA Apex installed: @@ -8,7 +46,7 @@ These params are for dual Titan RTX cards with NVIDIA Apex installed: ./distributed_train.sh 2 /imagenet/ --model efficientnet_b2 -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .016 ``` -## MixNet-XL with RandAugment - 80.5 top-1, 94.9 top-5 +### MixNet-XL with RandAugment - 80.5 top-1, 94.9 top-5 This params are for dual Titan RTX cards with NVIDIA Apex installed: @@ -16,45 +54,45 @@ This params are for dual Titan RTX cards with NVIDIA Apex installed: ./distributed_train.sh 2 /imagenet/ --model mixnet_xl -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .969 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.3 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.3 --amp --lr .016 --dist-bn reduce ``` -## SE-ResNeXt-26-D and SE-ResNeXt-26-T +### SE-ResNeXt-26-D and SE-ResNeXt-26-T These hparams (or similar) work well for a wide range of ResNet architecture, generally a good idea to increase the epoch # as the model size increases... ie approx 180-200 for ResNe(X)t50, and 220+ for larger. Increase batch size and LR proportionally for better GPUs or with AMP enabled. These params were for 2 1080Ti cards: ```bash ./distributed_train.sh 2 /imagenet/ --model seresnext26t_32x4d --lr 0.1 --warmup-epochs 5 --epochs 160 --weight-decay 1e-4 --sched cosine --reprob 0.4 --remode pixel -b 112 ``` -## EfficientNet-B3 with RandAugment - 81.5 top-1, 95.7 top-5 +### EfficientNet-B3 with RandAugment - 81.5 top-1, 95.7 top-5 The training of this model started with the same command line as EfficientNet-B2 w/ RA above. After almost three weeks of training the process crashed. The results weren't looking amazing so I resumed the training several times with tweaks to a few params (increase RE prob, decrease rand-aug, increase ema-decay). Nothing looked great. I ended up averaging the best checkpoints from all restarts. The result is mediocre at default res/crop but oddly performs much better with a full image test crop of 1.0. -## EfficientNet-B0 with RandAugment - 77.7 top-1, 95.3 top-5 +### EfficientNet-B0 with RandAugment - 77.7 top-1, 95.3 top-5 [Michael Klachko](https://github.com/michaelklachko) achieved these results with the command line for B2 adapted for larger batch size, with the recommended B0 dropout rate of 0.2. ```bash ./distributed_train.sh 2 /imagenet/ --model efficientnet_b0 -b 384 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .048 ``` -## ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5 +### ResNet50 with JSD loss and RandAugment (clean + 2x RA augs) - 79.04 top-1, 94.39 top-5 Trained on two older 1080Ti cards, this took a while. Only slightly, non statistically better ImageNet validation result than my first good AugMix training of 78.99. However, these weights are more robust on tests with ImageNetV2, ImageNet-Sketch, etc. Unlike my first AugMix runs, I've enabled SplitBatchNorm, disabled random erasing on the clean split, and cranked up random erasing prob on the 2 augmented paths. ```bash ./distributed_train.sh 2 /imagenet -b 64 --model resnet50 --sched cosine --epochs 200 --lr 0.05 --amp --remode pixel --reprob 0.6 --aug-splits 3 --aa rand-m9-mstd0.5-inc1 --resplit --split-bn --jsd --dist-bn reduce ``` -## EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5 +### EfficientNet-ES (EdgeTPU-Small) with RandAugment - 78.066 top-1, 93.926 top-5 Trained by [Andrew Lavin](https://github.com/andravin) with 8 V100 cards. Model EMA was not used, final checkpoint is the average of 8 best checkpoints during training. ```bash ./distributed_train.sh 8 /imagenet --model efficientnet_es -b 128 --sched step --epochs 450 --decay-epochs 2.4 --decay-rate .97 --opt rmsproptf --opt-eps .001 -j 8 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 ``` -## MobileNetV3-Large-100 - 75.766 top-1, 92,542 top-5 +### MobileNetV3-Large-100 - 75.766 top-1, 92,542 top-5 ```bash ./distributed_train.sh 2 /imagenet/ --model mobilenetv3_large_100 -b 512 --sched step --epochs 600 --decay-epochs 2.4 --decay-rate .973 --opt rmsproptf --opt-eps .001 -j 7 --warmup-lr 1e-6 --weight-decay 1e-5 --drop 0.2 --drop-path 0.2 --model-ema --model-ema-decay 0.9999 --aa rand-m9-mstd0.5 --remode pixel --reprob 0.2 --amp --lr .064 --lr-noise 0.42 0.9 ``` -## ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5 +### ResNeXt-50 32x4d w/ RandAugment - 79.762 top-1, 94.60 top-5 These params will also work well for SE-ResNeXt-50 and SK-ResNeXt-50 and likely 101. I used them for the SK-ResNeXt-50 32x4d that I trained with 2 GPU using a slightly higher LR per effective batch size (lr=0.18, b=192 per GPU). The cmd line below are tuned for 8 GPU training. diff --git a/inference.py b/inference.py index 1509b323..cfbe62d1 100755 --- a/inference.py +++ b/inference.py @@ -20,7 +20,7 @@ import torch from timm.data import create_dataset, create_loader, resolve_data_config from timm.layers import apply_test_time_pool from timm.models import create_model -from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser +from timm.utils import AverageMeter, setup_default_logging, set_jit_fuser, ParseKwargs try: from apex import amp @@ -72,6 +72,8 @@ parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)') parser.add_argument('--img-size', default=None, type=int, metavar='N', help='Input image dimension, uses model default if empty') +parser.add_argument('--in-chans', type=int, default=None, metavar='N', + help='Image input channels (default: None => 3)') parser.add_argument('--input-size', default=None, nargs=3, type=int, metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') parser.add_argument('--use-train-size', action='store_true', default=False, @@ -110,6 +112,7 @@ parser.add_argument('--amp-dtype', default='float16', type=str, help='lower precision AMP dtype (default: float16)') parser.add_argument('--fuser', default='', type=str, help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") +parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs) scripting_group = parser.add_mutually_exclusive_group() scripting_group.add_argument('--torchscript', default=False, action='store_true', @@ -170,12 +173,19 @@ def main(): set_jit_fuser(args.fuser) # create model + in_chans = 3 + if args.in_chans is not None: + in_chans = args.in_chans + elif args.input_size is not None: + in_chans = args.input_size[0] + model = create_model( args.model, num_classes=args.num_classes, - in_chans=3, + in_chans=in_chans, pretrained=args.pretrained, checkpoint_path=args.checkpoint, + **args.model_kwargs, ) if args.num_classes is None: assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.' diff --git a/mkdocs.yml b/mkdocs.yml index a72436c6..7adb4d34 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -44,3 +44,11 @@ markdown_extensions: plugins: - search - awesome-pages + - redirects: + redirect_maps: + 'index.md': 'https://huggingface.co/docs/timm/index' + 'models.md': 'https://huggingface.co/docs/timm/models' + 'results.md': 'https://huggingface.co/docs/timm/results' + 'scripts.md': 'https://huggingface.co/docs/timm/training_script' + 'training_hparam_examples.md': 'https://huggingface.co/docs/timm/training_script#training-examples' + 'feature_extraction.md': 'https://huggingface.co/docs/timm/feature_extraction' diff --git a/requirements-docs.txt b/requirements-docs.txt index 716a3bf7..d782d5fb 100644 --- a/requirements-docs.txt +++ b/requirements-docs.txt @@ -1,4 +1,5 @@ mkdocs mkdocs-material +mkdocs-redirects mdx_truly_sane_lists -mkdocs-awesome-pages-plugin \ No newline at end of file +mkdocs-awesome-pages-plugin diff --git a/results/README.md b/results/README.md index 4fabf64b..81f30061 100644 --- a/results/README.md +++ b/results/README.md @@ -38,7 +38,7 @@ An ImageNet test set of 10,000 images sampled from new images roughly 10 years a ### ImageNet-Adversarial - [`results-imagenet-a.csv`](results-imagenet-a.csv) -A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occuring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1. +A collection of 7500 images covering 200 of the 1000 ImageNet classes. Images are naturally occurring adversarial examples that confuse typical ImageNet classifiers. This is a challenging dataset, your typical ResNet-50 will score 0% top-1. For clean validation with same 200 classes, see [`results-imagenet-a-clean.csv`](results-imagenet-a-clean.csv) diff --git a/results/results-imagenet-a-clean.csv b/results/results-imagenet-a-clean.csv index 68822bd8..cd4afa70 100644 --- a/results/results-imagenet-a-clean.csv +++ b/results/results-imagenet-a-clean.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation -beit_large_patch16_512,98.560,1.440,99.840,0.160,305.67,512,1.000,bicubic -tf_efficientnet_l2_ns,98.550,1.450,99.820,0.180,480.31,800,0.960,bicubic -beit_large_patch16_384,98.520,1.480,99.820,0.180,305.00,384,1.000,bicubic -tf_efficientnet_l2_ns_475,98.500,1.500,99.830,0.170,480.31,475,0.936,bicubic +eva_giant_patch14_560.m30m_ft_in22k_in1k,98.820,1.180,99.900,0.100,"1,014.45",560,1.000,bicubic +eva_giant_patch14_336.clip_ft_in1k,98.820,1.180,99.820,0.180,"1,013.01",336,1.000,bicubic +eva_giant_patch14_336.m30m_ft_in22k_in1k,98.810,1.190,99.900,0.100,"1,013.01",336,1.000,bicubic +eva_large_patch14_336.in22k_ft_in22k_in1k,98.740,1.260,99.810,0.190,304.53,336,1.000,bicubic +eva_large_patch14_336.in22k_ft_in1k,98.710,1.290,99.870,0.130,304.53,336,1.000,bicubic +maxvit_base_tf_512.in21k_ft_in1k,98.630,1.370,99.800,0.200,119.88,512,1.000,bicubic +maxvit_xlarge_tf_512.in21k_ft_in1k,98.620,1.380,99.800,0.200,475.77,512,1.000,bicubic +maxvit_large_tf_512.in21k_ft_in1k,98.620,1.380,99.790,0.210,212.33,512,1.000,bicubic +beit_large_patch16_512.in22k_ft_in22k_in1k,98.560,1.440,99.840,0.160,305.67,512,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k,98.550,1.450,99.820,0.180,480.31,800,0.960,bicubic +beitv2_large_patch16_224.in1k_ft_in22k_in1k,98.540,1.460,99.760,0.240,304.43,224,0.950,bicubic +beit_large_patch16_384.in22k_ft_in22k_in1k,98.520,1.480,99.820,0.180,305.00,384,1.000,bicubic +maxvit_base_tf_384.in21k_ft_in1k,98.520,1.480,99.750,0.250,119.65,384,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k_475,98.500,1.500,99.830,0.170,480.31,475,0.936,bicubic +maxvit_xlarge_tf_384.in21k_ft_in1k,98.500,1.500,99.780,0.220,475.32,384,1.000,bicubic +maxvit_large_tf_384.in21k_ft_in1k,98.490,1.510,99.750,0.250,212.03,384,1.000,bicubic +eva_giant_patch14_224.clip_ft_in1k,98.480,1.520,99.820,0.180,"1,012.56",224,1.000,bicubic deit3_large_patch16_384_in21ft1k,98.460,1.540,99.760,0.240,304.76,384,1.000,bicubic -convnext_xlarge_384_in22ft1k,98.350,1.650,99.800,0.200,350.20,384,1.000,bicubic -vit_large_patch16_384,98.220,1.780,99.800,0.200,304.72,384,1.000,bicubic -convnext_large_384_in22ft1k,98.220,1.780,99.730,0.270,197.77,384,1.000,bicubic -beit_large_patch16_224,98.180,1.820,99.760,0.240,304.43,224,0.900,bicubic +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,98.430,1.570,99.810,0.190,632.46,336,1.000,bicubic +eva_large_patch14_196.in22k_ft_in22k_in1k,98.430,1.570,99.770,0.230,304.14,196,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k_384,98.420,1.580,99.810,0.190,350.20,384,1.000,bicubic +eva_large_patch14_196.in22k_ft_in1k,98.350,1.650,99.820,0.180,304.14,196,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,98.330,1.670,99.760,0.240,304.53,336,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,98.270,1.730,99.760,0.240,632.05,224,1.000,bicubic +vit_large_patch14_clip_336.openai_ft_in12k_in1k,98.260,1.740,99.770,0.230,304.53,336,1.000,bicubic +convnext_large.fb_in22k_ft_in1k_384,98.230,1.770,99.750,0.250,197.77,384,1.000,bicubic +vit_large_patch16_384.augreg_in21k_ft_in1k,98.220,1.780,99.800,0.200,304.72,384,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in12k_in1k,98.220,1.780,99.730,0.270,304.20,224,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in1k,98.220,1.780,99.720,0.280,304.53,336,1.000,bicubic +vit_base_patch16_clip_384.openai_ft_in12k_in1k,98.200,1.800,99.660,0.340,86.86,384,0.950,bicubic +beit_large_patch16_224.in22k_ft_in22k_in1k,98.180,1.820,99.760,0.240,304.43,224,0.900,bicubic deit3_large_patch16_224_in21ft1k,98.170,1.830,99.760,0.240,304.37,224,1.000,bicubic deit3_huge_patch14_224_in21ft1k,98.170,1.830,99.730,0.270,632.13,224,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in1k,98.160,1.840,99.660,0.340,304.20,224,1.000,bicubic swinv2_large_window12to24_192to384_22kft1k,98.150,1.850,99.690,0.310,196.74,384,1.000,bicubic swinv2_base_window12to24_192to384_22kft1k,98.140,1.860,99.780,0.220,87.92,384,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k,98.120,1.880,99.780,0.220,350.20,288,1.000,bicubic +convnext_large.fb_in22k_ft_in1k,98.120,1.880,99.750,0.250,197.77,288,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,98.080,1.920,99.760,0.240,304.20,224,1.000,bicubic +convnext_base.fb_in22k_ft_in1k_384,98.070,1.930,99.650,0.350,88.59,384,1.000,bicubic swin_large_patch4_window12_384,98.040,1.960,99.690,0.310,196.74,384,1.000,bicubic -convnext_base_384_in22ft1k,97.950,2.050,99.650,0.350,88.59,384,1.000,bicubic -tf_efficientnet_b7_ns,97.920,2.080,99.720,0.280,66.35,600,0.949,bicubic -convnext_xlarge_in22ft1k,97.920,2.080,99.680,0.320,350.20,224,0.875,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in1k,98.020,1.980,99.720,0.280,632.05,224,1.000,bicubic +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,98.010,1.990,99.660,0.340,86.86,384,1.000,bicubic +tf_efficientnet_b7.ns_jft_in1k,97.910,2.090,99.720,0.280,66.35,600,0.949,bicubic +tf_efficientnetv2_xl.in21k_ft_in1k,97.910,2.090,99.570,0.430,208.12,512,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in1k,97.900,2.100,99.650,0.350,304.20,224,1.000,bicubic swin_base_patch4_window12_384,97.890,2.110,99.710,0.290,87.90,384,1.000,bicubic -vit_large_r50_s32_384,97.860,2.140,99.670,0.330,329.09,384,1.000,bicubic +convnext_base.fb_in22k_ft_in1k,97.860,2.140,99.680,0.320,88.59,288,1.000,bicubic +vit_large_r50_s32_384.augreg_in21k_ft_in1k,97.860,2.140,99.670,0.330,329.09,384,1.000,bicubic swinv2_large_window12to16_192to256_22kft1k,97.860,2.140,99.650,0.350,196.74,256,0.900,bicubic -vit_base_patch16_384,97.840,2.160,99.670,0.330,86.86,384,1.000,bicubic -convnext_large_in22ft1k,97.830,2.170,99.690,0.310,197.77,224,0.875,bicubic -deit3_base_patch16_384_in21ft1k,97.830,2.170,99.680,0.320,86.88,384,1.000,bicubic -beit_base_patch16_384,97.820,2.180,99.700,0.300,86.74,384,1.000,bicubic +deit3_base_patch16_384_in21ft1k,97.840,2.160,99.680,0.320,86.88,384,1.000,bicubic +vit_base_patch16_384.augreg_in21k_ft_in1k,97.840,2.160,99.670,0.330,86.86,384,1.000,bicubic +maxvit_large_tf_512.in1k,97.830,2.170,99.560,0.440,212.33,512,1.000,bicubic +beit_base_patch16_384.in22k_ft_in22k_in1k,97.820,2.180,99.700,0.300,86.74,384,1.000,bicubic +tf_efficientnetv2_m.in21k_ft_in1k,97.820,2.180,99.600,0.400,54.14,480,1.000,bicubic +tf_efficientnetv2_l.in21k_ft_in1k,97.800,2.200,99.770,0.230,118.52,480,1.000,bicubic volo_d5_512,97.770,2.230,99.670,0.330,296.09,512,1.150,bicubic volo_d5_448,97.760,2.240,99.620,0.380,295.91,448,1.150,bicubic -tf_efficientnetv2_l_in21ft1k,97.700,2.300,99.670,0.330,118.52,480,1.000,bicubic +maxvit_small_tf_512.in1k,97.750,2.250,99.550,0.450,69.13,512,1.000,bicubic +maxvit_base_tf_512.in1k,97.740,2.260,99.610,0.390,119.88,512,1.000,bicubic +vit_base_patch16_clip_384.laion2b_ft_in1k,97.730,2.270,99.630,0.370,86.86,384,1.000,bicubic +vit_base_patch8_224.augreg2_in21k_ft_in1k,97.710,2.290,99.650,0.350,86.58,224,0.900,bicubic +beitv2_base_patch16_224.in1k_ft_in22k_in1k,97.690,2.310,99.680,0.320,86.53,224,0.900,bicubic volo_d4_448,97.670,2.330,99.610,0.390,193.41,448,1.150,bicubic -tf_efficientnetv2_xl_in21ft1k,97.660,2.340,99.490,0.510,208.12,512,1.000,bicubic swinv2_base_window12to16_192to256_22kft1k,97.650,2.350,99.720,0.280,87.92,256,0.900,bicubic swin_large_patch4_window7_224,97.650,2.350,99.580,0.420,196.53,224,0.900,bicubic -vit_large_patch16_224,97.640,2.360,99.590,0.410,304.33,224,0.900,bicubic -tf_efficientnet_b6_ns,97.630,2.370,99.580,0.420,43.04,528,0.942,bicubic +vit_large_patch16_224.augreg_in21k_ft_in1k,97.640,2.360,99.590,0.410,304.33,224,0.900,bicubic +tf_efficientnet_b6.ns_jft_in1k,97.630,2.370,99.580,0.420,43.04,528,0.942,bicubic ig_resnext101_32x48d,97.620,2.380,99.700,0.300,828.41,224,0.875,bilinear +convnext_small.fb_in22k_ft_in1k_384,97.600,2.400,99.600,0.400,50.22,384,1.000,bicubic dm_nfnet_f6,97.600,2.400,99.550,0.450,438.36,576,0.956,bicubic -vit_base_patch8_224,97.580,2.420,99.670,0.330,86.58,224,0.900,bicubic +vit_base_patch8_224.augreg_in21k_ft_in1k,97.580,2.420,99.670,0.330,86.58,224,0.900,bicubic dm_nfnet_f4,97.580,2.420,99.510,0.490,316.07,512,0.951,bicubic +maxvit_base_tf_384.in1k,97.570,2.430,99.590,0.410,119.65,384,1.000,bicubic +maxvit_tiny_tf_512.in1k,97.570,2.430,99.560,0.440,31.05,512,1.000,bicubic +maxvit_large_tf_384.in1k,97.570,2.430,99.530,0.470,212.03,384,1.000,bicubic +vit_base_patch16_clip_384.openai_ft_in1k,97.550,2.450,99.660,0.340,86.86,384,1.000,bicubic volo_d3_448,97.550,2.450,99.550,0.450,86.63,448,1.000,bicubic dm_nfnet_f5,97.540,2.460,99.570,0.430,377.21,544,0.954,bicubic xcit_large_24_p8_384_dist,97.520,2.480,99.540,0.460,188.93,384,1.000,bicubic +vit_base_patch16_clip_224.openai_ft_in12k_in1k,97.520,2.480,99.500,0.500,86.57,224,0.950,bicubic xcit_large_24_p16_384_dist,97.520,2.480,99.480,0.520,189.10,384,1.000,bicubic -tf_efficientnet_b5_ns,97.500,2.500,99.630,0.370,30.39,456,0.934,bicubic +tf_efficientnet_b5.ns_jft_in1k,97.500,2.500,99.630,0.370,30.39,456,0.934,bicubic resnetv2_152x4_bitm,97.490,2.510,99.610,0.390,936.53,480,1.000,bilinear -deit3_base_patch16_224_in21ft1k,97.490,2.510,99.600,0.400,86.59,224,1.000,bicubic +deit3_base_patch16_224_in21ft1k,97.480,2.520,99.600,0.400,86.59,224,1.000,bicubic cait_m48_448,97.480,2.520,99.550,0.450,356.46,448,1.000,bicubic -tf_efficientnetv2_m_in21ft1k,97.480,2.520,99.530,0.470,54.14,480,1.000,bicubic -convnext_base_in22ft1k,97.470,2.530,99.600,0.400,88.59,224,0.875,bicubic -convnext_small_384_in22ft1k,97.460,2.540,99.580,0.420,50.22,384,1.000,bicubic +tf_efficientnetv2_l.in1k,97.470,2.530,99.530,0.470,118.52,480,1.000,bicubic +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,97.450,2.550,99.540,0.460,86.57,224,0.950,bicubic +vit_medium_patch16_gap_384.in12k_ft_in1k,97.440,2.560,99.640,0.360,39.03,384,0.950,bicubic deit3_large_patch16_384,97.420,2.580,99.620,0.380,304.76,384,1.000,bicubic +maxvit_small_tf_384.in1k,97.420,2.580,99.510,0.490,69.02,384,1.000,bicubic +flexivit_large.1200ep_in1k,97.410,2.590,99.600,0.400,304.36,240,0.950,bicubic +efficientnet_b5.in12k_ft_in1k,97.410,2.590,99.540,0.460,30.39,448,1.000,bicubic cait_m36_384,97.400,2.600,99.510,0.490,271.22,384,1.000,bicubic -volo_d5_224,97.390,2.610,99.570,0.430,295.46,224,0.960,bicubic -ig_resnext101_32x32d,97.370,2.630,99.680,0.320,468.53,224,0.875,bilinear +volo_d5_224,97.380,2.620,99.570,0.430,295.46,224,0.960,bicubic +ig_resnext101_32x32d,97.360,2.640,99.680,0.320,468.53,224,0.875,bilinear +convnext_small.fb_in22k_ft_in1k,97.360,2.640,99.530,0.470,50.22,288,1.000,bicubic +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,97.360,2.640,99.520,0.480,88.30,384,1.000,bicubic dm_nfnet_f3,97.350,2.650,99.560,0.440,254.92,416,0.940,bicubic cait_s36_384,97.330,2.670,99.530,0.470,68.37,384,1.000,bicubic -volo_d2_384,97.310,2.690,99.600,0.400,58.87,384,1.000,bicubic +volo_d2_384,97.320,2.680,99.600,0.400,58.87,384,1.000,bicubic +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,97.320,2.680,99.480,0.520,88.34,448,1.000,bicubic volo_d4_224,97.300,2.700,99.520,0.480,192.96,224,0.960,bicubic +maxvit_tiny_tf_384.in1k,97.300,2.700,99.500,0.500,30.98,384,1.000,bicubic xcit_medium_24_p8_384_dist,97.290,2.710,99.510,0.490,84.32,384,1.000,bicubic -tf_efficientnetv2_l,97.280,2.720,99.550,0.450,118.52,480,1.000,bicubic -xcit_medium_24_p16_384_dist,97.280,2.720,99.460,0.540,84.40,384,1.000,bicubic +flexivit_large.600ep_in1k,97.280,2.720,99.590,0.410,304.36,240,0.950,bicubic +xcit_medium_24_p16_384_dist,97.270,2.730,99.460,0.540,84.40,384,1.000,bicubic swin_base_patch4_window7_224,97.250,2.750,99.530,0.470,87.77,224,0.900,bicubic -xcit_small_24_p8_384_dist,97.240,2.760,99.610,0.390,47.63,384,1.000,bicubic +flexivit_large.300ep_in1k,97.250,2.750,99.490,0.510,304.36,240,0.950,bicubic +xcit_small_24_p8_384_dist,97.240,2.760,99.600,0.400,47.63,384,1.000,bicubic xcit_small_12_p8_384_dist,97.230,2.770,99.480,0.520,26.21,384,1.000,bicubic +tf_efficientnetv2_m.in1k,97.210,2.790,99.530,0.470,54.14,480,1.000,bicubic swsl_resnext101_32x8d,97.200,2.800,99.570,0.430,88.79,224,0.875,bilinear -tf_efficientnet_b7_ap,97.200,2.800,99.540,0.460,66.35,600,0.949,bicubic +tf_efficientnet_b7.ap_in1k,97.200,2.800,99.540,0.460,66.35,600,0.949,bicubic regnetz_e8,97.200,2.800,99.500,0.500,57.70,320,1.000,bicubic -tf_efficientnet_b8,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic -vit_base_r50_s16_384,97.180,2.820,99.560,0.440,98.95,384,1.000,bicubic -tf_efficientnetv2_m,97.140,2.860,99.410,0.590,54.14,480,1.000,bicubic +tf_efficientnet_b8.ra_in1k,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic +vit_base_r50_s16_384.orig_in21k_ft_in1k,97.180,2.820,99.560,0.440,98.95,384,1.000,bicubic +vit_base_patch16_224.augreg2_in21k_ft_in1k,97.150,2.850,99.540,0.460,86.57,224,0.900,bicubic deit3_small_patch16_384_in21ft1k,97.130,2.870,99.500,0.500,22.21,384,1.000,bicubic -xcit_small_24_p16_384_dist,97.120,2.880,99.450,0.550,47.67,384,1.000,bicubic -tf_efficientnet_b8_ap,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic -beit_base_patch16_224,97.090,2.910,99.610,0.390,86.53,224,0.900,bicubic +vit_base_patch16_clip_224.laion2b_ft_in1k,97.130,2.870,99.460,0.540,86.57,224,1.000,bicubic +xcit_small_24_p16_384_dist,97.120,2.880,99.460,0.540,47.67,384,1.000,bicubic +tf_efficientnet_b8.ap_in1k,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic +vit_base_patch32_clip_384.openai_ft_in12k_in1k,97.110,2.890,99.500,0.500,88.30,384,0.950,bicubic +convnext_large.fb_in1k,97.100,2.900,99.450,0.550,197.77,288,1.000,bicubic +beit_base_patch16_224.in22k_ft_in22k_in1k,97.090,2.910,99.610,0.390,86.53,224,0.900,bicubic eca_nfnet_l2,97.090,2.910,99.510,0.490,56.72,384,1.000,bicubic volo_d3_224,97.090,2.910,99.470,0.530,86.33,224,0.960,bicubic -tf_efficientnet_b6_ap,97.080,2.920,99.620,0.380,43.04,528,0.942,bicubic +tf_efficientnet_b6.ap_in1k,97.080,2.920,99.620,0.380,43.04,528,0.942,bicubic +convnext_tiny.fb_in22k_ft_in1k_384,97.080,2.920,99.510,0.490,28.59,384,1.000,bicubic +vit_base_patch16_clip_224.openai_ft_in1k,97.080,2.920,99.490,0.510,86.57,224,0.900,bicubic ecaresnet269d,97.080,2.920,99.470,0.530,102.09,352,1.000,bicubic cait_s24_384,97.070,2.930,99.430,0.570,47.06,384,1.000,bicubic -xcit_large_24_p8_224_dist,97.070,2.930,99.420,0.580,188.93,224,1.000,bicubic +xcit_large_24_p8_224_dist,97.060,2.940,99.420,0.580,188.93,224,1.000,bicubic dm_nfnet_f2,97.020,2.980,99.440,0.560,193.78,352,0.920,bicubic deit3_base_patch16_384,97.020,2.980,99.390,0.610,86.88,384,1.000,bicubic resnetv2_152x2_bitm,97.010,2.990,99.590,0.410,236.34,448,1.000,bilinear -tf_efficientnet_b7,97.010,2.990,99.520,0.480,66.35,600,0.949,bicubic -volo_d2_224,97.000,3.000,99.390,0.610,58.68,224,0.960,bicubic +tf_efficientnet_b7.ra_in1k,97.010,2.990,99.520,0.480,66.35,600,0.949,bicubic resnetv2_101x3_bitm,96.990,3.010,99.490,0.510,387.93,448,1.000,bilinear -convnext_small_in22ft1k,96.990,3.010,99.410,0.590,50.22,224,0.875,bicubic -efficientnetv2_rw_m,96.980,3.020,99.540,0.460,53.24,416,1.000,bicubic +volo_d2_224,96.990,3.010,99.390,0.610,58.68,224,0.960,bicubic +efficientnetv2_rw_m.agc_in1k,96.980,3.020,99.540,0.460,53.24,416,1.000,bicubic +deit3_medium_patch16_224_in21ft1k,96.970,3.030,99.430,0.570,38.85,224,1.000,bicubic deit_base_distilled_patch16_384,96.960,3.040,99.480,0.520,87.63,384,1.000,bicubic -tf_efficientnet_b4_ns,96.950,3.050,99.580,0.420,19.34,380,0.922,bicubic +maxvit_large_tf_224.in1k,96.960,3.040,99.250,0.750,211.79,224,0.950,bicubic +tf_efficientnet_b4.ns_jft_in1k,96.950,3.050,99.580,0.420,19.34,380,0.922,bicubic +mvitv2_large,96.950,3.050,99.400,0.600,217.99,224,0.900,bicubic seresnextaa101d_32x8d,96.950,3.050,99.390,0.610,93.59,288,1.000,bicubic +maxvit_base_tf_224.in1k,96.950,3.050,99.260,0.740,119.47,224,0.950,bicubic deit3_large_patch16_224,96.940,3.060,99.340,0.660,304.37,224,0.900,bicubic xcit_small_12_p16_384_dist,96.930,3.070,99.400,0.600,26.25,384,1.000,bicubic +volo_d1_384,96.920,3.080,99.520,0.480,26.78,384,1.000,bicubic +dm_nfnet_f1,96.920,3.080,99.410,0.590,132.63,320,0.910,bicubic xcit_medium_24_p8_224_dist,96.920,3.080,99.390,0.610,84.32,224,1.000,bicubic -volo_d1_384,96.910,3.090,99.520,0.480,26.78,384,1.000,bicubic resnetrs420,96.910,3.090,99.460,0.540,191.89,416,1.000,bicubic -dm_nfnet_f1,96.910,3.090,99.410,0.590,132.63,320,0.910,bicubic -deit3_huge_patch14_224,96.890,3.110,99.480,0.520,632.13,224,0.900,bicubic -vit_base_patch16_224,96.880,3.120,99.530,0.470,86.57,224,0.900,bicubic -convnext_tiny_384_in22ft1k,96.880,3.120,99.470,0.530,28.59,384,1.000,bicubic +vit_base_patch16_224.augreg_in21k_ft_in1k,96.880,3.120,99.530,0.470,86.57,224,0.900,bicubic +deit3_huge_patch14_224,96.880,3.120,99.480,0.520,632.13,224,0.900,bicubic xcit_small_24_p8_224_dist,96.870,3.130,99.480,0.520,47.63,224,1.000,bicubic resnetv2_152x2_bit_teacher_384,96.830,3.170,99.450,0.550,236.34,384,1.000,bicubic -ig_resnext101_32x16d,96.810,3.190,99.600,0.400,194.03,224,0.875,bilinear +ig_resnext101_32x16d,96.820,3.180,99.590,0.410,194.03,224,0.875,bilinear +convnext_base.fb_in1k,96.820,3.180,99.410,0.590,88.59,288,1.000,bicubic +maxxvit_rmlp_small_rw_256,96.810,3.190,99.380,0.620,66.01,256,0.950,bicubic xcit_large_24_p16_224_dist,96.800,3.200,99.350,0.650,189.10,224,1.000,bicubic -vit_large_r50_s32_224,96.790,3.210,99.350,0.650,328.99,224,0.900,bicubic +vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.790,3.210,99.350,0.650,328.99,224,0.900,bicubic seresnet152d,96.770,3.230,99.450,0.550,66.84,320,1.000,bicubic -seresnext101_32x8d,96.770,3.230,99.350,0.650,93.57,288,1.000,bicubic +mvitv2_base,96.770,3.230,99.270,0.730,51.47,224,0.900,bicubic resnetrs350,96.760,3.240,99.370,0.630,163.96,384,1.000,bicubic -swinv2_base_window16_256,96.760,3.240,99.350,0.650,87.92,256,0.900,bicubic -convnext_large,96.760,3.240,99.300,0.700,197.77,224,0.875,bicubic -tf_efficientnetv2_s_in21ft1k,96.720,3.280,99.420,0.580,21.46,384,1.000,bicubic +flexivit_base.1200ep_in1k,96.760,3.240,99.360,0.640,86.59,240,0.950,bicubic +seresnext101_32x8d,96.760,3.240,99.340,0.660,93.57,288,1.000,bicubic +swinv2_base_window16_256,96.750,3.250,99.350,0.650,87.92,256,0.900,bicubic +tf_efficientnetv2_s.in21k_ft_in1k,96.730,3.270,99.420,0.580,21.46,384,1.000,bicubic +seresnext101d_32x8d,96.730,3.270,99.360,0.640,93.59,288,1.000,bicubic resnet200d,96.720,3.280,99.330,0.670,64.69,320,1.000,bicubic resnetv2_50x3_bitm,96.710,3.290,99.550,0.450,217.32,448,1.000,bilinear regnetz_040h,96.710,3.290,99.500,0.500,28.94,320,1.000,bicubic regnetz_040,96.710,3.290,99.470,0.530,27.12,320,1.000,bicubic -seresnext101d_32x8d,96.710,3.290,99.360,0.640,93.59,288,1.000,bicubic -vit_small_patch16_384,96.700,3.300,99.480,0.520,22.20,384,1.000,bicubic +vit_base_patch16_384.orig_in21k_ft_in1k,96.700,3.300,99.510,0.490,86.86,384,1.000,bicubic +vit_small_patch16_384.augreg_in21k_ft_in1k,96.700,3.300,99.480,0.520,22.20,384,1.000,bicubic +edgenext_base,96.700,3.300,99.430,0.570,18.51,320,1.000,bicubic resnetrs200,96.700,3.300,99.370,0.630,93.21,320,1.000,bicubic eca_nfnet_l1,96.700,3.300,99.290,0.710,41.41,320,1.000,bicubic xcit_small_12_p8_224_dist,96.690,3.310,99.390,0.610,26.21,224,1.000,bicubic +maxvit_small_tf_224.in1k,96.690,3.310,99.370,0.630,68.93,224,0.950,bicubic resnetrs270,96.690,3.310,99.350,0.650,129.86,352,1.000,bicubic -vit_small_r26_s32_384,96.680,3.320,99.580,0.420,36.47,384,1.000,bicubic -tf_efficientnet_b5_ap,96.680,3.320,99.460,0.540,30.39,456,0.934,bicubic -tf_efficientnet_b6,96.670,3.330,99.370,0.630,43.04,528,0.942,bicubic -pit_b_distilled_224,96.670,3.330,99.350,0.650,74.79,224,0.900,bicubic +vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.680,3.320,99.570,0.430,36.47,384,1.000,bicubic +tf_efficientnet_b5.ap_in1k,96.680,3.320,99.460,0.540,30.39,456,0.934,bicubic +pit_b_distilled_224,96.680,3.320,99.350,0.650,74.79,224,0.900,bicubic +tf_efficientnet_b6.aa_in1k,96.670,3.330,99.370,0.630,43.04,528,0.942,bicubic +vit_medium_patch16_gap_256.in12k_ft_in1k,96.660,3.340,99.510,0.490,38.86,256,0.950,bicubic deit3_small_patch16_224_in21ft1k,96.660,3.340,99.330,0.670,22.06,224,1.000,bicubic +flexivit_base.600ep_in1k,96.630,3.370,99.330,0.670,86.59,240,0.950,bicubic resmlp_big_24_224_in22ft1k,96.620,3.380,99.510,0.490,129.14,224,0.875,bicubic regnetz_d8,96.620,3.380,99.450,0.550,23.37,320,1.000,bicubic -regnetz_d8_evos,96.610,3.390,99.440,0.560,23.46,320,0.950,bicubic +flexivit_base.300ep_in1k,96.620,3.380,99.270,0.730,86.59,240,0.950,bicubic +regnetz_d8_evos,96.610,3.390,99.450,0.550,23.46,320,0.950,bicubic resnest200e,96.610,3.390,99.350,0.650,70.20,320,0.909,bicubic -swsl_resnext101_32x16d,96.600,3.400,99.530,0.470,194.03,224,0.875,bilinear +swsl_resnext101_32x16d,96.600,3.400,99.520,0.480,194.03,224,0.875,bilinear regnetz_d32,96.600,3.400,99.380,0.620,27.58,320,0.950,bicubic xcit_medium_24_p16_224_dist,96.590,3.410,99.270,0.730,84.40,224,1.000,bicubic +maxvit_rmlp_small_rw_224,96.590,3.410,99.110,0.890,64.90,224,0.900,bicubic resnetrs152,96.580,3.420,99.240,0.760,86.62,320,1.000,bicubic +gcvit_base,96.570,3.430,99.230,0.770,90.32,224,0.875,bicubic +convnext_small.fb_in1k,96.560,3.440,99.340,0.660,50.22,288,1.000,bicubic +cait_xs24_384,96.550,3.450,99.420,0.580,26.67,384,1.000,bicubic xcit_tiny_24_p8_384_dist,96.550,3.450,99.320,0.680,12.11,384,1.000,bicubic -cait_xs24_384,96.540,3.460,99.420,0.580,26.67,384,1.000,bicubic -efficientnetv2_rw_s,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic +efficientnetv2_rw_s.ra2_in1k,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic swinv2_base_window8_256,96.540,3.460,99.270,0.730,87.92,256,0.900,bicubic +coatnet_rmlp_2_rw_224,96.540,3.460,99.100,0.900,73.88,224,0.950,bicubic regnety_080,96.530,3.470,99.320,0.680,39.18,288,1.000,bicubic crossvit_18_dagger_408,96.530,3.470,99.260,0.740,44.61,408,1.000,bicubic resnest269e,96.520,3.480,99.350,0.650,110.93,416,0.928,bicubic -vit_base_patch32_384,96.490,3.510,99.410,0.590,88.30,384,1.000,bicubic -convnext_base,96.470,3.530,99.230,0.770,88.59,224,0.875,bicubic -swinv2_small_window16_256,96.460,3.540,99.200,0.800,49.73,256,0.900,bicubic +vit_base_patch32_384.augreg_in21k_ft_in1k,96.490,3.510,99.410,0.590,88.30,384,1.000,bicubic +swinv2_small_window16_256,96.470,3.530,99.200,0.800,49.73,256,0.900,bicubic +vit_base_patch16_224_miil.in21k_ft_in1k,96.460,3.540,99.300,0.700,86.54,224,0.875,bilinear resmlp_big_24_distilled_224,96.450,3.550,99.310,0.690,129.14,224,0.875,bicubic -vit_base_patch16_224_miil,96.450,3.550,99.300,0.700,86.54,224,0.875,bilinear cs3se_edgenet_x,96.440,3.560,99.400,0.600,50.72,320,1.000,bicubic -swsl_resnext101_32x4d,96.430,3.570,99.470,0.530,44.18,224,0.875,bilinear +swsl_resnext101_32x4d,96.420,3.580,99.470,0.530,44.18,224,0.875,bilinear +maxvit_rmlp_tiny_rw_256,96.410,3.590,99.390,0.610,29.15,256,0.950,bicubic regnetv_064,96.410,3.590,99.360,0.640,30.58,288,1.000,bicubic xcit_large_24_p8_224,96.410,3.590,98.980,1.020,188.93,224,1.000,bicubic xcit_small_24_p8_224,96.400,3.600,99.150,0.850,47.63,224,1.000,bicubic -tf_efficientnet_b3_ns,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic +tf_efficientnet_b3.ns_jft_in1k,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic crossvit_15_dagger_408,96.390,3.610,99.160,0.840,28.50,408,1.000,bicubic cait_s24_224,96.380,3.620,99.150,0.850,46.92,224,1.000,bicubic resnet152d,96.360,3.640,99.390,0.610,60.21,320,1.000,bicubic regnety_064,96.360,3.640,99.230,0.770,30.58,288,1.000,bicubic +mvitv2_small,96.360,3.640,99.200,0.800,34.87,224,0.900,bicubic +pvt_v2_b5,96.360,3.640,99.170,0.830,81.96,224,0.900,bicubic regnety_160,96.350,3.650,99.330,0.670,83.59,288,1.000,bicubic -tf_efficientnet_b5,96.350,3.650,99.310,0.690,30.39,456,0.934,bicubic +tf_efficientnet_b5.ra_in1k,96.350,3.650,99.310,0.690,30.39,456,0.934,bicubic xception65,96.350,3.650,99.240,0.760,39.92,299,0.940,bicubic -tf_efficientnetv2_s,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic +tf_efficientnetv2_s.in1k,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic volo_d1_224,96.330,3.670,99.310,0.690,26.63,224,0.960,bicubic -ig_resnext101_32x8d,96.310,3.690,99.430,0.570,88.79,224,0.875,bilinear -resnet101d,96.300,3.700,99.230,0.770,44.57,320,1.000,bicubic +pvt_v2_b4,96.330,3.670,99.180,0.820,62.56,224,0.900,bicubic +ig_resnext101_32x8d,96.320,3.680,99.430,0.570,88.79,224,0.875,bilinear deit3_base_patch16_224,96.300,3.700,99.180,0.820,86.59,224,0.900,bicubic -swinv2_small_window8_256,96.290,3.710,99.210,0.790,49.73,256,0.900,bicubic +gcvit_small,96.300,3.700,99.140,0.860,51.09,224,0.875,bicubic +resnet101d,96.290,3.710,99.230,0.770,44.57,320,1.000,bicubic +swinv2_small_window8_256,96.270,3.730,99.210,0.790,49.73,256,0.900,bicubic twins_svt_large,96.270,3.730,99.170,0.830,99.27,224,0.900,bicubic jx_nest_base,96.250,3.750,99.210,0.790,67.72,224,0.875,bicubic swin_s3_base_224,96.250,3.750,99.140,0.860,71.13,224,0.900,bicubic -swin_s3_small_224,96.230,3.770,99.090,0.910,49.74,224,0.900,bicubic -convnext_tiny_in22ft1k,96.220,3.780,99.340,0.660,28.59,224,0.875,bicubic +maxvit_tiny_rw_224,96.240,3.760,99.120,0.880,29.06,224,0.950,bicubic +tf_efficientnetv2_b3.in21k_ft_in1k,96.220,3.780,99.230,0.770,14.36,300,0.900,bicubic +swin_s3_small_224,96.220,3.780,99.080,0.920,49.74,224,0.900,bicubic xcit_small_24_p16_224_dist,96.210,3.790,99.210,0.790,47.67,224,1.000,bicubic xception65p,96.210,3.790,99.180,0.820,39.82,299,0.940,bicubic deit3_small_patch16_384,96.200,3.800,99.290,0.710,22.21,384,1.000,bicubic -regnetv_040,96.190,3.810,99.330,0.670,20.64,288,1.000,bicubic +regnetv_040,96.180,3.820,99.330,0.670,20.64,288,1.000,bicubic swinv2_cr_small_ns_224,96.180,3.820,99.140,0.860,49.70,224,0.900,bicubic mobilevitv2_175_384_in22ft1k,96.180,3.820,99.130,0.870,14.25,384,1.000,bicubic -convnext_small,96.170,3.830,99.100,0.900,50.22,224,0.875,bicubic -tf_efficientnet_b4_ap,96.160,3.840,99.280,0.720,19.34,380,0.922,bicubic +gcvit_tiny,96.170,3.830,99.240,0.760,28.22,224,0.875,bicubic +tf_efficientnet_b4.ap_in1k,96.160,3.840,99.280,0.720,19.34,380,0.922,bicubic +tresnet_v2_l,96.160,3.840,99.240,0.760,46.17,224,0.875,bilinear twins_svt_base,96.160,3.840,99.060,0.940,56.07,224,0.900,bicubic dm_nfnet_f0,96.150,3.850,99.250,0.750,71.49,256,0.900,bicubic -efficientnet_b4,96.150,3.850,99.190,0.810,19.34,384,1.000,bicubic +efficientnet_b4.ra2_in1k,96.150,3.850,99.200,0.800,19.34,384,1.000,bicubic twins_pcpvt_large,96.150,3.850,99.180,0.820,60.99,224,0.900,bicubic deit_base_patch16_384,96.150,3.850,99.140,0.860,86.86,384,1.000,bicubic +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,96.140,3.860,99.210,0.790,88.22,224,0.900,bicubic sequencer2d_l,96.140,3.860,99.160,0.840,54.30,224,0.875,bicubic -regnetz_c16_evos,96.130,3.870,99.360,0.640,13.49,320,0.950,bicubic -resnetv2_50x1_bit_distilled,96.120,3.880,99.280,0.720,25.55,224,0.875,bicubic +resnetv2_50x1_bit_distilled,96.130,3.870,99.280,0.720,25.55,224,0.875,bicubic +regnetz_c16_evos,96.120,3.880,99.360,0.640,13.49,320,0.950,bicubic nfnet_l0,96.120,3.880,99.240,0.760,35.07,288,1.000,bicubic +efficientformer_l7,96.110,3.890,99.270,0.730,82.23,224,0.950,bicubic xcit_small_12_p8_224,96.110,3.890,99.160,0.840,26.21,224,1.000,bicubic xcit_medium_24_p8_224,96.110,3.890,98.890,1.110,84.32,224,1.000,bicubic resnetv2_101x1_bitm,96.100,3.900,99.280,0.720,44.54,448,1.000,bilinear -resnetv2_152x2_bit_teacher,96.100,3.900,99.270,0.730,236.34,224,0.875,bicubic +resnetv2_152x2_bit_teacher,96.100,3.900,99.280,0.720,236.34,224,0.875,bicubic +maxvit_tiny_tf_224.in1k,96.100,3.900,99.250,0.750,30.92,224,0.950,bicubic deit_base_distilled_patch16_224,96.090,3.910,99.190,0.810,87.34,224,0.900,bicubic resnext101_64x4d,96.080,3.920,99.240,0.760,83.46,288,1.000,bicubic xcit_tiny_12_p8_384_dist,96.080,3.920,99.140,0.860,6.71,384,1.000,bicubic -swinv2_cr_small_224,96.060,3.940,98.870,1.130,49.70,224,0.900,bicubic -cs3edgenet_x,96.050,3.950,99.140,0.860,47.82,288,1.000,bicubic -cs3sedarknet_x,96.040,3.960,99.110,0.890,35.40,288,1.000,bicubic +deit3_medium_patch16_224,96.070,3.930,99.200,0.800,38.85,224,0.900,bicubic +swinv2_cr_small_224,96.070,3.930,98.870,1.130,49.70,224,0.900,bicubic mobilevitv2_200_384_in22ft1k,96.040,3.960,99.080,0.920,18.45,384,1.000,bicubic -xcit_small_12_p16_224_dist,96.020,3.980,99.130,0.870,26.25,224,1.000,bicubic -regnety_040,96.010,3.990,99.180,0.820,20.65,288,1.000,bicubic +maxxvit_rmlp_nano_rw_256,96.030,3.970,99.260,0.740,16.78,256,0.950,bicubic +xcit_small_12_p16_224_dist,96.030,3.970,99.150,0.850,26.25,224,1.000,bicubic +cs3edgenet_x,96.030,3.970,99.140,0.860,47.82,288,1.000,bicubic +cs3sedarknet_x,96.030,3.970,99.110,0.890,35.40,288,1.000,bicubic +coatnet_1_rw_224,96.030,3.970,99.050,0.950,41.72,224,0.950,bicubic +regnety_040,96.020,3.980,99.190,0.810,20.65,288,1.000,bicubic +convnext_tiny_hnf.a2h_in1k,96.020,3.980,99.070,0.930,28.59,288,1.000,bicubic +pvt_v2_b3,95.990,4.010,99.190,0.810,45.24,224,0.900,bicubic sequencer2d_s,95.990,4.010,99.050,0.950,27.65,224,0.875,bicubic +convnext_nano.in12k_ft_in1k,95.980,4.020,99.320,0.680,15.59,288,1.000,bicubic +maxvit_rmlp_nano_rw_256,95.980,4.020,98.970,1.030,15.50,256,0.950,bicubic regnety_032,95.970,4.030,99.190,0.810,19.44,288,1.000,bicubic tresnet_xl_448,95.970,4.030,99.130,0.870,78.44,448,0.875,bilinear jx_nest_small,95.960,4.040,99.030,0.970,38.35,224,0.875,bicubic +xcit_tiny_24_p16_384_dist,95.950,4.050,99.220,0.780,12.12,384,1.000,bicubic eca_nfnet_l0,95.950,4.050,99.210,0.790,24.14,288,1.000,bicubic -swinv2_tiny_window16_256,95.940,4.060,99.140,0.860,28.35,256,0.900,bicubic -xcit_tiny_24_p16_384_dist,95.920,4.080,99.220,0.780,12.12,384,1.000,bicubic +coatnet_rmlp_1_rw_224,95.950,4.050,99.160,0.840,41.69,224,0.950,bicubic +swinv2_tiny_window16_256,95.930,4.070,99.140,0.860,28.35,256,0.900,bicubic +maxvit_nano_rw_256,95.930,4.070,99.000,1.000,15.45,256,0.950,bicubic swin_small_patch4_window7_224,95.910,4.090,99.020,0.980,49.61,224,0.900,bicubic -tf_efficientnet_b4,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic -resnet152,95.900,4.100,99.080,0.920,60.19,224,0.950,bicubic -resnet51q,95.870,4.130,99.130,0.870,35.70,288,1.000,bilinear -swsl_resnext50_32x4d,95.860,4.140,99.250,0.750,25.03,224,0.875,bilinear +tf_efficientnet_b4.aa_in1k,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic +resnet152,95.880,4.120,99.070,0.930,60.19,224,0.950,bicubic +swsl_resnext50_32x4d,95.870,4.130,99.250,0.750,25.03,224,0.875,bilinear +mvitv2_tiny,95.870,4.130,99.070,0.930,24.17,224,0.900,bicubic resnest101e,95.860,4.140,99.210,0.790,48.28,256,0.875,bilinear cs3darknet_x,95.860,4.140,99.180,0.820,35.05,288,1.000,bicubic +resnet51q,95.860,4.140,99.120,0.880,35.70,288,1.000,bilinear tresnet_l_448,95.860,4.140,99.120,0.880,55.99,448,0.875,bilinear -cait_xxs36_384,95.840,4.160,99.090,0.910,17.37,384,1.000,bicubic -vit_large_patch32_384,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic +cait_xxs36_384,95.850,4.150,99.090,0.910,17.37,384,1.000,bicubic +vit_large_patch32_384.orig_in21k_ft_in1k,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic xcit_tiny_24_p8_224_dist,95.810,4.190,99.210,0.790,12.11,224,1.000,bicubic sequencer2d_m,95.810,4.190,99.110,0.890,38.31,224,0.875,bicubic +ssl_resnext101_32x16d,95.800,4.200,99.180,0.820,194.03,224,0.875,bilinear regnetz_c16,95.800,4.200,99.100,0.900,13.46,320,0.940,bicubic -ssl_resnext101_32x16d,95.790,4.210,99.180,0.820,194.03,224,0.875,bilinear +convnext_tiny.fb_in1k,95.790,4.210,99.160,0.840,28.59,288,1.000,bicubic twins_pcpvt_base,95.790,4.210,99.130,0.870,43.83,224,0.900,bicubic -resnet61q,95.780,4.220,98.990,1.010,36.85,288,1.000,bicubic -tf_efficientnet_b2_ns,95.760,4.240,99.120,0.880,9.11,260,0.890,bicubic -vit_relpos_base_patch16_clsgap_224,95.760,4.240,99.040,0.960,86.43,224,0.900,bicubic -gc_efficientnetv2_rw_t,95.740,4.260,99.020,0.980,13.68,288,1.000,bicubic -efficientnet_b3,95.710,4.290,99.040,0.960,12.23,320,1.000,bicubic -tresnet_m,95.710,4.290,99.030,0.970,31.39,224,0.875,bilinear +tf_efficientnet_b2.ns_jft_in1k,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic +resnet61q,95.770,4.230,98.990,1.010,36.85,288,1.000,bicubic +vit_relpos_base_patch16_clsgap_224.sw_in1k,95.760,4.240,99.040,0.960,86.43,224,0.900,bicubic +gc_efficientnetv2_rw_t.agc_in1k,95.740,4.260,99.020,0.980,13.68,288,1.000,bicubic +tresnet_m,95.720,4.280,99.030,0.970,31.39,224,0.875,bilinear +efficientnet_b3.ra2_in1k,95.710,4.290,99.040,0.960,12.23,320,1.000,bicubic pnasnet5large,95.710,4.290,98.920,1.080,86.06,331,0.911,bicubic mobilevitv2_150_384_in22ft1k,95.700,4.300,99.140,0.860,10.59,384,1.000,bicubic +coatnet_bn_0_rw_224,95.700,4.300,99.050,0.950,27.44,224,0.950,bicubic crossvit_15_dagger_240,95.690,4.310,98.830,1.170,28.21,240,0.875,bicubic +flexivit_small.600ep_in1k,95.680,4.320,99.050,0.950,22.06,240,0.950,bicubic nasnetalarge,95.680,4.320,98.930,1.070,88.75,331,0.911,bicubic xcit_tiny_24_p8_224,95.670,4.330,99.050,0.950,12.11,224,1.000,bicubic -vit_small_r26_s32_224,95.640,4.360,99.190,0.810,36.43,224,0.900,bicubic +resnetv2_101,95.640,4.360,98.990,1.010,44.54,224,0.950,bicubic poolformer_m48,95.640,4.360,98.940,1.060,73.47,224,0.950,bicubic -pit_b_224,95.640,4.360,98.670,1.330,73.76,224,0.900,bicubic -resnetv2_101,95.620,4.380,98.990,1.010,44.54,224,0.950,bicubic +pit_b_224,95.640,4.360,98.660,1.340,73.76,224,0.900,bicubic +vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.630,4.370,99.190,0.810,36.43,224,0.900,bicubic +efficientnetv2_rw_t.ra2_in1k,95.610,4.390,99.070,0.930,13.65,288,1.000,bicubic resnetv2_50d_evos,95.610,4.390,99.030,0.970,25.59,288,0.950,bicubic -efficientnetv2_rw_t,95.600,4.400,99.070,0.930,13.65,288,1.000,bicubic +efficientformer_l3,95.600,4.400,99.160,0.840,31.41,224,0.950,bicubic +gcvit_xtiny,95.580,4.420,99.040,0.960,19.98,224,0.875,bicubic crossvit_18_dagger_240,95.570,4.430,99.060,0.940,44.27,240,0.875,bicubic -vit_relpos_base_patch16_224,95.570,4.430,99.030,0.970,86.43,224,0.900,bicubic -convnext_tiny,95.550,4.450,99.000,1.000,28.59,224,0.875,bicubic +flexivit_small.1200ep_in1k,95.560,4.440,99.120,0.880,22.06,240,0.950,bicubic +vit_relpos_base_patch16_224.sw_in1k,95.560,4.440,99.030,0.970,86.43,224,0.900,bicubic +pvt_v2_b2_li,95.550,4.450,98.990,1.010,22.55,224,0.900,bicubic convit_base,95.550,4.450,98.870,1.130,86.54,224,0.875,bicubic coat_lite_small,95.540,4.460,98.860,1.140,19.84,224,0.900,bicubic ecaresnet101d,95.530,4.470,99.130,0.870,44.57,224,0.875,bicubic levit_384,95.530,4.470,99.050,0.950,39.13,224,0.900,bicubic +vit_base_patch32_clip_224.laion2b_ft_in1k,95.530,4.470,98.860,1.140,88.22,224,0.900,bicubic +crossvit_base_240,95.530,4.470,98.820,1.180,105.03,240,0.875,bicubic xcit_small_24_p16_224,95.530,4.470,98.770,1.230,47.67,224,1.000,bicubic xcit_medium_24_p16_224,95.530,4.470,98.740,1.260,84.40,224,1.000,bicubic -crossvit_base_240,95.520,4.480,98.820,1.180,105.03,240,0.875,bicubic +fbnetv3_g.ra2_in1k,95.520,4.480,98.990,1.010,16.62,288,0.950,bilinear +xception41p,95.520,4.480,98.920,1.080,26.91,299,0.940,bicubic ecaresnet50t,95.510,4.490,99.120,0.880,25.57,320,0.950,bicubic -vit_relpos_medium_patch16_rpn_224,95.510,4.490,99.080,0.920,38.73,224,0.900,bicubic -convnext_tiny_hnf,95.510,4.490,99.020,0.980,28.59,224,0.950,bicubic -fbnetv3_g,95.510,4.490,98.990,1.010,16.62,288,0.950,bilinear -xception41p,95.510,4.490,98.910,1.090,26.91,299,0.940,bicubic +vit_relpos_medium_patch16_rpn_224.sw_in1k,95.510,4.490,99.080,0.920,38.73,224,0.900,bicubic swinv2_tiny_window8_256,95.500,4.500,99.120,0.880,28.35,256,0.900,bicubic -ssl_resnext101_32x8d,95.490,4.510,99.120,0.880,88.79,224,0.875,bilinear -vit_relpos_medium_patch16_cls_224,95.480,4.520,98.950,1.050,38.76,224,0.900,bicubic -visformer_small,95.470,4.530,98.900,1.100,40.22,224,0.900,bicubic -vit_relpos_medium_patch16_224,95.460,4.540,98.960,1.040,38.75,224,0.900,bicubic +pvt_v2_b2,95.500,4.500,99.000,1.000,25.36,224,0.900,bicubic +flexivit_small.300ep_in1k,95.500,4.500,98.960,1.040,22.06,240,0.950,bicubic +visformer_small,95.490,4.510,98.900,1.100,40.22,224,0.900,bicubic +vit_relpos_medium_patch16_cls_224.sw_in1k,95.480,4.520,98.950,1.050,38.76,224,0.900,bicubic +ssl_resnext101_32x8d,95.470,4.530,99.110,0.890,88.79,224,0.875,bilinear +vit_relpos_medium_patch16_224.sw_in1k,95.460,4.540,98.960,1.040,38.75,224,0.900,bicubic ssl_resnext101_32x4d,95.440,4.560,99.130,0.870,44.18,224,0.875,bilinear tresnet_xl,95.440,4.560,99.050,0.950,78.44,224,0.875,bilinear deit_base_patch16_224,95.440,4.560,98.840,1.160,86.57,224,0.900,bicubic crossvit_18_240,95.440,4.560,98.790,1.210,43.27,240,0.875,bicubic resnetv2_50d_gn,95.430,4.570,99.040,0.960,25.57,288,0.950,bicubic resnetrs101,95.430,4.570,99.030,0.970,63.62,288,0.940,bicubic -halo2botnet50ts_256,95.420,4.580,99.010,0.990,22.64,256,0.950,bicubic +coatnext_nano_rw_224,95.430,4.570,99.000,1.000,14.70,224,0.900,bicubic +coatnet_rmlp_nano_rw_224,95.430,4.570,98.990,1.010,15.15,224,0.900,bicubic +coatnet_0_rw_224,95.430,4.570,98.720,1.280,27.44,224,0.950,bicubic xcit_small_12_p16_224,95.420,4.580,98.840,1.160,26.25,224,1.000,bicubic -xcit_large_24_p16_224,95.420,4.580,98.620,1.380,189.10,224,1.000,bicubic -swsl_resnet50,95.410,4.590,99.300,0.700,25.56,224,0.875,bilinear -edgenext_small,95.410,4.590,99.100,0.900,5.59,320,1.000,bicubic -vit_base_patch16_rpn_224,95.380,4.620,98.930,1.070,86.54,224,0.900,bicubic +xcit_large_24_p16_224,95.420,4.580,98.610,1.390,189.10,224,1.000,bicubic +swsl_resnet50,95.410,4.590,99.290,0.710,25.56,224,0.875,bilinear +edgenext_small,95.400,4.600,99.100,0.900,5.59,320,1.000,bicubic +halo2botnet50ts_256,95.390,4.610,99.010,0.990,22.64,256,0.950,bicubic +vit_base_patch16_rpn_224.in1k,95.380,4.620,98.930,1.070,86.54,224,0.900,bicubic poolformer_m36,95.380,4.620,98.850,1.150,56.17,224,0.950,bicubic -vit_small_patch16_224,95.370,4.630,99.150,0.850,22.05,224,0.900,bicubic -swinv2_cr_tiny_ns_224,95.370,4.630,98.940,1.060,28.33,224,0.900,bicubic -resnet101,95.360,4.640,98.860,1.140,44.55,224,0.950,bicubic -convnext_nano,95.360,4.640,98.850,1.150,15.59,288,1.000,bicubic -tf_efficientnet_b3_ap,95.320,4.680,98.900,1.100,12.23,300,0.904,bicubic -cs3sedarknet_l,95.310,4.690,99.130,0.870,21.91,288,0.950,bicubic +vit_small_patch16_224.augreg_in21k_ft_in1k,95.370,4.630,99.150,0.850,22.05,224,0.900,bicubic +swinv2_cr_tiny_ns_224,95.370,4.630,98.930,1.070,28.33,224,0.900,bicubic +convnext_nano.d1h_in1k,95.350,4.650,98.860,1.140,15.59,288,1.000,bicubic +resnet101,95.350,4.650,98.860,1.140,44.55,224,0.950,bicubic +vit_base_patch16_224.orig_in21k_ft_in1k,95.330,4.670,99.000,1.000,86.57,224,0.900,bicubic +tf_efficientnet_b3.ap_in1k,95.320,4.680,98.900,1.100,12.23,300,0.904,bicubic +cs3sedarknet_l,95.310,4.690,99.120,0.880,21.91,288,0.950,bicubic mixer_b16_224_miil,95.300,4.700,98.880,1.120,59.88,224,0.875,bilinear -tresnet_l,95.290,4.710,99.010,0.990,55.99,224,0.875,bilinear -cait_xxs24_384,95.280,4.720,98.960,1.040,12.03,384,1.000,bicubic +vit_small_patch16_384.augreg_in1k,95.290,4.710,99.000,1.000,22.20,384,1.000,bicubic +tresnet_l,95.280,4.720,99.010,0.990,55.99,224,0.875,bilinear +cait_xxs24_384,95.260,4.740,98.960,1.040,12.03,384,1.000,bicubic +jx_nest_tiny,95.250,4.750,98.980,1.020,17.06,224,0.875,bicubic +coatnet_nano_rw_224,95.250,4.750,98.870,1.130,15.14,224,0.900,bicubic pit_s_distilled_224,95.240,4.760,99.050,0.950,24.04,224,0.900,bicubic -jx_nest_tiny,95.240,4.760,98.980,1.020,17.06,224,0.875,bicubic -vit_srelpos_medium_patch16_224,95.230,4.770,98.990,1.010,38.74,224,0.900,bicubic +vit_srelpos_medium_patch16_224.sw_in1k,95.230,4.770,98.990,1.010,38.74,224,0.900,bicubic mobilevitv2_175_in22ft1k,95.230,4.770,98.790,1.210,14.25,256,0.888,bicubic resnetaa50,95.210,4.790,98.930,1.070,25.56,288,1.000,bicubic twins_pcpvt_small,95.210,4.790,98.880,1.120,24.11,224,0.900,bicubic convit_small,95.200,4.800,98.900,1.100,27.78,224,0.875,bicubic twins_svt_small,95.200,4.800,98.880,1.120,24.06,224,0.900,bicubic -tf_efficientnet_b1_ns,95.180,4.820,99.110,0.890,7.79,240,0.882,bicubic +tf_efficientnet_b1.ns_jft_in1k,95.170,4.830,99.110,0.890,7.79,240,0.882,bicubic cs3darknet_focus_l,95.170,4.830,98.960,1.040,21.15,288,0.950,bicubic -mobilevitv2_200_in22ft1k,95.160,4.840,98.950,1.050,18.45,256,0.888,bicubic -vit_relpos_small_patch16_224,95.160,4.840,98.950,1.050,21.98,224,0.900,bicubic +vit_relpos_small_patch16_224.sw_in1k,95.160,4.840,98.950,1.050,21.98,224,0.900,bicubic swin_s3_tiny_224,95.160,4.840,98.940,1.060,28.33,224,0.900,bicubic -tf_efficientnetv2_b3,95.160,4.840,98.820,1.180,14.36,300,0.904,bicubic +mobilevitv2_200_in22ft1k,95.160,4.840,98.930,1.070,18.45,256,0.888,bicubic +lamhalobotnet50ts_256,95.160,4.840,98.880,1.120,22.57,256,0.950,bicubic +tf_efficientnetv2_b3.in1k,95.160,4.840,98.820,1.180,14.36,300,0.904,bicubic crossvit_15_240,95.150,4.850,98.930,1.070,27.53,240,0.875,bicubic -lamhalobotnet50ts_256,95.150,4.850,98.880,1.120,22.57,256,0.950,bicubic mobilevitv2_150_in22ft1k,95.140,4.860,98.860,1.140,10.59,256,0.888,bicubic -halonet50ts,95.140,4.860,98.770,1.230,22.73,256,0.940,bicubic +swin_tiny_patch4_window7_224,95.140,4.860,98.850,1.150,28.29,224,0.900,bicubic xcit_tiny_12_p16_384_dist,95.130,4.870,99.020,0.980,6.72,384,1.000,bicubic -swin_tiny_patch4_window7_224,95.130,4.870,98.850,1.150,28.29,224,0.900,bicubic +convnext_nano_ols.d1h_in1k,95.130,4.870,98.720,1.280,15.65,288,1.000,bicubic +efficientnet_el.ra_in1k,95.120,4.880,98.990,1.010,10.59,300,0.904,bicubic cs3darknet_l,95.120,4.880,98.980,1.020,21.16,288,0.950,bicubic -efficientnet_el,95.120,4.880,98.980,1.020,10.59,300,0.904,bicubic -xcit_tiny_12_p8_224_dist,95.100,4.900,98.910,1.090,6.71,224,1.000,bicubic +vit_base_patch32_clip_224.openai_ft_in1k,95.120,4.880,98.980,1.020,88.22,224,0.900,bicubic +halonet50ts,95.100,4.900,98.780,1.220,22.73,256,0.940,bicubic poolformer_s36,95.090,4.910,98.910,1.090,30.86,224,0.900,bicubic gernet_l,95.090,4.910,98.900,1.100,31.08,256,0.875,bilinear ecaresnet101d_pruned,95.080,4.920,98.980,1.020,24.88,224,0.875,bicubic wide_resnet50_2,95.080,4.920,98.970,1.030,68.88,224,0.875,bicubic -convmixer_1536_20,95.070,4.930,99.030,0.970,51.63,224,0.960,bicubic +xcit_tiny_12_p8_224_dist,95.080,4.920,98.910,1.090,6.71,224,1.000,bicubic +regnetz_b16,95.070,4.930,99.050,0.950,9.72,288,0.940,bicubic legacy_senet154,95.070,4.930,98.830,1.170,115.09,224,0.875,bilinear -regnetz_b16,95.060,4.940,99.050,0.950,9.72,288,0.940,bicubic -vit_small_patch32_384,95.050,4.950,98.990,1.010,22.92,384,1.000,bicubic +convmixer_1536_20,95.060,4.940,99.030,0.970,51.63,224,0.960,bicubic +vit_small_patch32_384.augreg_in21k_ft_in1k,95.050,4.950,98.990,1.010,22.92,384,1.000,bicubic +vit_srelpos_small_patch16_224.sw_in1k,95.040,4.960,98.960,1.040,21.97,224,0.900,bicubic gluon_resnet152_v1s,95.040,4.960,98.930,1.070,60.32,224,0.875,bicubic +seresnext50_32x4d,95.040,4.960,98.880,1.120,27.56,224,0.875,bicubic tnt_s_patch16_224,95.040,4.960,98.830,1.170,23.76,224,0.900,bicubic -vit_srelpos_small_patch16_224,95.030,4.970,98.960,1.040,21.97,224,0.900,bicubic -seresnext50_32x4d,95.030,4.970,98.880,1.120,27.56,224,0.875,bicubic resnetv2_50x1_bitm,95.010,4.990,99.060,0.940,25.55,448,1.000,bilinear -tf_efficientnet_b3,95.010,4.990,98.910,1.090,12.23,300,0.904,bicubic +tf_efficientnet_b3.aa_in1k,95.010,4.990,98.910,1.090,12.23,300,0.904,bicubic levit_256,95.010,4.990,98.890,1.110,18.89,224,0.900,bicubic -vit_base_patch32_224,95.000,5.000,99.030,0.970,88.22,224,0.900,bicubic +vit_base_patch32_224.augreg_in21k_ft_in1k,95.000,5.000,99.030,0.970,88.22,224,0.900,bicubic deit3_small_patch16_224,95.000,5.000,98.460,1.540,22.06,224,0.900,bicubic tresnet_m_448,94.990,5.010,98.980,1.020,31.39,448,0.875,bilinear coat_mini,94.970,5.030,98.780,1.220,10.34,224,0.900,bicubic resnest50d_4s2x40d,94.960,5.040,99.070,0.930,30.42,224,0.875,bicubic -rexnet_200,94.950,5.050,99.010,0.990,16.37,224,0.875,bicubic -gluon_seresnext101_64x4d,94.920,5.080,98.830,1.170,88.23,224,0.875,bicubic +rexnet_200,94.940,5.060,99.010,0.990,16.37,224,0.875,bicubic +vit_base_patch16_384.augreg_in1k,94.940,5.060,98.890,1.110,86.86,384,1.000,bicubic +gluon_seresnext101_64x4d,94.930,5.070,98.830,1.170,88.23,224,0.875,bicubic gluon_seresnext101_32x4d,94.920,5.080,98.810,1.190,48.96,224,0.875,bicubic gluon_senet154,94.920,5.080,98.760,1.240,115.09,224,0.875,bicubic -mobilevitv2_175,94.890,5.110,98.860,1.140,14.25,256,0.888,bicubic -tf_efficientnet_lite4,94.880,5.120,99.020,0.980,13.01,380,0.920,bilinear -resmlp_36_distilled_224,94.880,5.120,98.840,1.160,44.69,224,0.875,bicubic -ssl_resnext50_32x4d,94.870,5.130,98.890,1.110,25.03,224,0.875,bilinear +mobilevitv2_175,94.900,5.100,98.870,1.130,14.25,256,0.888,bicubic +tf_efficientnet_lite4.in1k,94.890,5.110,99.020,0.980,13.01,380,0.920,bilinear +resmlp_36_distilled_224,94.890,5.110,98.850,1.150,44.69,224,0.875,bicubic +ssl_resnext50_32x4d,94.870,5.130,98.880,1.120,25.03,224,0.875,bilinear seresnet33ts,94.860,5.140,98.790,1.210,19.78,256,0.900,bicubic -resnest50d,94.850,5.150,98.880,1.120,27.48,224,0.875,bilinear +convnext_tiny.fb_in22k_ft_in1k,94.860,5.140,98.530,1.470,28.59,288,1.000,bicubic gcresnet50t,94.850,5.150,98.790,1.210,25.90,256,0.900,bicubic +cspresnext50,94.840,5.160,98.770,1.230,20.57,256,0.887,bilinear crossvit_small_240,94.830,5.170,99.020,0.980,26.86,240,0.875,bicubic -cspresnext50,94.830,5.170,98.770,1.230,20.57,256,0.887,bilinear +resnest50d,94.830,5.170,98.880,1.120,27.48,224,0.875,bilinear mobilevitv2_200,94.830,5.170,98.710,1.290,18.45,256,0.888,bicubic -sehalonet33ts,94.780,5.220,98.570,1.430,13.69,256,0.940,bicubic -lambda_resnet50ts,94.780,5.220,98.460,1.540,21.54,256,0.950,bicubic ecaresnetlight,94.770,5.230,98.800,1.200,30.16,224,0.875,bicubic +sehalonet33ts,94.770,5.230,98.570,1.430,13.69,256,0.940,bicubic +lambda_resnet50ts,94.770,5.230,98.470,1.530,21.54,256,0.950,bicubic resnest50d_1s4x24d,94.750,5.250,98.980,1.020,25.68,224,0.875,bicubic gluon_resnet152_v1d,94.740,5.260,98.740,1.260,60.21,224,0.875,bicubic +convnext_pico.d1_in1k,94.740,5.260,98.700,1.300,9.05,288,0.950,bicubic gluon_resnet101_v1s,94.720,5.280,98.820,1.180,44.67,224,0.875,bicubic deit_small_distilled_patch16_224,94.710,5.290,99.030,0.970,22.44,224,0.900,bicubic haloregnetz_b,94.700,5.300,98.660,1.340,11.68,224,0.940,bicubic -xcit_tiny_12_p8_224,94.690,5.310,98.830,1.170,6.71,224,1.000,bicubic +xcit_tiny_12_p8_224,94.680,5.320,98.830,1.170,6.71,224,1.000,bicubic +gluon_resnext101_64x4d,94.670,5.330,98.650,1.350,83.46,224,0.875,bicubic cspdarknet53,94.660,5.340,98.800,1.200,27.64,256,0.887,bilinear edgenext_small_rw,94.660,5.340,98.790,1.210,7.83,320,1.000,bicubic -gluon_resnext101_64x4d,94.660,5.340,98.650,1.350,83.46,224,0.875,bicubic resmlp_big_24_224,94.660,5.340,98.480,1.520,129.14,224,0.875,bicubic darknet53,94.630,5.370,98.890,1.110,41.61,288,1.000,bicubic -efficientnet_b3_pruned,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic -ecaresnet50d,94.620,5.380,98.890,1.110,25.58,224,0.875,bicubic +ecaresnet50d,94.630,5.370,98.890,1.110,25.58,224,0.875,bicubic +maxvit_rmlp_pico_rw_256,94.630,5.370,98.820,1.180,7.52,256,0.950,bicubic +efficientnet_b3_pruned.in1k,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic gernet_m,94.620,5.380,98.860,1.140,21.14,224,0.875,bilinear -efficientnet_b2,94.610,5.390,98.710,1.290,9.11,288,1.000,bicubic -pit_s_224,94.590,5.410,98.700,1.300,23.46,224,0.900,bicubic -sebotnet33ts_256,94.580,5.420,98.500,1.500,13.70,256,0.940,bicubic +convnext_pico_ols.d1_in1k,94.620,5.380,98.770,1.230,9.06,288,1.000,bicubic +efficientnet_b2.ra_in1k,94.610,5.390,98.710,1.290,9.11,288,1.000,bicubic +pit_s_224,94.590,5.410,98.710,1.290,23.46,224,0.900,bicubic +sebotnet33ts_256,94.590,5.410,98.500,1.500,13.70,256,0.940,bicubic repvgg_b3,94.570,5.430,98.780,1.220,123.09,224,0.875,bilinear -poolformer_s24,94.550,5.450,98.880,1.120,21.39,224,0.900,bicubic -nf_resnet50,94.550,5.450,98.790,1.210,25.56,288,0.940,bicubic +poolformer_s24,94.560,5.440,98.880,1.120,21.39,224,0.900,bicubic +nf_resnet50,94.560,5.440,98.790,1.210,25.56,288,0.940,bicubic +resnext50_32x4d,94.560,5.440,98.610,1.390,25.03,224,0.950,bicubic seresnet50,94.550,5.450,98.750,1.250,28.09,224,0.875,bicubic mobilevitv2_150,94.550,5.450,98.710,1.290,10.59,256,0.888,bicubic -regnety_320,94.540,5.460,98.860,1.140,145.05,224,0.875,bicubic -gluon_resnext101_32x4d,94.540,5.460,98.630,1.370,44.18,224,0.875,bicubic -resnext50_32x4d,94.540,5.460,98.610,1.390,25.03,224,0.950,bicubic -inception_resnet_v2,94.530,5.470,98.780,1.220,55.84,299,0.897,bicubic +regnety_320,94.540,5.460,98.850,1.150,145.05,224,0.875,bicubic +inception_resnet_v2,94.540,5.460,98.790,1.210,55.84,299,0.897,bicubic xcit_tiny_24_p16_224_dist,94.530,5.470,98.780,1.220,12.12,224,1.000,bicubic +gluon_resnext101_32x4d,94.530,5.470,98.630,1.370,44.18,224,0.875,bicubic repvgg_b3g4,94.520,5.480,98.970,1.030,83.83,224,0.875,bilinear convmixer_768_32,94.500,5.500,98.850,1.150,21.11,224,0.960,bicubic +efficientformer_l1,94.490,5.510,98.830,1.170,12.29,224,0.950,bicubic gcresnext50ts,94.490,5.510,98.670,1.330,15.67,256,0.900,bicubic -tf_efficientnet_b2_ap,94.490,5.510,98.620,1.380,9.11,260,0.890,bicubic +tf_efficientnet_b2.ap_in1k,94.490,5.510,98.620,1.380,9.11,260,0.890,bicubic regnety_120,94.480,5.520,98.810,1.190,51.82,224,0.875,bicubic rexnet_150,94.480,5.520,98.790,1.210,9.73,224,0.875,bicubic +darknetaa53,94.470,5.530,98.770,1.230,36.02,288,1.000,bilinear gcresnet33ts,94.470,5.530,98.770,1.230,19.88,256,0.900,bicubic -darknetaa53,94.470,5.530,98.760,1.240,36.02,288,1.000,bilinear resmlp_24_distilled_224,94.460,5.540,98.770,1.230,30.02,224,0.875,bicubic regnetx_320,94.460,5.540,98.740,1.260,107.81,224,0.875,bicubic -ssl_resnet50,94.440,5.560,98.920,1.080,25.56,224,0.875,bilinear -resnetv2_50,94.430,5.570,98.730,1.270,25.55,224,0.950,bicubic -tf_efficientnetv2_b2,94.420,5.580,98.570,1.430,10.10,260,0.890,bicubic -efficientnet_el_pruned,94.400,5.600,98.740,1.260,10.59,300,0.904,bicubic -tf_efficientnet_el,94.400,5.600,98.710,1.290,10.59,300,0.904,bicubic -deit_small_patch16_224,94.390,5.610,98.690,1.310,22.05,224,0.900,bicubic +ssl_resnet50,94.450,5.550,98.920,1.080,25.56,224,0.875,bilinear +resnetv2_50,94.440,5.560,98.740,1.260,25.55,224,0.950,bicubic +tf_efficientnetv2_b2.in1k,94.420,5.580,98.570,1.430,10.10,260,0.890,bicubic +tf_efficientnet_el.in1k,94.410,5.590,98.710,1.290,10.59,300,0.904,bicubic +gcvit_xxtiny,94.400,5.600,98.900,1.100,12.00,224,0.875,bicubic +efficientnet_el_pruned.in1k,94.400,5.600,98.740,1.260,10.59,300,0.904,bicubic +deit_small_patch16_224,94.400,5.600,98.690,1.310,22.05,224,0.900,bicubic inception_v4,94.380,5.620,98.580,1.420,42.68,299,0.875,bicubic legacy_seresnext101_32x4d,94.370,5.630,98.650,1.350,48.96,224,0.875,bilinear -tf_efficientnet_b2,94.360,5.640,98.610,1.390,9.11,260,0.890,bicubic +tf_efficientnet_b2.aa_in1k,94.360,5.640,98.610,1.390,9.11,260,0.890,bicubic resnet50_gn,94.350,5.650,98.710,1.290,25.56,224,0.940,bicubic -resnet50,94.340,5.660,98.440,1.560,25.56,224,0.950,bicubic -gluon_seresnext50_32x4d,94.330,5.670,98.610,1.390,27.56,224,0.875,bicubic -ecaresnet26t,94.320,5.680,98.720,1.280,16.01,320,0.950,bicubic -dpn107,94.310,5.690,98.470,1.530,86.92,224,0.875,bicubic -resnetrs50,94.300,5.700,98.640,1.360,35.69,224,0.910,bicubic +gluon_seresnext50_32x4d,94.340,5.660,98.610,1.390,27.56,224,0.875,bicubic +resnet50,94.320,5.680,98.440,1.560,25.56,224,0.950,bicubic +ecaresnet26t,94.310,5.690,98.720,1.280,16.01,320,0.950,bicubic +resnetrs50,94.310,5.690,98.640,1.360,35.69,224,0.910,bicubic +dpn107,94.310,5.690,98.480,1.520,86.92,224,0.875,bicubic xception71,94.280,5.720,98.640,1.360,42.34,299,0.903,bicubic cait_xxs36_224,94.260,5.740,98.720,1.280,17.30,224,1.000,bicubic resnet50d,94.260,5.740,98.720,1.280,25.58,224,0.875,bicubic gluon_xception65,94.260,5.740,98.570,1.430,39.92,299,0.903,bicubic -skresnext50_32x4d,94.250,5.750,98.460,1.540,27.48,224,0.875,bicubic +skresnext50_32x4d,94.260,5.740,98.460,1.540,27.48,224,0.875,bicubic regnetx_120,94.240,5.760,98.650,1.350,46.11,224,0.875,bicubic dpn92,94.230,5.770,98.730,1.270,37.67,224,0.875,bicubic -gluon_resnet101_v1d,94.230,5.770,98.550,1.450,44.57,224,0.875,bicubic ecaresnet50d_pruned,94.220,5.780,98.730,1.270,19.94,224,0.875,bicubic -tf_efficientnet_lite3,94.210,5.790,98.640,1.360,8.20,300,0.904,bilinear -resmlp_36_224,94.200,5.800,98.660,1.340,44.69,224,0.875,bicubic +gluon_resnet101_v1d,94.220,5.780,98.550,1.450,44.57,224,0.875,bicubic +tf_efficientnet_lite3.in1k,94.200,5.800,98.640,1.360,8.20,300,0.904,bilinear eca_resnet33ts,94.190,5.810,98.760,1.240,19.68,256,0.900,bicubic -resnext50d_32x4d,94.190,5.810,98.560,1.440,25.05,224,0.875,bicubic -mixnet_xl,94.190,5.810,98.340,1.660,11.90,224,0.875,bicubic -levit_192,94.180,5.820,98.540,1.460,10.95,224,0.900,bicubic +resmlp_36_224,94.190,5.810,98.660,1.340,44.69,224,0.875,bicubic +mixnet_xl.ra_in1k,94.190,5.810,98.340,1.660,11.90,224,0.875,bicubic +resnext50d_32x4d,94.180,5.820,98.570,1.430,25.05,224,0.875,bicubic +levit_192,94.170,5.830,98.540,1.460,10.95,224,0.900,bicubic gluon_resnet152_v1c,94.160,5.840,98.640,1.360,60.21,224,0.875,bicubic ens_adv_inception_resnet_v2,94.160,5.840,98.600,1.400,55.84,299,0.897,bicubic gmlp_s16_224,94.160,5.840,98.500,1.500,19.42,224,0.875,bicubic -efficientnet_b2_pruned,94.150,5.850,98.530,1.470,8.31,260,0.890,bicubic -vit_base_patch16_224_sam,94.140,5.860,98.670,1.330,86.57,224,0.900,bicubic -regnetx_160,94.130,5.870,98.740,1.260,54.28,224,0.875,bicubic -dpn98,94.120,5.880,98.580,1.420,61.57,224,0.875,bicubic -nf_regnet_b1,94.110,5.890,98.630,1.370,10.22,288,0.900,bicubic +vit_base_patch16_224.sam,94.140,5.860,98.670,1.330,86.57,224,0.900,bicubic +efficientnet_b2_pruned.in1k,94.140,5.860,98.530,1.470,8.31,260,0.890,bicubic +dpn98,94.130,5.870,98.570,1.430,61.57,224,0.875,bicubic +regnetx_160,94.120,5.880,98.740,1.260,54.28,224,0.875,bicubic +nf_regnet_b1,94.120,5.880,98.630,1.370,10.22,288,0.900,bicubic ese_vovnet39b,94.090,5.910,98.660,1.340,24.57,224,0.875,bicubic -xcit_tiny_24_p16_224,94.070,5.930,98.530,1.470,12.12,224,1.000,bicubic -gluon_resnet152_v1b,94.070,5.930,98.460,1.540,60.19,224,0.875,bicubic -coat_lite_mini,94.050,5.950,98.560,1.440,11.01,224,0.900,bicubic +xcit_tiny_24_p16_224,94.080,5.920,98.520,1.480,12.12,224,1.000,bicubic +gluon_resnet152_v1b,94.080,5.920,98.450,1.550,60.19,224,0.875,bicubic +coat_lite_mini,94.060,5.940,98.560,1.440,11.01,224,0.900,bicubic eca_halonext26ts,94.040,5.960,98.490,1.510,10.76,256,0.940,bicubic -hrnet_w64,94.020,5.980,98.620,1.380,128.06,224,0.875,bilinear resmlp_24_224,94.020,5.980,98.330,1.670,30.02,224,0.875,bicubic -halonet26t,94.010,5.990,98.500,1.500,12.48,256,0.950,bicubic -dpn131,93.990,6.010,98.720,1.280,79.25,224,0.875,bicubic -fbnetv3_b,93.970,6.030,98.630,1.370,8.60,256,0.950,bilinear -mobilevitv2_125,93.970,6.030,98.560,1.440,7.48,256,0.888,bicubic -dla102x2,93.970,6.030,98.500,1.500,41.28,224,0.875,bilinear -tf_efficientnetv2_b1,93.940,6.060,98.620,1.380,8.14,240,0.882,bicubic -resnetblur50,93.940,6.060,98.580,1.420,25.56,224,0.875,bicubic -fbnetv3_d,93.930,6.070,98.740,1.260,10.31,256,0.950,bilinear +dpn131,94.010,5.990,98.720,1.280,79.25,224,0.875,bicubic +hrnet_w64,94.010,5.990,98.610,1.390,128.06,224,0.875,bilinear +halonet26t,93.980,6.020,98.500,1.500,12.48,256,0.950,bicubic +fbnetv3_b.ra2_in1k,93.960,6.040,98.630,1.370,8.60,256,0.950,bilinear +resnetblur50,93.960,6.040,98.590,1.410,25.56,224,0.875,bicubic +mobilevitv2_125,93.960,6.040,98.560,1.440,7.48,256,0.888,bicubic +dla102x2,93.950,6.050,98.490,1.510,41.28,224,0.875,bilinear +tf_efficientnetv2_b1.in1k,93.940,6.060,98.620,1.380,8.14,240,0.882,bicubic +fbnetv3_d.ra2_in1k,93.930,6.070,98.740,1.260,10.31,256,0.950,bilinear +convnext_femto_ols.d1_in1k,93.920,6.080,98.610,1.390,5.23,288,0.950,bicubic hrnet_w48,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear -tf_efficientnet_cc_b1_8e,93.910,6.090,98.260,1.740,39.72,240,0.882,bicubic +convnext_femto.d1_in1k,93.920,6.080,98.520,1.480,5.22,288,0.950,bicubic rexnet_130,93.900,6.100,98.400,1.600,7.56,224,0.875,bicubic +tf_efficientnet_cc_b1_8e.in1k,93.900,6.100,98.260,1.740,39.72,240,0.882,bicubic regnetx_064,93.890,6.110,98.630,1.370,26.21,224,0.875,bicubic +vit_small_patch16_224.augreg_in1k,93.890,6.110,98.440,1.560,22.05,224,0.900,bicubic regnetx_080,93.870,6.130,98.520,1.480,39.57,224,0.875,bicubic -efficientnet_em,93.840,6.160,98.810,1.190,6.90,240,0.882,bicubic -repvgg_b2g4,93.840,6.160,98.590,1.410,61.76,224,0.875,bilinear -lambda_resnet26t,93.830,6.170,98.650,1.350,10.96,256,0.940,bicubic -pit_xs_distilled_224,93.820,6.180,98.670,1.330,11.00,224,0.900,bicubic -resnext101_32x8d,93.820,6.180,98.580,1.420,88.79,224,0.875,bilinear +repvgg_b2g4,93.860,6.140,98.590,1.410,61.76,224,0.875,bilinear +efficientnet_em.ra2_in1k,93.840,6.160,98.810,1.190,6.90,240,0.882,bicubic +lambda_resnet26t,93.840,6.160,98.640,1.360,10.96,256,0.940,bicubic +resnext101_32x8d,93.830,6.170,98.580,1.420,88.79,224,0.875,bilinear +pvt_v2_b1,93.820,6.180,98.660,1.340,14.01,224,0.900,bicubic +pit_xs_distilled_224,93.810,6.190,98.670,1.330,11.00,224,0.900,bicubic gluon_resnext50_32x4d,93.810,6.190,98.410,1.590,25.03,224,0.875,bicubic -eca_botnext26ts_256,93.780,6.220,98.500,1.500,10.59,256,0.950,bicubic +eca_botnext26ts_256,93.790,6.210,98.500,1.500,10.59,256,0.950,bicubic gluon_resnet50_v1d,93.770,6.230,98.390,1.610,25.58,224,0.875,bicubic gluon_resnet101_v1b,93.750,6.250,98.380,1.620,44.55,224,0.875,bicubic res2net101_26w_4s,93.750,6.250,98.310,1.690,45.21,224,0.875,bilinear -cspresnet50,93.730,6.270,98.640,1.360,21.62,256,0.887,bilinear +cspresnet50,93.740,6.260,98.640,1.360,21.62,256,0.887,bilinear legacy_seresnext50_32x4d,93.730,6.270,98.580,1.420,27.56,224,0.875,bilinear -vit_relpos_base_patch32_plus_rpn_256,93.730,6.270,98.070,1.930,119.42,256,0.900,bicubic -lambda_resnet26rpt_256,93.720,6.280,98.520,1.480,10.99,256,0.940,bicubic -wide_resnet101_2,93.710,6.290,98.540,1.460,126.89,224,0.875,bilinear -dpn68b,93.690,6.310,98.520,1.480,12.61,224,0.875,bicubic -tf_efficientnet_b1_ap,93.680,6.320,98.360,1.640,7.79,240,0.882,bicubic +wide_resnet101_2,93.720,6.280,98.540,1.460,126.89,224,0.875,bilinear +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.720,6.280,98.070,1.930,119.42,256,0.900,bicubic +lambda_resnet26rpt_256,93.710,6.290,98.510,1.490,10.99,256,0.940,bicubic +dpn68b,93.690,6.310,98.510,1.490,12.61,224,0.875,bicubic +tf_efficientnet_b1.ap_in1k,93.690,6.310,98.360,1.640,7.79,240,0.882,bicubic gluon_resnet101_v1c,93.670,6.330,98.420,1.580,44.57,224,0.875,bicubic -vit_tiny_patch16_384,93.650,6.350,98.600,1.400,5.79,384,1.000,bicubic -tf_efficientnet_b0_ns,93.630,6.370,98.640,1.360,5.29,224,0.875,bicubic +vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.650,6.350,98.600,1.400,5.79,384,1.000,bicubic +vit_base_patch32_384.augreg_in1k,93.640,6.360,98.400,1.600,88.30,384,1.000,bicubic +tf_efficientnet_b0.ns_jft_in1k,93.630,6.370,98.640,1.360,5.29,224,0.875,bicubic +vit_base_patch16_224.augreg_in1k,93.630,6.370,98.240,1.760,86.57,224,0.900,bicubic gluon_resnet50_v1s,93.620,6.380,98.460,1.540,25.68,224,0.875,bicubic -resnet33ts,93.600,6.400,98.530,1.470,19.68,256,0.900,bicubic +resnet33ts,93.600,6.400,98.540,1.460,19.68,256,0.900,bicubic cait_xxs24_224,93.600,6.400,98.440,1.560,11.96,224,1.000,bicubic -coat_tiny,93.590,6.410,98.420,1.580,5.50,224,0.900,bicubic -regnetx_040,93.560,6.440,98.550,1.450,22.12,224,0.875,bicubic +coat_tiny,93.590,6.410,98.430,1.570,5.50,224,0.900,bicubic +regnetx_040,93.560,6.440,98.540,1.460,22.12,224,0.875,bicubic hrnet_w44,93.550,6.450,98.700,1.300,67.06,224,0.875,bilinear -hrnet_w32,93.530,6.470,98.460,1.540,41.23,224,0.875,bilinear -xcit_nano_12_p8_384_dist,93.520,6.480,98.540,1.460,3.05,384,1.000,bicubic -dla102x,93.520,6.480,98.500,1.500,26.31,224,0.875,bilinear -botnet26t_256,93.510,6.490,98.300,1.700,12.49,256,0.950,bicubic -tf_efficientnet_b1,93.500,6.500,98.360,1.640,7.79,240,0.882,bicubic -repvgg_b2,93.490,6.510,98.730,1.270,89.02,224,0.875,bilinear +res2net50_26w_8s,93.540,6.460,98.260,1.740,48.40,224,0.875,bilinear +hrnet_w32,93.530,6.470,98.450,1.550,41.23,224,0.875,bilinear +dla102x,93.520,6.480,98.510,1.490,26.31,224,0.875,bilinear +botnet26t_256,93.520,6.480,98.300,1.700,12.49,256,0.950,bicubic +repvgg_b2,93.500,6.500,98.730,1.270,89.02,224,0.875,bilinear +xcit_nano_12_p8_384_dist,93.500,6.500,98.530,1.470,3.05,384,1.000,bicubic +tf_efficientnet_b1.aa_in1k,93.500,6.500,98.360,1.640,7.79,240,0.882,bicubic hrnet_w40,93.490,6.510,98.580,1.420,57.56,224,0.875,bilinear -xception,93.470,6.530,98.530,1.470,22.86,299,0.897,bicubic +gluon_inception_v3,93.460,6.540,98.570,1.430,23.83,299,0.875,bicubic +xception,93.460,6.540,98.530,1.470,22.86,299,0.897,bicubic resnet32ts,93.460,6.540,98.490,1.510,17.96,256,0.900,bicubic -gluon_inception_v3,93.450,6.550,98.570,1.430,23.83,299,0.875,bicubic -mixnet_l,93.450,6.550,98.220,1.780,7.33,224,0.875,bicubic +mixnet_l.ft_in1k,93.450,6.550,98.220,1.780,7.33,224,0.875,bicubic xception41,93.430,6.570,98.430,1.570,26.97,299,0.903,bicubic -res2net50_26w_8s,93.420,6.580,98.170,1.830,48.40,224,0.875,bilinear res2net50_26w_6s,93.410,6.590,98.280,1.720,37.05,224,0.875,bilinear -xcit_tiny_12_p16_224_dist,93.400,6.600,98.480,1.520,6.72,224,1.000,bicubic -legacy_seresnet152,93.390,6.610,98.340,1.660,66.82,224,0.875,bilinear -cs3darknet_m,93.360,6.640,98.600,1.400,9.31,288,0.950,bicubic -dla169,93.340,6.660,98.590,1.410,53.39,224,0.875,bilinear +legacy_seresnet152,93.400,6.600,98.350,1.650,66.82,224,0.875,bilinear +xcit_tiny_12_p16_224_dist,93.390,6.610,98.500,1.500,6.72,224,1.000,bicubic +cs3darknet_m,93.350,6.650,98.600,1.400,9.31,288,0.950,bicubic +dla169,93.340,6.660,98.600,1.400,53.39,224,0.875,bilinear +levit_128,93.340,6.660,98.380,1.620,9.21,224,0.900,bicubic resnest26d,93.330,6.670,98.630,1.370,17.07,224,0.875,bilinear -levit_128,93.330,6.670,98.380,1.620,9.21,224,0.900,bicubic +repvgg_b1,93.330,6.670,98.510,1.490,57.42,224,0.875,bilinear bat_resnext26ts,93.330,6.670,98.350,1.650,10.73,256,0.900,bicubic -repvgg_b1,93.320,6.680,98.510,1.490,57.42,224,0.875,bilinear tf_inception_v3,93.320,6.680,98.030,1.970,23.83,299,0.875,bicubic -tf_mixnet_l,93.320,6.680,98.030,1.970,7.33,224,0.875,bicubic -tv_resnet152,93.310,6.690,98.390,1.610,60.19,224,0.875,bilinear -mobilevitv2_100,93.300,6.700,98.280,1.720,4.90,256,0.888,bicubic -legacy_seresnet101,93.290,6.710,98.510,1.490,49.33,224,0.875,bilinear -selecsls60b,93.290,6.710,98.280,1.720,32.77,224,0.875,bicubic -efficientnet_b1,93.240,6.760,98.300,1.700,7.79,256,1.000,bicubic -coat_lite_tiny,93.230,6.770,98.260,1.740,5.72,224,0.900,bicubic +tf_mixnet_l.in1k,93.310,6.690,98.030,1.970,7.33,224,0.875,bicubic +tv_resnet152,93.300,6.700,98.390,1.610,60.19,224,0.875,bilinear +selecsls60b,93.300,6.700,98.280,1.720,32.77,224,0.875,bicubic +legacy_seresnet101,93.280,6.720,98.510,1.490,49.33,224,0.875,bilinear +mobilevitv2_100,93.270,6.730,98.280,1.720,4.90,256,0.888,bicubic +efficientnet_b1.ft_in1k,93.250,6.750,98.290,1.710,7.79,256,1.000,bicubic +coat_lite_tiny,93.240,6.760,98.260,1.740,5.72,224,0.900,bicubic hrnet_w30,93.200,6.800,98.410,1.590,37.71,224,0.875,bilinear -mobilevit_s,93.180,6.820,98.440,1.560,5.58,256,0.900,bicubic -dla60_res2next,93.170,6.830,98.400,1.600,17.03,224,0.875,bilinear -dla60_res2net,93.160,6.840,98.400,1.600,20.85,224,0.875,bilinear -efficientnet_es,93.140,6.860,98.420,1.580,5.44,224,0.875,bicubic +mobilevit_s,93.180,6.820,98.430,1.570,5.58,256,0.900,bicubic +dla60_res2net,93.180,6.820,98.420,1.580,20.85,224,0.875,bilinear +dla60_res2next,93.180,6.820,98.410,1.590,17.03,224,0.875,bilinear +efficientnet_es.ra_in1k,93.140,6.860,98.420,1.580,5.44,224,0.875,bicubic dla60x,93.120,6.880,98.510,1.490,17.35,224,0.875,bilinear regnetx_032,93.120,6.880,98.390,1.610,15.30,224,0.875,bicubic -pit_xs_224,93.120,6.880,98.330,1.670,10.62,224,0.900,bicubic -tf_efficientnetv2_b0,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic -dla102,93.060,6.940,98.550,1.450,33.27,224,0.875,bilinear +tf_efficientnetv2_b0.in1k,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic +pit_xs_224,93.110,6.890,98.310,1.690,10.62,224,0.900,bicubic +convnext_atto_ols.a2_in1k,93.080,6.920,98.470,1.530,3.70,288,0.950,bicubic +dla102,93.060,6.940,98.540,1.460,33.27,224,0.875,bilinear gluon_resnet50_v1c,93.030,6.970,98.390,1.610,25.58,224,0.875,bicubic -regnety_016,93.030,6.970,98.350,1.650,11.20,224,0.875,bicubic +regnety_016,93.030,6.970,98.360,1.640,11.20,224,0.875,bicubic +selecsls60,93.030,6.970,98.300,1.700,30.67,224,0.875,bicubic rexnet_100,93.030,6.970,98.190,1.810,4.80,224,0.875,bicubic -selecsls60,93.020,6.980,98.300,1.700,30.67,224,0.875,bicubic repvgg_b1g4,92.980,7.020,98.430,1.570,39.97,224,0.875,bilinear -cs3darknet_focus_m,92.970,7.030,98.390,1.610,9.30,288,0.950,bicubic -legacy_seresnet50,92.970,7.030,98.190,1.810,28.09,224,0.875,bilinear -hardcorenas_f,92.960,7.040,98.160,1.840,8.20,224,0.875,bilinear -tf_efficientnet_em,92.930,7.070,98.200,1.800,6.90,240,0.882,bicubic -crossvit_9_dagger_240,92.890,7.110,98.250,1.750,8.78,240,0.875,bicubic +legacy_seresnet50,92.960,7.040,98.190,1.810,28.09,224,0.875,bilinear +cs3darknet_focus_m,92.950,7.050,98.390,1.610,9.30,288,0.950,bicubic +hardcorenas_f,92.950,7.050,98.160,1.840,8.20,224,0.875,bilinear +tf_efficientnet_em.in1k,92.930,7.070,98.190,1.810,6.90,240,0.882,bicubic +crossvit_9_dagger_240,92.900,7.100,98.240,1.760,8.78,240,0.875,bicubic adv_inception_v3,92.880,7.120,98.140,1.860,23.83,299,0.875,bicubic -res2next50,92.860,7.140,98.190,1.810,24.67,224,0.875,bilinear -resmlp_12_distilled_224,92.840,7.160,98.140,1.860,15.35,224,0.875,bicubic -tf_efficientnet_cc_b0_8e,92.830,7.170,98.180,1.820,24.01,224,0.875,bicubic +res2next50,92.840,7.160,98.180,1.820,24.67,224,0.875,bilinear +tf_efficientnet_cc_b0_8e.in1k,92.830,7.170,98.180,1.820,24.01,224,0.875,bicubic +resmlp_12_distilled_224,92.830,7.170,98.140,1.860,15.35,224,0.875,bicubic gmixer_24_224,92.830,7.170,97.880,2.120,24.72,224,0.875,bicubic seresnext26t_32x4d,92.820,7.180,98.370,1.630,16.81,224,0.875,bicubic -tv_resnet101,92.820,7.180,98.250,1.750,44.55,224,0.875,bilinear -gcresnext26ts,92.780,7.220,98.260,1.740,10.48,256,0.900,bicubic -efficientnet_b1_pruned,92.770,7.230,98.040,1.960,6.33,240,0.882,bicubic -tv_resnext50_32x4d,92.750,7.250,98.280,1.720,25.03,224,0.875,bilinear -resnet26t,92.750,7.250,98.230,1.770,16.01,256,0.940,bicubic -densenet201,92.740,7.260,98.230,1.770,20.01,224,0.875,bicubic +tv_resnet101,92.810,7.190,98.250,1.750,44.55,224,0.875,bilinear +convnext_atto.d2_in1k,92.790,7.210,98.060,1.940,3.70,288,0.950,bicubic +gcresnext26ts,92.770,7.230,98.260,1.740,10.48,256,0.900,bicubic +efficientnet_b1_pruned.in1k,92.770,7.230,98.040,1.960,6.33,240,0.882,bicubic +densenet201,92.750,7.250,98.230,1.770,20.01,224,0.875,bicubic +resnet26t,92.750,7.250,98.210,1.790,16.01,256,0.940,bicubic +tv_resnext50_32x4d,92.740,7.260,98.270,1.730,25.03,224,0.875,bilinear res2net50_14w_8s,92.740,7.260,98.180,1.820,25.06,224,0.875,bilinear inception_v3,92.720,7.280,97.970,2.030,23.83,299,0.875,bicubic +seresnext26d_32x4d,92.700,7.300,98.150,1.850,16.81,224,0.875,bicubic seresnext26ts,92.690,7.310,98.290,1.710,10.39,256,0.900,bicubic -seresnext26d_32x4d,92.690,7.310,98.150,1.850,16.81,224,0.875,bicubic -efficientnet_b0,92.690,7.310,98.070,1.930,5.29,224,0.875,bicubic +efficientnet_b0.ra_in1k,92.690,7.310,98.070,1.930,5.29,224,0.875,bicubic resnet34d,92.680,7.320,98.310,1.690,21.82,224,0.875,bicubic -tf_efficientnet_lite2,92.650,7.350,98.230,1.770,6.09,260,0.890,bicubic +tf_efficientnet_lite2.in1k,92.650,7.350,98.230,1.770,6.09,260,0.890,bicubic legacy_seresnext26_32x4d,92.640,7.360,98.130,1.870,16.79,224,0.875,bicubic -poolformer_s12,92.630,7.370,98.200,1.800,11.92,224,0.900,bicubic -tf_efficientnet_lite1,92.620,7.380,98.080,1.920,5.42,240,0.882,bicubic +poolformer_s12,92.620,7.380,98.200,1.800,11.92,224,0.900,bicubic +tf_efficientnet_lite1.in1k,92.620,7.380,98.080,1.920,5.42,240,0.882,bicubic eca_resnext26ts,92.610,7.390,98.260,1.740,10.30,256,0.900,bicubic -tf_efficientnet_cc_b0_4e,92.590,7.410,98.080,1.920,13.31,224,0.875,bicubic -hardcorenas_e,92.570,7.430,98.100,1.900,8.07,224,0.875,bilinear +tf_efficientnet_cc_b0_4e.in1k,92.590,7.410,98.080,1.920,13.31,224,0.875,bicubic +hardcorenas_e,92.570,7.430,98.110,1.890,8.07,224,0.875,bilinear +res2net50_48w_2s,92.550,7.450,98.080,1.920,25.29,224,0.875,bilinear gluon_resnet50_v1b,92.540,7.460,98.170,1.830,25.56,224,0.875,bicubic -res2net50_48w_2s,92.540,7.460,98.080,1.920,25.29,224,0.875,bilinear densenet161,92.500,7.500,98.290,1.710,28.68,224,0.875,bicubic xcit_tiny_12_p16_224,92.500,7.500,98.240,1.760,6.72,224,1.000,bicubic -res2net50_26w_4s,92.490,7.510,98.060,1.940,25.70,224,0.875,bilinear -tinynet_a,92.440,7.560,98.080,1.920,6.19,192,0.875,bicubic +res2net50_26w_4s,92.500,7.500,98.060,1.940,25.70,224,0.875,bilinear +tinynet_a.in1k,92.440,7.560,98.080,1.920,6.19,192,0.875,bicubic convmixer_1024_20_ks9_p14,92.430,7.570,98.270,1.730,24.38,224,0.960,bicubic -mixnet_m,92.430,7.570,97.860,2.140,5.01,224,0.875,bicubic -mobilenetv2_120d,92.400,7.600,98.050,1.950,5.83,224,0.875,bicubic +mixnet_m.ft_in1k,92.430,7.570,97.870,2.130,5.01,224,0.875,bicubic +hardcorenas_d,92.400,7.600,98.070,1.930,7.50,224,0.875,bilinear +mobilenetv2_120d.ra_in1k,92.400,7.600,98.050,1.950,5.83,224,0.875,bicubic skresnet34,92.390,7.610,98.150,1.850,22.28,224,0.875,bicubic -hardcorenas_d,92.390,7.610,98.080,1.920,7.50,224,0.875,bilinear -hrnet_w18,92.320,7.680,98.250,1.750,21.30,224,0.875,bilinear -tf_mixnet_m,92.320,7.680,97.890,2.110,5.01,224,0.875,bicubic -selecsls42b,92.280,7.720,98.140,1.860,32.46,224,0.875,bicubic -ese_vovnet19b_dw,92.280,7.720,98.090,1.910,6.54,224,0.875,bicubic -mobilenetv3_large_100_miil,92.270,7.730,97.640,2.360,5.48,224,0.875,bilinear -tf_efficientnet_b0,92.250,7.750,97.990,2.010,5.29,224,0.875,bicubic +tf_mixnet_m.in1k,92.330,7.670,97.890,2.110,5.01,224,0.875,bicubic +hrnet_w18,92.320,7.680,98.240,1.760,21.30,224,0.875,bilinear +ese_vovnet19b_dw,92.290,7.710,98.090,1.910,6.54,224,0.875,bicubic +selecsls42b,92.280,7.720,98.150,1.850,32.46,224,0.875,bicubic +mobilenetv3_large_100.miil_in21k_ft_in1k,92.260,7.740,97.640,2.360,5.48,224,0.875,bilinear +tf_efficientnet_b0.aa_in1k,92.250,7.750,98.000,2.000,5.29,224,0.875,bicubic +dla60,92.230,7.770,98.110,1.890,22.04,224,0.875,bilinear resmlp_12_224,92.210,7.790,98.160,1.840,15.35,224,0.875,bicubic -dla60,92.210,7.790,98.100,1.900,22.04,224,0.875,bilinear -tf_efficientnet_b0_ap,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic -regnetx_016,92.160,7.840,98.200,1.800,9.19,224,0.875,bicubic -gernet_s,92.140,7.860,98.200,1.800,8.17,224,0.875,bilinear -xcit_nano_12_p8_224_dist,92.100,7.900,98.150,1.850,3.05,224,1.000,bicubic -resnet26d,92.070,7.930,97.970,2.030,16.01,224,0.875,bicubic -vit_tiny_r_s16_p8_384,92.040,7.960,98.290,1.710,6.36,384,1.000,bicubic -vit_small_patch32_224,92.030,7.970,98.230,1.770,22.88,224,0.900,bicubic -dpn68,92.030,7.970,98.050,1.950,12.61,224,0.875,bicubic -hardcorenas_c,92.030,7.970,97.840,2.160,5.52,224,0.875,bilinear -tf_efficientnet_es,91.980,8.020,97.860,2.140,5.44,224,0.875,bicubic +tf_efficientnet_b0.ap_in1k,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic +regnetx_016,92.170,7.830,98.210,1.790,9.19,224,0.875,bicubic +gernet_s,92.140,7.860,98.190,1.810,8.17,224,0.875,bilinear +xcit_nano_12_p8_224_dist,92.090,7.910,98.160,1.840,3.05,224,1.000,bicubic +resnet26d,92.070,7.930,97.960,2.040,16.01,224,0.875,bicubic +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,92.040,7.960,98.290,1.710,6.36,384,1.000,bicubic +vit_small_patch32_224.augreg_in21k_ft_in1k,92.040,7.960,98.230,1.770,22.88,224,0.900,bicubic +hardcorenas_c,92.020,7.980,97.840,2.160,5.52,224,0.875,bilinear +dpn68,92.010,7.990,98.050,1.950,12.61,224,0.875,bicubic +tf_efficientnet_es.in1k,91.980,8.020,97.860,2.140,5.44,224,0.875,bicubic +levit_128s,91.970,8.030,98.060,1.940,7.78,224,0.900,bicubic repvgg_a2,91.940,8.060,98.150,1.850,28.21,224,0.875,bilinear -levit_128s,91.930,8.070,98.070,1.930,7.78,224,0.900,bicubic -densenet169,91.920,8.080,98.100,1.900,14.15,224,0.875,bicubic +densenet169,91.930,8.070,98.100,1.900,14.15,224,0.875,bicubic densenetblur121d,91.910,8.090,98.070,1.930,8.00,224,0.875,bicubic -tv_resnet50,91.900,8.100,98.040,1.960,25.56,224,0.875,bilinear +tv_resnet50,91.880,8.120,98.040,1.960,25.56,224,0.875,bilinear resnext26ts,91.870,8.130,97.920,2.080,10.30,256,0.900,bicubic mixer_b16_224,91.870,8.130,97.250,2.750,59.88,224,0.875,bicubic -xcit_nano_12_p16_384_dist,91.830,8.170,98.020,1.980,3.05,384,1.000,bicubic -mobilenetv2_140,91.830,8.170,97.850,2.150,6.11,224,0.875,bicubic -mixnet_s,91.820,8.180,97.690,2.310,4.13,224,0.875,bicubic -vit_tiny_patch16_224,91.770,8.230,98.040,1.960,5.72,224,0.900,bicubic +mobilenetv2_140.ra_in1k,91.830,8.170,97.860,2.140,6.11,224,0.875,bicubic +mixnet_s.ft_in1k,91.830,8.170,97.690,2.310,4.13,224,0.875,bicubic +xcit_nano_12_p16_384_dist,91.820,8.180,98.020,1.980,3.05,384,1.000,bicubic +hardcorenas_b,91.770,8.230,97.780,2.220,5.18,224,0.875,bilinear +vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.760,8.240,98.040,1.960,5.72,224,0.900,bicubic mobilevitv2_075,91.760,8.240,97.860,2.140,2.87,256,0.888,bicubic -hardcorenas_b,91.760,8.240,97.780,2.220,5.18,224,0.875,bilinear -regnety_008,91.720,8.280,98.180,1.820,6.26,224,0.875,bicubic +regnety_008,91.750,8.250,98.180,1.820,6.26,224,0.875,bicubic resnest14d,91.720,8.280,97.870,2.130,10.61,224,0.875,bilinear -densenet121,91.580,8.420,98.030,1.970,7.98,224,0.875,bicubic -tf_mixnet_s,91.510,8.490,97.610,2.390,4.13,224,0.875,bicubic -repvgg_b0,91.400,8.600,97.990,2.010,15.82,224,0.875,bilinear +edgenext_x_small,91.720,8.280,97.610,2.390,2.34,288,1.000,bicubic +densenet121,91.570,8.430,98.030,1.970,7.98,224,0.875,bicubic +tf_mixnet_s.in1k,91.510,8.490,97.620,2.380,4.13,224,0.875,bicubic +repvgg_b0,91.430,8.570,97.990,2.010,15.82,224,0.875,bilinear regnety_006,91.370,8.630,97.710,2.290,6.06,224,0.875,bicubic hardcorenas_a,91.350,8.650,97.860,2.140,5.26,224,0.875,bilinear -mobilenetv3_large_100,91.330,8.670,97.720,2.280,5.48,224,0.875,bicubic -semnasnet_100,91.280,8.720,97.560,2.440,3.89,224,0.875,bicubic -tf_mobilenetv3_large_100,91.220,8.780,97.660,2.340,5.48,224,0.875,bilinear -mobilenetv3_rw,91.210,8.790,97.660,2.340,5.48,224,0.875,bicubic +mobilenetv3_large_100.ra_in1k,91.320,8.680,97.710,2.290,5.48,224,0.875,bicubic +semnasnet_100.rmsp_in1k,91.280,8.720,97.560,2.440,3.89,224,0.875,bicubic +tf_mobilenetv3_large_100.in1k,91.240,8.760,97.660,2.340,5.48,224,0.875,bilinear +mobilenetv3_rw.rmsp_in1k,91.210,8.790,97.660,2.340,5.48,224,0.875,bicubic hrnet_w18_small_v2,91.190,8.810,97.900,2.100,15.60,224,0.875,bilinear -efficientnet_es_pruned,91.180,8.820,97.750,2.250,5.44,224,0.875,bicubic +vit_base_patch32_224.augreg_in1k,91.190,8.810,97.380,2.620,88.22,224,0.900,bicubic +efficientnet_es_pruned.in1k,91.180,8.820,97.750,2.250,5.44,224,0.875,bicubic +efficientnet_lite0.ra_in1k,91.140,8.860,97.630,2.370,4.65,224,0.875,bicubic resnet34,91.130,8.870,97.620,2.380,21.80,224,0.875,bilinear -resnet26,91.120,8.880,97.750,2.250,16.00,224,0.875,bicubic -efficientnet_lite0,91.110,8.890,97.630,2.370,4.65,224,0.875,bicubic -edgenext_x_small,91.090,8.910,97.550,2.450,2.34,256,0.900,bicubic +resnet26,91.110,8.890,97.740,2.260,16.00,224,0.875,bicubic regnetx_008,91.050,8.950,97.710,2.290,7.26,224,0.875,bicubic -tf_efficientnet_lite0,91.040,8.960,97.590,2.410,4.65,224,0.875,bicubic -xcit_nano_12_p8_224,91.020,8.980,97.790,2.210,3.05,224,1.000,bicubic -gluon_resnet34_v1b,90.960,9.040,97.640,2.360,21.80,224,0.875,bicubic -mobilenetv2_110d,90.960,9.040,97.560,2.440,4.52,224,0.875,bicubic -tinynet_b,90.920,9.080,97.670,2.330,3.73,188,0.875,bicubic -pit_ti_distilled_224,90.900,9.100,97.720,2.280,5.10,224,0.900,bicubic -legacy_seresnet34,90.900,9.100,97.580,2.420,21.96,224,0.875,bilinear +tf_efficientnet_lite0.in1k,91.040,8.960,97.590,2.410,4.65,224,0.875,bicubic +xcit_nano_12_p8_224,91.020,8.980,97.800,2.200,3.05,224,1.000,bicubic +gluon_resnet34_v1b,90.960,9.040,97.630,2.370,21.80,224,0.875,bicubic +mobilenetv2_110d.ra_in1k,90.950,9.050,97.550,2.450,4.52,224,0.875,bicubic +tinynet_b.in1k,90.920,9.080,97.670,2.330,3.73,188,0.875,bicubic +pit_ti_distilled_224,90.900,9.100,97.700,2.300,5.10,224,0.900,bicubic tv_densenet121,90.890,9.110,97.710,2.290,7.98,224,0.875,bicubic -mobilevit_xs,90.820,9.180,97.920,2.080,2.32,256,0.900,bicubic -dla34,90.780,9.220,97.660,2.340,15.74,224,0.875,bilinear -deit_tiny_distilled_patch16_224,90.710,9.290,97.570,2.430,5.91,224,0.900,bicubic -fbnetc_100,90.710,9.290,97.210,2.790,5.57,224,0.875,bilinear +legacy_seresnet34,90.890,9.110,97.580,2.420,21.96,224,0.875,bilinear +mobilevit_xs,90.830,9.170,97.920,2.080,2.32,256,0.900,bicubic +dla34,90.760,9.240,97.660,2.340,15.74,224,0.875,bilinear +deit_tiny_distilled_patch16_224,90.700,9.300,97.570,2.430,5.91,224,0.900,bicubic +fbnetc_100.rmsp_in1k,90.700,9.300,97.210,2.790,5.57,224,0.875,bilinear swsl_resnet18,90.690,9.310,97.700,2.300,11.69,224,0.875,bilinear -convit_tiny,90.640,9.360,97.740,2.260,5.71,224,0.875,bicubic -crossvit_9_240,90.630,9.370,97.740,2.260,8.55,240,0.875,bicubic -regnety_004,90.510,9.490,97.540,2.460,4.34,224,0.875,bicubic -mnasnet_100,90.510,9.490,97.470,2.530,4.38,224,0.875,bicubic -regnetx_006,90.360,9.640,97.430,2.570,6.20,224,0.875,bicubic -spnasnet_100,90.340,9.660,97.190,2.810,4.42,224,0.875,bilinear -crossvit_tiny_240,90.240,9.760,97.590,2.410,7.01,240,0.875,bicubic -ssl_resnet18,90.210,9.790,97.550,2.450,11.69,224,0.875,bilinear +crossvit_9_240,90.640,9.360,97.740,2.260,8.55,240,0.875,bicubic +convit_tiny,90.630,9.370,97.740,2.260,5.71,224,0.875,bicubic +mnasnet_100.rmsp_in1k,90.510,9.490,97.470,2.530,4.38,224,0.875,bicubic +regnety_004,90.500,9.500,97.540,2.460,4.34,224,0.875,bicubic +regnetx_006,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic +spnasnet_100.rmsp_in1k,90.350,9.650,97.190,2.810,4.42,224,0.875,bilinear +crossvit_tiny_240,90.250,9.750,97.590,2.410,7.01,240,0.875,bicubic +ssl_resnet18,90.220,9.780,97.550,2.450,11.69,224,0.875,bilinear vgg16_bn,90.090,9.910,97.370,2.630,138.37,224,0.875,bilinear vgg19_bn,90.080,9.920,97.580,2.420,143.68,224,0.875,bilinear -semnasnet_075,90.060,9.940,97.430,2.570,2.91,224,0.875,bicubic -ghostnet_100,90.030,9.970,97.370,2.630,5.18,224,0.875,bilinear -pit_ti_224,89.950,10.050,97.440,2.560,4.85,224,0.900,bicubic -tv_resnet34,89.930,10.070,97.340,2.660,21.80,224,0.875,bilinear -vit_base_patch32_224_sam,89.750,10.250,97.000,3.000,88.22,224,0.900,bicubic -xcit_nano_12_p16_224_dist,89.690,10.310,97.100,2.900,3.05,224,1.000,bicubic -tf_mobilenetv3_large_075,89.680,10.320,97.210,2.790,3.99,224,0.875,bilinear -deit_tiny_patch16_224,89.660,10.340,97.450,2.550,5.72,224,0.900,bicubic -skresnet18,89.660,10.340,97.240,2.760,11.96,224,0.875,bicubic -mobilenetv2_100,89.610,10.390,97.150,2.850,3.50,224,0.875,bicubic -resnet18d,89.280,10.720,97.140,2.860,11.71,224,0.875,bicubic -vit_tiny_r_s16_p8_224,89.180,10.820,97.230,2.770,6.34,224,0.900,bicubic +semnasnet_075.rmsp_in1k,90.070,9.930,97.430,2.570,2.91,224,0.875,bicubic +ghostnet_100,90.020,9.980,97.370,2.630,5.18,224,0.875,bilinear +pit_ti_224,89.940,10.060,97.450,2.550,4.85,224,0.900,bicubic +tv_resnet34,89.940,10.060,97.340,2.660,21.80,224,0.875,bilinear +vit_base_patch32_224.sam,89.750,10.250,97.000,3.000,88.22,224,0.900,bicubic +tf_mobilenetv3_large_075.in1k,89.680,10.320,97.210,2.790,3.99,224,0.875,bilinear +xcit_nano_12_p16_224_dist,89.680,10.320,97.090,2.910,3.05,224,1.000,bicubic +deit_tiny_patch16_224,89.670,10.330,97.450,2.550,5.72,224,0.900,bicubic +skresnet18,89.660,10.340,97.230,2.770,11.96,224,0.875,bicubic +mobilenetv2_100.ra_in1k,89.600,10.400,97.140,2.860,3.50,224,0.875,bicubic +resnet18d,89.280,10.720,97.150,2.850,11.71,224,0.875,bicubic +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.170,10.830,97.230,2.770,6.34,224,0.900,bicubic +hrnet_w18_small,89.050,10.950,97.110,2.890,13.19,224,0.875,bilinear +resnet14t,89.050,10.950,96.620,3.380,10.08,224,0.950,bilinear vgg19,89.040,10.960,96.870,3.130,143.67,224,0.875,bilinear -resnet14t,89.040,10.960,96.600,3.400,10.08,224,0.950,bilinear -hrnet_w18_small,89.030,10.970,97.110,2.890,13.19,224,0.875,bilinear -tf_mobilenetv3_large_minimal_100,88.970,11.030,96.850,3.150,3.92,224,0.875,bilinear +tf_mobilenetv3_large_minimal_100.in1k,88.970,11.030,96.860,3.140,3.92,224,0.875,bilinear regnetx_004,88.900,11.100,97.120,2.880,5.16,224,0.875,bicubic legacy_seresnet18,88.880,11.120,96.980,3.020,11.78,224,0.875,bicubic -lcnet_100,88.790,11.210,96.730,3.270,2.95,224,0.875,bicubic +edgenext_xx_small,88.880,11.120,96.690,3.310,1.33,288,1.000,bicubic +pvt_v2_b0,88.790,11.210,96.860,3.140,3.67,224,0.900,bicubic +lcnet_100.ra2_in1k,88.790,11.210,96.730,3.270,2.95,224,0.875,bicubic vgg13_bn,88.760,11.240,96.970,3.030,133.05,224,0.875,bilinear xcit_nano_12_p16_224,88.610,11.390,96.790,3.210,3.05,224,1.000,bicubic vgg16,88.550,11.450,96.790,3.210,138.36,224,0.875,bilinear gluon_resnet18_v1b,88.400,11.600,96.680,3.320,11.69,224,0.875,bicubic -edgenext_xx_small,88.350,11.650,96.520,3.480,1.33,256,0.900,bicubic mobilevitv2_050,88.230,11.770,96.990,3.010,1.37,256,0.888,bicubic -tinynet_c,87.780,12.220,96.370,3.630,2.46,184,0.875,bicubic +tinynet_c.in1k,87.770,12.230,96.370,3.630,2.46,184,0.875,bicubic vgg11_bn,87.500,12.500,96.820,3.180,132.87,224,0.875,bilinear resnet18,87.390,12.610,96.290,3.710,11.69,224,0.875,bilinear regnety_002,87.380,12.620,96.590,3.410,3.16,224,0.875,bicubic -mobilevit_xxs,87.190,12.810,96.100,3.900,1.27,256,0.900,bicubic -mixer_l16_224,87.140,12.860,93.520,6.480,208.20,224,0.875,bicubic +mobilevit_xxs,87.170,12.830,96.100,3.900,1.27,256,0.900,bicubic +mixer_l16_224,87.150,12.850,93.520,6.480,208.20,224,0.875,bicubic vgg13,87.050,12.950,96.320,3.680,133.05,224,0.875,bilinear vgg11,86.550,13.450,96.280,3.720,132.86,224,0.875,bilinear -dla60x_c,86.270,13.730,96.170,3.830,1.32,224,0.875,bilinear -resnet10t,86.210,13.790,95.660,4.340,5.44,224,0.950,bilinear -regnetx_002,86.200,13.800,95.980,4.020,2.68,224,0.875,bicubic -lcnet_075,85.990,14.010,95.690,4.310,2.36,224,0.875,bicubic -mobilenetv3_small_100,85.220,14.780,95.630,4.370,2.54,224,0.875,bicubic -tf_mobilenetv3_small_100,85.190,14.810,95.770,4.230,2.54,224,0.875,bilinear -tinynet_d,84.760,15.240,95.180,4.820,2.34,152,0.875,bicubic -mnasnet_small,84.440,15.560,95.180,4.820,2.03,224,0.875,bicubic -dla46x_c,84.250,15.750,95.260,4.740,1.07,224,0.875,bilinear -mobilenetv2_050,83.890,16.110,94.720,5.280,1.97,224,0.875,bicubic -dla46_c,83.640,16.360,94.920,5.080,1.30,224,0.875,bilinear -tf_mobilenetv3_small_075,83.520,16.480,94.800,5.200,2.04,224,0.875,bilinear -mobilenetv3_small_075,83.040,16.960,94.100,5.900,2.04,224,0.875,bicubic -lcnet_050,81.780,18.220,93.720,6.280,1.88,224,0.875,bicubic -tf_mobilenetv3_small_minimal_100,81.400,18.600,93.680,6.320,2.04,224,0.875,bilinear -tinynet_e,78.900,21.100,92.560,7.440,2.04,106,0.875,bicubic -mobilenetv3_small_050,76.990,23.010,91.300,8.700,1.59,224,0.875,bicubic +dla60x_c,86.290,13.710,96.160,3.840,1.32,224,0.875,bilinear +resnet10t,86.200,13.800,95.650,4.350,5.44,224,0.950,bilinear +regnetx_002,86.190,13.810,95.980,4.020,2.68,224,0.875,bicubic +lcnet_075.ra2_in1k,85.990,14.010,95.680,4.320,2.36,224,0.875,bicubic +mobilenetv3_small_100.lamb_in1k,85.220,14.780,95.620,4.380,2.54,224,0.875,bicubic +tf_mobilenetv3_small_100.in1k,85.190,14.810,95.770,4.230,2.54,224,0.875,bilinear +tinynet_d.in1k,84.750,15.250,95.180,4.820,2.34,152,0.875,bicubic +mnasnet_small.lamb_in1k,84.440,15.560,95.180,4.820,2.03,224,0.875,bicubic +dla46x_c,84.250,15.750,95.270,4.730,1.07,224,0.875,bilinear +mobilenetv2_050.lamb_in1k,83.890,16.110,94.720,5.280,1.97,224,0.875,bicubic +dla46_c,83.650,16.350,94.920,5.080,1.30,224,0.875,bilinear +tf_mobilenetv3_small_075.in1k,83.520,16.480,94.790,5.210,2.04,224,0.875,bilinear +mobilenetv3_small_075.lamb_in1k,83.040,16.960,94.100,5.900,2.04,224,0.875,bicubic +lcnet_050.ra2_in1k,81.780,18.220,93.710,6.290,1.88,224,0.875,bicubic +tf_mobilenetv3_small_minimal_100.in1k,81.380,18.620,93.670,6.330,2.04,224,0.875,bilinear +tinynet_e.in1k,78.900,21.100,92.560,7.440,2.04,106,0.875,bicubic +mobilenetv3_small_050.lamb_in1k,76.990,23.010,91.300,8.700,1.59,224,0.875,bicubic diff --git a/results/results-imagenet-a.csv b/results/results-imagenet-a.csv index 4e306fba..0ed6790c 100644 --- a/results/results-imagenet-a.csv +++ b/results/results-imagenet-a.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff -tf_efficientnet_l2_ns,84.760,15.240,96.147,3.853,480.31,800,0.960,bicubic,-13.790,-3.673,+1 -tf_efficientnet_l2_ns_475,83.400,16.600,95.453,4.547,480.31,475,0.936,bicubic,-15.100,-4.377,+2 -beit_large_patch16_512,81.653,18.347,94.880,5.120,305.67,512,1.000,bicubic,-16.907,-4.960,-2 -deit3_large_patch16_384_in21ft1k,79.213,20.787,93.627,6.373,304.76,384,1.000,bicubic,-19.247,-6.133,+1 -beit_large_patch16_384,79.120,20.880,94.280,5.720,305.00,384,1.000,bicubic,-19.400,-5.540,-2 -swinv2_large_window12to24_192to384_22kft1k,73.867,26.133,91.747,8.253,196.74,384,1.000,bicubic,-24.283,-7.943,+6 -deit3_base_patch16_384_in21ft1k,71.280,28.720,89.947,10.053,86.88,384,1.000,bicubic,-26.550,-9.733,+16 -swinv2_base_window12to24_192to384_22kft1k,71.267,28.733,91.280,8.720,87.92,384,1.000,bicubic,-26.873,-8.500,+5 -vit_large_patch16_384,71.227,28.773,89.853,10.147,304.72,384,1.000,bicubic,-26.993,-9.947,-2 -convnext_xlarge_384_in22ft1k,70.787,29.213,90.400,9.600,350.20,384,1.000,bicubic,-27.563,-9.400,-4 -deit3_huge_patch14_224_in21ft1k,70.227,29.773,90.720,9.280,632.13,224,1.000,bicubic,-27.943,-9.010,0 -volo_d5_512,69.653,30.347,90.413,9.587,296.09,512,1.150,bicubic,-28.117,-9.257,+13 -swin_large_patch4_window12_384,69.613,30.387,89.573,10.427,196.74,384,1.000,bicubic,-28.427,-10.117,+1 -deit3_large_patch16_224_in21ft1k,68.707,31.293,90.013,9.987,304.37,224,1.000,bicubic,-29.463,-9.747,-4 -beit_large_patch16_224,68.507,31.493,89.573,10.427,304.43,224,0.900,bicubic,-29.673,-10.187,-6 -volo_d5_448,68.107,31.893,89.707,10.293,295.91,448,1.150,bicubic,-29.653,-9.913,+10 -convnext_large_384_in22ft1k,67.947,32.053,89.200,10.800,197.77,384,1.000,bicubic,-30.273,-10.530,-9 -swinv2_large_window12to16_192to256_22kft1k,67.280,32.720,88.013,11.987,196.74,256,0.900,bicubic,-30.580,-11.637,+2 -tf_efficientnet_b7_ns,67.080,32.920,88.640,11.360,66.35,600,0.949,bicubic,-30.840,-11.080,-3 -tf_efficientnetv2_xl_in21ft1k,67.000,33.000,86.867,13.133,208.12,512,1.000,bicubic,-30.660,-12.623,+9 -volo_d4_448,66.680,33.320,88.987,11.013,193.41,448,1.150,bicubic,-30.990,-10.623,+7 -tf_efficientnetv2_l_in21ft1k,66.320,33.680,87.840,12.160,118.52,480,1.000,bicubic,-31.380,-11.830,+5 -beit_base_patch16_384,65.880,34.120,88.507,11.493,86.74,384,1.000,bicubic,-31.940,-11.193,+1 -volo_d3_448,65.427,34.573,87.560,12.440,86.63,448,1.000,bicubic,-32.123,-11.990,+14 -convnext_base_384_in22ft1k,65.000,35.000,87.867,12.133,88.59,384,1.000,bicubic,-32.950,-11.783,-10 -swin_base_patch4_window12_384,64.467,35.533,87.493,12.507,87.90,384,1.000,bicubic,-33.423,-12.217,-8 -vit_base_patch16_384,63.693,36.307,86.707,13.293,86.86,384,1.000,bicubic,-34.147,-12.963,-6 -swinv2_base_window12to16_192to256_22kft1k,63.227,36.773,87.493,12.507,87.92,256,0.900,bicubic,-34.423,-12.227,+2 -convnext_xlarge_in22ft1k,62.627,37.373,86.000,14.000,350.20,224,0.875,bicubic,-35.293,-13.680,-12 -cait_m48_448,62.347,37.653,86.453,13.547,356.46,448,1.000,bicubic,-35.133,-13.097,+15 -tf_efficientnet_b6_ns,62.267,37.733,85.173,14.827,43.04,528,0.942,bicubic,-35.363,-14.407,+2 -vit_large_r50_s32_384,61.493,38.507,83.960,16.040,329.09,384,1.000,bicubic,-36.367,-15.710,-13 -tf_efficientnetv2_m_in21ft1k,61.387,38.613,85.413,14.587,54.14,480,1.000,bicubic,-36.093,-14.117,+13 -ig_resnext101_32x48d,61.013,38.987,83.333,16.667,828.41,224,0.875,bilinear,-36.607,-16.367,0 -swin_large_patch4_window7_224,60.907,39.093,85.867,14.133,196.53,224,0.900,bicubic,-36.743,-13.713,-4 -resnetv2_152x4_bitm,60.787,39.213,83.560,16.440,936.53,480,1.000,bilinear,-36.703,-16.050,+7 -deit3_large_patch16_384,60.507,39.493,85.707,14.293,304.76,384,1.000,bicubic,-36.913,-13.913,+12 -tf_efficientnet_b5_ns,60.293,39.707,84.480,15.520,30.39,456,0.934,bicubic,-37.207,-15.150,+4 -xcit_large_24_p8_384_dist,59.880,40.120,85.480,14.520,188.93,384,1.000,bicubic,-37.640,-14.060,+1 -convnext_large_in22ft1k,59.773,40.227,84.040,15.960,197.77,224,0.875,bicubic,-38.057,-15.650,-18 -dm_nfnet_f6,59.173,40.827,82.333,17.667,438.36,576,0.956,bicubic,-38.427,-17.217,-6 -vit_base_patch8_224,58.920,41.080,82.733,17.267,86.58,224,0.900,bicubic,-38.660,-16.937,-6 -volo_d2_384,58.600,41.400,84.253,15.747,58.87,384,1.000,bicubic,-38.710,-15.347,+12 -dm_nfnet_f5,58.560,41.440,82.773,17.227,377.21,544,0.954,bicubic,-38.980,-16.797,-5 -dm_nfnet_f4,58.133,41.867,81.973,18.027,316.07,512,0.951,bicubic,-39.447,-17.537,-8 -ig_resnext101_32x32d,58.040,41.960,80.613,19.387,468.53,224,0.875,bilinear,-39.330,-19.067,+6 -cait_m36_384,57.813,42.187,84.827,15.173,271.22,384,1.000,bicubic,-39.587,-14.683,+3 -deit3_base_patch16_224_in21ft1k,57.253,42.747,83.520,16.480,86.59,224,1.000,bicubic,-40.237,-16.080,-4 -volo_d5_224,57.120,42.880,82.720,17.280,295.46,224,0.960,bicubic,-40.270,-16.850,+2 -deit3_small_patch16_384_in21ft1k,57.067,42.933,83.080,16.920,22.21,384,1.000,bicubic,-40.063,-16.420,+19 -xcit_medium_24_p8_384_dist,56.693,43.307,83.400,16.600,84.32,384,1.000,bicubic,-40.597,-16.110,+6 -convnext_small_384_in22ft1k,56.187,43.813,83.760,16.240,50.22,384,1.000,bicubic,-41.273,-15.820,-4 -dm_nfnet_f3,55.827,44.173,80.947,19.053,254.92,416,0.940,bicubic,-41.523,-18.613,0 -vit_large_patch16_224,55.627,44.373,80.080,19.920,304.33,224,0.900,bicubic,-42.013,-19.510,-22 -convnext_base_in22ft1k,54.627,45.373,82.173,17.827,88.59,224,0.875,bicubic,-42.843,-17.427,-8 -vit_base_r50_s16_384,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.553,-18.347,+11 -cait_s36_384,54.387,45.613,81.360,18.640,68.37,384,1.000,bicubic,-42.943,-18.170,-3 -volo_d1_384,54.333,45.667,80.973,19.027,26.78,384,1.000,bicubic,-42.577,-18.547,+35 -deit3_huge_patch14_224,54.320,45.680,82.093,17.907,632.13,224,0.900,bicubic,-42.570,-17.387,+37 -xcit_small_24_p8_384_dist,54.267,45.733,81.533,18.467,47.63,384,1.000,bicubic,-42.973,-18.077,+1 -resnetv2_101x3_bitm,54.027,45.973,81.040,18.960,387.93,448,1.000,bilinear,-42.963,-18.450,+23 -resnetv2_152x2_bitm,54.013,45.987,82.013,17.987,236.34,448,1.000,bilinear,-42.997,-17.577,+19 -deit3_base_patch16_384,53.427,46.573,80.573,19.427,86.88,384,1.000,bicubic,-43.593,-18.817,+17 -tf_efficientnetv2_l,53.173,46.827,79.147,20.853,118.52,480,1.000,bicubic,-44.107,-20.403,-6 -ig_resnext101_32x16d,53.093,46.907,76.933,23.067,194.03,224,0.875,bilinear,-43.717,-22.667,+36 -volo_d4_224,52.933,47.067,80.440,19.560,192.96,224,0.960,bicubic,-44.367,-19.080,-10 -xcit_large_24_p16_384_dist,52.840,47.160,81.827,18.173,189.10,384,1.000,bicubic,-44.680,-17.653,-26 -swin_base_patch4_window7_224,51.427,48.573,79.973,20.027,87.77,224,0.900,bicubic,-45.823,-19.557,-8 -tf_efficientnet_b4_ns,51.253,48.747,79.173,20.827,19.34,380,0.922,bicubic,-45.697,-20.407,+19 -swsl_resnext101_32x8d,51.227,48.773,78.240,21.760,88.79,224,0.875,bilinear,-45.973,-21.330,-7 -resnetv2_152x2_bit_teacher_384,51.173,48.827,78.480,21.520,236.34,384,1.000,bicubic,-45.657,-20.970,+29 -beit_base_patch16_224,50.707,49.293,79.693,20.307,86.53,224,0.900,bicubic,-46.383,-19.917,0 -xcit_small_12_p8_384_dist,50.587,49.413,79.600,20.400,26.21,384,1.000,bicubic,-46.643,-19.880,-11 -volo_d3_224,50.253,49.747,78.173,21.827,86.33,224,0.960,bicubic,-46.837,-21.297,0 -cait_s24_384,49.733,50.267,78.733,21.267,47.06,384,1.000,bicubic,-47.337,-20.697,+2 -xcit_medium_24_p16_384_dist,49.333,50.667,79.853,20.147,84.40,384,1.000,bicubic,-47.947,-19.607,-17 -deit_base_distilled_patch16_384,49.320,50.680,79.253,20.747,87.63,384,1.000,bicubic,-47.640,-20.227,+10 -tf_efficientnet_b8,48.960,51.040,77.240,22.760,87.41,672,0.954,bicubic,-48.240,-22.260,-12 -dm_nfnet_f2,48.920,51.080,77.147,22.853,193.78,352,0.920,bicubic,-48.100,-22.293,0 -deit3_large_patch16_224,48.627,51.373,78.133,21.867,304.37,224,0.900,bicubic,-48.313,-21.207,+10 -tf_efficientnetv2_s_in21ft1k,48.507,51.493,77.893,22.107,21.46,384,1.000,bicubic,-48.213,-21.527,+28 -resnest269e,48.200,51.800,74.333,25.667,110.93,416,0.928,bicubic,-48.320,-25.017,+57 -xcit_large_24_p8_224_dist,48.120,51.880,79.107,20.893,188.93,224,1.000,bicubic,-48.950,-20.313,-5 -regnetz_e8,47.827,52.173,76.200,23.800,57.70,320,1.000,bicubic,-49.373,-23.300,-19 -resnetv2_50x3_bitm,47.280,52.720,77.333,22.667,217.32,448,1.000,bilinear,-49.430,-22.217,+26 -xcit_large_24_p8_224,47.160,52.840,74.400,25.600,188.93,224,1.000,bicubic,-49.250,-24.580,+62 -xcit_small_24_p16_384_dist,46.947,53.053,77.160,22.840,47.67,384,1.000,bicubic,-50.173,-22.290,-17 -tf_efficientnet_b8_ap,46.893,53.107,76.507,23.493,87.41,672,0.954,bicubic,-50.217,-23.153,-17 -efficientnetv2_rw_m,46.280,53.720,75.680,24.320,53.24,416,1.000,bicubic,-50.700,-23.860,-3 -swinv2_base_window16_256,46.267,53.733,75.187,24.813,87.92,256,0.900,bicubic,-50.493,-24.163,+17 -swsl_resnext101_32x16d,46.133,53.867,72.253,27.747,194.03,224,0.875,bilinear,-50.467,-27.277,+38 -volo_d2_224,46.080,53.920,75.253,24.747,58.68,224,0.960,bicubic,-50.920,-24.137,-9 -vit_small_patch16_384,45.920,54.080,76.707,23.293,22.20,384,1.000,bicubic,-50.780,-22.773,+22 -ecaresnet269d,45.880,54.120,75.133,24.867,102.09,352,1.000,bicubic,-51.200,-24.337,-18 -vit_small_r26_s32_384,45.733,54.267,76.053,23.947,36.47,384,1.000,bicubic,-50.947,-23.527,+25 -tf_efficientnetv2_m,45.533,54.467,74.533,25.467,54.14,480,1.000,bicubic,-51.607,-24.877,-28 -tf_efficientnet_b7_ap,45.373,54.627,74.213,25.787,66.35,600,0.949,bicubic,-51.827,-25.327,-33 -dm_nfnet_f1,45.320,54.680,74.107,25.893,132.63,320,0.910,bicubic,-51.590,-25.303,-3 -ig_resnext101_32x8d,45.293,54.707,70.853,29.147,88.79,224,0.875,bilinear,-51.017,-28.577,+61 -xcit_medium_24_p8_224_dist,45.213,54.787,76.720,23.280,84.32,224,1.000,bicubic,-51.707,-22.670,-8 -eca_nfnet_l2,44.960,55.040,75.893,24.107,56.72,384,1.000,bicubic,-52.130,-23.617,-28 -convnext_tiny_384_in22ft1k,44.840,55.160,76.680,23.320,28.59,384,1.000,bicubic,-52.040,-22.790,-4 -convnext_small_in22ft1k,44.813,55.187,77.373,22.627,50.22,224,0.875,bicubic,-52.177,-22.037,-18 -crossvit_18_dagger_408,44.293,55.707,73.827,26.173,44.61,408,1.000,bicubic,-52.237,-25.433,+34 -resnest200e,44.133,55.867,73.467,26.533,70.20,320,0.909,bicubic,-52.477,-25.883,+23 -cait_xs24_384,43.947,56.053,75.160,24.840,26.67,384,1.000,bicubic,-52.593,-24.260,+28 -seresnextaa101d_32x8d,43.947,56.053,73.400,26.600,93.59,288,1.000,bicubic,-53.003,-25.990,-18 -resnetrs200,43.747,56.253,72.813,27.187,93.21,320,1.000,bicubic,-52.953,-26.557,+8 -tresnet_xl_448,43.467,56.533,72.453,27.547,78.44,448,0.875,bilinear,-52.503,-26.677,+92 -xcit_small_12_p16_384_dist,43.267,56.733,73.880,26.120,26.25,384,1.000,bicubic,-53.663,-25.520,-19 -vit_base_patch16_224,43.253,56.747,72.893,27.107,86.57,224,0.900,bicubic,-53.627,-26.637,-14 -resnetrs420,43.147,56.853,70.467,29.533,191.89,416,1.000,bicubic,-53.763,-28.993,-18 -xcit_medium_24_p8_224,43.093,56.907,70.347,29.653,84.32,224,1.000,bicubic,-53.017,-28.543,+74 -tf_efficientnet_b7,42.960,57.040,73.133,26.867,66.35,600,0.949,bicubic,-54.050,-26.387,-32 -xcit_tiny_24_p8_384_dist,42.467,57.533,72.867,27.133,12.11,384,1.000,bicubic,-54.083,-26.453,+18 -swinv2_small_window16_256,42.293,57.707,72.920,27.080,49.73,256,0.900,bicubic,-54.167,-26.280,+26 -crossvit_15_dagger_408,41.907,58.093,72.067,27.933,28.50,408,1.000,bicubic,-54.483,-27.093,+34 -xcit_small_24_p8_224_dist,41.893,58.107,73.680,26.320,47.63,224,1.000,bicubic,-54.977,-25.800,-19 -xcit_small_24_p8_224,41.773,58.227,71.013,28.987,47.63,224,1.000,bicubic,-54.627,-28.137,+30 -vit_large_r50_s32_224,41.653,58.347,70.253,29.747,328.99,224,0.900,bicubic,-55.137,-29.097,-17 -swsl_resnext101_32x4d,41.560,58.440,71.747,28.253,44.18,224,0.875,bilinear,-54.870,-27.723,+25 -swinv2_base_window8_256,41.507,58.493,72.440,27.560,87.92,256,0.900,bicubic,-55.033,-26.830,+14 -convnext_large,41.373,58.627,73.293,26.707,197.77,224,0.875,bicubic,-55.387,-26.007,-15 -deit3_small_patch16_224_in21ft1k,41.240,58.760,71.933,28.067,22.06,224,1.000,bicubic,-55.420,-27.397,0 -seresnext101d_32x8d,41.133,58.867,70.880,29.120,93.59,288,1.000,bicubic,-55.577,-28.480,-11 -tf_efficientnet_b6_ap,40.813,59.187,71.627,28.373,43.04,528,0.942,bicubic,-56.267,-27.993,-51 -resmlp_big_24_224_in22ft1k,40.373,59.627,74.787,25.213,129.14,224,0.875,bicubic,-56.247,-24.723,-2 -deit3_small_patch16_384,40.307,59.693,70.333,29.667,22.21,384,1.000,bicubic,-55.893,-28.957,+43 -tresnet_l_448,40.213,59.787,69.907,30.093,55.99,448,0.875,bilinear,-55.647,-29.213,+84 -deit_base_patch16_384,40.173,59.827,70.760,29.240,86.86,384,1.000,bicubic,-55.977,-28.380,+51 -regnetz_d8_evos,40.093,59.907,72.187,27.813,23.46,320,0.950,bicubic,-56.517,-27.253,-4 -regnetz_040h,40.000,60.000,71.333,28.667,28.94,320,1.000,bicubic,-56.710,-28.167,-20 -resnetrs350,39.947,60.053,68.933,31.067,163.96,384,1.000,bicubic,-56.813,-30.437,-27 -regnetz_d8,39.933,60.067,71.640,28.360,23.37,320,1.000,bicubic,-56.687,-27.810,-8 -swin_s3_base_224,39.787,60.213,70.493,29.507,71.13,224,0.900,bicubic,-56.463,-28.647,+31 -seresnext101_32x8d,39.547,60.453,69.467,30.533,93.57,288,1.000,bicubic,-57.223,-29.883,-31 -deit3_base_patch16_224,39.200,60.800,71.027,28.973,86.59,224,0.900,bicubic,-57.100,-28.153,+25 -volo_d1_224,38.947,61.053,70.267,29.733,26.63,224,0.960,bicubic,-57.383,-29.043,+21 -resnetv2_101x1_bitm,38.933,61.067,71.040,28.960,44.54,448,1.000,bilinear,-57.167,-28.240,+49 -vit_large_patch32_384,38.933,61.067,68.947,31.053,306.63,384,1.000,bicubic,-56.897,-30.203,+75 -regnetz_040,38.733,61.267,70.413,29.587,27.12,320,1.000,bicubic,-57.977,-29.057,-28 -xcit_small_12_p8_224_dist,38.213,61.787,71.280,28.720,26.21,224,1.000,bicubic,-58.477,-28.110,-24 -resnet200d,38.147,61.853,68.627,31.373,64.69,320,1.000,bicubic,-58.573,-30.703,-33 -swinv2_small_window8_256,37.787,62.213,69.867,30.133,49.73,256,0.900,bicubic,-58.503,-29.343,+19 -xcit_large_24_p16_224_dist,37.680,62.320,71.587,28.413,189.10,224,1.000,bicubic,-59.120,-27.763,-43 -seresnet152d,37.653,62.347,69.480,30.520,66.84,320,1.000,bicubic,-59.117,-29.970,-42 -eca_nfnet_l1,37.533,62.467,70.960,29.040,41.41,320,1.000,bicubic,-59.167,-28.330,-30 -xcit_small_12_p8_224,37.533,62.467,68.213,31.787,26.21,224,1.000,bicubic,-58.577,-30.947,+38 -twins_svt_large,37.213,62.787,69.227,30.773,99.27,224,0.900,bicubic,-59.057,-29.943,+15 -regnetz_d32,37.133,62.867,70.480,29.520,27.58,320,0.950,bicubic,-59.467,-28.900,-20 -vit_base_patch32_384,37.107,62.893,69.787,30.213,88.30,384,1.000,bicubic,-59.383,-29.623,-11 -regnety_064,37.000,63.000,68.187,31.813,30.58,288,1.000,bicubic,-59.360,-31.043,+2 -swin_s3_small_224,36.867,63.133,68.213,31.787,49.74,224,0.900,bicubic,-59.363,-30.877,+14 -efficientnetv2_rw_s,36.813,63.187,68.320,31.680,23.94,384,1.000,bicubic,-59.727,-31.040,-19 -regnety_160,36.787,63.213,69.107,30.893,83.59,288,1.000,bicubic,-59.563,-30.223,0 -convnext_base,36.747,63.253,70.413,29.587,88.59,224,0.875,bicubic,-59.723,-28.817,-15 -resnext101_64x4d,36.720,63.280,66.653,33.347,83.46,288,1.000,bicubic,-59.360,-32.587,+34 -convnext_tiny_in22ft1k,36.267,63.733,69.560,30.440,28.59,224,0.875,bicubic,-59.953,-29.780,+10 -cait_xxs36_384,36.253,63.747,67.800,32.200,17.37,384,1.000,bicubic,-59.587,-31.290,+55 -jx_nest_base,36.067,63.933,66.760,33.240,67.72,224,0.875,bicubic,-60.183,-32.450,+5 -pit_b_distilled_224,35.627,64.373,69.120,30.880,74.79,224,0.900,bicubic,-61.043,-30.230,-38 -sequencer2d_l,35.560,64.440,67.333,32.667,54.30,224,0.875,bicubic,-60.580,-31.827,+20 -regnety_080,35.560,64.440,67.240,32.760,39.18,288,1.000,bicubic,-60.970,-32.080,-26 -tf_efficientnet_b3_ns,35.507,64.493,67.747,32.253,12.23,300,0.904,bicubic,-60.883,-31.603,-14 -cs3se_edgenet_x,35.427,64.573,67.280,32.720,50.72,320,1.000,bicubic,-61.013,-32.120,-20 -tf_efficientnet_b6,35.227,64.773,67.720,32.280,43.04,528,0.942,bicubic,-61.443,-31.650,-44 -resnetrs270,35.000,65.000,65.480,34.520,129.86,352,1.000,bicubic,-61.690,-33.870,-48 -tf_efficientnet_b5_ap,34.800,65.200,67.467,32.533,30.39,456,0.934,bicubic,-61.880,-31.993,-47 -xcit_tiny_12_p8_384_dist,34.653,65.347,66.280,33.720,6.71,384,1.000,bicubic,-61.427,-32.860,+23 -vit_base_patch16_224_miil,34.520,65.480,65.000,35.000,86.54,224,0.875,bilinear,-61.930,-34.300,-26 -xcit_medium_24_p16_224_dist,34.320,65.680,67.893,32.107,84.40,224,1.000,bicubic,-62.270,-31.377,-40 -resnet152d,34.307,65.693,65.907,34.093,60.21,320,1.000,bicubic,-62.053,-33.483,-19 -tresnet_m_448,34.107,65.893,64.507,35.493,31.39,448,0.875,bilinear,-60.883,-34.473,+147 -resmlp_big_24_distilled_224,34.067,65.933,69.600,30.400,129.14,224,0.875,bicubic,-62.383,-29.710,-31 -regnetv_064,33.987,66.013,67.867,32.133,30.58,288,1.000,bicubic,-62.423,-31.493,-28 -xcit_tiny_24_p16_384_dist,33.827,66.173,65.387,34.613,12.12,384,1.000,bicubic,-62.093,-33.833,+29 -twins_pcpvt_large,33.413,66.587,67.933,32.067,60.99,224,0.900,bicubic,-62.737,-31.247,+3 -twins_svt_base,33.173,66.827,65.773,34.227,56.07,224,0.900,bicubic,-62.987,-33.287,-1 -pit_b_224,33.160,66.840,62.347,37.653,73.76,224,0.900,bicubic,-62.480,-36.323,+55 -resnetv2_152x2_bit_teacher,33.053,66.947,64.253,35.747,236.34,224,0.875,bicubic,-63.047,-35.017,+9 -swsl_resnext50_32x4d,33.027,66.973,65.080,34.920,25.03,224,0.875,bilinear,-62.833,-34.170,+29 -mobilevitv2_200_384_in22ft1k,32.960,67.040,65.480,34.520,18.45,384,1.000,bicubic,-63.080,-33.600,+14 -swinv2_cr_small_ns_224,32.933,67.067,65.960,34.040,49.70,224,0.900,bicubic,-63.247,-33.180,-10 -xception65,32.760,67.240,62.973,37.027,39.92,299,0.940,bicubic,-63.590,-36.267,-27 -xcit_large_24_p16_224,32.760,67.240,62.120,37.880,189.10,224,1.000,bicubic,-62.660,-36.500,+81 -ssl_resnext101_32x16d,32.653,67.347,64.040,35.960,194.03,224,0.875,bilinear,-63.137,-35.140,+33 -swin_small_patch4_window7_224,32.587,67.413,65.453,34.547,49.61,224,0.900,bicubic,-63.323,-33.567,+19 -mobilevitv2_175_384_in22ft1k,32.453,67.547,64.720,35.280,14.25,384,1.000,bicubic,-63.727,-34.410,-14 -jx_nest_small,32.280,67.720,63.733,36.267,38.35,224,0.875,bicubic,-63.680,-35.297,+13 -tf_efficientnet_b5,31.853,68.147,65.307,34.693,30.39,456,0.934,bicubic,-64.497,-34.003,-34 -swinv2_tiny_window16_256,31.720,68.280,65.587,34.413,28.35,256,0.900,bicubic,-64.220,-33.553,+13 -swinv2_cr_small_224,31.680,68.320,62.507,37.493,49.70,224,0.900,bicubic,-64.380,-36.363,+1 -regnetz_c16_evos,31.493,68.507,66.280,33.720,13.49,320,0.950,bicubic,-64.637,-33.080,-10 -resnest101e,31.400,68.600,64.347,35.653,48.28,256,0.875,bilinear,-64.460,-34.863,+17 -crossvit_base_240,31.347,68.653,61.293,38.707,105.03,240,0.875,bicubic,-64.173,-37.527,+52 -regnetv_040,31.333,68.667,64.667,35.333,20.64,288,1.000,bicubic,-64.857,-34.663,-24 -convnext_small,31.320,68.680,66.040,33.960,50.22,224,0.875,bicubic,-64.850,-33.060,-22 -cait_s24_224,31.200,68.800,64.560,35.440,46.92,224,1.000,bicubic,-65.180,-34.590,-46 -efficientnet_b4,30.840,69.160,64.600,35.400,19.34,384,1.000,bicubic,-65.310,-34.590,-20 -regnety_040,30.613,69.387,63.827,36.173,20.65,288,1.000,bicubic,-65.397,-35.353,-2 -sequencer2d_m,30.600,69.400,62.933,37.067,38.31,224,0.875,bicubic,-65.210,-36.177,+16 -crossvit_18_240,30.600,69.400,61.947,38.053,43.27,240,0.875,bicubic,-64.840,-36.843,+59 -dm_nfnet_f0,30.547,69.453,62.867,37.133,71.49,256,0.900,bicubic,-65.603,-36.383,-25 -xcit_small_24_p16_224_dist,30.520,69.480,64.760,35.240,47.67,224,1.000,bicubic,-65.690,-34.450,-35 -crossvit_18_dagger_240,30.507,69.493,61.840,38.160,44.27,240,0.875,bicubic,-65.063,-37.220,+33 -xcit_medium_24_p16_224,30.187,69.813,59.333,40.667,84.40,224,1.000,bicubic,-65.343,-39.407,+40 -cait_xxs24_384,30.040,69.960,63.920,36.080,12.03,384,1.000,bicubic,-65.240,-35.040,+72 -twins_pcpvt_base,29.973,70.027,64.600,35.400,43.83,224,0.900,bicubic,-65.817,-34.530,+12 -swsl_resnet50,29.840,70.160,63.827,36.173,25.56,224,0.875,bilinear,-65.570,-35.473,+58 -mobilevitv2_150_384_in22ft1k,29.840,70.160,62.213,37.787,10.59,384,1.000,bicubic,-65.860,-36.927,+18 -vit_relpos_base_patch16_clsgap_224,29.720,70.280,62.867,37.133,86.43,224,0.900,bicubic,-66.040,-36.173,+12 -deit_base_distilled_patch16_224,29.600,70.400,64.440,35.560,87.34,224,0.900,bicubic,-66.490,-34.750,-22 -cs3sedarknet_x,29.573,70.427,61.493,38.507,35.40,288,1.000,bicubic,-66.467,-37.617,-18 -convit_base,29.507,70.493,61.760,38.240,86.54,224,0.875,bicubic,-66.043,-37.110,+27 -vit_relpos_medium_patch16_cls_224,29.320,70.680,60.653,39.347,38.76,224,0.900,bicubic,-66.160,-38.297,+40 -ssl_resnext101_32x8d,29.120,70.880,61.013,38.987,88.79,224,0.875,bilinear,-66.370,-38.107,+38 -tf_efficientnetv2_s,29.053,70.947,61.227,38.773,21.46,384,1.000,bicubic,-67.287,-37.973,-59 -resnet101d,28.987,71.013,62.040,37.960,44.57,320,1.000,bicubic,-67.313,-37.190,-57 -xception65p,28.987,71.013,59.920,40.080,39.82,299,0.940,bicubic,-67.223,-39.260,-49 -resnetrs152,28.920,71.080,60.507,39.493,86.62,320,1.000,bicubic,-67.660,-38.733,-88 -regnetz_c16,28.907,71.093,63.347,36.653,13.46,320,0.940,bicubic,-66.893,-35.753,-3 -vit_relpos_medium_patch16_224,28.840,71.160,62.013,37.987,38.75,224,0.900,bicubic,-66.620,-36.947,+35 -xcit_tiny_24_p8_224_dist,28.733,71.267,61.373,38.627,12.11,224,1.000,bicubic,-67.077,-37.837,-7 -xcit_tiny_24_p8_224,28.707,71.293,60.440,39.560,12.11,224,1.000,bicubic,-66.963,-38.610,+7 -crossvit_15_dagger_240,28.533,71.467,60.333,39.667,28.21,240,0.875,bicubic,-67.157,-38.497,+4 -xcit_small_24_p16_224,28.347,71.653,58.707,41.293,47.67,224,1.000,bicubic,-67.183,-40.063,+19 -cs3edgenet_x,28.333,71.667,60.813,39.187,47.82,288,1.000,bicubic,-67.717,-38.327,-33 -coat_lite_small,27.547,72.453,58.560,41.440,19.84,224,0.900,bicubic,-67.993,-40.300,+14 -deit_base_patch16_224,27.440,72.560,58.893,41.107,86.57,224,0.900,bicubic,-68.000,-39.947,+31 -vit_relpos_base_patch16_224,27.347,72.653,61.147,38.853,86.43,224,0.900,bicubic,-68.223,-37.883,+9 -resnetv2_50x1_bitm,27.307,72.693,62.853,37.147,25.55,448,1.000,bilinear,-67.703,-36.207,+84 -xcit_small_12_p16_224_dist,27.120,72.880,59.800,40.200,26.25,224,1.000,bicubic,-68.900,-39.330,-35 -vit_small_patch16_224,27.013,72.987,59.187,40.813,22.05,224,0.900,bicubic,-68.357,-39.963,+38 -sequencer2d_s,26.813,73.187,60.613,39.387,27.65,224,0.875,bicubic,-69.177,-38.437,-35 -mobilevitv2_200_in22ft1k,26.680,73.320,59.373,40.627,18.45,256,0.888,bicubic,-68.480,-39.577,+55 -swin_s3_tiny_224,26.520,73.480,60.320,39.680,28.33,224,0.900,bicubic,-68.640,-38.620,+56 -swinv2_tiny_window8_256,26.413,73.587,60.560,39.440,28.35,256,0.900,bicubic,-69.087,-38.560,+16 -tf_efficientnet_b4,26.320,73.680,60.107,39.893,19.34,380,0.922,bicubic,-69.580,-39.063,-31 -tf_efficientnet_b4_ap,26.240,73.760,60.213,39.787,19.34,380,0.922,bicubic,-69.920,-39.067,-63 -nfnet_l0,26.213,73.787,61.720,38.280,35.07,288,1.000,bicubic,-69.907,-37.520,-55 -deit3_small_patch16_224,26.213,73.787,54.413,45.587,22.06,224,0.900,bicubic,-68.787,-44.047,+78 -regnety_032,26.200,73.800,60.973,39.027,19.44,288,1.000,bicubic,-69.770,-38.217,-42 -fbnetv3_g,26.120,73.880,61.067,38.933,16.62,288,0.950,bilinear,-69.390,-37.923,+8 -ecaresnet50t,26.120,73.880,59.987,40.013,25.57,320,0.950,bicubic,-69.390,-39.133,+4 -ecaresnet101d,26.040,73.960,59.000,41.000,44.57,224,0.875,bicubic,-69.490,-40.130,-2 -mobilevitv2_175_in22ft1k,26.040,73.960,58.453,41.547,14.25,256,0.888,bicubic,-69.190,-40.337,+37 -visformer_small,25.840,74.160,58.907,41.093,40.22,224,0.900,bicubic,-69.630,-39.993,+9 -halo2botnet50ts_256,25.587,74.413,56.853,43.147,22.64,256,0.950,bicubic,-69.833,-42.157,+16 -coat_mini,25.493,74.507,57.707,42.293,10.34,224,0.900,bicubic,-69.477,-41.073,+72 -vit_relpos_medium_patch16_rpn_224,25.453,74.547,58.627,41.373,38.73,224,0.900,bicubic,-70.057,-40.453,-1 -crossvit_15_240,25.453,74.547,57.547,42.453,27.53,240,0.875,bicubic,-69.697,-41.383,+43 -vit_srelpos_medium_patch16_224,25.387,74.613,58.480,41.520,38.74,224,0.900,bicubic,-69.843,-40.510,+30 -xcit_small_12_p16_224,25.173,74.827,56.080,43.920,26.25,224,1.000,bicubic,-70.247,-42.760,+12 -resnetv2_50x1_bit_distilled,25.133,74.867,59.653,40.347,25.55,224,0.875,bicubic,-70.987,-39.627,-70 -convit_small,25.107,74.893,57.280,42.720,27.78,224,0.875,bicubic,-70.093,-41.620,+31 -vit_base_patch16_rpn_224,25.080,74.920,58.653,41.347,86.54,224,0.900,bicubic,-70.300,-40.277,+13 -gc_efficientnetv2_rw_t,25.053,74.947,57.720,42.280,13.68,288,1.000,bicubic,-70.687,-41.300,-33 -eca_nfnet_l0,24.813,75.187,60.093,39.907,24.14,288,1.000,bicubic,-71.137,-39.117,-55 -xception41p,24.800,75.200,55.173,44.827,26.91,299,0.940,bicubic,-70.710,-43.737,-7 -tnt_s_patch16_224,24.720,75.280,58.187,41.813,23.76,224,0.900,bicubic,-70.320,-40.643,+52 -resnetv2_50d_evos,24.467,75.533,56.387,43.613,25.59,288,0.950,bicubic,-71.143,-42.643,-25 -xcit_tiny_12_p16_384_dist,24.440,75.560,57.067,42.933,6.72,384,1.000,bicubic,-70.690,-41.953,+36 -cs3darknet_x,24.360,75.640,57.813,42.187,35.05,288,1.000,bicubic,-71.500,-41.367,-51 -efficientnetv2_rw_t,24.280,75.720,57.360,42.640,13.65,288,1.000,bicubic,-71.320,-41.710,-27 -convnext_tiny,24.267,75.733,59.333,40.667,28.59,224,0.875,bicubic,-71.283,-39.667,-25 -ssl_resnext101_32x4d,24.173,75.827,57.413,42.587,44.18,224,0.875,bilinear,-71.267,-41.717,-8 -swinv2_cr_tiny_ns_224,24.120,75.880,58.227,41.773,28.33,224,0.900,bicubic,-71.250,-40.713,+5 -twins_svt_small,24.107,75.893,57.133,42.867,24.06,224,0.900,bicubic,-71.093,-41.747,+19 -vit_small_r26_s32_224,24.080,75.920,56.173,43.827,36.43,224,0.900,bicubic,-71.560,-43.017,-37 -mobilevitv2_150_in22ft1k,24.053,75.947,55.987,44.013,10.59,256,0.888,bicubic,-71.087,-42.873,+26 -vit_relpos_small_patch16_224,24.027,75.973,58.200,41.800,21.98,224,0.900,bicubic,-71.133,-40.750,+20 -poolformer_m48,24.027,75.973,57.280,42.720,73.47,224,0.950,bicubic,-71.613,-41.660,-39 -tf_efficientnet_b2_ns,24.013,75.987,57.280,42.720,9.11,260,0.890,bicubic,-71.747,-41.840,-51 -cs3sedarknet_l,23.960,76.040,58.707,41.293,21.91,288,0.950,bicubic,-71.350,-40.423,+2 -resnetv2_50d_gn,23.920,76.080,56.307,43.693,25.57,288,0.950,bicubic,-71.510,-42.733,-13 -vit_small_patch32_384,23.760,76.240,57.293,42.707,22.92,384,1.000,bicubic,-71.290,-41.697,+34 -convnext_nano,23.640,76.360,55.800,44.200,15.59,288,1.000,bicubic,-71.720,-43.050,-3 -lamhalobotnet50ts_256,23.573,76.427,55.333,44.667,22.57,256,0.950,bicubic,-71.577,-43.547,+17 -resnet152,23.560,76.440,53.680,46.320,60.19,224,0.950,bicubic,-72.340,-45.400,-71 -nasnetalarge,23.467,76.533,55.013,44.987,88.75,331,0.911,bicubic,-72.213,-43.917,-50 -crossvit_small_240,23.440,76.560,56.813,43.187,26.86,240,0.875,bicubic,-71.390,-42.207,+53 -levit_384,23.427,76.573,56.373,43.627,39.13,224,0.900,bicubic,-72.103,-42.677,-38 -pnasnet5large,23.320,76.680,53.640,46.360,86.06,331,0.911,bicubic,-72.390,-45.280,-56 -convnext_tiny_hnf,23.227,76.773,55.200,44.800,28.59,224,0.950,bicubic,-72.283,-43.820,-34 -efficientnet_b3,23.213,76.787,55.960,44.040,12.23,320,1.000,bicubic,-72.497,-43.080,-60 -jx_nest_tiny,23.173,76.827,56.213,43.787,17.06,224,0.875,bicubic,-72.067,-42.767,-5 -resnet61q,22.987,77.013,55.747,44.253,36.85,288,1.000,bicubic,-72.793,-43.243,-66 -halonet50ts,22.920,77.080,54.000,46.000,22.73,256,0.940,bicubic,-72.220,-44.770,+9 -vit_srelpos_small_patch16_224,22.907,77.093,55.733,44.267,21.97,224,0.900,bicubic,-72.123,-43.227,+24 -resmlp_big_24_224,22.853,77.147,54.293,45.707,129.14,224,0.875,bicubic,-71.807,-44.187,+59 -twins_pcpvt_small,22.707,77.293,56.853,43.147,24.11,224,0.900,bicubic,-72.503,-42.027,-6 -poolformer_m36,22.507,77.493,55.293,44.707,56.17,224,0.950,bicubic,-72.873,-43.557,-22 -vit_base_patch32_224,22.400,77.600,53.987,46.013,88.22,224,0.900,bicubic,-72.600,-45.043,+25 -pit_s_distilled_224,22.360,77.640,57.093,42.907,24.04,224,0.900,bicubic,-72.880,-41.957,-14 -xcit_tiny_12_p8_224_dist,22.067,77.933,54.280,45.720,6.71,224,1.000,bicubic,-73.033,-44.630,+7 -tresnet_m,21.667,78.333,53.840,46.160,31.39,224,0.875,bilinear,-74.043,-45.190,-70 -convmixer_1536_20,21.213,78.787,55.520,44.480,51.63,224,0.960,bicubic,-73.857,-43.510,+10 -swin_tiny_patch4_window7_224,21.147,78.853,55.973,44.027,28.29,224,0.900,bicubic,-73.983,-42.877,+1 -pit_s_224,21.093,78.907,53.587,46.413,23.46,224,0.900,bicubic,-73.497,-45.113,+56 -xcit_tiny_12_p8_224,21.027,78.973,52.467,47.533,6.71,224,1.000,bicubic,-73.663,-46.363,+45 -resnet51q,20.960,79.040,55.693,44.307,35.70,288,1.000,bilinear,-74.910,-43.437,-92 -regnetz_b16,20.933,79.067,53.853,46.147,9.72,288,0.940,bicubic,-74.127,-45.197,+7 -resnetrs101,20.867,79.133,52.813,47.187,63.62,288,0.940,bicubic,-74.563,-46.217,-40 -sebotnet33ts_256,20.733,79.267,48.787,51.213,13.70,256,0.940,bicubic,-73.847,-49.713,+52 -deit_small_distilled_patch16_224,20.707,79.293,55.147,44.853,22.44,224,0.900,bicubic,-74.003,-43.883,+38 -resnest50d_4s2x40d,20.373,79.627,52.827,47.173,30.42,224,0.875,bicubic,-74.587,-46.243,+16 -resnetaa50,20.093,79.907,52.000,48.000,25.56,288,1.000,bicubic,-75.117,-46.930,-23 -ssl_resnext50_32x4d,20.013,79.987,53.627,46.373,25.03,224,0.875,bilinear,-74.857,-45.263,+22 -haloregnetz_b,19.987,80.013,50.013,49.987,11.68,224,0.940,bicubic,-74.713,-48.647,+35 -resnetv2_101,19.960,80.040,49.227,50.773,44.54,224,0.950,bicubic,-75.660,-49.763,-75 -xcit_nano_12_p8_384_dist,19.800,80.200,50.573,49.427,3.05,384,1.000,bicubic,-73.720,-47.967,+151 -tresnet_xl,19.640,80.360,53.133,46.867,78.44,224,0.875,bilinear,-75.800,-45.917,-53 -gluon_senet154,19.333,80.667,47.573,52.427,115.09,224,0.875,bicubic,-75.587,-51.187,+13 -resnet101,19.320,80.680,49.587,50.413,44.55,224,0.950,bicubic,-76.040,-49.273,-41 -rexnet_200,19.227,80.773,52.720,47.280,16.37,224,0.875,bicubic,-75.723,-46.290,+8 -levit_256,19.187,80.813,50.093,49.907,18.89,224,0.900,bicubic,-75.823,-48.797,+1 -repvgg_b3,19.133,80.867,50.280,49.720,123.09,224,0.875,bilinear,-75.437,-48.500,+40 -lambda_resnet50ts,19.133,80.867,49.307,50.693,21.54,256,0.950,bicubic,-75.647,-49.153,+20 -mixer_b16_224_miil,19.040,80.960,51.227,48.773,59.88,224,0.875,bilinear,-76.260,-47.653,-42 -legacy_senet154,19.027,80.973,47.960,52.040,115.09,224,0.875,bilinear,-76.043,-50.870,-12 -gluon_seresnext101_64x4d,18.933,81.067,49.160,50.840,88.23,224,0.875,bicubic,-75.987,-49.670,+3 -deit_small_patch16_224,18.920,81.080,51.400,48.600,22.05,224,0.900,bicubic,-75.470,-47.290,+60 -mobilevitv2_200,18.920,81.080,50.560,49.440,18.45,256,0.888,bicubic,-75.910,-48.150,+13 -edgenext_small,18.667,81.333,53.600,46.400,5.59,320,1.000,bicubic,-76.743,-45.500,-56 -tf_efficientnet_b1_ns,18.667,81.333,51.693,48.307,7.79,240,0.882,bicubic,-76.513,-47.417,-37 -poolformer_s36,18.400,81.600,51.867,48.133,30.86,224,0.900,bicubic,-76.690,-47.043,-23 -seresnext50_32x4d,18.360,81.640,50.960,49.040,27.56,224,0.875,bicubic,-76.670,-47.920,-13 -cs3darknet_l,18.307,81.693,51.867,48.133,21.16,288,0.950,bicubic,-76.813,-47.113,-28 -ecaresnet50d,18.267,81.733,51.840,48.160,25.58,224,0.875,bicubic,-76.353,-47.050,+23 -cait_xxs36_224,18.267,81.733,49.427,50.573,17.30,224,1.000,bicubic,-75.993,-49.293,+63 -sehalonet33ts,18.227,81.773,47.787,52.213,13.69,256,0.940,bicubic,-76.553,-50.783,+6 -tf_efficientnet_lite4,18.133,81.867,50.720,49.280,13.01,380,0.920,bilinear,-76.747,-48.300,-4 -vit_tiny_patch16_384,18.013,81.987,50.333,49.667,5.79,384,1.000,bicubic,-75.637,-48.267,+120 -mobilevitv2_175,17.773,82.227,49.760,50.240,14.25,256,0.888,bicubic,-77.117,-49.100,-7 -resnest50d_1s4x24d,17.693,82.307,49.800,50.200,25.68,224,0.875,bicubic,-77.057,-49.180,+5 -resnest50d,17.360,82.640,50.733,49.267,27.48,224,0.875,bilinear,-77.490,-48.147,-4 -gluon_seresnext101_32x4d,17.360,82.640,46.373,53.627,48.96,224,0.875,bicubic,-77.560,-52.437,-12 -efficientnet_el,17.320,82.680,50.000,50.000,10.59,300,0.904,bicubic,-77.800,-48.980,-37 -inception_v4,17.280,82.720,45.933,54.067,42.68,299,0.875,bicubic,-77.100,-52.647,+44 -tf_efficientnet_b3_ap,17.200,82.800,49.667,50.333,12.23,300,0.904,bicubic,-78.120,-49.233,-65 -xcit_tiny_24_p16_224_dist,17.187,82.813,47.480,52.520,12.12,224,1.000,bicubic,-77.343,-51.300,+25 -tf_efficientnet_b3,17.000,83.000,49.267,50.733,12.23,300,0.904,bicubic,-78.010,-49.643,-26 -xception71,17.000,83.000,45.533,54.467,42.34,299,0.903,bicubic,-77.280,-53.107,+49 -cs3darknet_focus_l,16.973,83.027,50.480,49.520,21.15,288,0.950,bicubic,-78.197,-48.480,-55 -resmlp_36_distilled_224,16.880,83.120,51.480,48.520,44.69,224,0.875,bicubic,-78.000,-47.360,-16 -gluon_resnext101_64x4d,16.880,83.120,44.173,55.827,83.46,224,0.875,bicubic,-77.780,-54.477,+2 -tf_efficientnetv2_b3,16.667,83.333,48.680,51.320,14.36,300,0.904,bicubic,-78.493,-50.140,-54 -gluon_resnet152_v1d,16.600,83.400,44.293,55.707,60.21,224,0.875,bicubic,-78.140,-54.447,-7 -tresnet_l,16.573,83.427,49.947,50.053,55.99,224,0.875,bilinear,-78.717,-49.063,-71 -inception_resnet_v2,16.573,83.427,44.933,55.067,55.84,299,0.897,bicubic,-77.957,-53.847,+15 -gluon_resnet152_v1s,16.560,83.440,44.507,55.493,60.32,224,0.875,bicubic,-78.480,-54.423,-40 -gmlp_s16_224,16.547,83.453,45.120,54.880,19.42,224,0.875,bicubic,-77.613,-53.380,+57 -mobilevitv2_150,16.480,83.520,48.453,51.547,10.59,256,0.888,bicubic,-78.070,-50.257,+8 -resmlp_24_distilled_224,16.440,83.560,50.373,49.627,30.02,224,0.875,bicubic,-78.020,-48.397,+21 -gluon_xception65,16.440,83.560,46.040,53.960,39.92,299,0.903,bicubic,-77.820,-52.530,+40 -gcresnet50t,16.373,83.627,48.227,51.773,25.90,256,0.900,bicubic,-78.477,-50.563,-23 -gernet_l,16.347,83.653,47.213,52.787,31.08,256,0.875,bilinear,-78.743,-51.687,-53 -wide_resnet50_2,16.307,83.693,48.400,51.600,68.88,224,0.875,bicubic,-78.773,-50.570,-52 -xcit_tiny_24_p16_224,16.280,83.720,45.973,54.027,12.12,224,1.000,bicubic,-77.790,-52.557,+57 -gcresnext50ts,16.240,83.760,46.533,53.467,15.67,256,0.900,bicubic,-78.250,-52.137,+9 -repvgg_b3g4,16.227,83.773,47.640,52.360,83.83,224,0.875,bilinear,-78.293,-51.330,+6 -ens_adv_inception_resnet_v2,16.213,83.787,43.613,56.387,55.84,299,0.897,bicubic,-77.947,-54.987,+46 -edgenext_small_rw,15.960,84.040,49.667,50.333,7.83,320,1.000,bicubic,-78.700,-49.123,-16 -ssl_resnet50,15.920,84.080,49.400,50.600,25.56,224,0.875,bilinear,-78.520,-49.520,+13 -regnety_320,15.653,84.347,44.827,55.173,145.05,224,0.875,bicubic,-78.887,-54.033,-3 -ecaresnet101d_pruned,15.600,84.400,48.053,51.947,24.88,224,0.875,bicubic,-79.480,-50.927,-61 -convmixer_768_32,15.533,84.467,47.960,52.040,21.11,224,0.960,bicubic,-78.967,-50.890,+1 -ecaresnet26t,15.467,84.533,47.907,52.093,16.01,320,0.950,bicubic,-78.853,-50.813,+21 -coat_tiny,15.387,84.613,45.640,54.360,5.50,224,0.900,bicubic,-78.203,-52.780,+89 -skresnext50_32x4d,15.347,84.653,44.507,55.493,27.48,224,0.875,bicubic,-78.903,-53.953,+26 -vit_relpos_base_patch32_plus_rpn_256,15.240,84.760,42.613,57.387,119.42,256,0.900,bicubic,-78.490,-55.457,+76 -cait_xxs24_224,15.160,84.840,44.947,55.053,11.96,224,1.000,bicubic,-78.440,-53.493,+85 -ecaresnetlight,15.147,84.853,45.800,54.200,30.16,224,0.875,bicubic,-79.623,-53.000,-34 -levit_192,14.893,85.107,44.920,55.080,10.95,224,0.900,bicubic,-79.287,-53.620,+32 -rexnet_150,14.707,85.293,46.920,53.080,9.73,224,0.875,bicubic,-79.773,-51.870,-3 -darknet53,14.680,85.320,47.120,52.880,41.61,288,1.000,bicubic,-79.950,-51.770,-26 -darknetaa53,14.573,85.427,45.453,54.547,36.02,288,1.000,bilinear,-79.897,-53.307,-3 -resnext50_32x4d,14.533,85.467,44.187,55.813,25.03,224,0.950,bicubic,-80.007,-54.423,-14 -coat_lite_mini,14.493,85.507,44.547,55.453,11.01,224,0.900,bicubic,-79.557,-54.013,+39 -efficientnet_el_pruned,14.480,85.520,46.080,53.920,10.59,300,0.904,bicubic,-79.920,-52.660,0 -efficientnet_b2,14.440,85.560,46.067,53.933,9.11,288,1.000,bicubic,-80.170,-52.643,-27 -seresnet33ts,14.427,85.573,46.133,53.867,19.78,256,0.900,bicubic,-80.433,-52.657,-51 -poolformer_s24,14.267,85.733,47.227,52.773,21.39,224,0.900,bicubic,-80.283,-51.653,-25 -legacy_seresnext101_32x4d,14.160,85.840,43.013,56.987,48.96,224,0.875,bilinear,-80.210,-55.637,0 -seresnet50,14.147,85.853,45.507,54.493,28.09,224,0.875,bicubic,-80.403,-53.243,-25 -fbnetv3_d,14.107,85.893,46.480,53.520,10.31,256,0.950,bilinear,-79.823,-52.260,+43 -eca_resnet33ts,14.080,85.920,47.360,52.640,19.68,256,0.900,bicubic,-80.110,-51.400,+16 -gernet_m,14.053,85.947,46.013,53.987,21.14,224,0.875,bilinear,-80.567,-52.847,-35 -mobilevitv2_125,14.000,86.000,44.987,55.013,7.48,256,0.888,bicubic,-79.970,-53.573,+36 -gluon_resnext101_32x4d,13.867,86.133,41.653,58.347,44.18,224,0.875,bicubic,-80.673,-56.977,-27 -gcresnet33ts,13.760,86.240,45.053,54.947,19.88,256,0.900,bicubic,-80.710,-53.717,-18 -gluon_seresnext50_32x4d,13.613,86.387,43.720,56.280,27.56,224,0.875,bicubic,-80.717,-54.890,-4 -resmlp_36_224,13.520,86.480,46.693,53.307,44.69,224,0.875,bicubic,-80.680,-51.967,+9 -resnet50_gn,13.467,86.533,42.747,57.253,25.56,224,0.940,bicubic,-80.883,-55.963,-8 -repvgg_b2g4,13.427,86.573,43.827,56.173,61.76,224,0.875,bilinear,-80.413,-54.763,+41 -eca_botnext26ts_256,13.373,86.627,42.173,57.827,10.59,256,0.950,bicubic,-80.407,-56.327,+45 -ese_vovnet39b,13.333,86.667,43.813,56.187,24.57,224,0.875,bicubic,-80.757,-54.847,+18 -regnetx_320,13.320,86.680,40.720,59.280,107.81,224,0.875,bicubic,-81.140,-58.020,-22 -pit_xs_distilled_224,13.267,86.733,44.560,55.440,11.00,224,0.900,bicubic,-80.553,-54.110,+39 -efficientnet_b3_pruned,13.173,86.827,45.227,54.773,9.86,300,0.904,bicubic,-81.457,-53.533,-49 -gluon_resnet101_v1d,13.173,86.827,41.480,58.520,44.57,224,0.875,bicubic,-81.057,-57.070,-2 -mixnet_xl,13.120,86.880,43.240,56.760,11.90,224,0.875,bicubic,-81.070,-55.100,+3 -cspresnext50,13.053,86.947,45.000,55.000,20.57,256,0.887,bilinear,-81.777,-53.770,-68 -nf_regnet_b1,12.947,87.053,44.387,55.613,10.22,288,0.900,bicubic,-81.163,-54.243,+10 -eca_halonext26ts,12.933,87.067,42.800,57.200,10.76,256,0.940,bicubic,-81.107,-55.690,+14 -mobilevit_s,12.880,87.120,40.787,59.213,5.58,256,0.900,bicubic,-80.300,-57.653,+86 -pit_xs_224,12.813,87.187,42.827,57.173,10.62,224,0.900,bicubic,-80.307,-55.503,+91 -gluon_inception_v3,12.640,87.360,40.493,59.507,23.83,299,0.875,bicubic,-80.810,-58.077,+62 -crossvit_9_dagger_240,12.573,87.427,41.787,58.213,8.78,240,0.875,bicubic,-80.317,-56.463,+101 -coat_lite_tiny,12.547,87.453,41.133,58.867,5.72,224,0.900,bicubic,-80.683,-57.127,+80 -resmlp_24_224,12.507,87.493,43.427,56.573,30.02,224,0.875,bicubic,-81.513,-54.903,+10 -regnety_120,12.400,87.600,42.213,57.787,51.82,224,0.875,bicubic,-82.080,-56.597,-41 -efficientnet_em,12.360,87.640,43.853,56.147,6.90,240,0.882,bicubic,-81.480,-54.957,+22 -cspdarknet53,12.027,87.973,43.280,56.720,27.64,256,0.887,bilinear,-82.633,-55.520,-68 -hrnet_w64,12.000,88.000,40.813,59.187,128.06,224,0.875,bilinear,-82.020,-57.807,+5 -xcit_tiny_12_p16_224_dist,11.973,88.027,40.107,59.893,6.72,224,1.000,bicubic,-81.427,-58.373,+59 -gluon_resnet101_v1s,11.880,88.120,40.973,59.027,44.67,224,0.875,bicubic,-82.840,-57.847,-75 -gmixer_24_224,11.867,88.133,37.787,62.213,24.72,224,0.875,bicubic,-80.963,-60.093,+97 -nf_resnet50,11.760,88.240,45.933,54.067,25.56,288,0.940,bicubic,-82.790,-52.857,-60 -fbnetv3_b,11.733,88.267,44.387,55.613,8.60,256,0.950,bilinear,-82.237,-54.243,+4 -resnet50d,11.720,88.280,42.467,57.533,25.58,224,0.875,bicubic,-82.540,-56.253,-27 -dpn92,11.613,88.387,40.293,59.707,37.67,224,0.875,bicubic,-82.617,-58.437,-24 -dla102x2,11.600,88.400,41.267,58.733,41.28,224,0.875,bilinear,-82.370,-57.233,+3 -xception41,11.600,88.400,39.147,60.853,26.97,299,0.903,bicubic,-81.830,-59.283,+48 -botnet26t_256,11.587,88.413,40.133,59.867,12.49,256,0.950,bicubic,-81.923,-58.167,+39 -vit_small_patch32_224,11.480,88.520,39.547,60.453,22.88,224,0.900,bicubic,-80.550,-58.683,+135 -levit_128,11.387,88.613,40.240,59.760,9.21,224,0.900,bicubic,-81.943,-58.140,+53 -tf_efficientnet_el,11.373,88.627,42.040,57.960,10.59,300,0.904,bicubic,-83.027,-56.670,-47 -lambda_resnet26t,11.373,88.627,40.173,59.827,10.96,256,0.940,bicubic,-82.457,-58.477,+8 -efficientnet_b2_pruned,11.347,88.653,42.013,57.987,8.31,260,0.890,bicubic,-82.803,-56.517,-20 -xcit_nano_12_p16_384_dist,11.227,88.773,39.853,60.147,3.05,384,1.000,bicubic,-80.603,-58.167,+141 -halonet26t,11.120,88.880,38.813,61.187,12.48,256,0.950,bicubic,-82.890,-59.687,-10 -hrnet_w48,11.093,88.907,40.293,59.707,77.47,224,0.875,bilinear,-82.827,-58.317,-3 -vit_tiny_r_s16_p8_384,11.093,88.907,39.973,60.027,6.36,384,1.000,bicubic,-80.947,-58.317,+126 -gluon_resnet152_v1c,11.093,88.907,37.120,62.880,60.21,224,0.875,bicubic,-83.067,-61.520,-28 -dpn107,11.053,88.947,38.640,61.360,86.92,224,0.875,bicubic,-83.257,-59.830,-46 -ecaresnet50d_pruned,11.013,88.987,41.960,58.040,19.94,224,0.875,bicubic,-83.207,-56.770,-37 -mobilevitv2_100,11.013,88.987,40.653,59.347,4.90,256,0.888,bicubic,-82.287,-57.627,+48 -tf_efficientnetv2_b2,11.000,89.000,39.760,60.240,10.10,260,0.890,bicubic,-83.420,-58.810,-60 -adv_inception_v3,11.000,89.000,36.720,63.280,23.83,299,0.875,bicubic,-81.880,-61.420,+71 -tf_efficientnet_b0_ns,10.973,89.027,40.067,59.933,5.29,224,0.875,bicubic,-82.657,-58.573,+13 -xcit_tiny_12_p16_224,10.973,89.027,37.027,62.973,6.72,224,1.000,bicubic,-81.527,-61.213,+97 -resnetv2_50,10.960,89.040,39.333,60.667,25.55,224,0.950,bicubic,-83.470,-59.397,-65 -tf_inception_v3,10.813,89.187,36.840,63.160,23.83,299,0.875,bicubic,-82.507,-61.190,+39 -xcit_nano_12_p8_224_dist,10.800,89.200,38.120,61.880,3.05,224,1.000,bicubic,-81.300,-60.030,+113 -dpn131,10.720,89.280,37.187,62.813,79.25,224,0.875,bicubic,-83.270,-61.533,-23 -tf_efficientnet_b2_ap,10.533,89.467,40.107,59.893,9.11,260,0.890,bicubic,-83.957,-58.513,-77 -resnext50d_32x4d,10.413,89.587,39.733,60.267,25.05,224,0.875,bicubic,-83.777,-58.827,-44 -rexnet_130,10.400,89.600,41.547,58.453,7.56,224,0.875,bicubic,-83.500,-56.853,-17 -hrnet_w44,10.307,89.693,39.480,60.520,67.06,224,0.875,bilinear,-83.243,-59.220,+10 -xcit_nano_12_p8_224,10.293,89.707,37.000,63.000,3.05,224,1.000,bicubic,-80.727,-60.790,+146 -lambda_resnet26rpt_256,10.253,89.747,38.107,61.893,10.99,256,0.940,bicubic,-83.467,-60.413,-4 -resnext101_32x8d,10.173,89.827,37.800,62.200,88.79,224,0.875,bilinear,-83.647,-60.780,-14 -regnetx_160,10.147,89.853,38.000,62.000,54.28,224,0.875,bicubic,-83.983,-60.740,-42 -dpn98,10.133,89.867,36.587,63.413,61.57,224,0.875,bicubic,-83.987,-61.993,-42 -resnet50,10.120,89.880,37.907,62.093,25.56,224,0.950,bicubic,-84.220,-60.533,-69 -legacy_seresnext50_32x4d,10.093,89.907,39.200,60.800,27.56,224,0.875,bilinear,-83.637,-59.380,-11 -resnetrs50,10.067,89.933,37.507,62.493,35.69,224,0.910,bicubic,-84.233,-61.133,-67 -inception_v3,10.027,89.973,35.227,64.773,23.83,299,0.875,bicubic,-82.693,-62.743,+65 -xception,9.987,90.013,38.040,61.960,22.86,299,0.897,bicubic,-83.483,-60.490,+8 -efficientnet_b1,9.987,90.013,37.560,62.440,7.79,256,1.000,bicubic,-83.253,-60.740,+28 -dpn68b,9.787,90.213,38.027,61.973,12.61,224,0.875,bicubic,-83.903,-60.493,-12 -gluon_resnet152_v1b,9.747,90.253,36.080,63.920,60.19,224,0.875,bicubic,-84.323,-62.380,-46 -tf_efficientnet_lite3,9.653,90.347,38.987,61.013,8.20,300,0.904,bilinear,-84.557,-59.653,-63 -tf_efficientnet_b2,9.653,90.347,38.893,61.107,9.11,260,0.890,bicubic,-84.707,-59.717,-80 -tf_efficientnet_cc_b1_8e,9.600,90.400,36.787,63.213,39.72,240,0.882,bicubic,-84.310,-61.473,-35 -res2net101_26w_4s,9.520,90.480,35.027,64.973,45.21,224,0.875,bilinear,-84.230,-63.283,-23 -legacy_seresnet152,9.333,90.667,37.427,62.573,66.82,224,0.875,bilinear,-84.057,-60.913,+8 -cspresnet50,9.293,90.707,39.613,60.387,21.62,256,0.887,bilinear,-84.437,-59.027,-24 -resnet33ts,9.240,90.760,38.667,61.333,19.68,256,0.900,bicubic,-84.360,-59.863,-14 -hrnet_w40,9.227,90.773,36.880,63.120,57.56,224,0.875,bilinear,-84.263,-61.700,-4 -regnetx_120,9.187,90.813,37.187,62.813,46.11,224,0.875,bicubic,-85.053,-61.463,-75 -seresnext26d_32x4d,9.147,90.853,36.813,63.187,16.81,224,0.875,bicubic,-83.543,-61.337,+53 -crossvit_tiny_240,9.107,90.893,34.600,65.400,7.01,240,0.875,bicubic,-81.133,-62.990,+141 -resnest26d,9.067,90.933,37.840,62.160,17.07,224,0.875,bilinear,-84.263,-60.790,+4 -vit_tiny_patch16_224,9.053,90.947,34.627,65.373,5.72,224,0.900,bicubic,-82.717,-63.413,+99 -vit_base_patch16_224_sam,8.987,91.013,36.173,63.827,86.57,224,0.900,bicubic,-85.153,-62.497,-66 -gluon_resnext50_32x4d,8.973,91.027,36.307,63.693,25.03,224,0.875,bicubic,-84.837,-62.103,-38 -rexnet_100,8.907,91.093,36.373,63.627,4.80,224,0.875,bicubic,-84.123,-61.817,+24 -seresnext26t_32x4d,8.893,91.107,36.893,63.107,16.81,224,0.875,bicubic,-83.927,-61.477,+36 -bat_resnext26ts,8.867,91.133,36.413,63.587,10.73,256,0.900,bicubic,-84.463,-61.937,0 -mixnet_l,8.853,91.147,36.213,63.787,7.33,224,0.875,bicubic,-84.597,-62.007,-11 -mobilenetv3_large_100_miil,8.853,91.147,33.080,66.920,5.48,224,0.875,bilinear,-83.417,-64.560,+68 -convit_tiny,8.813,91.187,34.360,65.640,5.71,224,0.875,bicubic,-81.827,-63.380,+125 -resnet32ts,8.760,91.240,37.227,62.773,17.96,256,0.900,bicubic,-84.700,-61.263,-16 -gcresnext26ts,8.707,91.293,35.733,64.267,10.48,256,0.900,bicubic,-84.073,-62.527,+32 -levit_128s,8.707,91.293,33.160,66.840,7.78,224,0.900,bicubic,-83.223,-64.910,+79 -dla169,8.653,91.347,35.947,64.053,53.39,224,0.875,bilinear,-84.687,-62.643,-10 -hrnet_w30,8.600,91.400,37.027,62.973,37.71,224,0.875,bilinear,-84.600,-61.383,+2 -mixer_b16_224,8.600,91.400,29.440,70.560,59.88,224,0.875,bicubic,-83.270,-67.810,+81 -legacy_seresnet101,8.533,91.467,36.013,63.987,49.33,224,0.875,bilinear,-84.757,-62.497,-4 -tf_efficientnet_b1_ap,8.453,91.547,35.227,64.773,7.79,240,0.882,bicubic,-85.227,-63.133,-41 -repvgg_b2,8.427,91.573,36.453,63.547,89.02,224,0.875,bilinear,-85.063,-62.277,-27 -resmlp_12_distilled_224,8.307,91.693,36.840,63.160,15.35,224,0.875,bicubic,-84.533,-61.300,+19 -resnetblur50,8.253,91.747,37.373,62.627,25.56,224,0.875,bicubic,-85.687,-61.207,-67 -crossvit_9_240,8.253,91.747,34.107,65.893,8.55,240,0.875,bicubic,-82.377,-63.633,+114 -dla102x,8.213,91.787,37.040,62.960,26.31,224,0.875,bilinear,-85.307,-61.460,-34 -eca_resnext26ts,8.080,91.920,35.960,64.040,10.30,256,0.900,bicubic,-84.530,-62.300,+35 -hrnet_w32,8.027,91.973,37.520,62.480,41.23,224,0.875,bilinear,-85.503,-60.940,-38 -cs3darknet_m,7.987,92.013,36.507,63.493,9.31,288,0.950,bicubic,-85.373,-62.093,-23 -gluon_resnet101_v1c,7.987,92.013,33.360,66.640,44.57,224,0.875,bicubic,-85.683,-65.060,-49 -gluon_resnet50_v1d,7.933,92.067,35.000,65.000,25.58,224,0.875,bicubic,-85.837,-63.390,-60 -dla60_res2next,7.827,92.173,34.987,65.013,17.03,224,0.875,bilinear,-85.343,-63.413,-10 -res2net50_26w_8s,7.827,92.173,33.720,66.280,48.40,224,0.875,bilinear,-85.593,-64.450,-31 -mobilevitv2_075,7.827,92.173,33.693,66.307,2.87,256,0.888,bicubic,-83.933,-64.167,+71 -mobilevit_xs,7.747,92.253,32.533,67.467,2.32,256,0.900,bicubic,-83.073,-65.387,+98 -densenetblur121d,7.733,92.267,34.773,65.227,8.00,224,0.875,bicubic,-84.177,-63.297,+61 -tf_efficientnetv2_b1,7.707,92.293,34.653,65.347,8.14,240,0.882,bicubic,-86.233,-63.967,-81 -deit_tiny_distilled_patch16_224,7.693,92.307,33.547,66.453,5.91,224,0.900,bicubic,-83.017,-64.023,+97 -dla60_res2net,7.600,92.400,34.600,65.400,20.85,224,0.875,bilinear,-85.560,-63.800,-16 -efficientnet_b1_pruned,7.427,92.573,34.507,65.493,6.33,240,0.882,bicubic,-85.343,-63.533,+8 -wide_resnet101_2,7.360,92.640,34.160,65.840,126.89,224,0.875,bilinear,-86.350,-64.380,-63 -regnetx_064,7.347,92.653,34.373,65.627,26.21,224,0.875,bicubic,-86.543,-64.257,-80 -deit_tiny_patch16_224,7.320,92.680,30.707,69.293,5.72,224,0.900,bicubic,-82.340,-66.743,+112 -hardcorenas_e,7.253,92.747,33.293,66.707,8.07,224,0.875,bilinear,-85.317,-64.807,+20 -gluon_resnet101_v1b,7.240,92.760,32.773,67.227,44.55,224,0.875,bicubic,-86.510,-65.607,-73 -efficientnet_b0,7.227,92.773,34.013,65.987,5.29,224,0.875,bicubic,-85.463,-64.057,+10 -gluon_resnet50_v1s,7.213,92.787,33.493,66.507,25.68,224,0.875,bicubic,-86.407,-64.967,-63 -tf_efficientnet_b1,7.147,92.853,33.053,66.947,7.79,240,0.882,bicubic,-86.353,-65.307,-54 -tf_mixnet_l,7.147,92.853,31.627,68.373,7.33,224,0.875,bicubic,-86.173,-66.403,-36 -tf_efficientnet_cc_b0_8e,7.120,92.880,31.827,68.173,24.01,224,0.875,bicubic,-85.710,-66.353,-7 -convmixer_1024_20_ks9_p14,7.080,92.920,33.067,66.933,24.38,224,0.960,bicubic,-85.350,-65.203,+20 -seresnext26ts,7.053,92.947,34.920,65.080,10.39,256,0.900,bicubic,-85.637,-63.370,+2 -resmlp_12_224,7.013,92.987,33.947,66.053,15.35,224,0.875,bicubic,-85.197,-64.213,+29 -cs3darknet_focus_m,6.947,93.053,34.640,65.360,9.30,288,0.950,bicubic,-86.023,-63.750,-19 -hardcorenas_f,6.853,93.147,34.067,65.933,8.20,224,0.875,bilinear,-86.107,-64.093,-18 -selecsls60b,6.733,93.267,33.253,66.747,32.77,224,0.875,bicubic,-86.557,-65.027,-39 -res2net50_26w_6s,6.720,93.280,31.640,68.360,37.05,224,0.875,bilinear,-86.690,-66.640,-54 -ese_vovnet19b_dw,6.707,93.293,33.400,66.600,6.54,224,0.875,bicubic,-85.573,-64.690,+21 -efficientnet_es,6.680,93.320,33.827,66.173,5.44,224,0.875,bicubic,-86.460,-64.593,-35 -tinynet_a,6.640,93.360,32.227,67.773,6.19,192,0.875,bicubic,-85.800,-65.853,+10 -mixnet_m,6.640,93.360,32.053,67.947,5.01,224,0.875,bicubic,-85.790,-65.807,+11 -pit_ti_distilled_224,6.627,93.373,30.747,69.253,5.10,224,0.900,bicubic,-84.273,-66.973,+68 -legacy_seresnext26_32x4d,6.613,93.387,33.280,66.720,16.79,224,0.875,bicubic,-86.027,-64.850,-4 -poolformer_s12,6.560,93.440,34.453,65.547,11.92,224,0.900,bicubic,-86.070,-63.747,-4 -repvgg_b1,6.467,93.533,33.813,66.187,57.42,224,0.875,bilinear,-86.853,-64.697,-54 -skresnet34,6.467,93.533,31.587,68.413,22.28,224,0.875,bicubic,-85.923,-66.563,+8 -dla60x,6.427,93.573,34.120,65.880,17.35,224,0.875,bilinear,-86.693,-64.390,-42 -hardcorenas_d,6.413,93.587,32.200,67.800,7.50,224,0.875,bilinear,-85.977,-65.880,+7 -resnet34d,6.400,93.600,31.507,68.493,21.82,224,0.875,bicubic,-86.280,-66.803,-12 -edgenext_x_small,6.373,93.627,29.720,70.280,2.34,256,0.900,bicubic,-84.717,-67.830,+53 -regnetx_080,6.307,93.693,32.333,67.667,39.57,224,0.875,bicubic,-87.563,-66.187,-108 -swsl_resnet18,6.253,93.747,31.600,68.400,11.69,224,0.875,bilinear,-84.437,-66.100,+65 -legacy_seresnet50,6.187,93.813,32.653,67.347,28.09,224,0.875,bilinear,-86.783,-65.537,-37 -resnet26t,6.133,93.867,32.227,67.773,16.01,256,0.940,bicubic,-86.617,-66.003,-24 -pit_ti_224,6.107,93.893,30.240,69.760,4.85,224,0.900,bicubic,-83.843,-67.200,+75 -tv_resnet152,6.027,93.973,32.067,67.933,60.19,224,0.875,bilinear,-87.283,-66.323,-62 -tf_efficientnet_cc_b0_4e,5.987,94.013,29.587,70.413,13.31,224,0.875,bicubic,-86.603,-68.493,-14 -regnetx_040,5.973,94.027,31.587,68.413,22.12,224,0.875,bicubic,-87.587,-66.963,-90 -tf_efficientnetv2_b0,5.893,94.107,30.773,69.227,7.14,224,0.875,bicubic,-87.217,-67.617,-51 -mixer_l16_224,5.867,94.133,18.533,81.467,208.20,224,0.875,bicubic,-81.273,-74.987,+98 -dla102,5.853,94.147,32.720,67.280,33.27,224,0.875,bilinear,-87.207,-65.830,-52 -selecsls60,5.667,94.333,32.493,67.507,30.67,224,0.875,bicubic,-87.353,-65.807,-49 -regnety_016,5.653,94.347,30.440,69.560,11.20,224,0.875,bicubic,-87.377,-67.910,-52 -res2next50,5.640,94.360,30.867,69.133,24.67,224,0.875,bilinear,-87.220,-67.323,-43 -hardcorenas_c,5.640,94.360,30.453,69.547,5.52,224,0.875,bilinear,-86.390,-67.387,+7 -hrnet_w18,5.493,94.507,30.987,69.013,21.30,224,0.875,bilinear,-86.827,-67.263,-10 -resnest14d,5.467,94.533,28.547,71.453,10.61,224,0.875,bilinear,-86.253,-69.323,+21 -tf_efficientnet_lite2,5.360,94.640,30.920,69.080,6.09,260,0.890,bicubic,-87.290,-67.310,-30 -tf_efficientnet_em,5.347,94.653,31.107,68.893,6.90,240,0.882,bicubic,-87.583,-67.093,-51 -tf_efficientnet_b0_ap,5.307,94.693,28.827,71.173,5.29,224,0.875,bicubic,-86.893,-69.193,-6 -gernet_s,5.293,94.707,30.107,69.893,8.17,224,0.875,bilinear,-86.847,-68.093,-5 -densenet121,5.293,94.707,29.893,70.107,7.98,224,0.875,bicubic,-86.287,-68.137,+17 -repvgg_b1g4,5.280,94.720,30.800,69.200,39.97,224,0.875,bilinear,-87.700,-67.630,-59 -xcit_nano_12_p16_224_dist,5.240,94.760,26.560,73.440,3.05,224,1.000,bicubic,-84.450,-70.540,+59 -res2net50_26w_4s,5.160,94.840,29.373,70.627,25.70,224,0.875,bilinear,-87.330,-68.687,-26 -vit_tiny_r_s16_p8_224,5.080,94.920,27.067,72.933,6.34,224,0.900,bicubic,-84.100,-70.163,+63 -mobilenetv3_large_100,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.263,-69.533,+17 -tf_mixnet_m,5.067,94.933,28.147,71.853,5.01,224,0.875,bicubic,-87.253,-69.743,-21 -tf_efficientnet_b0,5.053,94.947,28.720,71.280,5.29,224,0.875,bicubic,-87.197,-69.270,-18 -res2net50_14w_8s,5.040,94.960,28.773,71.227,25.06,224,0.875,bilinear,-87.700,-69.407,-48 -hardcorenas_b,4.947,95.053,28.067,71.933,5.18,224,0.875,bilinear,-86.813,-69.713,+5 -mixnet_s,4.920,95.080,28.547,71.453,4.13,224,0.875,bicubic,-86.900,-69.143,+1 -mobilenetv3_rw,4.907,95.093,29.853,70.147,5.48,224,0.875,bicubic,-86.303,-67.807,+14 -gluon_resnet50_v1c,4.893,95.107,28.147,71.853,25.58,224,0.875,bicubic,-88.137,-70.243,-74 -hardcorenas_a,4.880,95.120,28.093,71.907,5.26,224,0.875,bilinear,-86.470,-69.767,+8 -regnetx_032,4.853,95.147,30.253,69.747,15.30,224,0.875,bicubic,-88.267,-68.137,-80 -xcit_nano_12_p16_224,4.853,95.147,25.453,74.547,3.05,224,1.000,bicubic,-83.757,-71.337,+61 -tv_resnext50_32x4d,4.840,95.160,30.280,69.720,25.03,224,0.875,bilinear,-87.910,-68.000,-59 -densenet161,4.720,95.280,29.560,70.440,28.68,224,0.875,bicubic,-87.780,-68.730,-42 -tv_resnet101,4.720,95.280,29.373,70.627,44.55,224,0.875,bilinear,-88.100,-68.877,-64 -resnext26ts,4.680,95.320,29.013,70.987,10.30,256,0.900,bicubic,-87.190,-68.907,-12 -selecsls42b,4.667,95.333,28.613,71.387,32.46,224,0.875,bicubic,-87.613,-69.527,-34 -tf_efficientnet_lite1,4.613,95.387,28.413,71.587,5.42,240,0.882,bicubic,-88.007,-69.667,-52 -mobilenetv2_120d,4.533,95.467,29.293,70.707,5.83,224,0.875,bicubic,-87.867,-68.757,-41 -vit_base_patch32_224_sam,4.333,95.667,24.387,75.613,88.22,224,0.900,bicubic,-85.417,-72.613,+37 -tinynet_b,4.187,95.813,26.720,73.280,3.73,188,0.875,bicubic,-86.733,-70.950,+13 -efficientnet_es_pruned,4.187,95.813,26.547,73.453,5.44,224,0.875,bicubic,-86.993,-71.203,+2 -fbnetc_100,4.133,95.867,25.933,74.067,5.57,224,0.875,bilinear,-86.577,-71.277,+18 -densenet201,4.120,95.880,27.533,72.467,20.01,224,0.875,bicubic,-88.620,-70.697,-68 -gluon_resnet50_v1b,4.120,95.880,26.920,73.080,25.56,224,0.875,bicubic,-88.420,-71.250,-55 -resnet26d,4.040,95.960,28.507,71.493,16.01,224,0.875,bicubic,-88.030,-69.463,-33 -semnasnet_100,3.960,96.040,26.933,73.067,3.89,224,0.875,bicubic,-87.320,-70.627,-7 -repvgg_a2,3.933,96.067,27.280,72.720,28.21,224,0.875,bilinear,-88.007,-70.870,-29 -mobilevitv2_050,3.933,96.067,23.867,76.133,1.37,256,0.888,bicubic,-84.297,-73.123,+48 -tf_mixnet_s,3.893,96.107,25.280,74.720,4.13,224,0.875,bicubic,-87.617,-72.330,-15 -semnasnet_075,3.867,96.133,27.000,73.000,2.91,224,0.875,bicubic,-86.193,-70.430,+22 -dpn68,3.867,96.133,26.067,73.933,12.61,224,0.875,bicubic,-88.163,-71.983,-36 -mobilevit_xxs,3.827,96.173,21.707,78.293,1.27,256,0.900,bicubic,-83.363,-74.393,+49 -regnety_008,3.813,96.187,27.160,72.840,6.26,224,0.875,bicubic,-87.907,-71.020,-22 -tf_efficientnet_es,3.813,96.187,26.120,73.880,5.44,224,0.875,bicubic,-88.167,-71.740,-37 -edgenext_xx_small,3.787,96.213,23.693,76.307,1.33,256,0.900,bicubic,-84.563,-72.827,+40 -dla60,3.773,96.227,27.973,72.027,22.04,224,0.875,bilinear,-88.437,-70.127,-49 -ssl_resnet18,3.747,96.253,25.440,74.560,11.69,224,0.875,bilinear,-86.463,-72.110,+12 -mobilenetv2_140,3.720,96.280,26.747,73.253,6.11,224,0.875,bicubic,-88.110,-71.103,-32 -densenet169,3.693,96.307,25.600,74.400,14.15,224,0.875,bicubic,-88.227,-72.500,-39 -regnetx_016,3.613,96.387,26.253,73.747,9.19,224,0.875,bicubic,-88.547,-71.947,-51 -res2net50_48w_2s,3.587,96.413,26.613,73.387,25.29,224,0.875,bilinear,-88.953,-71.467,-71 -tf_mobilenetv3_large_100,3.560,96.440,25.093,74.907,5.48,224,0.875,bilinear,-87.660,-72.567,-22 -spnasnet_100,3.560,96.440,24.293,75.707,4.42,224,0.875,bilinear,-86.780,-72.897,+4 -regnety_006,3.467,96.533,24.893,75.107,6.06,224,0.875,bicubic,-87.903,-72.817,-28 -legacy_seresnet34,3.333,96.667,23.813,76.187,21.96,224,0.875,bilinear,-87.567,-73.767,-10 -efficientnet_lite0,3.253,96.747,25.880,74.120,4.65,224,0.875,bicubic,-87.857,-71.750,-20 -dla34,3.253,96.747,23.613,76.387,15.74,224,0.875,bilinear,-87.527,-74.047,-9 -ghostnet_100,3.227,96.773,24.867,75.133,5.18,224,0.875,bilinear,-86.803,-72.503,+5 -regnety_004,3.200,96.800,22.667,77.333,4.34,224,0.875,bicubic,-87.310,-74.873,-5 -mobilenetv2_110d,3.187,96.813,24.573,75.427,4.52,224,0.875,bicubic,-87.773,-72.987,-18 -mnasnet_100,3.107,96.893,24.200,75.800,4.38,224,0.875,bicubic,-87.403,-73.270,-6 -tinynet_c,3.107,96.893,21.533,78.467,2.46,184,0.875,bicubic,-84.673,-74.837,+25 -tf_efficientnet_lite0,3.093,96.907,22.920,77.080,4.65,224,0.875,bicubic,-87.947,-74.670,-24 -skresnet18,3.000,97.000,22.773,77.227,11.96,224,0.875,bicubic,-86.660,-74.467,+6 -vgg19_bn,2.947,97.053,23.480,76.520,143.68,224,0.875,bilinear,-87.133,-74.100,-4 -resnet34,2.920,97.080,23.693,76.307,21.80,224,0.875,bilinear,-88.210,-73.927,-32 -tf_mobilenetv3_large_075,2.867,97.133,21.560,78.440,3.99,224,0.875,bilinear,-86.813,-75.650,+1 -tinynet_d,2.867,97.133,17.787,82.213,2.34,152,0.875,bicubic,-81.893,-77.393,+33 -resnet14t,2.760,97.240,19.280,80.720,10.08,224,0.950,bilinear,-86.280,-77.320,+6 -hrnet_w18_small_v2,2.707,97.293,23.707,76.293,15.60,224,0.875,bilinear,-88.483,-74.193,-38 -regnetx_008,2.667,97.333,22.467,77.533,7.26,224,0.875,bicubic,-88.383,-75.243,-33 -gluon_resnet34_v1b,2.667,97.333,21.667,78.333,21.80,224,0.875,bicubic,-88.293,-75.973,-31 -vgg16_bn,2.653,97.347,23.787,76.213,138.37,224,0.875,bilinear,-87.437,-73.583,-13 -vgg16,2.627,97.373,20.427,79.573,138.36,224,0.875,bilinear,-85.923,-76.363,+9 -lcnet_100,2.613,97.387,20.867,79.133,2.95,224,0.875,bicubic,-86.177,-75.863,+5 -resnet18d,2.600,97.400,21.600,78.400,11.71,224,0.875,bicubic,-86.680,-75.540,-4 -tv_densenet121,2.560,97.440,22.667,77.333,7.98,224,0.875,bicubic,-88.330,-75.043,-31 -repvgg_b0,2.547,97.453,24.000,76.000,15.82,224,0.875,bilinear,-88.853,-73.990,-53 -regnetx_006,2.507,97.493,20.627,79.373,6.20,224,0.875,bicubic,-87.853,-76.803,-23 -legacy_seresnet18,2.493,97.507,20.080,79.920,11.78,224,0.875,bicubic,-86.387,-76.900,-1 -resnet26,2.480,97.520,23.053,76.947,16.00,224,0.875,bicubic,-88.640,-74.697,-46 -lcnet_075,2.307,97.693,17.160,82.840,2.36,224,0.875,bicubic,-83.683,-78.530,+16 -mobilenetv3_small_075,2.293,97.707,15.907,84.093,2.04,224,0.875,bicubic,-80.747,-78.193,+24 -regnety_002,2.160,97.840,18.893,81.107,3.16,224,0.875,bicubic,-85.220,-77.697,+6 -mobilenetv2_100,2.147,97.853,19.907,80.093,3.50,224,0.875,bicubic,-87.463,-77.243,-14 -vgg19,2.107,97.893,20.747,79.253,143.67,224,0.875,bilinear,-86.933,-76.123,-12 -vgg13_bn,2.093,97.907,20.307,79.693,133.05,224,0.875,bilinear,-86.667,-76.663,-6 -tf_mobilenetv3_small_100,2.013,97.987,15.853,84.147,2.54,224,0.875,bilinear,-83.177,-79.917,+12 -mobilenetv3_small_100,2.000,98.000,17.080,82.920,2.54,224,0.875,bicubic,-83.220,-78.550,+10 -tf_mobilenetv3_small_075,2.000,98.000,14.813,85.187,2.04,224,0.875,bilinear,-81.520,-79.987,+16 -regnetx_004,1.947,98.053,19.160,80.840,5.16,224,0.875,bicubic,-86.953,-77.960,-13 -tv_resnet34,1.867,98.133,20.000,80.000,21.80,224,0.875,bilinear,-88.063,-77.340,-27 +eva_giant_patch14_560.m30m_ft_in22k_in1k,87.533,12.467,96.893,3.107,"1,014.45",560,1.000,bicubic,-11.287,-2.927,0 +eva_giant_patch14_336.clip_ft_in1k,85.280,14.720,95.733,4.267,"1,013.01",336,1.000,bicubic,-13.540,-4.167,0 +eva_giant_patch14_336.m30m_ft_in22k_in1k,85.160,14.840,96.360,3.640,"1,013.01",336,1.000,bicubic,-13.650,-3.540,0 +tf_efficientnet_l2.ns_jft_in1k,84.760,15.240,96.147,3.853,480.31,800,0.960,bicubic,-13.790,-3.673,+6 +eva_large_patch14_336.in22k_ft_in22k_in1k,83.853,16.147,95.347,4.653,304.53,336,1.000,bicubic,-14.887,-4.463,-1 +maxvit_xlarge_tf_512.in21k_ft_in1k,83.400,16.600,95.533,4.467,475.77,512,1.000,bicubic,-15.220,-4.257,+1 +tf_efficientnet_l2.ns_jft_in1k_475,83.373,16.627,95.453,4.547,480.31,475,0.936,bicubic,-15.127,-4.327,+7 +eva_large_patch14_336.in22k_ft_in1k,82.747,17.253,95.520,4.480,304.53,336,1.000,bicubic,-15.963,-4.350,-3 +maxvit_large_tf_512.in21k_ft_in1k,81.747,18.253,95.040,4.960,212.33,512,1.000,bicubic,-16.873,-4.760,-1 +beit_large_patch16_512.in22k_ft_in22k_in1k,81.613,18.387,94.880,5.120,305.67,512,1.000,bicubic,-16.947,-4.960,-1 +maxvit_base_tf_512.in21k_ft_in1k,81.333,18.667,94.467,5.533,119.88,512,1.000,bicubic,-17.297,-5.333,-5 +maxvit_xlarge_tf_384.in21k_ft_in1k,81.067,18.933,94.640,5.360,475.32,384,1.000,bicubic,-17.433,-5.190,+3 +eva_giant_patch14_224.clip_ft_in1k,80.813,19.187,94.320,5.680,"1,012.56",224,1.000,bicubic,-17.667,-5.500,+4 +deit3_large_patch16_384_in21ft1k,79.213,20.787,93.627,6.373,304.76,384,1.000,bicubic,-19.247,-6.133,+4 +beit_large_patch16_384.in22k_ft_in22k_in1k,79.120,20.880,94.280,5.720,305.00,384,1.000,bicubic,-19.400,-5.540,-3 +maxvit_large_tf_384.in21k_ft_in1k,78.027,21.973,93.280,6.720,212.03,384,1.000,bicubic,-20.463,-6.470,0 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,77.293,22.707,93.613,6.387,304.53,336,1.000,bicubic,-20.967,-6.157,+8 +maxvit_base_tf_384.in21k_ft_in1k,76.813,23.187,92.587,7.413,119.65,384,1.000,bicubic,-21.707,-7.163,-5 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.733,23.267,93.160,6.840,304.43,224,0.950,bicubic,-21.807,-6.600,-8 +eva_large_patch14_196.in22k_ft_in22k_in1k,75.547,24.453,91.747,8.253,304.14,196,1.000,bicubic,-22.883,-8.063,0 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,74.227,25.773,92.267,7.733,632.46,336,1.000,bicubic,-24.203,-7.503,-2 +swinv2_large_window12to24_192to384_22kft1k,73.867,26.133,91.747,8.253,196.74,384,1.000,bicubic,-24.283,-7.943,+13 +eva_large_patch14_196.in22k_ft_in1k,73.187,26.813,91.440,8.560,304.14,196,1.000,bicubic,-25.163,-8.380,-1 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,72.333,27.667,90.827,9.173,304.20,224,1.000,bicubic,-25.887,-8.893,+4 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,71.853,28.147,90.213,9.787,304.53,336,1.000,bicubic,-26.477,-9.547,-2 +vit_large_patch14_clip_224.openai_ft_in1k,71.760,28.240,91.480,8.520,304.20,224,1.000,bicubic,-26.400,-8.180,+8 +deit3_base_patch16_384_in21ft1k,71.293,28.707,89.960,10.040,86.88,384,1.000,bicubic,-26.547,-9.720,+24 +swinv2_base_window12to24_192to384_22kft1k,71.267,28.733,91.293,8.707,87.92,384,1.000,bicubic,-26.873,-8.487,+8 +vit_large_patch16_384.augreg_in21k_ft_in1k,71.227,28.773,89.840,10.160,304.72,384,1.000,bicubic,-26.993,-9.890,-2 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,70.733,29.267,90.387,9.613,632.05,224,1.000,bicubic,-27.537,-9.373,-6 +deit3_huge_patch14_224_in21ft1k,70.240,29.760,90.720,9.280,632.13,224,1.000,bicubic,-27.930,-9.040,+2 +volo_d5_512,69.653,30.347,90.427,9.573,296.09,512,1.150,bicubic,-28.117,-9.243,+25 +swin_large_patch4_window12_384,69.627,30.373,89.560,10.440,196.74,384,1.000,bicubic,-28.413,-10.130,+8 +convnext_xlarge.fb_in22k_ft_in1k_384,69.320,30.680,89.293,10.707,350.20,384,1.000,bicubic,-29.100,-10.517,-13 +deit3_large_patch16_224_in21ft1k,68.693,31.307,90.000,10.000,304.37,224,1.000,bicubic,-29.477,-9.730,-3 +beit_large_patch16_224.in22k_ft_in22k_in1k,68.507,31.493,89.560,10.440,304.43,224,0.900,bicubic,-29.673,-10.200,-5 +volo_d5_448,68.107,31.893,89.707,10.293,295.91,448,1.150,bicubic,-29.653,-9.913,+21 +maxvit_base_tf_512.in1k,67.920,32.080,88.507,11.493,119.88,512,1.000,bicubic,-29.820,-11.103,+22 +maxvit_large_tf_512.in1k,67.867,32.133,87.640,12.360,212.33,512,1.000,bicubic,-29.963,-11.920,+14 +tf_efficientnetv2_xl.in21k_ft_in1k,67.787,32.213,87.373,12.627,208.12,512,1.000,bicubic,-30.123,-12.197,+5 +swinv2_large_window12to16_192to256_22kft1k,67.293,32.707,88.027,11.973,196.74,256,0.900,bicubic,-30.567,-11.643,+9 +vit_large_patch14_clip_336.laion2b_ft_in1k,67.040,32.960,89.453,10.547,304.53,336,1.000,bicubic,-31.180,-10.347,-14 +tf_efficientnet_b7.ns_jft_in1k,67.040,32.960,88.667,11.333,66.35,600,0.949,bicubic,-30.870,-11.053,+2 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,67.027,32.973,87.987,12.013,304.20,224,1.000,bicubic,-31.053,-11.773,-5 +convnext_xlarge.fb_in22k_ft_in1k,66.947,33.053,88.947,11.053,350.20,288,1.000,bicubic,-31.173,-10.803,-8 +volo_d4_448,66.640,33.360,88.987,11.013,193.41,448,1.150,bicubic,-31.030,-10.623,+18 +beit_base_patch16_384.in22k_ft_in22k_in1k,65.880,34.120,88.507,11.493,86.74,384,1.000,bicubic,-31.940,-11.193,+7 +vit_huge_patch14_clip_224.laion2b_ft_in1k,65.560,34.440,87.707,12.293,632.05,224,1.000,bicubic,-32.460,-12.013,-6 +convnext_large.fb_in22k_ft_in1k_384,65.547,34.453,87.467,12.533,197.77,384,1.000,bicubic,-32.683,-12.283,-23 +volo_d3_448,65.440,34.560,87.573,12.427,86.63,448,1.000,bicubic,-32.110,-11.977,+28 +convnext_large.fb_in22k_ft_in1k,65.027,34.973,87.933,12.067,197.77,288,1.000,bicubic,-33.093,-11.847,-13 +tf_efficientnetv2_l.in21k_ft_in1k,64.973,35.027,87.813,12.187,118.52,480,1.000,bicubic,-32.827,-11.957,+4 +swin_base_patch4_window12_384,64.480,35.520,87.493,12.507,87.90,384,1.000,bicubic,-33.410,-12.217,-6 +vit_base_patch16_384.augreg_in21k_ft_in1k,63.693,36.307,86.707,13.293,86.86,384,1.000,bicubic,-34.147,-12.963,-2 +maxvit_large_tf_384.in1k,63.467,36.533,85.107,14.893,212.03,384,1.000,bicubic,-34.103,-14.453,+21 +swinv2_base_window12to16_192to256_22kft1k,63.227,36.773,87.493,12.507,87.92,256,0.900,bicubic,-34.423,-12.087,+9 +maxvit_small_tf_512.in1k,62.867,37.133,86.293,13.707,69.13,512,1.000,bicubic,-34.883,-13.257,+2 +maxvit_base_tf_384.in1k,62.600,37.400,85.200,14.800,119.65,384,1.000,bicubic,-34.970,-14.390,+16 +cait_m48_448,62.373,37.627,86.453,13.547,356.46,448,1.000,bicubic,-35.107,-13.147,+27 +convnext_base.fb_in22k_ft_in1k_384,62.347,37.653,86.213,13.787,88.59,384,1.000,bicubic,-35.723,-13.437,-20 +tf_efficientnet_b6.ns_jft_in1k,62.267,37.733,85.173,14.827,43.04,528,0.942,bicubic,-35.363,-14.407,+7 +vit_base_patch8_224.augreg2_in21k_ft_in1k,62.093,37.907,85.867,14.133,86.58,224,0.900,bicubic,-35.617,-13.783,0 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,61.693,38.307,85.507,14.493,86.53,224,0.900,bicubic,-35.997,-14.173,0 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,61.507,38.493,83.960,16.040,329.09,384,1.000,bicubic,-36.353,-15.690,-15 +ig_resnext101_32x48d,61.013,38.987,83.347,16.653,828.41,224,0.875,bilinear,-36.607,-16.353,+4 +convnext_base.fb_in22k_ft_in1k,60.960,39.040,86.133,13.867,88.59,288,1.000,bicubic,-36.900,-13.547,-18 +swin_large_patch4_window7_224,60.893,39.107,85.840,14.160,196.53,224,0.900,bicubic,-36.757,-13.880,-1 +resnetv2_152x4_bitm,60.787,39.213,83.573,16.427,936.53,480,1.000,bilinear,-36.703,-16.037,+16 +deit3_large_patch16_384,60.507,39.493,85.693,14.307,304.76,384,1.000,bicubic,-36.913,-13.927,+21 +tf_efficientnet_b5.ns_jft_in1k,60.320,39.680,84.493,15.507,30.39,456,0.934,bicubic,-37.180,-15.137,+13 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,60.253,39.747,84.613,15.387,86.86,384,0.950,bicubic,-37.947,-15.047,-41 +vit_large_patch14_clip_224.laion2b_ft_in1k,59.893,40.107,85.733,14.267,304.20,224,1.000,bicubic,-38.007,-13.917,-26 +xcit_large_24_p8_384_dist,59.893,40.107,85.493,14.507,188.93,384,1.000,bicubic,-37.627,-14.007,+7 +tf_efficientnetv2_m.in21k_ft_in1k,59.387,40.613,84.573,15.427,54.14,480,1.000,bicubic,-38.433,-15.027,-19 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,59.160,40.840,83.267,16.733,86.86,384,1.000,bicubic,-38.850,-16.393,-33 +dm_nfnet_f6,59.160,40.840,82.333,17.667,438.36,576,0.956,bicubic,-38.440,-17.217,-4 +vit_base_patch8_224.augreg_in21k_ft_in1k,58.933,41.067,82.733,17.267,86.58,224,0.900,bicubic,-38.647,-16.777,-5 +maxvit_tiny_tf_512.in1k,58.840,41.160,84.547,15.453,31.05,512,1.000,bicubic,-38.730,-14.983,-3 +volo_d2_384,58.613,41.387,84.267,15.733,58.87,384,1.000,bicubic,-38.707,-15.213,+22 +dm_nfnet_f5,58.573,41.427,82.773,17.227,377.21,544,0.954,bicubic,-38.967,-16.797,-1 +dm_nfnet_f4,58.120,41.880,81.987,18.013,316.07,512,0.951,bicubic,-39.460,-17.683,-8 +ig_resnext101_32x32d,58.093,41.907,80.653,19.347,468.53,224,0.875,bilinear,-39.267,-18.877,+14 +cait_m36_384,57.840,42.160,84.813,15.187,271.22,384,1.000,bicubic,-39.560,-14.697,+11 +deit3_base_patch16_224_in21ft1k,57.253,42.747,83.520,16.480,86.59,224,1.000,bicubic,-40.227,-16.030,+1 +volo_d5_224,57.147,42.853,82.720,17.280,295.46,224,0.960,bicubic,-40.233,-16.850,+10 +deit3_small_patch16_384_in21ft1k,57.080,42.920,83.053,16.947,22.21,384,1.000,bicubic,-40.050,-16.447,+33 +xcit_medium_24_p8_384_dist,56.680,43.320,83.413,16.587,84.32,384,1.000,bicubic,-40.610,-16.097,+18 +maxvit_small_tf_384.in1k,56.587,43.413,82.307,17.693,69.02,384,1.000,bicubic,-40.833,-17.203,+3 +dm_nfnet_f3,55.827,44.173,80.947,19.053,254.92,416,0.940,bicubic,-41.523,-18.613,+10 +vit_large_patch16_224.augreg_in21k_ft_in1k,55.627,44.373,80.093,19.907,304.33,224,0.900,bicubic,-42.013,-19.497,-23 +vit_base_patch16_clip_384.openai_ft_in1k,54.960,45.040,82.600,17.400,86.86,384,1.000,bicubic,-42.590,-17.060,-14 +vit_base_r50_s16_384.orig_in21k_ft_in1k,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.553,-18.347,+25 +cait_s36_384,54.413,45.587,81.360,18.640,68.37,384,1.000,bicubic,-42.917,-18.170,+7 +deit3_huge_patch14_224,54.320,45.680,82.093,17.907,632.13,224,0.900,bicubic,-42.560,-17.437,+61 +volo_d1_384,54.307,45.693,80.973,19.027,26.78,384,1.000,bicubic,-42.613,-18.437,+55 +xcit_small_24_p8_384_dist,54.280,45.720,81.533,18.467,47.63,384,1.000,bicubic,-42.960,-18.067,+14 +vit_base_patch16_clip_384.laion2b_ft_in1k,54.227,45.773,80.893,19.107,86.86,384,1.000,bicubic,-43.503,-18.737,-36 +vit_medium_patch16_gap_384.in12k_ft_in1k,54.147,45.853,81.680,18.320,39.03,384,0.950,bicubic,-43.293,-17.960,-9 +resnetv2_101x3_bitm,54.027,45.973,81.027,18.973,387.93,448,1.000,bilinear,-42.963,-18.463,+39 +resnetv2_152x2_bitm,54.013,45.987,82.000,18.000,236.34,448,1.000,bilinear,-42.997,-17.590,+36 +deit3_base_patch16_384,53.440,46.560,80.560,19.440,86.88,384,1.000,bicubic,-43.580,-18.880,+34 +ig_resnext101_32x16d,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.753,-22.503,+56 +volo_d4_224,52.920,47.080,80.467,19.533,192.96,224,0.960,bicubic,-44.380,-19.033,0 +xcit_large_24_p16_384_dist,52.827,47.173,81.827,18.173,189.10,384,1.000,bicubic,-44.693,-17.653,-22 +convnext_small.fb_in22k_ft_in1k_384,52.467,47.533,80.827,19.173,50.22,384,1.000,bicubic,-45.133,-18.773,-35 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,52.307,47.693,79.773,20.227,88.34,448,1.000,bicubic,-45.013,-19.827,-4 +maxvit_tiny_tf_384.in1k,52.133,47.867,79.800,20.200,30.98,384,1.000,bicubic,-45.167,-19.720,-3 +swin_base_patch4_window7_224,51.453,48.547,79.973,20.027,87.77,224,0.900,bicubic,-45.797,-19.517,0 +efficientnet_b5.in12k_ft_in1k,51.253,48.747,78.853,21.147,30.39,448,1.000,bicubic,-46.157,-20.747,-16 +tf_efficientnet_b4.ns_jft_in1k,51.213,48.787,79.187,20.813,19.34,380,0.922,bicubic,-45.737,-20.073,+34 +flexivit_large.1200ep_in1k,51.200,48.800,80.693,19.307,304.36,240,0.950,bicubic,-46.210,-18.847,-19 +resnetv2_152x2_bit_teacher_384,51.187,48.813,78.493,21.507,236.34,384,1.000,bicubic,-45.643,-20.957,+45 +swsl_resnext101_32x8d,51.187,48.813,78.240,21.760,88.79,224,0.875,bilinear,-46.013,-21.260,0 +convnext_small.fb_in22k_ft_in1k,51.120,48.880,80.867,19.133,50.22,288,1.000,bicubic,-46.240,-18.813,-17 +mvitv2_large,50.907,49.093,78.467,21.533,217.99,224,0.900,bicubic,-46.043,-20.933,+30 +beit_base_patch16_224.in22k_ft_in22k_in1k,50.707,49.293,79.693,20.307,86.53,224,0.900,bicubic,-46.383,-19.917,+9 +tf_efficientnetv2_l.in1k,50.680,49.320,77.613,22.387,118.52,480,1.000,bicubic,-46.790,-21.917,-30 +vit_base_patch16_384.orig_in21k_ft_in1k,50.613,49.387,78.200,21.800,86.86,384,1.000,bicubic,-46.087,-21.090,+57 +xcit_small_12_p8_384_dist,50.573,49.427,79.573,20.427,26.21,384,1.000,bicubic,-46.657,-19.907,-8 +flexivit_large.600ep_in1k,50.240,49.760,80.027,19.973,304.36,240,0.950,bicubic,-47.040,-19.563,-14 +volo_d3_224,50.240,49.760,78.173,21.827,86.33,224,0.960,bicubic,-46.850,-21.297,+6 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,50.093,49.907,78.080,21.920,86.57,224,0.950,bicubic,-47.357,-21.460,-34 +vit_base_patch16_224.augreg2_in21k_ft_in1k,49.840,50.160,78.987,21.013,86.57,224,0.900,bicubic,-47.310,-20.553,-5 +cait_s24_384,49.733,50.267,78.733,21.267,47.06,384,1.000,bicubic,-47.337,-20.697,+8 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.733,50.267,77.040,22.960,86.57,224,0.950,bicubic,-47.787,-22.500,-44 +xcit_medium_24_p16_384_dist,49.333,50.667,79.813,20.187,84.40,384,1.000,bicubic,-47.937,-19.647,-20 +deit_base_distilled_patch16_384,49.333,50.667,79.253,20.747,87.63,384,1.000,bicubic,-47.627,-20.227,+16 +tf_efficientnet_b8.ra_in1k,48.947,51.053,77.240,22.760,87.41,672,0.954,bicubic,-48.253,-22.260,-12 +dm_nfnet_f2,48.920,51.080,77.160,22.840,193.78,352,0.920,bicubic,-48.100,-22.230,+5 +deit3_large_patch16_224,48.627,51.373,78.160,21.840,304.37,224,0.900,bicubic,-48.313,-21.180,+18 +flexivit_large.300ep_in1k,48.587,51.413,78.667,21.333,304.36,240,0.950,bicubic,-48.663,-20.863,-22 +tf_efficientnetv2_s.in21k_ft_in1k,48.507,51.493,77.880,22.120,21.46,384,1.000,bicubic,-48.223,-21.480,+37 +deit3_medium_patch16_224_in21ft1k,48.213,51.787,77.067,22.933,38.85,224,1.000,bicubic,-48.757,-22.363,+8 +resnest269e,48.187,51.813,74.333,25.667,110.93,416,0.928,bicubic,-48.333,-25.017,+75 +xcit_large_24_p8_224_dist,48.107,51.893,79.080,20.920,188.93,224,1.000,bicubic,-48.953,-20.340,-2 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.933,52.067,76.867,23.133,88.30,384,1.000,bicubic,-49.427,-22.653,-38 +regnetz_e8,47.813,52.187,76.200,23.800,57.70,320,1.000,bicubic,-49.387,-23.340,-22 +resnetv2_50x3_bitm,47.293,52.707,77.333,22.667,217.32,448,1.000,bilinear,-49.417,-22.137,+34 +vit_base_patch16_clip_224.openai_ft_in1k,47.227,52.773,77.627,22.373,86.57,224,0.900,bicubic,-49.853,-21.993,-9 +xcit_large_24_p8_224,47.173,52.827,74.400,25.600,188.93,224,1.000,bicubic,-49.237,-24.580,+78 +xcit_small_24_p16_384_dist,46.960,53.040,77.147,22.853,47.67,384,1.000,bicubic,-50.160,-22.313,-20 +tf_efficientnet_b8.ap_in1k,46.893,53.107,76.507,23.493,87.41,672,0.954,bicubic,-50.217,-23.153,-20 +convnext_large.fb_in1k,46.840,53.160,76.613,23.387,197.77,288,1.000,bicubic,-50.260,-22.837,-19 +efficientnetv2_rw_m.agc_in1k,46.280,53.720,75.707,24.293,53.24,416,1.000,bicubic,-50.700,-23.833,-4 +swinv2_base_window16_256,46.240,53.760,75.173,24.827,87.92,256,0.900,bicubic,-50.510,-24.177,+23 +swsl_resnext101_32x16d,46.200,53.800,72.200,27.800,194.03,224,0.875,bilinear,-50.400,-27.180,+49 +volo_d2_224,46.080,53.920,75.253,24.747,58.68,224,0.960,bicubic,-50.910,-24.137,-8 +vit_small_patch16_384.augreg_in21k_ft_in1k,45.933,54.067,76.720,23.280,22.20,384,1.000,bicubic,-50.767,-22.710,+28 +ecaresnet269d,45.893,54.107,75.133,24.867,102.09,352,1.000,bicubic,-51.187,-24.357,-18 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,45.720,54.280,76.067,23.933,36.47,384,1.000,bicubic,-50.960,-23.283,+33 +tf_efficientnet_b7.ap_in1k,45.373,54.627,74.213,25.787,66.35,600,0.949,bicubic,-51.827,-25.357,-37 +dm_nfnet_f1,45.333,54.667,74.107,25.893,132.63,320,0.910,bicubic,-51.587,-25.413,-1 +ig_resnext101_32x8d,45.320,54.680,70.867,29.133,88.79,224,0.875,bilinear,-51.000,-28.563,+80 +xcit_medium_24_p8_224_dist,45.213,54.787,76.720,23.280,84.32,224,1.000,bicubic,-51.707,-22.670,-2 +eca_nfnet_l2,44.960,55.040,75.893,24.107,56.72,384,1.000,bicubic,-52.130,-23.617,-29 +maxxvit_rmlp_small_rw_256,44.547,55.453,75.053,24.947,66.01,256,0.950,bicubic,-52.263,-24.327,+4 +crossvit_18_dagger_408,44.293,55.707,73.840,26.160,44.61,408,1.000,bicubic,-52.237,-25.480,+51 +resnest200e,44.147,55.853,73.467,26.533,70.20,320,0.909,bicubic,-52.463,-25.883,+36 +cait_xs24_384,43.947,56.053,75.187,24.813,26.67,384,1.000,bicubic,-52.603,-24.233,+43 +seresnextaa101d_32x8d,43.933,56.067,73.387,26.613,93.59,288,1.000,bicubic,-53.017,-26.003,-14 +mvitv2_base,43.747,56.253,74.520,25.480,51.47,224,0.900,bicubic,-53.023,-24.930,+3 +resnetrs200,43.733,56.267,72.827,27.173,93.21,320,1.000,bicubic,-52.967,-26.683,+16 +tresnet_xl_448,43.480,56.520,72.453,27.547,78.44,448,0.875,bilinear,-52.490,-26.677,+124 +xcit_small_12_p16_384_dist,43.240,56.760,73.880,26.120,26.25,384,1.000,bicubic,-53.690,-25.520,-16 +vit_base_patch16_224.augreg_in21k_ft_in1k,43.240,56.760,72.920,27.080,86.57,224,0.900,bicubic,-53.640,-26.560,-10 +resnetrs420,43.147,56.853,70.453,29.547,191.89,416,1.000,bicubic,-53.763,-29.007,-13 +xcit_medium_24_p8_224,43.093,56.907,70.347,29.653,84.32,224,1.000,bicubic,-53.017,-28.813,+98 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,43.040,56.960,73.227,26.773,88.30,384,0.950,bicubic,-54.070,-26.273,-45 +coatnet_rmlp_2_rw_224,43.000,57.000,71.680,28.320,73.88,224,0.950,bicubic,-53.540,-27.590,+37 +tf_efficientnet_b7.ra_in1k,42.960,57.040,73.133,26.867,66.35,600,0.949,bicubic,-54.050,-26.387,-33 +tf_efficientnetv2_m.in1k,42.867,57.133,72.627,27.373,54.14,480,1.000,bicubic,-54.343,-26.903,-59 +vit_medium_patch16_gap_256.in12k_ft_in1k,42.693,57.307,74.320,25.680,38.86,256,0.950,bicubic,-53.967,-25.010,+15 +gcvit_base,42.507,57.493,73.813,26.187,90.32,224,0.875,bicubic,-54.063,-25.417,+27 +xcit_tiny_24_p8_384_dist,42.453,57.547,72.880,27.120,12.11,384,1.000,bicubic,-54.097,-26.440,+29 +swinv2_small_window16_256,42.320,57.680,72.907,27.093,49.73,256,0.900,bicubic,-54.150,-26.293,+36 +maxvit_rmlp_small_rw_224,42.240,57.760,72.333,27.667,64.90,224,0.900,bicubic,-54.350,-26.937,+22 +convnext_base.fb_in1k,42.000,58.000,73.973,26.027,88.59,288,1.000,bicubic,-54.820,-25.617,-18 +maxvit_base_tf_224.in1k,41.947,58.053,70.107,29.893,119.47,224,0.950,bicubic,-55.003,-29.473,-31 +crossvit_15_dagger_408,41.907,58.093,72.067,27.933,28.50,408,1.000,bicubic,-54.483,-27.283,+42 +xcit_small_24_p8_224_dist,41.893,58.107,73.720,26.280,47.63,224,1.000,bicubic,-54.977,-25.760,-24 +maxvit_large_tf_224.in1k,41.840,58.160,68.613,31.387,211.79,224,0.950,bicubic,-55.120,-30.637,-38 +xcit_small_24_p8_224,41.760,58.240,71.013,28.987,47.63,224,1.000,bicubic,-54.640,-28.137,+37 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,41.640,58.360,70.227,29.773,328.99,224,0.900,bicubic,-55.150,-29.123,-21 +vit_base_patch16_clip_224.laion2b_ft_in1k,41.573,58.427,73.627,26.373,86.57,224,1.000,bicubic,-55.557,-25.833,-64 +swsl_resnext101_32x4d,41.560,58.440,71.760,28.240,44.18,224,0.875,bilinear,-54.860,-27.710,+30 +swinv2_base_window8_256,41.507,58.493,72.440,27.560,87.92,256,0.900,bicubic,-55.033,-26.920,+19 +deit3_small_patch16_224_in21ft1k,41.227,58.773,71.920,28.080,22.06,224,1.000,bicubic,-55.433,-27.590,+1 +seresnext101d_32x8d,41.173,58.827,70.853,29.147,93.59,288,1.000,bicubic,-55.557,-28.567,-18 +maxvit_rmlp_tiny_rw_256,41.160,58.840,71.213,28.787,29.15,256,0.950,bicubic,-55.250,-28.177,+27 +convnext_tiny.fb_in22k_ft_in1k_384,41.013,58.987,72.507,27.493,28.59,384,1.000,bicubic,-56.067,-26.963,-61 +tf_efficientnet_b6.ap_in1k,40.800,59.200,71.627,28.373,43.04,528,0.942,bicubic,-56.280,-27.883,-63 +flexivit_base.1200ep_in1k,40.613,59.387,72.320,27.680,86.59,240,0.950,bicubic,-56.147,-27.050,-26 +resmlp_big_24_224_in22ft1k,40.373,59.627,74.760,25.240,129.14,224,0.875,bicubic,-56.247,-24.510,-3 +deit3_small_patch16_384,40.320,59.680,70.293,29.707,22.21,384,1.000,bicubic,-55.880,-28.997,+52 +tresnet_l_448,40.200,59.800,69.893,30.107,55.99,448,0.875,bilinear,-55.660,-29.227,+107 +deit_base_patch16_384,40.173,59.827,70.760,29.240,86.86,384,1.000,bicubic,-55.977,-28.420,+61 +regnetz_d8_evos,40.080,59.920,72.200,27.800,23.46,320,0.950,bicubic,-56.530,-27.250,-4 +regnetz_040h,40.013,59.987,71.320,28.680,28.94,320,1.000,bicubic,-56.697,-28.180,-25 +resnetrs350,39.960,60.040,68.907,31.093,163.96,384,1.000,bicubic,-56.800,-30.453,-34 +regnetz_d8,39.933,60.067,71.640,28.360,23.37,320,1.000,bicubic,-56.687,-27.810,-9 +flexivit_base.600ep_in1k,39.907,60.093,71.867,28.133,86.59,240,0.950,bicubic,-56.723,-27.463,-12 +swin_s3_base_224,39.787,60.213,70.467,29.533,71.13,224,0.900,bicubic,-56.463,-28.673,+38 +seresnext101_32x8d,39.587,60.413,69.440,30.560,93.57,288,1.000,bicubic,-57.173,-29.900,-36 +flexivit_base.300ep_in1k,39.547,60.453,70.987,29.013,86.59,240,0.950,bicubic,-57.073,-28.523,-12 +gcvit_small,39.427,60.573,70.520,29.480,51.09,224,0.875,bicubic,-56.873,-28.620,+30 +deit3_base_patch16_224,39.200,60.800,71.000,29.000,86.59,224,0.900,bicubic,-57.100,-28.180,+28 +volo_d1_224,38.960,61.040,70.240,29.760,26.63,224,0.960,bicubic,-57.370,-28.940,+24 +vit_large_patch32_384.orig_in21k_ft_in1k,38.933,61.067,68.920,31.080,306.63,384,1.000,bicubic,-56.897,-30.230,+96 +resnetv2_101x1_bitm,38.920,61.080,71.040,28.960,44.54,448,1.000,bilinear,-57.180,-28.210,+57 +regnetz_040,38.760,61.240,70.400,29.600,27.12,320,1.000,bicubic,-57.950,-29.150,-36 +mvitv2_small,38.747,61.253,70.413,29.587,34.87,224,0.900,bicubic,-57.613,-28.817,+14 +xcit_small_12_p8_224_dist,38.200,61.800,71.293,28.707,26.21,224,1.000,bicubic,-58.490,-28.077,-32 +resnet200d,38.147,61.853,68.613,31.387,64.69,320,1.000,bicubic,-58.573,-30.717,-42 +swinv2_small_window8_256,37.773,62.227,69.853,30.147,49.73,256,0.900,bicubic,-58.497,-29.357,+23 +xcit_large_24_p16_224_dist,37.693,62.307,71.587,28.413,189.10,224,1.000,bicubic,-59.107,-27.763,-54 +seresnet152d,37.640,62.360,69.480,30.520,66.84,320,1.000,bicubic,-59.130,-29.790,-53 +eca_nfnet_l1,37.533,62.467,70.947,29.053,41.41,320,1.000,bicubic,-59.167,-28.533,-38 +maxvit_small_tf_224.in1k,37.520,62.480,68.040,31.960,68.93,224,0.950,bicubic,-59.170,-31.310,-37 +xcit_small_12_p8_224,37.507,62.493,68.173,31.827,26.21,224,1.000,bicubic,-58.603,-30.717,+45 +twins_svt_large,37.200,62.800,69.227,30.773,99.27,224,0.900,bicubic,-59.070,-29.943,+18 +regnetz_d32,37.160,62.840,70.467,29.533,27.58,320,0.950,bicubic,-59.440,-29.053,-25 +vit_base_patch32_384.augreg_in21k_ft_in1k,37.080,62.920,69.760,30.240,88.30,384,1.000,bicubic,-59.410,-29.650,-12 +regnety_064,36.973,63.027,68.160,31.840,30.58,288,1.000,bicubic,-59.387,-31.010,+1 +swin_s3_small_224,36.880,63.120,68.240,31.760,49.74,224,0.900,bicubic,-59.340,-30.990,+19 +efficientnetv2_rw_s.ra2_in1k,36.787,63.213,68.320,31.680,23.94,384,1.000,bicubic,-59.753,-30.780,-21 +resnext101_64x4d,36.787,63.213,66.680,33.320,83.46,288,1.000,bicubic,-59.293,-32.560,+44 +regnety_160,36.747,63.253,69.107,30.893,83.59,288,1.000,bicubic,-59.603,-30.223,0 +convnext_small.fb_in1k,36.667,63.333,71.093,28.907,50.22,288,1.000,bicubic,-59.893,-28.247,-27 +pvt_v2_b4,36.640,63.360,68.667,31.333,62.56,224,0.900,bicubic,-59.690,-30.643,+3 +pvt_v2_b5,36.280,63.720,68.453,31.547,81.96,224,0.900,bicubic,-60.080,-30.937,-4 +cait_xxs36_384,36.227,63.773,67.800,32.200,17.37,384,1.000,bicubic,-59.623,-31.290,+72 +jx_nest_base,36.080,63.920,66.760,33.240,67.72,224,0.875,bicubic,-60.170,-32.450,+7 +coatnet_1_rw_224,36.040,63.960,67.080,32.920,41.72,224,0.950,bicubic,-59.990,-32.070,+46 +maxvit_tiny_rw_224,35.920,64.080,65.547,34.453,29.06,224,0.950,bicubic,-60.320,-33.573,+7 +cs3se_edgenet_x,35.653,64.347,67.787,32.213,50.72,320,1.000,bicubic,-60.787,-31.613,-21 +pit_b_distilled_224,35.627,64.373,69.120,30.880,74.79,224,0.900,bicubic,-61.053,-30.450,-51 +regnety_080,35.560,64.440,67.240,32.760,39.18,288,1.000,bicubic,-60.970,-32.020,-30 +sequencer2d_l,35.547,64.453,67.347,32.653,54.30,224,0.875,bicubic,-60.593,-31.863,+21 +tf_efficientnet_b3.ns_jft_in1k,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.387,-19 +tf_efficientnet_b6.aa_in1k,35.213,64.787,67.720,32.280,43.04,528,0.942,bicubic,-61.457,-31.650,-54 +resnetrs270,35.013,64.987,65.480,34.520,129.86,352,1.000,bicubic,-61.677,-33.910,-59 +gcvit_tiny,34.907,65.093,66.853,33.147,28.22,224,0.875,bicubic,-61.263,-32.387,+8 +tf_efficientnet_b5.ap_in1k,34.787,65.213,67.493,32.507,30.39,456,0.934,bicubic,-61.893,-31.967,-59 +xcit_tiny_12_p8_384_dist,34.653,65.347,66.280,33.720,6.71,384,1.000,bicubic,-61.427,-32.860,+27 +vit_base_patch16_224_miil.in21k_ft_in1k,34.507,65.493,65.000,35.000,86.54,224,0.875,bilinear,-61.953,-34.300,-33 +xcit_medium_24_p16_224_dist,34.373,65.627,67.920,32.080,84.40,224,1.000,bicubic,-62.217,-31.190,-49 +resnet152d,34.320,65.680,65.907,34.093,60.21,320,1.000,bicubic,-62.040,-33.293,-24 +deit3_medium_patch16_224,34.187,65.813,66.027,33.973,38.85,224,0.900,bicubic,-61.883,-33.173,+24 +tresnet_m_448,34.107,65.893,64.493,35.507,31.39,448,0.875,bilinear,-60.883,-34.487,+176 +resmlp_big_24_distilled_224,34.067,65.933,69.600,30.400,129.14,224,0.875,bicubic,-62.383,-29.710,-37 +regnetv_064,33.973,66.027,67.867,32.133,30.58,288,1.000,bicubic,-62.437,-31.493,-34 +xcit_tiny_24_p16_384_dist,33.827,66.173,65.400,34.600,12.12,384,1.000,bicubic,-62.123,-33.760,+37 +pvt_v2_b3,33.653,66.347,67.653,32.347,45.24,224,0.900,bicubic,-62.337,-31.537,+29 +coatnet_rmlp_1_rw_224,33.533,66.467,65.627,34.373,41.69,224,0.950,bicubic,-62.417,-33.593,+37 +twins_pcpvt_large,33.387,66.613,67.933,32.067,60.99,224,0.900,bicubic,-62.763,-31.267,+1 +twins_svt_base,33.173,66.827,65.773,34.227,56.07,224,0.900,bicubic,-62.987,-33.287,-4 +pit_b_224,33.173,66.827,62.320,37.680,73.76,224,0.900,bicubic,-62.467,-36.670,+70 +resnetv2_152x2_bit_teacher,33.053,66.947,64.267,35.733,236.34,224,0.875,bicubic,-63.047,-35.013,+9 +swsl_resnext50_32x4d,33.013,66.987,65.067,34.933,25.03,224,0.875,bilinear,-62.857,-34.003,+38 +mobilevitv2_200_384_in22ft1k,32.987,67.013,65.507,34.493,18.45,384,1.000,bicubic,-63.053,-33.573,+14 +swinv2_cr_small_ns_224,32.933,67.067,65.960,34.040,49.70,224,0.900,bicubic,-63.247,-33.370,-13 +xception65,32.747,67.253,62.960,37.040,39.92,299,0.940,bicubic,-63.603,-36.280,-33 +xcit_large_24_p16_224,32.733,67.267,62.107,37.893,189.10,224,1.000,bicubic,-62.687,-36.733,+102 +swin_small_patch4_window7_224,32.600,67.400,65.440,34.560,49.61,224,0.900,bicubic,-63.310,-33.580,+29 +ssl_resnext101_32x16d,32.600,67.400,64.000,36.000,194.03,224,0.875,bilinear,-63.200,-35.100,+43 +mobilevitv2_175_384_in22ft1k,32.467,67.533,64.680,35.320,14.25,384,1.000,bicubic,-63.713,-34.460,-17 +tf_efficientnetv2_b3.in21k_ft_in1k,32.373,67.627,66.107,33.893,14.36,300,0.900,bicubic,-63.847,-32.973,-25 +jx_nest_small,32.280,67.720,63.760,36.240,38.35,224,0.875,bicubic,-63.680,-35.270,+20 +convnext_tiny_hnf.a2h_in1k,32.227,67.773,62.853,37.147,28.59,288,1.000,bicubic,-63.793,-36.337,+12 +vit_base_patch16_224.orig_in21k_ft_in1k,32.053,67.947,61.573,38.427,86.57,224,0.900,bicubic,-63.277,-37.427,+105 +maxvit_nano_rw_256,31.853,68.147,64.187,35.813,15.45,256,0.950,bicubic,-64.077,-34.953,+22 +tf_efficientnet_b5.ra_in1k,31.840,68.160,65.293,34.707,30.39,456,0.934,bicubic,-64.510,-34.017,-44 +swinv2_tiny_window16_256,31.707,68.293,65.613,34.387,28.35,256,0.900,bicubic,-64.223,-33.387,+19 +swinv2_cr_small_224,31.693,68.307,62.520,37.480,49.70,224,0.900,bicubic,-64.377,-36.350,-1 +regnetz_c16_evos,31.493,68.507,66.280,33.720,13.49,320,0.950,bicubic,-64.627,-32.960,-14 +maxvit_rmlp_nano_rw_256,31.440,68.560,63.373,36.627,15.50,256,0.950,bicubic,-64.540,-35.597,+9 +resnest101e,31.413,68.587,64.360,35.640,48.28,256,0.875,bilinear,-64.447,-34.820,+22 +maxxvit_rmlp_nano_rw_256,31.360,68.640,64.440,35.560,16.78,256,0.950,bicubic,-64.670,-34.610,-4 +crossvit_base_240,31.360,68.640,61.293,38.707,105.03,240,0.875,bicubic,-64.170,-37.567,+63 +regnetv_040,31.333,68.667,64.693,35.307,20.64,288,1.000,bicubic,-64.847,-34.437,-33 +cait_s24_224,31.200,68.800,64.560,35.440,46.92,224,1.000,bicubic,-65.180,-34.590,-59 +convnext_nano.in12k_ft_in1k,31.107,68.893,67.333,32.667,15.59,288,1.000,bicubic,-64.873,-31.987,+2 +efficientnet_b4.ra2_in1k,30.867,69.133,64.600,35.400,19.34,384,1.000,bicubic,-65.283,-34.650,-28 +regnety_040,30.613,69.387,63.840,36.160,20.65,288,1.000,bicubic,-65.407,-35.230,-4 +sequencer2d_m,30.600,69.400,62.920,37.080,38.31,224,0.875,bicubic,-65.210,-36.290,+21 +maxvit_tiny_tf_224.in1k,30.587,69.413,62.760,37.240,30.92,224,0.950,bicubic,-65.513,-36.520,-19 +crossvit_18_240,30.587,69.413,61.960,38.040,43.27,240,0.875,bicubic,-64.853,-37.090,+72 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,30.573,69.427,62.080,37.920,88.22,224,0.900,bicubic,-65.567,-37.080,-30 +dm_nfnet_f0,30.547,69.453,62.867,37.133,71.49,256,0.900,bicubic,-65.603,-36.273,-35 +crossvit_18_dagger_240,30.480,69.520,61.813,38.187,44.27,240,0.875,bicubic,-65.090,-37.247,+42 +xcit_small_24_p16_224_dist,30.440,69.560,64.720,35.280,47.67,224,1.000,bicubic,-65.770,-34.460,-47 +xcit_medium_24_p16_224,30.187,69.813,59.347,40.653,84.40,224,1.000,bicubic,-65.343,-39.423,+51 +mvitv2_tiny,30.160,69.840,64.333,35.667,24.17,224,0.900,bicubic,-65.710,-34.917,+5 +cait_xxs24_384,30.027,69.973,63.933,36.067,12.03,384,1.000,bicubic,-65.233,-35.027,+87 +twins_pcpvt_base,29.960,70.040,64.587,35.413,43.83,224,0.900,bicubic,-65.830,-34.543,+15 +convnext_tiny.fb_in1k,29.933,70.067,65.107,34.893,28.59,288,1.000,bicubic,-65.857,-34.053,+13 +swsl_resnet50,29.867,70.133,63.853,36.147,25.56,224,0.875,bilinear,-65.543,-35.437,+69 +cs3sedarknet_x,29.827,70.173,61.987,38.013,35.40,288,1.000,bicubic,-66.203,-37.273,-20 +mobilevitv2_150_384_in22ft1k,29.787,70.213,62.187,37.813,10.59,384,1.000,bicubic,-65.913,-36.863,+19 +vit_relpos_base_patch16_clsgap_224.sw_in1k,29.720,70.280,62.893,37.107,86.43,224,0.900,bicubic,-66.040,-36.147,+13 +deit_base_distilled_patch16_224,29.600,70.400,64.453,35.547,87.34,224,0.900,bicubic,-66.490,-34.737,-32 +convit_base,29.520,70.480,61.787,38.213,86.54,224,0.875,bicubic,-66.030,-37.203,+34 +vit_relpos_medium_patch16_cls_224.sw_in1k,29.347,70.653,60.653,39.347,38.76,224,0.900,bicubic,-66.133,-38.297,+49 +tf_efficientnetv2_s.in1k,29.040,70.960,61.213,38.787,21.46,384,1.000,bicubic,-67.300,-37.987,-75 +ssl_resnext101_32x8d,29.040,70.960,60.973,39.027,88.79,224,0.875,bilinear,-66.430,-38.137,+49 +edgenext_base,29.027,70.973,64.920,35.080,18.51,320,1.000,bicubic,-67.673,-34.450,-129 +convnext_tiny.fb_in22k_ft_in1k,29.000,71.000,55.640,44.360,28.59,288,1.000,bicubic,-65.860,-43.150,+131 +resnet101d,28.987,71.013,62.053,37.947,44.57,320,1.000,bicubic,-67.303,-37.177,-72 +xception65p,28.987,71.013,59.933,40.067,39.82,299,0.940,bicubic,-67.223,-39.277,-64 +regnetz_c16,28.920,71.080,63.333,36.667,13.46,320,0.940,bicubic,-66.880,-35.847,-2 +resnetrs152,28.920,71.080,60.520,39.480,86.62,320,1.000,bicubic,-67.660,-38.720,-112 +vit_relpos_medium_patch16_224.sw_in1k,28.853,71.147,61.987,38.013,38.75,224,0.900,bicubic,-66.607,-36.973,+42 +xcit_tiny_24_p8_224_dist,28.720,71.280,61.413,38.587,12.11,224,1.000,bicubic,-67.090,-37.697,-8 +xcit_tiny_24_p8_224,28.707,71.293,60.453,39.547,12.11,224,1.000,bicubic,-66.963,-38.597,+9 +cs3edgenet_x,28.533,71.467,61.147,38.853,47.82,288,1.000,bicubic,-67.497,-37.963,-39 +crossvit_15_dagger_240,28.533,71.467,60.293,39.707,28.21,240,0.875,bicubic,-67.157,-38.537,+5 +pvt_v2_b2_li,28.493,71.507,62.040,37.960,22.55,224,0.900,bicubic,-67.057,-36.830,+18 +xcit_small_24_p16_224,28.333,71.667,58.813,41.187,47.67,224,1.000,bicubic,-67.197,-39.927,+24 +efficientformer_l7,28.027,71.973,62.987,37.013,82.23,224,0.950,bicubic,-68.083,-36.283,-56 +flexivit_small.1200ep_in1k,27.840,72.160,58.667,41.333,22.06,240,0.950,bicubic,-67.720,-40.453,+13 +pvt_v2_b2,27.640,72.360,60.707,39.293,25.36,224,0.900,bicubic,-67.860,-38.293,+28 +vit_base_patch16_384.augreg_in1k,27.560,72.440,57.280,42.720,86.86,384,1.000,bicubic,-67.380,-41.610,+107 +coat_lite_small,27.547,72.453,58.547,41.453,19.84,224,0.900,bicubic,-67.993,-40.313,+14 +deit_base_patch16_224,27.440,72.560,58.893,41.107,86.57,224,0.900,bicubic,-68.000,-40.237,+33 +vit_relpos_base_patch16_224.sw_in1k,27.333,72.667,61.133,38.867,86.43,224,0.900,bicubic,-68.227,-37.897,+9 +resnetv2_50x1_bitm,27.293,72.707,62.853,37.147,25.55,448,1.000,bilinear,-67.717,-36.037,+94 +coatnet_bn_0_rw_224,27.200,72.800,61.280,38.720,27.44,224,0.950,bicubic,-68.500,-37.860,-8 +xcit_small_12_p16_224_dist,27.120,72.880,59.800,40.200,26.25,224,1.000,bicubic,-68.910,-39.340,-52 +vit_small_patch16_224.augreg_in21k_ft_in1k,27.053,72.947,59.213,40.787,22.05,224,0.900,bicubic,-68.317,-39.717,+42 +coatnet_0_rw_224,27.027,72.973,59.387,40.613,27.44,224,0.950,bicubic,-68.403,-39.653,+33 +flexivit_small.600ep_in1k,26.933,73.067,57.280,42.720,22.06,240,0.950,bicubic,-68.747,-41.770,-10 +sequencer2d_s,26.827,73.173,60.587,39.413,27.65,224,0.875,bicubic,-69.163,-38.463,-49 +tresnet_v2_l,26.760,73.240,59.800,40.200,46.17,224,0.875,bilinear,-69.400,-39.440,-81 +mobilevitv2_200_in22ft1k,26.667,73.333,59.400,40.600,18.45,256,0.888,bicubic,-68.493,-39.540,+61 +gcvit_xtiny,26.627,73.373,60.867,39.133,19.98,224,0.875,bicubic,-68.953,-38.173,-4 +swin_s3_tiny_224,26.507,73.493,60.320,39.680,28.33,224,0.900,bicubic,-68.653,-38.610,+58 +coatnet_rmlp_nano_rw_224,26.440,73.560,60.520,39.480,15.15,224,0.900,bicubic,-68.990,-38.510,+25 +swinv2_tiny_window8_256,26.413,73.587,60.573,39.427,28.35,256,0.900,bicubic,-69.087,-38.387,+10 +tf_efficientnet_b4.aa_in1k,26.293,73.707,60.107,39.893,19.34,380,0.922,bicubic,-69.607,-39.063,-44 +tf_efficientnet_b4.ap_in1k,26.240,73.760,60.227,39.773,19.34,380,0.922,bicubic,-69.920,-39.053,-89 +nfnet_l0,26.213,73.787,61.720,38.280,35.07,288,1.000,bicubic,-69.907,-37.640,-79 +regnety_032,26.213,73.787,60.987,39.013,19.44,288,1.000,bicubic,-69.757,-38.203,-56 +deit3_small_patch16_224,26.187,73.813,54.440,45.560,22.06,224,0.900,bicubic,-68.813,-44.590,+81 +coatnext_nano_rw_224,26.173,73.827,59.560,40.440,14.70,224,0.900,bicubic,-69.257,-39.440,+17 +ecaresnet50t,26.133,73.867,60.027,39.973,25.57,320,0.950,bicubic,-69.377,-39.093,+1 +fbnetv3_g.ra2_in1k,26.107,73.893,61.053,38.947,16.62,288,0.950,bilinear,-69.413,-37.937,-2 +ecaresnet101d,26.027,73.973,58.987,41.013,44.57,224,0.875,bicubic,-69.503,-39.833,-9 +mobilevitv2_175_in22ft1k,26.013,73.987,58.480,41.520,14.25,256,0.888,bicubic,-69.217,-40.510,+38 +flexivit_small.300ep_in1k,25.933,74.067,57.053,42.947,22.06,240,0.950,bicubic,-69.567,-42.067,+1 +visformer_small,25.840,74.160,58.907,41.093,40.22,224,0.900,bicubic,-69.650,-39.993,+1 +vit_small_patch16_384.augreg_in1k,25.813,74.187,57.613,42.387,22.20,384,1.000,bicubic,-69.477,-41.387,+28 +halo2botnet50ts_256,25.587,74.413,56.893,43.107,22.64,256,0.950,bicubic,-69.803,-42.117,+16 +coat_mini,25.520,74.480,57.693,42.307,10.34,224,0.900,bicubic,-69.450,-41.087,+73 +vit_relpos_medium_patch16_rpn_224.sw_in1k,25.453,74.547,58.640,41.360,38.73,224,0.900,bicubic,-70.057,-40.440,-8 +crossvit_15_240,25.453,74.547,57.560,42.440,27.53,240,0.875,bicubic,-69.697,-41.370,+44 +vit_srelpos_medium_patch16_224.sw_in1k,25.400,74.600,58.453,41.547,38.74,224,0.900,bicubic,-69.830,-40.337,+29 +xcit_small_12_p16_224,25.133,74.867,56.040,43.960,26.25,224,1.000,bicubic,-70.287,-42.570,+7 +resnetv2_50x1_bit_distilled,25.107,74.893,59.613,40.387,25.55,224,0.875,bicubic,-71.023,-39.667,-98 +convit_small,25.093,74.907,57.280,42.720,27.78,224,0.875,bicubic,-70.107,-41.620,+30 +vit_base_patch16_rpn_224.in1k,25.080,74.920,58.653,41.347,86.54,224,0.900,bicubic,-70.300,-40.197,+9 +gc_efficientnetv2_rw_t.agc_in1k,25.053,74.947,57.707,42.293,13.68,288,1.000,bicubic,-70.687,-41.313,-47 +eca_nfnet_l0,24.827,75.173,60.093,39.907,24.14,288,1.000,bicubic,-71.123,-39.117,-72 +xception41p,24.773,75.227,55.227,44.773,26.91,299,0.940,bicubic,-70.747,-43.693,-18 +tnt_s_patch16_224,24.733,75.267,58.187,41.813,23.76,224,0.900,bicubic,-70.307,-40.773,+55 +convnext_nano_ols.d1h_in1k,24.520,75.480,57.053,42.947,15.65,288,1.000,bicubic,-70.610,-41.967,+37 +resnetv2_50d_evos,24.480,75.520,56.387,43.613,25.59,288,0.950,bicubic,-71.130,-42.643,-37 +xcit_tiny_12_p16_384_dist,24.453,75.547,57.080,42.920,6.72,384,1.000,bicubic,-70.677,-41.640,+34 +cs3darknet_x,24.373,75.627,57.813,42.187,35.05,288,1.000,bicubic,-71.487,-41.397,-68 +efficientnetv2_rw_t.ra2_in1k,24.333,75.667,57.400,42.600,13.65,288,1.000,bicubic,-71.277,-41.670,-41 +ssl_resnext101_32x4d,24.173,75.827,57.413,42.587,44.18,224,0.875,bilinear,-71.267,-41.377,-15 +swinv2_cr_tiny_ns_224,24.133,75.867,58.213,41.787,28.33,224,0.900,bicubic,-71.237,-40.937,+1 +twins_svt_small,24.133,75.867,57.147,42.853,24.06,224,0.900,bicubic,-71.067,-41.733,+18 +coatnet_nano_rw_224,24.120,75.880,57.173,42.827,15.14,224,0.900,bicubic,-71.130,-41.807,+10 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,24.080,75.920,56.213,43.787,36.43,224,0.900,bicubic,-71.550,-42.977,-47 +vit_relpos_small_patch16_224.sw_in1k,24.040,75.960,58.173,41.827,21.98,224,0.900,bicubic,-71.120,-40.707,+16 +poolformer_m48,24.040,75.960,57.293,42.707,73.47,224,0.950,bicubic,-71.600,-41.647,-51 +mobilevitv2_150_in22ft1k,24.040,75.960,55.933,44.067,10.59,256,0.888,bicubic,-71.100,-42.927,+24 +tf_efficientnet_b2.ns_jft_in1k,24.013,75.987,57.293,42.707,9.11,260,0.890,bicubic,-71.757,-41.697,-67 +convnext_nano.d1h_in1k,24.000,76.000,56.187,43.813,15.59,288,1.000,bicubic,-71.350,-42.673,-6 +cs3sedarknet_l,23.973,76.027,58.720,41.280,21.91,288,0.950,bicubic,-71.337,-40.400,-3 +resnetv2_50d_gn,23.920,76.080,56.320,43.680,25.57,288,0.950,bicubic,-71.510,-42.400,-22 +vit_small_patch32_384.augreg_in21k_ft_in1k,23.773,76.227,57.307,42.693,22.92,384,1.000,bicubic,-71.277,-41.683,+33 +lamhalobotnet50ts_256,23.600,76.400,55.267,44.733,22.57,256,0.950,bicubic,-71.560,-43.553,+13 +resnet152,23.560,76.440,53.653,46.347,60.19,224,0.950,bicubic,-72.320,-45.417,-88 +nasnetalarge,23.493,76.507,55.027,44.973,88.75,331,0.911,bicubic,-72.187,-43.903,-63 +crossvit_small_240,23.453,76.547,56.827,43.173,26.86,240,0.875,bicubic,-71.377,-42.193,+55 +levit_384,23.440,76.560,56.387,43.613,39.13,224,0.900,bicubic,-72.090,-42.743,-48 +pnasnet5large,23.333,76.667,53.640,46.360,86.06,331,0.911,bicubic,-72.377,-45.280,-71 +efficientnet_b3.ra2_in1k,23.213,76.787,55.960,44.040,12.23,320,1.000,bicubic,-72.497,-43.080,-73 +jx_nest_tiny,23.200,76.800,56.227,43.773,17.06,224,0.875,bicubic,-72.050,-42.643,-8 +efficientformer_l3,23.160,76.840,57.120,42.880,31.41,224,0.950,bicubic,-72.440,-42.040,-61 +resnet61q,22.987,77.013,55.760,44.240,36.85,288,1.000,bicubic,-72.783,-43.360,-80 +vit_srelpos_small_patch16_224.sw_in1k,22.920,77.080,55.720,44.280,21.97,224,0.900,bicubic,-72.120,-43.210,+23 +vit_base_patch32_clip_224.laion2b_ft_in1k,22.867,77.133,54.973,45.027,88.22,224,0.900,bicubic,-72.663,-44.077,-54 +resmlp_big_24_224,22.853,77.147,54.307,45.693,129.14,224,0.875,bicubic,-71.807,-44.173,+62 +halonet50ts,22.813,77.187,54.013,45.987,22.73,256,0.940,bicubic,-72.287,-44.767,+10 +twins_pcpvt_small,22.720,77.280,56.853,43.147,24.11,224,0.900,bicubic,-72.490,-42.027,-9 +vit_base_patch32_clip_224.openai_ft_in1k,22.573,77.427,55.293,44.707,88.22,224,0.900,bicubic,-72.547,-43.687,+7 +poolformer_m36,22.520,77.480,55.253,44.747,56.17,224,0.950,bicubic,-72.860,-43.677,-29 +vit_base_patch32_224.augreg_in21k_ft_in1k,22.400,77.600,53.933,46.067,88.22,224,0.900,bicubic,-72.600,-44.527,+23 +pit_s_distilled_224,22.360,77.640,57.120,42.880,24.04,224,0.900,bicubic,-72.880,-41.930,-17 +xcit_tiny_12_p8_224_dist,22.080,77.920,54.307,45.693,6.71,224,1.000,bicubic,-73.000,-44.603,+9 +tresnet_m,21.680,78.320,53.840,46.160,31.39,224,0.875,bilinear,-74.040,-45.190,-88 +maxvit_rmlp_pico_rw_256,21.253,78.747,51.880,48.120,7.52,256,0.950,bicubic,-73.377,-46.880,+56 +convmixer_1536_20,21.200,78.800,55.560,44.440,51.63,224,0.960,bicubic,-73.860,-43.470,+9 +swin_tiny_patch4_window7_224,21.173,78.827,55.973,44.027,28.29,224,0.900,bicubic,-73.967,-42.877,-6 +pit_s_224,21.080,78.920,53.573,46.427,23.46,224,0.900,bicubic,-73.510,-45.137,+58 +xcit_tiny_12_p8_224,21.027,78.973,52.453,47.547,6.71,224,1.000,bicubic,-73.653,-46.377,+45 +resnet51q,20.960,79.040,55.720,44.280,35.70,288,1.000,bilinear,-74.900,-43.400,-109 +regnetz_b16,20.960,79.040,53.853,46.147,9.72,288,0.940,bicubic,-74.110,-44.977,+3 +resnetrs101,20.893,79.107,52.813,47.187,63.62,288,0.940,bicubic,-74.537,-46.177,-51 +deit_small_distilled_patch16_224,20.707,79.293,55.133,44.867,22.44,224,0.900,bicubic,-74.003,-43.897,+39 +sebotnet33ts_256,20.707,79.293,48.800,51.200,13.70,256,0.940,bicubic,-73.883,-49.700,+53 +resnest50d_4s2x40d,20.387,79.613,52.800,47.200,30.42,224,0.875,bicubic,-74.573,-46.270,+13 +resnetaa50,20.093,79.907,51.920,48.080,25.56,288,1.000,bicubic,-75.117,-47.010,-28 +ssl_resnext50_32x4d,20.000,80.000,53.613,46.387,25.03,224,0.875,bilinear,-74.870,-45.267,+20 +haloregnetz_b,19.987,80.013,49.987,50.013,11.68,224,0.940,bicubic,-74.713,-48.673,+35 +resnetv2_101,19.920,80.080,49.280,50.720,44.54,224,0.950,bicubic,-75.720,-49.380,-94 +xcit_nano_12_p8_384_dist,19.787,80.213,50.587,49.413,3.05,384,1.000,bicubic,-73.713,-47.773,+165 +tresnet_xl,19.640,80.360,53.133,46.867,78.44,224,0.875,bilinear,-75.800,-45.707,-64 +resnet101,19.307,80.693,49.507,50.493,44.55,224,0.950,bicubic,-76.043,-49.353,-48 +gluon_senet154,19.307,80.693,47.533,52.467,115.09,224,0.875,bicubic,-75.613,-51.277,+11 +rexnet_200,19.227,80.773,52.720,47.280,16.37,224,0.875,bicubic,-75.713,-46.290,+5 +levit_256,19.200,80.800,50.067,49.933,18.89,224,0.900,bicubic,-75.810,-48.843,-2 +repvgg_b3,19.107,80.893,50.253,49.747,123.09,224,0.875,bilinear,-75.463,-48.527,+42 +lambda_resnet50ts,19.093,80.907,49.267,50.733,21.54,256,0.950,bicubic,-75.677,-49.303,+20 +mixer_b16_224_miil,19.053,80.947,51.227,48.773,59.88,224,0.875,bilinear,-76.247,-47.653,-50 +legacy_senet154,19.053,80.947,47.947,52.053,115.09,224,0.875,bilinear,-76.017,-51.103,-14 +mobilevitv2_200,18.920,81.080,50.533,49.467,18.45,256,0.888,bicubic,-75.910,-48.347,+14 +deit_small_patch16_224,18.907,81.093,51.413,48.587,22.05,224,0.900,bicubic,-75.493,-47.487,+64 +gluon_seresnext101_64x4d,18.907,81.093,49.187,50.813,88.23,224,0.875,bicubic,-76.023,-49.643,-1 +gcvit_xxtiny,18.720,81.280,53.347,46.653,12.00,224,0.875,bicubic,-75.680,-45.343,+60 +tf_efficientnet_b1.ns_jft_in1k,18.693,81.307,51.667,48.333,7.79,240,0.882,bicubic,-76.477,-47.293,-42 +edgenext_small,18.653,81.347,53.613,46.387,5.59,320,1.000,bicubic,-76.747,-45.487,-67 +poolformer_s36,18.400,81.600,51.867,48.133,30.86,224,0.900,bicubic,-76.690,-47.033,-28 +seresnext50_32x4d,18.360,81.640,50.973,49.027,27.56,224,0.875,bicubic,-76.680,-47.857,-18 +cs3darknet_l,18.307,81.693,51.880,48.120,21.16,288,0.950,bicubic,-76.813,-47.110,-33 +cait_xxs36_224,18.253,81.747,49.427,50.573,17.30,224,1.000,bicubic,-76.007,-49.293,+67 +ecaresnet50d,18.227,81.773,51.893,48.107,25.58,224,0.875,bicubic,-76.403,-46.997,+20 +sehalonet33ts,18.200,81.800,47.787,52.213,13.69,256,0.940,bicubic,-76.570,-50.683,+5 +tf_efficientnet_lite4.in1k,18.133,81.867,50.707,49.293,13.01,380,0.920,bilinear,-76.757,-48.143,-7 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,18.027,81.973,50.307,49.693,5.79,384,1.000,bicubic,-75.623,-48.293,+127 +mobilevitv2_175,17.773,82.227,49.760,50.240,14.25,256,0.888,bicubic,-77.127,-49.110,-10 +resnest50d_1s4x24d,17.693,82.307,49.800,50.200,25.68,224,0.875,bicubic,-77.057,-49.180,+3 +resnest50d,17.373,82.627,50.707,49.293,27.48,224,0.875,bilinear,-77.457,-48.003,-4 +gluon_seresnext101_32x4d,17.373,82.627,46.373,53.627,48.96,224,0.875,bicubic,-77.547,-52.387,-14 +efficientnet_el.ra_in1k,17.347,82.653,49.987,50.013,10.59,300,0.904,bicubic,-77.773,-48.993,-44 +convnext_pico.d1_in1k,17.333,82.667,50.213,49.787,9.05,288,0.950,bicubic,-77.407,-48.527,+1 +inception_v4,17.267,82.733,45.920,54.080,42.68,299,0.875,bicubic,-77.113,-52.660,+46 +tf_efficientnet_b3.ap_in1k,17.187,82.813,49.680,50.320,12.23,300,0.904,bicubic,-78.133,-49.220,-74 +xcit_tiny_24_p16_224_dist,17.173,82.827,47.453,52.547,12.12,224,1.000,bicubic,-77.357,-51.177,+24 +tf_efficientnet_b3.aa_in1k,17.000,83.000,49.267,50.733,12.23,300,0.904,bicubic,-78.010,-49.793,-31 +xception71,17.000,83.000,45.520,54.480,42.34,299,0.903,bicubic,-77.280,-53.120,+51 +cs3darknet_focus_l,16.960,83.040,50.453,49.547,21.15,288,0.950,bicubic,-78.210,-48.657,-62 +resmlp_36_distilled_224,16.880,83.120,51.467,48.533,44.69,224,0.875,bicubic,-78.010,-47.553,-20 +gluon_resnext101_64x4d,16.853,83.147,44.213,55.787,83.46,224,0.875,bicubic,-77.817,-54.437,-2 +tf_efficientnetv2_b3.in1k,16.667,83.333,48.680,51.320,14.36,300,0.904,bicubic,-78.493,-50.270,-60 +tresnet_l,16.600,83.400,49.920,50.080,55.99,224,0.875,bilinear,-78.680,-49.090,-78 +inception_resnet_v2,16.573,83.427,44.960,55.040,55.84,299,0.897,bicubic,-77.967,-53.890,+13 +gluon_resnet152_v1s,16.573,83.427,44.533,55.467,60.32,224,0.875,bicubic,-78.467,-54.347,-43 +gluon_resnet152_v1d,16.573,83.427,44.280,55.720,60.21,224,0.875,bicubic,-78.167,-54.420,-11 +convnext_pico_ols.d1_in1k,16.520,83.480,49.733,50.267,9.06,288,1.000,bicubic,-78.100,-49.127,+1 +gmlp_s16_224,16.520,83.480,45.120,54.880,19.42,224,0.875,bicubic,-77.640,-53.380,+58 +resmlp_24_distilled_224,16.467,83.533,50.387,49.613,30.02,224,0.875,bicubic,-77.993,-48.353,+22 +mobilevitv2_150,16.453,83.547,48.453,51.547,10.59,256,0.888,bicubic,-78.097,-50.297,+7 +gluon_xception65,16.440,83.560,46.027,53.973,39.92,299,0.903,bicubic,-77.820,-52.693,+41 +gernet_l,16.373,83.627,47.213,52.787,31.08,256,0.875,bilinear,-78.717,-51.697,-59 +gcresnet50t,16.360,83.640,48.240,51.760,25.90,256,0.900,bicubic,-78.490,-50.550,-29 +xcit_tiny_24_p16_224,16.307,83.693,45.960,54.040,12.12,224,1.000,bicubic,-77.773,-52.490,+59 +wide_resnet50_2,16.280,83.720,48.347,51.653,68.88,224,0.875,bicubic,-78.800,-50.623,-60 +gcresnext50ts,16.240,83.760,46.533,53.467,15.67,256,0.900,bicubic,-78.250,-52.137,+8 +ens_adv_inception_resnet_v2,16.240,83.760,43.640,56.360,55.84,299,0.897,bicubic,-77.920,-55.000,+49 +repvgg_b3g4,16.213,83.787,47.653,52.347,83.83,224,0.875,bilinear,-78.307,-51.317,+4 +ssl_resnet50,15.960,84.040,49.467,50.533,25.56,224,0.875,bilinear,-78.490,-49.453,+14 +edgenext_small_rw,15.933,84.067,49.653,50.347,7.83,320,1.000,bicubic,-78.727,-49.137,-19 +regnety_320,15.627,84.373,44.827,55.173,145.05,224,0.875,bicubic,-78.913,-53.963,-3 +vit_base_patch32_384.augreg_in1k,15.613,84.387,44.107,55.893,88.30,384,1.000,bicubic,-78.027,-54.293,+93 +ecaresnet101d_pruned,15.600,84.400,48.027,51.973,24.88,224,0.875,bicubic,-79.480,-50.953,-69 +convmixer_768_32,15.533,84.467,47.933,52.067,21.11,224,0.960,bicubic,-78.967,-50.917,-1 +ecaresnet26t,15.467,84.533,47.920,52.080,16.01,320,0.950,bicubic,-78.843,-50.560,+21 +coat_tiny,15.413,84.587,45.600,54.400,5.50,224,0.900,bicubic,-78.177,-52.830,+95 +skresnext50_32x4d,15.373,84.627,44.493,55.507,27.48,224,0.875,bicubic,-78.887,-53.967,+26 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,15.267,84.733,42.627,57.373,119.42,256,0.900,bicubic,-78.453,-55.913,+81 +ecaresnetlight,15.160,84.840,45.827,54.173,30.16,224,0.875,bicubic,-79.610,-52.973,-41 +cait_xxs24_224,15.160,84.840,44.960,55.040,11.96,224,1.000,bicubic,-78.440,-53.580,+91 +vit_base_patch16_224.augreg_in1k,14.987,85.013,41.987,58.013,86.57,224,0.900,bicubic,-78.643,-56.253,+86 +levit_192,14.893,85.107,44.920,55.080,10.95,224,0.900,bicubic,-79.277,-53.620,+31 +rexnet_150,14.720,85.280,46.907,53.093,9.73,224,0.875,bicubic,-79.760,-51.883,-5 +darknet53,14.693,85.307,47.120,52.880,41.61,288,1.000,bicubic,-79.937,-51.770,-31 +resnext50_32x4d,14.613,85.387,44.187,55.813,25.03,224,0.950,bicubic,-79.947,-54.423,-20 +darknetaa53,14.573,85.427,45.413,54.587,36.02,288,1.000,bilinear,-79.897,-53.357,-7 +coat_lite_mini,14.507,85.493,44.507,55.493,11.01,224,0.900,bicubic,-79.553,-54.053,+38 +efficientnet_el_pruned.in1k,14.480,85.520,46.120,53.880,10.59,300,0.904,bicubic,-79.920,-52.620,0 +seresnet33ts,14.440,85.560,46.107,53.893,19.78,256,0.900,bicubic,-80.420,-52.423,-58 +efficientnet_b2.ra_in1k,14.440,85.560,46.080,53.920,9.11,288,1.000,bicubic,-80.170,-52.630,-30 +poolformer_s24,14.267,85.733,47.240,52.760,21.39,224,0.900,bicubic,-80.293,-51.550,-28 +seresnet50,14.147,85.853,45.467,54.533,28.09,224,0.875,bicubic,-80.403,-53.243,-27 +legacy_seresnext101_32x4d,14.147,85.853,42.973,57.027,48.96,224,0.875,bilinear,-80.223,-55.677,-1 +fbnetv3_d.ra2_in1k,14.133,85.867,46.453,53.547,10.31,256,0.950,bilinear,-79.797,-52.287,+42 +eca_resnet33ts,14.093,85.907,47.347,52.653,19.68,256,0.900,bicubic,-80.097,-51.413,+14 +gernet_m,14.013,85.987,46.067,53.933,21.14,224,0.875,bilinear,-80.607,-52.703,-39 +pvt_v2_b1,14.000,86.000,47.720,52.280,14.01,224,0.900,bicubic,-79.820,-50.940,+52 +mobilevitv2_125,13.987,86.013,44.933,55.067,7.48,256,0.888,bicubic,-79.973,-53.657,+35 +gluon_resnext101_32x4d,13.867,86.133,41.653,58.347,44.18,224,0.875,bicubic,-80.663,-57.127,-28 +gcresnet33ts,13.760,86.240,45.053,54.947,19.88,256,0.900,bicubic,-80.710,-53.717,-20 +gluon_seresnext50_32x4d,13.600,86.400,43.760,56.240,27.56,224,0.875,bicubic,-80.740,-54.850,-7 +resmlp_36_224,13.507,86.493,46.693,53.307,44.69,224,0.875,bicubic,-80.683,-51.647,+8 +resnet50_gn,13.453,86.547,42.747,57.253,25.56,224,0.940,bicubic,-80.897,-55.963,-10 +repvgg_b2g4,13.440,86.560,43.787,56.213,61.76,224,0.875,bilinear,-80.420,-54.803,+41 +vit_small_patch16_224.augreg_in1k,13.400,86.600,41.400,58.600,22.05,224,0.900,bicubic,-80.490,-57.040,+38 +eca_botnext26ts_256,13.373,86.627,42.160,57.840,10.59,256,0.950,bicubic,-80.417,-56.340,+46 +ese_vovnet39b,13.320,86.680,43.813,56.187,24.57,224,0.875,bicubic,-80.770,-54.847,+15 +regnetx_320,13.307,86.693,40.720,59.280,107.81,224,0.875,bicubic,-81.153,-58.050,-26 +pit_xs_distilled_224,13.240,86.760,44.573,55.427,11.00,224,0.900,bicubic,-80.570,-53.837,+41 +efficientnet_b3_pruned.in1k,13.173,86.827,45.213,54.787,9.86,300,0.904,bicubic,-81.457,-53.607,-54 +gluon_resnet101_v1d,13.160,86.840,41.493,58.507,44.57,224,0.875,bicubic,-81.060,-57.057,-4 +mixnet_xl.ra_in1k,13.120,86.880,43.253,56.747,11.90,224,0.875,bicubic,-81.070,-55.407,-1 +cspresnext50,13.080,86.920,44.920,55.080,20.57,256,0.887,bilinear,-81.760,-53.850,-78 +efficientformer_l1,13.027,86.973,45.600,54.400,12.29,224,0.950,bicubic,-81.463,-53.230,-40 +eca_halonext26ts,12.960,87.040,42.773,57.227,10.76,256,0.940,bicubic,-81.080,-55.717,+11 +nf_regnet_b1,12.947,87.053,44.400,55.600,10.22,288,0.900,bicubic,-81.173,-54.340,+5 +mobilevit_s,12.880,87.120,40.773,59.227,5.58,256,0.900,bicubic,-80.300,-57.647,+88 +pit_xs_224,12.813,87.187,42.840,57.160,10.62,224,0.900,bicubic,-80.297,-55.550,+94 +gluon_inception_v3,12.640,87.360,40.493,59.507,23.83,299,0.875,bicubic,-80.820,-58.077,+63 +crossvit_9_dagger_240,12.573,87.427,41.773,58.227,8.78,240,0.875,bicubic,-80.327,-56.467,+104 +coat_lite_tiny,12.520,87.480,41.160,58.840,5.72,224,0.900,bicubic,-80.720,-57.100,+82 +convnext_femto_ols.d1_in1k,12.493,87.507,44.000,56.000,5.23,288,0.950,bicubic,-81.427,-54.520,+15 +resmlp_24_224,12.493,87.507,43.427,56.573,30.02,224,0.875,bicubic,-81.527,-54.903,+4 +regnety_120,12.427,87.573,42.200,57.800,51.82,224,0.875,bicubic,-82.053,-56.610,-47 +efficientnet_em.ra2_in1k,12.360,87.640,43.880,56.120,6.90,240,0.882,bicubic,-81.480,-54.930,+21 +hrnet_w64,12.027,87.973,40.787,59.213,128.06,224,0.875,bilinear,-81.983,-57.823,+3 +cspdarknet53,12.013,87.987,43.253,56.747,27.64,256,0.887,bilinear,-82.647,-55.547,-77 +xcit_tiny_12_p16_224_dist,11.947,88.053,40.133,59.867,6.72,224,1.000,bicubic,-81.443,-58.367,+61 +gluon_resnet101_v1s,11.880,88.120,40.973,59.027,44.67,224,0.875,bicubic,-82.840,-57.847,-84 +gmixer_24_224,11.853,88.147,37.773,62.227,24.72,224,0.875,bicubic,-80.977,-60.407,+99 +nf_resnet50,11.760,88.240,45.933,54.067,25.56,288,0.940,bicubic,-82.800,-52.947,-67 +fbnetv3_b.ra2_in1k,11.747,88.253,44.387,55.613,8.60,256,0.950,bilinear,-82.213,-54.243,-1 +resnet50d,11.693,88.307,42.453,57.547,25.58,224,0.875,bicubic,-82.567,-56.117,-32 +dpn92,11.627,88.373,40.267,59.733,37.67,224,0.875,bicubic,-82.603,-58.463,-29 +xception41,11.600,88.400,39.133,60.867,26.97,299,0.903,bicubic,-81.830,-59.297,+51 +botnet26t_256,11.587,88.413,40.147,59.853,12.49,256,0.950,bicubic,-81.933,-58.363,+41 +dla102x2,11.573,88.427,41.293,58.707,41.28,224,0.875,bilinear,-82.377,-57.197,-3 +vit_small_patch32_224.augreg_in21k_ft_in1k,11.480,88.520,39.573,60.427,22.88,224,0.900,bicubic,-80.560,-58.717,+138 +levit_128,11.427,88.573,40.267,59.733,9.21,224,0.900,bicubic,-81.913,-58.113,+53 +lambda_resnet26t,11.373,88.627,40.240,59.760,10.96,256,0.940,bicubic,-82.467,-58.400,+7 +efficientnet_b2_pruned.in1k,11.360,88.640,42.027,57.973,8.31,260,0.890,bicubic,-82.780,-56.643,-23 +tf_efficientnet_el.in1k,11.333,88.667,42.040,57.960,10.59,300,0.904,bicubic,-83.077,-56.670,-56 +xcit_nano_12_p16_384_dist,11.253,88.747,39.867,60.133,3.05,384,1.000,bicubic,-80.567,-58.153,+146 +convnext_femto.d1_in1k,11.240,88.760,42.827,57.173,5.22,288,0.950,bicubic,-82.680,-55.783,-5 +halonet26t,11.133,88.867,38.800,61.200,12.48,256,0.950,bicubic,-82.847,-59.700,-15 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,11.093,88.907,39.987,60.013,6.36,384,1.000,bicubic,-80.947,-58.243,+128 +gluon_resnet152_v1c,11.093,88.907,37.120,62.880,60.21,224,0.875,bicubic,-83.067,-61.480,-32 +hrnet_w48,11.080,88.920,40.320,59.680,77.47,224,0.875,bilinear,-82.840,-58.290,-11 +dpn107,11.080,88.920,38.693,61.307,86.92,224,0.875,bicubic,-83.230,-59.947,-50 +ecaresnet50d_pruned,11.027,88.973,41.947,58.053,19.94,224,0.875,bicubic,-83.193,-56.783,-44 +mobilevitv2_100,11.027,88.973,40.600,59.400,4.90,256,0.888,bicubic,-82.243,-57.680,+50 +tf_efficientnetv2_b2.in1k,11.027,88.973,39.760,60.240,10.10,260,0.890,bicubic,-83.393,-58.810,-67 +adv_inception_v3,11.013,88.987,36.720,63.280,23.83,299,0.875,bicubic,-81.867,-61.420,+72 +resnetv2_50,10.960,89.040,39.373,60.627,25.55,224,0.950,bicubic,-83.480,-59.367,-70 +xcit_tiny_12_p16_224,10.960,89.040,37.040,62.960,6.72,224,1.000,bicubic,-81.540,-61.020,+99 +tf_efficientnet_b0.ns_jft_in1k,10.933,89.067,40.067,59.933,5.29,224,0.875,bicubic,-82.697,-58.573,+10 +tf_inception_v3,10.840,89.160,36.880,63.120,23.83,299,0.875,bicubic,-82.480,-61.150,+39 +xcit_nano_12_p8_224_dist,10.787,89.213,38.080,61.920,3.05,224,1.000,bicubic,-81.303,-60.080,+114 +dpn131,10.787,89.213,37.200,62.800,79.25,224,0.875,bicubic,-83.223,-61.520,-30 +tf_efficientnet_b2.ap_in1k,10.533,89.467,40.107,59.893,9.11,260,0.890,bicubic,-83.957,-58.513,-84 +resnext50d_32x4d,10.413,89.587,39.733,60.267,25.05,224,0.875,bicubic,-83.767,-58.837,-49 +rexnet_130,10.400,89.600,41.547,58.453,7.56,224,0.875,bicubic,-83.500,-56.853,-22 +xcit_nano_12_p8_224,10.333,89.667,36.987,63.013,3.05,224,1.000,bicubic,-80.687,-60.813,+150 +hrnet_w44,10.320,89.680,39.507,60.493,67.06,224,0.875,bilinear,-83.230,-59.193,+9 +lambda_resnet26rpt_256,10.253,89.747,38.093,61.907,10.99,256,0.940,bicubic,-83.457,-60.417,-5 +resnext101_32x8d,10.187,89.813,37.827,62.173,88.79,224,0.875,bilinear,-83.643,-60.753,-18 +regnetx_160,10.147,89.853,38.000,62.000,54.28,224,0.875,bicubic,-83.973,-60.630,-47 +resnet50,10.133,89.867,37.867,62.133,25.56,224,0.950,bicubic,-84.187,-60.573,-74 +dpn98,10.133,89.867,36.587,63.413,61.57,224,0.875,bicubic,-83.997,-61.983,-49 +legacy_seresnext50_32x4d,10.107,89.893,39.200,60.800,27.56,224,0.875,bilinear,-83.623,-59.380,-13 +resnetrs50,10.093,89.907,37.507,62.493,35.69,224,0.910,bicubic,-84.217,-61.213,-74 +inception_v3,10.027,89.973,35.227,64.773,23.83,299,0.875,bicubic,-82.693,-62.743,+67 +efficientnet_b1.ft_in1k,10.013,89.987,37.547,62.453,7.79,256,1.000,bicubic,-83.237,-60.743,+29 +xception,9.987,90.013,38.027,61.973,22.86,299,0.897,bicubic,-83.473,-60.463,+9 +dpn68b,9.787,90.213,38.053,61.947,12.61,224,0.875,bicubic,-83.903,-60.457,-14 +gluon_resnet152_v1b,9.747,90.253,36.067,63.933,60.19,224,0.875,bicubic,-84.333,-62.453,-52 +tf_efficientnet_lite3.in1k,9.667,90.333,39.000,61.000,8.20,300,0.904,bilinear,-84.533,-59.640,-69 +tf_efficientnet_b2.aa_in1k,9.653,90.347,38.880,61.120,9.11,260,0.890,bicubic,-84.707,-59.730,-86 +tf_efficientnet_cc_b1_8e.in1k,9.573,90.427,36.773,63.227,39.72,240,0.882,bicubic,-84.327,-61.487,-38 +res2net101_26w_4s,9.520,90.480,35.027,64.973,45.21,224,0.875,bilinear,-84.230,-63.283,-25 +legacy_seresnet152,9.347,90.653,37.413,62.587,66.82,224,0.875,bilinear,-84.053,-60.937,+7 +cspresnet50,9.253,90.747,39.640,60.360,21.62,256,0.887,bilinear,-84.487,-59.000,-26 +resnet33ts,9.240,90.760,38.667,61.333,19.68,256,0.900,bicubic,-84.360,-59.773,-14 +hrnet_w40,9.227,90.773,36.893,63.107,57.56,224,0.875,bilinear,-84.263,-61.687,-3 +regnetx_120,9.187,90.813,37.200,62.800,46.11,224,0.875,bicubic,-85.053,-61.450,-81 +seresnext26d_32x4d,9.147,90.853,36.840,63.160,16.81,224,0.875,bicubic,-83.553,-61.310,+54 +resnest26d,9.080,90.920,37.853,62.147,17.07,224,0.875,bilinear,-84.250,-60.497,+5 +crossvit_tiny_240,9.080,90.920,34.600,65.400,7.01,240,0.875,bicubic,-81.170,-62.990,+144 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,9.067,90.933,34.573,65.427,5.72,224,0.900,bicubic,-82.693,-63.287,+102 +vit_base_patch16_224.sam,8.987,91.013,36.173,63.827,86.57,224,0.900,bicubic,-85.153,-62.357,-73 +gluon_resnext50_32x4d,8.947,91.053,36.333,63.667,25.03,224,0.875,bicubic,-84.863,-62.337,-40 +seresnext26t_32x4d,8.893,91.107,36.907,63.093,16.81,224,0.875,bicubic,-83.927,-61.463,+37 +rexnet_100,8.893,91.107,36.373,63.627,4.80,224,0.875,bicubic,-84.137,-61.927,+26 +bat_resnext26ts,8.867,91.133,36.427,63.573,10.73,256,0.900,bicubic,-84.463,-62.203,+1 +mixnet_l.ft_in1k,8.853,91.147,36.187,63.813,7.33,224,0.875,bicubic,-84.597,-62.033,-10 +convit_tiny,8.840,91.160,34.360,65.640,5.71,224,0.875,bicubic,-81.790,-63.380,+130 +mobilenetv3_large_100.miil_in21k_ft_in1k,8.840,91.160,32.973,67.027,5.48,224,0.875,bilinear,-83.420,-64.667,+69 +resnet32ts,8.760,91.240,37.200,62.800,17.96,256,0.900,bicubic,-84.700,-61.330,-14 +gcresnext26ts,8.680,91.320,35.733,64.267,10.48,256,0.900,bicubic,-84.090,-62.307,+34 +levit_128s,8.653,91.347,33.107,66.893,7.78,224,0.900,bicubic,-83.317,-64.953,+80 +dla169,8.640,91.360,36.040,63.960,53.39,224,0.875,bilinear,-84.700,-62.560,-10 +hrnet_w30,8.613,91.387,37.040,62.960,37.71,224,0.875,bilinear,-84.587,-61.370,+2 +mixer_b16_224,8.600,91.400,29.413,70.587,59.88,224,0.875,bicubic,-83.270,-68.507,+83 +convnext_atto_ols.a2_in1k,8.560,91.440,35.067,64.933,3.70,288,0.950,bicubic,-84.520,-63.403,+9 +legacy_seresnet101,8.533,91.467,36.013,63.987,49.33,224,0.875,bilinear,-84.747,-62.497,-5 +tf_efficientnet_b1.ap_in1k,8.453,91.547,35.253,64.747,7.79,240,0.882,bicubic,-85.237,-63.107,-44 +repvgg_b2,8.427,91.573,36.467,63.533,89.02,224,0.875,bilinear,-85.073,-62.263,-29 +resmlp_12_distilled_224,8.307,91.693,36.853,63.147,15.35,224,0.875,bicubic,-84.523,-61.287,+20 +crossvit_9_240,8.280,91.720,34.107,65.893,8.55,240,0.875,bicubic,-82.360,-63.633,+116 +resnetblur50,8.240,91.760,37.400,62.600,25.56,224,0.875,bicubic,-85.720,-61.160,-78 +dla102x,8.200,91.800,37.013,62.987,26.31,224,0.875,bilinear,-85.320,-61.287,-35 +eca_resnext26ts,8.080,91.920,35.960,64.040,10.30,256,0.900,bicubic,-84.530,-62.300,+36 +hrnet_w32,8.040,91.960,37.507,62.493,41.23,224,0.875,bilinear,-85.490,-60.943,-38 +cs3darknet_m,8.000,92.000,36.520,63.480,9.31,288,0.950,bicubic,-85.350,-62.080,-24 +res2net50_26w_8s,8.000,92.000,33.853,66.147,48.40,224,0.875,bilinear,-85.540,-64.407,-41 +vit_base_patch32_224.augreg_in1k,8.000,92.000,30.467,69.533,88.22,224,0.900,bicubic,-83.190,-66.913,+90 +gluon_resnet101_v1c,7.987,92.013,33.360,66.640,44.57,224,0.875,bicubic,-85.683,-65.060,-54 +gluon_resnet50_v1d,7.920,92.080,35.000,65.000,25.58,224,0.875,bicubic,-85.850,-63.390,-65 +mobilevitv2_075,7.800,92.200,33.640,66.360,2.87,256,0.888,bicubic,-83.960,-64.400,+73 +dla60_res2next,7.787,92.213,34.987,65.013,17.03,224,0.875,bilinear,-85.393,-63.443,-13 +mobilevit_xs,7.733,92.267,32.507,67.493,2.32,256,0.900,bicubic,-83.097,-65.413,+99 +densenetblur121d,7.720,92.280,34.733,65.267,8.00,224,0.875,bicubic,-84.190,-63.337,+61 +deit_tiny_distilled_patch16_224,7.707,92.293,33.560,66.440,5.91,224,0.900,bicubic,-82.993,-64.010,+99 +tf_efficientnetv2_b1.in1k,7.693,92.307,34.653,65.347,8.14,240,0.882,bicubic,-86.247,-63.967,-89 +convnext_atto.d2_in1k,7.600,92.400,35.040,64.960,3.70,288,0.950,bicubic,-85.190,-63.020,+7 +dla60_res2net,7.560,92.440,34.627,65.373,20.85,224,0.875,bilinear,-85.620,-63.783,-20 +efficientnet_b1_pruned.in1k,7.440,92.560,34.533,65.467,6.33,240,0.882,bicubic,-85.330,-63.727,+7 +wide_resnet101_2,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.360,-63.923,-70 +regnetx_064,7.333,92.667,34.373,65.627,26.21,224,0.875,bicubic,-86.557,-64.257,-87 +deit_tiny_patch16_224,7.307,92.693,30.707,69.293,5.72,224,0.900,bicubic,-82.363,-66.743,+112 +edgenext_x_small,7.280,92.720,30.947,69.053,2.34,288,1.000,bicubic,-84.440,-66.923,+64 +hardcorenas_e,7.240,92.760,33.293,66.707,8.07,224,0.875,bilinear,-85.330,-64.817,+18 +gluon_resnet101_v1b,7.227,92.773,32.773,67.227,44.55,224,0.875,bicubic,-86.523,-65.607,-79 +efficientnet_b0.ra_in1k,7.213,92.787,34.013,65.987,5.29,224,0.875,bicubic,-85.477,-64.277,+8 +gluon_resnet50_v1s,7.213,92.787,33.507,66.493,25.68,224,0.875,bicubic,-86.407,-64.953,-67 +tf_mixnet_l.in1k,7.147,92.853,31.613,68.387,7.33,224,0.875,bicubic,-86.163,-66.417,-39 +tf_efficientnet_b1.aa_in1k,7.133,92.867,33.040,66.960,7.79,240,0.882,bicubic,-86.367,-65.490,-57 +tf_efficientnet_cc_b0_8e.in1k,7.120,92.880,31.787,68.213,24.01,224,0.875,bicubic,-85.710,-66.093,-11 +convmixer_1024_20_ks9_p14,7.093,92.907,33.053,66.947,24.38,224,0.960,bicubic,-85.337,-65.217,+18 +seresnext26ts,7.040,92.960,34.933,65.067,10.39,256,0.900,bicubic,-85.650,-63.137,+1 +resmlp_12_224,7.013,92.987,33.947,66.053,15.35,224,0.875,bicubic,-85.197,-64.213,+28 +cs3darknet_focus_m,6.947,93.053,34.600,65.400,9.30,288,0.950,bicubic,-86.003,-63.790,-21 +hardcorenas_f,6.827,93.173,34.093,65.907,8.20,224,0.875,bilinear,-86.123,-64.067,-21 +ese_vovnet19b_dw,6.733,93.267,33.413,66.587,6.54,224,0.875,bicubic,-85.557,-64.677,+20 +selecsls60b,6.733,93.267,33.267,66.733,32.77,224,0.875,bicubic,-86.567,-65.123,-46 +efficientnet_es.ra_in1k,6.707,93.293,33.840,66.160,5.44,224,0.875,bicubic,-86.433,-64.580,-38 +res2net50_26w_6s,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.717,-66.627,-60 +legacy_seresnext26_32x4d,6.627,93.373,33.253,66.747,16.79,224,0.875,bicubic,-86.013,-64.877,-3 +tinynet_a.in1k,6.627,93.373,32.213,67.787,6.19,192,0.875,bicubic,-85.813,-65.867,+5 +mixnet_m.ft_in1k,6.627,93.373,32.053,67.947,5.01,224,0.875,bicubic,-85.803,-65.817,+9 +pit_ti_distilled_224,6.627,93.373,30.760,69.240,5.10,224,0.900,bicubic,-84.273,-66.940,+67 +poolformer_s12,6.560,93.440,34.467,65.533,11.92,224,0.900,bicubic,-86.060,-63.733,-6 +skresnet34,6.480,93.520,31.547,68.453,22.28,224,0.875,bicubic,-85.910,-66.603,+8 +repvgg_b1,6.467,93.533,33.827,66.173,57.42,224,0.875,bilinear,-86.863,-64.683,-60 +hardcorenas_d,6.440,93.560,32.213,67.787,7.50,224,0.875,bilinear,-85.960,-65.857,+4 +dla60x,6.427,93.573,34.080,65.920,17.35,224,0.875,bilinear,-86.693,-64.430,-47 +resnet34d,6.400,93.600,31.493,68.507,21.82,224,0.875,bicubic,-86.280,-66.817,-14 +regnetx_080,6.307,93.693,32.320,67.680,39.57,224,0.875,bicubic,-87.563,-66.200,-114 +swsl_resnet18,6.240,93.760,31.600,68.400,11.69,224,0.875,bilinear,-84.450,-66.100,+65 +legacy_seresnet50,6.187,93.813,32.653,67.347,28.09,224,0.875,bilinear,-86.773,-65.537,-40 +resnet26t,6.120,93.880,32.280,67.720,16.01,256,0.940,bicubic,-86.630,-65.930,-26 +pit_ti_224,6.120,93.880,30.227,69.773,4.85,224,0.900,bicubic,-83.820,-67.223,+76 +tv_resnet152,6.040,93.960,32.053,67.947,60.19,224,0.875,bilinear,-87.260,-66.227,-65 +regnetx_040,5.973,94.027,31.547,68.453,22.12,224,0.875,bicubic,-87.587,-66.993,-92 +tf_efficientnet_cc_b0_4e.in1k,5.973,94.027,29.600,70.400,13.31,224,0.875,bicubic,-86.617,-68.480,-16 +tf_efficientnetv2_b0.in1k,5.893,94.107,30.773,69.227,7.14,224,0.875,bicubic,-87.217,-67.537,-55 +dla102,5.880,94.120,32.707,67.293,33.27,224,0.875,bilinear,-87.180,-65.833,-53 +mixer_l16_224,5.867,94.133,18.533,81.467,208.20,224,0.875,bicubic,-81.283,-74.987,+98 +regnety_016,5.680,94.320,30.413,69.587,11.20,224,0.875,bicubic,-87.350,-67.947,-53 +selecsls60,5.653,94.347,32.507,67.493,30.67,224,0.875,bicubic,-87.377,-65.683,-53 +hardcorenas_c,5.640,94.360,30.400,69.600,5.52,224,0.875,bilinear,-86.380,-67.440,+6 +res2next50,5.627,94.373,30.867,69.133,24.67,224,0.875,bilinear,-87.213,-67.313,-46 +hrnet_w18,5.493,94.507,30.960,69.040,21.30,224,0.875,bilinear,-86.827,-67.280,-10 +resnest14d,5.480,94.520,28.547,71.453,10.61,224,0.875,bilinear,-86.240,-69.063,+20 +tf_efficientnet_lite2.in1k,5.360,94.640,30.907,69.093,6.09,260,0.890,bicubic,-87.290,-67.323,-31 +tf_efficientnet_em.in1k,5.347,94.653,31.107,68.893,6.90,240,0.882,bicubic,-87.583,-67.083,-53 +gernet_s,5.307,94.693,30.133,69.867,8.17,224,0.875,bilinear,-86.833,-68.057,-5 +tf_efficientnet_b0.ap_in1k,5.307,94.693,28.813,71.187,5.29,224,0.875,bicubic,-86.893,-69.207,-8 +repvgg_b1g4,5.293,94.707,30.813,69.187,39.97,224,0.875,bilinear,-87.687,-67.617,-61 +densenet121,5.293,94.707,29.907,70.093,7.98,224,0.875,bicubic,-86.277,-68.123,+17 +xcit_nano_12_p16_224_dist,5.240,94.760,26.560,73.440,3.05,224,1.000,bicubic,-84.440,-70.530,+60 +res2net50_26w_4s,5.160,94.840,29.360,70.640,25.70,224,0.875,bilinear,-87.340,-68.880,-27 +tf_mixnet_m.in1k,5.080,94.920,28.147,71.853,5.01,224,0.875,bicubic,-87.250,-69.743,-21 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,5.080,94.920,27.080,72.920,6.34,224,0.900,bicubic,-84.090,-70.150,+62 +tf_efficientnet_b0.aa_in1k,5.067,94.933,28.800,71.200,5.29,224,0.875,bicubic,-87.183,-69.200,-19 +mobilenetv3_large_100.ra_in1k,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.253,-69.523,+16 +res2net50_14w_8s,5.040,94.960,28.773,71.227,25.06,224,0.875,bilinear,-87.700,-69.497,-49 +hardcorenas_b,4.947,95.053,28.120,71.880,5.18,224,0.875,bilinear,-86.823,-69.660,+2 +mobilenetv3_rw.rmsp_in1k,4.907,95.093,29.853,70.147,5.48,224,0.875,bicubic,-86.303,-67.807,+14 +mixnet_s.ft_in1k,4.907,95.093,28.573,71.427,4.13,224,0.875,bicubic,-86.923,-69.287,-1 +gluon_resnet50_v1c,4.893,95.107,28.147,71.853,25.58,224,0.875,bicubic,-88.137,-70.243,-76 +hardcorenas_a,4.867,95.133,28.093,71.907,5.26,224,0.875,bilinear,-86.483,-69.767,+8 +regnetx_032,4.853,95.147,30.280,69.720,15.30,224,0.875,bicubic,-88.267,-68.110,-83 +xcit_nano_12_p16_224,4.853,95.147,25.467,74.533,3.05,224,1.000,bicubic,-83.757,-71.323,+63 +tv_resnext50_32x4d,4.840,95.160,30.307,69.693,25.03,224,0.875,bilinear,-87.900,-67.873,-58 +tv_resnet101,4.707,95.293,29.333,70.667,44.55,224,0.875,bilinear,-88.103,-68.917,-65 +densenet161,4.693,95.307,29.547,70.453,28.68,224,0.875,bicubic,-87.807,-68.743,-44 +resnext26ts,4.693,95.307,29.027,70.973,10.30,256,0.900,bicubic,-87.177,-68.223,-13 +selecsls42b,4.667,95.333,28.587,71.413,32.46,224,0.875,bicubic,-87.613,-69.563,-34 +tf_efficientnet_lite1.in1k,4.613,95.387,28.387,71.613,5.42,240,0.882,bicubic,-88.007,-69.693,-53 +mobilenetv2_120d.ra_in1k,4.533,95.467,29.280,70.720,5.83,224,0.875,bicubic,-87.867,-68.770,-41 +pvt_v2_b0,4.347,95.653,25.907,74.093,3.67,224,0.900,bicubic,-84.443,-70.823,+52 +vit_base_patch32_224.sam,4.333,95.667,24.387,75.613,88.22,224,0.900,bicubic,-85.417,-72.613,+36 +edgenext_xx_small,4.240,95.760,24.093,75.907,1.33,288,1.000,bicubic,-84.640,-72.887,+49 +tinynet_b.in1k,4.187,95.813,26.720,73.280,3.73,188,0.875,bicubic,-86.733,-70.950,+10 +efficientnet_es_pruned.in1k,4.187,95.813,26.520,73.480,5.44,224,0.875,bicubic,-86.993,-71.230,+2 +fbnetc_100.rmsp_in1k,4.133,95.867,25.933,74.067,5.57,224,0.875,bilinear,-86.567,-71.277,+16 +densenet201,4.120,95.880,27.547,72.453,20.01,224,0.875,bicubic,-88.630,-70.683,-73 +gluon_resnet50_v1b,4.120,95.880,26.933,73.067,25.56,224,0.875,bicubic,-88.420,-71.237,-57 +resnet26d,4.040,95.960,28.520,71.480,16.01,224,0.875,bicubic,-88.030,-69.440,-36 +semnasnet_100.rmsp_in1k,3.960,96.040,26.947,73.053,3.89,224,0.875,bicubic,-87.320,-70.613,-9 +repvgg_a2,3.947,96.053,27.267,72.733,28.21,224,0.875,bilinear,-87.993,-70.883,-31 +mobilevitv2_050,3.933,96.067,23.880,76.120,1.37,256,0.888,bicubic,-84.297,-73.110,+47 +tf_mixnet_s.in1k,3.880,96.120,25.253,74.747,4.13,224,0.875,bicubic,-87.630,-72.367,-17 +semnasnet_075.rmsp_in1k,3.867,96.133,27.027,72.973,2.91,224,0.875,bicubic,-86.203,-70.403,+19 +dpn68,3.867,96.133,26.080,73.920,12.61,224,0.875,bicubic,-88.143,-71.970,-37 +tf_efficientnet_es.in1k,3.827,96.173,26.107,73.893,5.44,224,0.875,bicubic,-88.153,-71.753,-39 +mobilevit_xxs,3.827,96.173,21.747,78.253,1.27,256,0.900,bicubic,-83.343,-74.353,+48 +regnety_008,3.813,96.187,27.133,72.867,6.26,224,0.875,bicubic,-87.937,-71.047,-26 +dla60,3.773,96.227,27.933,72.067,22.04,224,0.875,bilinear,-88.457,-70.177,-52 +ssl_resnet18,3.747,96.253,25.427,74.573,11.69,224,0.875,bilinear,-86.473,-72.123,+11 +mobilenetv2_140.ra_in1k,3.720,96.280,26.747,73.253,6.11,224,0.875,bicubic,-88.110,-70.943,-35 +densenet169,3.707,96.293,25.613,74.387,14.15,224,0.875,bicubic,-88.223,-72.487,-41 +regnetx_016,3.627,96.373,26.293,73.707,9.19,224,0.875,bicubic,-88.543,-71.917,-53 +res2net50_48w_2s,3.587,96.413,26.613,73.387,25.29,224,0.875,bilinear,-88.963,-71.467,-74 +tf_mobilenetv3_large_100.in1k,3.547,96.453,25.053,74.947,5.48,224,0.875,bilinear,-87.693,-72.607,-24 +spnasnet_100.rmsp_in1k,3.547,96.453,24.293,75.707,4.42,224,0.875,bilinear,-86.803,-72.897,+4 +regnety_006,3.467,96.533,24.893,75.107,6.06,224,0.875,bicubic,-87.903,-72.817,-29 +legacy_seresnet34,3.333,96.667,23.800,76.200,21.96,224,0.875,bilinear,-87.557,-73.910,-10 +efficientnet_lite0.ra_in1k,3.253,96.747,25.867,74.133,4.65,224,0.875,bicubic,-87.887,-71.763,-22 +ghostnet_100,3.227,96.773,24.853,75.147,5.18,224,0.875,bilinear,-86.793,-72.517,+4 +dla34,3.227,96.773,23.573,76.427,15.74,224,0.875,bilinear,-87.533,-74.087,-10 +regnety_004,3.200,96.800,22.653,77.347,4.34,224,0.875,bicubic,-87.300,-74.887,-5 +mobilenetv2_110d.ra_in1k,3.173,96.827,24.587,75.413,4.52,224,0.875,bicubic,-87.777,-72.963,-19 +mnasnet_100.rmsp_in1k,3.120,96.880,24.227,75.773,4.38,224,0.875,bicubic,-87.390,-73.243,-8 +tinynet_c.in1k,3.093,96.907,21.533,78.467,2.46,184,0.875,bicubic,-84.677,-74.837,+25 +tf_efficientnet_lite0.in1k,3.080,96.920,22.907,77.093,4.65,224,0.875,bicubic,-87.960,-74.683,-25 +skresnet18,3.013,96.987,22.800,77.200,11.96,224,0.875,bicubic,-86.647,-74.430,+5 +vgg19_bn,2.947,97.053,23.480,76.520,143.68,224,0.875,bilinear,-87.133,-74.100,-5 +resnet34,2.920,97.080,23.680,76.320,21.80,224,0.875,bilinear,-88.210,-73.940,-31 +tf_mobilenetv3_large_075.in1k,2.867,97.133,21.573,78.427,3.99,224,0.875,bilinear,-86.813,-75.637,-1 +tinynet_d.in1k,2.867,97.133,17.813,82.187,2.34,152,0.875,bicubic,-81.883,-77.367,+33 +resnet14t,2.787,97.213,19.267,80.733,10.08,224,0.950,bilinear,-86.263,-77.353,+5 +hrnet_w18_small_v2,2.720,97.280,23.693,76.307,15.60,224,0.875,bilinear,-88.470,-74.207,-39 +gluon_resnet34_v1b,2.667,97.333,21.680,78.320,21.80,224,0.875,bicubic,-88.293,-75.950,-31 +vgg16_bn,2.653,97.347,23.773,76.227,138.37,224,0.875,bilinear,-87.437,-73.597,-14 +regnetx_008,2.653,97.347,22.453,77.547,7.26,224,0.875,bicubic,-88.397,-75.257,-35 +vgg16,2.640,97.360,20.427,79.573,138.36,224,0.875,bilinear,-85.910,-76.363,+10 +lcnet_100.ra2_in1k,2.613,97.387,20.893,79.107,2.95,224,0.875,bicubic,-86.177,-75.967,+6 +resnet18d,2.600,97.400,21.613,78.387,11.71,224,0.875,bicubic,-86.680,-75.537,-5 +tv_densenet121,2.560,97.440,22.667,77.333,7.98,224,0.875,bicubic,-88.330,-74.913,-33 +repvgg_b0,2.547,97.453,24.013,75.987,15.82,224,0.875,bilinear,-88.883,-73.977,-54 +regnetx_006,2.507,97.493,20.653,79.347,6.20,224,0.875,bicubic,-87.843,-76.777,-24 +legacy_seresnet18,2.493,97.507,20.080,79.920,11.78,224,0.875,bicubic,-86.387,-76.610,-2 +resnet26,2.480,97.520,22.987,77.013,16.00,224,0.875,bicubic,-88.630,-74.753,-45 +lcnet_075.ra2_in1k,2.320,97.680,17.120,82.880,2.36,224,0.875,bicubic,-83.670,-78.560,+16 +mobilenetv3_small_075.lamb_in1k,2.307,97.693,15.920,84.080,2.04,224,0.875,bicubic,-80.733,-78.180,+24 +mobilenetv2_100.ra_in1k,2.147,97.853,19.907,80.093,3.50,224,0.875,bicubic,-87.453,-77.233,-14 +regnety_002,2.147,97.853,18.880,81.120,3.16,224,0.875,bicubic,-85.233,-77.710,+5 +vgg19,2.107,97.893,20.733,79.267,143.67,224,0.875,bilinear,-86.933,-76.137,-11 +vgg13_bn,2.093,97.907,20.307,79.693,133.05,224,0.875,bilinear,-86.667,-76.663,-5 +tf_mobilenetv3_small_100.in1k,2.013,97.987,15.867,84.133,2.54,224,0.875,bilinear,-83.177,-79.903,+12 +mobilenetv3_small_100.lamb_in1k,2.000,98.000,17.080,82.920,2.54,224,0.875,bicubic,-83.220,-78.540,+10 +tf_mobilenetv3_small_075.in1k,2.000,98.000,14.813,85.187,2.04,224,0.875,bilinear,-81.520,-79.977,+16 +regnetx_004,1.960,98.040,19.173,80.827,5.16,224,0.875,bicubic,-86.940,-77.947,-14 +tv_resnet34,1.867,98.133,20.000,80.000,21.80,224,0.875,bilinear,-88.073,-77.340,-28 vgg13,1.867,98.133,17.960,82.040,133.05,224,0.875,bilinear,-85.183,-78.360,0 -tinynet_e,1.853,98.147,14.013,85.987,2.04,106,0.875,bicubic,-77.047,-78.547,+16 -mobilenetv3_small_050,1.840,98.160,12.507,87.493,1.59,224,0.875,bicubic,-75.150,-78.793,+16 -lcnet_050,1.813,98.187,13.893,86.107,1.88,224,0.875,bicubic,-79.967,-79.827,+12 -dla46x_c,1.760,98.240,16.493,83.507,1.07,224,0.875,bilinear,-82.490,-78.767,+6 -mnasnet_small,1.760,98.240,15.093,84.907,2.03,224,0.875,bicubic,-82.680,-80.087,+4 -resnet10t,1.733,98.267,15.813,84.187,5.44,224,0.950,bilinear,-84.477,-79.847,-3 -vgg11_bn,1.720,98.280,18.080,81.920,132.87,224,0.875,bilinear,-85.780,-78.740,-12 -dla60x_c,1.627,98.373,18.053,81.947,1.32,224,0.875,bilinear,-84.643,-78.117,-6 -tf_mobilenetv3_large_minimal_100,1.613,98.387,17.120,82.880,3.92,224,0.875,bilinear,-87.357,-79.730,-25 -mobilenetv2_050,1.613,98.387,14.187,85.813,1.97,224,0.875,bicubic,-82.277,-80.533,+1 +tinynet_e.in1k,1.853,98.147,14.027,85.973,2.04,106,0.875,bicubic,-77.047,-78.533,+16 +mobilenetv3_small_050.lamb_in1k,1.840,98.160,12.507,87.493,1.59,224,0.875,bicubic,-75.150,-78.793,+16 +lcnet_050.ra2_in1k,1.813,98.187,13.880,86.120,1.88,224,0.875,bicubic,-79.967,-79.830,+12 +dla46x_c,1.760,98.240,16.480,83.520,1.07,224,0.875,bilinear,-82.490,-78.790,+6 +mnasnet_small.lamb_in1k,1.760,98.240,15.093,84.907,2.03,224,0.875,bicubic,-82.680,-80.087,+4 +resnet10t,1.733,98.267,15.813,84.187,5.44,224,0.950,bilinear,-84.467,-79.837,-3 +vgg11_bn,1.720,98.280,18.093,81.907,132.87,224,0.875,bilinear,-85.780,-78.727,-12 +tf_mobilenetv3_large_minimal_100.in1k,1.627,98.373,17.120,82.880,3.92,224,0.875,bilinear,-87.343,-79.740,-25 +dla60x_c,1.613,98.387,18.040,81.960,1.32,224,0.875,bilinear,-84.677,-78.120,-7 +mobilenetv2_050.lamb_in1k,1.613,98.387,14.200,85.800,1.97,224,0.875,bicubic,-82.277,-80.520,+1 vgg11,1.560,98.440,16.227,83.773,132.86,224,0.875,bilinear,-84.990,-80.053,-10 -gluon_resnet18_v1b,1.547,98.453,16.613,83.387,11.69,224,0.875,bicubic,-86.853,-80.067,-21 -hrnet_w18_small,1.533,98.467,18.133,81.867,13.19,224,0.875,bilinear,-87.497,-78.977,-30 -dla46_c,1.520,98.480,15.253,84.747,1.30,224,0.875,bilinear,-82.120,-79.667,-2 -regnetx_002,1.373,98.627,15.027,84.973,2.68,224,0.875,bicubic,-84.827,-80.953,-11 -resnet18,1.160,98.840,16.227,83.773,11.69,224,0.875,bilinear,-86.230,-80.063,-20 -tf_mobilenetv3_small_minimal_100,1.013,98.987,11.453,88.547,2.04,224,0.875,bilinear,-80.387,-82.227,-1 -tv_resnet50,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.900,-83.587,-99 +gluon_resnet18_v1b,1.547,98.453,16.613,83.387,11.69,224,0.875,bicubic,-86.853,-80.067,-20 +hrnet_w18_small,1.533,98.467,18.120,81.880,13.19,224,0.875,bilinear,-87.517,-78.990,-33 +dla46_c,1.520,98.480,15.267,84.733,1.30,224,0.875,bilinear,-82.130,-79.653,-2 +regnetx_002,1.373,98.627,15.027,84.973,2.68,224,0.875,bicubic,-84.817,-80.953,-11 +resnet18,1.160,98.840,16.213,83.787,11.69,224,0.875,bilinear,-86.230,-80.077,-20 +tf_mobilenetv3_small_minimal_100.in1k,1.013,98.987,11.493,88.507,2.04,224,0.875,bilinear,-80.367,-82.177,-1 +tv_resnet50,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.880,-83.587,-101 diff --git a/results/results-imagenet-r-clean.csv b/results/results-imagenet-r-clean.csv index 0e42fcda..d26ae416 100644 --- a/results/results-imagenet-r-clean.csv +++ b/results/results-imagenet-r-clean.csv @@ -1,109 +1,166 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation -beit_large_patch16_384,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic -tf_efficientnet_l2_ns,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic -beit_large_patch16_512,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic -tf_efficientnet_l2_ns_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic +eva_giant_patch14_336.m30m_ft_in22k_in1k,98.000,2.000,99.900,0.100,"1,013.01",336,1.000,bicubic +eva_giant_patch14_560.m30m_ft_in22k_in1k,97.990,2.010,99.860,0.140,"1,014.45",560,1.000,bicubic +eva_large_patch14_336.in22k_ft_in22k_in1k,97.860,2.140,99.880,0.120,304.53,336,1.000,bicubic +eva_giant_patch14_336.clip_ft_in1k,97.860,2.140,99.790,0.210,"1,013.01",336,1.000,bicubic +eva_large_patch14_336.in22k_ft_in1k,97.810,2.190,99.840,0.160,304.53,336,1.000,bicubic +beit_large_patch16_384.in22k_ft_in22k_in1k,97.810,2.190,99.790,0.210,305.00,384,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k,97.780,2.220,99.890,0.110,480.31,800,0.960,bicubic +beit_large_patch16_512.in22k_ft_in22k_in1k,97.780,2.220,99.820,0.180,305.67,512,1.000,bicubic +maxvit_base_tf_512.in21k_ft_in1k,97.760,2.240,99.860,0.140,119.88,512,1.000,bicubic +maxvit_xlarge_tf_512.in21k_ft_in1k,97.760,2.240,99.820,0.180,475.77,512,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic +beitv2_large_patch16_224.in1k_ft_in22k_in1k,97.750,2.250,99.790,0.210,304.43,224,0.950,bicubic +maxvit_xlarge_tf_384.in21k_ft_in1k,97.740,2.260,99.850,0.150,475.32,384,1.000,bicubic +eva_giant_patch14_224.clip_ft_in1k,97.680,2.320,99.750,0.250,"1,012.56",224,1.000,bicubic +maxvit_large_tf_384.in21k_ft_in1k,97.670,2.330,99.820,0.180,212.03,384,1.000,bicubic +maxvit_large_tf_512.in21k_ft_in1k,97.670,2.330,99.730,0.270,212.33,512,1.000,bicubic +eva_large_patch14_196.in22k_ft_in22k_in1k,97.610,2.390,99.810,0.190,304.14,196,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in12k_in1k,97.610,2.390,99.730,0.270,304.20,224,1.000,bicubic +vit_large_patch14_clip_336.openai_ft_in12k_in1k,97.610,2.390,99.730,0.270,304.53,336,1.000,bicubic +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,97.600,2.400,99.780,0.220,632.46,336,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k_384,97.590,2.410,99.770,0.230,350.20,384,1.000,bicubic +maxvit_base_tf_384.in21k_ft_in1k,97.560,2.440,99.760,0.240,119.65,384,1.000,bicubic deit3_large_patch16_384_in21ft1k,97.560,2.440,99.710,0.290,304.76,384,1.000,bicubic -convnext_xlarge_384_in22ft1k,97.550,2.450,99.800,0.200,350.20,384,1.000,bicubic -beit_large_patch16_224,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic -convnext_large_384_in22ft1k,97.440,2.560,99.780,0.220,197.77,384,1.000,bicubic -vit_large_patch16_384,97.420,2.580,99.780,0.220,304.72,384,1.000,bicubic -beit_base_patch16_384,97.330,2.670,99.720,0.280,86.74,384,1.000,bicubic +eva_large_patch14_196.in22k_ft_in1k,97.520,2.480,99.790,0.210,304.14,196,1.000,bicubic +beit_large_patch16_224.in22k_ft_in22k_in1k,97.480,2.520,99.690,0.310,304.43,224,0.900,bicubic +vit_large_patch14_clip_224.openai_ft_in1k,97.460,2.540,99.680,0.320,304.20,224,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k,97.450,2.550,99.820,0.180,350.20,288,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,97.450,2.550,99.780,0.220,304.53,336,1.000,bicubic +vit_large_patch16_384.augreg_in21k_ft_in1k,97.420,2.580,99.780,0.220,304.72,384,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,97.390,2.610,99.740,0.260,304.20,224,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,97.360,2.640,99.800,0.200,632.05,224,1.000,bicubic +beit_base_patch16_384.in22k_ft_in22k_in1k,97.330,2.670,99.720,0.280,86.74,384,1.000,bicubic +tf_efficientnetv2_xl.in21k_ft_in1k,97.330,2.670,99.600,0.400,208.12,512,1.000,bicubic +tf_efficientnetv2_l.in21k_ft_in1k,97.320,2.680,99.640,0.360,118.52,480,1.000,bicubic +convnext_large.fb_in22k_ft_in1k_384,97.310,2.690,99.760,0.240,197.77,384,1.000,bicubic deit3_large_patch16_224_in21ft1k,97.310,2.690,99.680,0.320,304.37,224,1.000,bicubic -convnext_base_384_in22ft1k,97.290,2.710,99.780,0.220,88.59,384,1.000,bicubic +swinv2_large_window12to24_192to384_22kft1k,97.290,2.710,99.780,0.220,196.74,384,1.000,bicubic volo_d5_512,97.290,2.710,99.760,0.240,296.09,512,1.150,bicubic -swinv2_large_window12to24_192to384_22kft1k,97.280,2.720,99.780,0.220,196.74,384,1.000,bicubic swinv2_base_window12to24_192to384_22kft1k,97.260,2.740,99.790,0.210,87.92,384,1.000,bicubic -convnext_large_in22ft1k,97.260,2.740,99.650,0.350,197.77,224,0.875,bicubic deit3_huge_patch14_224_in21ft1k,97.250,2.750,99.720,0.280,632.13,224,1.000,bicubic +convnext_base.fb_in22k_ft_in1k_384,97.250,2.750,99.710,0.290,88.59,384,1.000,bicubic volo_d5_448,97.240,2.760,99.740,0.260,295.91,448,1.150,bicubic -convnext_xlarge_in22ft1k,97.240,2.760,99.730,0.270,350.20,224,0.875,bicubic swinv2_large_window12to16_192to256_22kft1k,97.240,2.760,99.710,0.290,196.74,256,0.900,bicubic deit3_base_patch16_384_in21ft1k,97.240,2.760,99.670,0.330,86.88,384,1.000,bicubic -tf_efficientnet_b7_ns,97.190,2.810,99.700,0.300,66.35,600,0.949,bicubic -swin_large_patch4_window12_384,97.180,2.820,99.680,0.320,196.74,384,1.000,bicubic -tf_efficientnetv2_xl_in21ft1k,97.150,2.850,99.620,0.380,208.12,512,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in1k,97.230,2.770,99.720,0.280,304.53,336,1.000,bicubic +convnext_base.fb_in22k_ft_in1k,97.220,2.780,99.760,0.240,88.59,288,1.000,bicubic +convnext_large.fb_in22k_ft_in1k,97.220,2.780,99.730,0.270,197.77,288,1.000,bicubic +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,97.220,2.780,99.700,0.300,86.86,384,1.000,bicubic +tf_efficientnet_b7.ns_jft_in1k,97.200,2.800,99.700,0.300,66.35,600,0.949,bicubic +maxvit_base_tf_512.in1k,97.180,2.820,99.640,0.360,119.88,512,1.000,bicubic +maxvit_small_tf_512.in1k,97.180,2.820,99.620,0.380,69.13,512,1.000,bicubic +swin_large_patch4_window12_384,97.170,2.830,99.680,0.320,196.74,384,1.000,bicubic +vit_base_patch16_clip_384.openai_ft_in12k_in1k,97.140,2.860,99.640,0.360,86.86,384,0.950,bicubic swin_base_patch4_window12_384,97.120,2.880,99.780,0.220,87.90,384,1.000,bicubic -tf_efficientnetv2_l_in21ft1k,97.110,2.890,99.710,0.290,118.52,480,1.000,bicubic -convnext_small_384_in22ft1k,97.090,2.910,99.690,0.310,50.22,384,1.000,bicubic -vit_base_patch8_224,97.080,2.920,99.620,0.380,86.58,224,0.900,bicubic +maxvit_base_tf_384.in1k,97.120,2.880,99.570,0.430,119.65,384,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in1k,97.100,2.900,99.700,0.300,632.05,224,1.000,bicubic +convnext_small.fb_in22k_ft_in1k_384,97.100,2.900,99.640,0.360,50.22,384,1.000,bicubic +vit_base_patch8_224.augreg_in21k_ft_in1k,97.080,2.920,99.620,0.380,86.58,224,0.900,bicubic volo_d4_448,97.070,2.930,99.750,0.250,193.41,448,1.150,bicubic swinv2_base_window12to16_192to256_22kft1k,97.060,2.940,99.660,0.340,87.92,256,0.900,bicubic -tf_efficientnet_b6_ns,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic -vit_base_patch16_384,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic +maxvit_large_tf_512.in1k,97.050,2.950,99.590,0.410,212.33,512,1.000,bicubic +tf_efficientnet_b6.ns_jft_in1k,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic +vit_base_patch16_384.augreg_in21k_ft_in1k,97.020,2.980,99.710,0.290,86.86,384,1.000,bicubic volo_d3_448,97.020,2.980,99.680,0.320,86.63,448,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in1k,97.020,2.980,99.670,0.330,304.20,224,1.000,bicubic +tf_efficientnetv2_m.in21k_ft_in1k,97.000,3.000,99.630,0.370,54.14,480,1.000,bicubic ig_resnext101_32x48d,96.970,3.030,99.670,0.330,828.41,224,0.875,bilinear -tf_efficientnetv2_m_in21ft1k,96.970,3.030,99.610,0.390,54.14,480,1.000,bicubic -vit_large_r50_s32_384,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic +maxvit_tiny_tf_512.in1k,96.970,3.030,99.670,0.330,31.05,512,1.000,bicubic +vit_large_r50_s32_384.augreg_in21k_ft_in1k,96.950,3.050,99.710,0.290,329.09,384,1.000,bicubic swin_large_patch4_window7_224,96.950,3.050,99.660,0.340,196.53,224,0.900,bicubic -xcit_large_24_p16_384_dist,96.940,3.060,99.510,0.490,189.10,384,1.000,bicubic +vit_base_patch8_224.augreg2_in21k_ft_in1k,96.940,3.060,99.640,0.360,86.58,224,0.900,bicubic +maxvit_large_tf_384.in1k,96.940,3.060,99.580,0.420,212.03,384,1.000,bicubic +xcit_large_24_p16_384_dist,96.940,3.060,99.520,0.480,189.10,384,1.000,bicubic dm_nfnet_f6,96.920,3.080,99.720,0.280,438.36,576,0.956,bicubic +beitv2_base_patch16_224.in1k_ft_in22k_in1k,96.910,3.090,99.730,0.270,86.53,224,0.900,bicubic +vit_base_patch16_clip_384.laion2b_ft_in1k,96.910,3.090,99.670,0.330,86.86,384,1.000,bicubic volo_d5_224,96.880,3.120,99.670,0.330,295.46,224,0.960,bicubic -resnetv2_152x4_bitm,96.880,3.120,99.660,0.340,936.53,480,1.000,bilinear cait_m48_448,96.880,3.120,99.620,0.380,356.46,448,1.000,bicubic -tf_efficientnet_b5_ns,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic +resnetv2_152x4_bitm,96.870,3.130,99.660,0.340,936.53,480,1.000,bilinear +tf_efficientnet_b5.ns_jft_in1k,96.870,3.130,99.640,0.360,30.39,456,0.934,bicubic deit3_base_patch16_224_in21ft1k,96.870,3.130,99.620,0.380,86.59,224,1.000,bicubic deit3_large_patch16_384,96.850,3.150,99.620,0.380,304.76,384,1.000,bicubic -convnext_base_in22ft1k,96.840,3.160,99.650,0.350,88.59,224,0.875,bicubic cait_m36_384,96.830,3.170,99.660,0.340,271.22,384,1.000,bicubic +vit_base_patch16_clip_384.openai_ft_in1k,96.820,3.180,99.660,0.340,86.86,384,1.000,bicubic +xcit_small_24_p8_384_dist,96.820,3.180,99.630,0.370,47.63,384,1.000,bicubic dm_nfnet_f5,96.810,3.190,99.670,0.330,377.21,544,0.954,bicubic -xcit_small_24_p8_384_dist,96.810,3.190,99.630,0.370,47.63,384,1.000,bicubic +convnext_small.fb_in22k_ft_in1k,96.810,3.190,99.510,0.490,50.22,288,1.000,bicubic volo_d4_224,96.780,3.220,99.670,0.330,192.96,224,0.960,bicubic dm_nfnet_f4,96.780,3.220,99.620,0.380,316.07,512,0.951,bicubic +flexivit_large.1200ep_in1k,96.780,3.220,99.610,0.390,304.36,240,0.950,bicubic xcit_medium_24_p8_384_dist,96.780,3.220,99.610,0.390,84.32,384,1.000,bicubic ig_resnext101_32x32d,96.780,3.220,99.530,0.470,468.53,224,0.875,bilinear +efficientnet_b5.in12k_ft_in1k,96.770,3.230,99.600,0.400,30.39,448,1.000,bicubic xcit_large_24_p8_384_dist,96.760,3.240,99.560,0.440,188.93,384,1.000,bicubic +maxvit_small_tf_384.in1k,96.740,3.260,99.600,0.400,69.02,384,1.000,bicubic +flexivit_large.600ep_in1k,96.740,3.260,99.550,0.450,304.36,240,0.950,bicubic +tf_efficientnetv2_l.in1k,96.740,3.260,99.550,0.450,118.52,480,1.000,bicubic dm_nfnet_f3,96.730,3.270,99.630,0.370,254.92,416,0.940,bicubic -vit_large_patch16_224,96.710,3.290,99.650,0.350,304.33,224,0.900,bicubic -tf_efficientnet_b4_ns,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic +vit_large_patch16_224.augreg_in21k_ft_in1k,96.710,3.290,99.650,0.350,304.33,224,0.900,bicubic +tf_efficientnet_b4.ns_jft_in1k,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic volo_d2_384,96.710,3.290,99.600,0.400,58.87,384,1.000,bicubic xcit_medium_24_p16_384_dist,96.700,3.300,99.600,0.400,84.40,384,1.000,bicubic -tf_efficientnet_b8,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic +tf_efficientnet_b8.ra_in1k,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic +flexivit_large.300ep_in1k,96.690,3.310,99.580,0.420,304.36,240,0.950,bicubic swin_base_patch4_window7_224,96.680,3.320,99.660,0.340,87.77,224,0.900,bicubic deit3_small_patch16_384_in21ft1k,96.670,3.330,99.640,0.360,22.21,384,1.000,bicubic -beit_base_patch16_224,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic -tf_efficientnetv2_l,96.650,3.350,99.560,0.440,118.52,480,1.000,bicubic +beit_base_patch16_224.in22k_ft_in22k_in1k,96.660,3.340,99.660,0.340,86.53,224,0.900,bicubic xcit_large_24_p8_224_dist,96.640,3.360,99.460,0.540,188.93,224,1.000,bicubic cait_s36_384,96.630,3.370,99.600,0.400,68.37,384,1.000,bicubic +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,96.610,3.390,99.480,0.520,88.30,384,1.000,bicubic regnetz_e8,96.600,3.400,99.610,0.390,57.70,320,1.000,bicubic -deit3_huge_patch14_224,96.580,3.420,99.520,0.480,632.13,224,0.900,bicubic -tf_efficientnet_b7,96.580,3.420,99.520,0.480,66.35,600,0.949,bicubic +maxvit_tiny_tf_384.in1k,96.600,3.400,99.560,0.440,30.98,384,1.000,bicubic +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,96.600,3.400,99.560,0.440,86.57,224,0.950,bicubic +tf_efficientnet_b7.ra_in1k,96.580,3.420,99.510,0.490,66.35,600,0.949,bicubic cait_s24_384,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic +deit3_huge_patch14_224,96.570,3.430,99.520,0.480,632.13,224,0.900,bicubic +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,96.570,3.430,99.520,0.480,88.34,448,1.000,bicubic xcit_small_24_p8_224_dist,96.550,3.450,99.570,0.430,47.63,224,1.000,bicubic -tf_efficientnet_b8_ap,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic -tf_efficientnetv2_m,96.540,3.460,99.570,0.430,54.14,480,1.000,bicubic +tf_efficientnet_b8.ap_in1k,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic resnetv2_152x2_bitm,96.520,3.480,99.590,0.410,236.34,448,1.000,bilinear xcit_medium_24_p8_224_dist,96.520,3.480,99.510,0.490,84.32,224,1.000,bicubic +vit_medium_patch16_gap_384.in12k_ft_in1k,96.510,3.490,99.620,0.380,39.03,384,0.950,bicubic deit_base_distilled_patch16_384,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic +vit_base_patch16_224.augreg2_in21k_ft_in1k,96.510,3.490,99.560,0.440,86.57,224,0.900,bicubic +vit_base_patch16_clip_224.openai_ft_in12k_in1k,96.510,3.490,99.550,0.450,86.57,224,0.950,bicubic +tf_efficientnetv2_m.in1k,96.480,3.520,99.610,0.390,54.14,480,1.000,bicubic xcit_small_12_p8_384_dist,96.480,3.520,99.490,0.510,26.21,384,1.000,bicubic -tf_efficientnetv2_s_in21ft1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic +tf_efficientnetv2_s.in21k_ft_in1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic volo_d1_384,96.470,3.530,99.550,0.450,26.78,384,1.000,bicubic ecaresnet269d,96.460,3.540,99.610,0.390,102.09,352,1.000,bicubic dm_nfnet_f2,96.460,3.540,99.540,0.460,193.78,352,0.920,bicubic -convnext_small_in22ft1k,96.460,3.540,99.470,0.530,50.22,224,0.875,bicubic -vit_base_r50_s16_384,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic +vit_base_r50_s16_384.orig_in21k_ft_in1k,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic eca_nfnet_l2,96.450,3.550,99.620,0.380,56.72,384,1.000,bicubic -volo_d3_224,96.440,3.560,99.620,0.380,86.33,224,0.960,bicubic +volo_d3_224,96.450,3.550,99.620,0.380,86.33,224,0.960,bicubic ig_resnext101_32x16d,96.440,3.560,99.540,0.460,194.03,224,0.875,bilinear seresnextaa101d_32x8d,96.420,3.580,99.520,0.480,93.59,288,1.000,bicubic volo_d2_224,96.420,3.580,99.500,0.500,58.68,224,0.960,bicubic -resnetrs420,96.410,3.590,99.540,0.460,191.89,416,1.000,bicubic -dm_nfnet_f1,96.380,3.620,99.470,0.530,132.63,320,0.910,bicubic -tf_efficientnet_b6_ap,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic +vit_base_patch32_clip_384.openai_ft_in12k_in1k,96.420,3.580,99.460,0.540,88.30,384,0.950,bicubic +mvitv2_large,96.410,3.590,99.450,0.550,217.99,224,0.900,bicubic +resnetrs420,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic +convnext_large.fb_in1k,96.400,3.600,99.530,0.470,197.77,288,1.000,bicubic +dm_nfnet_f1,96.390,3.610,99.470,0.530,132.63,320,0.910,bicubic +tf_efficientnet_b6.ap_in1k,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic seresnext101d_32x8d,96.360,3.640,99.470,0.530,93.59,288,1.000,bicubic -tf_efficientnet_b7_ap,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic +tf_efficientnet_b7.ap_in1k,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic resmlp_big_24_224_in22ft1k,96.350,3.650,99.520,0.480,129.14,224,0.875,bicubic +maxvit_base_tf_224.in1k,96.350,3.650,99.370,0.630,119.47,224,0.950,bicubic xcit_small_24_p16_384_dist,96.340,3.660,99.580,0.420,47.67,384,1.000,bicubic resnetrs200,96.340,3.660,99.550,0.450,93.21,320,1.000,bicubic +xcit_small_12_p16_384_dist,96.340,3.660,99.490,0.510,26.25,384,1.000,bicubic regnetz_040h,96.330,3.670,99.520,0.480,28.94,320,1.000,bicubic -xcit_small_12_p16_384_dist,96.330,3.670,99.490,0.510,26.25,384,1.000,bicubic +vit_base_patch16_clip_224.laion2b_ft_in1k,96.320,3.680,99.540,0.460,86.57,224,1.000,bicubic xcit_large_24_p16_224_dist,96.320,3.680,99.500,0.500,189.10,224,1.000,bicubic +maxvit_large_tf_224.in1k,96.320,3.680,99.410,0.590,211.79,224,0.950,bicubic +vit_base_patch16_clip_224.openai_ft_in1k,96.310,3.690,99.550,0.450,86.57,224,0.900,bicubic seresnet152d,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic -vit_base_patch16_224,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic -tf_efficientnet_b6,96.290,3.710,99.520,0.480,43.04,528,0.942,bicubic -swsl_resnext101_32x16d,96.280,3.720,99.500,0.500,194.03,224,0.875,bilinear +convnext_base.fb_in1k,96.310,3.690,99.500,0.500,88.59,288,1.000,bicubic +vit_base_patch16_224.augreg_in21k_ft_in1k,96.300,3.700,99.560,0.440,86.57,224,0.900,bicubic +tf_efficientnet_b6.aa_in1k,96.290,3.710,99.520,0.480,43.04,528,0.942,bicubic resnetv2_50x3_bitm,96.270,3.730,99.630,0.370,217.32,448,1.000,bilinear -efficientnetv2_rw_m,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic +efficientnetv2_rw_m.agc_in1k,96.270,3.730,99.560,0.440,53.24,416,1.000,bicubic +swsl_resnext101_32x16d,96.270,3.730,99.500,0.500,194.03,224,0.875,bilinear xcit_medium_24_p16_224_dist,96.260,3.740,99.410,0.590,84.40,224,1.000,bicubic resnetv2_101x3_bitm,96.250,3.750,99.590,0.410,387.93,448,1.000,bilinear swsl_resnext101_32x8d,96.240,3.760,99.590,0.410,88.79,224,0.875,bilinear @@ -111,559 +168,624 @@ resnetrs350,96.240,3.760,99.470,0.530,163.96,384,1.000,bicubic xcit_tiny_24_p8_384_dist,96.240,3.760,99.440,0.560,12.11,384,1.000,bicubic deit3_base_patch16_384,96.230,3.770,99.400,0.600,86.88,384,1.000,bicubic regnetz_d8_evos,96.220,3.780,99.490,0.510,23.46,320,0.950,bicubic +maxxvit_rmlp_small_rw_256,96.210,3.790,99.480,0.520,66.01,256,0.950,bicubic +maxvit_small_tf_224.in1k,96.210,3.790,99.460,0.540,68.93,224,0.950,bicubic +coatnet_rmlp_2_rw_224,96.200,3.800,99.280,0.720,73.88,224,0.950,bicubic +vit_base_patch16_384.orig_in21k_ft_in1k,96.190,3.810,99.530,0.470,86.86,384,1.000,bicubic resnetv2_152x2_bit_teacher_384,96.190,3.810,99.500,0.500,236.34,384,1.000,bicubic deit3_large_patch16_224,96.190,3.810,99.300,0.700,304.37,224,0.900,bicubic -vit_large_r50_s32_224,96.180,3.820,99.540,0.460,328.99,224,0.900,bicubic +vit_large_r50_s32_224.augreg_in21k_ft_in1k,96.180,3.820,99.530,0.470,328.99,224,0.900,bicubic regnetz_040,96.180,3.820,99.510,0.490,27.12,320,1.000,bicubic -convnext_tiny_384_in22ft1k,96.170,3.830,99.480,0.520,28.59,384,1.000,bicubic -swinv2_base_window16_256,96.170,3.830,99.400,0.600,87.92,256,0.900,bicubic +swinv2_base_window16_256,96.180,3.820,99.400,0.600,87.92,256,0.900,bicubic +convnext_tiny.fb_in22k_ft_in1k_384,96.170,3.830,99.500,0.500,28.59,384,1.000,bicubic +deit3_medium_patch16_224_in21ft1k,96.140,3.860,99.490,0.510,38.85,224,1.000,bicubic crossvit_18_dagger_408,96.130,3.870,99.470,0.530,44.61,408,1.000,bicubic -seresnext101_32x8d,96.130,3.870,99.360,0.640,93.57,288,1.000,bicubic resnest269e,96.120,3.880,99.520,0.480,110.93,416,0.928,bicubic +seresnext101_32x8d,96.120,3.880,99.360,0.640,93.57,288,1.000,bicubic resnet200d,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic -tf_efficientnet_b3_ns,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic -tf_efficientnet_b5_ap,96.080,3.920,99.540,0.460,30.39,456,0.934,bicubic +flexivit_base.1200ep_in1k,96.110,3.890,99.400,0.600,86.59,240,0.950,bicubic +tf_efficientnet_b3.ns_jft_in1k,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic +tf_efficientnet_b5.ap_in1k,96.080,3.920,99.540,0.460,30.39,456,0.934,bicubic xcit_large_24_p8_224,96.080,3.920,99.150,0.850,188.93,224,1.000,bicubic resnest200e,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic swinv2_base_window8_256,96.070,3.930,99.420,0.580,87.92,256,0.900,bicubic pit_b_distilled_224,96.070,3.930,99.380,0.620,74.79,224,0.900,bicubic swinv2_small_window16_256,96.070,3.930,99.340,0.660,49.73,256,0.900,bicubic -vit_small_r26_s32_384,96.060,3.940,99.550,0.450,36.47,384,1.000,bicubic -resnetrs270,96.060,3.940,99.480,0.520,129.86,352,1.000,bicubic -swsl_resnext101_32x4d,96.040,3.960,99.530,0.470,44.18,224,0.875,bilinear +vit_small_r26_s32_384.augreg_in21k_ft_in1k,96.060,3.940,99.560,0.440,36.47,384,1.000,bicubic +resnetrs270,96.060,3.940,99.490,0.510,129.86,352,1.000,bicubic +gcvit_base,96.060,3.940,99.380,0.620,90.32,224,0.875,bicubic +swsl_resnext101_32x4d,96.050,3.950,99.530,0.470,44.18,224,0.875,bilinear +maxvit_rmlp_tiny_rw_256,96.040,3.960,99.410,0.590,29.15,256,0.950,bicubic swin_s3_base_224,96.040,3.960,99.350,0.650,71.13,224,0.900,bicubic volo_d1_224,96.030,3.970,99.390,0.610,26.63,224,0.960,bicubic -vit_base_patch16_224_miil,96.030,3.970,99.350,0.650,86.54,224,0.875,bilinear -convnext_large,96.020,3.980,99.470,0.530,197.77,224,0.875,bicubic +vit_base_patch16_224_miil.in21k_ft_in1k,96.030,3.970,99.350,0.650,86.54,224,0.875,bilinear regnetz_d8,96.010,3.990,99.520,0.480,23.37,320,1.000,bicubic cs3se_edgenet_x,96.010,3.990,99.440,0.560,50.72,320,1.000,bicubic cait_xs24_384,96.010,3.990,99.430,0.570,26.67,384,1.000,bicubic -vit_small_patch16_384,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic -tf_efficientnet_b5,95.980,4.020,99.450,0.550,30.39,456,0.934,bicubic +mvitv2_base,96.010,3.990,99.330,0.670,51.47,224,0.900,bicubic +vit_small_patch16_384.augreg_in21k_ft_in1k,95.980,4.020,99.590,0.410,22.20,384,1.000,bicubic +vit_medium_patch16_gap_256.in12k_ft_in1k,95.980,4.020,99.500,0.500,38.86,256,0.950,bicubic +tf_efficientnet_b5.ra_in1k,95.980,4.020,99.450,0.550,30.39,456,0.934,bicubic +convnext_small.fb_in1k,95.980,4.020,99.430,0.570,50.22,288,1.000,bicubic +flexivit_base.300ep_in1k,95.970,4.030,99.370,0.630,86.59,240,0.950,bicubic +flexivit_base.600ep_in1k,95.960,4.040,99.420,0.580,86.59,240,0.950,bicubic xcit_small_12_p8_224_dist,95.960,4.040,99.420,0.580,26.21,224,1.000,bicubic resnetrs152,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic +maxvit_rmlp_small_rw_224,95.960,4.040,99.350,0.650,64.90,224,0.900,bicubic +pvt_v2_b5,95.950,4.050,99.390,0.610,81.96,224,0.900,bicubic eca_nfnet_l1,95.940,4.060,99.490,0.510,41.41,320,1.000,bicubic -convnext_base,95.940,4.060,99.380,0.620,88.59,224,0.875,bicubic -ig_resnext101_32x8d,95.940,4.060,99.380,0.620,88.79,224,0.875,bilinear -xcit_small_24_p8_224,95.910,4.090,99.180,0.820,47.63,224,1.000,bicubic -vit_base_patch32_384,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic +ig_resnext101_32x8d,95.930,4.070,99.380,0.620,88.79,224,0.875,bilinear +gcvit_small,95.930,4.070,99.280,0.720,51.09,224,0.875,bicubic +vit_base_patch32_384.augreg_in21k_ft_in1k,95.900,4.100,99.440,0.560,88.30,384,1.000,bicubic +pvt_v2_b4,95.900,4.100,99.350,0.650,62.56,224,0.900,bicubic +xcit_small_24_p8_224,95.900,4.100,99.180,0.820,47.63,224,1.000,bicubic +mvitv2_small,95.890,4.110,99.360,0.640,34.87,224,0.900,bicubic regnety_160,95.880,4.120,99.560,0.440,83.59,288,1.000,bicubic -sequencer2d_l,95.870,4.130,99.470,0.530,54.30,224,0.875,bicubic +sequencer2d_l,95.880,4.120,99.470,0.530,54.30,224,0.875,bicubic resmlp_big_24_distilled_224,95.870,4.130,99.440,0.560,129.14,224,0.875,bicubic -regnetz_d32,95.870,4.130,99.430,0.570,27.58,320,0.950,bicubic resnet152d,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic xcit_medium_24_p8_224,95.870,4.130,99.080,0.920,84.32,224,1.000,bicubic -regnety_080,95.850,4.150,99.440,0.560,39.18,288,1.000,bicubic +regnety_080,95.860,4.140,99.440,0.560,39.18,288,1.000,bicubic +regnetz_d32,95.860,4.140,99.430,0.570,27.58,320,0.950,bicubic swin_s3_small_224,95.840,4.160,99.200,0.800,49.74,224,0.900,bicubic -deit3_small_patch16_224_in21ft1k,95.820,4.180,99.400,0.600,22.06,224,1.000,bicubic +deit3_small_patch16_224_in21ft1k,95.820,4.180,99.410,0.590,22.06,224,1.000,bicubic crossvit_15_dagger_408,95.820,4.180,99.310,0.690,28.50,408,1.000,bicubic -xcit_small_24_p16_224_dist,95.790,4.210,99.350,0.650,47.67,224,1.000,bicubic +tresnet_v2_l,95.820,4.180,99.290,0.710,46.17,224,0.875,bilinear +maxvit_tiny_tf_224.in1k,95.810,4.190,99.260,0.740,30.92,224,0.950,bicubic +xcit_small_24_p16_224_dist,95.800,4.200,99.340,0.660,47.67,224,1.000,bicubic +edgenext_base,95.790,4.210,99.570,0.430,18.51,320,1.000,bicubic regnety_064,95.790,4.210,99.290,0.710,30.58,288,1.000,bicubic deit3_base_patch16_224,95.780,4.220,99.270,0.730,86.59,224,0.900,bicubic regnetv_064,95.770,4.230,99.420,0.580,30.58,288,1.000,bicubic resnet101d,95.750,4.250,99.440,0.560,44.57,320,1.000,bicubic resnetv2_152x2_bit_teacher,95.750,4.250,99.430,0.570,236.34,224,0.875,bicubic deit_base_distilled_patch16_224,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic +xcit_small_12_p16_224_dist,95.740,4.260,99.310,0.690,26.25,224,1.000,bicubic +maxvit_tiny_rw_224,95.740,4.260,99.160,0.840,29.06,224,0.950,bicubic regnetv_040,95.730,4.270,99.380,0.620,20.64,288,1.000,bicubic -convnext_tiny_in22ft1k,95.730,4.270,99.360,0.640,28.59,224,0.875,bicubic swinv2_small_window8_256,95.730,4.270,99.360,0.640,49.73,256,0.900,bicubic -xcit_small_12_p16_224_dist,95.730,4.270,99.300,0.700,26.25,224,1.000,bicubic twins_pcpvt_large,95.720,4.280,99.490,0.510,60.99,224,0.900,bicubic twins_svt_large,95.720,4.280,99.370,0.630,99.27,224,0.900,bicubic swin_small_patch4_window7_224,95.720,4.280,99.290,0.710,49.61,224,0.900,bicubic -tf_efficientnetv2_s,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic -efficientnetv2_rw_s,95.700,4.300,99.380,0.620,23.94,384,1.000,bicubic +tf_efficientnetv2_s.in1k,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic +efficientnetv2_rw_s.ra2_in1k,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic dm_nfnet_f0,95.690,4.310,99.330,0.670,71.49,256,0.900,bicubic swinv2_cr_small_ns_224,95.690,4.310,99.310,0.690,49.70,224,0.900,bicubic xception65,95.690,4.310,99.310,0.690,39.92,299,0.940,bicubic +gcvit_tiny,95.680,4.320,99.340,0.660,28.22,224,0.875,bicubic xception65p,95.660,4.340,99.270,0.730,39.82,299,0.940,bicubic +cait_s24_224,95.650,4.350,99.390,0.610,46.92,224,1.000,bicubic deit_base_patch16_384,95.650,4.350,99.240,0.760,86.86,384,1.000,bicubic -cait_s24_224,95.640,4.360,99.390,0.610,46.92,224,1.000,bicubic -regnetz_c16_evos,95.630,4.370,99.420,0.580,13.49,320,0.950,bicubic -swsl_resnext50_32x4d,95.610,4.390,99.440,0.560,25.03,224,0.875,bilinear -deit3_small_patch16_384,95.610,4.390,99.390,0.610,22.21,384,1.000,bicubic -convnext_small,95.610,4.390,99.260,0.740,50.22,224,0.875,bicubic -sequencer2d_m,95.600,4.400,99.270,0.730,38.31,224,0.875,bicubic -tf_efficientnet_b4,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic +swsl_resnext50_32x4d,95.620,4.380,99.440,0.560,25.03,224,0.875,bilinear +regnetz_c16_evos,95.620,4.380,99.420,0.580,13.49,320,0.950,bicubic +coatnet_1_rw_224,95.620,4.380,99.220,0.780,41.72,224,0.950,bicubic +efficientformer_l7,95.600,4.400,99.440,0.560,82.23,224,0.950,bicubic +deit3_small_patch16_384,95.600,4.400,99.390,0.610,22.21,384,1.000,bicubic +tf_efficientnet_b4.aa_in1k,95.590,4.410,99.330,0.670,19.34,380,0.922,bicubic +sequencer2d_m,95.590,4.410,99.280,0.720,38.31,224,0.875,bicubic +tf_efficientnetv2_b3.in21k_ft_in1k,95.590,4.410,99.280,0.720,14.36,300,0.900,bicubic +resnest101e,95.570,4.430,99.270,0.730,48.28,256,0.875,bilinear twins_svt_base,95.570,4.430,99.230,0.770,56.07,224,0.900,bicubic -resnest101e,95.560,4.440,99.270,0.730,48.28,256,0.875,bilinear -resnet152,95.550,4.450,99.260,0.740,60.19,224,0.950,bicubic +resnet152,95.550,4.450,99.270,0.730,60.19,224,0.950,bicubic jx_nest_base,95.540,4.460,99.300,0.700,67.72,224,0.875,bicubic resnext101_64x4d,95.540,4.460,99.290,0.710,83.46,288,1.000,bicubic -efficientnet_b4,95.530,4.470,99.400,0.600,19.34,384,1.000,bicubic -jx_nest_small,95.530,4.470,99.220,0.780,38.35,224,0.875,bicubic -tf_efficientnet_b2_ns,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic +jx_nest_small,95.530,4.470,99.210,0.790,38.35,224,0.875,bicubic +efficientnet_b4.ra2_in1k,95.520,4.480,99.390,0.610,19.34,384,1.000,bicubic +tf_efficientnet_b2.ns_jft_in1k,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic tresnet_xl_448,95.510,4.490,99.340,0.660,78.44,448,0.875,bilinear -tf_efficientnet_b4_ap,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic +tf_efficientnet_b4.ap_in1k,95.490,4.510,99.390,0.610,19.34,380,0.922,bicubic xcit_tiny_24_p16_384_dist,95.490,4.510,99.360,0.640,12.12,384,1.000,bicubic -regnety_032,95.480,4.520,99.320,0.680,19.44,288,1.000,bicubic -regnety_040,95.470,4.530,99.420,0.580,20.65,288,1.000,bicubic -cs3edgenet_x,95.470,4.530,99.280,0.720,47.82,288,1.000,bicubic +coatnet_rmlp_1_rw_224,95.490,4.510,99.240,0.760,41.69,224,0.950,bicubic +maxvit_nano_rw_256,95.490,4.510,99.130,0.870,15.45,256,0.950,bicubic +regnety_040,95.480,4.520,99.420,0.580,20.65,288,1.000,bicubic +regnety_032,95.470,4.530,99.320,0.680,19.44,288,1.000,bicubic +pvt_v2_b3,95.470,4.530,99.310,0.690,45.24,224,0.900,bicubic sequencer2d_s,95.470,4.530,99.270,0.730,27.65,224,0.875,bicubic twins_pcpvt_base,95.460,4.540,99.390,0.610,43.83,224,0.900,bicubic -xcit_tiny_24_p8_224_dist,95.460,4.540,99.360,0.640,12.11,224,1.000,bicubic eca_nfnet_l0,95.450,4.550,99.390,0.610,24.14,288,1.000,bicubic -cs3sedarknet_x,95.420,4.580,99.320,0.680,35.40,288,1.000,bicubic +xcit_tiny_24_p8_224_dist,95.450,4.550,99.360,0.640,12.11,224,1.000,bicubic +cs3edgenet_x,95.450,4.550,99.280,0.720,47.82,288,1.000,bicubic +maxvit_rmlp_nano_rw_256,95.440,4.560,99.060,0.940,15.50,256,0.950,bicubic xcit_small_12_p8_224,95.420,4.580,99.200,0.800,26.21,224,1.000,bicubic ssl_resnext101_32x16d,95.410,4.590,99.410,0.590,194.03,224,0.875,bilinear -resnetv2_50x1_bit_distilled,95.400,4.600,99.430,0.570,25.55,224,0.875,bicubic -tresnet_l_448,95.400,4.600,99.300,0.700,55.99,448,0.875,bilinear -swinv2_cr_small_224,95.400,4.600,99.050,0.950,49.70,224,0.900,bicubic +tresnet_l_448,95.410,4.590,99.300,0.700,55.99,448,0.875,bilinear +mvitv2_tiny,95.410,4.590,99.160,0.840,24.17,224,0.900,bicubic +swinv2_cr_small_224,95.410,4.590,99.060,0.940,49.70,224,0.900,bicubic +cs3sedarknet_x,95.400,4.600,99.320,0.680,35.40,288,1.000,bicubic +regnetz_c16,95.400,4.600,99.310,0.690,13.46,320,0.940,bicubic +resnetv2_50x1_bit_distilled,95.390,4.610,99.430,0.570,25.55,224,0.875,bicubic nfnet_l0,95.390,4.610,99.420,0.580,35.07,288,1.000,bicubic -regnetz_c16,95.390,4.610,99.310,0.690,13.46,320,0.940,bicubic mobilevitv2_200_384_in22ft1k,95.390,4.610,99.280,0.720,18.45,384,1.000,bicubic +deit3_medium_patch16_224,95.390,4.610,99.210,0.790,38.85,224,0.900,bicubic tresnet_m,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear +convnext_nano.in12k_ft_in1k,95.360,4.640,99.450,0.550,15.59,288,1.000,bicubic swinv2_tiny_window16_256,95.360,4.640,99.300,0.700,28.35,256,0.900,bicubic pnasnet5large,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic +maxxvit_rmlp_nano_rw_256,95.350,4.650,99.320,0.680,16.78,256,0.950,bicubic xcit_tiny_12_p8_384_dist,95.340,4.660,99.340,0.660,6.71,384,1.000,bicubic -mobilevitv2_150_384_in22ft1k,95.340,4.660,99.130,0.870,10.59,384,1.000,bicubic -ssl_resnext101_32x8d,95.330,4.670,99.310,0.690,88.79,224,0.875,bilinear +ssl_resnext101_32x8d,95.340,4.660,99.320,0.680,88.79,224,0.875,bilinear +mobilevitv2_150_384_in22ft1k,95.330,4.670,99.130,0.870,10.59,384,1.000,bicubic resnetv2_101x1_bitm,95.320,4.680,99.370,0.630,44.54,448,1.000,bilinear -vit_relpos_medium_patch16_cls_224,95.300,4.700,99.090,0.910,38.76,224,0.900,bicubic -gc_efficientnetv2_rw_t,95.290,4.710,99.220,0.780,13.68,288,1.000,bicubic -cs3darknet_x,95.270,4.730,99.280,0.720,35.05,288,1.000,bicubic +vit_relpos_medium_patch16_cls_224.sw_in1k,95.300,4.700,99.090,0.910,38.76,224,0.900,bicubic +cs3darknet_x,95.280,4.720,99.280,0.720,35.05,288,1.000,bicubic +gc_efficientnetv2_rw_t.agc_in1k,95.280,4.720,99.220,0.780,13.68,288,1.000,bicubic +flexivit_small.600ep_in1k,95.270,4.730,99.160,0.840,22.06,240,0.950,bicubic +convnext_tiny_hnf.a2h_in1k,95.270,4.730,98.980,1.020,28.59,288,1.000,bicubic +mobilevitv2_175_384_in22ft1k,95.260,4.740,99.380,0.620,14.25,384,1.000,bicubic resnetrs101,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic -vit_relpos_base_patch16_clsgap_224,95.250,4.750,99.200,0.800,86.43,224,0.900,bicubic -mobilevitv2_175_384_in22ft1k,95.240,4.760,99.380,0.620,14.25,384,1.000,bicubic -vit_large_patch32_384,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic +vit_relpos_base_patch16_clsgap_224.sw_in1k,95.250,4.750,99.200,0.800,86.43,224,0.900,bicubic +vit_large_patch32_384.orig_in21k_ft_in1k,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,95.240,4.760,99.240,0.760,88.22,224,0.900,bicubic cait_xxs36_384,95.220,4.780,99.320,0.680,17.37,384,1.000,bicubic +efficientformer_l3,95.210,4.790,99.310,0.690,31.41,224,0.950,bicubic +vit_base_patch16_224.orig_in21k_ft_in1k,95.210,4.790,99.230,0.770,86.57,224,0.900,bicubic +vit_relpos_medium_patch16_224.sw_in1k,95.210,4.790,99.220,0.780,38.75,224,0.900,bicubic levit_384,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic swsl_resnet50,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear +convnext_tiny.fb_in1k,95.200,4.800,99.330,0.670,28.59,288,1.000,bicubic resnet51q,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear -vit_relpos_medium_patch16_224,95.200,4.800,99.220,0.780,38.75,224,0.900,bicubic +pvt_v2_b2_li,95.200,4.800,99.260,0.740,22.55,224,0.900,bicubic +flexivit_small.1200ep_in1k,95.200,4.800,99.170,0.830,22.06,240,0.950,bicubic crossvit_18_dagger_240,95.180,4.820,99.120,0.880,44.27,240,0.875,bicubic +ssl_resnext101_32x4d,95.160,4.840,99.300,0.700,44.18,224,0.875,bilinear ecaresnet101d,95.160,4.840,99.230,0.770,44.57,224,0.875,bicubic -ssl_resnext101_32x4d,95.150,4.850,99.300,0.700,44.18,224,0.875,bilinear +vit_relpos_base_patch16_224.sw_in1k,95.150,4.850,99.300,0.700,86.43,224,0.900,bicubic +flexivit_small.300ep_in1k,95.150,4.850,99.160,0.840,22.06,240,0.950,bicubic nasnetalarge,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic -efficientnet_b3,95.140,4.860,99.210,0.790,12.23,320,1.000,bicubic -vit_relpos_base_patch16_224,95.130,4.870,99.300,0.700,86.43,224,0.900,bicubic -fbnetv3_g,95.130,4.870,99.200,0.800,16.62,288,0.950,bilinear +efficientnet_b3.ra2_in1k,95.140,4.860,99.210,0.790,12.23,320,1.000,bicubic +resnetv2_50d_evos,95.130,4.870,99.230,0.770,25.59,288,0.950,bicubic +vit_small_r26_s32_224.augreg_in21k_ft_in1k,95.130,4.870,99.220,0.780,36.43,224,0.900,bicubic +fbnetv3_g.ra2_in1k,95.130,4.870,99.200,0.800,16.62,288,0.950,bilinear poolformer_m48,95.130,4.870,99.120,0.880,73.47,224,0.950,bicubic -xcit_medium_24_p16_224,95.130,4.870,98.930,1.070,84.40,224,1.000,bicubic -resnetv2_50d_evos,95.120,4.880,99.230,0.770,25.59,288,0.950,bicubic -vit_small_r26_s32_224,95.120,4.880,99.220,0.780,36.43,224,0.900,bicubic -cs3sedarknet_l,95.120,4.880,99.210,0.790,21.91,288,0.950,bicubic -tf_efficientnetv2_b3,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic +xcit_medium_24_p16_224,95.130,4.870,98.920,1.080,84.40,224,1.000,bicubic +tf_efficientnetv2_b3.in1k,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic resnet61q,95.120,4.880,99.080,0.920,36.85,288,1.000,bicubic +cs3sedarknet_l,95.110,4.890,99.210,0.790,21.91,288,0.950,bicubic convit_base,95.100,4.900,99.140,0.860,86.54,224,0.875,bicubic resnetv2_50d_gn,95.100,4.900,99.060,0.940,25.57,288,0.950,bicubic -xcit_small_24_p16_224,95.080,4.920,99.070,0.930,47.67,224,1.000,bicubic -coat_lite_small,95.080,4.920,99.030,0.970,19.84,224,0.900,bicubic +coatnet_rmlp_nano_rw_224,95.090,4.910,99.170,0.830,15.15,224,0.900,bicubic +xcit_small_24_p16_224,95.080,4.920,99.060,0.940,47.67,224,1.000,bicubic +coat_lite_small,95.080,4.920,99.020,0.980,19.84,224,0.900,bicubic ecaresnet50t,95.070,4.930,99.290,0.710,25.57,320,0.950,bicubic -efficientnetv2_rw_t,95.070,4.930,99.220,0.780,13.65,288,1.000,bicubic -vit_relpos_medium_patch16_rpn_224,95.070,4.930,99.190,0.810,38.73,224,0.900,bicubic +efficientnetv2_rw_t.ra2_in1k,95.070,4.930,99.220,0.780,13.65,288,1.000,bicubic +vit_relpos_medium_patch16_rpn_224.sw_in1k,95.070,4.930,99.200,0.800,38.73,224,0.900,bicubic +xception41p,95.070,4.930,99.150,0.850,26.91,299,0.940,bicubic crossvit_18_240,95.070,4.930,99.120,0.880,43.27,240,0.875,bicubic crossvit_base_240,95.070,4.930,98.980,1.020,105.03,240,0.875,bicubic tresnet_xl,95.060,4.940,99.260,0.740,78.44,224,0.875,bilinear -xception41p,95.060,4.940,99.150,0.850,26.91,299,0.940,bicubic +coatnet_nano_rw_224,95.050,4.950,99.150,0.850,15.14,224,0.900,bicubic mobilevitv2_200_in22ft1k,95.050,4.950,99.080,0.920,18.45,256,0.888,bicubic swinv2_tiny_window8_256,95.030,4.970,99.170,0.830,28.35,256,0.900,bicubic +halo2botnet50ts_256,95.030,4.970,99.030,0.970,22.64,256,0.950,bicubic +gcvit_xtiny,95.010,4.990,99.180,0.820,19.98,224,0.875,bicubic +pvt_v2_b2,95.010,4.990,99.140,0.860,25.36,224,0.900,bicubic poolformer_m36,95.010,4.990,99.100,0.900,56.17,224,0.950,bicubic -halo2botnet50ts_256,95.010,4.990,99.040,0.960,22.64,256,0.950,bicubic deit_base_patch16_224,95.010,4.990,98.980,1.020,86.57,224,0.900,bicubic +coatnet_bn_0_rw_224,94.980,5.020,99.230,0.770,27.44,224,0.950,bicubic crossvit_15_dagger_240,94.980,5.020,99.160,0.840,28.21,240,0.875,bicubic -resnet101,94.980,5.020,99.080,0.920,44.55,224,0.950,bicubic -visformer_small,94.970,5.030,99.210,0.790,40.22,224,0.900,bicubic convmixer_1536_20,94.970,5.030,99.170,0.830,51.63,224,0.960,bicubic -tf_efficientnet_b3_ap,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic -convnext_tiny,94.960,5.040,99.200,0.800,28.59,224,0.875,bicubic +tf_efficientnet_b3.ap_in1k,94.970,5.030,99.110,0.890,12.23,300,0.904,bicubic +visformer_small,94.960,5.040,99.210,0.790,40.22,224,0.900,bicubic jx_nest_tiny,94.950,5.050,99.100,0.900,17.06,224,0.875,bicubic -xcit_large_24_p16_224,94.950,5.050,98.830,1.170,189.10,224,1.000,bicubic +resnet101,94.950,5.050,99.070,0.930,44.55,224,0.950,bicubic +xcit_large_24_p16_224,94.940,5.060,98.830,1.170,189.10,224,1.000,bicubic gernet_l,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear -cait_xxs24_384,94.930,5.070,99.140,0.860,12.03,384,1.000,bicubic -resnetv2_101,94.930,5.070,99.120,0.880,44.54,224,0.950,bicubic +cait_xxs24_384,94.920,5.080,99.140,0.860,12.03,384,1.000,bicubic +resnetv2_101,94.920,5.080,99.120,0.880,44.54,224,0.950,bicubic convit_small,94.920,5.080,99.110,0.890,27.78,224,0.875,bicubic -tf_efficientnet_b3,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic -vit_srelpos_medium_patch16_224,94.900,5.100,99.200,0.800,38.74,224,0.900,bicubic +tf_efficientnet_b3.aa_in1k,94.910,5.090,99.110,0.890,12.23,300,0.904,bicubic +coatnet_0_rw_224,94.910,5.090,99.020,0.980,27.44,224,0.950,bicubic +vit_srelpos_medium_patch16_224.sw_in1k,94.900,5.100,99.200,0.800,38.74,224,0.900,bicubic swin_s3_tiny_224,94.900,5.100,99.160,0.840,28.33,224,0.900,bicubic -tresnet_l,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear xcit_tiny_24_p8_224,94.890,5.110,99.190,0.810,12.11,224,1.000,bicubic -mixer_b16_224_miil,94.890,5.110,99.080,0.920,59.88,224,0.875,bilinear -vit_small_patch16_224,94.880,5.120,99.270,0.730,22.05,224,0.900,bicubic -resnetaa50,94.880,5.120,99.130,0.870,25.56,288,1.000,bicubic -tf_efficientnet_lite4,94.870,5.130,99.090,0.910,13.01,380,0.920,bilinear -tf_efficientnet_b1_ns,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic -convnext_nano,94.860,5.140,99.150,0.850,15.59,288,1.000,bicubic +tresnet_l,94.890,5.110,99.030,0.970,55.99,224,0.875,bilinear +vit_small_patch16_224.augreg_in21k_ft_in1k,94.880,5.120,99.270,0.730,22.05,224,0.900,bicubic +mixer_b16_224_miil,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear +convnext_nano.d1h_in1k,94.870,5.130,99.140,0.860,15.59,288,1.000,bicubic +resnetaa50,94.870,5.130,99.120,0.880,25.56,288,1.000,bicubic +tf_efficientnet_lite4.in1k,94.870,5.130,99.090,0.910,13.01,380,0.920,bilinear +tf_efficientnet_b1.ns_jft_in1k,94.860,5.140,99.250,0.750,7.79,240,0.882,bicubic +coatnext_nano_rw_224,94.850,5.150,99.200,0.800,14.70,224,0.900,bicubic edgenext_small,94.830,5.170,99.410,0.590,5.59,320,1.000,bicubic -vit_base_patch16_rpn_224,94.820,5.180,99.090,0.910,86.54,224,0.900,bicubic -xcit_small_12_p16_224,94.820,5.180,99.060,0.940,26.25,224,1.000,bicubic -seresnext50_32x4d,94.810,5.190,99.130,0.870,27.56,224,0.875,bicubic +vit_base_patch16_rpn_224.in1k,94.830,5.170,99.090,0.910,86.54,224,0.900,bicubic +xcit_small_12_p16_224,94.830,5.170,99.060,0.940,26.25,224,1.000,bicubic +seresnext50_32x4d,94.820,5.180,99.130,0.870,27.56,224,0.875,bicubic cs3darknet_focus_l,94.790,5.210,99.150,0.850,21.15,288,0.950,bicubic +mobilevitv2_175_in22ft1k,94.790,5.210,99.090,0.910,14.25,256,0.888,bicubic pit_b_224,94.790,5.210,98.820,1.180,73.76,224,0.900,bicubic -mobilevitv2_175_in22ft1k,94.780,5.220,99.100,0.900,14.25,256,0.888,bicubic -convnext_tiny_hnf,94.770,5.230,99.160,0.840,28.59,224,0.950,bicubic +lamhalobotnet50ts_256,94.780,5.220,98.980,1.020,22.57,256,0.950,bicubic twins_svt_small,94.770,5.230,99.080,0.920,24.06,224,0.900,bicubic -lamhalobotnet50ts_256,94.770,5.230,98.980,1.020,22.57,256,0.950,bicubic coat_mini,94.770,5.230,98.950,1.050,10.34,224,0.900,bicubic swinv2_cr_tiny_ns_224,94.760,5.240,99.110,0.890,28.33,224,0.900,bicubic -resnetv2_50x1_bitm,94.750,5.250,99.180,0.820,25.55,448,1.000,bilinear -pit_s_distilled_224,94.740,5.260,99.180,0.820,24.04,224,0.900,bicubic +vit_base_patch32_clip_224.laion2b_ft_in1k,94.740,5.260,99.070,0.930,88.22,224,0.900,bicubic +pit_s_distilled_224,94.730,5.270,99.190,0.810,24.04,224,0.900,bicubic +resnetv2_50x1_bitm,94.730,5.270,99.180,0.820,25.55,448,1.000,bilinear legacy_senet154,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear xcit_tiny_12_p8_224_dist,94.720,5.280,99.180,0.820,6.71,224,1.000,bicubic crossvit_15_240,94.720,5.280,99.080,0.920,27.53,240,0.875,bicubic gluon_resnet152_v1s,94.720,5.280,99.060,0.940,60.32,224,0.875,bicubic -resnest50d_4s2x40d,94.710,5.290,99.140,0.860,30.42,224,0.875,bicubic +resnest50d_4s2x40d,94.710,5.290,99.130,0.870,30.42,224,0.875,bicubic gluon_senet154,94.710,5.290,98.970,1.030,115.09,224,0.875,bicubic -halonet50ts,94.710,5.290,98.830,1.170,22.73,256,0.940,bicubic ssl_resnext50_32x4d,94.700,5.300,99.240,0.760,25.03,224,0.875,bilinear -vit_relpos_small_patch16_224,94.690,5.310,99.100,0.900,21.98,224,0.900,bicubic -mobilevitv2_150_in22ft1k,94.690,5.310,98.920,1.080,10.59,256,0.888,bicubic +mobilevitv2_150_in22ft1k,94.700,5.300,98.920,1.080,10.59,256,0.888,bicubic +halonet50ts,94.700,5.300,98.830,1.170,22.73,256,0.940,bicubic +vit_relpos_small_patch16_224.sw_in1k,94.690,5.310,99.100,0.900,21.98,224,0.900,bicubic deit3_small_patch16_224,94.690,5.310,98.750,1.250,22.06,224,0.900,bicubic cs3darknet_l,94.680,5.320,99.220,0.780,21.16,288,0.950,bicubic regnetz_b16,94.680,5.320,99.160,0.840,9.72,288,0.940,bicubic -efficientnet_el,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic +efficientnet_el.ra_in1k,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic +wide_resnet50_2,94.670,5.330,99.050,0.950,68.88,224,0.875,bicubic tresnet_m_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear rexnet_200,94.660,5.340,99.090,0.910,16.37,224,0.875,bicubic -wide_resnet50_2,94.660,5.340,99.050,0.950,68.88,224,0.875,bicubic -gluon_seresnext101_64x4d,94.660,5.340,98.980,1.020,88.23,224,0.875,bicubic +gluon_seresnext101_64x4d,94.650,5.350,98.980,1.020,88.23,224,0.875,bicubic +poolformer_s36,94.630,5.370,99.050,0.950,30.86,224,0.900,bicubic +vit_small_patch16_384.augreg_in1k,94.620,5.380,99.140,0.860,22.20,384,1.000,bicubic swin_tiny_patch4_window7_224,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic -poolformer_s36,94.620,5.380,99.050,0.950,30.86,224,0.900,bicubic resnest50d,94.620,5.380,99.030,0.970,27.48,224,0.875,bilinear gcresnet50t,94.620,5.380,98.980,1.020,25.90,256,0.900,bicubic twins_pcpvt_small,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic -vit_small_patch32_384,94.600,5.400,99.140,0.860,22.92,384,1.000,bicubic -deit_small_distilled_patch16_224,94.600,5.400,99.100,0.900,22.44,224,0.900,bicubic -crossvit_small_240,94.580,5.420,99.120,0.880,26.86,240,0.875,bicubic -efficientnet_b3_pruned,94.580,5.420,99.070,0.930,9.86,300,0.904,bicubic -pit_s_224,94.580,5.420,98.930,1.070,23.46,224,0.900,bicubic -resnext50_32x4d,94.580,5.420,98.800,1.200,25.03,224,0.950,bicubic -tnt_s_patch16_224,94.570,5.430,99.180,0.820,23.76,224,0.900,bicubic -lambda_resnet50ts,94.570,5.430,98.650,1.350,21.54,256,0.950,bicubic -repvgg_b3,94.560,5.440,98.910,1.090,123.09,224,0.875,bilinear -resmlp_36_distilled_224,94.550,5.450,99.160,0.840,44.69,224,0.875,bicubic -vit_srelpos_small_patch16_224,94.550,5.450,99.140,0.860,21.97,224,0.900,bicubic +vit_small_patch32_384.augreg_in21k_ft_in1k,94.590,5.410,99.140,0.860,22.92,384,1.000,bicubic +deit_small_distilled_patch16_224,94.590,5.410,99.100,0.900,22.44,224,0.900,bicubic +pit_s_224,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic +tnt_s_patch16_224,94.580,5.420,99.180,0.820,23.76,224,0.900,bicubic +crossvit_small_240,94.580,5.420,99.110,0.890,26.86,240,0.875,bicubic +efficientnet_b3_pruned.in1k,94.580,5.420,99.070,0.930,9.86,300,0.904,bicubic +convnext_nano_ols.d1h_in1k,94.580,5.420,99.050,0.950,15.65,288,1.000,bicubic +resmlp_36_distilled_224,94.570,5.430,99.160,0.840,44.69,224,0.875,bicubic +resnext50_32x4d,94.570,5.430,98.800,1.200,25.03,224,0.950,bicubic +vit_srelpos_small_patch16_224.sw_in1k,94.550,5.450,99.140,0.860,21.97,224,0.900,bicubic gernet_m,94.550,5.450,98.930,1.070,21.14,224,0.875,bilinear -sehalonet33ts,94.540,5.460,98.760,1.240,13.69,256,0.940,bicubic +repvgg_b3,94.550,5.450,98.910,1.090,123.09,224,0.875,bilinear +lambda_resnet50ts,94.550,5.450,98.660,1.340,21.54,256,0.950,bicubic xcit_tiny_12_p16_384_dist,94.530,5.470,99.170,0.830,6.72,384,1.000,bicubic regnety_320,94.520,5.480,99.170,0.830,145.05,224,0.875,bicubic -haloregnetz_b,94.520,5.480,98.960,1.040,11.68,224,0.940,bicubic +sehalonet33ts,94.520,5.480,98.760,1.240,13.69,256,0.940,bicubic mobilevitv2_200,94.510,5.490,98.970,1.030,18.45,256,0.888,bicubic -repvgg_b3g4,94.500,5.500,99.020,0.980,83.83,224,0.875,bilinear -ecaresnet101d_pruned,94.460,5.540,99.090,0.910,24.88,224,0.875,bicubic +haloregnetz_b,94.510,5.490,98.960,1.040,11.68,224,0.940,bicubic +repvgg_b3g4,94.490,5.510,99.020,0.980,83.83,224,0.875,bilinear +ecaresnet101d_pruned,94.450,5.550,99.100,0.900,24.88,224,0.875,bicubic gluon_seresnext101_32x4d,94.450,5.550,99.090,0.910,48.96,224,0.875,bicubic +vit_base_patch32_clip_224.openai_ft_in1k,94.440,5.560,99.180,0.820,88.22,224,0.900,bicubic +vit_base_patch16_384.augreg_in1k,94.440,5.560,99.020,0.980,86.86,384,1.000,bicubic gluon_resnet152_v1d,94.440,5.560,99.010,0.990,60.21,224,0.875,bicubic -convmixer_768_32,94.430,5.570,99.110,0.890,21.11,224,0.960,bicubic -levit_256,94.410,5.590,99.060,0.940,18.89,224,0.900,bicubic +convmixer_768_32,94.420,5.580,99.110,0.890,21.11,224,0.960,bicubic gcresnext50ts,94.410,5.590,98.990,1.010,15.67,256,0.900,bicubic -nf_resnet50,94.390,5.610,99.070,0.930,25.56,288,0.940,bicubic +nf_resnet50,94.400,5.600,99.070,0.930,25.56,288,0.940,bicubic +levit_256,94.400,5.600,99.060,0.940,18.89,224,0.900,bicubic resnest50d_1s4x24d,94.390,5.610,99.070,0.930,25.68,224,0.875,bicubic -vit_base_patch32_224,94.390,5.610,99.060,0.940,88.22,224,0.900,bicubic +vit_base_patch32_224.augreg_in21k_ft_in1k,94.390,5.610,99.060,0.940,88.22,224,0.900,bicubic inception_v4,94.380,5.620,98.820,1.180,42.68,299,0.875,bicubic -efficientnet_b2,94.370,5.630,99.050,0.950,9.11,288,1.000,bicubic -tf_efficientnet_el,94.360,5.640,99.100,0.900,10.59,300,0.904,bicubic +darknet53,94.370,5.630,99.050,0.950,41.61,288,1.000,bicubic +efficientnet_b2.ra_in1k,94.370,5.630,99.050,0.950,9.11,288,1.000,bicubic +tf_efficientnet_el.in1k,94.360,5.640,99.100,0.900,10.59,300,0.904,bicubic xcit_tiny_12_p8_224,94.360,5.640,99.070,0.930,6.71,224,1.000,bicubic -darknet53,94.360,5.640,99.050,0.950,41.61,288,1.000,bicubic edgenext_small_rw,94.360,5.640,99.040,0.960,7.83,320,1.000,bicubic gluon_resnext101_64x4d,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic -resmlp_24_distilled_224,94.340,5.660,99.090,0.910,30.02,224,0.875,bicubic +inception_resnet_v2,94.340,5.660,98.800,1.200,55.84,299,0.897,bicubic +resmlp_24_distilled_224,94.330,5.670,99.090,0.910,30.02,224,0.875,bicubic poolformer_s24,94.330,5.670,99.060,0.940,21.39,224,0.900,bicubic -inception_resnet_v2,94.330,5.670,98.800,1.200,55.84,299,0.897,bicubic -ssl_resnet50,94.320,5.680,99.150,0.850,25.56,224,0.875,bilinear -sebotnet33ts_256,94.310,5.690,98.600,1.400,13.70,256,0.940,bicubic -rexnet_150,94.280,5.720,99.080,0.920,9.73,224,0.875,bicubic -tf_efficientnet_b2_ap,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic -resnetv2_50,94.270,5.730,98.930,1.070,25.55,224,0.950,bicubic +sebotnet33ts_256,94.330,5.670,98.580,1.420,13.70,256,0.940,bicubic +ssl_resnet50,94.310,5.690,99.150,0.850,25.56,224,0.875,bilinear +resnetv2_50,94.290,5.710,98.930,1.070,25.55,224,0.950,bicubic +regnetx_120,94.270,5.730,99.190,0.810,46.11,224,0.875,bicubic +rexnet_150,94.270,5.730,99.080,0.920,9.73,224,0.875,bicubic +tf_efficientnet_b2.ap_in1k,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic seresnet33ts,94.270,5.730,98.780,1.220,19.78,256,0.900,bicubic -regnetx_120,94.260,5.740,99.190,0.810,46.11,224,0.875,bicubic resmlp_big_24_224,94.260,5.740,98.820,1.180,129.14,224,0.875,bicubic -cspresnext50,94.240,5.760,99.050,0.950,20.57,256,0.887,bilinear +cspresnext50,94.250,5.750,99.050,0.950,20.57,256,0.887,bilinear +xcit_tiny_24_p16_224_dist,94.230,5.770,98.960,1.040,12.12,224,1.000,bicubic mobilevitv2_175,94.230,5.770,98.930,1.070,14.25,256,0.888,bicubic -mixnet_xl,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic -regnetx_320,94.220,5.780,99.050,0.950,107.81,224,0.875,bicubic -xcit_tiny_24_p16_224_dist,94.220,5.780,98.960,1.040,12.12,224,1.000,bicubic -tf_efficientnet_b2,94.210,5.790,99.040,0.960,9.11,260,0.890,bicubic +mixnet_xl.ra_in1k,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic +maxvit_rmlp_pico_rw_256,94.220,5.780,99.000,1.000,7.52,256,0.950,bicubic +regnetx_320,94.210,5.790,99.050,0.950,107.81,224,0.875,bicubic +tf_efficientnet_b2.aa_in1k,94.210,5.790,99.030,0.970,9.11,260,0.890,bicubic darknetaa53,94.210,5.790,98.950,1.050,36.02,288,1.000,bilinear -ecaresnet50d,94.200,5.800,99.020,0.980,25.58,224,0.875,bicubic -gluon_resnet101_v1d,94.180,5.820,98.940,1.060,44.57,224,0.875,bicubic -dpn92,94.180,5.820,98.930,1.070,37.67,224,0.875,bicubic +ecaresnet50d,94.190,5.810,99.020,0.980,25.58,224,0.875,bicubic +dpn92,94.190,5.810,98.930,1.070,37.67,224,0.875,bicubic resnet50_gn,94.180,5.820,98.920,1.080,25.56,224,0.940,bicubic gluon_resnet101_v1s,94.170,5.830,99.010,0.990,44.67,224,0.875,bicubic +gluon_resnet101_v1d,94.170,5.830,98.940,1.060,44.57,224,0.875,bicubic gluon_seresnext50_32x4d,94.170,5.830,98.910,1.090,27.56,224,0.875,bicubic ecaresnetlight,94.140,5.860,98.950,1.050,30.16,224,0.875,bicubic -legacy_seresnext101_32x4d,94.120,5.880,98.970,1.030,48.96,224,0.875,bilinear +legacy_seresnext101_32x4d,94.130,5.870,98.970,1.030,48.96,224,0.875,bilinear +tf_efficientnet_lite3.in1k,94.130,5.870,98.960,1.040,8.20,300,0.904,bilinear +ens_adv_inception_resnet_v2,94.130,5.870,98.790,1.210,55.84,299,0.897,bicubic gluon_resnext101_32x4d,94.120,5.880,98.930,1.070,44.18,224,0.875,bicubic -ens_adv_inception_resnet_v2,94.120,5.880,98.790,1.210,55.84,299,0.897,bicubic -tf_efficientnet_lite3,94.110,5.890,98.960,1.040,8.20,300,0.904,bilinear -efficientnet_el_pruned,94.090,5.910,99.010,0.990,10.59,300,0.904,bicubic +efficientnet_el_pruned.in1k,94.090,5.910,99.010,0.990,10.59,300,0.904,bicubic cspdarknet53,94.090,5.910,98.980,1.020,27.64,256,0.887,bilinear -seresnet50,94.080,5.920,98.950,1.050,28.09,224,0.875,bicubic +seresnet50,94.080,5.920,98.970,1.030,28.09,224,0.875,bicubic +tf_efficientnetv2_b2.in1k,94.070,5.930,98.930,1.070,10.10,260,0.890,bicubic resnet50d,94.070,5.930,98.920,1.080,25.58,224,0.875,bicubic -mobilevitv2_150,94.070,5.930,98.900,1.100,10.59,256,0.888,bicubic -tf_efficientnetv2_b2,94.060,5.940,98.930,1.070,10.10,260,0.890,bicubic -hrnet_w48,94.030,5.970,99.030,0.970,77.47,224,0.875,bilinear -gluon_resnet152_v1b,94.030,5.970,98.750,1.250,60.19,224,0.875,bicubic +gcvit_xxtiny,94.050,5.950,99.070,0.930,12.00,224,0.875,bicubic +mobilevitv2_150,94.050,5.950,98.900,1.100,10.59,256,0.888,bicubic +hrnet_w48,94.030,5.970,99.040,0.960,77.47,224,0.875,bilinear +convnext_pico.d1_in1k,94.030,5.970,99.000,1.000,9.05,288,0.950,bicubic +convnext_pico_ols.d1_in1k,94.030,5.970,98.940,1.060,9.06,288,1.000,bicubic +gluon_resnet152_v1b,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic resnetrs50,94.020,5.980,98.850,1.150,35.69,224,0.910,bicubic regnety_120,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic gluon_xception65,94.010,5.990,99.020,0.980,39.92,299,0.903,bicubic dla102x2,94.000,6.000,99.030,0.970,41.28,224,0.875,bilinear -deit_small_patch16_224,93.990,6.010,98.960,1.040,22.05,224,0.900,bicubic -ecaresnet26t,93.960,6.040,98.920,1.080,16.01,320,0.950,bicubic -dpn107,93.960,6.040,98.830,1.170,86.92,224,0.875,bicubic -skresnext50_32x4d,93.950,6.050,98.830,1.170,27.48,224,0.875,bicubic -dpn98,93.930,6.070,98.920,1.080,61.57,224,0.875,bicubic -cait_xxs36_224,93.930,6.070,98.890,1.110,17.30,224,1.000,bicubic -resnet50,93.930,6.070,98.470,1.530,25.56,224,0.950,bicubic -regnetx_160,93.890,6.110,99.090,0.910,54.28,224,0.875,bicubic -vit_base_patch16_224_sam,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic -gluon_resnet152_v1c,93.890,6.110,98.800,1.200,60.21,224,0.875,bicubic -xception71,93.880,6.120,98.950,1.050,42.34,299,0.903,bicubic -nf_regnet_b1,93.880,6.120,98.750,1.250,10.22,288,0.900,bicubic +deit_small_patch16_224,94.000,6.000,98.960,1.040,22.05,224,0.900,bicubic +dpn107,93.960,6.040,98.840,1.160,86.92,224,0.875,bicubic +skresnext50_32x4d,93.950,6.050,98.820,1.180,27.48,224,0.875,bicubic +efficientformer_l1,93.940,6.060,99.030,0.970,12.29,224,0.950,bicubic +dpn98,93.940,6.060,98.920,1.080,61.57,224,0.875,bicubic +ecaresnet26t,93.940,6.060,98.920,1.080,16.01,320,0.950,bicubic +cait_xxs36_224,93.940,6.060,98.890,1.110,17.30,224,1.000,bicubic +resnet50,93.920,6.080,98.470,1.530,25.56,224,0.950,bicubic +xception71,93.890,6.110,98.950,1.050,42.34,299,0.903,bicubic +vit_base_patch16_224.sam,93.890,6.110,98.890,1.110,86.57,224,0.900,bicubic +regnetx_160,93.880,6.120,99.090,0.910,54.28,224,0.875,bicubic +gluon_resnet152_v1c,93.880,6.120,98.800,1.200,60.21,224,0.875,bicubic +nf_regnet_b1,93.880,6.120,98.740,1.260,10.22,288,0.900,bicubic eca_resnet33ts,93.860,6.140,98.890,1.110,19.68,256,0.900,bicubic -cspresnet50,93.860,6.140,98.860,1.140,21.62,256,0.887,bilinear -fbnetv3_d,93.850,6.150,98.910,1.090,10.31,256,0.950,bilinear +cspresnet50,93.860,6.140,98.870,1.130,21.62,256,0.887,bilinear ese_vovnet39b,93.850,6.150,98.900,1.100,24.57,224,0.875,bicubic -xcit_tiny_24_p16_224,93.840,6.160,98.760,1.240,12.12,224,1.000,bicubic -hrnet_w64,93.830,6.170,98.920,1.080,128.06,224,0.875,bilinear -gcresnet33ts,93.830,6.170,98.910,1.090,19.88,256,0.900,bicubic +xcit_tiny_24_p16_224,93.850,6.150,98.760,1.240,12.12,224,1.000,bicubic +fbnetv3_d.ra2_in1k,93.840,6.160,98.910,1.090,10.31,256,0.950,bilinear +hrnet_w64,93.830,6.170,98.930,1.070,128.06,224,0.875,bilinear ecaresnet50d_pruned,93.820,6.180,99.000,1.000,19.94,224,0.875,bicubic repvgg_b2g4,93.820,6.180,98.930,1.070,61.76,224,0.875,bilinear -resnext50d_32x4d,93.820,6.180,98.740,1.260,25.05,224,0.875,bicubic -efficientnet_b2_pruned,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic -regnetx_080,93.790,6.210,98.900,1.100,39.57,224,0.875,bicubic -dla169,93.790,6.210,98.830,1.170,53.39,224,0.875,bilinear +gcresnet33ts,93.820,6.180,98.910,1.090,19.88,256,0.900,bicubic +resnext50d_32x4d,93.810,6.190,98.740,1.260,25.05,224,0.875,bicubic +efficientnet_b2_pruned.in1k,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic +dla169,93.800,6.200,98.840,1.160,53.39,224,0.875,bilinear +regnetx_080,93.790,6.210,98.910,1.090,39.57,224,0.875,bicubic resnext101_32x8d,93.770,6.230,98.950,1.050,88.79,224,0.875,bilinear +dpn131,93.760,6.240,98.800,1.200,79.25,224,0.875,bicubic gluon_resnet101_v1b,93.760,6.240,98.700,1.300,44.55,224,0.875,bicubic -tf_efficientnet_b0_ns,93.750,6.250,98.970,1.030,5.29,224,0.875,bicubic -dpn131,93.750,6.250,98.830,1.170,79.25,224,0.875,bicubic -efficientnet_em,93.740,6.260,98.930,1.070,6.90,240,0.882,bicubic -wide_resnet101_2,93.720,6.280,98.810,1.190,126.89,224,0.875,bilinear -levit_192,93.720,6.280,98.790,1.210,10.95,224,0.900,bicubic -tf_efficientnetv2_b1,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic +tf_efficientnet_b0.ns_jft_in1k,93.740,6.260,98.980,1.020,5.29,224,0.875,bicubic +efficientnet_em.ra2_in1k,93.740,6.260,98.930,1.070,6.90,240,0.882,bicubic +wide_resnet101_2,93.730,6.270,98.810,1.190,126.89,224,0.875,bilinear +tf_efficientnetv2_b1.in1k,93.710,6.290,98.820,1.180,8.14,240,0.882,bicubic resnetblur50,93.710,6.290,98.810,1.190,25.56,224,0.875,bicubic hrnet_w40,93.710,6.290,98.800,1.200,57.56,224,0.875,bilinear -tf_efficientnet_b1,93.710,6.290,98.800,1.200,7.79,240,0.882,bicubic -gluon_resnet101_v1c,93.680,6.320,98.760,1.240,44.57,224,0.875,bicubic -regnetx_040,93.670,6.330,98.940,1.060,22.12,224,0.875,bicubic -rexnet_130,93.670,6.330,98.700,1.300,7.56,224,0.875,bicubic +tf_efficientnet_b1.aa_in1k,93.710,6.290,98.800,1.200,7.79,240,0.882,bicubic +levit_192,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic +gluon_resnet101_v1c,93.690,6.310,98.760,1.240,44.57,224,0.875,bicubic +regnetx_040,93.680,6.320,98.940,1.060,22.12,224,0.875,bicubic +rexnet_130,93.670,6.330,98.710,1.290,7.56,224,0.875,bicubic resmlp_36_224,93.650,6.350,98.950,1.050,44.69,224,0.875,bicubic +fbnetv3_b.ra2_in1k,93.650,6.350,98.910,1.090,8.60,256,0.950,bilinear gluon_resnext50_32x4d,93.650,6.350,98.690,1.310,25.03,224,0.875,bicubic -xception,93.640,6.360,98.760,1.240,22.86,299,0.897,bicubic -fbnetv3_b,93.630,6.370,98.910,1.090,8.60,256,0.950,bilinear -tf_efficientnet_b1_ap,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic -resnet33ts,93.630,6.370,98.760,1.240,19.68,256,0.900,bicubic -regnetx_064,93.620,6.380,99.050,0.950,26.21,224,0.875,bicubic +xception,93.640,6.360,98.770,1.230,22.86,299,0.897,bicubic +regnetx_064,93.630,6.370,99.050,0.950,26.21,224,0.875,bicubic +tf_efficientnet_b1.ap_in1k,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic +resnet33ts,93.630,6.370,98.750,1.250,19.68,256,0.900,bicubic +hrnet_w44,93.620,6.380,98.960,1.040,67.06,224,0.875,bilinear dpn68b,93.620,6.380,98.700,1.300,12.61,224,0.875,bicubic -hrnet_w44,93.610,6.390,98.960,1.040,67.06,224,0.875,bilinear halonet26t,93.610,6.390,98.640,1.360,12.48,256,0.950,bicubic -res2net50_26w_6s,93.600,6.400,98.750,1.250,37.05,224,0.875,bilinear repvgg_b2,93.590,6.410,99.070,0.930,89.02,224,0.875,bilinear gluon_resnet50_v1s,93.590,6.410,98.840,1.160,25.68,224,0.875,bicubic -tf_efficientnet_cc_b1_8e,93.570,6.430,98.690,1.310,39.72,240,0.882,bicubic -resnet32ts,93.560,6.440,98.750,1.250,17.96,256,0.900,bicubic -eca_halonext26ts,93.560,6.440,98.680,1.320,10.76,256,0.940,bicubic -dla60_res2next,93.550,6.450,98.780,1.220,17.03,224,0.875,bilinear +res2net50_26w_6s,93.590,6.410,98.750,1.250,37.05,224,0.875,bilinear +dla60_res2next,93.570,6.430,98.800,1.200,17.03,224,0.875,bilinear +resnet32ts,93.570,6.430,98.750,1.250,17.96,256,0.900,bicubic +tf_efficientnet_cc_b1_8e.in1k,93.570,6.430,98.690,1.310,39.72,240,0.882,bicubic +eca_halonext26ts,93.550,6.450,98.680,1.320,10.76,256,0.940,bicubic +convnext_tiny.fb_in22k_ft_in1k,93.550,6.450,98.580,1.420,28.59,288,1.000,bicubic gluon_inception_v3,93.540,6.460,98.830,1.170,23.83,299,0.875,bicubic dla102x,93.530,6.470,98.850,1.150,26.31,224,0.875,bilinear gluon_resnet50_v1d,93.530,6.470,98.710,1.290,25.58,224,0.875,bicubic -res2net101_26w_4s,93.530,6.470,98.600,1.400,45.21,224,0.875,bilinear -gmlp_s16_224,93.510,6.490,98.780,1.220,19.42,224,0.875,bicubic +res2net101_26w_4s,93.520,6.480,98.600,1.400,45.21,224,0.875,bilinear coat_tiny,93.510,6.490,98.690,1.310,5.50,224,0.900,bicubic selecsls60b,93.500,6.500,98.840,1.160,32.77,224,0.875,bicubic +gmlp_s16_224,93.500,6.500,98.780,1.220,19.42,224,0.875,bicubic +pvt_v2_b1,93.490,6.510,98.860,1.140,14.01,224,0.900,bicubic cait_xxs24_224,93.490,6.510,98.770,1.230,11.96,224,1.000,bicubic xception41,93.480,6.520,98.750,1.250,26.97,299,0.903,bicubic mobilevitv2_125,93.460,6.540,98.860,1.140,7.48,256,0.888,bicubic -coat_lite_mini,93.460,6.540,98.780,1.220,11.01,224,0.900,bicubic -vit_tiny_patch16_384,93.440,6.560,98.830,1.170,5.79,384,1.000,bicubic +coat_lite_mini,93.450,6.550,98.780,1.220,11.01,224,0.900,bicubic +res2net50_26w_8s,93.450,6.550,98.700,1.300,48.40,224,0.875,bilinear +botnet26t_256,93.450,6.550,98.650,1.350,12.49,256,0.950,bicubic +legacy_seresnet152,93.440,6.560,98.850,1.150,66.82,224,0.875,bilinear +convnext_femto.d1_in1k,93.440,6.560,98.810,1.190,5.22,288,0.950,bicubic resmlp_24_224,93.440,6.560,98.810,1.190,30.02,224,0.875,bicubic -res2net50_26w_8s,93.440,6.560,98.690,1.310,48.40,224,0.875,bilinear lambda_resnet26rpt_256,93.430,6.570,98.880,1.120,10.99,256,0.940,bicubic -legacy_seresnet152,93.430,6.570,98.850,1.150,66.82,224,0.875,bilinear -botnet26t_256,93.430,6.570,98.650,1.350,12.49,256,0.950,bicubic -legacy_seresnext50_32x4d,93.420,6.580,98.800,1.200,27.56,224,0.875,bilinear +legacy_seresnext50_32x4d,93.430,6.570,98.800,1.200,27.56,224,0.875,bilinear +vit_small_patch16_224.augreg_in1k,93.430,6.570,98.780,1.220,22.05,224,0.900,bicubic +vit_tiny_patch16_384.augreg_in21k_ft_in1k,93.420,6.580,98.830,1.170,5.79,384,1.000,bicubic repvgg_b1,93.410,6.590,98.790,1.210,57.42,224,0.875,bilinear -lambda_resnet26t,93.400,6.600,98.730,1.270,10.96,256,0.940,bicubic -hrnet_w30,93.380,6.620,98.830,1.170,37.71,224,0.875,bilinear -dla60_res2net,93.370,6.630,98.840,1.160,20.85,224,0.875,bilinear -eca_botnext26ts_256,93.370,6.630,98.700,1.300,10.59,256,0.950,bicubic +lambda_resnet26t,93.400,6.600,98.740,1.260,10.96,256,0.940,bicubic +convnext_femto_ols.d1_in1k,93.390,6.610,98.910,1.090,5.23,288,0.950,bicubic +dla60_res2net,93.380,6.620,98.860,1.140,20.85,224,0.875,bilinear +hrnet_w30,93.370,6.630,98.830,1.170,37.71,224,0.875,bilinear +eca_botnext26ts_256,93.360,6.640,98.700,1.300,10.59,256,0.950,bicubic xcit_tiny_12_p16_224_dist,93.350,6.650,98.740,1.260,6.72,224,1.000,bicubic -xcit_nano_12_p8_384_dist,93.270,6.730,98.850,1.150,3.05,384,1.000,bicubic -legacy_seresnet101,93.270,6.730,98.740,1.260,49.33,224,0.875,bilinear -mixnet_l,93.270,6.730,98.700,1.300,7.33,224,0.875,bicubic -dla102,93.260,6.740,98.770,1.230,33.27,224,0.875,bilinear -cs3darknet_m,93.260,6.740,98.720,1.280,9.31,288,0.950,bicubic -tv_resnet152,93.250,6.750,98.750,1.250,60.19,224,0.875,bilinear +vit_base_patch16_224.augreg_in1k,93.350,6.650,98.670,1.330,86.57,224,0.900,bicubic +cs3darknet_m,93.280,6.720,98.720,1.280,9.31,288,0.950,bicubic +dla102,93.260,6.740,98.780,1.220,33.27,224,0.875,bilinear +legacy_seresnet101,93.260,6.740,98.740,1.260,49.33,224,0.875,bilinear +mixnet_l.ft_in1k,93.260,6.740,98.700,1.300,7.33,224,0.875,bicubic +xcit_nano_12_p8_384_dist,93.250,6.750,98.850,1.150,3.05,384,1.000,bicubic regnetx_032,93.250,6.750,98.730,1.270,15.30,224,0.875,bicubic resnest26d,93.240,6.760,98.850,1.150,17.07,224,0.875,bilinear -pit_xs_distilled_224,93.240,6.760,98.830,1.170,11.00,224,0.900,bicubic +pit_xs_distilled_224,93.240,6.760,98.820,1.180,11.00,224,0.900,bicubic +tv_resnet152,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear tf_inception_v3,93.200,6.800,98.480,1.520,23.83,299,0.875,bicubic dla60x,93.190,6.810,98.710,1.290,17.35,224,0.875,bilinear res2net50_26w_4s,93.180,6.820,98.670,1.330,25.70,224,0.875,bilinear -tf_efficientnet_em,93.170,6.830,98.670,1.330,6.90,240,0.882,bicubic +tf_efficientnet_em.in1k,93.170,6.830,98.670,1.330,6.90,240,0.882,bicubic mobilevit_s,93.160,6.840,98.770,1.230,5.58,256,0.900,bicubic -res2next50,93.160,6.840,98.650,1.350,24.67,224,0.875,bilinear -vit_relpos_base_patch32_plus_rpn_256,93.160,6.840,98.320,1.680,119.42,256,0.900,bicubic -mobilevitv2_100,93.130,6.870,98.760,1.240,4.90,256,0.888,bicubic +vit_base_patch32_384.augreg_in1k,93.160,6.840,98.610,1.390,88.30,384,1.000,bicubic +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,93.160,6.840,98.310,1.690,119.42,256,0.900,bicubic +res2next50,93.150,6.850,98.660,1.340,24.67,224,0.875,bilinear +mobilevitv2_100,93.140,6.860,98.760,1.240,4.90,256,0.888,bicubic cs3darknet_focus_m,93.110,6.890,98.740,1.260,9.30,288,0.950,bicubic -bat_resnext26ts,93.100,6.900,98.730,1.270,10.73,256,0.900,bicubic -tf_efficientnetv2_b0,93.060,6.940,98.690,1.310,7.14,224,0.875,bicubic -levit_128,93.050,6.950,98.700,1.300,9.21,224,0.900,bicubic -res2net50_14w_8s,93.040,6.960,98.700,1.300,25.06,224,0.875,bilinear -tf_mixnet_l,93.040,6.960,98.540,1.460,7.33,224,0.875,bicubic +bat_resnext26ts,93.100,6.900,98.720,1.280,10.73,256,0.900,bicubic +tf_efficientnetv2_b0.in1k,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic +levit_128,93.050,6.950,98.690,1.310,9.21,224,0.900,bicubic +tf_mixnet_l.in1k,93.040,6.960,98.540,1.460,7.33,224,0.875,bicubic repvgg_b1g4,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear -efficientnet_b1,93.020,6.980,98.710,1.290,7.79,256,1.000,bicubic +efficientnet_b1.ft_in1k,93.030,6.970,98.710,1.290,7.79,256,1.000,bicubic +res2net50_14w_8s,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear selecsls60,93.010,6.990,98.830,1.170,30.67,224,0.875,bicubic adv_inception_v3,93.010,6.990,98.490,1.510,23.83,299,0.875,bicubic regnety_016,93.000,7.000,98.680,1.320,11.20,224,0.875,bicubic +convnext_atto_ols.a2_in1k,92.980,7.020,98.680,1.320,3.70,288,0.950,bicubic hardcorenas_f,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear -efficientnet_b1_pruned,92.970,7.030,98.520,1.480,6.33,240,0.882,bicubic +efficientnet_b1_pruned.in1k,92.980,7.020,98.530,1.470,6.33,240,0.882,bicubic hrnet_w32,92.950,7.050,98.840,1.160,41.23,224,0.875,bilinear -hardcorenas_e,92.940,7.060,98.580,1.420,8.07,224,0.875,bilinear -efficientnet_es,92.920,7.080,98.690,1.310,5.44,224,0.875,bicubic +hardcorenas_e,92.950,7.050,98.570,1.430,8.07,224,0.875,bilinear pit_xs_224,92.910,7.090,98.780,1.220,10.62,224,0.900,bicubic -tv_resnext50_32x4d,92.910,7.090,98.720,1.280,25.03,224,0.875,bilinear -gluon_resnet50_v1c,92.910,7.090,98.700,1.300,25.58,224,0.875,bicubic +gluon_resnet50_v1c,92.910,7.090,98.710,1.290,25.58,224,0.875,bicubic +efficientnet_es.ra_in1k,92.910,7.090,98.690,1.310,5.44,224,0.875,bicubic densenet161,92.900,7.100,98.810,1.190,28.68,224,0.875,bicubic +tv_resnext50_32x4d,92.900,7.100,98.720,1.280,25.03,224,0.875,bilinear inception_v3,92.900,7.100,98.330,1.670,23.83,299,0.875,bicubic tv_resnet101,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear -resmlp_12_distilled_224,92.870,7.130,98.620,1.380,15.35,224,0.875,bicubic -tf_efficientnet_cc_b0_8e,92.870,7.130,98.450,1.550,24.01,224,0.875,bicubic -coat_lite_tiny,92.860,7.140,98.640,1.360,5.72,224,0.900,bicubic +resmlp_12_distilled_224,92.870,7.130,98.630,1.370,15.35,224,0.875,bicubic +tf_efficientnet_cc_b0_8e.in1k,92.870,7.130,98.460,1.540,24.01,224,0.875,bicubic +coat_lite_tiny,92.850,7.150,98.640,1.360,5.72,224,0.900,bicubic rexnet_100,92.850,7.150,98.620,1.380,4.80,224,0.875,bicubic -tf_efficientnet_cc_b0_4e,92.830,7.170,98.440,1.560,13.31,224,0.875,bicubic -seresnext26ts,92.820,7.180,98.600,1.400,10.39,256,0.900,bicubic +tf_efficientnet_cc_b0_4e.in1k,92.840,7.160,98.440,1.560,13.31,224,0.875,bicubic +seresnext26ts,92.830,7.170,98.600,1.400,10.39,256,0.900,bicubic seresnext26t_32x4d,92.820,7.180,98.560,1.440,16.81,224,0.875,bicubic -tinynet_a,92.810,7.190,98.560,1.440,6.19,192,0.875,bicubic -res2net50_48w_2s,92.790,7.210,98.480,1.520,25.29,224,0.875,bilinear +tinynet_a.in1k,92.800,7.200,98.560,1.440,6.19,192,0.875,bicubic +res2net50_48w_2s,92.790,7.210,98.470,1.530,25.29,224,0.875,bilinear hrnet_w18,92.760,7.240,98.660,1.340,21.30,224,0.875,bilinear +convnext_atto.d2_in1k,92.760,7.240,98.610,1.390,3.70,288,0.950,bicubic crossvit_9_dagger_240,92.760,7.240,98.510,1.490,8.78,240,0.875,bicubic -densenet201,92.700,7.300,98.650,1.350,20.01,224,0.875,bicubic +densenet201,92.690,7.310,98.650,1.350,20.01,224,0.875,bicubic +resnet26t,92.680,7.320,98.580,1.420,16.01,256,0.940,bicubic repvgg_a2,92.680,7.320,98.520,1.480,28.21,224,0.875,bilinear gmixer_24_224,92.680,7.320,98.280,1.720,24.72,224,0.875,bicubic legacy_seresnet50,92.670,7.330,98.650,1.350,28.09,224,0.875,bilinear -resnet26t,92.670,7.330,98.580,1.420,16.01,256,0.940,bicubic -dla60,92.660,7.340,98.630,1.370,22.04,224,0.875,bilinear -resnet34d,92.650,7.350,98.420,1.580,21.82,224,0.875,bicubic -tf_efficientnet_b0_ap,92.620,7.380,98.370,1.630,5.29,224,0.875,bicubic -mobilenetv2_120d,92.610,7.390,98.500,1.500,5.83,224,0.875,bicubic -tf_efficientnet_lite2,92.600,7.400,98.550,1.450,6.09,260,0.890,bicubic +dla60,92.670,7.330,98.630,1.370,22.04,224,0.875,bilinear +resnet34d,92.640,7.360,98.420,1.580,21.82,224,0.875,bicubic +mobilenetv2_120d.ra_in1k,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic +tf_efficientnet_b0.ap_in1k,92.610,7.390,98.370,1.630,5.29,224,0.875,bicubic hardcorenas_d,92.600,7.400,98.430,1.570,7.50,224,0.875,bilinear -legacy_seresnext26_32x4d,92.590,7.410,98.410,1.590,16.79,224,0.875,bicubic +tf_efficientnet_lite2.in1k,92.590,7.410,98.550,1.450,6.09,260,0.890,bicubic skresnet34,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic +legacy_seresnext26_32x4d,92.570,7.430,98.420,1.580,16.79,224,0.875,bicubic gluon_resnet50_v1b,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic -regnetx_016,92.530,7.470,98.550,1.450,9.19,224,0.875,bicubic -efficientnet_b0,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic +regnetx_016,92.540,7.460,98.550,1.450,9.19,224,0.875,bicubic +efficientnet_b0.ra_in1k,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic selecsls42b,92.480,7.520,98.440,1.560,32.46,224,0.875,bicubic poolformer_s12,92.470,7.530,98.350,1.650,11.92,224,0.900,bicubic xcit_tiny_12_p16_224,92.460,7.540,98.630,1.370,6.72,224,1.000,bicubic gcresnext26ts,92.460,7.540,98.490,1.510,10.48,256,0.900,bicubic +seresnext26d_32x4d,92.440,7.560,98.540,1.460,16.81,224,0.875,bicubic gernet_s,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear -seresnext26d_32x4d,92.430,7.570,98.540,1.460,16.81,224,0.875,bicubic -xcit_nano_12_p8_224_dist,92.420,7.580,98.520,1.480,3.05,224,1.000,bicubic -eca_resnext26ts,92.410,7.590,98.620,1.380,10.30,256,0.900,bicubic -densenetblur121d,92.410,7.590,98.420,1.580,8.00,224,0.875,bicubic -tf_efficientnet_b0,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic -hardcorenas_c,92.360,7.640,98.350,1.650,5.52,224,0.875,bilinear -convmixer_1024_20_ks9_p14,92.350,7.650,98.420,1.580,24.38,224,0.960,bicubic -tf_efficientnet_lite1,92.310,7.690,98.490,1.510,5.42,240,0.882,bicubic -densenet169,92.290,7.710,98.590,1.410,14.15,224,0.875,bicubic -mixnet_m,92.270,7.730,98.350,1.650,5.01,224,0.875,bicubic -mobilenetv3_large_100_miil,92.270,7.730,98.240,1.760,5.48,224,0.875,bilinear -resnet26d,92.260,7.740,98.450,1.550,16.01,224,0.875,bicubic -dpn68,92.250,7.750,98.610,1.390,12.61,224,0.875,bicubic -resnext26ts,92.220,7.780,98.250,1.750,10.30,256,0.900,bicubic -tf_mixnet_m,92.210,7.790,98.420,1.580,5.01,224,0.875,bicubic -vit_small_patch32_224,92.160,7.840,98.510,1.490,22.88,224,0.900,bicubic -xcit_nano_12_p16_384_dist,92.130,7.870,98.520,1.480,3.05,384,1.000,bicubic -tv_resnet50,92.130,7.870,98.420,1.580,25.56,224,0.875,bilinear +xcit_nano_12_p8_224_dist,92.430,7.570,98.530,1.470,3.05,224,1.000,bicubic +eca_resnext26ts,92.420,7.580,98.620,1.380,10.30,256,0.900,bicubic +tf_efficientnet_b0.aa_in1k,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic +densenetblur121d,92.400,7.600,98.410,1.590,8.00,224,0.875,bicubic +convmixer_1024_20_ks9_p14,92.340,7.660,98.430,1.570,24.38,224,0.960,bicubic +hardcorenas_c,92.330,7.670,98.340,1.660,5.52,224,0.875,bilinear +tf_efficientnet_lite1.in1k,92.310,7.690,98.490,1.510,5.42,240,0.882,bicubic +densenet169,92.300,7.700,98.590,1.410,14.15,224,0.875,bicubic +mixnet_m.ft_in1k,92.270,7.730,98.350,1.650,5.01,224,0.875,bicubic +mobilenetv3_large_100.miil_in21k_ft_in1k,92.250,7.750,98.250,1.750,5.48,224,0.875,bilinear +dpn68,92.240,7.760,98.610,1.390,12.61,224,0.875,bicubic +resnet26d,92.230,7.770,98.450,1.550,16.01,224,0.875,bicubic +resnext26ts,92.210,7.790,98.250,1.750,10.30,256,0.900,bicubic +tf_mixnet_m.in1k,92.200,7.800,98.420,1.580,5.01,224,0.875,bicubic +vit_small_patch32_224.augreg_in21k_ft_in1k,92.150,7.850,98.510,1.490,22.88,224,0.900,bicubic +tv_resnet50,92.140,7.860,98.420,1.580,25.56,224,0.875,bilinear resmlp_12_224,92.120,7.880,98.570,1.430,15.35,224,0.875,bicubic -tf_efficientnet_es,92.120,7.880,98.430,1.570,5.44,224,0.875,bicubic -mobilenetv2_140,92.040,7.960,98.250,1.750,6.11,224,0.875,bicubic -ese_vovnet19b_dw,92.000,8.000,98.510,1.490,6.54,224,0.875,bicubic -mobilevitv2_075,91.970,8.030,98.300,1.700,2.87,256,0.888,bicubic +xcit_nano_12_p16_384_dist,92.110,7.890,98.520,1.480,3.05,384,1.000,bicubic +tf_efficientnet_es.in1k,92.100,7.900,98.440,1.560,5.44,224,0.875,bicubic +mobilenetv2_140.ra_in1k,92.030,7.970,98.250,1.750,6.11,224,0.875,bicubic +ese_vovnet19b_dw,92.010,7.990,98.510,1.490,6.54,224,0.875,bicubic +mobilevitv2_075,91.980,8.020,98.300,1.700,2.87,256,0.888,bicubic +hardcorenas_b,91.940,8.060,98.400,1.600,5.18,224,0.875,bilinear densenet121,91.940,8.060,98.280,1.720,7.98,224,0.875,bicubic -hardcorenas_b,91.930,8.070,98.400,1.600,5.18,224,0.875,bilinear -vit_tiny_patch16_224,91.910,8.090,98.340,1.660,5.72,224,0.900,bicubic +vit_tiny_patch16_224.augreg_in21k_ft_in1k,91.930,8.070,98.340,1.660,5.72,224,0.900,bicubic regnety_008,91.900,8.100,98.420,1.580,6.26,224,0.875,bicubic -mixnet_s,91.770,8.230,98.300,1.700,4.13,224,0.875,bicubic -vit_tiny_r_s16_p8_384,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic -efficientnet_es_pruned,91.710,8.290,98.410,1.590,5.44,224,0.875,bicubic -tf_mixnet_s,91.690,8.310,98.240,1.760,4.13,224,0.875,bicubic +mixnet_s.ft_in1k,91.780,8.220,98.300,1.700,4.13,224,0.875,bicubic +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,91.730,8.270,98.430,1.570,6.36,384,1.000,bicubic +efficientnet_es_pruned.in1k,91.700,8.300,98.420,1.580,5.44,224,0.875,bicubic repvgg_b0,91.680,8.320,98.450,1.550,15.82,224,0.875,bilinear -semnasnet_100,91.660,8.340,98.270,1.730,3.89,224,0.875,bicubic +tf_mixnet_s.in1k,91.680,8.320,98.240,1.760,4.13,224,0.875,bicubic +semnasnet_100.rmsp_in1k,91.660,8.340,98.270,1.730,3.89,224,0.875,bicubic hardcorenas_a,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear -regnety_006,91.550,8.450,98.430,1.570,6.06,224,0.875,bicubic -mobilenetv3_rw,91.550,8.450,98.280,1.720,5.48,224,0.875,bicubic +regnety_006,91.570,8.430,98.430,1.570,6.06,224,0.875,bicubic +edgenext_x_small,91.570,8.430,98.180,1.820,2.34,288,1.000,bicubic +mobilenetv3_rw.rmsp_in1k,91.550,8.450,98.270,1.730,5.48,224,0.875,bicubic levit_128s,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic -legacy_seresnet34,91.490,8.510,98.200,1.800,21.96,224,0.875,bilinear -mobilenetv3_large_100,91.480,8.520,98.320,1.680,5.48,224,0.875,bicubic -resnet26,91.440,8.560,98.260,1.740,16.00,224,0.875,bicubic -tf_mobilenetv3_large_100,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear +mobilenetv3_large_100.ra_in1k,91.480,8.520,98.320,1.680,5.48,224,0.875,bicubic +legacy_seresnet34,91.480,8.520,98.200,1.800,21.96,224,0.875,bilinear +resnet26,91.440,8.560,98.280,1.720,16.00,224,0.875,bicubic +tf_mobilenetv3_large_100.in1k,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear tv_densenet121,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic -edgenext_x_small,91.400,8.600,98.160,1.840,2.34,256,0.900,bicubic -mobilenetv2_110d,91.330,8.670,98.190,1.810,4.52,224,0.875,bicubic -tf_efficientnet_lite0,91.300,8.700,98.090,1.910,4.65,224,0.875,bicubic -efficientnet_lite0,91.260,8.740,98.250,1.750,4.65,224,0.875,bicubic -fbnetc_100,91.250,8.750,97.850,2.150,5.57,224,0.875,bilinear -dla34,91.230,8.770,98.170,1.830,15.74,224,0.875,bilinear -mnasnet_100,91.210,8.790,98.050,1.950,4.38,224,0.875,bicubic +mobilenetv2_110d.ra_in1k,91.350,8.650,98.190,1.810,4.52,224,0.875,bicubic +tf_efficientnet_lite0.in1k,91.300,8.700,98.090,1.910,4.65,224,0.875,bicubic +fbnetc_100.rmsp_in1k,91.270,8.730,97.830,2.170,5.57,224,0.875,bilinear +efficientnet_lite0.ra_in1k,91.260,8.740,98.250,1.750,4.65,224,0.875,bicubic +dla34,91.240,8.760,98.180,1.820,15.74,224,0.875,bilinear resnet34,91.200,8.800,98.240,1.760,21.80,224,0.875,bilinear -mobilevit_xs,91.200,8.800,98.220,1.780,2.32,256,0.900,bicubic -hrnet_w18_small_v2,91.170,8.830,98.330,1.670,15.60,224,0.875,bilinear -regnetx_008,91.160,8.840,98.380,1.620,7.26,224,0.875,bicubic -mixer_b16_224,91.150,8.850,97.400,2.600,59.88,224,0.875,bicubic -tinynet_b,91.140,8.860,98.060,1.940,3.73,188,0.875,bicubic +mnasnet_100.rmsp_in1k,91.200,8.800,98.050,1.950,4.38,224,0.875,bicubic +mobilevit_xs,91.190,8.810,98.220,1.780,2.32,256,0.900,bicubic +regnetx_008,91.180,8.820,98.380,1.620,7.26,224,0.875,bicubic +hrnet_w18_small_v2,91.170,8.830,98.340,1.660,15.60,224,0.875,bilinear +mixer_b16_224,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic resnest14d,91.130,8.870,98.330,1.670,10.61,224,0.875,bilinear -xcit_nano_12_p8_224,91.130,8.870,98.230,1.770,3.05,224,1.000,bicubic +xcit_nano_12_p8_224,91.120,8.880,98.240,1.760,3.05,224,1.000,bicubic +tinynet_b.in1k,91.120,8.880,98.070,1.930,3.73,188,0.875,bicubic +deit_tiny_distilled_patch16_224,91.100,8.900,98.270,1.730,5.91,224,0.900,bicubic gluon_resnet34_v1b,91.100,8.900,98.180,1.820,21.80,224,0.875,bicubic -deit_tiny_distilled_patch16_224,91.080,8.920,98.270,1.730,5.91,224,0.900,bicubic -swsl_resnet18,91.070,8.930,98.210,1.790,11.69,224,0.875,bilinear +swsl_resnet18,91.090,8.910,98.210,1.790,11.69,224,0.875,bilinear crossvit_9_240,91.050,8.950,98.310,1.690,8.55,240,0.875,bicubic vgg19_bn,90.990,9.010,98.110,1.890,143.68,224,0.875,bilinear pit_ti_distilled_224,90.900,9.100,98.220,1.780,5.10,224,0.900,bicubic -regnetx_006,90.770,9.230,98.100,1.900,6.20,224,0.875,bicubic -regnety_004,90.770,9.230,98.080,1.920,4.34,224,0.875,bicubic -ssl_resnet18,90.700,9.300,98.030,1.970,11.69,224,0.875,bilinear -spnasnet_100,90.600,9.400,97.960,2.040,4.42,224,0.875,bilinear -convit_tiny,90.550,9.450,98.220,1.780,5.71,224,0.875,bicubic +regnety_004,90.780,9.220,98.080,1.920,4.34,224,0.875,bicubic +regnetx_006,90.760,9.240,98.100,1.900,6.20,224,0.875,bicubic +ssl_resnet18,90.700,9.300,98.020,1.980,11.69,224,0.875,bilinear +spnasnet_100.rmsp_in1k,90.610,9.390,97.950,2.050,4.42,224,0.875,bilinear +vit_base_patch32_224.augreg_in1k,90.590,9.410,97.720,2.280,88.22,224,0.900,bicubic vgg16_bn,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear crossvit_tiny_240,90.540,9.460,97.940,2.060,7.01,240,0.875,bicubic -pit_ti_224,90.440,9.560,98.010,1.990,4.85,224,0.900,bicubic +convit_tiny,90.530,9.470,98.210,1.790,5.71,224,0.875,bicubic ghostnet_100,90.440,9.560,97.830,2.170,5.18,224,0.875,bilinear -tf_mobilenetv3_large_075,90.330,9.670,97.880,2.120,3.99,224,0.875,bilinear +pit_ti_224,90.420,9.580,98.010,1.990,4.85,224,0.900,bicubic +tf_mobilenetv3_large_075.in1k,90.320,9.680,97.870,2.130,3.99,224,0.875,bilinear tv_resnet34,90.290,9.710,97.980,2.020,21.80,224,0.875,bilinear -semnasnet_075,90.210,9.790,97.970,2.030,2.91,224,0.875,bicubic -skresnet18,90.170,9.830,97.780,2.220,11.96,224,0.875,bicubic -xcit_nano_12_p16_224_dist,90.170,9.830,97.750,2.250,3.05,224,1.000,bicubic -resnet18d,89.980,10.020,97.830,2.170,11.71,224,0.875,bicubic -hrnet_w18_small,89.870,10.130,97.890,2.110,13.19,224,0.875,bilinear -vit_base_patch32_224_sam,89.860,10.140,97.600,2.400,88.22,224,0.900,bicubic -mobilenetv2_100,89.820,10.180,97.830,2.170,3.50,224,0.875,bicubic +semnasnet_075.rmsp_in1k,90.200,9.800,97.970,2.030,2.91,224,0.875,bicubic +skresnet18,90.160,9.840,97.780,2.220,11.96,224,0.875,bicubic +xcit_nano_12_p16_224_dist,90.150,9.850,97.760,2.240,3.05,224,1.000,bicubic +resnet18d,89.990,10.010,97.830,2.170,11.71,224,0.875,bicubic +hrnet_w18_small,89.880,10.120,97.900,2.100,13.19,224,0.875,bilinear +vit_base_patch32_224.sam,89.860,10.140,97.600,2.400,88.22,224,0.900,bicubic +mobilenetv2_100.ra_in1k,89.830,10.170,97.830,2.170,3.50,224,0.875,bicubic +edgenext_xx_small,89.780,10.220,97.520,2.480,1.33,288,1.000,bicubic vgg19,89.680,10.320,97.550,2.450,143.67,224,0.875,bilinear deit_tiny_patch16_224,89.620,10.380,97.960,2.040,5.72,224,0.900,bicubic -regnetx_004,89.470,10.530,97.770,2.230,5.16,224,0.875,bicubic +regnetx_004,89.460,10.540,97.770,2.230,5.16,224,0.875,bicubic vgg16,89.360,10.640,97.520,2.480,138.36,224,0.875,bilinear -vit_tiny_r_s16_p8_224,89.350,10.650,97.700,2.300,6.34,224,0.900,bicubic -legacy_seresnet18,89.260,10.740,97.690,2.310,11.78,224,0.875,bicubic -edgenext_xx_small,89.230,10.770,97.260,2.740,1.33,256,0.900,bicubic -vgg13_bn,89.210,10.790,97.520,2.480,133.05,224,0.875,bilinear -tf_mobilenetv3_large_minimal_100,89.180,10.820,97.320,2.680,3.92,224,0.875,bilinear +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,89.340,10.660,97.700,2.300,6.34,224,0.900,bicubic +legacy_seresnet18,89.270,10.730,97.680,2.320,11.78,224,0.875,bicubic +vgg13_bn,89.200,10.800,97.530,2.470,133.05,224,0.875,bilinear +tf_mobilenetv3_large_minimal_100.in1k,89.180,10.820,97.320,2.680,3.92,224,0.875,bilinear resnet14t,89.110,10.890,97.370,2.630,10.08,224,0.950,bilinear -mobilevitv2_050,89.050,10.950,97.590,2.410,1.37,256,0.888,bicubic -lcnet_100,88.970,11.030,97.380,2.620,2.95,224,0.875,bicubic -xcit_nano_12_p16_224,88.960,11.040,97.400,2.600,3.05,224,1.000,bicubic +mobilevitv2_050,89.030,10.970,97.590,2.410,1.37,256,0.888,bicubic +pvt_v2_b0,88.980,11.020,97.690,2.310,3.67,224,0.900,bicubic +xcit_nano_12_p16_224,88.960,11.040,97.390,2.610,3.05,224,1.000,bicubic +lcnet_100.ra2_in1k,88.960,11.040,97.360,2.640,2.95,224,0.875,bicubic gluon_resnet18_v1b,88.660,11.340,97.100,2.900,11.69,224,0.875,bicubic -tinynet_c,88.420,11.580,97.270,2.730,2.46,184,0.875,bicubic +tinynet_c.in1k,88.420,11.580,97.270,2.730,2.46,184,0.875,bicubic vgg11_bn,88.390,11.610,97.270,2.730,132.87,224,0.875,bilinear -regnety_002,88.190,11.810,97.440,2.560,3.16,224,0.875,bicubic +regnety_002,88.200,11.800,97.430,2.570,3.16,224,0.875,bicubic resnet18,88.150,11.850,97.120,2.880,11.69,224,0.875,bilinear mobilevit_xxs,87.950,12.050,97.180,2.820,1.27,256,0.900,bicubic vgg13,87.570,12.430,97.120,2.880,133.05,224,0.875,bilinear regnetx_002,87.380,12.620,96.990,3.010,2.68,224,0.875,bicubic vgg11,87.340,12.660,97.110,2.890,132.86,224,0.875,bilinear -dla60x_c,87.130,12.870,97.140,2.860,1.32,224,0.875,bilinear -mixer_l16_224,86.970,13.030,94.050,5.950,208.20,224,0.875,bicubic -lcnet_075,86.940,13.060,96.530,3.470,2.36,224,0.875,bicubic -resnet10t,86.730,13.270,96.670,3.330,5.44,224,0.950,bilinear -mobilenetv3_small_100,86.180,13.820,96.460,3.540,2.54,224,0.875,bicubic -tf_mobilenetv3_small_100,85.970,14.030,96.400,3.600,2.54,224,0.875,bilinear -mnasnet_small,85.510,14.490,95.980,4.020,2.03,224,0.875,bicubic -dla46x_c,85.460,14.540,96.450,3.550,1.07,224,0.875,bilinear -tinynet_d,85.420,14.580,96.020,3.980,2.34,152,0.875,bicubic -mobilenetv2_050,85.010,14.990,95.620,4.380,1.97,224,0.875,bicubic -dla46_c,84.660,15.340,96.210,3.790,1.30,224,0.875,bilinear -tf_mobilenetv3_small_075,84.520,15.480,95.890,4.110,2.04,224,0.875,bilinear -mobilenetv3_small_075,84.120,15.880,95.500,4.500,2.04,224,0.875,bicubic -lcnet_050,83.010,16.990,95.010,4.990,1.88,224,0.875,bicubic -tf_mobilenetv3_small_minimal_100,82.690,17.310,95.000,5.000,2.04,224,0.875,bilinear -tinynet_e,79.800,20.200,93.980,6.020,2.04,106,0.875,bicubic -mobilenetv3_small_050,78.100,21.900,93.010,6.990,1.59,224,0.875,bicubic +dla60x_c,87.110,12.890,97.140,2.860,1.32,224,0.875,bilinear +mixer_l16_224,86.970,13.030,94.060,5.940,208.20,224,0.875,bicubic +lcnet_075.ra2_in1k,86.940,13.060,96.530,3.470,2.36,224,0.875,bicubic +resnet10t,86.690,13.310,96.670,3.330,5.44,224,0.950,bilinear +mobilenetv3_small_100.lamb_in1k,86.170,13.830,96.470,3.530,2.54,224,0.875,bicubic +tf_mobilenetv3_small_100.in1k,85.960,14.040,96.400,3.600,2.54,224,0.875,bilinear +mnasnet_small.lamb_in1k,85.510,14.490,95.980,4.020,2.03,224,0.875,bicubic +dla46x_c,85.480,14.520,96.440,3.560,1.07,224,0.875,bilinear +tinynet_d.in1k,85.430,14.570,96.010,3.990,2.34,152,0.875,bicubic +mobilenetv2_050.lamb_in1k,84.990,15.010,95.620,4.380,1.97,224,0.875,bicubic +dla46_c,84.660,15.340,96.200,3.800,1.30,224,0.875,bilinear +tf_mobilenetv3_small_075.in1k,84.530,15.470,95.890,4.110,2.04,224,0.875,bilinear +mobilenetv3_small_075.lamb_in1k,84.120,15.880,95.500,4.500,2.04,224,0.875,bicubic +lcnet_050.ra2_in1k,83.000,17.000,95.010,4.990,1.88,224,0.875,bicubic +tf_mobilenetv3_small_minimal_100.in1k,82.670,17.330,95.000,5.000,2.04,224,0.875,bilinear +tinynet_e.in1k,79.810,20.190,93.980,6.020,2.04,106,0.875,bicubic +mobilenetv3_small_050.lamb_in1k,78.100,21.900,93.010,6.990,1.59,224,0.875,bicubic diff --git a/results/results-imagenet-r.csv b/results/results-imagenet-r.csv index 507e75d3..9e2fee5b 100644 --- a/results/results-imagenet-r.csv +++ b/results/results-imagenet-r.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff -ig_resnext101_32x48d,79.650,20.350,89.393,10.607,828.41,224,0.875,bilinear,-17.320,-10.277,+33 -ig_resnext101_32x32d,79.467,20.533,89.180,10.820,468.53,224,0.875,bilinear,-17.313,-10.350,+51 -ig_resnext101_32x16d,78.817,21.183,88.477,11.523,194.03,224,0.875,bilinear,-17.623,-11.063,+83 -tf_efficientnet_l2_ns_475,76.470,23.530,88.653,11.347,480.31,475,0.936,bicubic,-21.280,-11.167,0 -swsl_resnext101_32x16d,76.307,23.693,87.740,12.260,194.03,224,0.875,bilinear,-19.973,-11.760,+98 -ig_resnext101_32x8d,75.800,24.200,86.213,13.787,88.79,224,0.875,bilinear,-20.140,-13.167,+140 -swsl_resnext101_32x8d,75.583,24.417,86.940,13.060,88.79,224,0.875,bilinear,-20.657,-12.650,+101 -tf_efficientnet_l2_ns,74.657,25.343,87.547,12.453,480.31,800,0.960,bicubic,-23.123,-12.343,-6 -beit_large_patch16_384,73.280,26.720,85.023,14.977,305.00,384,1.000,bicubic,-24.530,-14.767,-8 -beit_large_patch16_512,73.157,26.843,85.080,14.920,305.67,512,1.000,bicubic,-24.623,-14.740,-7 -swsl_resnext101_32x4d,72.657,27.343,85.157,14.843,44.18,224,0.875,bilinear,-23.383,-14.373,+121 -beit_large_patch16_224,71.043,28.957,83.420,16.580,304.43,224,0.900,bicubic,-26.437,-16.270,-5 -deit3_huge_patch14_224_in21ft1k,70.813,29.187,82.193,17.807,632.13,224,1.000,bicubic,-26.437,-17.527,+4 -deit3_large_patch16_384_in21ft1k,70.563,29.437,82.437,17.563,304.76,384,1.000,bicubic,-26.997,-17.273,-9 -deit3_large_patch16_224_in21ft1k,69.720,30.280,81.197,18.803,304.37,224,1.000,bicubic,-27.590,-18.483,-4 -swsl_resnext50_32x4d,68.970,31.030,82.807,17.193,25.03,224,0.875,bilinear,-26.640,-16.633,+166 -swsl_resnet50,68.293,31.707,83.300,16.700,25.56,224,0.875,bilinear,-26.907,-16.090,+213 -swinv2_large_window12to24_192to384_22kft1k,67.673,32.327,80.097,19.903,196.74,384,1.000,bicubic,-29.607,-19.683,-4 -tf_efficientnet_b7_ns,67.537,32.463,81.380,18.620,66.35,600,0.949,bicubic,-29.653,-18.320,+3 -vit_large_patch16_384,67.060,32.940,78.703,21.297,304.72,384,1.000,bicubic,-30.360,-21.077,-11 -convnext_xlarge_384_in22ft1k,66.967,33.033,79.703,20.297,350.20,384,1.000,bicubic,-30.583,-20.097,-15 -swin_large_patch4_window12_384,66.290,33.710,79.783,20.217,196.74,384,1.000,bicubic,-30.890,-19.897,+1 -convnext_large_384_in22ft1k,65.980,34.020,79.203,20.797,197.77,384,1.000,bicubic,-31.460,-20.577,-15 -swinv2_base_window12to24_192to384_22kft1k,65.740,34.260,79.310,20.690,87.92,384,1.000,bicubic,-31.520,-20.480,-9 -swinv2_large_window12to16_192to256_22kft1k,65.633,34.367,78.460,21.540,196.74,256,0.900,bicubic,-31.607,-21.250,-5 -tf_efficientnet_b6_ns,65.590,34.410,79.560,20.440,43.04,528,0.942,bicubic,-31.430,-20.150,+5 -convnext_xlarge_in22ft1k,65.423,34.577,78.243,21.757,350.20,224,0.875,bicubic,-31.817,-21.487,-8 -vit_large_patch16_224,64.353,35.647,76.187,23.813,304.33,224,0.900,bicubic,-32.357,-23.463,+28 -convnext_large_in22ft1k,64.177,35.823,77.580,22.420,197.77,224,0.875,bicubic,-33.083,-22.070,-13 -vit_large_r50_s32_384,64.103,35.897,75.850,24.150,329.09,384,1.000,bicubic,-32.847,-23.860,+6 -convnext_base_384_in22ft1k,64.093,35.907,77.733,22.267,88.59,384,1.000,bicubic,-33.197,-22.047,-19 -swin_large_patch4_window7_224,63.867,36.133,78.177,21.823,196.53,224,0.900,bicubic,-33.083,-21.483,+5 -beit_base_patch16_384,63.617,36.383,78.113,21.887,86.74,384,1.000,bicubic,-33.713,-21.607,-23 -swin_base_patch4_window12_384,63.470,36.530,78.080,21.920,87.90,384,1.000,bicubic,-33.650,-21.700,-9 -swinv2_base_window12to16_192to256_22kft1k,63.200,36.800,77.120,22.880,87.92,256,0.900,bicubic,-33.860,-22.540,-5 -tf_efficientnet_b5_ns,63.043,36.957,77.773,22.227,30.39,456,0.934,bicubic,-33.827,-21.867,+7 -deit3_base_patch16_384_in21ft1k,62.637,37.363,75.550,24.450,86.88,384,1.000,bicubic,-34.603,-24.120,-16 -vit_base_patch8_224,62.197,37.803,75.617,24.383,86.58,224,0.900,bicubic,-34.883,-24.003,-10 -convnext_base_in22ft1k,62.010,37.990,76.037,23.963,88.59,224,0.875,bicubic,-34.830,-23.613,+7 -deit3_base_patch16_224_in21ft1k,61.787,38.213,74.723,25.277,86.59,224,1.000,bicubic,-35.083,-24.897,+4 -tf_efficientnet_b4_ns,61.233,38.767,76.160,23.840,19.34,380,0.922,bicubic,-35.477,-23.480,+16 -tf_efficientnetv2_l_in21ft1k,60.953,39.047,75.843,24.157,118.52,480,1.000,bicubic,-36.157,-23.867,-16 -tf_efficientnetv2_xl_in21ft1k,60.680,39.320,74.397,25.603,208.12,512,1.000,bicubic,-36.470,-25.223,-19 -beit_base_patch16_224,60.317,39.683,75.597,24.403,86.53,224,0.900,bicubic,-36.343,-24.063,+19 -vit_base_patch16_384,60.187,39.813,73.837,26.163,86.86,384,1.000,bicubic,-36.833,-25.873,-13 -swin_base_patch4_window7_224,59.537,40.463,74.240,25.760,87.77,224,0.900,bicubic,-37.143,-25.420,+15 -convnext_small_384_in22ft1k,59.110,40.890,73.903,26.097,50.22,384,1.000,bicubic,-37.980,-25.787,-20 -volo_d5_512,58.920,41.080,73.200,26.800,296.09,512,1.150,bicubic,-38.370,-26.560,-35 -volo_d5_448,58.793,41.207,73.057,26.943,295.91,448,1.150,bicubic,-38.447,-26.683,-31 -tf_efficientnetv2_m_in21ft1k,58.643,41.357,73.980,26.020,54.14,480,1.000,bicubic,-38.327,-25.630,-15 -vit_large_r50_s32_224,58.633,41.367,71.720,28.280,328.99,224,0.900,bicubic,-37.547,-27.820,+64 -deit3_large_patch16_384,58.357,41.643,72.970,27.030,304.76,384,1.000,bicubic,-38.493,-26.650,-7 -deit3_huge_patch14_224,58.110,41.890,72.130,27.870,632.13,224,0.900,bicubic,-38.470,-27.390,+15 -tf_efficientnet_b8_ap,57.830,42.170,72.953,27.047,87.41,672,0.954,bicubic,-38.720,-26.587,+18 -convnext_small_in22ft1k,57.533,42.467,72.677,27.323,50.22,224,0.875,bicubic,-38.927,-26.793,+27 -cait_m48_448,57.477,42.523,71.867,28.133,356.46,448,1.000,bicubic,-39.403,-27.753,-14 -cait_m36_384,57.467,42.533,72.320,27.680,271.22,384,1.000,bicubic,-39.363,-27.340,-10 -tf_efficientnet_b3_ns,57.413,42.587,72.387,27.613,12.23,300,0.904,bicubic,-38.687,-27.093,+65 -volo_d4_448,57.293,42.707,71.533,28.467,193.41,448,1.150,bicubic,-39.777,-28.217,-30 -vit_base_patch16_224,56.840,43.160,70.637,29.363,86.57,224,0.900,bicubic,-39.460,-28.923,+41 -volo_d5_224,56.490,43.510,70.647,29.353,295.46,224,0.960,bicubic,-40.390,-29.023,-21 -deit3_large_patch16_224,56.463,43.537,70.463,29.537,304.37,224,0.900,bicubic,-39.727,-28.837,+52 -xcit_large_24_p8_384_dist,56.350,43.650,71.320,28.680,188.93,384,1.000,bicubic,-40.410,-28.240,-9 -xcit_large_24_p8_224_dist,56.027,43.973,70.663,29.337,188.93,224,1.000,bicubic,-40.613,-28.797,+1 -xcit_large_24_p16_384_dist,54.910,45.090,69.863,30.137,189.10,384,1.000,bicubic,-42.030,-29.647,-27 -volo_d4_224,54.743,45.257,68.860,31.140,192.96,224,0.960,bicubic,-42.037,-30.810,-16 -deit3_small_patch16_384_in21ft1k,54.467,45.533,68.310,31.690,22.21,384,1.000,bicubic,-42.203,-31.330,-5 -vit_base_r50_s16_384,54.400,45.600,69.563,30.437,98.95,384,1.000,bicubic,-42.050,-30.097,+15 -resnetv2_152x4_bitm,54.323,45.677,70.173,29.827,936.53,480,1.000,bilinear,-42.557,-29.487,-28 -xcit_large_24_p16_224_dist,54.260,45.740,68.980,31.020,189.10,224,1.000,bicubic,-42.060,-30.520,+29 -vit_small_r26_s32_384,54.203,45.797,68.747,31.253,36.47,384,1.000,bicubic,-41.857,-30.803,+59 -volo_d3_448,53.993,46.007,68.023,31.977,86.63,448,1.000,bicubic,-43.027,-31.657,-39 -tf_efficientnet_b5_ap,53.867,46.133,69.163,30.837,30.39,456,0.934,bicubic,-42.213,-30.377,+51 -xcit_medium_24_p8_224_dist,53.663,46.337,68.407,31.593,84.32,224,1.000,bicubic,-42.857,-31.103,+1 -tf_efficientnet_b2_ns,53.597,46.403,70.277,29.723,9.11,260,0.890,bicubic,-41.923,-29.063,+119 -tf_efficientnet_b6_ap,53.567,46.433,68.550,31.450,43.04,528,0.942,bicubic,-42.803,-31.000,+15 -cait_s36_384,53.550,46.450,68.020,31.980,68.37,384,1.000,bicubic,-43.080,-31.580,-11 -convnext_large,53.533,46.467,68.183,31.817,197.77,224,0.875,bicubic,-42.487,-31.287,+58 -deit3_base_patch16_384,53.510,46.490,67.630,32.370,86.88,384,1.000,bicubic,-42.720,-31.770,+32 -deit3_base_patch16_224,53.457,46.543,67.593,32.407,86.59,224,0.900,bicubic,-42.323,-31.677,+81 -tf_efficientnet_b8,53.410,46.590,69.090,30.910,87.41,672,0.954,bicubic,-43.290,-30.440,-21 -xcit_medium_24_p8_384_dist,53.407,46.593,68.137,31.863,84.32,384,1.000,bicubic,-43.373,-31.473,-30 -vit_base_patch32_384,53.300,46.700,68.043,31.957,88.30,384,1.000,bicubic,-42.600,-31.397,+65 -tf_efficientnet_b7_ap,53.260,46.740,68.867,31.133,66.35,600,0.949,bicubic,-43.090,-30.723,+9 -xcit_medium_24_p16_384_dist,53.213,46.787,68.057,31.943,84.40,384,1.000,bicubic,-43.487,-31.543,-26 -tf_efficientnetv2_s_in21ft1k,53.143,46.857,69.007,30.993,21.46,384,1.000,bicubic,-43.327,-30.563,-8 -tf_efficientnet_b4_ap,53.093,46.907,68.213,31.787,19.34,380,0.922,bicubic,-42.397,-31.177,+109 -regnetz_e8,53.017,46.983,67.140,32.860,57.70,320,1.000,bicubic,-43.583,-32.470,-21 -dm_nfnet_f5,52.870,47.130,67.427,32.573,377.21,544,0.954,bicubic,-43.940,-32.243,-41 -volo_d3_224,52.703,47.297,66.320,33.680,86.33,224,0.960,bicubic,-43.737,-33.300,-5 -deit3_small_patch16_224_in21ft1k,52.690,47.310,66.877,33.123,22.06,224,1.000,bicubic,-43.130,-32.523,+66 -dm_nfnet_f6,52.447,47.553,67.113,32.887,438.36,576,0.956,bicubic,-44.473,-32.607,-53 -tf_efficientnet_b7,52.393,47.607,68.230,31.770,66.35,600,0.949,bicubic,-44.187,-31.290,-24 -tf_efficientnetv2_l,52.387,47.613,67.240,32.760,118.52,480,1.000,bicubic,-44.263,-32.320,-30 -xcit_small_24_p8_384_dist,52.360,47.640,66.833,33.167,47.63,384,1.000,bicubic,-44.450,-32.797,-46 -swsl_resnet18,52.337,47.663,70.477,29.523,11.69,224,0.875,bilinear,-38.733,-27.733,+512 -efficientnetv2_rw_m,52.323,47.677,67.210,32.790,53.24,416,1.000,bicubic,-43.947,-32.350,+8 -deit_base_distilled_patch16_384,52.253,47.747,67.737,32.263,87.63,384,1.000,bicubic,-44.257,-31.853,-22 -xcit_medium_24_p16_224_dist,52.197,47.803,66.893,33.107,84.40,224,1.000,bicubic,-44.063,-32.517,+7 -xcit_small_24_p8_224_dist,52.197,47.803,66.767,33.233,47.63,224,1.000,bicubic,-44.353,-32.803,-29 -dm_nfnet_f3,52.130,47.870,66.740,33.260,254.92,416,0.940,bicubic,-44.600,-32.890,-46 -resnetv2_152x2_bit_teacher_384,51.943,48.057,68.663,31.337,236.34,384,1.000,bicubic,-44.247,-30.837,+11 -resmlp_big_24_224_in22ft1k,51.893,48.107,68.470,31.530,129.14,224,0.875,bicubic,-44.457,-31.050,-9 -xcit_small_24_p16_384_dist,51.883,48.117,66.367,33.633,47.67,384,1.000,bicubic,-44.457,-33.213,-9 -cait_s24_384,51.783,48.217,66.320,33.680,47.06,384,1.000,bicubic,-44.787,-33.230,-35 -resnetv2_152x2_bitm,51.757,48.243,69.247,30.753,236.34,448,1.000,bilinear,-44.763,-30.343,-32 -ecaresnet269d,51.663,48.337,66.043,33.957,102.09,352,1.000,bicubic,-44.797,-33.567,-27 -vit_base_patch16_224_miil,51.550,48.450,65.207,34.793,86.54,224,0.875,bilinear,-44.480,-34.143,+27 -convnext_tiny_384_in22ft1k,51.453,48.547,66.427,33.573,28.59,384,1.000,bicubic,-44.717,-33.053,+8 -convnext_base,51.247,48.753,66.190,33.810,88.59,224,0.875,bicubic,-44.693,-33.190,+35 -pit_b_distilled_224,51.157,48.843,66.773,33.227,74.79,224,0.900,bicubic,-44.913,-32.607,+17 -xcit_small_12_p8_384_dist,51.093,48.907,65.833,34.167,26.21,384,1.000,bicubic,-45.387,-33.657,-35 -convnext_tiny_in22ft1k,51.083,48.917,66.620,33.380,28.59,224,0.875,bicubic,-44.647,-32.740,+54 -dm_nfnet_f4,50.907,49.093,65.563,34.437,316.07,512,0.951,bicubic,-45.873,-34.057,-63 -tf_efficientnet_b1_ns,50.900,49.100,67.927,32.073,7.79,240,0.882,bicubic,-43.960,-31.323,+169 -volo_d2_384,50.883,49.117,65.637,34.363,58.87,384,1.000,bicubic,-45.827,-33.963,-58 -xcit_small_24_p16_224_dist,50.730,49.270,65.033,34.967,47.67,224,1.000,bicubic,-45.060,-34.317,+42 -tf_efficientnetv2_m,50.560,49.440,66.000,34.000,54.14,480,1.000,bicubic,-45.980,-33.570,-45 -xcit_small_12_p16_384_dist,50.527,49.473,65.297,34.703,26.25,384,1.000,bicubic,-45.803,-34.193,-21 -efficientnet_b4,50.503,49.497,65.707,34.293,19.34,384,1.000,bicubic,-45.027,-33.693,+72 -volo_d1_384,50.473,49.527,64.927,35.073,26.78,384,1.000,bicubic,-45.997,-34.623,-42 -xcit_small_12_p8_224_dist,50.440,49.560,65.433,34.567,26.21,224,1.000,bicubic,-45.520,-33.987,+20 -resnetv2_101x3_bitm,50.403,49.597,67.787,32.213,387.93,448,1.000,bilinear,-45.847,-31.803,-16 -regnetz_040h,50.330,49.670,65.630,34.370,28.94,320,1.000,bicubic,-46.000,-33.890,-27 -ssl_resnext101_32x16d,50.250,49.750,66.017,33.983,194.03,224,0.875,bilinear,-45.160,-33.393,+82 -cait_s24_224,50.240,49.760,65.023,34.977,46.92,224,1.000,bicubic,-45.400,-34.367,+54 -eca_nfnet_l2,50.233,49.767,65.453,34.547,56.72,384,1.000,bicubic,-46.217,-34.167,-43 -vit_small_patch16_384,50.167,49.833,65.807,34.193,22.20,384,1.000,bicubic,-45.813,-33.783,+12 -resnest269e,50.153,49.847,64.663,35.337,110.93,416,0.928,bicubic,-45.967,-34.857,-8 -deit_base_distilled_patch16_224,50.060,49.940,66.223,33.777,87.34,224,0.900,bicubic,-45.690,-33.057,+35 -tf_efficientnet_b3_ap,50.043,49.957,65.213,34.787,12.23,300,0.904,bicubic,-44.927,-33.897,+136 -resnest200e,49.877,50.123,64.740,35.260,70.20,320,0.909,bicubic,-46.193,-34.740,-6 -volo_d2_224,49.813,50.187,64.587,35.413,58.68,224,0.960,bicubic,-46.607,-34.913,-45 -seresnextaa101d_32x8d,49.767,50.233,64.420,35.580,93.59,288,1.000,bicubic,-46.653,-35.100,-47 -xception65,49.757,50.243,63.523,36.477,39.92,299,0.940,bicubic,-45.933,-35.787,+42 -swinv2_base_window16_256,49.667,50.333,63.810,36.190,87.92,256,0.900,bicubic,-46.503,-35.590,-18 -convnext_small,49.573,50.427,64.830,35.170,50.22,224,0.875,bicubic,-46.037,-34.430,+47 -cait_xs24_384,49.537,50.463,64.900,35.100,26.67,384,1.000,bicubic,-46.473,-34.530,+1 -tf_efficientnet_b5,49.510,50.490,65.650,34.350,30.39,456,0.934,bicubic,-46.470,-33.800,+2 -resnetv2_152x2_bit_teacher,49.487,50.513,65.620,34.380,236.34,224,0.875,bicubic,-46.263,-33.810,+24 -resnet200d,49.473,50.527,64.327,35.673,64.69,320,1.000,bicubic,-46.637,-35.133,-19 -xcit_small_12_p16_224_dist,49.420,50.580,63.840,36.160,26.25,224,1.000,bicubic,-46.310,-35.460,+27 -resnest101e,49.363,50.637,65.597,34.403,48.28,256,0.875,bilinear,-46.197,-33.673,+45 -regnetz_040,49.283,50.717,64.063,35.937,27.12,320,1.000,bicubic,-46.897,-35.447,-28 -resnet152d,49.250,50.750,64.417,35.583,60.21,320,1.000,bicubic,-46.620,-35.013,+8 -vit_base_patch32_224,49.250,50.750,64.343,35.657,88.22,224,0.900,bicubic,-45.140,-34.717,+203 -seresnet152d,49.250,50.750,64.177,35.823,66.84,320,1.000,bicubic,-47.060,-35.333,-47 -xcit_large_24_p8_224,49.237,50.763,62.840,37.160,188.93,224,1.000,bicubic,-46.843,-36.310,-23 -ssl_resnext101_32x8d,49.097,50.903,65.483,34.517,88.79,224,0.875,bilinear,-46.233,-33.827,+70 -resmlp_big_24_distilled_224,49.097,50.903,65.477,34.523,129.14,224,0.875,bicubic,-46.773,-33.963,+1 -volo_d1_224,48.970,51.030,63.190,36.810,26.63,224,0.960,bicubic,-47.060,-36.200,-17 -repvgg_b3,48.920,51.080,64.880,35.120,123.09,224,0.875,bilinear,-45.640,-34.030,+179 -resnetrs420,48.857,51.143,63.427,36.573,191.89,416,1.000,bicubic,-47.553,-36.113,-64 -deit3_small_patch16_384,48.670,51.330,62.823,37.177,22.21,384,1.000,bicubic,-46.940,-36.567,+29 -seresnext101d_32x8d,48.597,51.403,62.960,37.040,93.59,288,1.000,bicubic,-47.763,-36.510,-63 -efficientnetv2_rw_s,48.593,51.407,63.837,36.163,23.94,384,1.000,bicubic,-47.107,-35.543,+18 -regnetz_d32,48.590,51.410,65.190,34.810,27.58,320,0.950,bicubic,-47.280,-34.240,-5 -swinv2_small_window16_256,48.577,51.423,62.763,37.237,49.73,256,0.900,bicubic,-47.493,-36.577,-29 -efficientnet_b3,48.567,51.433,64.250,35.750,12.23,320,1.000,bicubic,-46.573,-34.960,+78 -ecaresnet101d,48.537,51.463,64.097,35.903,44.57,224,0.875,bicubic,-46.623,-35.133,+74 -vit_small_r26_s32_224,48.367,51.633,63.800,36.200,36.43,224,0.900,bicubic,-46.753,-35.420,+82 -dm_nfnet_f2,48.367,51.633,63.230,36.770,193.78,352,0.920,bicubic,-48.093,-36.310,-81 -swinv2_base_window8_256,48.340,51.660,63.597,36.403,87.92,256,0.900,bicubic,-47.730,-35.823,-36 -repvgg_b3g4,48.303,51.697,64.793,35.207,83.83,224,0.875,bilinear,-46.197,-34.227,+176 -vit_large_patch32_384,48.247,51.753,61.823,38.177,306.63,384,1.000,bicubic,-46.993,-37.497,+62 -convit_base,48.220,51.780,63.007,36.993,86.54,224,0.875,bicubic,-46.880,-36.133,+81 -swin_s3_base_224,48.140,51.860,62.263,37.737,71.13,224,0.900,bicubic,-47.900,-37.087,-34 -sequencer2d_l,48.107,51.893,62.343,37.657,54.30,224,0.875,bicubic,-47.763,-37.127,-18 -resnetrs350,48.057,51.943,62.650,37.350,163.96,384,1.000,bicubic,-48.183,-36.820,-60 -regnetz_d8,48.013,51.987,64.410,35.590,23.37,320,1.000,bicubic,-47.997,-35.110,-33 -twins_svt_large,47.947,52.053,62.910,37.090,99.27,224,0.900,bicubic,-47.773,-36.460,0 -vit_relpos_base_patch16_224,47.937,52.063,62.847,37.153,86.43,224,0.900,bicubic,-47.193,-36.453,+66 -mixer_b16_224_miil,47.800,52.200,63.397,36.603,59.88,224,0.875,bilinear,-47.090,-35.683,+107 -repvgg_b2g4,47.793,52.207,64.377,35.623,61.76,224,0.875,bilinear,-46.027,-34.553,+244 -vit_relpos_base_patch16_clsgap_224,47.763,52.237,62.410,37.590,86.43,224,0.900,bicubic,-47.487,-36.790,+50 -vit_relpos_medium_patch16_cls_224,47.660,52.340,61.803,38.197,38.76,224,0.900,bicubic,-47.640,-37.287,+45 -seresnext101_32x8d,47.653,52.347,61.447,38.553,93.57,288,1.000,bicubic,-48.477,-37.913,-57 -eca_nfnet_l1,47.643,52.357,62.763,37.237,41.41,320,1.000,bicubic,-48.297,-36.727,-34 -resnetv2_50x3_bitm,47.593,52.407,65.603,34.397,217.32,448,1.000,bilinear,-48.677,-34.027,-75 -pit_s_distilled_224,47.547,52.453,63.497,36.503,24.04,224,0.900,bicubic,-47.193,-35.683,+119 -resnest50d_4s2x40d,47.490,52.510,63.817,36.183,30.42,224,0.875,bicubic,-47.220,-35.323,+123 -efficientnet_b3_pruned,47.443,52.557,62.787,37.213,9.86,300,0.904,bicubic,-47.137,-36.283,+144 -crossvit_18_dagger_408,47.380,52.620,60.943,39.057,44.61,408,1.000,bicubic,-48.750,-38.527,-64 -xcit_small_24_p8_224,47.297,52.703,60.983,39.017,47.63,224,1.000,bicubic,-48.613,-38.197,-37 -tresnet_m,47.217,52.783,62.000,38.000,31.39,224,0.875,bilinear,-48.163,-37.150,+29 -tf_efficientnet_b6,47.207,52.793,63.110,36.890,43.04,528,0.942,bicubic,-49.083,-36.410,-84 -convnext_tiny,47.180,52.820,63.217,36.783,28.59,224,0.875,bicubic,-47.780,-35.983,+81 -ssl_resnext101_32x4d,47.167,52.833,63.367,36.633,44.18,224,0.875,bilinear,-47.983,-35.933,+47 -resnetrs270,47.107,52.893,62.013,37.987,129.86,352,1.000,bicubic,-48.953,-37.467,-58 -regnetz_d8_evos,47.080,52.920,63.390,36.610,23.46,320,0.950,bicubic,-49.140,-36.100,-78 -tf_efficientnet_b4,47.080,52.920,62.857,37.143,19.34,380,0.922,bicubic,-48.510,-36.473,-5 -vit_base_patch16_rpn_224,47.063,52.937,62.403,37.597,86.54,224,0.900,bicubic,-47.757,-36.687,+95 -swinv2_small_window8_256,47.030,52.970,62.297,37.703,49.73,256,0.900,bicubic,-48.700,-37.063,-25 -xcit_small_12_p8_224,46.983,53.017,60.537,39.463,26.21,224,1.000,bicubic,-48.437,-38.663,+12 -xcit_large_24_p16_224,46.960,53.040,60.670,39.330,189.10,224,1.000,bicubic,-47.990,-38.160,+75 -convnext_tiny_hnf,46.937,53.063,61.200,38.800,28.59,224,0.950,bicubic,-47.833,-37.960,+97 -xception65p,46.933,53.067,61.083,38.917,39.82,299,0.940,bicubic,-48.727,-38.187,-19 -resnet101d,46.893,53.107,62.323,37.677,44.57,320,1.000,bicubic,-48.857,-37.117,-35 -resnet152,46.800,53.200,60.410,39.590,60.19,224,0.950,bicubic,-48.750,-38.850,-10 -gluon_seresnext101_64x4d,46.677,53.323,61.297,38.703,88.23,224,0.875,bicubic,-47.983,-37.683,+117 -twins_pcpvt_large,46.627,53.373,62.233,37.767,60.99,224,0.900,bicubic,-49.093,-37.257,-31 -dm_nfnet_f1,46.547,53.453,61.403,38.597,132.63,320,0.910,bicubic,-49.833,-38.067,-112 -regnetv_064,46.480,53.520,62.253,37.747,30.58,288,1.000,bicubic,-49.290,-37.167,-41 -xcit_medium_24_p8_224,46.473,53.527,59.647,40.353,84.32,224,1.000,bicubic,-49.397,-39.433,-50 -crossvit_15_dagger_408,46.457,53.543,60.487,39.513,28.50,408,1.000,bicubic,-49.363,-38.823,-47 -resnetrs200,46.430,53.570,61.060,38.940,93.21,320,1.000,bicubic,-49.910,-38.490,-110 -swin_s3_small_224,46.393,53.607,60.897,39.103,49.74,224,0.900,bicubic,-49.447,-38.303,-51 -fbnetv3_g,46.347,53.653,62.403,37.597,16.62,288,0.950,bilinear,-48.783,-36.797,+31 -sequencer2d_m,46.297,53.703,60.903,39.097,38.31,224,0.875,bicubic,-49.303,-38.367,-24 -tresnet_xl,46.280,53.720,61.950,38.050,78.44,224,0.875,bilinear,-48.780,-37.310,+46 -xcit_tiny_24_p8_384_dist,46.263,53.737,60.713,39.287,12.11,384,1.000,bicubic,-49.977,-38.727,-101 -xcit_tiny_24_p8_224_dist,46.257,53.743,60.607,39.393,12.11,224,1.000,bicubic,-49.203,-38.753,-9 -gernet_m,46.170,53.830,62.700,37.300,21.14,224,0.875,bilinear,-48.380,-36.230,+121 -deit_small_distilled_patch16_224,46.163,53.837,62.403,37.597,22.44,224,0.900,bicubic,-48.437,-36.697,+110 -regnety_160,46.163,53.837,61.843,38.157,83.59,288,1.000,bicubic,-49.717,-37.717,-66 -crossvit_base_240,46.133,53.867,60.223,39.777,105.03,240,0.875,bicubic,-48.937,-38.757,+39 -swinv2_cr_small_ns_224,46.123,53.877,60.787,39.213,49.70,224,0.900,bicubic,-49.567,-38.523,-41 -resnest50d_1s4x24d,46.093,53.907,62.377,37.623,25.68,224,0.875,bicubic,-48.297,-36.693,+130 -tf_efficientnet_b0_ns,46.053,53.947,63.270,36.730,5.29,224,0.875,bicubic,-47.697,-35.700,+206 -jx_nest_base,46.040,53.960,60.093,39.907,67.72,224,0.875,bicubic,-49.500,-39.207,-30 -resnet51q,46.027,53.973,60.903,39.097,35.70,288,1.000,bilinear,-49.173,-38.377,+10 -vit_small_patch16_224,46.000,54.000,61.820,38.180,22.05,224,0.900,bicubic,-48.880,-37.450,+59 -vit_relpos_medium_patch16_224,45.960,54.040,61.030,38.970,38.75,224,0.900,bicubic,-49.240,-38.190,+9 -regnety_080,45.953,54.047,60.880,39.120,39.18,288,1.000,bicubic,-49.897,-38.560,-69 -resnest50d,45.943,54.057,62.630,37.370,27.48,224,0.875,bilinear,-48.677,-36.400,+95 -deit3_small_patch16_224,45.923,54.077,58.893,41.107,22.06,224,0.900,bicubic,-48.767,-39.857,+84 -crossvit_18_240,45.903,54.097,60.383,39.617,43.27,240,0.875,bicubic,-49.167,-38.737,+27 -twins_pcpvt_base,45.893,54.107,61.343,38.657,43.83,224,0.900,bicubic,-49.567,-38.047,-26 -regnety_032,45.883,54.117,61.533,38.467,19.44,288,1.000,bicubic,-49.597,-37.787,-31 -levit_384,45.873,54.127,61.690,38.310,39.13,224,0.900,bicubic,-49.337,-37.470,-1 -twins_svt_base,45.873,54.127,60.967,39.033,56.07,224,0.900,bicubic,-49.697,-38.263,-44 -crossvit_18_dagger_240,45.850,54.150,59.923,40.077,44.27,240,0.875,bicubic,-49.330,-39.197,+1 -vit_relpos_medium_patch16_rpn_224,45.753,54.247,60.957,39.043,38.73,224,0.900,bicubic,-49.317,-38.233,+20 -vit_srelpos_medium_patch16_224,45.730,54.270,61.070,38.930,38.74,224,0.900,bicubic,-49.170,-38.130,+42 -crossvit_15_dagger_240,45.697,54.303,60.090,39.910,28.21,240,0.875,bicubic,-49.283,-39.070,+28 -regnetz_c16,45.690,54.310,62.517,37.483,13.46,320,0.940,bicubic,-49.700,-36.793,-24 -convmixer_1536_20,45.660,54.340,61.770,38.230,51.63,224,0.960,bicubic,-49.310,-37.400,+29 -gc_efficientnetv2_rw_t,45.657,54.343,60.200,39.800,13.68,288,1.000,bicubic,-49.633,-39.020,-16 -efficientnetv2_rw_t,45.607,54.393,60.187,39.813,13.65,288,1.000,bicubic,-49.463,-39.033,+13 -gluon_seresnext101_32x4d,45.597,54.403,61.140,38.860,48.96,224,0.875,bicubic,-48.853,-37.950,+102 -xcit_tiny_24_p16_384_dist,45.587,54.413,60.510,39.490,12.12,384,1.000,bicubic,-49.903,-38.850,-44 -xcit_medium_24_p16_224,45.527,54.473,59.000,41.000,84.40,224,1.000,bicubic,-49.603,-39.930,-1 -xcit_small_24_p16_224,45.517,54.483,58.887,41.113,47.67,224,1.000,bicubic,-49.563,-40.183,+6 -dm_nfnet_f0,45.480,54.520,60.990,39.010,71.49,256,0.900,bicubic,-50.210,-38.340,-69 -resnext101_64x4d,45.453,54.547,59.040,40.960,83.46,288,1.000,bicubic,-50.087,-40.250,-54 -gluon_resnet152_v1d,45.437,54.563,60.083,39.917,60.21,224,0.875,bicubic,-49.003,-38.927,+97 -nfnet_l0,45.423,54.577,62.073,37.927,35.07,288,1.000,bicubic,-49.967,-37.347,-36 -ssl_resnext50_32x4d,45.403,54.597,62.033,37.967,25.03,224,0.875,bilinear,-49.297,-37.207,+59 -resnetv2_50x1_bit_distilled,45.397,54.603,62.310,37.690,25.55,224,0.875,bicubic,-50.003,-37.120,-41 -xcit_small_12_p16_224,45.397,54.603,59.417,40.583,26.25,224,1.000,bicubic,-49.423,-39.643,+38 -jx_nest_small,45.353,54.647,59.010,40.990,38.35,224,0.875,bicubic,-50.177,-40.210,-58 -cs3se_edgenet_x,45.327,54.673,60.383,39.617,50.72,320,1.000,bicubic,-50.683,-39.057,-114 -resnet61q,45.283,54.717,59.400,40.600,36.85,288,1.000,bicubic,-49.837,-39.680,-7 -cs3edgenet_x,45.280,54.720,60.287,39.713,47.82,288,1.000,bicubic,-50.190,-38.993,-54 -tresnet_xl_448,45.223,54.777,61.440,38.560,78.44,448,0.875,bilinear,-50.287,-37.900,-60 -nasnetalarge,45.207,54.793,57.880,42.120,88.75,331,0.911,bicubic,-49.943,-41.250,-20 -convit_small,45.197,54.803,60.497,39.503,27.78,224,0.875,bicubic,-49.723,-38.613,+17 -swin_small_patch4_window7_224,45.157,54.843,60.333,39.667,49.61,224,0.900,bicubic,-50.563,-38.957,-86 -tf_efficientnet_b3,45.100,54.900,60.643,39.357,12.23,300,0.904,bicubic,-49.810,-38.467,+16 -resnet101,45.087,54.913,59.577,40.423,44.55,224,0.950,bicubic,-49.893,-39.503,+4 -sequencer2d_s,45.083,54.917,60.067,39.933,27.65,224,0.875,bicubic,-50.387,-39.203,-60 -rexnet_200,45.057,54.943,62.313,37.687,16.37,224,0.875,bicubic,-49.603,-36.777,+53 -resnetrs152,44.957,55.043,59.707,40.293,86.62,320,1.000,bicubic,-51.003,-39.673,-120 -resnetv2_101,44.933,55.067,58.837,41.163,44.54,224,0.950,bicubic,-49.997,-40.283,+9 -ecaresnetlight,44.893,55.107,60.777,39.223,30.16,224,0.875,bicubic,-49.247,-38.173,+116 -deit_base_patch16_224,44.873,55.127,59.190,40.810,86.57,224,0.900,bicubic,-50.137,-39.790,-4 -cait_xxs36_384,44.777,55.223,59.367,40.633,17.37,384,1.000,bicubic,-50.443,-39.953,-39 -deit_base_patch16_384,44.770,55.230,59.627,40.373,86.86,384,1.000,bicubic,-50.880,-39.613,-89 -resmlp_36_distilled_224,44.757,55.243,61.073,38.927,44.69,224,0.875,bicubic,-49.793,-38.087,+63 -gernet_l,44.730,55.270,58.947,41.053,31.08,256,0.875,bilinear,-50.200,-40.253,+1 -xcit_tiny_24_p16_224_dist,44.720,55.280,59.420,40.580,12.12,224,1.000,bicubic,-49.500,-39.540,+101 -resmlp_24_distilled_224,44.710,55.290,61.463,38.537,30.02,224,0.875,bicubic,-49.630,-37.627,+85 -tf_efficientnet_b2_ap,44.707,55.293,60.680,39.320,9.11,260,0.890,bicubic,-49.563,-38.270,+90 -swinv2_tiny_window16_256,44.573,55.427,59.577,40.423,28.35,256,0.900,bicubic,-50.787,-39.723,-59 -vit_relpos_small_patch16_224,44.550,55.450,60.203,39.797,21.98,224,0.900,bicubic,-50.140,-38.897,+33 -gmlp_s16_224,44.483,55.517,58.627,41.373,19.42,224,0.875,bicubic,-49.027,-40.153,+182 -ens_adv_inception_resnet_v2,44.390,55.610,58.110,41.890,55.84,299,0.897,bicubic,-49.730,-40.680,+107 -tresnet_l,44.360,55.640,59.947,40.053,55.99,224,0.875,bilinear,-50.540,-39.083,0 -gluon_resnext101_32x4d,44.287,55.713,59.090,40.910,44.18,224,0.875,bicubic,-49.833,-39.840,+104 -poolformer_m48,44.270,55.730,59.300,40.700,73.47,224,0.950,bicubic,-50.860,-39.820,-40 -wide_resnet50_2,44.180,55.820,59.697,40.303,68.88,224,0.875,bicubic,-50.480,-39.353,+35 -regnetz_c16_evos,44.160,55.840,61.057,38.943,13.49,320,0.950,bicubic,-51.470,-38.363,-101 -vit_srelpos_small_patch16_224,44.137,55.863,59.710,40.290,21.97,224,0.900,bicubic,-50.413,-39.430,+50 -crossvit_15_240,44.123,55.877,59.130,40.870,27.53,240,0.875,bicubic,-50.597,-39.950,+18 -seresnext50_32x4d,44.120,55.880,59.480,40.520,27.56,224,0.875,bicubic,-50.690,-39.650,+4 -resnetv2_101x1_bitm,44.113,55.887,61.980,38.020,44.54,448,1.000,bilinear,-51.207,-37.390,-66 -gluon_resnet152_v1s,44.070,55.930,58.700,41.300,60.32,224,0.875,bicubic,-50.650,-40.360,+16 -pit_b_224,44.067,55.933,58.017,41.983,73.76,224,0.900,bicubic,-50.723,-40.803,+3 -ssl_resnet50,44.020,55.980,61.910,38.090,25.56,224,0.875,bilinear,-50.300,-37.240,+71 -poolformer_m36,44.020,55.980,59.067,40.933,56.17,224,0.950,bicubic,-50.990,-40.033,-30 -inception_resnet_v2,44.007,55.993,57.907,42.093,55.84,299,0.897,bicubic,-50.323,-40.893,+68 -pnasnet5large,43.953,56.047,56.723,43.277,86.06,331,0.911,bicubic,-51.407,-42.407,-76 -pit_s_224,43.893,56.107,58.637,41.363,23.46,224,0.900,bicubic,-50.687,-40.293,+34 -gluon_resnext101_64x4d,43.880,56.120,58.703,41.297,83.46,224,0.875,bicubic,-50.470,-40.177,+62 -coat_lite_small,43.813,56.187,57.143,42.857,19.84,224,0.900,bicubic,-51.267,-41.887,-45 -regnetv_040,43.793,56.207,58.460,41.540,20.64,288,1.000,bicubic,-51.937,-40.920,-130 -tnt_s_patch16_224,43.777,56.223,59.197,40.803,23.76,224,0.900,bicubic,-50.793,-39.983,+32 -mobilevitv2_200_in22ft1k,43.770,56.230,59.500,40.500,18.45,256,0.888,bicubic,-51.280,-39.580,-40 -swinv2_cr_small_224,43.770,56.230,57.690,42.310,49.70,224,0.900,bicubic,-51.630,-41.360,-89 -cspresnext50,43.763,56.237,60.143,39.857,20.57,256,0.887,bilinear,-50.477,-38.907,+68 -cait_xxs36_224,43.760,56.240,58.730,41.270,17.30,224,1.000,bicubic,-50.170,-40.160,+102 -ecaresnet50d,43.743,56.257,60.373,39.627,25.58,224,0.875,bicubic,-50.457,-38.647,+73 -ecaresnet101d_pruned,43.740,56.260,59.607,40.393,24.88,224,0.875,bicubic,-50.720,-39.483,+38 -swin_s3_tiny_224,43.717,56.283,59.510,40.490,28.33,224,0.900,bicubic,-51.183,-39.650,-27 -tf_efficientnetv2_s,43.707,56.293,58.597,41.403,21.46,384,1.000,bicubic,-52.003,-40.803,-132 -rexnet_150,43.687,56.313,60.890,39.110,9.73,224,0.875,bicubic,-50.593,-38.190,+56 -pit_xs_distilled_224,43.660,56.340,60.707,39.293,11.00,224,0.900,bicubic,-49.580,-38.123,+179 -xcit_tiny_12_p8_224_dist,43.640,56.360,58.457,41.543,6.71,224,1.000,bicubic,-51.080,-40.723,-7 -edgenext_small,43.617,56.383,59.883,40.117,5.59,320,1.000,bicubic,-51.213,-39.527,-23 -crossvit_small_240,43.473,56.527,58.940,41.060,26.86,240,0.875,bicubic,-51.107,-40.180,+15 -cs3sedarknet_x,43.460,56.540,58.843,41.157,35.40,288,1.000,bicubic,-51.960,-40.477,-106 -gluon_resnet101_v1d,43.430,56.570,58.610,41.390,44.57,224,0.875,bicubic,-50.750,-40.330,+64 -ecaresnet50t,43.413,56.587,59.300,40.700,25.57,320,0.950,bicubic,-51.657,-39.990,-62 -gluon_resnet101_v1s,43.363,56.637,58.510,41.490,44.67,224,0.875,bicubic,-50.807,-40.500,+65 -cspdarknet53,43.353,56.647,59.430,40.570,27.64,256,0.887,bilinear,-50.737,-39.550,+72 -xcit_tiny_24_p8_224,43.303,56.697,57.273,42.727,12.11,224,1.000,bicubic,-51.587,-41.917,-37 -xcit_tiny_12_p8_384_dist,43.300,56.700,58.177,41.823,6.71,384,1.000,bicubic,-52.040,-41.163,-100 -dpn68b,43.277,56.723,58.673,41.327,12.61,224,0.875,bicubic,-50.343,-40.027,+126 -convmixer_768_32,43.267,56.733,59.367,40.633,21.11,224,0.960,bicubic,-51.163,-39.743,+25 -visformer_small,43.257,56.743,57.980,42.020,40.22,224,0.900,bicubic,-51.713,-41.230,-55 -eca_nfnet_l0,43.233,56.767,59.907,40.093,24.14,288,1.000,bicubic,-52.217,-39.483,-117 -regnety_064,43.223,56.777,57.230,42.770,30.58,288,1.000,bicubic,-52.567,-42.060,-162 -vit_relpos_base_patch32_plus_rpn_256,43.167,56.833,58.430,41.570,119.42,256,0.900,bicubic,-49.993,-39.890,+170 -vit_small_patch32_384,43.143,56.857,59.293,40.707,22.92,384,1.000,bicubic,-51.457,-39.847,-1 -resnest26d,43.140,56.860,60.637,39.363,17.07,224,0.875,bilinear,-50.100,-38.213,+160 -twins_pcpvt_small,43.087,56.913,58.877,41.123,24.11,224,0.900,bicubic,-51.513,-40.273,-4 -resmlp_36_224,43.050,56.950,59.313,40.687,44.69,224,0.875,bicubic,-50.600,-39.637,+110 -cspresnet50,43.047,56.953,59.167,40.833,21.62,256,0.887,bilinear,-50.813,-39.693,+83 -dpn131,43.040,56.960,57.420,42.580,79.25,224,0.875,bicubic,-50.710,-41.410,+97 -tf_efficientnet_lite4,42.980,57.020,57.640,42.360,13.01,380,0.920,bilinear,-51.890,-41.450,-47 -twins_svt_small,42.930,57.070,58.467,41.533,24.06,224,0.900,bicubic,-51.840,-40.613,-37 -mobilevitv2_200_384_in22ft1k,42.917,57.083,58.987,41.013,18.45,384,1.000,bicubic,-52.473,-40.293,-119 -gluon_resnet152_v1b,42.893,57.107,57.740,42.260,60.19,224,0.875,bicubic,-51.137,-41.010,+60 -fbnetv3_d,42.890,57.110,59.690,40.310,10.31,256,0.950,bilinear,-50.960,-39.220,+78 -dpn107,42.860,57.140,57.363,42.637,86.92,224,0.875,bicubic,-51.100,-41.467,+65 -levit_256,42.813,57.187,57.903,42.097,18.89,224,0.900,bicubic,-51.597,-41.157,+9 -gluon_resnet152_v1c,42.810,57.190,57.737,42.263,60.21,224,0.875,bicubic,-51.080,-41.063,+70 -tf_efficientnet_b1_ap,42.800,57.200,58.817,41.183,7.79,240,0.882,bicubic,-50.830,-39.983,+103 -gcresnet50t,42.790,57.210,59.190,40.810,25.90,256,0.900,bicubic,-51.830,-39.790,-18 -gluon_xception65,42.790,57.210,58.820,41.180,39.92,299,0.903,bicubic,-51.220,-40.200,+56 -tresnet_l_448,42.750,57.250,58.943,41.057,55.99,448,0.875,bilinear,-52.650,-40.357,-132 -cs3darknet_x,42.717,57.283,58.197,41.803,35.05,288,1.000,bicubic,-52.553,-41.083,-119 -resnet50d,42.697,57.303,58.687,41.313,25.58,224,0.875,bicubic,-51.373,-40.233,+46 -gluon_seresnext50_32x4d,42.683,57.317,58.700,41.300,27.56,224,0.875,bicubic,-51.487,-40.210,+36 -convnext_nano,42.590,57.410,57.497,42.503,15.59,288,1.000,bicubic,-52.270,-41.653,-60 -xcit_tiny_12_p16_384_dist,42.587,57.413,58.087,41.913,6.72,384,1.000,bicubic,-51.943,-41.083,-10 -resnext101_32x8d,42.570,57.430,58.293,41.707,88.79,224,0.875,bilinear,-51.200,-40.657,+76 -regnety_040,42.567,57.433,57.037,42.963,20.65,288,1.000,bicubic,-52.903,-42.383,-149 -seresnet50,42.513,57.487,58.677,41.323,28.09,224,0.875,bicubic,-51.567,-40.273,+39 -nf_resnet50,42.507,57.493,59.520,40.480,25.56,288,0.940,bicubic,-51.883,-39.550,-3 -mobilevitv2_175_in22ft1k,42.500,57.500,58.133,41.867,14.25,256,0.888,bicubic,-52.280,-40.967,-59 -resnetrs101,42.443,57.557,57.290,42.710,63.62,288,0.940,bicubic,-52.807,-41.920,-128 -poolformer_s36,42.333,57.667,58.737,41.263,30.86,224,0.900,bicubic,-52.287,-40.313,-34 -jx_nest_tiny,42.330,57.670,57.043,42.957,17.06,224,0.875,bicubic,-52.620,-42.057,-85 -tf_efficientnetv2_b3,42.310,57.690,57.943,42.057,14.36,300,0.904,bicubic,-52.810,-41.257,-110 -convmixer_1024_20_ks9_p14,42.277,57.723,59.713,40.287,24.38,224,0.960,bicubic,-50.073,-38.707,+199 -dpn98,42.273,57.727,56.883,43.117,61.57,224,0.875,bicubic,-51.657,-42.037,+45 -xcit_tiny_24_p16_224,42.273,57.727,56.830,43.170,12.12,224,1.000,bicubic,-51.567,-41.930,+56 -deit_small_patch16_224,42.267,57.733,58.013,41.987,22.05,224,0.900,bicubic,-51.723,-40.947,+39 -tf_efficientnet_cc_b1_8e,42.220,57.780,58.430,41.570,39.72,240,0.882,bicubic,-51.350,-40.260,+90 -legacy_senet154,42.213,57.787,56.593,43.407,115.09,224,0.875,bilinear,-52.517,-42.507,-61 -cait_xxs24_384,42.183,57.817,57.460,42.540,12.03,384,1.000,bicubic,-52.747,-41.680,-90 -xception41p,42.163,57.837,56.890,43.110,26.91,299,0.940,bicubic,-52.897,-42.260,-106 -tf_efficientnet_b2,42.117,57.883,58.197,41.803,9.11,260,0.890,bicubic,-52.093,-40.843,+9 -gluon_resnext50_32x4d,42.043,57.957,57.670,42.330,25.03,224,0.875,bicubic,-51.607,-41.020,+73 -resnext50_32x4d,41.963,58.037,56.757,43.243,25.03,224,0.950,bicubic,-52.617,-42.043,-38 -ecaresnet50d_pruned,41.950,58.050,58.217,41.783,19.94,224,0.875,bicubic,-51.870,-40.783,+50 -efficientnet_b2,41.933,58.067,58.287,41.713,9.11,288,1.000,bicubic,-52.437,-40.763,-17 -mobilevitv2_150_in22ft1k,41.920,58.080,57.923,42.077,10.59,256,0.888,bicubic,-52.770,-40.997,-60 -xcit_tiny_12_p16_224_dist,41.920,58.080,57.227,42.773,6.72,224,1.000,bicubic,-51.430,-41.513,+107 -mobilevitv2_150_384_in22ft1k,41.777,58.223,57.820,42.180,10.59,384,1.000,bicubic,-53.563,-41.310,-153 -mobilevitv2_175_384_in22ft1k,41.670,58.330,58.010,41.990,14.25,384,1.000,bicubic,-53.570,-41.370,-146 -edgenext_small_rw,41.663,58.337,58.520,41.480,7.83,320,1.000,bicubic,-52.697,-40.520,-18 -dla102x2,41.643,58.357,57.940,42.060,41.28,224,0.875,bilinear,-52.357,-41.090,+23 -hrnet_w64,41.640,58.360,57.123,42.877,128.06,224,0.875,bilinear,-52.190,-41.797,+40 -gluon_senet154,41.617,58.383,56.377,43.623,115.09,224,0.875,bicubic,-53.093,-42.593,-71 -poolformer_s24,41.607,58.393,58.440,41.560,21.39,224,0.900,bicubic,-52.723,-40.620,-19 -inception_v4,41.580,58.420,55.390,44.610,42.68,299,0.875,bicubic,-52.800,-43.430,-28 -swinv2_cr_tiny_ns_224,41.543,58.457,57.190,42.810,28.33,224,0.900,bicubic,-53.217,-41.920,-82 -haloregnetz_b,41.540,58.460,57.080,42.920,11.68,224,0.940,bicubic,-52.980,-41.880,-42 -cs3sedarknet_l,41.533,58.467,57.347,42.653,21.91,288,0.950,bicubic,-53.587,-41.863,-137 -efficientnet_em,41.490,58.510,58.880,41.120,6.90,240,0.882,bicubic,-52.250,-40.050,+45 -tf_efficientnet_cc_b0_8e,41.490,58.510,57.380,42.620,24.01,224,0.875,bicubic,-51.380,-41.070,+135 -efficientnet_el,41.483,58.517,58.313,41.687,10.59,300,0.904,bicubic,-53.187,-40.817,-71 -halo2botnet50ts_256,41.467,58.533,56.207,43.793,22.64,256,0.950,bicubic,-53.543,-42.833,-124 -swin_tiny_patch4_window7_224,41.460,58.540,57.307,42.693,28.29,224,0.900,bicubic,-53.160,-41.813,-68 -resnetv2_50,41.387,58.613,56.747,43.253,25.55,224,0.950,bicubic,-52.883,-42.183,-23 -swinv2_tiny_window8_256,41.383,58.617,57.117,42.883,28.35,256,0.900,bicubic,-53.647,-42.053,-129 -cait_xxs24_224,41.380,58.620,57.523,42.477,11.96,224,1.000,bicubic,-52.110,-41.247,+72 -tv_resnet152,41.333,58.667,57.523,42.477,60.19,224,0.875,bilinear,-51.917,-41.227,+93 -gcresnext50ts,41.283,58.717,57.147,42.853,15.67,256,0.900,bicubic,-53.127,-41.843,-45 -cs3darknet_l,41.280,58.720,57.347,42.653,21.16,288,0.950,bicubic,-53.400,-41.873,-81 -dpn92,41.277,58.723,56.340,43.660,37.67,224,0.875,bicubic,-52.903,-42.590,-16 -xception71,41.273,58.727,55.877,44.123,42.34,299,0.903,bicubic,-52.607,-43.073,+14 -adv_inception_v3,41.260,58.740,56.317,43.683,23.83,299,0.875,bicubic,-51.750,-42.173,+109 -gernet_s,41.250,58.750,58.827,41.173,8.17,224,0.875,bilinear,-51.190,-39.673,+152 -resnetv2_50d_evos,41.133,58.867,56.050,43.950,25.59,288,0.950,bicubic,-53.987,-43.180,-155 -resnetblur50,41.077,58.923,57.080,42.920,25.56,224,0.875,bicubic,-52.633,-41.730,+33 -nf_regnet_b1,41.027,58.973,58.113,41.887,10.22,288,0.900,bicubic,-52.853,-40.637,+10 -gluon_resnet50_v1d,40.970,59.030,57.137,42.863,25.58,224,0.875,bicubic,-52.560,-41.573,+56 -fbnetv3_b,40.953,59.047,58.653,41.347,8.60,256,0.950,bilinear,-52.677,-40.257,+39 -gluon_inception_v3,40.907,59.093,55.620,44.380,23.83,299,0.875,bicubic,-52.633,-43.210,+52 -cs3darknet_focus_l,40.893,59.107,56.630,43.370,21.15,288,0.950,bicubic,-53.897,-42.520,-113 -ese_vovnet39b,40.867,59.133,56.950,43.050,24.57,224,0.875,bicubic,-52.983,-41.950,+9 -levit_192,40.837,59.163,56.690,43.310,10.95,224,0.900,bicubic,-52.883,-42.100,+24 -regnety_320,40.803,59.197,56.113,43.887,145.05,224,0.875,bicubic,-53.717,-43.057,-69 -resnet34d,40.800,59.200,56.523,43.477,21.82,224,0.875,bicubic,-51.850,-41.897,+127 -resnetv2_50d_gn,40.783,59.217,56.207,43.793,25.57,288,0.950,bicubic,-54.317,-42.853,-160 -xception,40.773,59.227,56.383,43.617,22.86,299,0.897,bicubic,-52.867,-42.377,+30 -lamhalobotnet50ts_256,40.747,59.253,56.093,43.907,22.57,256,0.950,bicubic,-54.023,-42.887,-115 -resnet50_gn,40.737,59.263,55.750,44.250,25.56,224,0.940,bicubic,-53.443,-43.170,-33 -skresnext50_32x4d,40.700,59.300,56.030,43.970,27.48,224,0.875,bicubic,-53.250,-42.800,-11 -gluon_resnet101_v1b,40.683,59.317,56.123,43.877,44.55,224,0.875,bicubic,-53.077,-42.577,+11 -hrnet_w40,40.663,59.337,56.757,43.243,57.56,224,0.875,bilinear,-53.047,-42.043,+18 -resmlp_24_224,40.643,59.357,56.570,43.430,30.02,224,0.875,bicubic,-52.797,-42.240,+51 -repvgg_b1,40.593,59.407,57.830,42.170,57.42,224,0.875,bilinear,-52.817,-40.960,+56 -halonet50ts,40.577,59.423,55.193,44.807,22.73,256,0.940,bicubic,-54.133,-43.637,-111 -tf_efficientnet_lite3,40.563,59.437,56.473,43.527,8.20,300,0.904,bilinear,-53.547,-42.487,-33 -xcit_tiny_12_p8_224,40.533,59.467,55.623,44.377,6.71,224,1.000,bicubic,-53.827,-43.447,-66 -mobilevitv2_175,40.530,59.470,56.277,43.723,14.25,256,0.888,bicubic,-53.700,-42.653,-51 -tresnet_m_448,40.527,59.473,56.703,43.297,31.39,448,0.875,bilinear,-54.133,-42.447,-107 -dla169,40.523,59.477,57.257,42.743,53.39,224,0.875,bilinear,-53.267,-41.573,0 -pit_xs_224,40.487,59.513,56.533,43.467,10.62,224,0.900,bicubic,-52.423,-42.247,+88 -resnetaa50,40.473,59.527,56.027,43.973,25.56,288,1.000,bicubic,-54.407,-43.103,-142 -repvgg_b2,40.463,59.537,57.773,42.227,89.02,224,0.875,bilinear,-53.127,-41.297,+23 -regnetx_320,40.447,59.553,55.667,44.333,107.81,224,0.875,bicubic,-53.773,-43.383,-55 -coat_mini,40.420,59.580,55.157,44.843,10.34,224,0.900,bicubic,-54.350,-43.793,-131 -skresnet34,40.393,59.607,56.740,43.260,22.28,224,0.875,bicubic,-52.177,-41.780,+112 -efficientnet_el_pruned,40.390,59.610,56.887,43.113,10.59,300,0.904,bicubic,-53.700,-42.123,-43 -resnet50,40.383,59.617,54.663,45.337,25.56,224,0.950,bicubic,-53.547,-43.807,-26 -efficientnet_b2_pruned,40.380,59.620,56.533,43.467,8.31,260,0.890,bicubic,-53.420,-42.377,-11 -wide_resnet101_2,40.360,59.640,55.787,44.213,126.89,224,0.875,bilinear,-53.360,-43.023,-4 -coat_lite_mini,40.353,59.647,55.723,44.277,11.01,224,0.900,bicubic,-53.107,-43.057,+31 -legacy_seresnext101_32x4d,40.353,59.647,54.823,45.177,48.96,224,0.875,bilinear,-53.767,-44.147,-52 -sebotnet33ts_256,40.340,59.660,53.217,46.783,13.70,256,0.940,bicubic,-53.970,-45.383,-74 -tf_efficientnet_b0_ap,40.333,59.667,56.793,43.207,5.29,224,0.875,bicubic,-52.287,-41.577,+99 -regnetx_160,40.273,59.727,56.060,43.940,54.28,224,0.875,bicubic,-53.617,-43.030,-32 -densenet201,40.270,59.730,56.713,43.287,20.01,224,0.875,bicubic,-52.430,-41.937,+90 -resnext50d_32x4d,40.157,59.843,55.490,44.510,25.05,224,0.875,bicubic,-53.663,-43.250,-20 -eca_resnet33ts,40.137,59.863,57.003,42.997,19.68,256,0.900,bicubic,-53.723,-41.887,-30 -mobilevitv2_200,40.133,59.867,55.510,44.490,18.45,256,0.888,bicubic,-54.377,-43.460,-102 -darknetaa53,40.120,59.880,55.787,44.213,36.02,288,1.000,bilinear,-54.090,-43.163,-68 -hrnet_w48,40.097,59.903,56.647,43.353,77.47,224,0.875,bilinear,-53.933,-42.383,-51 -vit_base_patch16_224_sam,40.093,59.907,55.433,44.567,86.57,224,0.900,bicubic,-53.797,-43.457,-38 -legacy_seresnet152,40.037,59.963,55.820,44.180,66.82,224,0.875,bilinear,-53.393,-43.030,+24 -hrnet_w30,40.030,59.970,57.100,42.900,37.71,224,0.875,bilinear,-53.350,-41.730,+28 -regnetz_b16,40.000,60.000,55.623,44.377,9.72,288,0.940,bicubic,-54.680,-43.537,-135 -regnetx_080,39.997,60.003,55.963,44.037,39.57,224,0.875,bicubic,-53.793,-42.937,-27 -tf_efficientnet_b1,39.980,60.020,56.133,43.867,7.79,240,0.882,bicubic,-53.730,-42.667,-16 -gluon_resnet101_v1c,39.950,60.050,55.310,44.690,44.57,224,0.875,bicubic,-53.730,-43.450,-16 -resmlp_12_distilled_224,39.833,60.167,57.440,42.560,15.35,224,0.875,bicubic,-53.037,-41.180,+66 -seresnet33ts,39.823,60.177,56.523,43.477,19.78,256,0.900,bicubic,-54.447,-42.257,-87 -res2net50_26w_8s,39.807,60.193,54.910,45.090,48.40,224,0.875,bilinear,-53.633,-43.780,+14 -tf_efficientnetv2_b0,39.787,60.213,56.290,43.710,7.14,224,0.875,bicubic,-53.273,-42.400,+43 -darknet53,39.733,60.267,55.283,44.717,41.61,288,1.000,bicubic,-54.627,-43.767,-101 -lambda_resnet50ts,39.733,60.267,54.340,45.660,21.54,256,0.950,bicubic,-54.837,-44.310,-126 -res2net101_26w_4s,39.713,60.287,54.550,45.450,45.21,224,0.875,bilinear,-53.817,-44.050,0 -regnetx_120,39.690,60.310,55.650,44.350,46.11,224,0.875,bicubic,-54.570,-43.540,-92 -vit_small_patch32_224,39.687,60.313,55.260,44.740,22.88,224,0.900,bicubic,-52.473,-43.250,+105 -hrnet_w44,39.680,60.320,55.333,44.667,67.06,224,0.875,bilinear,-53.930,-43.627,-15 -densenet161,39.623,60.377,56.130,43.870,28.68,224,0.875,bicubic,-53.277,-42.680,+53 -resmlp_big_24_224,39.623,60.377,54.820,45.180,129.14,224,0.875,bicubic,-54.637,-44.000,-95 -mixnet_xl,39.613,60.387,55.883,44.117,11.90,224,0.875,bicubic,-54.617,-42.937,-93 -xception41,39.607,60.393,55.047,44.953,26.97,299,0.903,bicubic,-53.873,-43.703,-2 -tf_efficientnetv2_b1,39.573,60.427,55.353,44.647,8.14,240,0.882,bicubic,-54.137,-43.467,-35 -gcresnet33ts,39.557,60.443,55.823,44.177,19.88,256,0.900,bicubic,-54.273,-43.087,-50 -dla102x,39.543,60.457,56.310,43.690,26.31,224,0.875,bilinear,-53.987,-42.540,-12 -xcit_tiny_12_p16_224,39.543,60.457,55.023,44.977,6.72,224,1.000,bicubic,-52.917,-43.607,+78 -sehalonet33ts,39.533,60.467,54.013,45.987,13.69,256,0.940,bicubic,-55.007,-44.747,-134 -rexnet_130,39.490,60.510,56.643,43.357,7.56,224,0.875,bicubic,-54.180,-42.057,-34 -hrnet_w32,39.463,60.537,56.137,43.863,41.23,224,0.875,bilinear,-53.487,-42.703,+37 -resnetv2_50x1_bitm,39.433,60.567,57.857,42.143,25.55,448,1.000,bilinear,-55.317,-41.323,-174 -levit_128,39.423,60.577,55.350,44.650,9.21,224,0.900,bicubic,-53.627,-43.350,+25 -densenetblur121d,39.380,60.620,56.630,43.370,8.00,224,0.875,bicubic,-53.030,-41.790,+78 -regnety_120,39.353,60.647,55.277,44.723,51.82,224,0.875,bicubic,-54.657,-43.753,-80 -mobilevitv2_150,39.340,60.660,55.203,44.797,10.59,256,0.888,bicubic,-54.730,-43.697,-86 -tf_efficientnet_el,39.307,60.693,55.380,44.620,10.59,300,0.904,bicubic,-55.053,-43.720,-125 -tv_resnet101,39.293,60.707,55.793,44.207,44.55,224,0.875,bilinear,-53.587,-42.867,+38 -tf_inception_v3,39.250,60.750,54.303,45.697,23.83,299,0.875,bicubic,-53.950,-44.177,+8 -gluon_resnet50_v1s,39.237,60.763,55.010,44.990,25.68,224,0.875,bicubic,-54.353,-43.830,-31 -densenet169,39.173,60.827,55.847,44.153,14.15,224,0.875,bicubic,-53.117,-42.743,+76 -tf_efficientnetv2_b2,39.173,60.827,54.567,45.433,10.10,260,0.890,bicubic,-54.887,-44.363,-91 -legacy_seresnet101,39.033,60.967,55.007,44.993,49.33,224,0.875,bilinear,-54.237,-43.733,-4 -efficientnet_b1_pruned,39.003,60.997,55.633,44.367,6.33,240,0.882,bicubic,-53.967,-42.887,+23 -repvgg_b1g4,38.987,61.013,56.347,43.653,39.97,224,0.875,bilinear,-54.043,-42.473,+16 -crossvit_9_dagger_240,38.973,61.027,54.860,45.140,8.78,240,0.875,bicubic,-53.787,-43.650,+41 -inception_v3,38.957,61.043,53.840,46.160,23.83,299,0.875,bicubic,-53.943,-44.490,+28 -resnet33ts,38.930,61.070,55.580,44.420,19.68,256,0.900,bicubic,-54.700,-43.180,-46 -dpn68,38.917,61.083,54.930,45.070,12.61,224,0.875,bicubic,-53.333,-43.680,+72 -legacy_seresnext50_32x4d,38.883,61.117,54.597,45.403,27.56,224,0.875,bilinear,-54.537,-44.203,-19 -dla102,38.833,61.167,55.330,44.670,33.27,224,0.875,bilinear,-54.427,-43.440,-10 -densenet121,38.787,61.213,56.273,43.727,7.98,224,0.875,bicubic,-53.153,-42.007,+80 -resnet32ts,38.773,61.227,55.813,44.187,17.96,256,0.900,bicubic,-54.787,-42.937,-42 -regnetx_040,38.707,61.293,55.343,44.657,22.12,224,0.875,bicubic,-54.963,-43.597,-59 -res2net50_14w_8s,38.697,61.303,54.073,45.927,25.06,224,0.875,bilinear,-54.343,-44.627,+4 -regnetx_032,38.683,61.317,55.160,44.840,15.30,224,0.875,bicubic,-54.567,-43.570,-12 -res2net50_26w_6s,38.683,61.317,53.757,46.243,37.05,224,0.875,bilinear,-54.917,-44.993,-50 -selecsls60,38.617,61.383,55.633,44.367,30.67,224,0.875,bicubic,-54.393,-43.197,+5 -dla60x,38.617,61.383,55.387,44.613,17.35,224,0.875,bilinear,-54.573,-43.323,-11 -dla60_res2net,38.607,61.393,54.547,45.453,20.85,224,0.875,bilinear,-54.763,-44.293,-25 -tf_efficientnet_b0,38.577,61.423,55.963,44.037,5.29,224,0.875,bicubic,-53.823,-42.507,+52 -selecsls60b,38.563,61.437,55.287,44.713,32.77,224,0.875,bicubic,-54.937,-43.553,-42 -repvgg_a2,38.557,61.443,55.760,44.240,28.21,224,0.875,bilinear,-54.123,-42.760,+26 -hardcorenas_f,38.503,61.497,55.650,44.350,8.20,224,0.875,bilinear,-54.477,-42.970,+2 -resmlp_12_224,38.443,61.557,56.320,43.680,15.35,224,0.875,bicubic,-53.677,-42.250,+62 -dla60_res2next,38.433,61.567,54.947,45.053,17.03,224,0.875,bilinear,-55.117,-43.833,-53 -regnetx_064,38.420,61.580,54.990,45.010,26.21,224,0.875,bicubic,-55.200,-44.060,-64 -gluon_resnet50_v1b,38.413,61.587,54.817,45.183,25.56,224,0.875,bicubic,-54.147,-43.733,+33 -tf_efficientnet_cc_b0_4e,38.400,61.600,55.157,44.843,13.31,224,0.875,bicubic,-54.430,-43.283,+12 -hrnet_w18,38.273,61.727,55.653,44.347,21.30,224,0.875,bilinear,-54.487,-43.007,+16 -tinynet_a,38.223,61.777,55.177,44.823,6.19,192,0.875,bicubic,-54.587,-43.383,+13 -poolformer_s12,38.163,61.837,56.190,43.810,11.92,224,0.900,bicubic,-54.307,-42.160,+33 -mixnet_l,38.160,61.840,54.753,45.247,7.33,224,0.875,bicubic,-55.110,-43.947,-33 -hardcorenas_e,38.150,61.850,55.167,44.833,8.07,224,0.875,bilinear,-54.790,-43.413,-5 -efficientnet_b1,38.090,61.910,54.020,45.980,7.79,256,1.000,bicubic,-54.930,-44.690,-13 -coat_lite_tiny,38.070,61.930,53.460,46.540,5.72,224,0.900,bicubic,-54.790,-45.180,+3 -gmixer_24_224,38.063,61.937,52.077,47.923,24.72,224,0.875,bicubic,-54.617,-46.203,+13 -resnetrs50,37.970,62.030,53.313,46.687,35.69,224,0.910,bicubic,-56.050,-45.537,-124 -mobilevitv2_125,37.883,62.117,54.060,45.940,7.48,256,0.888,bicubic,-55.577,-44.800,-56 -hardcorenas_c,37.873,62.127,55.713,44.287,5.52,224,0.875,bilinear,-54.487,-42.637,+34 -gluon_resnet50_v1c,37.850,62.150,54.117,45.883,25.58,224,0.875,bicubic,-55.060,-44.583,-8 -res2net50_26w_4s,37.830,62.170,53.070,46.930,25.70,224,0.875,bilinear,-55.350,-45.600,-33 -efficientnet_es,37.787,62.213,54.980,45.020,5.44,224,0.875,bicubic,-55.133,-43.710,-13 -resnest14d,37.773,62.227,56.450,43.550,10.61,224,0.875,bilinear,-53.357,-41.880,+80 -tv_resnext50_32x4d,37.740,62.260,54.120,45.880,25.03,224,0.875,bilinear,-55.170,-44.600,-13 -resnet26t,37.690,62.310,55.260,44.740,16.01,256,0.940,bicubic,-54.980,-43.320,+6 -ecaresnet26t,37.647,62.353,54.347,45.653,16.01,320,0.950,bicubic,-56.313,-44.573,-128 -hardcorenas_d,37.533,62.467,54.713,45.287,7.50,224,0.875,bilinear,-55.067,-43.717,+10 -res2next50,37.483,62.517,52.863,47.137,24.67,224,0.875,bilinear,-55.677,-45.787,-37 -resnet34,37.453,62.547,54.303,45.697,21.80,224,0.875,bilinear,-53.747,-43.937,+68 -pit_ti_distilled_224,37.323,62.677,55.133,44.867,5.10,224,0.900,bicubic,-53.577,-43.087,+80 -lambda_resnet26t,37.297,62.703,53.580,46.420,10.96,256,0.940,bicubic,-56.103,-45.150,-59 -hardcorenas_b,37.240,62.760,55.050,44.950,5.18,224,0.875,bilinear,-54.690,-43.350,+40 -mobilenetv3_large_100_miil,37.220,62.780,53.547,46.453,5.48,224,0.875,bilinear,-55.050,-44.693,+25 -eca_halonext26ts,37.183,62.817,53.120,46.880,10.76,256,0.940,bicubic,-56.377,-45.560,-83 -cs3darknet_focus_m,37.140,62.860,53.917,46.083,9.30,288,0.950,bicubic,-55.970,-44.823,-41 -res2net50_48w_2s,37.127,62.873,53.347,46.653,25.29,224,0.875,bilinear,-55.663,-45.133,-12 -lambda_resnet26rpt_256,37.077,62.923,53.840,46.160,10.99,256,0.940,bicubic,-56.353,-45.040,-70 -dla60,37.073,62.927,54.193,45.807,22.04,224,0.875,bilinear,-55.587,-44.437,-6 -rexnet_100,37.063,62.937,54.037,45.963,4.80,224,0.875,bicubic,-55.787,-44.583,-20 -bat_resnext26ts,37.063,62.937,53.753,46.247,10.73,256,0.900,bicubic,-56.037,-44.977,-45 -regnety_016,37.010,62.990,54.080,45.920,11.20,224,0.875,bicubic,-55.990,-44.600,-37 -tf_mixnet_l,36.973,63.027,52.587,47.413,7.33,224,0.875,bicubic,-56.067,-45.953,-43 -botnet26t_256,36.957,63.043,53.083,46.917,12.49,256,0.950,bicubic,-56.473,-45.567,-74 -legacy_seresnet50,36.867,63.133,53.473,46.527,28.09,224,0.875,bilinear,-55.803,-45.177,-14 -halonet26t,36.850,63.150,52.277,47.723,12.48,256,0.950,bicubic,-56.760,-46.363,-100 -tv_densenet121,36.813,63.187,54.030,45.970,7.98,224,0.875,bicubic,-54.587,-44.220,+43 -tf_efficientnet_lite2,36.803,63.197,53.320,46.680,6.09,260,0.890,bicubic,-55.797,-45.230,-11 -mobilenetv2_120d,36.793,63.207,54.050,45.950,5.83,224,0.875,bicubic,-55.817,-44.450,-13 -tf_efficientnet_lite1,36.727,63.273,53.580,46.420,5.42,240,0.882,bicubic,-55.583,-44.910,+6 -eca_botnext26ts_256,36.687,63.313,52.483,47.517,10.59,256,0.950,bicubic,-56.683,-46.217,-75 -regnetx_016,36.677,63.323,53.293,46.707,9.19,224,0.875,bicubic,-55.853,-45.257,-10 -hardcorenas_a,36.673,63.327,54.917,45.083,5.26,224,0.875,bilinear,-54.947,-43.253,+29 -levit_128s,36.633,63.367,53.123,46.877,7.78,224,0.900,bicubic,-54.867,-45.277,+31 -efficientnet_b0,36.597,63.403,53.487,46.513,5.29,224,0.875,bicubic,-55.883,-45.193,-12 -vit_base_patch32_224_sam,36.550,63.450,53.043,46.957,88.22,224,0.900,bicubic,-53.310,-44.557,+72 -xcit_nano_12_p8_224_dist,36.533,63.467,52.883,47.117,3.05,224,1.000,bicubic,-55.887,-45.637,-7 -cs3darknet_m,36.457,63.543,53.230,46.770,9.31,288,0.950,bicubic,-56.803,-45.490,-76 -mobilevitv2_100,36.393,63.607,53.067,46.933,4.90,256,0.888,bicubic,-56.737,-45.693,-65 -tf_efficientnet_em,36.387,63.613,52.833,47.167,6.90,240,0.882,bicubic,-56.783,-45.837,-70 -skresnet18,36.323,63.677,54.187,45.813,11.96,224,0.875,bicubic,-53.847,-43.593,+63 -repvgg_b0,36.283,63.717,54.067,45.933,15.82,224,0.875,bilinear,-55.397,-44.383,+18 -tv_resnet50,36.167,63.833,52.807,47.193,25.56,224,0.875,bilinear,-55.963,-45.613,+3 -xcit_nano_12_p16_384_dist,36.160,63.840,53.247,46.753,3.05,384,1.000,bicubic,-55.970,-45.273,+1 -legacy_seresnet34,36.140,63.860,52.550,47.450,21.96,224,0.875,bilinear,-55.350,-45.650,+21 -coat_tiny,36.120,63.880,51.060,48.940,5.50,224,0.900,bicubic,-57.390,-47.630,-107 -tv_resnet34,36.077,63.923,53.533,46.467,21.80,224,0.875,bilinear,-54.213,-44.447,+55 -deit_tiny_distilled_patch16_224,36.023,63.977,54.237,45.763,5.91,224,0.900,bicubic,-55.057,-44.033,+39 -mobilenetv2_140,36.010,63.990,53.957,46.043,6.11,224,0.875,bicubic,-56.030,-44.293,0 -tf_efficientnet_lite0,35.917,64.083,53.473,46.527,4.65,224,0.875,bicubic,-55.383,-44.617,+23 -seresnext26ts,35.830,64.170,53.933,46.067,10.39,256,0.900,bicubic,-56.990,-44.667,-49 -selecsls42b,35.807,64.193,52.493,47.507,32.46,224,0.875,bicubic,-56.673,-45.947,-28 -xcit_nano_12_p8_384_dist,35.770,64.230,52.297,47.703,3.05,384,1.000,bicubic,-57.500,-46.553,-95 -gluon_resnet34_v1b,35.760,64.240,52.183,47.817,21.80,224,0.875,bicubic,-55.340,-45.997,+32 -dla34,35.640,64.360,52.787,47.213,15.74,224,0.875,bilinear,-55.590,-45.383,+21 -efficientnet_lite0,35.637,64.363,53.637,46.363,4.65,224,0.875,bicubic,-55.623,-44.613,+18 -mixnet_m,35.637,64.363,52.423,47.577,5.01,224,0.875,bicubic,-56.633,-45.927,-19 -ssl_resnet18,35.593,64.407,53.740,46.260,11.69,224,0.875,bilinear,-55.107,-44.290,+36 -mobilenetv3_rw,35.537,64.463,53.707,46.293,5.48,224,0.875,bicubic,-56.013,-44.573,+5 -efficientnet_es_pruned,35.390,64.610,52.847,47.153,5.44,224,0.875,bicubic,-56.320,-45.563,-2 -mobilenetv2_110d,35.307,64.693,52.847,47.153,4.52,224,0.875,bicubic,-56.023,-45.343,+11 -tf_mixnet_m,35.180,64.820,50.983,49.017,5.01,224,0.875,bicubic,-57.030,-47.437,-19 -hrnet_w18_small_v2,35.170,64.830,52.430,47.570,15.60,224,0.875,bilinear,-56.000,-45.900,+17 -resnet18d,35.133,64.867,52.890,47.110,11.71,224,0.875,bicubic,-54.847,-44.940,+42 -xcit_nano_12_p16_224_dist,35.120,64.880,52.543,47.457,3.05,224,1.000,bicubic,-55.050,-45.207,+40 -eca_resnext26ts,35.050,64.950,52.310,47.690,10.30,256,0.900,bicubic,-57.360,-46.310,-35 -convit_tiny,35.047,64.953,51.770,48.230,5.71,224,0.875,bicubic,-55.503,-46.450,+29 -resnext26ts,35.043,64.957,53.420,46.580,10.30,256,0.900,bicubic,-57.177,-44.830,-26 -gcresnext26ts,34.930,65.070,51.673,48.327,10.48,256,0.900,bicubic,-57.530,-46.817,-42 -tinynet_b,34.863,65.137,52.010,47.990,3.73,188,0.875,bicubic,-56.277,-46.050,+13 -ese_vovnet19b_dw,34.833,65.167,52.033,47.967,6.54,224,0.875,bicubic,-57.167,-46.477,-21 -regnety_008,34.810,65.190,51.750,48.250,6.26,224,0.875,bicubic,-57.090,-46.670,-17 -pit_ti_224,34.677,65.323,52.160,47.840,4.85,224,0.900,bicubic,-55.763,-45.850,+26 -mobilenetv3_large_100,34.600,65.400,52.863,47.137,5.48,224,0.875,bicubic,-56.880,-45.457,-7 -crossvit_9_240,34.597,65.403,51.763,48.237,8.55,240,0.875,bicubic,-56.453,-46.547,+14 -seresnext26t_32x4d,34.543,65.457,51.380,48.620,16.81,224,0.875,bicubic,-58.277,-47.180,-73 -seresnext26d_32x4d,34.533,65.467,51.553,48.447,16.81,224,0.875,bicubic,-57.897,-46.987,-48 -mixer_b16_224,34.423,65.577,48.080,51.920,59.88,224,0.875,bicubic,-56.727,-49.320,+4 -resnet26d,34.283,65.717,51.687,48.313,16.01,224,0.875,bicubic,-57.977,-46.763,-39 -tf_efficientnet_es,34.263,65.737,51.347,48.653,5.44,224,0.875,bicubic,-57.857,-47.083,-32 -fbnetc_100,34.253,65.747,51.187,48.813,5.57,224,0.875,bilinear,-56.997,-46.663,-6 -regnety_006,34.147,65.853,51.270,48.730,6.06,224,0.875,bicubic,-57.403,-47.160,-19 -tf_mobilenetv3_large_100,33.940,66.060,51.483,48.517,5.48,224,0.875,bilinear,-57.480,-46.777,-14 -semnasnet_075,33.780,66.220,52.427,47.573,2.91,224,0.875,bicubic,-56.430,-45.543,+19 -mnasnet_100,33.780,66.220,51.177,48.823,4.38,224,0.875,bicubic,-57.430,-46.873,-8 -regnetx_008,33.773,66.227,50.540,49.460,7.26,224,0.875,bicubic,-57.387,-47.840,-5 -lcnet_100,33.750,66.250,52.090,47.910,2.95,224,0.875,bicubic,-55.220,-45.290,+34 -vit_tiny_r_s16_p8_384,33.647,66.353,50.683,49.317,6.36,384,1.000,bicubic,-58.083,-47.747,-31 -mobilevit_s,33.637,66.363,49.280,50.720,5.58,256,0.900,bicubic,-59.523,-49.490,-118 -xcit_nano_12_p8_224,33.580,66.420,50.213,49.787,3.05,224,1.000,bicubic,-57.550,-48.017,-5 -vit_tiny_patch16_384,33.543,66.457,51.077,48.923,5.79,384,1.000,bicubic,-59.897,-47.753,-146 -semnasnet_100,33.523,66.477,50.787,49.213,3.89,224,0.875,bicubic,-58.137,-47.483,-31 -resnet26,33.493,66.507,50.930,49.070,16.00,224,0.875,bicubic,-57.947,-47.330,-25 -spnasnet_100,33.477,66.523,51.270,48.730,4.42,224,0.875,bilinear,-57.123,-46.690,+1 -mixnet_s,33.477,66.523,51.010,48.990,4.13,224,0.875,bicubic,-58.293,-47.290,-39 -crossvit_tiny_240,33.353,66.647,49.893,50.107,7.01,240,0.875,bicubic,-57.187,-48.047,+2 -mobilevitv2_075,33.350,66.650,50.077,49.923,2.87,256,0.888,bicubic,-58.620,-48.223,-46 -vgg19_bn,33.230,66.770,50.803,49.197,143.68,224,0.875,bilinear,-57.760,-47.307,-8 -ghostnet_100,33.207,66.793,51.160,48.840,5.18,224,0.875,bilinear,-57.233,-46.670,+1 -regnetx_006,33.147,66.853,50.253,49.747,6.20,224,0.875,bicubic,-57.623,-47.847,-8 -resnet18,33.070,66.930,51.180,48.820,11.69,224,0.875,bilinear,-55.080,-45.940,+26 -xcit_nano_12_p16_224,32.953,67.047,49.993,50.007,3.05,224,1.000,bicubic,-56.007,-47.407,+20 -legacy_seresnext26_32x4d,32.763,67.237,49.250,50.750,16.79,224,0.875,bicubic,-59.827,-49.160,-84 -edgenext_x_small,32.720,67.280,48.640,51.360,2.34,256,0.900,bicubic,-58.680,-49.520,-33 -hrnet_w18_small,32.667,67.333,50.597,49.403,13.19,224,0.875,bilinear,-57.203,-47.293,+2 -deit_tiny_patch16_224,32.663,67.337,50.270,49.730,5.72,224,0.900,bicubic,-56.957,-47.690,+5 -legacy_seresnet18,32.593,67.407,50.323,49.677,11.78,224,0.875,bicubic,-56.667,-47.367,+8 -mobilenetv2_100,32.523,67.477,50.820,49.180,3.50,224,0.875,bicubic,-57.297,-47.010,+1 -regnetx_004,32.510,67.490,49.337,50.663,5.16,224,0.875,bicubic,-56.960,-48.433,+3 +eva_giant_patch14_336.clip_ft_in1k,90.553,9.447,97.230,2.770,"1,013.01",336,1.000,bicubic,-7.307,-2.650,+3 +eva_giant_patch14_224.clip_ft_in1k,90.227,9.773,97.173,2.827,"1,012.56",224,1.000,bicubic,-7.453,-2.577,+12 +eva_giant_patch14_336.m30m_ft_in22k_in1k,88.583,11.417,95.930,4.070,"1,013.01",336,1.000,bicubic,-9.417,-3.970,-2 +eva_giant_patch14_560.m30m_ft_in22k_in1k,88.410,11.590,95.613,4.387,"1,014.45",560,1.000,bicubic,-9.580,-4.247,-2 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,83.910,16.090,93.880,6.120,304.53,336,1.000,bicubic,-13.700,-5.850,+14 +vit_large_patch14_clip_336.laion2b_ft_in1k,83.587,16.413,93.507,6.493,304.53,336,1.000,bicubic,-13.643,-6.213,+39 +eva_large_patch14_336.in22k_ft_in1k,83.523,16.477,93.100,6.900,304.53,336,1.000,bicubic,-14.287,-6.690,-2 +vit_huge_patch14_clip_224.laion2b_ft_in1k,83.280,16.720,93.103,6.897,632.05,224,1.000,bicubic,-13.820,-6.537,+48 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,83.063,16.937,92.850,7.150,632.46,336,1.000,bicubic,-14.537,-6.930,+11 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,82.810,17.190,92.620,7.380,632.05,224,1.000,bicubic,-14.550,-7.180,+21 +vit_large_patch14_clip_224.openai_ft_in1k,82.317,17.683,92.913,7.087,304.20,224,1.000,bicubic,-15.143,-6.767,+15 +vit_large_patch14_clip_224.laion2b_ft_in1k,81.703,18.297,92.270,7.730,304.20,224,1.000,bicubic,-15.317,-7.410,+53 +eva_large_patch14_196.in22k_ft_in1k,81.290,18.710,91.550,8.450,304.14,196,1.000,bicubic,-16.230,-8.240,+11 +eva_large_patch14_336.in22k_ft_in22k_in1k,80.077,19.923,89.363,10.637,304.53,336,1.000,bicubic,-17.783,-10.427,-11 +ig_resnext101_32x48d,79.650,20.350,89.393,10.607,828.41,224,0.875,bilinear,-17.320,-10.277,+52 +ig_resnext101_32x32d,79.457,20.543,89.183,10.817,468.53,224,0.875,bilinear,-17.323,-10.427,+76 +ig_resnext101_32x16d,78.837,21.163,88.480,11.520,194.03,224,0.875,bilinear,-17.603,-11.060,+118 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,78.687,21.313,88.920,11.080,304.20,224,1.000,bicubic,-18.923,-10.810,0 +eva_large_patch14_196.in22k_ft_in22k_in1k,78.503,21.497,88.320,11.680,304.14,196,1.000,bicubic,-19.107,-11.490,-2 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.427,21.573,88.503,11.497,304.53,336,1.000,bicubic,-19.023,-11.277,+8 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.263,21.737,88.673,11.327,304.20,224,1.000,bicubic,-19.127,-11.067,+9 +tf_efficientnet_l2.ns_jft_in1k_475,76.480,23.520,88.653,11.347,480.31,475,0.936,bicubic,-21.270,-11.137,-11 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,76.370,23.630,87.093,12.907,304.43,224,0.950,bicubic,-21.380,-12.727,-11 +swsl_resnext101_32x16d,76.303,23.697,87.733,12.267,194.03,224,0.875,bilinear,-19.967,-11.767,+138 +ig_resnext101_32x8d,75.813,24.187,86.200,13.800,88.79,224,0.875,bilinear,-20.117,-13.080,+191 +swsl_resnext101_32x8d,75.590,24.410,86.937,13.063,88.79,224,0.875,bilinear,-20.650,-12.533,+139 +tf_efficientnet_l2.ns_jft_in1k,74.650,25.350,87.543,12.457,480.31,800,0.960,bicubic,-23.130,-12.277,-20 +beit_large_patch16_384.in22k_ft_in22k_in1k,73.277,26.723,85.017,14.983,305.00,384,1.000,bicubic,-24.533,-14.823,-22 +beit_large_patch16_512.in22k_ft_in22k_in1k,73.163,26.837,85.080,14.920,305.67,512,1.000,bicubic,-24.617,-14.810,-21 +swsl_resnext101_32x4d,72.660,27.340,85.157,14.843,44.18,224,0.875,bilinear,-23.390,-14.373,+166 +maxvit_xlarge_tf_512.in21k_ft_in1k,71.893,28.107,82.920,17.080,475.77,512,1.000,bicubic,-25.867,-16.900,-21 +maxvit_xlarge_tf_384.in21k_ft_in1k,71.697,28.303,82.727,17.273,475.32,384,1.000,bicubic,-26.043,-17.123,-19 +beit_large_patch16_224.in22k_ft_in22k_in1k,71.043,28.957,83.420,16.580,304.43,224,0.900,bicubic,-26.437,-16.270,-8 +deit3_huge_patch14_224_in21ft1k,70.810,29.190,82.197,17.803,632.13,224,1.000,bicubic,-26.440,-17.513,+6 +vit_base_patch16_clip_384.laion2b_ft_in1k,70.793,29.207,83.810,16.190,86.86,384,1.000,bicubic,-26.117,-15.860,+41 +deit3_large_patch16_384_in21ft1k,70.570,29.430,82.437,17.563,304.76,384,1.000,bicubic,-26.990,-17.323,-13 +maxvit_base_tf_512.in21k_ft_in1k,70.397,29.603,81.597,18.403,119.88,512,1.000,bicubic,-27.363,-18.263,-28 +maxvit_large_tf_512.in21k_ft_in1k,70.390,29.610,81.647,18.353,212.33,512,1.000,bicubic,-27.280,-18.083,-22 +maxvit_large_tf_384.in21k_ft_in1k,70.030,29.970,81.033,18.967,212.03,384,1.000,bicubic,-27.640,-18.787,-24 +deit3_large_patch16_224_in21ft1k,69.717,30.283,81.190,18.810,304.37,224,1.000,bicubic,-27.593,-18.490,-4 +maxvit_base_tf_384.in21k_ft_in1k,69.557,30.443,80.733,19.267,119.65,384,1.000,bicubic,-28.003,-18.977,-19 +swsl_resnext50_32x4d,68.977,31.023,82.810,17.190,25.03,224,0.875,bilinear,-26.643,-16.410,+216 +vit_base_patch16_clip_224.laion2b_ft_in1k,68.747,31.253,82.500,17.500,86.57,224,1.000,bicubic,-27.573,-16.910,+109 +swsl_resnet50,68.297,31.703,83.313,16.687,25.56,224,0.875,bilinear,-26.903,-16.017,+279 +convnext_xlarge.fb_in22k_ft_in1k_384,68.160,31.840,80.467,19.533,350.20,384,1.000,bicubic,-29.430,-19.303,-24 +swinv2_large_window12to24_192to384_22kft1k,67.670,32.330,80.100,19.900,196.74,384,1.000,bicubic,-29.620,-19.680,-9 +tf_efficientnet_b7.ns_jft_in1k,67.510,32.490,81.383,18.617,66.35,600,0.949,bicubic,-29.690,-18.317,+2 +vit_base_patch16_clip_384.openai_ft_in1k,67.357,32.643,81.690,18.310,86.86,384,1.000,bicubic,-29.463,-17.970,+36 +vit_large_patch16_384.augreg_in21k_ft_in1k,67.053,32.947,78.707,21.293,304.72,384,1.000,bicubic,-30.367,-21.073,-20 +convnext_large.fb_in22k_ft_in1k_384,66.667,33.333,79.807,20.193,197.77,384,1.000,bicubic,-30.643,-19.953,-15 +swin_large_patch4_window12_384,66.283,33.717,79.783,20.217,196.74,384,1.000,bicubic,-30.887,-19.897,+1 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,66.160,33.840,78.890,21.110,86.86,384,1.000,bicubic,-31.060,-20.810,-4 +vit_base_patch16_clip_224.openai_ft_in1k,66.023,33.977,80.990,19.010,86.57,224,0.900,bicubic,-30.287,-18.510,+102 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,65.757,34.243,78.890,21.110,86.53,224,0.900,bicubic,-31.153,-20.840,+21 +swinv2_base_window12to24_192to384_22kft1k,65.743,34.257,79.313,20.687,87.92,384,1.000,bicubic,-31.517,-20.477,-16 +swinv2_large_window12to16_192to256_22kft1k,65.627,34.373,78.460,21.540,196.74,256,0.900,bicubic,-31.613,-21.250,-13 +tf_efficientnet_b6.ns_jft_in1k,65.587,34.413,79.553,20.447,43.04,528,0.942,bicubic,-31.433,-20.157,+5 +convnext_xlarge.fb_in22k_ft_in1k,65.390,34.610,78.340,21.660,350.20,288,1.000,bicubic,-32.060,-21.480,-31 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,65.357,34.643,78.943,21.057,86.86,384,0.950,bicubic,-31.783,-20.697,-6 +convnext_base.fb_in22k_ft_in1k_384,64.883,35.117,78.393,21.607,88.59,384,1.000,bicubic,-32.367,-21.327,-19 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,64.767,35.233,77.787,22.213,86.57,224,0.950,bicubic,-31.833,-21.773,+52 +vit_large_patch16_224.augreg_in21k_ft_in1k,64.347,35.653,76.190,23.810,304.33,224,0.900,bicubic,-32.363,-23.450,+37 +convnext_large.fb_in22k_ft_in1k,64.270,35.730,77.787,22.213,197.77,288,1.000,bicubic,-32.950,-21.943,-16 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,64.100,35.900,75.850,24.150,329.09,384,1.000,bicubic,-32.850,-23.810,+5 +swin_large_patch4_window7_224,63.870,36.130,78.180,21.820,196.53,224,0.900,bicubic,-33.080,-21.530,+5 +beit_base_patch16_384.in22k_ft_in22k_in1k,63.617,36.383,78.107,21.893,86.74,384,1.000,bicubic,-33.713,-21.613,-34 +swin_base_patch4_window12_384,63.470,36.530,78.063,21.937,87.90,384,1.000,bicubic,-33.650,-21.507,-13 +swinv2_base_window12to16_192to256_22kft1k,63.180,36.820,77.117,22.883,87.92,256,0.900,bicubic,-33.880,-22.543,-8 +tf_efficientnet_b5.ns_jft_in1k,63.047,36.953,77.777,22.223,30.39,456,0.934,bicubic,-33.823,-21.883,+11 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,62.947,37.053,76.610,23.390,86.57,224,0.950,bicubic,-33.563,-23.010,+55 +deit3_base_patch16_384_in21ft1k,62.640,37.360,75.553,24.447,86.88,384,1.000,bicubic,-34.600,-24.187,-27 +convnext_base.fb_in22k_ft_in1k,62.520,37.480,76.563,23.437,88.59,288,1.000,bicubic,-34.700,-23.197,-26 +vit_base_patch8_224.augreg2_in21k_ft_in1k,62.407,37.593,76.607,23.393,86.58,224,0.900,bicubic,-34.533,-22.973,-2 +tf_efficientnetv2_l.in21k_ft_in1k,62.367,37.633,76.743,23.257,118.52,480,1.000,bicubic,-34.953,-22.897,-40 +vit_base_patch8_224.augreg_in21k_ft_in1k,62.190,37.810,75.610,24.390,86.58,224,0.900,bicubic,-34.890,-24.010,-17 +tf_efficientnetv2_xl.in21k_ft_in1k,62.090,37.910,75.650,24.350,208.12,512,1.000,bicubic,-35.240,-23.950,-43 +deit3_base_patch16_224_in21ft1k,61.780,38.220,74.713,25.287,86.59,224,1.000,bicubic,-35.090,-24.927,+4 +tf_efficientnet_b4.ns_jft_in1k,61.230,38.770,76.173,23.827,19.34,380,0.922,bicubic,-35.480,-23.477,+22 +maxvit_base_tf_512.in1k,61.113,38.887,74.057,25.943,119.88,512,1.000,bicubic,-36.067,-25.583,-29 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,60.373,39.627,73.810,26.190,88.30,384,1.000,bicubic,-36.237,-25.670,+30 +beit_base_patch16_224.in22k_ft_in22k_in1k,60.317,39.683,75.597,24.403,86.53,224,0.900,bicubic,-36.343,-24.063,+26 +tf_efficientnetv2_m.in21k_ft_in1k,60.280,39.720,75.070,24.930,54.14,480,1.000,bicubic,-36.720,-24.560,-16 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,60.240,39.760,73.540,26.460,88.34,448,1.000,bicubic,-36.330,-25.980,+34 +vit_base_patch16_384.augreg_in21k_ft_in1k,60.180,39.820,73.843,26.157,86.86,384,1.000,bicubic,-36.840,-25.867,-21 +convnext_small.fb_in22k_ft_in1k_384,59.947,40.053,74.470,25.530,50.22,384,1.000,bicubic,-37.153,-25.230,-28 +maxvit_large_tf_512.in1k,59.877,40.123,72.847,27.153,212.33,512,1.000,bicubic,-37.173,-26.743,-25 +swin_base_patch4_window7_224,59.537,40.463,74.247,25.753,87.77,224,0.900,bicubic,-37.143,-25.413,+18 +vit_base_patch32_clip_224.laion2b_ft_in1k,59.170,40.830,73.897,26.103,88.22,224,0.900,bicubic,-35.570,-25.173,+311 +maxvit_base_tf_384.in1k,59.110,40.890,71.700,28.300,119.65,384,1.000,bicubic,-38.010,-28.080,-34 +vit_base_patch16_224.augreg2_in21k_ft_in1k,59.047,40.953,73.640,26.360,86.57,224,0.900,bicubic,-37.463,-25.910,+34 +volo_d5_512,58.917,41.083,73.200,26.800,296.09,512,1.150,bicubic,-38.373,-26.560,-53 +volo_d5_448,58.793,41.207,73.057,26.943,295.91,448,1.150,bicubic,-38.447,-26.613,-50 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,58.633,41.367,71.720,28.280,328.99,224,0.900,bicubic,-37.547,-27.790,+83 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,58.603,41.397,73.140,26.860,88.30,384,0.950,bicubic,-37.817,-26.360,+44 +maxvit_large_tf_384.in1k,58.453,41.547,71.167,28.833,212.03,384,1.000,bicubic,-38.487,-28.473,-23 +deit3_large_patch16_384,58.360,41.640,72.970,27.030,304.76,384,1.000,bicubic,-38.490,-26.650,-14 +deit3_huge_patch14_224,58.107,41.893,72.130,27.870,632.13,224,0.900,bicubic,-38.463,-27.390,+19 +tf_efficientnet_b8.ap_in1k,57.830,42.170,72.957,27.043,87.41,672,0.954,bicubic,-38.720,-26.613,+21 +convnext_small.fb_in22k_ft_in1k,57.743,42.257,72.790,27.210,50.22,288,1.000,bicubic,-39.067,-26.880,-12 +mvitv2_large,57.487,42.513,70.773,29.227,217.99,224,0.900,bicubic,-38.923,-28.677,+39 +cait_m48_448,57.470,42.530,71.860,28.140,356.46,448,1.000,bicubic,-39.410,-27.810,-23 +cait_m36_384,57.467,42.533,72.313,27.687,271.22,384,1.000,bicubic,-39.363,-27.347,-19 +tf_efficientnet_b3.ns_jft_in1k,57.417,42.583,72.387,27.613,12.23,300,0.904,bicubic,-38.683,-27.093,+83 +volo_d4_448,57.283,42.717,71.537,28.463,193.41,448,1.150,bicubic,-39.787,-28.213,-45 +maxvit_small_tf_512.in1k,57.093,42.907,70.967,29.033,69.13,512,1.000,bicubic,-40.087,-28.653,-54 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,57.057,42.943,71.290,28.710,88.22,224,0.900,bicubic,-38.183,-28.030,+211 +vit_base_patch16_224.augreg_in21k_ft_in1k,56.823,43.177,70.633,29.367,86.57,224,0.900,bicubic,-39.477,-28.927,+51 +deit3_medium_patch16_224_in21ft1k,56.650,43.350,69.737,30.263,38.85,224,1.000,bicubic,-39.490,-29.753,+72 +volo_d5_224,56.480,43.520,70.643,29.357,295.46,224,0.960,bicubic,-40.400,-28.977,-32 +deit3_large_patch16_224,56.463,43.537,70.457,29.543,304.37,224,0.900,bicubic,-39.727,-29.073,+65 +xcit_large_24_p8_384_dist,56.353,43.647,71.323,28.677,188.93,384,1.000,bicubic,-40.407,-28.237,-17 +flexivit_large.1200ep_in1k,56.293,43.707,71.560,28.440,304.36,240,0.950,bicubic,-40.487,-27.970,-22 +flexivit_large.600ep_in1k,56.047,43.953,71.173,28.827,304.36,240,0.950,bicubic,-40.693,-28.427,-17 +xcit_large_24_p8_224_dist,56.027,43.973,70.663,29.337,188.93,224,1.000,bicubic,-40.613,-28.797,-6 +vit_base_patch32_clip_224.openai_ft_in1k,55.907,44.093,72.173,27.827,88.22,224,0.900,bicubic,-38.533,-26.837,+332 +vit_medium_patch16_gap_384.in12k_ft_in1k,55.787,44.213,70.993,29.007,39.03,384,0.950,bicubic,-40.723,-28.597,+6 +flexivit_large.300ep_in1k,55.700,44.300,70.713,29.287,304.36,240,0.950,bicubic,-40.990,-28.867,-13 +xcit_large_24_p16_384_dist,54.907,45.093,69.867,30.133,189.10,384,1.000,bicubic,-42.033,-29.653,-45 +volo_d4_224,54.747,45.253,68.867,31.133,192.96,224,0.960,bicubic,-42.033,-30.753,-31 +maxvit_tiny_tf_512.in1k,54.723,45.277,68.933,31.067,31.05,512,1.000,bicubic,-42.247,-30.737,-52 +deit3_small_patch16_384_in21ft1k,54.470,45.530,68.313,31.687,22.21,384,1.000,bicubic,-42.200,-31.327,-15 +efficientnet_b5.in12k_ft_in1k,54.447,45.553,69.857,30.143,30.39,448,1.000,bicubic,-42.323,-29.743,-29 +vit_base_r50_s16_384.orig_in21k_ft_in1k,54.403,45.597,69.560,30.440,98.95,384,1.000,bicubic,-42.047,-30.060,+9 +maxvit_small_tf_384.in1k,54.343,45.657,68.193,31.807,69.02,384,1.000,bicubic,-42.397,-31.357,-29 +resnetv2_152x4_bitm,54.320,45.680,70.167,29.833,936.53,480,1.000,bilinear,-42.550,-29.453,-46 +xcit_large_24_p16_224_dist,54.260,45.740,68.970,31.030,189.10,224,1.000,bicubic,-42.060,-30.570,+27 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,54.197,45.803,68.757,31.243,36.47,384,1.000,bicubic,-41.863,-30.623,+66 +volo_d3_448,53.990,46.010,68.020,31.980,86.63,448,1.000,bicubic,-43.030,-31.650,-64 +tf_efficientnet_b5.ap_in1k,53.870,46.130,69.160,30.840,30.39,456,0.934,bicubic,-42.210,-30.380,+58 +xcit_medium_24_p8_224_dist,53.660,46.340,68.410,31.590,84.32,224,1.000,bicubic,-42.860,-31.100,-9 +tf_efficientnet_b2.ns_jft_in1k,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+142 +tf_efficientnet_b6.ap_in1k,53.560,46.440,68.550,31.450,43.04,528,0.942,bicubic,-42.810,-31.000,+11 +cait_s36_384,53.550,46.450,68.000,32.000,68.37,384,1.000,bicubic,-43.080,-31.600,-24 +vit_medium_patch16_gap_256.in12k_ft_in1k,53.537,46.463,69.067,30.933,38.86,256,0.950,bicubic,-42.443,-30.383,+72 +deit3_base_patch16_384,53.513,46.487,67.637,32.363,86.88,384,1.000,bicubic,-42.717,-31.763,+33 +deit3_base_patch16_224,53.453,46.547,67.590,32.410,86.59,224,0.900,bicubic,-42.327,-31.680,+101 +tf_efficientnet_b8.ra_in1k,53.410,46.590,69.090,30.910,87.41,672,0.954,bicubic,-43.290,-30.510,-34 +xcit_medium_24_p8_384_dist,53.407,46.593,68.143,31.857,84.32,384,1.000,bicubic,-43.373,-31.527,-47 +vit_base_patch32_384.augreg_in21k_ft_in1k,53.307,46.693,68.047,31.953,88.30,384,1.000,bicubic,-42.593,-31.303,+79 +tf_efficientnet_b7.ap_in1k,53.260,46.740,68.873,31.127,66.35,600,0.949,bicubic,-43.090,-30.497,+5 +convnext_large.fb_in1k,53.227,46.773,67.877,32.123,197.77,288,1.000,bicubic,-43.173,-31.663,0 +xcit_medium_24_p16_384_dist,53.213,46.787,68.050,31.950,84.40,384,1.000,bicubic,-43.487,-31.480,-40 +maxvit_base_tf_224.in1k,53.210,46.790,66.147,33.853,119.47,224,0.950,bicubic,-43.140,-33.443,+4 +tf_efficientnetv2_l.in1k,53.163,46.837,67.833,32.167,118.52,480,1.000,bicubic,-43.577,-31.717,-47 +tf_efficientnetv2_s.in21k_ft_in1k,53.150,46.850,69.000,31.000,21.46,384,1.000,bicubic,-43.320,-30.570,-17 +tf_efficientnet_b4.ap_in1k,53.090,46.910,68.210,31.790,19.34,380,0.922,bicubic,-42.400,-31.030,+129 +regnetz_e8,53.017,46.983,67.140,32.860,57.70,320,1.000,bicubic,-43.583,-32.420,-36 +maxvit_large_tf_224.in1k,52.983,47.017,65.343,34.657,211.79,224,0.950,bicubic,-43.337,-34.157,+6 +dm_nfnet_f5,52.870,47.130,67.430,32.570,377.21,544,0.954,bicubic,-43.940,-32.080,-63 +volo_d3_224,52.703,47.297,66.317,33.683,86.33,224,0.960,bicubic,-43.747,-33.303,-16 +deit3_small_patch16_224_in21ft1k,52.690,47.310,66.877,33.123,22.06,224,1.000,bicubic,-43.130,-32.433,+79 +maxvit_tiny_tf_384.in1k,52.463,47.537,66.780,33.220,30.98,384,1.000,bicubic,-44.137,-32.830,-40 +dm_nfnet_f6,52.447,47.553,67.120,32.880,438.36,576,0.956,bicubic,-44.473,-32.600,-79 +tf_efficientnet_b7.ra_in1k,52.393,47.607,68.233,31.767,66.35,600,0.949,bicubic,-44.187,-31.277,-40 +xcit_small_24_p8_384_dist,52.360,47.640,66.840,33.160,47.63,384,1.000,bicubic,-44.460,-32.790,-70 +swsl_resnet18,52.327,47.673,70.480,29.520,11.69,224,0.875,bilinear,-38.763,-27.730,+572 +efficientnetv2_rw_m.agc_in1k,52.323,47.677,67.210,32.790,53.24,416,1.000,bicubic,-43.947,-32.420,+4 +deit_base_distilled_patch16_384,52.257,47.743,67.733,32.267,87.63,384,1.000,bicubic,-44.253,-31.827,-35 +xcit_medium_24_p16_224_dist,52.210,47.790,66.900,33.100,84.40,224,1.000,bicubic,-44.050,-32.510,+4 +xcit_small_24_p8_224_dist,52.197,47.803,66.767,33.233,47.63,224,1.000,bicubic,-44.353,-32.773,-42 +convnext_tiny.fb_in22k_ft_in1k_384,52.163,47.837,66.917,33.083,28.59,384,1.000,bicubic,-44.007,-32.583,+18 +dm_nfnet_f3,52.130,47.870,66.743,33.257,254.92,416,0.940,bicubic,-44.600,-32.887,-64 +resnetv2_152x2_bit_teacher_384,51.937,48.063,68.670,31.330,236.34,384,1.000,bicubic,-44.253,-30.830,+11 +resmlp_big_24_224_in22ft1k,51.903,48.097,68.463,31.537,129.14,224,0.875,bicubic,-44.447,-31.057,-18 +xcit_small_24_p16_384_dist,51.883,48.117,66.353,33.647,47.67,384,1.000,bicubic,-44.457,-33.197,-17 +cait_s24_384,51.783,48.217,66.313,33.687,47.06,384,1.000,bicubic,-44.787,-33.237,-51 +resnetv2_152x2_bitm,51.757,48.243,69.250,30.750,236.34,448,1.000,bilinear,-44.763,-30.340,-47 +ecaresnet269d,51.670,48.330,66.047,33.953,102.09,352,1.000,bicubic,-44.790,-33.493,-38 +mvitv2_base,51.567,48.433,65.623,34.377,51.47,224,0.900,bicubic,-44.443,-33.897,+35 +vit_base_patch16_224_miil.in21k_ft_in1k,51.557,48.443,65.207,34.793,86.54,224,0.875,bilinear,-44.473,-34.183,+30 +tf_efficientnetv2_m.in1k,51.437,48.563,66.630,33.370,54.14,480,1.000,bicubic,-45.043,-32.980,-45 +maxvit_rmlp_small_rw_224,51.423,48.577,65.180,34.820,64.90,224,0.900,bicubic,-44.537,-34.240,+41 +maxvit_small_tf_224.in1k,51.180,48.820,65.277,34.723,68.93,224,0.950,bicubic,-45.030,-34.203,-2 +pit_b_distilled_224,51.153,48.847,66.770,33.230,74.79,224,0.900,bicubic,-44.917,-32.650,+17 +xcit_small_12_p8_384_dist,51.100,48.900,65.833,34.167,26.21,384,1.000,bicubic,-45.380,-33.657,-48 +convnext_base.fb_in1k,51.073,48.927,65.883,34.117,88.59,288,1.000,bicubic,-45.237,-33.667,-19 +dm_nfnet_f4,50.900,49.100,65.557,34.443,316.07,512,0.951,bicubic,-45.880,-34.053,-88 +tf_efficientnet_b1.ns_jft_in1k,50.883,49.117,67.910,32.090,7.79,240,0.882,bicubic,-43.977,-31.340,+208 +vit_base_patch16_384.orig_in21k_ft_in1k,50.883,49.117,65.270,34.730,86.86,384,1.000,bicubic,-45.307,-34.030,-6 +volo_d2_384,50.873,49.127,65.637,34.363,58.87,384,1.000,bicubic,-45.837,-33.963,-79 +xcit_small_24_p16_224_dist,50.733,49.267,65.010,34.990,47.67,224,1.000,bicubic,-45.067,-34.330,+53 +flexivit_base.1200ep_in1k,50.693,49.307,65.117,34.883,86.59,240,0.950,bicubic,-45.417,-34.343,+3 +coatnet_rmlp_2_rw_224,50.563,49.437,63.370,36.630,73.88,224,0.950,bicubic,-45.637,-35.910,-11 +xcit_small_12_p16_384_dist,50.520,49.480,65.313,34.687,26.25,384,1.000,bicubic,-45.820,-34.267,-34 +efficientnet_b4.ra2_in1k,50.510,49.490,65.703,34.297,19.34,384,1.000,bicubic,-45.010,-33.687,+87 +volo_d1_384,50.477,49.523,64.917,35.083,26.78,384,1.000,bicubic,-45.993,-34.633,-57 +xcit_small_12_p8_224_dist,50.440,49.560,65.433,34.567,26.21,224,1.000,bicubic,-45.520,-33.917,+24 +resnetv2_101x3_bitm,50.407,49.593,67.790,32.210,387.93,448,1.000,bilinear,-45.843,-31.800,-24 +flexivit_base.600ep_in1k,50.357,49.643,64.613,35.387,86.59,240,0.950,bicubic,-45.603,-34.807,+21 +regnetz_040h,50.330,49.670,65.633,34.367,28.94,320,1.000,bicubic,-46.000,-33.887,-39 +ssl_resnext101_32x16d,50.257,49.743,66.033,33.967,194.03,224,0.875,bilinear,-45.153,-33.127,+98 +mvitv2_small,50.250,49.750,64.910,35.090,34.87,224,0.900,bicubic,-45.640,-34.450,+29 +cait_s24_224,50.243,49.757,65.027,34.973,46.92,224,1.000,bicubic,-45.407,-34.363,+63 +eca_nfnet_l2,50.237,49.763,65.450,34.550,56.72,384,1.000,bicubic,-46.213,-34.210,-61 +tresnet_v2_l,50.167,49.833,65.093,34.907,46.17,224,0.875,bilinear,-45.653,-34.197,+35 +pvt_v2_b5,50.167,49.833,65.037,34.963,81.96,224,0.900,bicubic,-45.783,-34.353,+18 +deit3_medium_patch16_224,50.167,49.833,64.697,35.303,38.85,224,0.900,bicubic,-45.223,-34.733,+103 +vit_small_patch16_384.augreg_in21k_ft_in1k,50.160,49.840,65.807,34.193,22.20,384,1.000,bicubic,-45.820,-33.623,+7 +resnest269e,50.153,49.847,64.670,35.330,110.93,416,0.928,bicubic,-45.967,-34.850,-17 +deit_base_distilled_patch16_224,50.063,49.937,66.227,33.773,87.34,224,0.900,bicubic,-45.687,-33.203,+41 +pvt_v2_b4,50.063,49.937,65.150,34.850,62.56,224,0.900,bicubic,-45.837,-34.290,+18 +tf_efficientnet_b3.ap_in1k,50.057,49.943,65.210,34.790,12.23,300,0.904,bicubic,-44.913,-33.900,+164 +flexivit_base.300ep_in1k,50.013,49.987,64.113,35.887,86.59,240,0.950,bicubic,-45.957,-35.257,+6 +resnest200e,49.873,50.127,64.743,35.257,70.20,320,0.909,bicubic,-46.197,-34.637,-15 +efficientformer_l7,49.837,50.163,66.020,33.980,82.23,224,0.950,bicubic,-45.763,-33.370,+56 +volo_d2_224,49.813,50.187,64.593,35.407,58.68,224,0.960,bicubic,-46.607,-34.867,-69 +seresnextaa101d_32x8d,49.760,50.240,64.410,35.590,93.59,288,1.000,bicubic,-46.660,-35.110,-71 +xception65,49.760,50.240,63.520,36.480,39.92,299,0.940,bicubic,-45.930,-35.790,+45 +swinv2_base_window16_256,49.680,50.320,63.813,36.187,87.92,256,0.900,bicubic,-46.500,-35.717,-31 +pvt_v2_b3,49.580,50.420,64.787,35.213,45.24,224,0.900,bicubic,-45.890,-34.533,+71 +cait_xs24_384,49.527,50.473,64.900,35.100,26.67,384,1.000,bicubic,-46.483,-34.430,-8 +maxvit_rmlp_tiny_rw_256,49.523,50.477,63.817,36.183,29.15,256,0.950,bicubic,-46.517,-35.593,-15 +tf_efficientnet_b5.ra_in1k,49.510,50.490,65.657,34.343,30.39,456,0.934,bicubic,-46.470,-33.843,-6 +resnetv2_152x2_bit_teacher,49.480,50.520,65.617,34.383,236.34,224,0.875,bicubic,-46.270,-33.823,+26 +resnet200d,49.470,50.530,64.330,35.670,64.69,320,1.000,bicubic,-46.640,-35.070,-31 +xcit_small_12_p16_224_dist,49.417,50.583,63.850,36.150,26.25,224,1.000,bicubic,-46.323,-35.310,+26 +resnest101e,49.367,50.633,65.587,34.413,48.28,256,0.875,bilinear,-46.203,-33.683,+49 +regnetz_040,49.280,50.720,64.067,35.933,27.12,320,1.000,bicubic,-46.900,-35.333,-41 +resnet152d,49.253,50.747,64.413,35.587,60.21,320,1.000,bicubic,-46.617,-35.017,+6 +vit_base_patch32_224.augreg_in21k_ft_in1k,49.253,50.747,64.340,35.660,88.22,224,0.900,bicubic,-45.137,-34.720,+235 +seresnet152d,49.247,50.753,64.170,35.830,66.84,320,1.000,bicubic,-47.063,-35.340,-65 +xcit_large_24_p8_224,49.247,50.753,62.850,37.150,188.93,224,1.000,bicubic,-46.833,-36.300,-34 +maxxvit_rmlp_small_rw_256,49.147,50.853,63.343,36.657,66.01,256,0.950,bicubic,-47.063,-36.117,-53 +gcvit_base,49.143,50.857,63.963,36.037,90.32,224,0.875,bicubic,-46.917,-35.597,-29 +resmlp_big_24_distilled_224,49.097,50.903,65.470,34.530,129.14,224,0.875,bicubic,-46.773,-33.970,-1 +convnext_small.fb_in1k,49.077,50.923,64.820,35.180,50.22,288,1.000,bicubic,-46.903,-34.770,-18 +ssl_resnext101_32x8d,49.067,50.933,65.480,34.520,88.79,224,0.875,bilinear,-46.273,-33.860,+78 +volo_d1_224,48.967,51.033,63.187,36.813,26.63,224,0.960,bicubic,-47.063,-36.163,-29 +repvgg_b3,48.917,51.083,64.887,35.113,123.09,224,0.875,bilinear,-45.633,-34.023,+208 +resnetrs420,48.857,51.143,63.427,36.573,191.89,416,1.000,bicubic,-47.543,-36.103,-90 +maxvit_tiny_tf_224.in1k,48.810,51.190,62.947,37.053,30.92,224,0.950,bicubic,-47.000,-36.313,+2 +deit3_small_patch16_384,48.680,51.320,62.833,37.167,22.21,384,1.000,bicubic,-46.920,-36.607,+30 +seresnext101d_32x8d,48.610,51.390,62.963,37.037,93.59,288,1.000,bicubic,-47.750,-36.507,-89 +efficientnetv2_rw_s.ra2_in1k,48.603,51.397,63.840,36.160,23.94,384,1.000,bicubic,-47.107,-35.560,+16 +regnetz_d32,48.590,51.410,65.187,34.813,27.58,320,0.950,bicubic,-47.270,-34.243,-7 +swinv2_small_window16_256,48.583,51.417,62.767,37.233,49.73,256,0.900,bicubic,-47.487,-36.573,-44 +efficientnet_b3.ra2_in1k,48.563,51.437,64.250,35.750,12.23,320,1.000,bicubic,-46.577,-34.960,+97 +ecaresnet101d,48.527,51.473,64.100,35.900,44.57,224,0.875,bicubic,-46.633,-35.200,+92 +edgenext_base,48.433,51.567,64.317,35.683,18.51,320,1.000,bicubic,-47.357,-35.253,-4 +dm_nfnet_f2,48.373,51.627,63.233,36.767,193.78,352,0.920,bicubic,-48.087,-36.377,-109 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,48.363,51.637,63.797,36.203,36.43,224,0.900,bicubic,-46.767,-35.323,+95 +swinv2_base_window8_256,48.340,51.660,63.610,36.390,87.92,256,0.900,bicubic,-47.730,-35.870,-52 +repvgg_b3g4,48.310,51.690,64.800,35.200,83.83,224,0.875,bilinear,-46.180,-34.220,+201 +vit_large_patch32_384.orig_in21k_ft_in1k,48.250,51.750,61.830,38.170,306.63,384,1.000,bicubic,-46.990,-37.410,+72 +convit_base,48.217,51.783,63.000,37.000,86.54,224,0.875,bicubic,-46.883,-36.140,+98 +swin_s3_base_224,48.147,51.853,62.250,37.750,71.13,224,0.900,bicubic,-47.893,-37.100,-48 +sequencer2d_l,48.100,51.900,62.350,37.650,54.30,224,0.875,bicubic,-47.780,-37.120,-24 +resnetrs350,48.050,51.950,62.653,37.347,163.96,384,1.000,bicubic,-48.190,-36.937,-82 +tf_efficientnetv2_b3.in21k_ft_in1k,48.037,51.963,64.730,35.270,14.36,300,0.900,bicubic,-47.553,-34.550,+16 +gcvit_small,48.033,51.967,62.700,37.300,51.09,224,0.875,bicubic,-47.897,-36.680,-33 +regnetz_d8,48.010,51.990,64.417,35.583,23.37,320,1.000,bicubic,-48.000,-35.013,-50 +twins_svt_large,47.947,52.053,62.907,37.093,99.27,224,0.900,bicubic,-47.773,-36.583,-5 +vit_relpos_base_patch16_224.sw_in1k,47.927,52.073,62.853,37.147,86.43,224,0.900,bicubic,-47.223,-36.307,+78 +mixer_b16_224_miil,47.790,52.210,63.400,36.600,59.88,224,0.875,bilinear,-47.090,-35.870,+128 +repvgg_b2g4,47.787,52.213,64.390,35.610,61.76,224,0.875,bilinear,-46.033,-34.520,+273 +vit_relpos_base_patch16_clsgap_224.sw_in1k,47.767,52.233,62.403,37.597,86.43,224,0.900,bicubic,-47.483,-36.797,+59 +mvitv2_tiny,47.693,52.307,62.810,37.190,24.17,224,0.900,bicubic,-47.717,-36.250,+34 +vit_relpos_medium_patch16_cls_224.sw_in1k,47.660,52.340,61.800,38.200,38.76,224,0.900,bicubic,-47.640,-37.290,+50 +eca_nfnet_l1,47.650,52.350,62.763,37.237,41.41,320,1.000,bicubic,-48.290,-36.727,-44 +seresnext101_32x8d,47.643,52.357,61.443,38.557,93.57,288,1.000,bicubic,-48.477,-37.917,-77 +resnetv2_50x3_bitm,47.593,52.407,65.603,34.397,217.32,448,1.000,bilinear,-48.677,-33.957,-101 +pit_s_distilled_224,47.543,52.457,63.493,36.507,24.04,224,0.900,bicubic,-47.187,-35.607,+138 +resnest50d_4s2x40d,47.483,52.517,63.807,36.193,30.42,224,0.875,bicubic,-47.227,-35.163,+143 +efficientnet_b3_pruned.in1k,47.447,52.553,62.793,37.207,9.86,300,0.904,bicubic,-47.133,-36.277,+167 +crossvit_18_dagger_408,47.380,52.620,60.943,39.057,44.61,408,1.000,bicubic,-48.750,-38.527,-84 +coatnet_rmlp_1_rw_224,47.370,52.630,61.430,38.570,41.69,224,0.950,bicubic,-48.120,-37.960,+11 +vit_base_patch16_224.orig_in21k_ft_in1k,47.340,52.660,61.607,38.393,86.57,224,0.900,bicubic,-47.870,-37.553,+53 +xcit_small_24_p8_224,47.287,52.713,60.990,39.010,47.63,224,1.000,bicubic,-48.613,-38.190,-48 +efficientformer_l3,47.230,52.770,63.400,36.600,31.41,224,0.950,bicubic,-47.980,-35.910,+50 +tresnet_m,47.230,52.770,61.993,38.007,31.39,224,0.875,bilinear,-48.150,-37.157,+29 +tf_efficientnet_b6.aa_in1k,47.213,52.787,63.110,36.890,43.04,528,0.942,bicubic,-49.077,-36.410,-112 +ssl_resnext101_32x4d,47.177,52.823,63.367,36.633,44.18,224,0.875,bilinear,-47.983,-35.863,+57 +resnetrs270,47.107,52.893,62.010,37.990,129.86,352,1.000,bicubic,-48.953,-37.480,-79 +tf_efficientnet_b4.aa_in1k,47.083,52.917,62.867,37.133,19.34,380,0.922,bicubic,-48.507,-36.413,-11 +regnetz_d8_evos,47.080,52.920,63.397,36.603,23.46,320,0.950,bicubic,-49.140,-36.093,-106 +vit_base_patch16_rpn_224.in1k,47.057,52.943,62.400,37.600,86.54,224,0.900,bicubic,-47.773,-36.690,+113 +swinv2_small_window8_256,47.027,52.973,62.307,37.693,49.73,256,0.900,bicubic,-48.703,-37.053,-32 +xcit_small_12_p8_224,46.983,53.017,60.533,39.467,26.21,224,1.000,bicubic,-48.437,-38.667,+10 +xcit_large_24_p16_224,46.957,53.043,60.670,39.330,189.10,224,1.000,bicubic,-47.983,-38.160,+91 +xception65p,46.937,53.063,61.083,38.917,39.82,299,0.940,bicubic,-48.723,-38.187,-25 +resnet101d,46.893,53.107,62.317,37.683,44.57,320,1.000,bicubic,-48.857,-36.963,-42 +maxvit_tiny_rw_224,46.887,53.113,60.897,39.103,29.06,224,0.950,bicubic,-48.853,-38.413,-39 +pvt_v2_b2_li,46.833,53.167,62.507,37.493,22.55,224,0.900,bicubic,-48.367,-36.773,+43 +resnet152,46.817,53.183,60.427,39.573,60.19,224,0.950,bicubic,-48.733,-38.843,-16 +gluon_seresnext101_64x4d,46.677,53.323,61.303,38.697,88.23,224,0.875,bicubic,-47.973,-37.677,+134 +twins_pcpvt_large,46.637,53.363,62.240,37.760,60.99,224,0.900,bicubic,-49.083,-37.050,-40 +convnext_tiny.fb_in1k,46.573,53.427,63.183,36.817,28.59,288,1.000,bicubic,-48.627,-35.987,+37 +dm_nfnet_f1,46.547,53.453,61.407,38.593,132.63,320,0.910,bicubic,-49.843,-38.063,-146 +regnetv_064,46.480,53.520,62.253,37.747,30.58,288,1.000,bicubic,-49.290,-37.167,-51 +crossvit_15_dagger_408,46.463,53.537,60.480,39.520,28.50,408,1.000,bicubic,-49.357,-38.930,-59 +xcit_medium_24_p8_224,46.463,53.537,59.647,40.353,84.32,224,1.000,bicubic,-49.407,-39.433,-65 +resnetrs200,46.423,53.577,61.060,38.940,93.21,320,1.000,bicubic,-49.917,-38.430,-143 +coatnet_1_rw_224,46.403,53.597,60.080,39.920,41.72,224,0.950,bicubic,-49.217,-39.360,-33 +swin_s3_small_224,46.400,53.600,60.900,39.100,49.74,224,0.900,bicubic,-49.440,-38.300,-65 +gcvit_tiny,46.373,53.627,61.603,38.397,28.22,224,0.875,bicubic,-49.307,-37.737,-41 +fbnetv3_g.ra2_in1k,46.337,53.663,62.417,37.583,16.62,288,0.950,bilinear,-48.793,-36.813,+41 +sequencer2d_m,46.300,53.700,60.897,39.103,38.31,224,0.875,bicubic,-49.290,-38.433,-33 +tresnet_xl,46.283,53.717,61.943,38.057,78.44,224,0.875,bilinear,-48.777,-37.317,+56 +xcit_tiny_24_p8_384_dist,46.267,53.733,60.713,39.287,12.11,384,1.000,bicubic,-49.973,-38.727,-132 +xcit_tiny_24_p8_224_dist,46.260,53.740,60.600,39.400,12.11,224,1.000,bicubic,-49.190,-38.790,-15 +deit_small_distilled_patch16_224,46.160,53.840,62.417,37.583,22.44,224,0.900,bicubic,-48.430,-36.513,+126 +regnety_160,46.153,53.847,61.837,38.163,83.59,288,1.000,bicubic,-49.727,-37.723,-80 +gernet_m,46.150,53.850,62.700,37.300,21.14,224,0.875,bilinear,-48.400,-35.960,+133 +crossvit_base_240,46.133,53.867,60.217,39.783,105.03,240,0.875,bicubic,-48.937,-38.933,+49 +swinv2_cr_small_ns_224,46.117,53.883,60.780,39.220,49.70,224,0.900,bicubic,-49.573,-38.530,-53 +resnest50d_1s4x24d,46.083,53.917,62.377,37.623,25.68,224,0.875,bicubic,-48.307,-36.693,+148 +tf_efficientnet_b0.ns_jft_in1k,46.047,53.953,63.253,36.747,5.29,224,0.875,bicubic,-47.693,-35.677,+230 +jx_nest_base,46.040,53.960,60.103,39.897,67.72,224,0.875,bicubic,-49.500,-39.197,-39 +resnet51q,46.027,53.973,60.910,39.090,35.70,288,1.000,bilinear,-49.173,-38.350,+16 +vit_small_patch16_224.augreg_in21k_ft_in1k,45.990,54.010,61.820,38.180,22.05,224,0.900,bicubic,-48.890,-37.260,+71 +regnety_080,45.960,54.040,60.850,39.150,39.18,288,1.000,bicubic,-49.900,-38.590,-84 +vit_relpos_medium_patch16_224.sw_in1k,45.947,54.053,61.030,38.970,38.75,224,0.900,bicubic,-49.263,-38.200,+9 +resnest50d,45.937,54.063,62.623,37.377,27.48,224,0.875,bilinear,-48.683,-36.497,+110 +deit3_small_patch16_224,45.927,54.073,58.903,41.097,22.06,224,0.900,bicubic,-48.763,-40.197,+98 +convnext_nano.in12k_ft_in1k,45.903,54.097,62.680,37.320,15.59,288,1.000,bicubic,-49.457,-36.770,-15 +crossvit_18_240,45.903,54.097,60.373,39.627,43.27,240,0.875,bicubic,-49.167,-38.827,+36 +regnety_032,45.893,54.107,61.537,38.463,19.44,288,1.000,bicubic,-49.577,-37.773,-37 +twins_pcpvt_base,45.893,54.107,61.337,38.663,43.83,224,0.900,bicubic,-49.567,-38.053,-35 +levit_384,45.877,54.123,61.693,38.307,39.13,224,0.900,bicubic,-49.333,-37.527,+3 +twins_svt_base,45.877,54.123,60.967,39.033,56.07,224,0.900,bicubic,-49.693,-38.263,-53 +crossvit_18_dagger_240,45.853,54.147,59.927,40.073,44.27,240,0.875,bicubic,-49.327,-39.193,+7 +convnext_tiny_hnf.a2h_in1k,45.850,54.150,60.183,39.817,28.59,288,1.000,bicubic,-49.420,-38.977,-10 +vit_relpos_medium_patch16_rpn_224.sw_in1k,45.737,54.263,60.963,39.037,38.73,224,0.900,bicubic,-49.333,-38.327,+27 +vit_srelpos_medium_patch16_224.sw_in1k,45.720,54.280,61.067,38.933,38.74,224,0.900,bicubic,-49.180,-38.093,+53 +crossvit_15_dagger_240,45.700,54.300,60.097,39.903,28.21,240,0.875,bicubic,-49.280,-39.063,+39 +regnetz_c16,45.687,54.313,62.520,37.480,13.46,320,0.940,bicubic,-49.713,-36.790,-32 +convmixer_1536_20,45.663,54.337,61.770,38.230,51.63,224,0.960,bicubic,-49.307,-37.400,+38 +gc_efficientnetv2_rw_t.agc_in1k,45.653,54.347,60.203,39.797,13.68,288,1.000,bicubic,-49.627,-39.017,-18 +flexivit_small.1200ep_in1k,45.610,54.390,59.883,40.117,22.06,240,0.950,bicubic,-49.590,-39.507,-2 +efficientnetv2_rw_t.ra2_in1k,45.607,54.393,60.183,39.817,13.65,288,1.000,bicubic,-49.463,-38.797,+19 +gluon_seresnext101_32x4d,45.590,54.410,61.143,38.857,48.96,224,0.875,bicubic,-48.860,-37.947,+115 +xcit_tiny_24_p16_384_dist,45.583,54.417,60.510,39.490,12.12,384,1.000,bicubic,-49.907,-38.620,-56 +xcit_small_24_p16_224,45.547,54.453,58.920,41.080,47.67,224,1.000,bicubic,-49.533,-40.100,+13 +xcit_medium_24_p16_224,45.540,54.460,59.000,41.000,84.40,224,1.000,bicubic,-49.590,-39.920,+5 +dm_nfnet_f0,45.483,54.517,60.983,39.017,71.49,256,0.900,bicubic,-50.207,-38.347,-84 +resnext101_64x4d,45.470,54.530,59.047,40.953,83.46,288,1.000,bicubic,-50.070,-40.243,-66 +gluon_resnet152_v1d,45.430,54.570,60.077,39.923,60.21,224,0.875,bicubic,-49.010,-39.103,+112 +nfnet_l0,45.420,54.580,62.080,37.920,35.07,288,1.000,bicubic,-49.970,-37.200,-42 +ssl_resnext50_32x4d,45.407,54.593,62.047,37.953,25.03,224,0.875,bilinear,-49.293,-36.783,+69 +resnetv2_50x1_bit_distilled,45.393,54.607,62.303,37.697,25.55,224,0.875,bicubic,-49.997,-36.907,-46 +cs3se_edgenet_x,45.393,54.607,60.427,39.573,50.72,320,1.000,bicubic,-50.617,-39.013,-138 +xcit_small_12_p16_224,45.387,54.613,59.417,40.583,26.25,224,1.000,bicubic,-49.443,-39.643,+48 +jx_nest_small,45.360,54.640,59.007,40.993,38.35,224,0.875,bicubic,-50.170,-40.203,-72 +pvt_v2_b2,45.297,54.703,60.620,39.380,25.36,224,0.900,bicubic,-49.713,-38.560,+16 +resnet61q,45.283,54.717,59.400,40.600,36.85,288,1.000,bicubic,-49.837,-39.800,-4 +cs3edgenet_x,45.253,54.747,60.257,39.743,47.82,288,1.000,bicubic,-50.197,-39.103,-60 +tresnet_xl_448,45.223,54.777,61.437,38.563,78.44,448,0.875,bilinear,-50.287,-37.903,-73 +nasnetalarge,45.210,54.790,57.883,42.117,88.75,331,0.911,bicubic,-49.940,-41.417,-15 +convit_small,45.203,54.797,60.510,39.490,27.78,224,0.875,bicubic,-49.717,-38.610,+25 +flexivit_small.600ep_in1k,45.197,54.803,59.413,40.587,22.06,240,0.950,bicubic,-50.073,-39.567,-39 +swin_small_patch4_window7_224,45.163,54.837,60.330,39.670,49.61,224,0.900,bicubic,-50.557,-39.040,-103 +resnet101,45.127,54.873,59.573,40.427,44.55,224,0.950,bicubic,-49.823,-39.497,+17 +tf_efficientnet_b3.aa_in1k,45.107,54.893,60.650,39.350,12.23,300,0.904,bicubic,-49.803,-38.370,+22 +sequencer2d_s,45.093,54.907,60.050,39.950,27.65,224,0.875,bicubic,-50.377,-39.220,-72 +rexnet_200,45.047,54.953,62.317,37.683,16.37,224,0.875,bicubic,-49.613,-36.833,+63 +maxxvit_rmlp_nano_rw_256,45.023,54.977,59.660,40.340,16.78,256,0.950,bicubic,-50.327,-39.660,-53 +resnetrs152,44.943,55.057,59.713,40.287,86.62,320,1.000,bicubic,-51.017,-39.667,-145 +resnetv2_101,44.937,55.063,58.850,41.150,44.54,224,0.950,bicubic,-49.983,-40.260,+15 +ecaresnetlight,44.890,55.110,60.770,39.230,30.16,224,0.875,bicubic,-49.250,-38.180,+129 +deit_base_patch16_224,44.870,55.130,59.177,40.823,86.57,224,0.900,bicubic,-50.140,-39.963,+2 +flexivit_small.300ep_in1k,44.867,55.133,59.343,40.657,22.06,240,0.950,bicubic,-50.283,-39.787,-29 +coatnet_bn_0_rw_224,44.813,55.187,60.903,39.097,27.44,224,0.950,bicubic,-50.167,-38.327,+1 +deit_base_patch16_384,44.777,55.223,59.617,40.383,86.86,384,1.000,bicubic,-50.873,-39.623,-106 +cait_xxs36_384,44.773,55.227,59.380,40.620,17.37,384,1.000,bicubic,-50.447,-39.940,-46 +resmlp_36_distilled_224,44.757,55.243,61.073,38.927,44.69,224,0.875,bicubic,-49.813,-38.087,+68 +gernet_l,44.740,55.260,58.943,41.057,31.08,256,0.875,bilinear,-50.190,-40.257,+5 +xcit_tiny_24_p16_224_dist,44.710,55.290,59.417,40.583,12.12,224,1.000,bicubic,-49.520,-39.403,+108 +resmlp_24_distilled_224,44.707,55.293,61.467,38.533,30.02,224,0.875,bicubic,-49.623,-37.593,+96 +tf_efficientnet_b2.ap_in1k,44.700,55.300,60.680,39.320,9.11,260,0.890,bicubic,-49.570,-38.100,+102 +swinv2_tiny_window16_256,44.573,55.427,59.570,40.430,28.35,256,0.900,bicubic,-50.787,-39.560,-69 +vit_relpos_small_patch16_224.sw_in1k,44.553,55.447,60.203,39.797,21.98,224,0.900,bicubic,-50.137,-38.547,+40 +gmlp_s16_224,44.473,55.527,58.630,41.370,19.42,224,0.875,bicubic,-49.027,-40.210,+200 +ens_adv_inception_resnet_v2,44.393,55.607,58.117,41.883,55.84,299,0.897,bicubic,-49.737,-40.843,+118 +tresnet_l,44.363,55.637,59.953,40.047,55.99,224,0.875,bilinear,-50.527,-39.237,+6 +gluon_resnext101_32x4d,44.290,55.710,59.090,40.910,44.18,224,0.875,bicubic,-49.830,-39.840,+117 +poolformer_m48,44.267,55.733,59.300,40.700,73.47,224,0.950,bicubic,-50.863,-39.920,-38 +gcvit_xtiny,44.237,55.763,59.963,40.037,19.98,224,0.875,bicubic,-50.773,-39.017,-18 +maxvit_rmlp_nano_rw_256,44.180,55.820,58.240,41.760,15.50,256,0.950,bicubic,-51.260,-40.820,-91 +wide_resnet50_2,44.177,55.823,59.727,40.273,68.88,224,0.875,bicubic,-50.493,-39.323,+37 +regnetz_c16_evos,44.160,55.840,61.060,38.940,13.49,320,0.950,bicubic,-51.460,-38.360,-121 +vit_srelpos_small_patch16_224.sw_in1k,44.137,55.863,59.710,40.290,21.97,224,0.900,bicubic,-50.413,-39.220,+54 +resnetv2_101x1_bitm,44.127,55.873,61.983,38.017,44.54,448,1.000,bilinear,-51.193,-37.387,-75 +seresnext50_32x4d,44.127,55.873,59.490,40.510,27.56,224,0.875,bicubic,-50.693,-39.640,+8 +crossvit_15_240,44.117,55.883,59.130,40.870,27.53,240,0.875,bicubic,-50.603,-39.930,+20 +gluon_resnet152_v1s,44.073,55.927,58.703,41.297,60.32,224,0.875,bicubic,-50.647,-40.477,+20 +pit_b_224,44.070,55.930,58.017,41.983,73.76,224,0.900,bicubic,-50.720,-40.803,+8 +poolformer_m36,44.027,55.973,59.060,40.940,56.17,224,0.950,bicubic,-50.983,-40.040,-26 +ssl_resnet50,44.010,55.990,61.887,38.113,25.56,224,0.875,bilinear,-50.300,-37.263,+79 +inception_resnet_v2,44.003,55.997,57.907,42.093,55.84,299,0.897,bicubic,-50.337,-40.893,+74 +pnasnet5large,43.950,56.050,56.730,43.270,86.06,331,0.911,bicubic,-51.410,-42.570,-88 +coatnext_nano_rw_224,43.907,56.093,58.653,41.347,14.70,224,0.900,bicubic,-50.943,-40.547,-4 +pit_s_224,43.890,56.110,58.627,41.373,23.46,224,0.900,bicubic,-50.700,-40.513,+36 +gluon_resnext101_64x4d,43.877,56.123,58.710,41.290,83.46,224,0.875,bicubic,-50.473,-40.170,+69 +coat_lite_small,43.823,56.177,57.147,42.853,19.84,224,0.900,bicubic,-51.257,-41.913,-47 +regnetv_040,43.787,56.213,58.457,41.543,20.64,288,1.000,bicubic,-51.943,-40.923,-151 +tnt_s_patch16_224,43.773,56.227,59.197,40.803,23.76,224,0.900,bicubic,-50.807,-39.853,+32 +swinv2_cr_small_224,43.773,56.227,57.703,42.297,49.70,224,0.900,bicubic,-51.637,-41.597,-104 +mobilevitv2_200_in22ft1k,43.770,56.230,59.493,40.507,18.45,256,0.888,bicubic,-51.280,-39.587,-42 +cait_xxs36_224,43.760,56.240,58.720,41.280,17.30,224,1.000,bicubic,-50.180,-40.310,+115 +cspresnext50,43.757,56.243,60.130,39.870,20.57,256,0.887,bilinear,-50.493,-38.920,+74 +ecaresnet50d,43.750,56.250,60.387,39.613,25.58,224,0.875,bicubic,-50.440,-38.543,+81 +ecaresnet101d_pruned,43.737,56.263,59.607,40.393,24.88,224,0.875,bicubic,-50.713,-39.493,+43 +swin_s3_tiny_224,43.710,56.290,59.497,40.503,28.33,224,0.900,bicubic,-51.190,-39.703,-25 +tf_efficientnetv2_s.in1k,43.710,56.290,58.597,41.403,21.46,384,1.000,bicubic,-52.000,-40.783,-155 +rexnet_150,43.690,56.310,60.897,39.103,9.73,224,0.875,bicubic,-50.580,-38.183,+65 +pit_xs_distilled_224,43.663,56.337,60.703,39.297,11.00,224,0.900,bicubic,-49.577,-38.147,+196 +xcit_tiny_12_p8_224_dist,43.640,56.360,58.457,41.543,6.71,224,1.000,bicubic,-51.080,-40.623,-4 +edgenext_small,43.620,56.380,59.887,40.113,5.59,320,1.000,bicubic,-51.210,-39.523,-20 +efficientformer_l1,43.587,56.413,59.927,40.073,12.29,224,0.950,bicubic,-50.353,-38.963,+102 +maxvit_nano_rw_256,43.537,56.463,57.600,42.400,15.45,256,0.950,bicubic,-51.953,-41.760,-132 +cs3sedarknet_x,43.520,56.480,58.793,41.207,35.40,288,1.000,bicubic,-51.880,-40.527,-118 +crossvit_small_240,43.470,56.530,58.933,41.067,26.86,240,0.875,bicubic,-51.110,-40.177,+18 +coatnet_rmlp_nano_rw_224,43.450,56.550,58.607,41.393,15.15,224,0.900,bicubic,-51.640,-40.563,-68 +gluon_resnet101_v1d,43.440,56.560,58.613,41.387,44.57,224,0.875,bicubic,-50.730,-40.397,+72 +ecaresnet50t,43.407,56.593,59.300,40.700,25.57,320,0.950,bicubic,-51.663,-39.820,-67 +gluon_resnet101_v1s,43.363,56.637,58.503,41.497,44.67,224,0.875,bicubic,-50.807,-40.437,+69 +cspdarknet53,43.357,56.643,59.430,40.570,27.64,256,0.887,bilinear,-50.733,-39.580,+77 +xcit_tiny_24_p8_224,43.313,56.687,57.280,42.720,12.11,224,1.000,bicubic,-51.577,-41.750,-39 +xcit_tiny_12_p8_384_dist,43.310,56.690,58.180,41.820,6.71,384,1.000,bicubic,-52.030,-41.140,-115 +dpn68b,43.287,56.713,58.673,41.327,12.61,224,0.875,bicubic,-50.333,-40.287,+136 +convmixer_768_32,43.267,56.733,59.367,40.633,21.11,224,0.960,bicubic,-51.153,-39.743,+29 +visformer_small,43.253,56.747,57.993,42.007,40.22,224,0.900,bicubic,-51.707,-41.217,-55 +eca_nfnet_l0,43.233,56.767,59.913,40.087,24.14,288,1.000,bicubic,-52.217,-39.367,-139 +regnety_064,43.227,56.773,57.237,42.763,30.58,288,1.000,bicubic,-52.563,-42.053,-188 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,43.163,56.837,58.427,41.573,119.42,256,0.900,bicubic,-49.997,-39.883,+185 +vit_small_patch32_384.augreg_in21k_ft_in1k,43.143,56.857,59.293,40.707,22.92,384,1.000,bicubic,-51.447,-39.807,0 +resnest26d,43.140,56.860,60.623,39.377,17.07,224,0.875,bilinear,-50.100,-38.197,+174 +twins_pcpvt_small,43.090,56.910,58.873,41.127,24.11,224,0.900,bicubic,-51.510,-40.277,-3 +resmlp_36_224,43.050,56.950,59.310,40.690,44.69,224,0.875,bicubic,-50.600,-39.600,+119 +coatnet_nano_rw_224,43.047,56.953,57.930,42.070,15.14,224,0.900,bicubic,-52.003,-41.220,-75 +dpn131,43.047,56.953,57.440,42.560,79.25,224,0.875,bicubic,-50.713,-41.360,+104 +cspresnet50,43.030,56.970,59.153,40.847,21.62,256,0.887,bilinear,-50.830,-39.737,+90 +tf_efficientnet_lite4.in1k,42.967,57.033,57.620,42.380,13.01,380,0.920,bilinear,-51.903,-41.470,-48 +twins_svt_small,42.923,57.077,58.453,41.547,24.06,224,0.900,bicubic,-51.847,-40.497,-38 +mobilevitv2_200_384_in22ft1k,42.920,57.080,58.980,41.020,18.45,384,1.000,bicubic,-52.470,-40.440,-138 +gluon_resnet152_v1b,42.903,57.097,57.750,42.250,60.19,224,0.875,bicubic,-51.127,-41.290,+67 +fbnetv3_d.ra2_in1k,42.873,57.127,59.693,40.307,10.31,256,0.950,bilinear,-50.967,-39.217,+88 +dpn107,42.857,57.143,57.367,42.633,86.92,224,0.875,bicubic,-51.103,-41.473,+71 +levit_256,42.823,57.177,57.897,42.103,18.89,224,0.900,bicubic,-51.577,-41.173,+14 +tf_efficientnet_b1.ap_in1k,42.803,57.197,58.813,41.187,7.79,240,0.882,bicubic,-50.827,-39.937,+113 +gcresnet50t,42.800,57.200,59.190,40.810,25.90,256,0.900,bicubic,-51.820,-39.950,-17 +gluon_resnet152_v1c,42.800,57.200,57.737,42.263,60.21,224,0.875,bicubic,-51.080,-41.003,+77 +gluon_xception65,42.793,57.207,58.820,41.180,39.92,299,0.903,bicubic,-51.217,-40.210,+63 +tresnet_l_448,42.753,57.247,58.947,41.053,55.99,448,0.875,bilinear,-52.657,-40.463,-154 +coatnet_0_rw_224,42.747,57.253,56.250,43.750,27.44,224,0.950,bicubic,-52.163,-42.860,-69 +cs3darknet_x,42.723,57.277,58.193,41.807,35.05,288,1.000,bicubic,-52.557,-41.087,-137 +resnet50d,42.707,57.293,58.697,41.303,25.58,224,0.875,bicubic,-51.363,-40.233,+50 +gluon_seresnext50_32x4d,42.683,57.317,58.710,41.290,27.56,224,0.875,bicubic,-51.487,-40.200,+39 +convnext_nano.d1h_in1k,42.673,57.327,57.560,42.440,15.59,288,1.000,bicubic,-52.197,-41.580,-66 +xcit_tiny_12_p16_384_dist,42.583,57.417,58.090,41.910,6.72,384,1.000,bicubic,-51.947,-41.080,-11 +regnety_040,42.570,57.430,57.027,42.973,20.65,288,1.000,bicubic,-52.910,-42.393,-172 +resnext101_32x8d,42.557,57.443,58.317,41.683,88.79,224,0.875,bilinear,-51.213,-40.633,+82 +nf_resnet50,42.510,57.490,59.520,40.480,25.56,288,0.940,bicubic,-51.890,-39.540,-1 +seresnet50,42.510,57.490,58.667,41.333,28.09,224,0.875,bicubic,-51.570,-40.303,+41 +mobilevitv2_175_in22ft1k,42.490,57.510,58.157,41.843,14.25,256,0.888,bicubic,-52.300,-40.933,-62 +resnetrs101,42.437,57.563,57.300,42.700,63.62,288,0.940,bicubic,-52.813,-41.910,-142 +poolformer_s36,42.323,57.677,58.743,41.257,30.86,224,0.900,bicubic,-52.307,-40.307,-38 +jx_nest_tiny,42.323,57.677,57.053,42.947,17.06,224,0.875,bicubic,-52.627,-42.047,-89 +tf_efficientnetv2_b3.in1k,42.313,57.687,57.940,42.060,14.36,300,0.904,bicubic,-52.807,-41.140,-119 +convmixer_1024_20_ks9_p14,42.280,57.720,59.713,40.287,24.38,224,0.960,bicubic,-50.060,-38.717,+214 +dpn98,42.280,57.720,56.880,43.120,61.57,224,0.875,bicubic,-51.660,-42.040,+51 +deit_small_patch16_224,42.263,57.737,58.020,41.980,22.05,224,0.900,bicubic,-51.737,-41.010,+46 +xcit_tiny_24_p16_224,42.260,57.740,56.830,43.170,12.12,224,1.000,bicubic,-51.590,-41.930,+61 +tf_efficientnet_cc_b1_8e.in1k,42.233,57.767,58.420,41.580,39.72,240,0.882,bicubic,-51.337,-40.270,+99 +legacy_senet154,42.207,57.793,56.597,43.403,115.09,224,0.875,bilinear,-52.523,-42.583,-63 +cait_xxs24_384,42.187,57.813,57.460,42.540,12.03,384,1.000,bicubic,-52.733,-41.680,-94 +xception41p,42.170,57.830,56.900,43.100,26.91,299,0.940,bicubic,-52.900,-42.320,-116 +tf_efficientnet_b2.aa_in1k,42.120,57.880,58.197,41.803,9.11,260,0.890,bicubic,-52.090,-40.853,+12 +convnext_nano_ols.d1h_in1k,42.047,57.953,56.880,43.120,15.65,288,1.000,bicubic,-52.533,-42.300,-37 +gluon_resnext50_32x4d,42.043,57.957,57.667,42.333,25.03,224,0.875,bicubic,-51.607,-41.283,+80 +resnext50_32x4d,41.987,58.013,56.767,43.233,25.03,224,0.950,bicubic,-52.583,-42.033,-37 +pvt_v2_b1,41.970,58.030,59.570,40.430,14.01,224,0.900,bicubic,-51.520,-39.200,+101 +ecaresnet50d_pruned,41.953,58.047,58.217,41.783,19.94,224,0.875,bicubic,-51.867,-40.783,+54 +mobilevitv2_150_in22ft1k,41.937,58.063,57.937,42.063,10.59,256,0.888,bicubic,-52.763,-40.983,-65 +efficientnet_b2.ra_in1k,41.933,58.067,58.300,41.700,9.11,288,1.000,bicubic,-52.437,-40.750,-17 +xcit_tiny_12_p16_224_dist,41.923,58.077,57.230,42.770,6.72,224,1.000,bicubic,-51.427,-41.440,+117 +gcvit_xxtiny,41.823,58.177,58.457,41.543,12.00,224,0.875,bicubic,-52.227,-40.613,+21 +mobilevitv2_150_384_in22ft1k,41.773,58.227,57.807,42.193,10.59,384,1.000,bicubic,-53.557,-41.323,-172 +edgenext_small_rw,41.687,58.313,58.513,41.487,7.83,320,1.000,bicubic,-52.673,-40.557,-18 +mobilevitv2_175_384_in22ft1k,41.683,58.317,57.997,42.003,14.25,384,1.000,bicubic,-53.577,-41.383,-167 +dla102x2,41.647,58.353,57.967,42.033,41.28,224,0.875,bilinear,-52.353,-40.993,+26 +hrnet_w64,41.637,58.363,57.130,42.870,128.06,224,0.875,bilinear,-52.193,-41.800,+44 +gluon_senet154,41.627,58.373,56.373,43.627,115.09,224,0.875,bicubic,-53.083,-42.757,-76 +poolformer_s24,41.600,58.400,58.437,41.563,21.39,224,0.900,bicubic,-52.730,-40.653,-19 +inception_v4,41.577,58.423,55.383,44.617,42.68,299,0.875,bicubic,-52.803,-43.437,-29 +haloregnetz_b,41.547,58.453,57.087,42.913,11.68,224,0.940,bicubic,-52.963,-41.883,-43 +swinv2_cr_tiny_ns_224,41.543,58.457,57.183,42.817,28.33,224,0.900,bicubic,-53.217,-41.927,-89 +cs3sedarknet_l,41.540,58.460,57.340,42.660,21.91,288,0.950,bicubic,-53.570,-41.870,-146 +convnext_tiny.fb_in22k_ft_in1k,41.533,58.467,55.470,44.530,28.59,288,1.000,bicubic,-52.017,-43.210,+76 +efficientnet_el.ra_in1k,41.497,58.503,58.303,41.697,10.59,300,0.904,bicubic,-53.173,-40.827,-75 +efficientnet_em.ra2_in1k,41.493,58.507,58.877,41.123,6.90,240,0.882,bicubic,-52.247,-40.103,+47 +tf_efficientnet_cc_b0_8e.in1k,41.487,58.513,57.377,42.623,24.01,224,0.875,bicubic,-51.383,-41.083,+145 +swin_tiny_patch4_window7_224,41.457,58.543,57.303,42.697,28.29,224,0.900,bicubic,-53.163,-41.727,-72 +halo2botnet50ts_256,41.457,58.543,56.210,43.790,22.64,256,0.950,bicubic,-53.573,-42.960,-135 +resnetv2_50,41.390,58.610,56.763,43.237,25.55,224,0.950,bicubic,-52.900,-42.167,-27 +swinv2_tiny_window8_256,41.387,58.613,57.117,42.883,28.35,256,0.900,bicubic,-53.643,-41.913,-139 +cait_xxs24_224,41.383,58.617,57.527,42.473,11.96,224,1.000,bicubic,-52.107,-41.333,+77 +tv_resnet152,41.327,58.673,57.520,42.480,60.19,224,0.875,bilinear,-51.913,-41.230,+105 +cs3darknet_l,41.290,58.710,57.353,42.647,21.16,288,0.950,bicubic,-53.390,-41.867,-86 +gcresnext50ts,41.270,58.730,57.140,42.860,15.67,256,0.900,bicubic,-53.140,-41.850,-49 +xception71,41.270,58.730,55.873,44.127,42.34,299,0.903,bicubic,-52.620,-43.017,+15 +dpn92,41.267,58.733,56.333,43.667,37.67,224,0.875,bicubic,-52.923,-42.687,-19 +adv_inception_v3,41.263,58.737,56.317,43.683,23.83,299,0.875,bicubic,-51.747,-42.513,+119 +gernet_s,41.247,58.753,58.830,41.170,8.17,224,0.875,bilinear,-51.193,-39.710,+165 +resnetv2_50d_evos,41.133,58.867,56.050,43.950,25.59,288,0.950,bicubic,-53.997,-43.150,-170 +resnetblur50,41.053,58.947,57.077,42.923,25.56,224,0.875,bicubic,-52.657,-41.713,+35 +nf_regnet_b1,41.013,58.987,58.120,41.880,10.22,288,0.900,bicubic,-52.867,-40.970,+13 +gluon_resnet50_v1d,40.970,59.030,57.137,42.863,25.58,224,0.875,bicubic,-52.560,-41.573,+60 +fbnetv3_b.ra2_in1k,40.947,59.053,58.653,41.347,8.60,256,0.950,bilinear,-52.703,-40.037,+40 +gluon_inception_v3,40.903,59.097,55.613,44.387,23.83,299,0.875,bicubic,-52.637,-43.217,+56 +cs3darknet_focus_l,40.890,59.110,56.637,43.363,21.15,288,0.950,bicubic,-53.900,-42.513,-119 +ese_vovnet39b,40.867,59.133,56.950,43.050,24.57,224,0.875,bicubic,-52.983,-41.950,+11 +levit_192,40.847,59.153,56.687,43.313,10.95,224,0.900,bicubic,-52.863,-42.133,+31 +regnety_320,40.813,59.187,56.117,43.883,145.05,224,0.875,bicubic,-53.707,-43.053,-74 +resnet34d,40.810,59.190,56.530,43.470,21.82,224,0.875,bicubic,-51.830,-41.890,+139 +resnetv2_50d_gn,40.777,59.223,56.210,43.790,25.57,288,0.950,bicubic,-54.323,-42.850,-172 +maxvit_rmlp_pico_rw_256,40.773,59.227,55.210,44.790,7.52,256,0.950,bicubic,-53.447,-43.790,-39 +xception,40.763,59.237,56.387,43.613,22.86,299,0.897,bicubic,-52.877,-42.383,+33 +lamhalobotnet50ts_256,40.747,59.253,56.090,43.910,22.57,256,0.950,bicubic,-54.033,-42.890,-124 +resnet50_gn,40.737,59.263,55.743,44.257,25.56,224,0.940,bicubic,-53.443,-43.177,-36 +skresnext50_32x4d,40.700,59.300,56.023,43.977,27.48,224,0.875,bicubic,-53.250,-42.797,-11 +vit_base_patch32_384.augreg_in1k,40.700,59.300,55.187,44.813,88.30,384,1.000,bicubic,-52.460,-43.423,+87 +gluon_resnet101_v1b,40.683,59.317,56.117,43.883,44.55,224,0.875,bicubic,-53.077,-42.583,+13 +hrnet_w40,40.660,59.340,56.753,43.247,57.56,224,0.875,bilinear,-53.050,-42.057,+18 +resmlp_24_224,40.653,59.347,56.573,43.427,30.02,224,0.875,bicubic,-52.787,-42.237,+57 +repvgg_b1,40.593,59.407,57.837,42.163,57.42,224,0.875,bilinear,-52.817,-40.953,+61 +halonet50ts,40.580,59.420,55.177,44.823,22.73,256,0.940,bicubic,-54.120,-44.063,-117 +tf_efficientnet_lite3.in1k,40.563,59.437,56.477,43.523,8.20,300,0.904,bilinear,-53.567,-42.493,-38 +tresnet_m_448,40.530,59.470,56.700,43.300,31.39,448,0.875,bilinear,-54.130,-42.390,-113 +mobilevitv2_175,40.530,59.470,56.280,43.720,14.25,256,0.888,bicubic,-53.700,-42.650,-53 +xcit_tiny_12_p8_224,40.530,59.470,55.630,44.370,6.71,224,1.000,bicubic,-53.830,-43.470,-71 +pit_xs_224,40.497,59.503,56.530,43.470,10.62,224,0.900,bicubic,-52.413,-42.160,+97 +dla169,40.493,59.507,57.263,42.737,53.39,224,0.875,bilinear,-53.307,-41.647,-1 +repvgg_b2,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.123,-41.060,+24 +resnetaa50,40.467,59.533,56.007,43.993,25.56,288,1.000,bicubic,-54.403,-43.113,-151 +vit_base_patch16_384.augreg_in1k,40.460,59.540,53.240,46.760,86.86,384,1.000,bicubic,-53.980,-45.780,-88 +regnetx_320,40.443,59.557,55.660,44.340,107.81,224,0.875,bicubic,-53.767,-43.290,-58 +coat_mini,40.420,59.580,55.167,44.833,10.34,224,0.900,bicubic,-54.350,-43.913,-141 +skresnet34,40.397,59.603,56.737,43.263,22.28,224,0.875,bicubic,-52.173,-41.683,+120 +efficientnet_el_pruned.in1k,40.390,59.610,56.903,43.097,10.59,300,0.904,bicubic,-53.700,-42.077,-47 +resnet50,40.387,59.613,54.673,45.327,25.56,224,0.950,bicubic,-53.533,-43.797,-26 +efficientnet_b2_pruned.in1k,40.383,59.617,56.537,43.463,8.31,260,0.890,bicubic,-53.417,-42.303,-11 +wide_resnet101_2,40.360,59.640,55.780,44.220,126.89,224,0.875,bilinear,-53.370,-43.030,-6 +coat_lite_mini,40.360,59.640,55.717,44.283,11.01,224,0.900,bicubic,-53.090,-42.933,+34 +legacy_seresnext101_32x4d,40.360,59.640,54.817,45.183,48.96,224,0.875,bilinear,-53.770,-43.973,-55 +sebotnet33ts_256,40.340,59.660,53.180,46.820,13.70,256,0.940,bicubic,-53.990,-45.400,-80 +tf_efficientnet_b0.ap_in1k,40.337,59.663,56.787,43.213,5.29,224,0.875,bicubic,-52.273,-41.583,+109 +regnetx_160,40.270,59.730,56.050,43.950,54.28,224,0.875,bicubic,-53.610,-42.750,-30 +densenet201,40.267,59.733,56.710,43.290,20.01,224,0.875,bicubic,-52.423,-41.940,+99 +resnext50d_32x4d,40.170,59.830,55.487,44.513,25.05,224,0.875,bicubic,-53.640,-43.253,-20 +eca_resnet33ts,40.137,59.863,57.003,42.997,19.68,256,0.900,bicubic,-53.723,-41.867,-30 +mobilevitv2_200,40.130,59.870,55.510,44.490,18.45,256,0.888,bicubic,-54.380,-43.450,-110 +darknetaa53,40.117,59.883,55.783,44.217,36.02,288,1.000,bilinear,-54.093,-43.247,-72 +vit_base_patch16_224.sam,40.097,59.903,55.430,44.570,86.57,224,0.900,bicubic,-53.793,-43.520,-37 +hrnet_w48,40.093,59.907,56.640,43.360,77.47,224,0.875,bilinear,-53.937,-42.360,-55 +legacy_seresnet152,40.043,59.957,55.820,44.180,66.82,224,0.875,bilinear,-53.397,-42.990,+24 +hrnet_w30,40.030,59.970,57.093,42.907,37.71,224,0.875,bilinear,-53.340,-41.737,+34 +regnetx_080,40.000,60.000,55.977,44.023,39.57,224,0.875,bicubic,-53.790,-42.933,-25 +regnetz_b16,40.000,60.000,55.627,44.373,9.72,288,0.940,bicubic,-54.680,-43.533,-145 +tf_efficientnet_b1.aa_in1k,39.977,60.023,56.137,43.863,7.79,240,0.882,bicubic,-53.733,-42.663,-17 +gluon_resnet101_v1c,39.953,60.047,55.300,44.700,44.57,224,0.875,bicubic,-53.737,-43.460,-16 +convnext_pico_ols.d1_in1k,39.870,60.130,55.620,44.380,9.06,288,1.000,bicubic,-54.160,-43.120,-60 +resmlp_12_distilled_224,39.843,60.157,57.440,42.560,15.35,224,0.875,bicubic,-53.027,-41.190,+73 +seresnet33ts,39.827,60.173,56.527,43.473,19.78,256,0.900,bicubic,-54.443,-42.423,-92 +tf_efficientnetv2_b0.in1k,39.787,60.213,56.283,43.717,7.14,224,0.875,bicubic,-53.273,-42.417,+50 +lambda_resnet50ts,39.733,60.267,54.337,45.663,21.54,256,0.950,bicubic,-54.817,-44.803,-128 +darknet53,39.723,60.277,55.293,44.707,41.61,288,1.000,bicubic,-54.647,-43.757,-110 +res2net101_26w_4s,39.717,60.283,54.550,45.450,45.21,224,0.875,bilinear,-53.803,-44.050,+1 +regnetx_120,39.687,60.313,55.633,44.367,46.11,224,0.875,bicubic,-54.583,-43.557,-100 +hrnet_w44,39.677,60.323,55.333,44.667,67.06,224,0.875,bilinear,-53.943,-43.367,-15 +vit_small_patch32_224.augreg_in21k_ft_in1k,39.670,60.330,55.253,44.747,22.88,224,0.900,bicubic,-52.480,-43.257,+113 +densenet161,39.620,60.380,56.133,43.867,28.68,224,0.875,bicubic,-53.280,-42.677,+60 +resmlp_big_24_224,39.620,60.380,54.817,45.183,129.14,224,0.875,bicubic,-54.640,-44.003,-100 +mixnet_xl.ra_in1k,39.617,60.383,55.887,44.113,11.90,224,0.875,bicubic,-54.613,-43.073,-97 +vit_small_patch16_384.augreg_in1k,39.617,60.383,54.253,45.747,22.20,384,1.000,bicubic,-55.003,-44.727,-154 +xception41,39.610,60.390,55.037,44.963,26.97,299,0.903,bicubic,-53.870,-43.713,-1 +res2net50_26w_8s,39.603,60.397,54.550,45.450,48.40,224,0.875,bilinear,-53.847,-44.230,+1 +tf_efficientnetv2_b1.in1k,39.570,60.430,55.343,44.657,8.14,240,0.882,bicubic,-54.140,-43.457,-38 +dla102x,39.553,60.447,56.323,43.677,26.31,224,0.875,bilinear,-53.977,-42.527,-12 +xcit_tiny_12_p16_224,39.553,60.447,55.027,44.973,6.72,224,1.000,bicubic,-52.907,-43.463,+86 +gcresnet33ts,39.550,60.450,55.830,44.170,19.88,256,0.900,bicubic,-54.270,-43.100,-52 +sehalonet33ts,39.550,60.450,54.020,45.980,13.69,256,0.940,bicubic,-54.970,-44.740,-141 +convnext_pico.d1_in1k,39.503,60.497,55.323,44.677,9.05,288,0.950,bicubic,-54.527,-43.617,-82 +rexnet_130,39.487,60.513,56.640,43.360,7.56,224,0.875,bicubic,-54.183,-42.070,-37 +hrnet_w32,39.463,60.537,56.123,43.877,41.23,224,0.875,bilinear,-53.487,-42.447,+42 +resnetv2_50x1_bitm,39.440,60.560,57.847,42.153,25.55,448,1.000,bilinear,-55.290,-41.343,-185 +levit_128,39.433,60.567,55.350,44.650,9.21,224,0.900,bicubic,-53.617,-43.340,+29 +densenetblur121d,39.380,60.620,56.640,43.360,8.00,224,0.875,bicubic,-53.020,-41.830,+85 +regnety_120,39.347,60.653,55.277,44.723,51.82,224,0.875,bicubic,-54.663,-43.743,-84 +mobilevitv2_150,39.333,60.667,55.210,44.790,10.59,256,0.888,bicubic,-54.717,-43.690,-91 +tv_resnet101,39.307,60.693,55.803,44.197,44.55,224,0.875,bilinear,-53.573,-42.857,+44 +tf_efficientnet_el.in1k,39.303,60.697,55.387,44.613,10.59,300,0.904,bicubic,-55.057,-43.653,-133 +tf_inception_v3,39.237,60.763,54.303,45.697,23.83,299,0.875,bicubic,-53.963,-44.177,+11 +gluon_resnet50_v1s,39.233,60.767,55.010,44.990,25.68,224,0.875,bicubic,-54.357,-44.060,-35 +tf_efficientnetv2_b2.in1k,39.180,60.820,54.570,45.430,10.10,260,0.890,bicubic,-54.890,-44.350,-99 +densenet169,39.167,60.833,55.843,44.157,14.15,224,0.875,bicubic,-53.133,-42.747,+81 +legacy_seresnet101,39.037,60.963,55.003,44.997,49.33,224,0.875,bilinear,-54.223,-43.737,0 +efficientnet_b1_pruned.in1k,39.010,60.990,55.647,44.353,6.33,240,0.882,bicubic,-53.970,-42.973,+28 +repvgg_b1g4,38.990,61.010,56.350,43.650,39.97,224,0.875,bilinear,-54.040,-42.360,+19 +crossvit_9_dagger_240,38.977,61.023,54.850,45.150,8.78,240,0.875,bicubic,-53.783,-43.810,+47 +inception_v3,38.963,61.037,53.850,46.150,23.83,299,0.875,bicubic,-53.937,-44.870,+33 +dpn68,38.933,61.067,54.933,45.067,12.61,224,0.875,bicubic,-53.307,-43.677,+78 +resnet33ts,38.920,61.080,55.580,44.420,19.68,256,0.900,bicubic,-54.710,-43.220,-49 +legacy_seresnext50_32x4d,38.877,61.123,54.593,45.407,27.56,224,0.875,bilinear,-54.553,-44.207,-20 +dla102,38.833,61.167,55.323,44.677,33.27,224,0.875,bilinear,-54.427,-43.457,-9 +densenet121,38.783,61.217,56.273,43.727,7.98,224,0.875,bicubic,-53.157,-42.127,+87 +resnet32ts,38.770,61.230,55.813,44.187,17.96,256,0.900,bicubic,-54.800,-42.937,-45 +res2net50_14w_8s,38.710,61.290,54.077,45.923,25.06,224,0.875,bilinear,-54.320,-44.623,+12 +regnetx_040,38.703,61.297,55.340,44.660,22.12,224,0.875,bicubic,-54.977,-43.600,-63 +res2net50_26w_6s,38.687,61.313,53.743,46.257,37.05,224,0.875,bilinear,-54.903,-45.007,-50 +regnetx_032,38.680,61.320,55.157,44.843,15.30,224,0.875,bicubic,-54.570,-43.693,-11 +selecsls60,38.623,61.377,55.630,44.370,30.67,224,0.875,bicubic,-54.387,-42.860,+9 +dla60x,38.617,61.383,55.383,44.617,17.35,224,0.875,bilinear,-54.573,-43.327,-8 +tf_efficientnet_b0.aa_in1k,38.600,61.400,55.957,44.043,5.29,224,0.875,bicubic,-53.800,-42.453,+58 +dla60_res2net,38.590,61.410,54.560,45.440,20.85,224,0.875,bilinear,-54.790,-44.300,-25 +selecsls60b,38.573,61.427,55.307,44.693,32.77,224,0.875,bicubic,-54.927,-43.473,-45 +repvgg_a2,38.563,61.437,55.770,44.230,28.21,224,0.875,bilinear,-54.117,-42.750,+33 +hardcorenas_f,38.500,61.500,55.657,44.343,8.20,224,0.875,bilinear,-54.480,-42.873,+7 +dla60_res2next,38.450,61.550,54.950,45.050,17.03,224,0.875,bilinear,-55.120,-43.850,-58 +resmlp_12_224,38.443,61.557,56.327,43.673,15.35,224,0.875,bicubic,-53.677,-42.243,+66 +regnetx_064,38.430,61.570,54.990,45.010,26.21,224,0.875,bicubic,-55.200,-44.060,-69 +tf_efficientnet_cc_b0_4e.in1k,38.413,61.587,55.150,44.850,13.31,224,0.875,bicubic,-54.427,-43.290,+18 +gluon_resnet50_v1b,38.407,61.593,54.833,45.167,25.56,224,0.875,bicubic,-54.153,-43.717,+38 +hrnet_w18,38.277,61.723,55.643,44.357,21.30,224,0.875,bilinear,-54.483,-42.967,+21 +tinynet_a.in1k,38.220,61.780,55.177,44.823,6.19,192,0.875,bicubic,-54.580,-43.383,+18 +poolformer_s12,38.173,61.827,56.187,43.813,11.92,224,0.900,bicubic,-54.297,-42.163,+39 +mixnet_l.ft_in1k,38.160,61.840,54.757,45.243,7.33,224,0.875,bicubic,-55.100,-43.943,-29 +hardcorenas_e,38.137,61.863,55.173,44.827,8.07,224,0.875,bilinear,-54.813,-43.667,0 +efficientnet_b1.ft_in1k,38.087,61.913,54.010,45.990,7.79,256,1.000,bicubic,-54.943,-44.810,-10 +coat_lite_tiny,38.070,61.930,53.453,46.547,5.72,224,0.900,bicubic,-54.780,-45.187,+8 +gmixer_24_224,38.050,61.950,52.083,47.917,24.72,224,0.875,bicubic,-54.630,-46.497,+20 +vit_base_patch16_224.augreg_in1k,38.037,61.963,50.687,49.313,86.57,224,0.900,bicubic,-55.313,-48.053,-38 +resnetrs50,37.957,62.043,53.310,46.690,35.69,224,0.910,bicubic,-56.063,-45.540,-129 +hardcorenas_c,37.883,62.117,55.717,44.283,5.52,224,0.875,bilinear,-54.447,-42.623,+41 +mobilevitv2_125,37.877,62.123,54.060,45.940,7.48,256,0.888,bicubic,-55.583,-44.800,-59 +gluon_resnet50_v1c,37.843,62.157,54.123,45.877,25.58,224,0.875,bicubic,-55.067,-44.587,-6 +res2net50_26w_4s,37.827,62.173,53.073,46.927,25.70,224,0.875,bilinear,-55.353,-45.597,-31 +efficientnet_es.ra_in1k,37.770,62.230,54.967,45.033,5.44,224,0.875,bicubic,-55.140,-43.813,-7 +resnest14d,37.767,62.233,56.470,43.530,10.61,224,0.875,bilinear,-53.363,-41.860,+84 +tv_resnext50_32x4d,37.750,62.250,54.113,45.887,25.03,224,0.875,bilinear,-55.150,-44.217,-7 +convnext_femto.d1_in1k,37.740,62.260,54.123,45.877,5.22,288,0.950,bicubic,-55.700,-44.727,-60 +resnet26t,37.707,62.293,55.257,44.743,16.01,256,0.940,bicubic,-54.973,-43.023,+7 +ecaresnet26t,37.650,62.350,54.350,45.650,16.01,320,0.950,bicubic,-56.290,-44.570,-130 +vit_base_patch32_224.augreg_in1k,37.557,62.443,51.813,48.187,88.22,224,0.900,bicubic,-53.033,-45.907,+92 +hardcorenas_d,37.550,62.450,54.723,45.277,7.50,224,0.875,bilinear,-55.050,-43.707,+12 +res2next50,37.477,62.523,52.853,47.147,24.67,224,0.875,bilinear,-55.673,-45.807,-35 +resnet34,37.443,62.557,54.297,45.703,21.80,224,0.875,bilinear,-53.757,-43.753,+70 +pit_ti_distilled_224,37.337,62.663,55.137,44.863,5.10,224,0.900,bicubic,-53.563,-43.083,+83 +lambda_resnet26t,37.300,62.700,53.570,46.430,10.96,256,0.940,bicubic,-56.100,-45.170,-61 +convnext_femto_ols.d1_in1k,37.260,62.740,53.053,46.947,5.23,288,0.950,bicubic,-56.130,-45.857,-61 +hardcorenas_b,37.243,62.757,55.073,44.927,5.18,224,0.875,bilinear,-54.697,-43.207,+41 +mobilenetv3_large_100.miil_in21k_ft_in1k,37.210,62.790,53.513,46.487,5.48,224,0.875,bilinear,-55.040,-44.737,+27 +eca_halonext26ts,37.187,62.813,53.113,46.887,10.76,256,0.940,bicubic,-56.363,-45.467,-89 +cs3darknet_focus_m,37.140,62.860,53.910,46.090,9.30,288,0.950,bicubic,-55.970,-44.830,-41 +res2net50_48w_2s,37.117,62.883,53.333,46.667,25.29,224,0.875,bilinear,-55.673,-45.137,-11 +lambda_resnet26rpt_256,37.093,62.907,53.860,46.140,10.99,256,0.940,bicubic,-56.337,-45.020,-73 +vit_small_patch16_224.augreg_in1k,37.093,62.907,51.533,48.467,22.05,224,0.900,bicubic,-56.337,-47.247,-72 +dla60,37.073,62.927,54.200,45.800,22.04,224,0.875,bilinear,-55.597,-44.450,-5 +rexnet_100,37.063,62.937,54.020,45.980,4.80,224,0.875,bicubic,-55.787,-44.600,-21 +bat_resnext26ts,37.063,62.937,53.743,46.257,10.73,256,0.900,bicubic,-56.037,-44.977,-45 +regnety_016,37.017,62.983,54.093,45.907,11.20,224,0.875,bicubic,-55.983,-44.587,-38 +tf_mixnet_l.in1k,36.987,63.013,52.583,47.417,7.33,224,0.875,bicubic,-56.053,-45.957,-45 +botnet26t_256,36.970,63.030,53.083,46.917,12.49,256,0.950,bicubic,-56.480,-45.617,-84 +legacy_seresnet50,36.873,63.127,53.487,46.513,28.09,224,0.875,bilinear,-55.797,-45.143,-12 +halonet26t,36.850,63.150,52.290,47.710,12.48,256,0.950,bicubic,-56.760,-46.350,-108 +tv_densenet121,36.810,63.190,54.033,45.967,7.98,224,0.875,bicubic,-54.590,-44.217,+45 +tf_efficientnet_lite2.in1k,36.807,63.193,53.320,46.680,6.09,260,0.890,bicubic,-55.783,-45.230,-9 +mobilenetv2_120d.ra_in1k,36.780,63.220,54.047,45.953,5.83,224,0.875,bicubic,-55.830,-44.463,-13 +tf_efficientnet_lite1.in1k,36.737,63.263,53.590,46.410,5.42,240,0.882,bicubic,-55.573,-44.900,+7 +regnetx_016,36.683,63.317,53.297,46.703,9.19,224,0.875,bicubic,-55.857,-45.253,-8 +eca_botnext26ts_256,36.670,63.330,52.467,47.533,10.59,256,0.950,bicubic,-56.690,-46.233,-79 +hardcorenas_a,36.640,63.360,54.910,45.090,5.26,224,0.875,bilinear,-54.980,-43.260,+30 +levit_128s,36.620,63.380,53.117,46.883,7.78,224,0.900,bicubic,-54.880,-45.283,+33 +efficientnet_b0.ra_in1k,36.600,63.400,53.497,46.503,5.29,224,0.875,bicubic,-55.880,-45.183,-11 +vit_base_patch32_224.sam,36.550,63.450,53.040,46.960,88.22,224,0.900,bicubic,-53.310,-44.560,+74 +xcit_nano_12_p8_224_dist,36.530,63.470,52.880,47.120,3.05,224,1.000,bicubic,-55.900,-45.650,-6 +cs3darknet_m,36.467,63.533,53.217,46.783,9.31,288,0.950,bicubic,-56.813,-45.503,-82 +mobilevitv2_100,36.387,63.613,53.070,46.930,4.90,256,0.888,bicubic,-56.753,-45.690,-66 +tf_efficientnet_em.in1k,36.380,63.620,52.840,47.160,6.90,240,0.882,bicubic,-56.790,-45.830,-72 +skresnet18,36.320,63.680,54.197,45.803,11.96,224,0.875,bicubic,-53.840,-43.583,+65 +repvgg_b0,36.287,63.713,54.057,45.943,15.82,224,0.875,bilinear,-55.393,-44.393,+18 +tv_resnet50,36.177,63.823,52.803,47.197,25.56,224,0.875,bilinear,-55.963,-45.617,+3 +xcit_nano_12_p16_384_dist,36.153,63.847,53.250,46.750,3.05,384,1.000,bicubic,-55.957,-45.270,+4 +legacy_seresnet34,36.143,63.857,52.553,47.447,21.96,224,0.875,bilinear,-55.337,-45.767,+24 +coat_tiny,36.123,63.877,51.063,48.937,5.50,224,0.900,bicubic,-57.387,-47.627,-115 +tv_resnet34,36.087,63.913,53.533,46.467,21.80,224,0.875,bilinear,-54.203,-44.447,+57 +deit_tiny_distilled_patch16_224,36.023,63.977,54.240,45.760,5.91,224,0.900,bicubic,-55.077,-44.030,+39 +mobilenetv2_140.ra_in1k,36.000,64.000,53.943,46.057,6.11,224,0.875,bicubic,-56.030,-44.307,+1 +tf_efficientnet_lite0.in1k,35.930,64.070,53.480,46.520,4.65,224,0.875,bicubic,-55.370,-44.610,+24 +seresnext26ts,35.833,64.167,53.913,46.087,10.39,256,0.900,bicubic,-56.997,-44.687,-49 +selecsls42b,35.813,64.187,52.487,47.513,32.46,224,0.875,bicubic,-56.667,-45.953,-27 +convnext_atto.d2_in1k,35.787,64.213,52.320,47.680,3.70,288,0.950,bicubic,-56.973,-46.190,-46 +xcit_nano_12_p8_384_dist,35.770,64.230,52.290,47.710,3.05,384,1.000,bicubic,-57.480,-46.440,-94 +gluon_resnet34_v1b,35.760,64.240,52.187,47.813,21.80,224,0.875,bicubic,-55.340,-45.993,+33 +dla34,35.643,64.357,52.783,47.217,15.74,224,0.875,bilinear,-55.597,-45.397,+21 +mixnet_m.ft_in1k,35.640,64.360,52.430,47.570,5.01,224,0.875,bicubic,-56.630,-45.920,-18 +efficientnet_lite0.ra_in1k,35.620,64.380,53.657,46.343,4.65,224,0.875,bicubic,-55.640,-44.593,+18 +ssl_resnet18,35.597,64.403,53.740,46.260,11.69,224,0.875,bilinear,-55.103,-44.280,+36 +mobilenetv3_rw.rmsp_in1k,35.547,64.453,53.713,46.287,5.48,224,0.875,bicubic,-56.003,-44.557,+6 +efficientnet_es_pruned.in1k,35.390,64.610,52.850,47.150,5.44,224,0.875,bicubic,-56.310,-45.570,-2 +convnext_atto_ols.a2_in1k,35.387,64.613,51.390,48.610,3.70,288,0.950,bicubic,-57.593,-47.290,-77 +mobilenetv2_110d.ra_in1k,35.293,64.707,52.830,47.170,4.52,224,0.875,bicubic,-56.057,-45.360,+10 +tf_mixnet_m.in1k,35.180,64.820,50.987,49.013,5.01,224,0.875,bicubic,-57.020,-47.433,-20 +hrnet_w18_small_v2,35.173,64.827,52.440,47.560,15.60,224,0.875,bilinear,-55.997,-45.900,+17 +resnet18d,35.127,64.873,52.890,47.110,11.71,224,0.875,bicubic,-54.863,-44.940,+42 +xcit_nano_12_p16_224_dist,35.123,64.877,52.557,47.443,3.05,224,1.000,bicubic,-55.027,-45.203,+40 +eca_resnext26ts,35.050,64.950,52.303,47.697,10.30,256,0.900,bicubic,-57.370,-46.317,-36 +convit_tiny,35.047,64.953,51.787,48.213,5.71,224,0.875,bicubic,-55.483,-46.423,+31 +resnext26ts,35.040,64.960,53.423,46.577,10.30,256,0.900,bicubic,-57.170,-44.827,-27 +gcresnext26ts,34.933,65.067,51.677,48.323,10.48,256,0.900,bicubic,-57.527,-46.953,-43 +tinynet_b.in1k,34.873,65.127,52.017,47.983,3.73,188,0.875,bicubic,-56.247,-46.223,+14 +ese_vovnet19b_dw,34.840,65.160,52.030,47.970,6.54,224,0.875,bicubic,-57.170,-46.480,-22 +regnety_008,34.807,65.193,51.743,48.257,6.26,224,0.875,bicubic,-57.093,-46.677,-18 +pit_ti_224,34.670,65.330,52.170,47.830,4.85,224,0.900,bicubic,-55.750,-45.840,+27 +mobilenetv3_large_100.ra_in1k,34.603,65.397,52.860,47.140,5.48,224,0.875,bicubic,-56.877,-45.340,-8 +crossvit_9_240,34.590,65.410,51.783,48.217,8.55,240,0.875,bicubic,-56.460,-46.527,+13 +seresnext26d_32x4d,34.543,65.457,51.543,48.457,16.81,224,0.875,bicubic,-57.897,-46.957,-49 +seresnext26t_32x4d,34.540,65.460,51.377,48.623,16.81,224,0.875,bicubic,-58.280,-47.183,-76 +mixer_b16_224,34.427,65.573,48.087,51.913,59.88,224,0.875,bicubic,-56.713,-49.313,+3 +pvt_v2_b0,34.393,65.607,53.093,46.907,3.67,224,0.900,bicubic,-54.587,-44.597,+42 +resnet26d,34.273,65.727,51.687,48.313,16.01,224,0.875,bicubic,-57.957,-46.763,-40 +tf_efficientnet_es.in1k,34.263,65.737,51.350,48.650,5.44,224,0.875,bicubic,-57.837,-47.090,-34 +fbnetc_100.rmsp_in1k,34.253,65.747,51.180,48.820,5.57,224,0.875,bilinear,-57.017,-46.650,-9 +regnety_006,34.150,65.850,51.277,48.723,6.06,224,0.875,bicubic,-57.420,-46.903,-21 +tf_mobilenetv3_large_100.in1k,33.947,66.053,51.490,48.510,5.48,224,0.875,bilinear,-57.473,-46.770,-15 +semnasnet_075.rmsp_in1k,33.790,66.210,52.427,47.573,2.91,224,0.875,bicubic,-56.410,-45.543,+18 +regnetx_008,33.770,66.230,50.547,49.453,7.26,224,0.875,bicubic,-57.410,-47.833,-7 +mnasnet_100.rmsp_in1k,33.763,66.237,51.170,48.830,4.38,224,0.875,bicubic,-57.437,-47.070,-10 +lcnet_100.ra2_in1k,33.750,66.250,52.103,47.897,2.95,224,0.875,bicubic,-55.210,-45.287,+35 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,33.650,66.350,50.683,49.317,6.36,384,1.000,bicubic,-58.080,-47.747,-33 +mobilevit_s,33.637,66.363,49.277,50.723,5.58,256,0.900,bicubic,-59.523,-49.493,-123 +xcit_nano_12_p8_224,33.583,66.417,50.220,49.780,3.05,224,1.000,bicubic,-57.537,-47.850,-8 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,33.550,66.450,51.077,48.923,5.79,384,1.000,bicubic,-59.870,-47.753,-147 +semnasnet_100.rmsp_in1k,33.520,66.480,50.787,49.213,3.89,224,0.875,bicubic,-58.140,-47.483,-33 +resnet26,33.500,66.500,50.927,49.073,16.00,224,0.875,bicubic,-57.940,-47.353,-26 +mixnet_s.ft_in1k,33.480,66.520,50.997,49.003,4.13,224,0.875,bicubic,-58.300,-47.303,-40 +spnasnet_100.rmsp_in1k,33.477,66.523,51.267,48.733,4.42,224,0.875,bilinear,-57.133,-46.683,-2 +mobilevitv2_075,33.360,66.640,50.100,49.900,2.87,256,0.888,bicubic,-58.620,-48.200,-47 +crossvit_tiny_240,33.357,66.643,49.900,50.100,7.01,240,0.875,bicubic,-57.183,-48.090,-1 +vgg19_bn,33.230,66.770,50.803,49.197,143.68,224,0.875,bilinear,-57.760,-47.307,-10 +ghostnet_100,33.207,66.793,51.163,48.837,5.18,224,0.875,bilinear,-57.233,-46.667,-1 +regnetx_006,33.157,66.843,50.250,49.750,6.20,224,0.875,bicubic,-57.603,-47.850,-9 +edgenext_x_small,33.113,66.887,48.977,51.023,2.34,288,1.000,bicubic,-58.457,-49.453,-39 +resnet18,33.067,66.933,51.170,48.830,11.69,224,0.875,bilinear,-55.083,-45.950,+25 +xcit_nano_12_p16_224,32.963,67.037,49.987,50.013,3.05,224,1.000,bicubic,-55.997,-47.373,+18 +legacy_seresnext26_32x4d,32.757,67.243,49.237,50.763,16.79,224,0.875,bicubic,-59.813,-49.283,-86 +hrnet_w18_small,32.667,67.333,50.587,49.413,13.19,224,0.875,bilinear,-57.213,-47.313,0 +deit_tiny_patch16_224,32.667,67.333,50.273,49.727,5.72,224,0.900,bicubic,-56.953,-47.687,+6 +legacy_seresnet18,32.600,67.400,50.340,49.660,11.78,224,0.875,bicubic,-56.670,-47.340,+8 +mobilenetv2_100.ra_in1k,32.523,67.477,50.800,49.200,3.50,224,0.875,bicubic,-57.307,-47.030,0 +regnetx_004,32.517,67.483,49.343,50.657,5.16,224,0.875,bicubic,-56.943,-48.427,+3 gluon_resnet18_v1b,32.407,67.593,49.727,50.273,11.69,224,0.875,bicubic,-56.253,-47.373,+13 -regnety_004,32.340,67.660,49.450,50.550,4.34,224,0.875,bicubic,-58.430,-48.630,-18 -tf_mixnet_s,32.190,67.810,48.503,51.497,4.13,224,0.875,bicubic,-59.500,-49.737,-53 -vit_tiny_patch16_224,32.020,67.980,49.023,50.977,5.72,224,0.900,bicubic,-59.890,-49.317,-59 -tf_mobilenetv3_large_075,31.857,68.143,49.113,50.887,3.99,224,0.875,bilinear,-58.473,-48.767,-13 -tf_mobilenetv3_large_minimal_100,31.593,68.407,49.340,50.660,3.92,224,0.875,bilinear,-57.587,-47.980,+3 -vit_tiny_r_s16_p8_224,30.797,69.203,47.643,52.357,6.34,224,0.900,bicubic,-58.553,-50.057,-2 -tinynet_c,30.510,69.490,48.490,51.510,2.46,184,0.875,bicubic,-57.910,-48.780,+7 -lcnet_075,30.383,69.617,48.753,51.247,2.36,224,0.875,bicubic,-56.557,-47.777,+16 -vgg16_bn,30.360,69.640,47.263,52.737,138.37,224,0.875,bilinear,-60.180,-50.727,-22 -regnety_002,29.687,70.313,46.800,53.200,3.16,224,0.875,bicubic,-58.503,-50.640,+6 -resnet10t,29.610,70.390,47.837,52.163,5.44,224,0.950,bilinear,-57.120,-48.833,+14 -mobilevit_xs,29.597,70.403,46.040,53.960,2.32,256,0.900,bicubic,-61.603,-52.180,-43 -edgenext_xx_small,29.420,70.580,46.500,53.500,1.33,256,0.900,bicubic,-59.810,-50.760,-7 -mobilenetv3_small_100,29.050,70.950,47.190,52.810,2.54,224,0.875,bicubic,-57.130,-49.270,+12 -mnasnet_small,28.950,71.050,47.267,52.733,2.03,224,0.875,bicubic,-56.560,-48.713,+13 -vgg13_bn,28.893,71.107,46.737,53.263,133.05,224,0.875,bilinear,-60.317,-50.783,-9 -regnetx_002,28.847,71.153,45.420,54.580,2.68,224,0.875,bicubic,-58.533,-51.570,+3 -mobilenetv2_050,28.663,71.337,46.593,53.407,1.97,224,0.875,bicubic,-56.347,-49.027,+13 +regnety_004,32.333,67.667,49.453,50.547,4.34,224,0.875,bicubic,-58.447,-48.627,-21 +tf_mixnet_s.in1k,32.183,67.817,48.493,51.507,4.13,224,0.875,bicubic,-59.497,-49.747,-54 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,32.023,67.977,49.017,50.983,5.72,224,0.900,bicubic,-59.907,-49.323,-61 +tf_mobilenetv3_large_075.in1k,31.867,68.133,49.110,50.890,3.99,224,0.875,bilinear,-58.453,-48.760,-14 +tf_mobilenetv3_large_minimal_100.in1k,31.597,68.403,49.337,50.663,3.92,224,0.875,bilinear,-57.583,-47.983,+2 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,30.807,69.193,47.657,52.343,6.34,224,0.900,bicubic,-58.533,-50.043,-2 +tinynet_c.in1k,30.507,69.493,48.487,51.513,2.46,184,0.875,bicubic,-57.913,-48.783,+7 +lcnet_075.ra2_in1k,30.367,69.633,48.760,51.240,2.36,224,0.875,bicubic,-56.573,-47.770,+16 +vgg16_bn,30.357,69.643,47.260,52.740,138.37,224,0.875,bilinear,-60.183,-50.680,-24 +edgenext_xx_small,29.730,70.270,46.493,53.507,1.33,288,1.000,bicubic,-60.050,-51.027,-11 +regnety_002,29.687,70.313,46.787,53.213,3.16,224,0.875,bicubic,-58.513,-50.643,+5 +resnet10t,29.613,70.387,47.843,52.157,5.44,224,0.950,bilinear,-57.077,-48.827,+13 +mobilevit_xs,29.590,70.410,46.003,53.997,2.32,256,0.900,bicubic,-61.600,-52.217,-46 +mobilenetv3_small_100.lamb_in1k,29.047,70.953,47.183,52.817,2.54,224,0.875,bicubic,-57.123,-49.287,+12 +mnasnet_small.lamb_in1k,28.950,71.050,47.270,52.730,2.03,224,0.875,bicubic,-56.560,-48.710,+13 +vgg13_bn,28.883,71.117,46.737,53.263,133.05,224,0.875,bilinear,-60.317,-50.793,-10 +regnetx_002,28.860,71.140,45.420,54.580,2.68,224,0.875,bicubic,-58.520,-51.570,+3 +mobilenetv2_050.lamb_in1k,28.677,71.323,46.600,53.400,1.97,224,0.875,bicubic,-56.313,-49.020,+13 vgg19,28.580,71.420,45.170,54.830,143.67,224,0.875,bilinear,-61.100,-52.380,-19 -mobilevitv2_050,28.560,71.440,45.193,54.807,1.37,256,0.888,bicubic,-60.490,-52.397,-10 -dla60x_c,28.437,71.563,46.213,53.787,1.32,224,0.875,bilinear,-58.693,-50.927,+1 -vgg11_bn,28.423,71.577,46.447,53.553,132.87,224,0.875,bilinear,-59.967,-50.823,-7 -resnet14t,28.097,71.903,45.297,54.703,10.08,224,0.950,bilinear,-61.013,-52.073,-14 -tinynet_d,27.963,72.037,45.863,54.137,2.34,152,0.875,bicubic,-57.457,-50.157,+6 +mobilevitv2_050,28.563,71.437,45.197,54.803,1.37,256,0.888,bicubic,-60.467,-52.393,-11 +dla60x_c,28.447,71.553,46.193,53.807,1.32,224,0.875,bilinear,-58.663,-50.947,+1 +vgg11_bn,28.423,71.577,46.453,53.547,132.87,224,0.875,bilinear,-59.967,-50.817,-7 +resnet14t,28.087,71.913,45.303,54.697,10.08,224,0.950,bilinear,-61.023,-52.067,-15 +tinynet_d.in1k,27.960,72.040,45.853,54.147,2.34,152,0.875,bicubic,-57.470,-50.157,+6 vgg16,27.877,72.123,44.673,55.327,138.36,224,0.875,bilinear,-61.483,-52.847,-22 -tf_mobilenetv3_small_100,27.287,72.713,44.420,55.580,2.54,224,0.875,bilinear,-58.683,-51.980,+1 -mixer_l16_224,26.857,73.143,37.923,62.077,208.20,224,0.875,bicubic,-60.113,-56.127,-4 -mobilenetv3_small_075,26.533,73.467,43.887,56.113,2.04,224,0.875,bicubic,-57.587,-51.613,+6 -vgg11,26.533,73.467,43.460,56.540,132.86,224,0.875,bilinear,-60.807,-53.650,-8 -mobilevit_xxs,26.347,73.653,43.030,56.970,1.27,256,0.900,bicubic,-61.603,-54.150,-12 -vgg13,26.270,73.730,43.373,56.627,133.05,224,0.875,bilinear,-61.300,-53.747,-12 -lcnet_050,26.220,73.780,44.607,55.393,1.88,224,0.875,bicubic,-56.790,-50.403,+3 -dla46x_c,26.220,73.780,43.770,56.230,1.07,224,0.875,bilinear,-59.240,-52.680,-4 -tf_mobilenetv3_small_075,26.197,73.803,43.640,56.360,2.04,224,0.875,bilinear,-58.323,-52.250,-1 -dla46_c,25.497,74.503,43.790,56.210,1.30,224,0.875,bilinear,-59.163,-52.420,-3 -tf_mobilenetv3_small_minimal_100,25.097,74.903,42.923,57.077,2.04,224,0.875,bilinear,-57.593,-52.077,0 -tinynet_e,23.363,76.637,41.083,58.917,2.04,106,0.875,bicubic,-56.437,-52.897,0 -mobilenetv3_small_050,21.743,78.257,38.757,61.243,1.59,224,0.875,bicubic,-56.357,-54.253,0 +tf_mobilenetv3_small_100.in1k,27.297,72.703,44.420,55.580,2.54,224,0.875,bilinear,-58.663,-51.980,+1 +mixer_l16_224,26.853,73.147,37.923,62.077,208.20,224,0.875,bicubic,-60.117,-56.137,-4 +vgg11,26.533,73.467,43.460,56.540,132.86,224,0.875,bilinear,-60.807,-53.650,-7 +mobilenetv3_small_075.lamb_in1k,26.530,73.470,43.887,56.113,2.04,224,0.875,bicubic,-57.590,-51.613,+5 +mobilevit_xxs,26.340,73.660,43.033,56.967,1.27,256,0.900,bicubic,-61.610,-54.147,-12 +vgg13,26.267,73.733,43.370,56.630,133.05,224,0.875,bilinear,-61.303,-53.750,-12 +lcnet_050.ra2_in1k,26.217,73.783,44.577,55.423,1.88,224,0.875,bicubic,-56.783,-50.433,+2 +dla46x_c,26.217,73.783,43.780,56.220,1.07,224,0.875,bilinear,-59.263,-52.660,-3 +tf_mobilenetv3_small_075.in1k,26.200,73.800,43.637,56.363,2.04,224,0.875,bilinear,-58.330,-52.253,-1 +dla46_c,25.490,74.510,43.800,56.200,1.30,224,0.875,bilinear,-59.170,-52.400,-3 +tf_mobilenetv3_small_minimal_100.in1k,25.087,74.913,42.930,57.070,2.04,224,0.875,bilinear,-57.583,-52.070,0 +tinynet_e.in1k,23.363,76.637,41.080,58.920,2.04,106,0.875,bicubic,-56.447,-52.900,0 +mobilenetv3_small_050.lamb_in1k,21.740,78.260,38.760,61.240,1.59,224,0.875,bicubic,-56.360,-54.250,0 diff --git a/results/results-imagenet-real.csv b/results/results-imagenet-real.csv index c0bc6be6..d6ce74ee 100644 --- a/results/results-imagenet-real.csv +++ b/results/results-imagenet-real.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff -beit_large_patch16_512,90.691,9.309,98.751,1.249,305.67,512,1.000,bicubic,+2.089,+0.095,0 -beit_large_patch16_384,90.610,9.390,98.764,1.236,305.00,384,1.000,bicubic,+2.204,+0.158,0 -volo_d5_512,90.610,9.390,98.698,1.302,296.09,512,1.150,bicubic,+3.570,+0.730,+11 -volo_d5_448,90.584,9.416,98.685,1.315,295.91,448,1.150,bicubic,+3.630,+0.745,+13 -tf_efficientnet_l2_ns,90.563,9.437,98.779,1.221,480.31,800,0.960,bicubic,+2.213,+0.129,-2 -tf_efficientnet_l2_ns_475,90.540,9.460,98.710,1.290,480.31,475,0.936,bicubic,+2.308,+0.164,-2 -volo_d4_448,90.507,9.492,98.591,1.409,193.41,448,1.150,bicubic,+3.715,+0.709,+14 -convnext_xlarge_384_in22ft1k,90.450,9.550,98.672,1.328,350.20,384,1.000,bicubic,+2.906,+0.186,-2 -swinv2_base_window12to24_192to384_22kft1k,90.401,9.599,98.740,1.260,87.92,384,1.000,bicubic,+3.293,+0.504,+3 -beit_base_patch16_384,90.371,9.629,98.725,1.275,86.74,384,1.000,bicubic,+3.573,+0.589,+10 -convnext_large_384_in22ft1k,90.258,9.742,98.663,1.337,197.77,384,1.000,bicubic,+2.862,+0.297,-2 +eva_giant_patch14_336.clip_ft_in1k,91.054,8.946,98.597,1.403,"1,013.01",336,1.000,bicubic,+1.578,-0.227,+2 +eva_giant_patch14_560.m30m_ft_in22k_in1k,90.973,9.027,98.678,1.322,"1,014.45",560,1.000,bicubic,+1.177,-0.314,-1 +eva_giant_patch14_224.clip_ft_in1k,90.954,9.046,98.723,1.277,"1,012.56",224,1.000,bicubic,+1.854,+0.007,+2 +eva_large_patch14_336.in22k_ft_in1k,90.905,9.095,98.785,1.215,304.53,336,1.000,bicubic,+2.241,+0.065,+2 +eva_giant_patch14_336.m30m_ft_in22k_in1k,90.903,9.098,98.663,1.337,"1,013.01",336,1.000,bicubic,+1.335,-0.289,-3 +eva_large_patch14_336.in22k_ft_in22k_in1k,90.871,9.130,98.721,1.279,304.53,336,1.000,bicubic,+1.667,-0.129,-2 +beit_large_patch16_512.in22k_ft_in22k_in1k,90.687,9.313,98.751,1.249,305.67,512,1.000,bicubic,+2.089,+0.095,0 +beit_large_patch16_384.in22k_ft_in22k_in1k,90.610,9.390,98.766,1.234,305.00,384,1.000,bicubic,+2.206,+0.158,+3 +volo_d5_512,90.608,9.392,98.698,1.302,296.09,512,1.150,bicubic,+3.564,+0.730,+32 +volo_d5_448,90.584,9.416,98.685,1.315,295.91,448,1.150,bicubic,+3.630,+0.747,+35 +eva_large_patch14_196.in22k_ft_in22k_in1k,90.567,9.433,98.698,1.302,304.14,196,1.000,bicubic,+1.981,+0.042,-3 +tf_efficientnet_l2.ns_jft_in1k,90.563,9.437,98.779,1.221,480.31,800,0.960,bicubic,+2.211,+0.129,+1 +maxvit_base_tf_512.in21k_ft_in1k,90.561,9.439,98.700,1.300,119.88,512,1.000,bicubic,+2.349,+0.168,+6 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,90.552,9.448,98.683,1.317,304.53,336,1.000,bicubic,+2.286,+0.151,+1 +tf_efficientnet_l2.ns_jft_in1k_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,+2 +eva_large_patch14_196.in22k_ft_in1k,90.533,9.467,98.779,1.221,304.14,196,1.000,bicubic,+2.595,+0.287,+7 +volo_d4_448,90.510,9.490,98.591,1.409,193.41,448,1.150,bicubic,+3.720,+0.709,+34 +maxvit_xlarge_tf_512.in21k_ft_in1k,90.503,9.497,98.580,1.420,475.77,512,1.000,bicubic,+1.965,-0.064,-8 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,90.501,9.499,98.640,1.360,632.46,336,1.000,bicubic,+1.927,-0.020,-10 +convnext_xlarge.fb_in22k_ft_in1k_384,90.495,9.505,98.766,1.234,350.20,384,1.000,bicubic,+2.747,+0.212,+8 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,90.420,9.580,98.638,1.362,304.53,336,1.000,bicubic,+2.238,+0.066,-1 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,90.414,9.586,98.648,1.351,632.05,224,1.000,bicubic,+2.168,+0.099,-6 +swinv2_base_window12to24_192to384_22kft1k,90.403,9.597,98.740,1.260,87.92,384,1.000,bicubic,+3.295,+0.504,+16 +maxvit_xlarge_tf_384.in21k_ft_in1k,90.379,9.621,98.587,1.413,475.32,384,1.000,bicubic,+2.073,+0.043,-10 +beit_base_patch16_384.in22k_ft_in22k_in1k,90.373,9.627,98.725,1.275,86.74,384,1.000,bicubic,+3.573,+0.587,+24 +maxvit_base_tf_384.in21k_ft_in1k,90.367,9.633,98.680,1.319,119.65,384,1.000,bicubic,+2.445,+0.138,-2 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,90.367,9.633,98.657,1.343,304.20,224,1.000,bicubic,+2.199,+0.113,-6 +maxvit_large_tf_512.in21k_ft_in1k,90.360,9.640,98.642,1.358,212.33,512,1.000,bicubic,+2.142,+0.044,-10 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,90.354,9.646,98.582,1.418,304.43,224,0.950,bicubic,+1.968,-0.016,-17 +vit_large_patch14_clip_336.laion2b_ft_in1k,90.341,9.659,98.593,1.407,304.53,336,1.000,bicubic,+2.493,+0.223,-3 +maxvit_large_tf_384.in21k_ft_in1k,90.317,9.682,98.685,1.315,212.03,384,1.000,bicubic,+2.325,+0.119,-9 +vit_large_patch14_clip_224.openai_ft_in1k,90.305,9.695,98.636,1.364,304.20,224,1.000,bicubic,+2.453,+0.208,-6 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,90.302,9.697,98.661,1.339,304.20,224,1.000,bicubic,+2.412,+0.251,-8 +convnext_large.fb_in22k_ft_in1k_384,90.279,9.721,98.655,1.345,197.77,384,1.000,bicubic,+2.807,+0.269,-2 +convnext_base.fb_in22k_ft_in1k_384,90.277,9.723,98.800,1.200,88.59,384,1.000,bicubic,+3.483,+0.536,+15 deit3_large_patch16_384_in21ft1k,90.249,9.751,98.625,1.375,304.76,384,1.000,bicubic,+2.533,+0.113,-7 -deit3_huge_patch14_224_in21ft1k,90.213,9.787,98.638,1.362,632.13,224,1.000,bicubic,+3.033,+0.378,-3 -vit_large_patch16_384,90.198,9.802,98.661,1.339,304.72,384,1.000,bicubic,+3.118,+0.361,-1 -cait_m48_448,90.189,9.811,98.484,1.516,356.46,448,1.000,bicubic,+3.701,+0.734,+11 -volo_d3_448,90.168,9.832,98.550,1.450,86.63,448,1.000,bicubic,+3.672,+0.840,+9 -swinv2_large_window12to24_192to384_22kft1k,90.157,9.843,98.604,1.396,196.74,384,1.000,bicubic,+2.701,+0.352,-9 -convnext_base_384_in22ft1k,90.151,9.849,98.728,1.272,88.59,384,1.000,bicubic,+3.609,+0.538,+6 -beit_large_patch16_224,90.151,9.849,98.723,1.277,304.43,224,0.900,bicubic,+2.675,+0.419,-12 -tf_efficientnet_b7_ns,90.093,9.907,98.614,1.386,66.35,600,0.949,bicubic,+3.261,+0.518,-1 -cait_m36_384,90.049,9.951,98.493,1.507,271.22,384,1.000,bicubic,+3.995,+0.763,+16 -dm_nfnet_f6,90.044,9.956,98.546,1.454,438.36,576,0.956,bicubic,+3.902,+0.816,+12 -swin_large_patch4_window12_384,90.027,9.973,98.663,1.337,196.74,384,1.000,bicubic,+2.875,+0.423,-12 -deit3_large_patch16_224_in21ft1k,90.006,9.994,98.661,1.339,304.37,224,1.000,bicubic,+3.024,+0.423,-8 -tf_efficientnetv2_l_in21ft1k,90.004,9.996,98.623,1.377,118.52,480,1.000,bicubic,+3.700,+0.643,+6 -swin_base_patch4_window12_384,89.995,10.005,98.695,1.304,87.90,384,1.000,bicubic,+3.563,+0.639,+2 -vit_base_patch16_384,89.987,10.014,98.680,1.319,86.86,384,1.000,bicubic,+3.981,+0.676,+12 -convnext_xlarge_in22ft1k,89.933,10.067,98.570,1.431,350.20,224,0.875,bicubic,+2.931,+0.358,-13 -swinv2_large_window12to16_192to256_22kft1k,89.922,10.078,98.510,1.490,196.74,256,0.900,bicubic,+2.976,+0.400,-11 -xcit_large_24_p8_384_dist,89.886,10.114,98.384,1.616,188.93,384,1.000,bicubic,+3.888,+0.700,+10 -deit3_base_patch16_384_in21ft1k,89.884,10.116,98.602,1.399,86.88,384,1.000,bicubic,+3.142,+0.490,-9 -volo_d5_224,89.882,10.118,98.493,1.507,295.46,224,0.960,bicubic,+3.812,+0.915,+4 -swinv2_base_window12to16_192to256_22kft1k,89.873,10.127,98.657,1.343,87.92,256,0.900,bicubic,+3.603,+0.761,-1g -cait_s36_384,89.844,10.156,98.424,1.576,68.37,384,1.000,bicubic,+4.384,+0.946,+22 -volo_d4_224,89.814,10.186,98.424,1.576,192.96,224,0.960,bicubic,+3.938,+0.956,+6 -convnext_large_in22ft1k,89.811,10.188,98.493,1.507,197.77,224,0.875,bicubic,+3.175,+0.465,-13 -xcit_medium_24_p8_384_dist,89.811,10.188,98.362,1.638,84.32,384,1.000,bicubic,+3.995,+0.770,+7 -convnext_small_384_in22ft1k,89.803,10.197,98.655,1.345,50.22,384,1.000,bicubic,+4.079,+0.791,+11 -swin_large_patch4_window7_224,89.794,10.206,98.642,1.358,196.53,224,0.900,bicubic,+3.474,+0.750,-9 -vit_large_r50_s32_384,89.792,10.208,98.516,1.484,329.09,384,1.000,bicubic,+3.612,+0.596,-7 -tf_efficientnet_b6_ns,89.784,10.216,98.512,1.488,43.04,528,0.942,bicubic,+3.334,+0.626,-14 -volo_d2_384,89.784,10.216,98.401,1.599,58.87,384,1.000,bicubic,+3.748,+0.827,-4 -tf_efficientnetv2_m_in21ft1k,89.779,10.221,98.501,1.499,54.14,480,1.000,bicubic,+4.193,+0.755,+9 -xcit_small_24_p8_384_dist,89.739,10.261,98.422,1.578,47.63,384,1.000,bicubic,+4.185,+0.850,+9 -volo_d1_384,89.698,10.302,98.294,1.706,26.78,384,1.000,bicubic,+4.448,+1.080,+20 -deit3_large_patch16_384,89.681,10.319,98.392,1.608,304.76,384,1.000,bicubic,+3.875,+0.796,0 -xcit_large_24_p16_384_dist,89.662,10.338,98.401,1.599,189.10,384,1.000,bicubic,+3.910,+0.863,+1 -tf_efficientnet_b5_ns,89.649,10.351,98.482,1.518,30.39,456,0.934,bicubic,+3.561,+0.730,-13 -convnext_base_in22ft1k,89.628,10.372,98.537,1.462,88.59,224,0.875,bicubic,+3.804,+0.671,-6 -tf_efficientnetv2_xl_in21ft1k,89.589,10.411,98.174,1.825,208.12,512,1.000,bicubic,+3.169,+0.306,-21 -tf_efficientnet_b8_ap,89.581,10.419,98.303,1.697,87.41,672,0.954,bicubic,+4.209,+1.009,+11 -volo_d3_224,89.557,10.443,98.375,1.625,86.33,224,0.960,bicubic,+4.145,+1.095,+8 -dm_nfnet_f4,89.557,10.443,98.303,1.697,316.07,512,0.951,bicubic,+3.843,+0.783,-2 -xcit_large_24_p8_224_dist,89.517,10.483,98.224,1.776,188.93,224,1.000,bicubic,+4.119,+0.814,+7 -xcit_small_12_p8_384_dist,89.515,10.485,98.303,1.697,26.21,384,1.000,bicubic,+4.435,+1.023,+18 -cait_s24_384,89.506,10.494,98.367,1.633,47.06,384,1.000,bicubic,+4.456,+1.019,+21 -dm_nfnet_f3,89.485,10.515,98.399,1.601,254.92,416,0.940,bicubic,+3.963,+0.937,-3 -xcit_medium_24_p16_384_dist,89.474,10.526,98.296,1.704,84.40,384,1.000,bicubic,+4.052,+0.890,0 -dm_nfnet_f5,89.463,10.537,98.324,1.676,377.21,544,0.954,bicubic,+3.647,+0.838,-14 -deit3_base_patch16_224_in21ft1k,89.451,10.549,98.557,1.443,86.59,224,1.000,bicubic,+3.735,+0.813,-10 -deit_base_distilled_patch16_384,89.429,10.571,98.439,1.561,87.63,384,1.000,bicubic,+4.007,+1.107,-2 -tf_efficientnet_b7_ap,89.429,10.571,98.345,1.655,66.35,600,0.949,bicubic,+4.309,+1.093,+8 -vit_base_patch8_224,89.427,10.573,98.486,1.514,86.58,224,0.900,bicubic,+3.637,+0.694,-16 -beit_base_patch16_224,89.410,10.590,98.525,1.475,86.53,224,0.900,bicubic,+4.182,+0.869,+2 -regnetz_e8,89.380,10.620,98.459,1.542,57.70,320,1.000,bicubic,+4.350,+1.195,+14 -tf_efficientnetv2_l,89.374,10.626,98.271,1.729,118.52,480,1.000,bicubic,+3.886,+0.899,-11 -deit3_small_patch16_384_in21ft1k,89.367,10.633,98.382,1.618,22.21,384,1.000,bicubic,+4.543,+0.898,+20 -tf_efficientnet_b8,89.352,10.648,98.303,1.697,87.41,672,0.954,bicubic,+3.984,+0.911,-5 -tf_efficientnet_b6_ap,89.344,10.656,98.281,1.719,43.04,528,0.942,bicubic,+4.558,+1.143,+20 -volo_d2_224,89.327,10.673,98.209,1.791,58.68,224,0.960,bicubic,+4.133,+1.021,-2 -vit_large_patch16_224,89.314,10.686,98.394,1.606,304.33,224,0.900,bicubic,+3.470,+0.572,-29 -tf_efficientnet_b4_ns,89.303,10.697,98.347,1.653,19.34,380,0.922,bicubic,+4.143,+0.877,-3 -xcit_small_24_p16_384_dist,89.295,10.705,98.328,1.672,47.67,384,1.000,bicubic,+4.207,+1.020,-1 -xcit_medium_24_p8_224_dist,89.290,10.710,98.192,1.808,84.32,224,1.000,bicubic,+4.220,+0.912,+1 -tf_efficientnetv2_m,89.286,10.714,98.236,1.764,54.14,480,1.000,bicubic,+4.250,+0.958,+3 -deit3_huge_patch14_224,89.212,10.789,98.166,1.834,632.13,224,0.900,bicubic,+4.006,+0.808,-9 -xcit_small_24_p8_224_dist,89.201,10.799,98.245,1.755,47.63,224,1.000,bicubic,+4.325,+1.057,+9 -xcit_small_12_p16_384_dist,89.194,10.806,98.219,1.781,26.25,384,1.000,bicubic,+4.486,+1.103,+14 -swin_base_patch4_window7_224,89.147,10.852,98.424,1.576,87.77,224,0.900,bicubic,+3.897,+0.862,-15 -eca_nfnet_l2,89.141,10.859,98.315,1.685,56.72,384,1.000,bicubic,+4.445,+1.051,+13 -cait_xs24_384,89.139,10.861,98.290,1.710,26.67,384,1.000,bicubic,+5.075,+1.400,+46 -convnext_small_in22ft1k,89.122,10.878,98.322,1.678,50.22,224,0.875,bicubic,+4.554,+0.926,+15 -ig_resnext101_32x48d,89.115,10.885,98.132,1.868,828.41,224,0.875,bilinear,+3.679,+0.556,-26 -ig_resnext101_32x32d,89.109,10.891,98.183,1.817,468.53,224,0.875,bilinear,+4.009,+0.749,-13 -tf_efficientnet_b7,89.083,10.917,98.185,1.815,66.35,600,0.949,bicubic,+4.149,+0.979,-2 -ecaresnet269d,89.069,10.931,98.232,1.768,102.09,352,1.000,bicubic,+4.095,+1.006,-4 -xcit_large_24_p16_224_dist,89.041,10.959,98.061,1.939,189.10,224,1.000,bicubic,+4.121,+0.929,-3 -resmlp_big_24_224_in22ft1k,89.011,10.989,98.215,1.785,129.14,224,0.875,bicubic,+4.613,+1.097,+18 -dm_nfnet_f2,89.011,10.989,98.189,1.810,193.78,352,0.920,bicubic,+3.945,+0.947,-13 -xcit_small_12_p8_224_dist,89.002,10.998,98.078,1.922,26.21,224,1.000,bicubic,+4.772,+1.204,+28 -efficientnetv2_rw_m,88.990,11.011,98.213,1.787,53.24,416,1.000,bicubic,+4.178,+1.067,-3 -regnetz_040h,88.953,11.047,98.202,1.798,28.94,320,1.000,bicubic,+4.457,+1.196,+9 -tf_efficientnet_b5_ap,88.942,11.057,98.164,1.836,30.39,456,0.934,bicubic,+4.688,+1.186,+23 -deit3_base_patch16_384,88.928,11.072,98.046,1.954,86.88,384,1.000,bicubic,+3.852,+0.792,-20 -dm_nfnet_f1,88.925,11.075,98.115,1.885,132.63,320,0.910,bicubic,+4.301,+1.017,-1 -volo_d1_224,88.906,11.094,98.031,1.968,26.63,224,0.960,bicubic,+4.742,+1.257,+27 -tf_efficientnetv2_s_in21ft1k,88.904,11.096,98.279,1.721,21.46,384,1.000,bicubic,+4.608,+1.025,+13 -vit_base_patch16_224,88.864,11.136,98.230,1.770,86.57,224,0.900,bicubic,+4.334,+0.934,0 -regnetz_d8,88.855,11.145,98.189,1.810,23.37,320,1.000,bicubic,+4.803,+1.193,+29 -resnetrs420,88.842,11.158,98.034,1.966,191.89,416,1.000,bicubic,+3.834,+0.910,-20 -regnetz_d8_evos,88.838,11.162,98.132,1.868,23.46,320,0.950,bicubic,+4.788,+1.136,+28 -resnetrs270,88.834,11.166,98.136,1.864,129.86,352,1.000,bicubic,+4.398,+1.162,+2 -ig_resnext101_32x16d,88.825,11.175,98.049,1.951,194.03,224,0.875,bilinear,+4.655,+0.851,+19 -vit_small_r26_s32_384,88.812,11.188,98.343,1.657,36.47,384,1.000,bicubic,+4.764,+1.015,+26 -vit_base_r50_s16_384,88.804,11.196,98.232,1.768,98.95,384,1.000,bicubic,+3.828,+0.942,-24 -xcit_medium_24_p16_224_dist,88.804,11.196,98.038,1.962,84.40,224,1.000,bicubic,+4.526,+1.098,+7 -seresnet152d,88.797,11.203,98.174,1.825,66.84,320,1.000,bicubic,+4.433,+1.130,+1 -xcit_tiny_24_p8_384_dist,88.778,11.222,98.164,1.836,12.11,384,1.000,bicubic,+5.032,+1.452,+43 -swsl_resnext101_32x8d,88.778,11.222,98.149,1.851,88.79,224,0.875,bilinear,+4.488,+0.967,+3 -convnext_tiny_384_in22ft1k,88.772,11.228,98.298,1.702,28.59,384,1.000,bicubic,+4.696,+1.140,+16 -resnetrs200,88.759,11.241,98.113,1.887,93.21,320,1.000,bicubic,+4.319,+1.033,-8 -tf_efficientnet_b6,88.759,11.241,98.068,1.932,43.04,528,0.942,bicubic,+4.651,+1.180,+13 -deit3_large_patch16_224,88.759,11.241,97.912,2.088,304.37,224,0.900,bicubic,+3.997,+0.874,-23 -resnetrs350,88.755,11.245,98.031,1.968,163.96,384,1.000,bicubic,+4.043,+1.041,-23 -vit_base_patch16_224_miil,88.742,11.258,98.027,1.973,86.54,224,0.875,bilinear,+4.470,+1.225,-1 -regnetz_040,88.727,11.273,98.091,1.909,27.12,320,1.000,bicubic,+4.491,+1.159,+1 -resnetv2_152x2_bitm,88.725,11.275,98.307,1.693,236.34,448,1.000,bilinear,+4.215,+0.873,-17 -regnety_160,88.699,11.301,98.068,1.932,83.59,288,1.000,bicubic,+5.007,+1.292,+38 -pit_b_distilled_224,88.674,11.326,98.091,1.909,74.79,224,0.900,bicubic,+4.532,+1.235,+5 -regnetz_d32,88.652,11.348,98.081,1.919,27.58,320,0.950,bicubic,+4.628,+1.213,+12 -vit_small_patch16_384,88.648,11.352,98.230,1.770,22.20,384,1.000,bicubic,+4.848,+1.130,+27 -regnety_080,88.635,11.365,97.972,2.028,39.18,288,1.000,bicubic,+4.707,+1.084,+15 -eca_nfnet_l1,88.624,11.376,98.134,1.866,41.41,320,1.000,bicubic,+4.612,+1.102,+11 -swinv2_base_window16_256,88.584,11.416,97.895,2.105,87.92,256,0.900,bicubic,+3.992,+0.821,-29 -convnext_large,88.577,11.423,97.854,2.146,197.77,224,0.875,bicubic,+4.281,+0.960,-14 -resnetv2_152x4_bitm,88.552,11.448,98.189,1.810,936.53,480,1.000,bilinear,+3.634,+0.747,-41 -resnet200d,88.545,11.455,97.959,2.041,64.69,320,1.000,bicubic,+4.585,+1.135,+8 -seresnextaa101d_32x8d,88.543,11.457,98.002,1.998,93.59,288,1.000,bicubic,+3.971,+0.932,-32 -xcit_small_24_p16_224_dist,88.535,11.465,98.002,1.998,47.67,224,1.000,bicubic,+4.665,+1.270,+10 -resnest269e,88.522,11.478,98.027,1.973,110.93,416,0.928,bicubic,+4.004,+1.041,-31 -swinv2_base_window8_256,88.518,11.482,97.893,2.107,87.92,256,0.900,bicubic,+4.256,+0.971,-16 -seresnext101_32x8d,88.505,11.495,97.888,2.112,93.57,288,1.000,bicubic,+4.301,+1.014,-12 -efficientnetv2_rw_s,88.475,11.525,97.972,2.028,23.94,384,1.000,bicubic,+4.665,+1.248,+14 -crossvit_18_dagger_408,88.475,11.525,97.893,2.107,44.61,408,1.000,bicubic,+4.281,+1.075,-13 -resnetv2_101x3_bitm,88.469,11.531,98.157,1.843,387.93,448,1.000,bilinear,+4.025,+0.775,-33 -cait_s24_224,88.451,11.549,97.957,2.043,46.92,224,1.000,bicubic,+4.993,+1.395,+27 -resnetv2_50x3_bitm,88.445,11.555,98.198,1.802,217.32,448,1.000,bilinear,+4.433,+1.072,-4 -resmlp_big_24_distilled_224,88.441,11.559,97.940,2.060,129.14,224,0.875,bicubic,+4.853,+1.292,+21 -regnetv_064,88.432,11.568,98.064,1.937,30.58,288,1.000,bicubic,+4.720,+1.318,+16 -resnest200e,88.430,11.570,98.044,1.956,70.20,320,0.909,bicubic,+4.602,+1.152,+5 -tf_efficientnet_b3_ns,88.428,11.572,98.027,1.973,12.23,300,0.904,bicubic,+4.380,+1.115,-10 -vit_large_r50_s32_224,88.424,11.576,98.085,1.915,328.99,224,0.900,bicubic,+3.994,+0.919,-37 -seresnext101d_32x8d,88.424,11.576,97.955,2.045,93.59,288,1.000,bicubic,+4.062,+1.037,-34 -tf_efficientnetv2_s,88.396,11.604,97.927,2.073,21.46,384,1.000,bicubic,+4.512,+1.229,-6 -regnetz_c16_evos,88.379,11.621,98.042,1.958,13.49,320,0.950,bicubic,+5.747,+1.566,+66 -efficientnet_b4,88.368,11.632,97.961,2.039,19.34,384,1.000,bicubic,+4.944,+1.363,+19 -swinv2_small_window16_256,88.364,11.636,97.852,2.148,49.73,256,0.900,bicubic,+4.154,+0.982,-28 -resnet152d,88.353,11.647,97.938,2.062,60.21,320,1.000,bicubic,+4.675,+1.198,+10 -tf_efficientnet_b4_ap,88.351,11.649,97.893,2.107,19.34,380,0.922,bicubic,+5.103,+1.501,+25 -convnext_base,88.347,11.653,97.784,2.216,88.59,224,0.875,bicubic,+4.507,+1.034,-8 -deit3_small_patch16_224_in21ft1k,88.334,11.666,98.127,1.873,22.06,224,1.000,bicubic,+5.258,+1.351,+36 -tf_efficientnet_b5,88.323,11.677,97.912,2.088,30.39,456,0.934,bicubic,+4.509,+1.164,-6 -regnety_064,88.319,11.681,97.861,2.139,30.58,288,1.000,bicubic,+4.599,+1.135,0 -crossvit_15_dagger_408,88.308,11.692,97.869,2.131,28.50,408,1.000,bicubic,+4.470,+1.089,-10 -deit3_small_patch16_384,88.298,11.702,97.888,2.112,22.21,384,1.000,bicubic,+4.870,+1.212,+9 -cs3se_edgenet_x,88.291,11.709,97.931,2.069,50.72,320,1.000,bicubic,+4.743,+1.265,+5 -resnetrs152,88.255,11.745,97.737,2.263,86.62,320,1.000,bicubic,+4.541,+1.123,-3 -deit3_base_patch16_224,88.251,11.749,97.807,2.193,86.59,224,0.900,bicubic,+4.459,+1.223,-9 -xcit_small_12_p16_224_dist,88.246,11.754,97.846,2.154,26.25,224,1.000,bicubic,+4.900,+1.428,+13 -regnetv_040,88.219,11.781,97.972,2.028,20.64,288,1.000,bicubic,+5.021,+1.308,+17 -deit_base_distilled_patch16_224,88.214,11.786,97.920,2.080,87.34,224,0.900,bicubic,+4.826,+1.432,+7 -xception65p,88.185,11.815,97.790,2.210,39.82,299,0.940,bicubic,+5.055,+1.310,+22 -swinv2_small_window8_256,88.185,11.815,97.775,2.225,49.73,256,0.900,bicubic,+4.331,+1.133,-23 -xcit_tiny_24_p16_384_dist,88.161,11.839,97.946,2.054,12.12,384,1.000,bicubic,+5.589,+1.658,+53 -xcit_large_24_p8_224,88.157,11.843,97.389,2.611,188.93,224,1.000,bicubic,+3.765,+0.731,-58 -ig_resnext101_32x8d,88.155,11.845,97.856,2.144,88.79,224,0.875,bilinear,+5.457,+1.224,+42 -resnetv2_152x2_bit_teacher_384,88.150,11.850,98.053,1.947,236.34,384,1.000,bicubic,+4.306,+0.937,-26 -cait_xxs36_384,88.138,11.862,97.908,2.092,17.37,384,1.000,bicubic,+5.946,+1.764,+82 -dm_nfnet_f0,88.125,11.875,97.854,2.146,71.49,256,0.900,bicubic,+4.741,+1.280,0 -xcit_tiny_12_p8_384_dist,88.101,11.899,97.923,2.077,6.71,384,1.000,bicubic,+5.715,+1.701,+59 -swsl_resnext101_32x4d,88.099,11.901,97.970,2.030,44.18,224,0.875,bilinear,+4.859,+1.210,+4 -xception65,88.071,11.929,97.750,2.250,39.92,299,0.940,bicubic,+4.897,+1.158,+6 -convnext_small,88.050,11.950,97.788,2.212,50.22,224,0.875,bicubic,+4.900,+1.358,+6 -swin_s3_base_224,88.050,11.950,97.660,2.340,71.13,224,0.900,bicubic,+4.118,+1.000,-38 -xcit_tiny_24_p8_224_dist,88.035,11.965,97.812,2.188,12.11,224,1.000,bicubic,+5.475,+1.644,+44 -convnext_tiny_in22ft1k,87.997,12.003,97.920,2.080,28.59,224,0.875,bicubic,+5.085,+1.296,+18 -cs3sedarknet_x,87.995,12.005,97.790,2.210,35.40,288,1.000,bicubic,+5.341,+1.444,+32 -eca_nfnet_l0,87.978,12.023,97.871,2.129,24.14,288,1.000,bicubic,+5.400,+1.381,+38 -nfnet_l0,87.971,12.029,97.867,2.133,35.07,288,1.000,bicubic,+5.219,+1.349,+25 -xcit_small_24_p8_224,87.969,12.031,97.581,2.419,47.63,224,1.000,bicubic,+4.129,+0.945,-37 -tf_efficientnet_b4,87.967,12.033,97.739,2.261,19.34,380,0.922,bicubic,+4.943,+1.439,+10 -regnety_032,87.941,12.059,97.888,2.112,19.44,288,1.000,bicubic,+5.217,+1.466,+23 -resnet101d,87.937,12.063,97.908,2.092,44.57,320,1.000,bicubic,+4.915,+1.462,+9 -mobilevitv2_200_384_in22ft1k,87.935,12.065,97.822,2.178,18.45,384,1.000,bicubic,+4.535,+1.240,-17 -swinv2_cr_small_ns_224,87.922,12.078,97.666,2.334,49.70,224,0.900,bicubic,+4.436,+1.182,-23 -sequencer2d_l,87.915,12.085,97.698,2.302,54.30,224,0.875,bicubic,+4.509,+1.198,-20 -regnety_040,87.913,12.087,97.884,2.116,20.65,288,1.000,bicubic,+4.877,+1.374,+3 -vit_base_patch32_384,87.911,12.089,98.012,1.988,88.30,384,1.000,bicubic,+4.559,+1.176,-18 -twins_svt_large,87.901,12.099,97.581,2.419,99.27,224,0.900,bicubic,+4.221,+0.987,-32 -twins_pcpvt_large,87.877,12.123,97.859,2.142,60.99,224,0.900,bicubic,+4.741,+1.255,-7 -swin_s3_small_224,87.860,12.140,97.434,2.566,49.74,224,0.900,bicubic,+4.086,+0.982,-41 -regnetz_c16,87.858,12.142,97.818,2.182,13.46,320,0.940,bicubic,+5.338,+1.458,+28 -deit_base_patch16_384,87.841,12.159,97.510,2.490,86.86,384,1.000,bicubic,+4.735,+1.140,-7 -mobilevitv2_175_384_in22ft1k,87.837,12.164,97.726,2.274,14.25,384,1.000,bicubic,+4.903,+1.296,-1 -xcit_small_12_p8_224,87.828,12.172,97.566,2.434,26.21,224,1.000,bicubic,+4.488,+1.086,-22 -tresnet_xl_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.748,+1.289,-7 -resnetv2_50x1_bit_distilled,87.792,12.208,97.899,2.101,25.55,224,0.875,bicubic,+4.970,+1.377,+1 -tresnet_m,87.740,12.259,97.523,2.477,31.39,224,0.875,bilinear,+4.666,+1.403,-10 -twins_pcpvt_base,87.732,12.268,97.728,2.272,43.83,224,0.900,bicubic,+5.024,+1.378,+8 -gc_efficientnetv2_rw_t,87.717,12.283,97.807,2.193,13.68,288,1.000,bicubic,+5.251,+1.509,+24 -resnetv2_101x1_bitm,87.683,12.317,97.938,2.062,44.54,448,1.000,bilinear,+5.351,+1.422,+35 -swin_small_patch4_window7_224,87.670,12.330,97.568,2.432,49.61,224,0.900,bicubic,+4.452,+1.242,-26 -mobilevitv2_150_384_in22ft1k,87.653,12.347,97.649,2.351,10.59,384,1.000,bicubic,+5.063,+1.333,+10 -twins_svt_base,87.644,12.356,97.525,2.474,56.07,224,0.900,bicubic,+4.506,+1.105,-23 -efficientnetv2_rw_t,87.642,12.358,97.688,2.312,13.65,288,1.000,bicubic,+5.298,+1.492,+28 -pnasnet5large,87.640,12.360,97.485,2.515,86.06,331,0.911,bicubic,+4.858,+1.443,-4 -cs3edgenet_x,87.632,12.368,97.662,2.338,47.82,288,1.000,bicubic,+4.910,+1.286,-1 -swinv2_tiny_window16_256,87.617,12.383,97.562,2.438,28.35,256,0.900,bicubic,+4.807,+1.332,-8 -swsl_resnext101_32x16d,87.608,12.392,97.820,2.180,194.03,224,0.875,bilinear,+4.258,+0.976,-38 -jx_nest_base,87.608,12.392,97.515,2.485,67.72,224,0.875,bicubic,+4.054,+1.151,-50 -xcit_medium_24_p8_224,87.606,12.394,97.197,2.803,84.32,224,1.000,bicubic,+3.868,+0.803,-59 -swsl_resnext50_32x4d,87.602,12.398,97.654,2.346,25.03,224,0.875,bilinear,+5.426,+1.422,+39 -sequencer2d_m,87.565,12.435,97.581,2.419,38.31,224,0.875,bicubic,+4.757,+1.313,-12 -tf_efficientnet_b2_ns,87.559,12.441,97.628,2.372,9.11,260,0.890,bicubic,+5.175,+1.382,+16 -levit_384,87.555,12.445,97.545,2.455,39.13,224,0.900,bicubic,+4.967,+1.527,-1 -ecaresnet50t,87.542,12.458,97.645,2.355,25.57,320,0.950,bicubic,+5.194,+1.507,+16 -vit_base_patch16_rpn_224,87.506,12.494,97.489,2.511,86.54,224,0.900,bicubic,+5.306,+1.493,+32 -edgenext_small,87.504,12.496,97.587,2.413,5.59,320,1.000,bicubic,+5.930,+1.873,+66 -resnetv2_152x2_bit_teacher,87.493,12.507,97.812,2.188,236.34,224,0.875,bicubic,+4.625,+1.244,-22 -jx_nest_small,87.491,12.509,97.521,2.479,38.35,224,0.875,bicubic,+4.371,+1.191,-35 -vit_relpos_base_patch16_clsgap_224,87.469,12.531,97.525,2.474,86.43,224,0.900,bicubic,+4.709,+1.351,-18 -vit_relpos_base_patch16_224,87.463,12.537,97.560,2.440,86.43,224,0.900,bicubic,+4.977,+1.418,+1 -resnet152,87.454,12.546,97.400,2.600,60.19,224,0.950,bicubic,+4.636,+1.268,-24 -fbnetv3_g,87.446,12.554,97.545,2.455,16.62,288,0.950,bilinear,+5.412,+1.479,+36 -resnext101_64x4d,87.444,12.556,97.442,2.558,83.46,288,1.000,bicubic,+4.300,+1.068,-45 -efficientnet_b3,87.435,12.565,97.679,2.321,12.23,320,1.000,bicubic,+5.195,+1.561,+18 -resnet61q,87.431,12.569,97.598,2.402,36.85,288,1.000,bicubic,+4.913,+1.468,-5 -cait_xxs24_384,87.414,12.586,97.619,2.381,12.03,384,1.000,bicubic,+6.452,+1.975,+101 -cs3sedarknet_l,87.407,12.593,97.572,2.428,21.91,288,0.950,bicubic,+5.631,+1.602,+47 -cs3darknet_x,87.399,12.601,97.607,2.393,35.05,288,1.000,bicubic,+5.175,+1.377,+16 -resnet51q,87.392,12.608,97.581,2.419,35.70,288,1.000,bilinear,+5.034,+1.403,0 -xcit_tiny_24_p8_224,87.380,12.620,97.626,2.374,12.11,224,1.000,bicubic,+5.484,+1.652,+37 -tresnet_l_448,87.380,12.620,97.487,2.513,55.99,448,0.875,bilinear,+5.110,+1.507,+10 -coat_lite_small,87.377,12.623,97.372,2.628,19.84,224,0.900,bicubic,+5.073,+1.522,+4 -sequencer2d_s,87.375,12.625,97.391,2.609,27.65,224,0.875,bicubic,+5.031,+1.357,-1 -swinv2_cr_small_224,87.371,12.629,97.344,2.656,49.70,224,0.900,bicubic,+4.233,+1.246,-54 -vit_relpos_medium_patch16_cls_224,87.369,12.631,97.453,2.547,38.76,224,0.900,bicubic,+4.807,+1.387,-19 -nasnetalarge,87.348,12.652,97.417,2.583,88.75,331,0.911,bicubic,+4.730,+1.373,-26 -crossvit_18_dagger_240,87.346,12.655,97.455,2.545,44.27,240,0.875,bicubic,+4.826,+1.387,-18 -resnetv2_101,87.322,12.678,97.325,2.675,44.54,224,0.950,bicubic,+5.276,+1.463,+19 -crossvit_18_240,87.316,12.684,97.487,2.513,43.27,240,0.875,bicubic,+4.918,+1.433,-13 -convnext_tiny,87.313,12.687,97.449,2.551,28.59,224,0.875,bicubic,+5.251,+1.595,+16 -resnest101e,87.286,12.714,97.560,2.440,48.28,256,0.875,bilinear,+4.398,+1.240,-47 -ecaresnet101d,87.284,12.716,97.562,2.438,44.57,224,0.875,bicubic,+5.114,+1.514,+8 -pit_s_distilled_224,87.275,12.725,97.500,2.500,24.04,224,0.900,bicubic,+5.281,+1.704,+16 -resnetv2_50d_gn,87.269,12.731,97.513,2.487,25.57,288,0.950,bicubic,+5.445,+1.589,+26 -vit_relpos_medium_patch16_rpn_224,87.256,12.744,97.442,2.558,38.73,224,0.900,bicubic,+4.962,+1.470,-7 -resnetrs101,87.243,12.757,97.457,2.543,63.62,288,0.940,bicubic,+4.959,+1.449,-6 -poolformer_m48,87.239,12.761,97.308,2.692,73.47,224,0.950,bicubic,+4.779,+1.350,-23 -mixer_b16_224_miil,87.230,12.770,97.410,2.590,59.88,224,0.875,bilinear,+4.926,+1.690,-11 -tresnet_xl,87.226,12.774,97.400,2.600,78.44,224,0.875,bilinear,+5.164,+1.464,+6 -xcit_tiny_12_p8_224_dist,87.219,12.780,97.449,2.551,6.71,224,1.000,bicubic,+6.011,+1.843,+58 -convit_base,87.207,12.793,97.286,2.714,86.54,224,0.875,bicubic,+4.915,+1.348,-12 -xcit_tiny_12_p16_384_dist,87.202,12.798,97.468,2.532,6.72,384,1.000,bicubic,+6.260,+2.060,+76 -resnetv2_50d_evos,87.194,12.806,97.359,2.641,25.59,288,0.950,bicubic,+5.216,+1.447,+8 -tf_efficientnet_b3_ap,87.188,12.812,97.380,2.620,12.23,300,0.904,bicubic,+5.364,+1.756,+17 -visformer_small,87.185,12.815,97.325,2.675,40.22,224,0.900,bicubic,+5.077,+1.449,-3 -crossvit_15_dagger_240,87.170,12.830,97.438,2.562,28.21,240,0.875,bicubic,+4.844,+1.482,-21 -vit_srelpos_medium_patch16_224,87.168,12.832,97.312,2.688,38.74,224,0.900,bicubic,+4.932,+1.378,-14 -vit_relpos_medium_patch16_224,87.138,12.862,97.506,2.494,38.75,224,0.900,bicubic,+4.676,+1.420,-35 -xcit_small_24_p16_224,87.134,12.866,97.263,2.737,47.67,224,1.000,bicubic,+4.550,+1.263,-46 -swin_s3_tiny_224,87.130,12.870,97.303,2.697,28.33,224,0.900,bicubic,+5.006,+1.353,-9 -resnet101,87.081,12.919,97.265,2.735,44.55,224,0.950,bicubic,+5.151,+1.499,+5 -swinv2_tiny_window8_256,87.079,12.921,97.517,2.483,28.35,256,0.900,bicubic,+5.269,+1.523,+10 -mobilevitv2_200_in22ft1k,87.059,12.941,97.425,2.575,18.45,256,0.888,bicubic,+4.725,+1.487,-30 -xception41p,87.057,12.943,97.201,2.799,26.91,299,0.940,bicubic,+5.089,+1.407,-1 -crossvit_15_240,87.055,12.945,97.423,2.577,27.53,240,0.875,bicubic,+5.511,+1.733,+19 -convit_small,87.051,12.949,97.350,2.650,27.78,224,0.875,bicubic,+5.623,+1.608,+28 -tf_efficientnetv2_b3,87.029,12.970,97.303,2.697,14.36,300,0.904,bicubic,+5.063,+1.521,-3 -xcit_small_12_p16_224,87.017,12.983,97.242,2.759,26.25,224,1.000,bicubic,+5.045,+1.430,-6 -regnetz_b16,87.012,12.988,97.425,2.575,9.72,288,0.940,bicubic,+6.300,+1.951,+73 -jx_nest_tiny,87.008,12.992,97.378,2.622,17.06,224,0.875,bicubic,+5.590,+1.760,+25 -deit3_small_patch16_224,87.004,12.996,97.167,2.833,22.06,224,0.900,bicubic,+5.622,+1.717,+28 -deit_small_distilled_patch16_224,87.002,12.998,97.316,2.684,22.44,224,0.900,bicubic,+5.794,+1.942,+37 -swinv2_cr_tiny_ns_224,86.998,13.002,97.282,2.718,28.33,224,0.900,bicubic,+5.212,+1.460,0 -resmlp_36_distilled_224,86.989,13.011,97.276,2.724,44.69,224,0.875,bicubic,+5.833,+1.790,+37 -xcit_large_24_p16_224,86.955,13.045,96.919,3.081,189.10,224,1.000,bicubic,+4.063,+1.041,-82 -mobilevitv2_175_in22ft1k,86.953,13.047,97.333,2.667,14.25,256,0.888,bicubic,+5.013,+1.543,-11 -poolformer_m36,86.946,13.054,97.148,2.852,56.17,224,0.950,bicubic,+4.838,+1.458,-24 -xcit_medium_24_p16_224,86.938,13.062,97.098,2.902,84.40,224,1.000,bicubic,+4.300,+1.120,-70 -convnext_tiny_hnf,86.918,13.082,97.280,2.720,28.59,224,0.950,bicubic,+4.698,+1.414,-34 -tnt_s_patch16_224,86.906,13.094,97.365,2.635,23.76,224,0.900,bicubic,+5.388,+1.619,+6 -vit_relpos_small_patch16_224,86.891,13.109,97.491,2.509,21.98,224,0.900,bicubic,+5.437,+1.663,+12 -vit_small_patch16_224,86.865,13.135,97.613,2.387,22.05,224,0.900,bicubic,+5.469,+1.475,+15 -ssl_resnext101_32x16d,86.865,13.135,97.519,2.481,194.03,224,0.875,bilinear,+5.009,+1.423,-14 -vit_small_r26_s32_224,86.856,13.143,97.528,2.472,36.43,224,0.900,bicubic,+4.994,+1.506,-16 -convmixer_1536_20,86.854,13.146,97.346,2.654,51.63,224,0.960,bicubic,+5.484,+1.734,+16 -rexnet_200,86.842,13.158,97.276,2.724,16.37,224,0.875,bicubic,+5.214,+1.608,-7 -tf_efficientnet_b3,86.837,13.163,97.297,2.703,12.23,300,0.904,bicubic,+5.199,+1.579,-9 -swsl_resnet50,86.835,13.165,97.493,2.507,25.56,224,0.875,bilinear,+5.655,+1.513,+22 -deit_base_patch16_224,86.827,13.173,97.052,2.949,86.57,224,0.900,bicubic,+4.833,+1.320,-29 -tresnet_m_448,86.814,13.186,97.216,2.784,31.39,448,0.875,bilinear,+5.108,+1.644,-15 -ssl_resnext101_32x8d,86.801,13.199,97.472,2.528,88.79,224,0.875,bilinear,+5.193,+1.430,-10 -tf_efficientnet_lite4,86.801,13.199,97.263,2.737,13.01,380,0.920,bilinear,+5.267,+1.597,-7 -coat_mini,86.790,13.210,97.158,2.842,10.34,224,0.900,bicubic,+5.524,+1.766,+12 -resnetaa50,86.771,13.229,97.389,2.611,25.56,288,1.000,bicubic,+5.153,+1.579,-14 -tresnet_l,86.763,13.237,97.271,2.729,55.99,224,0.875,bilinear,+5.273,+1.645,-6 -twins_svt_small,86.756,13.244,97.177,2.823,24.06,224,0.900,bicubic,+5.074,+1.511,-20 -cs3darknet_l,86.748,13.252,97.463,2.537,21.16,288,0.950,bicubic,+5.862,+1.795,+35 -mobilevitv2_150_in22ft1k,86.743,13.257,97.218,2.782,10.59,256,0.888,bicubic,+5.273,+1.550,-7 -levit_256,86.739,13.261,97.259,2.741,18.89,224,0.900,bicubic,+5.223,+1.769,-12 -cs3darknet_focus_l,86.735,13.265,97.380,2.620,21.15,288,0.950,bicubic,+5.861,+1.688,+34 -crossvit_base_240,86.735,13.265,97.122,2.878,105.03,240,0.875,bicubic,+4.519,+1.290,-55 -vit_srelpos_small_patch16_224,86.703,13.297,97.250,2.750,21.97,224,0.900,bicubic,+5.605,+1.678,+14 -halo2botnet50ts_256,86.692,13.308,97.096,2.904,22.64,256,0.950,bicubic,+4.624,+1.454,-49 -seresnext50_32x4d,86.690,13.310,97.222,2.778,27.56,224,0.875,bicubic,+5.428,+1.594,+2 -crossvit_small_240,86.688,13.312,97.273,2.727,26.86,240,0.875,bicubic,+5.672,+1.817,+17 -pit_b_224,86.688,13.312,96.898,3.102,73.76,224,0.900,bicubic,+4.244,+1.186,-81 -tf_efficientnet_b1_ns,86.666,13.334,97.378,2.622,7.79,240,0.882,bicubic,+5.280,+1.642,-9 -swin_tiny_patch4_window7_224,86.658,13.342,97.197,2.803,28.29,224,0.900,bicubic,+5.282,+1.655,-8 -wide_resnet50_2,86.641,13.359,97.212,2.788,68.88,224,0.875,bicubic,+5.185,+1.682,-17 -gernet_l,86.641,13.359,97.190,2.810,31.08,256,0.875,bilinear,+5.291,+1.654,-8 -poolformer_s36,86.639,13.361,97.158,2.842,30.86,224,0.900,bicubic,+5.221,+1.710,-15 -efficientnet_el,86.635,13.366,97.180,2.820,10.59,300,0.904,bicubic,+5.329,+1.646,-8 -twins_pcpvt_small,86.626,13.374,97.340,2.660,24.11,224,0.900,bicubic,+5.536,+1.698,+6 -resmlp_24_distilled_224,86.620,13.380,97.139,2.861,30.02,224,0.875,bicubic,+5.856,+1.917,+25 -nf_resnet50,86.605,13.395,97.293,2.707,25.56,288,0.940,bicubic,+5.951,+1.959,+28 -resnest50d_4s2x40d,86.583,13.417,97.269,2.731,30.42,224,0.875,bicubic,+5.475,+1.707,-1 -efficientnet_b3_pruned,86.579,13.421,97.188,2.812,9.86,300,0.904,bicubic,+5.721,+1.944,+19 -sebotnet33ts_256,86.573,13.427,96.791,3.209,13.70,256,0.940,bicubic,+5.419,+1.625,-6 -sehalonet33ts,86.570,13.430,97.009,2.991,13.69,256,0.940,bicubic,+5.598,+1.737,+7 -repvgg_b3,86.564,13.436,97.141,2.859,123.09,224,0.875,bilinear,+6.068,+1.877,+32 -xcit_tiny_24_p16_224_dist,86.534,13.466,97.216,2.784,12.12,224,1.000,bicubic,+6.086,+2.004,+38 -convnext_nano,86.532,13.468,97.182,2.818,15.59,288,1.000,bicubic,+5.056,+1.522,-32 -halonet50ts,86.500,13.500,97.152,2.848,22.73,256,0.940,bicubic,+4.848,+1.540,-46 -ssl_resnext101_32x4d,86.477,13.524,97.470,2.530,44.18,224,0.875,bilinear,+5.553,+1.744,+6 -gcresnet50t,86.474,13.526,97.141,2.859,25.90,256,0.900,bicubic,+5.540,+1.687,+4 -ecaresnet50d,86.472,13.528,97.184,2.816,25.58,224,0.875,bicubic,+5.874,+1.866,+20 -gluon_resnet152_v1s,86.462,13.538,97.109,2.891,60.32,224,0.875,bicubic,+5.448,+1.695,-4 -haloregnetz_b,86.462,13.538,96.943,3.057,11.68,224,0.940,bicubic,+5.418,+1.745,-8 -mobilevitv2_200,86.455,13.545,96.970,3.030,18.45,256,0.888,bicubic,+5.315,+1.602,-15 -resnetv2_50x1_bitm,86.442,13.558,97.600,2.400,25.55,448,1.000,bilinear,+6.100,+1.914,+36 -resnest50d_1s4x24d,86.440,13.560,97.152,2.848,25.68,224,0.875,bicubic,+5.456,+1.828,-7 -repvgg_b3g4,86.368,13.632,97.054,2.946,83.83,224,0.875,bilinear,+6.152,+1.946,+47 -darknetaa53,86.361,13.639,97.165,2.835,36.02,288,1.000,bilinear,+5.839,+1.839,+18 -darknet53,86.359,13.641,97.113,2.887,41.61,288,1.000,bicubic,+5.821,+1.693,+15 -lamhalobotnet50ts_256,86.357,13.643,97.062,2.938,22.57,256,0.950,bicubic,+4.805,+1.558,-52 -legacy_senet154,86.340,13.660,96.925,3.075,115.09,224,0.875,bilinear,+5.032,+1.429,-33 -cait_xxs36_224,86.338,13.662,97.111,2.889,17.30,224,1.000,bicubic,+6.590,+2.243,+67 -resnext50_32x4d,86.329,13.671,96.964,3.036,25.03,224,0.950,bicubic,+5.233,+1.638,-20 -pit_s_224,86.325,13.675,97.049,2.951,23.46,224,0.900,bicubic,+5.227,+1.717,-22 -vit_small_patch32_384,86.316,13.684,97.419,2.581,22.92,384,1.000,bicubic,+5.826,+1.819,+13 -gernet_m,86.316,13.684,97.098,2.902,21.14,224,0.875,bilinear,+5.586,+1.912,-1 -mobilevitv2_175,86.316,13.684,96.990,3.010,14.25,256,0.888,bicubic,+5.454,+1.728,-7 -efficientnet_b2,86.310,13.690,96.987,3.013,9.11,288,1.000,bicubic,+5.694,+1.671,+1 -gluon_senet154,86.278,13.722,96.945,3.055,115.09,224,0.875,bicubic,+5.048,+1.599,-37 -resnest50d,86.240,13.761,97.071,2.929,27.48,224,0.875,bilinear,+5.266,+1.691,-20 -convmixer_768_32,86.225,13.775,97.034,2.966,21.11,224,0.960,bicubic,+6.061,+1.962,+37 -ecaresnet101d_pruned,86.210,13.790,97.338,2.662,24.88,224,0.875,bicubic,+5.400,+1.710,-10 -efficientnet_el_pruned,86.195,13.805,97.022,2.978,10.59,300,0.904,bicubic,+5.897,+1.808,+24 -cspdarknet53,86.184,13.816,97.013,2.987,27.64,256,0.887,bilinear,+6.128,+1.927,+40 -inception_v4,86.167,13.833,96.915,3.085,42.68,299,0.875,bicubic,+5.999,+1.951,+31 -rexnet_150,86.156,13.844,97.060,2.940,9.73,224,0.875,bicubic,+5.842,+1.894,+19 -inception_resnet_v2,86.137,13.863,97.043,2.957,55.84,299,0.897,bicubic,+5.677,+1.737,+4 -xcit_tiny_12_p8_224,86.114,13.886,97.086,2.914,6.71,224,1.000,bicubic,+6.420,+2.038,+54 -tf_efficientnet_el,86.086,13.914,96.964,3.036,10.59,300,0.904,bicubic,+5.832,+1.836,+21 -ssl_resnext50_32x4d,86.084,13.916,97.212,2.788,25.03,224,0.875,bilinear,+5.758,+1.800,+13 -cspresnext50,86.073,13.927,97.103,2.897,20.57,256,0.887,bilinear,+5.529,+1.779,-8 -mobilevitv2_150,86.073,13.927,96.853,3.147,10.59,256,0.888,bicubic,+5.705,+1.789,+7 -ecaresnetlight,86.054,13.946,97.071,2.929,30.16,224,0.875,bicubic,+5.598,+1.825,-1 -gluon_resnet101_v1s,86.054,13.946,97.024,2.976,44.67,224,0.875,bicubic,+5.756,+1.862,+14 -edgenext_small_rw,86.049,13.950,96.925,3.075,7.83,320,1.000,bicubic,+5.597,+1.735,-2 -lambda_resnet50ts,86.039,13.961,96.746,3.254,21.54,256,0.950,bicubic,+4.887,+1.644,-48 -poolformer_s24,86.037,13.963,97.030,2.970,21.39,224,0.900,bicubic,+5.721,+1.988,+7 -gluon_seresnext101_32x4d,86.032,13.968,96.977,3.023,48.96,224,0.875,bicubic,+5.126,+1.681,-32 -resnetv2_50,86.015,13.985,96.902,3.098,25.55,224,0.950,bicubic,+5.603,+1.830,-3 -seresnet33ts,86.009,13.991,97.011,2.989,19.78,256,0.900,bicubic,+5.655,+1.905,0 -gcresnext50ts,86.009,13.991,96.966,3.034,15.67,256,0.900,bicubic,+5.431,+1.796,-19 -resnet50d,86.002,13.998,96.987,3.013,25.58,224,0.875,bicubic,+5.474,+1.819,-17 -ecaresnet26t,85.985,14.015,97.037,2.963,16.01,320,0.950,bicubic,+6.133,+1.953,+31 -tf_efficientnet_b2_ap,85.973,14.027,96.808,3.192,9.11,260,0.890,bicubic,+5.671,+1.780,+2 -vit_base_patch32_224,85.958,14.042,97.130,2.869,88.22,224,0.900,bicubic,+5.234,+1.564,-29 -gluon_seresnext101_64x4d,85.958,14.042,96.981,3.019,88.23,224,0.875,bicubic,+5.078,+1.685,-38 -fbnetv3_d,85.924,14.076,97.028,2.972,10.31,256,0.950,bilinear,+6.244,+2.088,+38 -vit_large_patch32_384,85.911,14.089,97.368,2.632,306.63,384,1.000,bicubic,+4.403,+1.278,-85 -tf_efficientnet_b2,85.909,14.091,96.862,3.139,9.11,260,0.890,bicubic,+5.821,+1.954,+11 -gluon_resnet152_v1d,85.906,14.094,96.806,3.194,60.21,224,0.875,bicubic,+5.430,+1.606,-20 -tf_efficientnetv2_b2,85.902,14.098,96.885,3.115,10.10,260,0.890,bicubic,+5.694,+1.841,+4 -resnet50_gn,85.881,14.119,96.849,3.151,25.56,224,0.940,bicubic,+5.821,+1.901,+11 -vit_base_patch16_224_sam,85.879,14.121,96.695,3.305,86.57,224,0.900,bicubic,+5.635,+1.941,-1 -seresnet50,85.853,14.147,97.007,2.993,28.09,224,0.875,bicubic,+5.587,+1.937,-5 -gluon_resnet101_v1d,85.851,14.149,96.663,3.337,44.57,224,0.875,bicubic,+5.433,+1.649,-20 -repvgg_b2g4,85.847,14.153,96.812,3.188,61.76,224,0.875,bilinear,+6.481,+2.124,+44 -gcresnet33ts,85.804,14.196,96.902,3.098,19.88,256,0.900,bicubic,+5.728,+1.908,+5 -mixnet_xl,85.798,14.202,96.710,3.290,11.90,224,0.875,bicubic,+5.320,+1.776,-29 -ens_adv_inception_resnet_v2,85.768,14.232,96.761,3.239,55.84,299,0.897,bicubic,+5.794,+1.819,+7 -tf_efficientnet_lite3,85.761,14.239,96.889,3.111,8.20,300,0.904,bilinear,+5.943,+1.975,+17 -legacy_seresnext101_32x4d,85.744,14.256,96.755,3.245,48.96,224,0.875,bilinear,+5.522,+1.741,-8 -ese_vovnet39b,85.742,14.258,96.894,3.107,24.57,224,0.875,bicubic,+6.430,+2.180,+42 -gluon_resnext101_32x4d,85.742,14.258,96.635,3.365,44.18,224,0.875,bicubic,+5.402,+1.709,-21 -eca_resnet33ts,85.740,14.260,96.902,3.098,19.68,256,0.900,bicubic,+5.660,+1.930,-3 -xcit_tiny_24_p16_224,85.736,14.264,96.938,3.062,12.12,224,1.000,bicubic,+6.292,+2.050,+32 -cspresnet50,85.727,14.273,96.799,3.200,21.62,256,0.887,bilinear,+6.145,+2.091,+24 -regnety_320,85.719,14.281,96.723,3.277,145.05,224,0.875,bicubic,+4.915,+1.479,-54 -resnet50,85.719,14.281,96.492,3.508,25.56,224,0.950,bicubic,+5.345,+1.878,-30 -gluon_resnext101_64x4d,85.693,14.307,96.644,3.356,83.46,224,0.875,bicubic,+5.089,+1.652,-49 -resmlp_big_24_224,85.693,14.307,96.424,3.576,129.14,224,0.875,bicubic,+4.663,+1.404,-74 -xception71,85.691,14.309,96.774,3.226,42.34,299,0.903,bicubic,+5.821,+1.850,0 -efficientnet_em,85.686,14.313,96.936,3.064,6.90,240,0.882,bicubic,+6.434,+2.144,+41 -deit_small_patch16_224,85.678,14.322,96.904,3.096,22.05,224,0.900,bicubic,+5.814,+1.856,-1 -pit_xs_distilled_224,85.659,14.341,96.665,3.335,11.00,224,0.900,bicubic,+6.351,+2.299,+31 -dpn107,85.650,14.350,96.725,3.275,86.92,224,0.875,bicubic,+5.482,+1.819,-18 -efficientnet_b2_pruned,85.640,14.360,96.746,3.254,8.31,260,0.890,bicubic,+5.722,+1.896,-9 -resmlp_36_224,85.625,14.375,96.795,3.205,44.69,224,0.875,bicubic,+5.855,+1.909,+1 -mobilevitv2_125,85.584,14.416,96.665,3.335,7.48,256,0.888,bicubic,+5.902,+1.817,+5 -gluon_resnet152_v1c,85.582,14.418,96.646,3.354,60.21,224,0.875,bicubic,+5.670,+1.804,-11 -levit_192,85.578,14.422,96.744,3.256,10.95,224,0.900,bicubic,+5.742,+1.954,-5 -ecaresnet50d_pruned,85.576,14.425,96.932,3.068,19.94,224,0.875,bicubic,+5.858,+2.056,0 -resnext50d_32x4d,85.571,14.429,96.748,3.252,25.05,224,0.875,bicubic,+5.895,+1.882,+4 -tf_efficientnetv2_b1,85.561,14.439,96.727,3.273,8.14,240,0.882,bicubic,+6.095,+2.005,+13 -regnety_120,85.543,14.457,96.785,3.215,51.82,224,0.875,bicubic,+5.167,+1.663,-47 -regnetx_320,85.522,14.478,96.669,3.331,107.81,224,0.875,bicubic,+5.278,+1.649,-34 -fbnetv3_b,85.514,14.486,96.862,3.139,8.60,256,0.950,bilinear,+6.372,+2.112,+33 -nf_regnet_b1,85.514,14.486,96.795,3.205,10.22,288,0.900,bicubic,+6.214,+2.041,+20 -dpn92,85.501,14.499,96.631,3.369,37.67,224,0.875,bicubic,+5.481,+1.801,-23 -rexnet_130,85.475,14.525,96.686,3.314,7.56,224,0.875,bicubic,+5.973,+2.004,+3 -gluon_resnet152_v1b,85.465,14.536,96.556,3.444,60.19,224,0.875,bicubic,+5.783,+1.820,-6 -resnetrs50,85.462,14.538,96.738,3.262,35.69,224,0.910,bicubic,+5.576,+1.768,-21 -dpn131,85.400,14.600,96.631,3.369,79.25,224,0.875,bicubic,+5.574,+1.923,-16 -regnetx_160,85.390,14.610,96.637,3.363,54.28,224,0.875,bicubic,+5.536,+1.807,-20 -dla102x2,85.377,14.623,96.629,3.371,41.28,224,0.875,bilinear,+5.935,+1.983,+4 -gmlp_s16_224,85.351,14.649,96.646,3.354,19.42,224,0.875,bicubic,+5.711,+2.022,-7 -gluon_seresnext50_32x4d,85.334,14.666,96.671,3.329,27.56,224,0.875,bicubic,+5.422,+1.839,-27 -botnet26t_256,85.332,14.668,96.631,3.369,12.49,256,0.950,bicubic,+6.074,+2.103,+15 -skresnext50_32x4d,85.317,14.683,96.394,3.606,27.48,224,0.875,bicubic,+5.163,+1.748,-39 -gluon_resnet101_v1c,85.311,14.689,96.407,3.593,44.57,224,0.875,bicubic,+5.775,+1.829,-8 -dpn98,85.304,14.696,96.466,3.534,61.57,224,0.875,bicubic,+5.660,+1.866,-13 -lambda_resnet26t,85.302,14.698,96.727,3.273,10.96,256,0.940,bicubic,+6.204,+2.137,+20 -resnetblur50,85.291,14.709,96.520,3.480,25.56,224,0.875,bicubic,+5.997,+1.886,+7 -dpn68b,85.291,14.709,96.464,3.536,12.61,224,0.875,bicubic,+6.075,+2.050,+12 -resmlp_24_224,85.264,14.736,96.496,3.504,30.02,224,0.875,bicubic,+5.886,+1.950,-5 -coat_lite_mini,85.255,14.745,96.680,3.320,11.01,224,0.900,bicubic,+6.167,+2.072,+17 -cait_xxs24_224,85.225,14.775,96.716,3.284,11.96,224,1.000,bicubic,+6.839,+2.408,+50 -resnet33ts,85.225,14.775,96.627,3.373,19.68,256,0.900,bicubic,+6.017,+2.053,+9 -xcit_tiny_12_p16_224_dist,85.215,14.785,96.599,3.401,6.72,224,1.000,bicubic,+6.637,+2.401,+35 -halonet26t,85.202,14.798,96.464,3.536,12.48,256,0.950,bicubic,+6.090,+2.150,+11 -resnext101_32x8d,85.195,14.805,96.451,3.549,88.79,224,0.875,bilinear,+5.879,+1.933,-8 -gluon_inception_v3,85.180,14.819,96.526,3.474,23.83,299,0.875,bicubic,+6.374,+2.156,+24 -resnet32ts,85.168,14.832,96.622,3.378,17.96,256,0.900,bicubic,+6.154,+2.266,+15 -gluon_xception65,85.155,14.845,96.597,3.403,39.92,299,0.903,bicubic,+5.433,+1.737,-33 -hrnet_w48,85.148,14.851,96.492,3.508,77.47,224,0.875,bilinear,+5.848,+1.978,-6 -gluon_resnet101_v1b,85.142,14.858,96.368,3.632,44.55,224,0.875,bicubic,+5.838,+1.848,-9 -eca_halonext26ts,85.127,14.873,96.586,3.414,10.76,256,0.940,bicubic,+5.639,+1.982,-23 -regnetx_120,85.127,14.873,96.473,3.527,46.11,224,0.875,bicubic,+5.535,+1.739,-28 -eca_botnext26ts_256,85.125,14.875,96.507,3.493,10.59,256,0.950,bicubic,+5.849,+1.891,-8 -tf_efficientnet_b1_ap,85.125,14.875,96.407,3.593,7.79,240,0.882,bicubic,+5.851,+2.099,-8 -xception,85.123,14.877,96.471,3.529,22.86,299,0.897,bicubic,+6.079,+2.077,+6 -hrnet_w64,85.114,14.886,96.746,3.254,128.06,224,0.875,bilinear,+5.644,+2.092,-26 -lambda_resnet26rpt_256,85.095,14.905,96.560,3.440,10.99,256,0.940,bicubic,+6.131,+2.134,+7 -res2net101_26w_4s,85.095,14.905,96.383,3.617,45.21,224,0.875,bilinear,+5.899,+1.947,-5 -ssl_resnet50,85.091,14.909,96.862,3.139,25.56,224,0.875,bilinear,+5.867,+2.032,-10 -tf_efficientnet_cc_b1_8e,85.065,14.935,96.422,3.578,39.72,240,0.882,bicubic,+5.751,+2.052,-22 -xcit_nano_12_p8_384_dist,85.025,14.975,96.631,3.369,3.05,384,1.000,bicubic,+7.209,+2.585,+62 -resnest26d,85.010,14.990,96.637,3.363,17.07,224,0.875,bilinear,+6.526,+2.343,+21 -gluon_resnext50_32x4d,85.008,14.992,96.428,3.572,25.03,224,0.875,bicubic,+5.648,+2.002,-27 -tf_efficientnet_b0_ns,84.997,15.003,96.505,3.495,5.29,224,0.875,bicubic,+6.333,+2.129,+12 -coat_tiny,84.980,15.020,96.409,3.591,5.50,224,0.900,bicubic,+6.544,+2.371,+23 -dla169,84.922,15.078,96.535,3.465,53.39,224,0.875,bilinear,+6.240,+2.199,+9 -tf_efficientnet_b1,84.914,15.086,96.362,3.638,7.79,240,0.882,bicubic,+6.086,+2.164,+2 -mobilevitv2_100,84.905,15.095,96.390,3.610,4.90,256,0.888,bicubic,+6.819,+2.230,+39 -legacy_seresnext50_32x4d,84.899,15.101,96.428,3.572,27.56,224,0.875,bilinear,+5.823,+1.994,-11 -hrnet_w44,84.886,15.114,96.437,3.563,67.06,224,0.875,bilinear,+5.990,+2.067,-3 -regnetx_080,84.867,15.133,96.428,3.572,39.57,224,0.875,bicubic,+5.665,+1.876,-19 -gluon_resnet50_v1s,84.858,15.142,96.441,3.559,25.68,224,0.875,bicubic,+6.152,+2.203,+2 -res2net50_26w_8s,84.847,15.153,96.355,3.645,48.40,224,0.875,bilinear,+5.895,+2.049,-8 -levit_128,84.839,15.161,96.353,3.647,9.21,224,0.900,bicubic,+6.357,+2.341,+10 -vit_tiny_patch16_384,84.832,15.168,96.712,3.288,5.79,384,1.000,bicubic,+6.402,+2.168,+14 -gluon_resnet50_v1d,84.830,15.170,96.398,3.602,25.58,224,0.875,bicubic,+5.760,+1.932,-16 -dla60_res2next,84.826,15.174,96.411,3.589,17.03,224,0.875,bilinear,+6.370,+2.265,+9 -mixnet_l,84.824,15.176,96.328,3.672,7.33,224,0.875,bicubic,+5.848,+2.150,-15 -tv_resnet152,84.818,15.182,96.221,3.779,60.19,224,0.875,bilinear,+6.498,+2.187,+16 -dla102x,84.807,15.193,96.548,3.452,26.31,224,0.875,bilinear,+6.295,+2.320,+1 -dla60_res2net,84.803,15.197,96.479,3.521,20.85,224,0.875,bilinear,+6.345,+2.283,+4 -pit_xs_224,84.794,15.206,96.494,3.506,10.62,224,0.900,bicubic,+6.604,+2.328,+20 -xception41,84.792,15.208,96.417,3.583,26.97,299,0.903,bicubic,+6.276,+2.137,-3 -regnetx_064,84.779,15.221,96.492,3.508,26.21,224,0.875,bicubic,+5.705,+2.032,-25 -hrnet_w40,84.741,15.259,96.554,3.446,57.56,224,0.875,bilinear,+5.819,+2.084,-19 -res2net50_26w_6s,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.156,+2.157,-7 -repvgg_b2,84.722,15.278,96.469,3.531,89.02,224,0.875,bilinear,+5.928,+2.051,-16 -resmlp_12_distilled_224,84.715,15.285,96.221,3.779,15.35,224,0.875,bicubic,+6.769,+2.661,+27 -legacy_seresnet152,84.702,15.298,96.415,3.585,66.82,224,0.875,bilinear,+6.050,+2.045,-12 -cs3darknet_m,84.692,15.308,96.492,3.508,9.31,288,0.950,bicubic,+7.066,+2.478,+39 -hrnet_w32,84.655,15.345,96.411,3.589,41.23,224,0.875,bilinear,+6.203,+2.223,-4 -selecsls60b,84.651,15.349,96.304,3.696,32.77,224,0.875,bicubic,+6.247,+2.132,-2 -bat_resnext26ts,84.636,15.364,96.268,3.732,10.73,256,0.900,bicubic,+6.388,+2.172,+5 -tf_efficientnetv2_b0,84.617,15.383,96.274,3.726,7.14,224,0.875,bicubic,+6.265,+2.248,0 -regnetx_040,84.604,15.396,96.379,3.621,22.12,224,0.875,bicubic,+6.116,+2.141,-13 -efficientnet_b1,84.604,15.396,96.336,3.664,7.79,256,1.000,bicubic,+5.816,+1.990,-24 -vit_relpos_base_patch32_plus_rpn_256,84.593,15.407,96.010,3.990,119.42,256,0.900,bicubic,+5.107,+1.870,-68 -efficientnet_es,84.581,15.419,96.317,3.683,5.44,224,0.875,bicubic,+6.523,+2.373,+10 -hrnet_w30,84.576,15.424,96.383,3.617,37.71,224,0.875,bilinear,+6.378,+2.159,+2 -tf_mixnet_l,84.564,15.437,96.242,3.758,7.33,224,0.875,bicubic,+5.786,+2.244,-27 -wide_resnet101_2,84.549,15.451,96.353,3.647,126.89,224,0.875,bilinear,+5.697,+2.065,-33 -dla60x,84.521,15.479,96.289,3.711,17.35,224,0.875,bilinear,+6.293,+2.265,-2 -legacy_seresnet101,84.506,15.494,96.330,3.670,49.33,224,0.875,bilinear,+6.126,+2.068,-11 -cs3darknet_focus_m,84.482,15.518,96.422,3.578,9.30,288,0.950,bicubic,+7.200,+2.450,+42 -resnet26t,84.467,15.533,96.217,3.783,16.01,256,0.940,bicubic,+6.603,+2.375,+13 -coat_lite_tiny,84.459,15.541,96.370,3.630,5.72,224,0.900,bicubic,+6.943,+2.456,+30 -tf_efficientnet_em,84.448,15.552,96.183,3.817,6.90,240,0.882,bicubic,+6.322,+2.137,-2 -repvgg_b1,84.416,15.584,96.215,3.785,57.42,224,0.875,bilinear,+6.048,+2.121,-15 -efficientnet_b1_pruned,84.397,15.603,96.140,3.860,6.33,240,0.882,bicubic,+6.153,+2.306,-10 -res2net50_26w_4s,84.363,15.637,96.080,3.920,25.70,224,0.875,bilinear,+6.401,+2.228,+4 -hardcorenas_f,84.329,15.671,96.025,3.975,8.20,224,0.875,bilinear,+6.227,+2.223,-5 -res2net50_14w_8s,84.305,15.695,96.072,3.929,25.06,224,0.875,bilinear,+6.161,+2.220,-8 -selecsls60,84.297,15.703,96.101,3.899,30.67,224,0.875,bicubic,+6.313,+2.269,-1 -mobilevit_s,84.269,15.731,96.266,3.734,5.58,256,0.900,bicubic,+5.959,+2.114,-18 -regnetx_032,84.243,15.757,96.251,3.749,15.30,224,0.875,bicubic,+6.059,+2.163,-12 -res2next50,84.237,15.763,95.999,4.001,24.67,224,0.875,bilinear,+5.979,+2.111,-19 -gluon_resnet50_v1c,84.211,15.789,96.163,3.837,25.58,224,0.875,bicubic,+6.203,+2.173,-6 -dla102,84.190,15.810,96.208,3.792,33.27,224,0.875,bilinear,+6.162,+2.258,-8 -gcresnext26ts,84.171,15.829,96.084,3.916,10.48,256,0.900,bicubic,+6.357,+2.248,+5 -rexnet_100,84.168,15.832,96.255,3.745,4.80,224,0.875,bicubic,+6.308,+2.381,-1 -seresnext26ts,84.147,15.853,96.069,3.931,10.39,256,0.900,bicubic,+6.289,+2.279,-1 -tf_inception_v3,84.139,15.861,95.918,4.082,23.83,299,0.875,bicubic,+6.287,+2.278,0 -res2net50_48w_2s,84.128,15.872,95.965,4.035,25.29,224,0.875,bilinear,+6.604,+2.415,+12 -resnet34d,84.096,15.904,95.978,4.022,21.82,224,0.875,bicubic,+6.980,+2.596,+27 -xcit_tiny_12_p16_224,84.094,15.906,96.234,3.766,6.72,224,1.000,bicubic,+6.970,+2.522,+25 -tf_efficientnet_lite2,84.085,15.915,96.076,3.924,6.09,260,0.890,bicubic,+6.619,+2.318,+11 -poolformer_s12,84.036,15.964,96.163,3.837,11.92,224,0.900,bicubic,+6.798,+2.657,+21 -efficientnet_b0,84.032,15.968,95.958,4.042,5.29,224,0.875,bicubic,+6.332,+2.426,-2 -crossvit_9_dagger_240,84.015,15.985,96.084,3.916,8.78,240,0.875,bicubic,+7.037,+2.470,+27 -tf_efficientnet_cc_b0_8e,83.970,16.030,96.074,3.926,24.01,224,0.875,bicubic,+6.070,+2.416,-13 -hardcorenas_e,83.966,16.034,95.903,4.097,8.07,224,0.875,bilinear,+6.180,+2.199,-6 -gmixer_24_224,83.966,16.034,95.854,4.146,24.72,224,0.875,bicubic,+5.930,+2.184,-23 -regnety_016,83.957,16.043,96.005,3.995,11.20,224,0.875,bicubic,+6.101,+2.285,-12 -tv_resnext50_32x4d,83.957,16.043,95.967,4.033,25.03,224,0.875,bilinear,+6.339,+2.267,-4 -gluon_resnet50_v1b,83.936,16.064,96.014,3.986,25.56,224,0.875,bicubic,+6.352,+2.294,-2 -densenet161,83.906,16.094,96.014,3.986,28.68,224,0.875,bicubic,+6.552,+2.378,+6 -adv_inception_v3,83.897,16.103,95.933,4.067,23.83,299,0.875,bicubic,+6.319,+2.195,-3 -mobilenetv2_120d,83.889,16.111,95.909,4.091,5.83,224,0.875,bicubic,+6.599,+2.409,+6 -seresnext26t_32x4d,83.874,16.126,95.935,4.065,16.81,224,0.875,bicubic,+5.906,+2.187,-26 -tv_resnet101,83.853,16.148,95.892,4.108,44.55,224,0.875,bilinear,+6.473,+2.348,+1 -tinynet_a,83.833,16.167,95.817,4.183,6.19,192,0.875,bicubic,+6.185,+2.281,-14 -inception_v3,83.763,16.237,95.877,4.123,23.83,299,0.875,bicubic,+6.325,+2.401,-3 -hardcorenas_d,83.759,16.241,95.736,4.264,7.50,224,0.875,bilinear,+6.329,+2.252,-3 -seresnext26d_32x4d,83.750,16.250,95.852,4.148,16.81,224,0.875,bicubic,+6.144,+2.246,-13 -xcit_nano_12_p8_224_dist,83.731,16.269,95.958,4.042,3.05,224,1.000,bicubic,+7.403,+2.864,+31 -dla60,83.720,16.280,95.926,4.074,22.04,224,0.875,bilinear,+6.698,+2.606,+9 -eca_resnext26ts,83.705,16.295,95.948,4.052,10.30,256,0.900,bicubic,+6.247,+2.380,-9 -repvgg_b1g4,83.697,16.303,96.025,3.975,39.97,224,0.875,bilinear,+6.109,+2.195,-16 -convmixer_1024_20_ks9_p14,83.686,16.314,95.894,4.106,24.38,224,0.960,bicubic,+6.744,+2.536,+10 -legacy_seresnet50,83.665,16.335,95.978,4.022,28.09,224,0.875,bilinear,+6.033,+2.228,-22 -tf_efficientnet_b0_ap,83.652,16.348,95.781,4.219,5.29,224,0.875,bicubic,+6.564,+2.523,+2 -tf_efficientnet_cc_b0_4e,83.639,16.361,95.743,4.257,13.31,224,0.875,bicubic,+6.329,+2.403,-9 -skresnet34,83.635,16.365,95.928,4.072,22.28,224,0.875,bicubic,+6.731,+2.608,+8 -resmlp_12_224,83.573,16.427,95.762,4.238,15.35,224,0.875,bicubic,+6.917,+2.582,+13 -mobilenetv3_large_100_miil,83.558,16.442,95.452,4.548,5.48,224,0.875,bilinear,+5.636,+2.532,-39 -densenet201,83.554,16.446,95.811,4.189,20.01,224,0.875,bicubic,+6.266,+2.331,-11 -mixnet_m,83.526,16.474,95.685,4.315,5.01,224,0.875,bicubic,+6.264,+2.263,-10 -legacy_seresnext26_32x4d,83.522,16.478,95.717,4.283,16.79,224,0.875,bicubic,+6.418,+2.401,-6 -gernet_s,83.517,16.483,95.796,4.204,8.17,224,0.875,bilinear,+6.601,+2.662,+1 -tf_efficientnet_b0,83.511,16.489,95.704,4.296,5.29,224,0.875,bicubic,+6.671,+2.486,+2 -hrnet_w18,83.502,16.498,95.909,4.091,21.30,224,0.875,bilinear,+6.742,+2.465,+4 -densenetblur121d,83.470,16.530,95.817,4.183,8.00,224,0.875,bicubic,+6.890,+2.629,+9 -resnext26ts,83.464,16.536,95.726,4.274,10.30,256,0.900,bicubic,+6.684,+2.594,+1 -selecsls42b,83.460,16.540,95.743,4.257,32.46,224,0.875,bicubic,+6.282,+2.351,-15 -hardcorenas_c,83.336,16.664,95.713,4.287,5.52,224,0.875,bilinear,+6.284,+2.553,-11 -tf_efficientnet_lite1,83.332,16.668,95.640,4.360,5.42,240,0.882,bicubic,+6.694,+2.416,+2 -regnetx_016,83.193,16.807,95.743,4.257,9.19,224,0.875,bicubic,+6.251,+2.319,-9 -dpn68,83.184,16.816,95.600,4.400,12.61,224,0.875,bicubic,+6.874,+2.622,+10 -mobilenetv2_140,83.180,16.820,95.687,4.313,6.11,224,0.875,bicubic,+6.668,+2.689,+5 -tf_efficientnet_es,83.176,16.824,95.585,4.415,5.44,224,0.875,bicubic,+6.578,+2.381,0 -tf_mixnet_m,83.176,16.824,95.459,4.541,5.01,224,0.875,bicubic,+6.230,+2.307,-14 -xcit_nano_12_p16_384_dist,83.174,16.826,95.751,4.249,3.05,384,1.000,bicubic,+7.718,+3.061,+22 -ese_vovnet19b_dw,83.109,16.890,95.775,4.225,6.54,224,0.875,bicubic,+6.315,+2.509,-10 -levit_128s,83.058,16.942,95.531,4.469,7.78,224,0.900,bicubic,+6.544,+2.661,-1 -resnet26d,83.056,16.944,95.610,4.390,16.01,224,0.875,bicubic,+6.354,+2.458,-9 -repvgg_a2,83.001,16.999,95.593,4.407,28.21,224,0.875,bilinear,+6.541,+2.583,-1 -tv_resnet50,82.956,17.044,95.474,4.526,25.56,224,0.875,bilinear,+6.822,+2.606,+2 -hardcorenas_b,82.866,17.134,95.390,4.610,5.18,224,0.875,bilinear,+6.330,+2.636,-6 -densenet121,82.826,17.174,95.580,4.420,7.98,224,0.875,bicubic,+7.246,+2.932,+10 -mobilevitv2_075,82.806,17.194,95.572,4.428,2.87,256,0.888,bicubic,+7.198,+2.814,+8 -vit_tiny_r_s16_p8_384,82.687,17.313,95.849,4.151,6.36,384,1.000,bicubic,+6.735,+2.587,+1 -densenet169,82.683,17.317,95.597,4.402,14.15,224,0.875,bicubic,+6.779,+2.573,+2 -mixnet_s,82.527,17.473,95.356,4.644,4.13,224,0.875,bicubic,+6.531,+2.556,-3 -vit_small_patch32_224,82.514,17.486,95.664,4.336,22.88,224,0.900,bicubic,+6.524,+2.396,-3 -regnety_008,82.493,17.508,95.491,4.509,6.26,224,0.875,bicubic,+6.179,+2.421,-8 -efficientnet_lite0,82.371,17.629,95.284,4.716,4.65,224,0.875,bicubic,+6.903,+2.768,+6 -resnest14d,82.354,17.646,95.346,4.654,10.61,224,0.875,bilinear,+6.846,+2.822,+4 -hardcorenas_a,82.324,17.676,95.290,4.710,5.26,224,0.875,bilinear,+6.394,+2.780,-5 -efficientnet_es_pruned,82.292,17.708,95.303,4.697,5.44,224,0.875,bicubic,+7.292,+2.861,+15 -mobilenetv3_rw,82.266,17.734,95.234,4.766,5.48,224,0.875,bicubic,+6.632,+2.526,-3 -semnasnet_100,82.251,17.749,95.226,4.774,3.89,224,0.875,bicubic,+6.801,+2.626,+4 -mobilenetv3_large_100,82.170,17.830,95.196,4.804,5.48,224,0.875,bicubic,+6.394,+2.656,-7 -resnet34,82.144,17.855,95.128,4.872,21.80,224,0.875,bilinear,+7.032,+2.844,+7 -vit_tiny_patch16_224,82.076,17.924,95.482,4.518,5.72,224,0.900,bicubic,+6.612,+2.638,-1 -mobilenetv2_110d,82.070,17.930,95.079,4.921,4.52,224,0.875,bicubic,+7.034,+2.887,+7 -tf_mixnet_s,82.040,17.960,95.121,4.879,4.13,224,0.875,bicubic,+6.388,+2.495,-10 -repvgg_b0,82.006,17.994,95.098,4.902,15.82,224,0.875,bilinear,+6.852,+2.682,+1 -deit_tiny_distilled_patch16_224,81.993,18.007,95.138,4.862,5.91,224,0.900,bicubic,+7.481,+3.248,+17 -mixer_b16_224,81.987,18.014,94.449,5.551,59.88,224,0.875,bicubic,+5.377,+2.219,-30 -pit_ti_distilled_224,81.969,18.031,95.147,4.853,5.10,224,0.900,bicubic,+7.435,+3.051,+14 -hrnet_w18_small_v2,81.961,18.039,95.164,4.836,15.60,224,0.875,bilinear,+6.851,+2.748,0 -tf_efficientnet_lite0,81.959,18.041,95.162,4.838,4.65,224,0.875,bicubic,+7.127,+2.988,+5 -resnet26,81.957,18.043,95.252,4.748,16.00,224,0.875,bicubic,+6.657,+2.672,-7 -edgenext_x_small,81.897,18.103,95.032,4.968,2.34,256,0.900,bicubic,+7.033,+2.732,+2 -tinynet_b,81.871,18.129,94.878,5.122,3.73,188,0.875,bicubic,+6.897,+2.696,0 -tf_mobilenetv3_large_100,81.848,18.152,95.066,4.934,5.48,224,0.875,bilinear,+6.336,+2.460,-16 -tv_densenet121,81.722,18.278,95.034,4.966,7.98,224,0.875,bicubic,+6.982,+2.886,+2 -regnety_006,81.703,18.297,95.121,4.879,6.06,224,0.875,bicubic,+6.451,+2.589,-11 -dla34,81.660,18.340,94.876,5.124,15.74,224,0.875,bilinear,+7.036,+2.804,+3 -xcit_nano_12_p8_224,81.645,18.355,95.267,4.733,3.05,224,1.000,bicubic,+7.729,+3.099,+11 -crossvit_9_240,81.613,18.387,94.974,5.026,8.55,240,0.875,bicubic,+7.653,+3.010,+9 -mobilevit_xs,81.574,18.426,95.030,4.970,2.32,256,0.900,bicubic,+6.940,+2.684,-1 -fbnetc_100,81.559,18.441,94.959,5.041,5.57,224,0.875,bilinear,+6.443,+2.573,-14 -legacy_seresnet34,81.538,18.462,94.897,5.103,21.96,224,0.875,bilinear,+6.728,+2.771,-6 -gluon_resnet34_v1b,81.498,18.503,94.808,5.192,21.80,224,0.875,bicubic,+6.906,+2.820,-2 -regnetx_008,81.481,18.520,95.064,4.936,7.26,224,0.875,bicubic,+6.447,+2.724,-13 -mnasnet_100,81.451,18.549,94.904,5.096,4.38,224,0.875,bicubic,+6.801,+2.790,-7 -vgg19_bn,81.442,18.558,94.767,5.233,143.68,224,0.875,bilinear,+7.228,+2.923,-2 -convit_tiny,81.126,18.874,95.047,4.953,5.71,224,0.875,bicubic,+8.012,+3.327,+10 -crossvit_tiny_240,81.096,18.904,94.985,5.015,7.01,240,0.875,bicubic,+7.758,+3.071,+6 -spnasnet_100,80.880,19.119,94.530,5.470,4.42,224,0.875,bilinear,+6.790,+2.714,-4 -ghostnet_100,80.703,19.297,94.291,5.709,5.18,224,0.875,bilinear,+6.723,+2.833,-3 -regnety_004,80.650,19.350,94.688,5.312,4.34,224,0.875,bicubic,+6.626,+2.932,-5 -skresnet18,80.639,19.361,94.376,5.624,11.96,224,0.875,bicubic,+7.605,+3.210,+6 -regnetx_006,80.633,19.367,94.526,5.474,6.20,224,0.875,bicubic,+6.777,+2.854,-3 -pit_ti_224,80.614,19.386,94.620,5.380,4.85,224,0.900,bicubic,+7.702,+3.214,+7 -swsl_resnet18,80.573,19.427,94.743,5.256,11.69,224,0.875,bilinear,+7.299,+3.007,+1 -vgg16_bn,80.556,19.444,94.592,5.408,138.37,224,0.875,bilinear,+7.206,+3.088,-3 -semnasnet_075,80.475,19.525,94.319,5.681,2.91,224,0.875,bicubic,+7.501,+3.185,+2 -resnet18d,80.392,19.608,94.246,5.754,11.71,224,0.875,bicubic,+8.134,+3.558,+10 -tv_resnet34,80.389,19.611,94.436,5.564,21.80,224,0.875,bilinear,+7.081,+3.012,-4 -mobilenetv2_100,80.236,19.764,94.193,5.807,3.50,224,0.875,bicubic,+7.280,+3.183,0 -xcit_nano_12_p16_224_dist,80.214,19.786,94.355,5.645,3.05,224,1.000,bicubic,+7.912,+3.493,+6 -vit_base_patch32_224_sam,80.208,19.792,93.821,6.179,88.22,224,0.900,bicubic,+6.516,+2.809,-11 -ssl_resnet18,80.099,19.901,94.590,5.410,11.69,224,0.875,bilinear,+7.495,+3.166,-1 -tf_mobilenetv3_large_075,80.093,19.907,94.184,5.816,3.99,224,0.875,bilinear,+6.653,+2.836,-12 -deit_tiny_patch16_224,80.018,19.982,94.447,5.553,5.72,224,0.900,bicubic,+7.844,+3.333,+5 -hrnet_w18_small,79.557,20.443,93.902,6.098,13.19,224,0.875,bilinear,+7.221,+3.222,0 -vgg19,79.476,20.524,93.870,6.130,143.67,224,0.875,bilinear,+7.110,+2.998,-3 -regnetx_004,79.429,20.571,93.853,6.147,5.16,224,0.875,bicubic,+7.033,+3.015,-5 -resnet14t,79.243,20.757,93.603,6.397,10.08,224,0.950,bilinear,+6.887,+3.263,-4 -tf_mobilenetv3_large_minimal_100,79.228,20.772,93.693,6.307,3.92,224,0.875,bilinear,+6.978,+3.073,-1 -legacy_seresnet18,79.155,20.845,93.781,6.219,11.78,224,0.875,bicubic,+7.415,+3.451,+3 -vgg16,79.034,20.966,93.646,6.354,138.36,224,0.875,bilinear,+7.444,+3.264,+4 -vgg13_bn,79.006,20.994,93.661,6.339,133.05,224,0.875,bilinear,+7.408,+3.285,+2 -vit_tiny_r_s16_p8_224,78.993,21.007,93.898,6.102,6.34,224,0.900,bicubic,+7.199,+3.080,-1 -lcnet_100,78.912,21.088,93.561,6.439,2.95,224,0.875,bicubic,+6.802,+3.183,-4 -edgenext_xx_small,78.698,21.302,93.503,6.497,1.33,256,0.900,bicubic,+7.592,+3.471,+2 -tinynet_c,78.436,21.564,93.140,6.860,2.46,184,0.875,bicubic,+7.208,+3.392,0 -gluon_resnet18_v1b,78.376,21.624,93.136,6.864,11.69,224,0.875,bicubic,+7.538,+3.374,+1 -mobilevitv2_050,78.124,21.876,93.573,6.426,1.37,256,0.888,bicubic,+7.984,+3.643,+3 +vit_base_patch16_clip_384.laion2b_ft_in1k,90.219,9.781,98.704,1.296,86.86,384,1.000,bicubic,+3.599,+0.694,+17 +deit3_huge_patch14_224_in21ft1k,90.215,9.785,98.638,1.362,632.13,224,1.000,bicubic,+3.031,+0.378,-1 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,90.211,9.789,98.653,1.347,86.86,384,0.950,bicubic,+3.177,+0.473,+3 +tf_efficientnetv2_l.in21k_ft_in1k,90.207,9.793,98.717,1.283,118.52,480,1.000,bicubic,+3.401,+0.583,+8 +vit_large_patch16_384.augreg_in21k_ft_in1k,90.196,9.804,98.661,1.339,304.72,384,1.000,bicubic,+3.116,+0.361,-2 +cait_m48_448,90.196,9.804,98.484,1.516,356.46,448,1.000,bicubic,+3.712,+0.730,+17 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,90.187,9.813,98.585,1.415,86.86,384,1.000,bicubic,+2.969,+0.550,-7 +vit_huge_patch14_clip_224.laion2b_ft_in1k,90.172,9.828,98.542,1.458,632.05,224,1.000,bicubic,+2.578,+0.322,-14 +volo_d3_448,90.168,9.832,98.550,1.450,86.63,448,1.000,bicubic,+3.674,+0.840,+12 +swinv2_large_window12to24_192to384_22kft1k,90.157,9.843,98.608,1.392,196.74,384,1.000,bicubic,+2.699,+0.356,-13 +beit_large_patch16_224.in22k_ft_in22k_in1k,90.151,9.849,98.723,1.277,304.43,224,0.900,bicubic,+2.675,+0.419,-16 +vit_large_patch14_clip_224.laion2b_ft_in1k,90.106,9.894,98.561,1.439,304.20,224,1.000,bicubic,+2.814,+0.315,-13 +tf_efficientnet_b7.ns_jft_in1k,90.100,9.900,98.614,1.386,66.35,600,0.949,bicubic,+3.260,+0.520,-2 +convnext_xlarge.fb_in22k_ft_in1k,90.066,9.934,98.619,1.381,350.20,288,1.000,bicubic,+2.728,+0.291,-16 +dm_nfnet_f6,90.046,9.954,98.546,1.454,438.36,576,0.956,bicubic,+3.902,+0.816,+19 +cait_m36_384,90.046,9.954,98.493,1.507,271.22,384,1.000,bicubic,+3.992,+0.763,+24 +swin_large_patch4_window12_384,90.027,9.973,98.657,1.343,196.74,384,1.000,bicubic,+2.879,+0.423,-15 +tf_efficientnetv2_m.in21k_ft_in1k,90.023,9.977,98.663,1.337,54.14,480,1.000,bicubic,+4.019,+0.721,+24 +deit3_large_patch16_224_in21ft1k,89.999,10.001,98.661,1.339,304.37,224,1.000,bicubic,+3.021,+0.423,-11 +swin_base_patch4_window12_384,89.995,10.005,98.695,1.304,87.90,384,1.000,bicubic,+3.563,+0.637,+5 +vit_base_patch16_384.augreg_in21k_ft_in1k,89.989,10.011,98.678,1.322,86.86,384,1.000,bicubic,+3.983,+0.678,+20 +maxvit_base_tf_512.in1k,89.980,10.020,98.435,1.565,119.88,512,1.000,bicubic,+3.382,+0.515,-3 +swinv2_large_window12to16_192to256_22kft1k,89.920,10.080,98.508,1.492,196.74,256,0.900,bicubic,+2.984,+0.400,-13 +convnext_small.fb_in22k_ft_in1k_384,89.916,10.084,98.680,1.319,50.22,384,1.000,bicubic,+4.138,+0.788,+29 +efficientnet_b5.in12k_ft_in1k,89.903,10.097,98.570,1.431,30.39,448,1.000,bicubic,+4.015,+0.838,+20 +xcit_large_24_p8_384_dist,89.886,10.114,98.384,1.616,188.93,384,1.000,bicubic,+3.886,+0.698,+17 +deit3_base_patch16_384_in21ft1k,89.884,10.116,98.602,1.399,86.88,384,1.000,bicubic,+3.140,+0.490,-10 +volo_d5_224,89.882,10.118,98.493,1.507,295.46,224,0.960,bicubic,+3.814,+0.915,+10 +swinv2_base_window12to16_192to256_22kft1k,89.876,10.124,98.657,1.343,87.92,256,0.900,bicubic,+3.602,+0.761,0 +convnext_large.fb_in22k_ft_in1k,89.871,10.129,98.597,1.403,197.77,288,1.000,bicubic,+2.855,+0.391,-23 +convnext_base.fb_in22k_ft_in1k,89.856,10.144,98.689,1.311,88.59,288,1.000,bicubic,+3.576,+0.599,-3 +cait_s36_384,89.844,10.156,98.427,1.573,68.37,384,1.000,bicubic,+4.384,+0.947,+34 +xcit_medium_24_p8_384_dist,89.811,10.188,98.362,1.638,84.32,384,1.000,bicubic,+3.995,+0.770,+15 +volo_d4_224,89.809,10.191,98.424,1.576,192.96,224,0.960,bicubic,+3.937,+0.956,+12 +swin_large_patch4_window7_224,89.796,10.204,98.640,1.360,196.53,224,0.900,bicubic,+3.477,+0.744,-9 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,89.794,10.206,98.514,1.486,329.09,384,1.000,bicubic,+3.610,+0.596,-4 +maxvit_large_tf_512.in1k,89.794,10.206,98.328,1.672,212.33,512,1.000,bicubic,+3.276,+0.444,-16 +volo_d2_384,89.784,10.216,98.399,1.601,58.87,384,1.000,bicubic,+3.748,+0.827,+2 +tf_efficientnet_b6.ns_jft_in1k,89.782,10.218,98.510,1.490,43.04,528,0.942,bicubic,+3.330,+0.628,-15 +tf_efficientnetv2_xl.in21k_ft_in1k,89.773,10.227,98.294,1.706,208.12,512,1.000,bicubic,+3.025,+0.276,-24 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,89.747,10.253,98.580,1.420,86.53,224,0.900,bicubic,+3.267,+0.532,-18 +xcit_small_24_p8_384_dist,89.739,10.261,98.422,1.578,47.63,384,1.000,bicubic,+4.183,+0.850,+18 +vit_base_patch8_224.augreg2_in21k_ft_in1k,89.728,10.272,98.510,1.490,86.58,224,0.900,bicubic,+3.516,+0.678,-12 +vit_base_patch16_clip_384.openai_ft_in1k,89.709,10.291,98.510,1.490,86.86,384,1.000,bicubic,+3.503,+0.636,-12 +volo_d1_384,89.698,10.302,98.292,1.708,26.78,384,1.000,bicubic,+4.448,+1.096,+34 +deit3_large_patch16_384,89.679,10.321,98.392,1.608,304.76,384,1.000,bicubic,+3.869,+0.796,+4 +xcit_large_24_p16_384_dist,89.662,10.338,98.401,1.599,189.10,384,1.000,bicubic,+3.908,+0.863,+7 +tf_efficientnet_b5.ns_jft_in1k,89.651,10.349,98.482,1.518,30.39,456,0.934,bicubic,+3.563,+0.730,-11 +tf_efficientnet_b8.ap_in1k,89.581,10.419,98.305,1.695,87.41,672,0.954,bicubic,+4.211,+0.915,+25 +maxvit_base_tf_384.in1k,89.579,10.421,98.324,1.676,119.65,384,1.000,bicubic,+3.285,+0.520,-23 +maxvit_tiny_tf_512.in1k,89.564,10.436,98.335,1.665,31.05,512,1.000,bicubic,+3.902,+0.755,+7 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,89.559,10.441,98.414,1.586,86.57,224,0.950,bicubic,+3.389,+0.660,-18 +dm_nfnet_f4,89.557,10.443,98.303,1.697,316.07,512,0.951,bicubic,+3.843,+0.783,+3 +volo_d3_224,89.555,10.445,98.375,1.625,86.33,224,0.960,bicubic,+4.147,+1.095,+15 +maxvit_large_tf_384.in1k,89.555,10.445,98.187,1.813,212.03,384,1.000,bicubic,+3.319,+0.497,-24 +tf_efficientnetv2_l.in1k,89.549,10.451,98.339,1.661,118.52,480,1.000,bicubic,+3.879,+0.865,+1 +flexivit_large.1200ep_in1k,89.540,10.460,98.418,1.582,304.36,240,0.950,bicubic,+3.896,+0.876,+2 +xcit_small_12_p8_384_dist,89.517,10.483,98.305,1.695,26.21,384,1.000,bicubic,+4.429,+1.023,+33 +xcit_large_24_p8_224_dist,89.517,10.483,98.224,1.776,188.93,224,1.000,bicubic,+4.121,+0.814,+13 +flexivit_large.600ep_in1k,89.510,10.490,98.394,1.606,304.36,240,0.950,bicubic,+3.972,+0.902,+1 +cait_s24_384,89.502,10.498,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+35 +dm_nfnet_f3,89.485,10.515,98.399,1.601,254.92,416,0.940,bicubic,+3.963,+0.937,+2 +xcit_medium_24_p16_384_dist,89.468,10.532,98.296,1.704,84.40,384,1.000,bicubic,+4.056,+0.890,+6 +dm_nfnet_f5,89.461,10.539,98.324,1.676,377.21,544,0.954,bicubic,+3.647,+0.836,-15 +deit3_base_patch16_224_in21ft1k,89.457,10.543,98.557,1.443,86.59,224,1.000,bicubic,+3.743,+0.813,-10 +maxvit_small_tf_512.in1k,89.451,10.549,98.350,1.650,69.13,512,1.000,bicubic,+3.363,+0.592,-30 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,89.448,10.552,98.401,1.599,88.34,448,1.000,bicubic,+3.664,+0.767,-15 +vit_base_patch16_224.augreg2_in21k_ft_in1k,89.446,10.554,98.439,1.561,86.57,224,0.900,bicubic,+4.340,+1.059,+20 +deit_base_distilled_patch16_384,89.429,10.571,98.441,1.559,87.63,384,1.000,bicubic,+4.007,+1.109,-1 +tf_efficientnet_b7.ap_in1k,89.429,10.571,98.347,1.653,66.35,600,0.949,bicubic,+4.309,+1.096,+17 +vit_base_patch8_224.augreg_in21k_ft_in1k,89.427,10.573,98.486,1.514,86.58,224,0.900,bicubic,+3.631,+0.696,-20 +vit_base_patch16_clip_224.laion2b_ft_in1k,89.427,10.573,98.473,1.527,86.57,224,1.000,bicubic,+3.959,+0.897,-7 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,89.410,10.590,98.401,1.599,86.57,224,0.950,bicubic,+3.480,+0.677,-29 +beit_base_patch16_224.in22k_ft_in22k_in1k,89.401,10.598,98.525,1.475,86.53,224,0.900,bicubic,+4.165,+0.869,+7 +regnetz_e8,89.382,10.618,98.459,1.542,57.70,320,1.000,bicubic,+4.352,+1.195,+22 +deit3_small_patch16_384_in21ft1k,89.367,10.633,98.384,1.616,22.21,384,1.000,bicubic,+4.543,+0.898,+32 +tf_efficientnetv2_m.in1k,89.355,10.645,98.330,1.670,54.14,480,1.000,bicubic,+4.147,+0.962,+5 +tf_efficientnet_b8.ra_in1k,89.355,10.645,98.303,1.697,87.41,672,0.954,bicubic,+3.985,+1.009,-4 +vit_medium_patch16_gap_384.in12k_ft_in1k,89.342,10.658,98.493,1.507,39.03,384,0.950,bicubic,+3.806,+0.859,-18 +tf_efficientnet_b6.ap_in1k,89.342,10.658,98.281,1.719,43.04,528,0.942,bicubic,+4.554,+1.143,+31 +volo_d2_224,89.327,10.673,98.209,1.791,58.68,224,0.960,bicubic,+4.131,+1.021,+4 +vit_large_patch16_224.augreg_in21k_ft_in1k,89.314,10.686,98.392,1.608,304.33,224,0.900,bicubic,+3.472,+0.568,-35 +convnext_small.fb_in22k_ft_in1k,89.305,10.694,98.360,1.640,50.22,288,1.000,bicubic,+4.043,+0.676,-6 +tf_efficientnet_b4.ns_jft_in1k,89.305,10.694,98.347,1.653,19.34,380,0.922,bicubic,+4.143,+0.877,+2 +flexivit_large.300ep_in1k,89.303,10.697,98.324,1.676,304.36,240,0.950,bicubic,+4.023,+0.884,-9 +xcit_small_24_p16_384_dist,89.299,10.701,98.330,1.670,47.67,384,1.000,bicubic,+4.201,+1.020,+4 +xcit_medium_24_p8_224_dist,89.293,10.707,98.187,1.813,84.32,224,1.000,bicubic,+4.221,+0.933,+6 +deit3_huge_patch14_224,89.214,10.786,98.166,1.834,632.13,224,0.900,bicubic,+4.010,+0.808,-4 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,89.201,10.799,98.356,1.644,88.30,384,1.000,bicubic,+3.829,+0.692,-17 +xcit_small_24_p8_224_dist,89.201,10.799,98.243,1.757,47.63,224,1.000,bicubic,+4.325,+1.055,+15 +xcit_small_12_p16_384_dist,89.197,10.803,98.219,1.781,26.25,384,1.000,bicubic,+4.491,+1.101,+22 +vit_base_patch16_clip_224.openai_ft_in1k,89.154,10.846,98.273,1.727,86.57,224,0.900,bicubic,+3.874,+0.867,-17 +swin_base_patch4_window7_224,89.145,10.855,98.429,1.571,87.77,224,0.900,bicubic,+3.893,+0.867,-15 +eca_nfnet_l2,89.141,10.859,98.315,1.685,56.72,384,1.000,bicubic,+4.443,+1.051,+20 +cait_xs24_384,89.139,10.861,98.290,1.710,26.67,384,1.000,bicubic,+5.077,+1.402,+65 +maxvit_tiny_tf_384.in1k,89.120,10.880,98.211,1.789,30.98,384,1.000,bicubic,+4.014,+0.677,-8 +ig_resnext101_32x48d,89.120,10.880,98.130,1.870,828.41,224,0.875,bilinear,+3.692,+0.558,-29 +ig_resnext101_32x32d,89.111,10.889,98.181,1.819,468.53,224,0.875,bilinear,+4.017,+0.743,-7 +maxvit_small_tf_384.in1k,89.103,10.897,98.164,1.836,69.02,384,1.000,bicubic,+3.569,+0.700,-36 +tf_efficientnet_b7.ra_in1k,89.086,10.914,98.183,1.817,66.35,600,0.949,bicubic,+4.150,+0.979,+1 +ecaresnet269d,89.069,10.931,98.234,1.766,102.09,352,1.000,bicubic,+4.093,+1.008,-2 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,89.051,10.949,98.285,1.714,88.30,384,0.950,bicubic,+3.839,+0.883,-20 +xcit_large_24_p16_224_dist,89.041,10.959,98.064,1.937,189.10,224,1.000,bicubic,+4.123,+0.931,0 +resmlp_big_24_224_in22ft1k,89.011,10.989,98.215,1.785,129.14,224,0.875,bicubic,+4.617,+1.333,+33 +dm_nfnet_f2,89.009,10.991,98.189,1.810,193.78,352,0.920,bicubic,+3.945,+0.950,-10 +xcit_small_12_p8_224_dist,89.002,10.998,98.078,1.922,26.21,224,1.000,bicubic,+4.770,+1.300,+43 +efficientnetv2_rw_m.agc_in1k,88.987,11.013,98.213,1.787,53.24,416,1.000,bicubic,+4.179,+1.065,+2 +convnext_large.fb_in1k,88.985,11.015,98.040,1.960,197.77,288,1.000,bicubic,+4.139,+0.828,-1 +regnetz_040h,88.951,11.049,98.202,1.798,28.94,320,1.000,bicubic,+4.457,+1.196,+17 +mvitv2_large,88.945,11.055,97.967,2.033,217.99,224,0.900,bicubic,+3.695,+0.753,-30 +tf_efficientnet_b5.ap_in1k,88.938,11.062,98.164,1.836,30.39,456,0.934,bicubic,+4.686,+1.190,+36 +dm_nfnet_f1,88.925,11.075,98.115,1.885,132.63,320,0.910,bicubic,+4.299,+1.015,+5 +deit3_medium_patch16_224_in21ft1k,88.921,11.079,98.300,1.700,38.85,224,1.000,bicubic,+4.361,+1.112,+7 +deit3_base_patch16_384,88.921,11.079,98.046,1.954,86.88,384,1.000,bicubic,+3.849,+0.768,-19 +volo_d1_224,88.908,11.092,98.031,1.968,26.63,224,0.960,bicubic,+4.744,+1.255,+41 +tf_efficientnetv2_s.in21k_ft_in1k,88.904,11.096,98.277,1.723,21.46,384,1.000,bicubic,+4.602,+1.025,+26 +vit_base_patch16_224.augreg_in21k_ft_in1k,88.866,11.134,98.230,1.770,86.57,224,0.900,bicubic,+4.334,+0.936,+5 +regnetz_d8,88.855,11.145,98.189,1.810,23.37,320,1.000,bicubic,+4.805,+1.191,+43 +convnext_tiny.fb_in22k_ft_in1k_384,88.846,11.154,98.298,1.702,28.59,384,1.000,bicubic,+4.766,+1.156,+40 +regnetz_d8_evos,88.842,11.158,98.132,1.868,23.46,320,0.950,bicubic,+4.792,+1.138,+42 +resnetrs420,88.840,11.160,98.034,1.966,191.89,416,1.000,bicubic,+3.832,+0.910,-23 +resnetrs270,88.834,11.166,98.136,1.864,129.86,352,1.000,bicubic,+4.400,+1.166,+10 +ig_resnext101_32x16d,88.834,11.166,98.049,1.951,194.03,224,0.875,bilinear,+4.664,+0.853,+33 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,88.819,11.181,98.337,1.663,36.47,384,1.000,bicubic,+4.773,+1.009,+40 +vit_base_r50_s16_384.orig_in21k_ft_in1k,88.808,11.192,98.232,1.768,98.95,384,1.000,bicubic,+3.836,+0.944,-25 +xcit_medium_24_p16_224_dist,88.797,11.203,98.036,1.964,84.40,224,1.000,bicubic,+4.523,+1.096,+18 +seresnet152d,88.795,11.205,98.172,1.828,66.84,320,1.000,bicubic,+4.433,+1.132,+14 +maxxvit_rmlp_small_rw_256,88.791,11.209,98.061,1.939,66.01,256,0.950,bicubic,+4.163,+0.999,-12 +xcit_tiny_24_p8_384_dist,88.774,11.226,98.160,1.840,12.11,384,1.000,bicubic,+5.034,+1.526,+58 +swsl_resnext101_32x8d,88.770,11.230,98.147,1.853,88.79,224,0.875,bilinear,+4.486,+0.971,+13 +resnetrs200,88.763,11.237,98.113,1.887,93.21,320,1.000,bicubic,+4.315,+1.269,-3 +tf_efficientnet_b6.aa_in1k,88.761,11.239,98.064,1.937,43.04,528,0.942,bicubic,+4.651,+1.178,+25 +convnext_base.fb_in1k,88.761,11.239,97.931,2.069,88.59,288,1.000,bicubic,+4.327,+1.111,0 +resnetrs350,88.759,11.241,98.029,1.971,163.96,384,1.000,bicubic,+4.039,+1.041,-22 +deit3_large_patch16_224,88.755,11.245,97.914,2.086,304.37,224,0.900,bicubic,+3.991,+0.876,-24 +edgenext_base,88.744,11.256,98.145,1.855,18.51,320,1.000,bicubic,+4.784,+1.377,+33 +vit_base_patch16_224_miil.in21k_ft_in1k,88.737,11.262,98.027,1.973,86.54,224,0.875,bilinear,+4.469,+1.225,+8 +regnetz_040,88.731,11.269,98.091,1.909,27.12,320,1.000,bicubic,+4.495,+1.159,+10 +resnetv2_152x2_bitm,88.725,11.275,98.307,1.693,236.34,448,1.000,bilinear,+4.215,+0.875,-14 +regnety_160,88.697,11.303,98.068,1.932,83.59,288,1.000,bicubic,+5.011,+1.292,+55 +pit_b_distilled_224,88.676,11.324,98.093,1.907,74.79,224,0.900,bicubic,+4.532,+1.237,+16 +vit_small_patch16_384.augreg_in21k_ft_in1k,88.652,11.348,98.232,1.768,22.20,384,1.000,bicubic,+4.850,+1.130,+41 +regnetz_d32,88.650,11.350,98.081,1.919,27.58,320,0.950,bicubic,+4.628,+1.215,+22 +flexivit_base.1200ep_in1k,88.646,11.354,97.935,2.065,86.59,240,0.950,bicubic,+3.982,+0.943,-29 +regnety_080,88.635,11.365,97.970,2.030,39.18,288,1.000,bicubic,+4.703,+1.082,+25 +vit_medium_patch16_gap_256.in12k_ft_in1k,88.631,11.369,98.189,1.810,38.86,256,0.950,bicubic,+4.201,+0.977,-11 +eca_nfnet_l1,88.624,11.376,98.132,1.868,41.41,320,1.000,bicubic,+4.614,+1.104,+20 +mvitv2_base,88.620,11.380,97.820,2.180,51.47,224,0.900,bicubic,+4.198,+0.956,-12 +maxvit_base_tf_224.in1k,88.586,11.414,97.848,2.152,119.47,224,0.950,bicubic,+3.726,+0.858,-43 +swinv2_base_window16_256,88.584,11.416,97.893,2.107,87.92,256,0.900,bicubic,+3.990,+0.819,-31 +flexivit_base.600ep_in1k,88.550,11.450,97.935,2.065,86.59,240,0.950,bicubic,+4.032,+0.949,-27 +resnetv2_152x4_bitm,88.545,11.455,98.189,1.810,936.53,480,1.000,bilinear,+3.629,+0.749,-48 +seresnextaa101d_32x8d,88.545,11.455,98.002,1.998,93.59,288,1.000,bicubic,+3.977,+0.932,-33 +resnet200d,88.543,11.457,97.959,2.041,64.69,320,1.000,bicubic,+4.581,+1.135,+14 +xcit_small_24_p16_224_dist,88.530,11.470,97.999,2.001,47.67,224,1.000,bicubic,+4.668,+1.271,+20 +resnest269e,88.522,11.478,98.027,1.973,110.93,416,0.928,bicubic,+4.004,+1.091,-33 +maxvit_rmlp_small_rw_224,88.520,11.480,97.775,2.225,64.90,224,0.900,bicubic,+4.036,+1.013,-30 +swinv2_base_window8_256,88.513,11.487,97.893,2.107,87.92,256,0.900,bicubic,+4.251,+0.971,-12 +coatnet_rmlp_2_rw_224,88.509,11.491,97.572,2.428,73.88,224,0.950,bicubic,+3.909,+0.836,-41 +seresnext101_32x8d,88.501,11.499,97.888,2.112,93.57,288,1.000,bicubic,+4.309,+1.014,-6 +gcvit_base,88.498,11.502,97.775,2.225,90.32,224,0.875,bicubic,+4.050,+0.693,-32 +crossvit_18_dagger_408,88.477,11.523,97.893,2.107,44.61,408,1.000,bicubic,+4.281,+1.075,-9 +efficientnetv2_rw_s.ra2_in1k,88.473,11.527,97.974,2.026,23.94,384,1.000,bicubic,+4.665,+1.250,+19 +flexivit_base.300ep_in1k,88.471,11.529,97.841,2.159,86.59,240,0.950,bicubic,+4.077,+0.721,-26 +resnetv2_101x3_bitm,88.464,11.536,98.157,1.843,387.93,448,1.000,bilinear,+4.024,+0.775,-35 +maxvit_small_tf_224.in1k,88.458,11.542,97.880,2.120,68.93,224,0.950,bicubic,+4.024,+0.716,-32 +maxvit_large_tf_224.in1k,88.456,11.544,97.805,2.195,211.79,224,0.950,bicubic,+3.530,+0.833,-65 +cait_s24_224,88.447,11.553,97.957,2.043,46.92,224,1.000,bicubic,+4.995,+1.393,+36 +resnetv2_50x3_bitm,88.443,11.557,98.200,1.800,217.32,448,1.000,bilinear,+4.429,+1.076,-4 +resmlp_big_24_distilled_224,88.443,11.557,97.940,2.060,129.14,224,0.875,bicubic,+4.855,+1.292,+30 +regnetv_064,88.432,11.568,98.061,1.939,30.58,288,1.000,bicubic,+4.720,+1.313,+21 +resnest200e,88.432,11.568,98.042,1.958,70.20,320,0.909,bicubic,+4.600,+1.148,+8 +seresnext101d_32x8d,88.428,11.572,97.957,2.043,93.59,288,1.000,bicubic,+4.058,+1.041,-33 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,88.426,11.574,98.085,1.915,328.99,224,0.900,bicubic,+3.992,+1.113,-44 +tf_efficientnet_b3.ns_jft_in1k,88.426,11.574,98.029,1.971,12.23,300,0.904,bicubic,+4.378,+1.119,-11 +convnext_small.fb_in1k,88.413,11.587,98.008,1.992,50.22,288,1.000,bicubic,+4.707,+1.198,+18 +tf_efficientnetv2_s.in1k,88.402,11.598,97.927,2.073,21.46,384,1.000,bicubic,+4.508,+1.229,-4 +vit_base_patch16_384.orig_in21k_ft_in1k,88.389,11.611,98.155,1.845,86.86,384,1.000,bicubic,+4.180,+0.937,-27 +tresnet_v2_l,88.381,11.619,97.925,2.075,46.17,224,0.875,bilinear,+4.479,+1.433,-7 +regnetz_c16_evos,88.377,11.623,98.040,1.960,13.49,320,0.950,bicubic,+5.747,+1.566,+83 +efficientnet_b4.ra2_in1k,88.372,11.628,97.961,2.039,19.34,384,1.000,bicubic,+4.944,+1.365,+24 +resnet152d,88.355,11.645,97.935,2.065,60.21,320,1.000,bicubic,+4.675,+1.197,+14 +swinv2_small_window16_256,88.355,11.645,97.848,2.152,49.73,256,0.900,bicubic,+4.149,+0.978,-31 +tf_efficientnet_b4.ap_in1k,88.349,11.651,97.893,2.107,19.34,380,0.922,bicubic,+5.101,+1.501,+36 +maxvit_rmlp_tiny_rw_256,88.345,11.655,97.824,2.176,29.15,256,0.950,bicubic,+4.113,+0.948,-35 +deit3_small_patch16_224_in21ft1k,88.328,11.672,98.130,1.870,22.06,224,1.000,bicubic,+5.258,+1.350,+49 +tf_efficientnet_b5.ra_in1k,88.321,11.679,97.912,2.088,30.39,456,0.934,bicubic,+4.509,+1.164,-6 +regnety_064,88.317,11.683,97.859,2.142,30.58,288,1.000,bicubic,+4.601,+1.184,+2 +crossvit_15_dagger_408,88.308,11.692,97.871,2.129,28.50,408,1.000,bicubic,+4.470,+1.089,-11 +deit3_small_patch16_384,88.300,11.700,97.888,2.112,22.21,384,1.000,bicubic,+4.874,+1.212,+16 +cs3se_edgenet_x,88.291,11.709,97.935,2.065,50.72,320,1.000,bicubic,+4.743,+1.265,+10 +pvt_v2_b4,88.285,11.715,97.814,2.186,62.56,224,0.900,bicubic,+4.569,+1.094,-1 +efficientformer_l7,88.278,11.722,97.882,2.118,82.23,224,0.950,bicubic,+4.892,+1.342,+20 +mvitv2_small,88.261,11.739,97.688,2.312,34.87,224,0.900,bicubic,+4.493,+1.118,-8 +xcit_small_12_p16_224_dist,88.251,11.749,97.844,2.156,26.25,224,1.000,bicubic,+4.901,+1.430,+20 +resnetrs152,88.251,11.749,97.737,2.263,86.62,320,1.000,bicubic,+4.539,+1.123,-2 +deit3_base_patch16_224,88.249,11.751,97.809,2.191,86.59,224,0.900,bicubic,+4.457,+1.225,-13 +gcvit_small,88.219,11.781,97.786,2.214,51.09,224,0.875,bicubic,+4.335,+1.128,-24 +regnetv_040,88.214,11.786,97.972,2.028,20.64,288,1.000,bicubic,+5.020,+1.312,+23 +deit_base_distilled_patch16_224,88.214,11.786,97.914,2.086,87.34,224,0.900,bicubic,+4.826,+1.426,+12 +xception65p,88.180,11.820,97.790,2.210,39.82,299,0.940,bicubic,+5.050,+1.310,+27 +swinv2_small_window8_256,88.180,11.820,97.775,2.225,49.73,256,0.900,bicubic,+4.324,+1.135,-25 +xcit_tiny_24_p16_384_dist,88.161,11.839,97.946,2.054,12.12,384,1.000,bicubic,+5.591,+1.660,+68 +xcit_large_24_p8_224,88.157,11.843,97.387,2.613,188.93,224,1.000,bicubic,+3.765,+0.731,-65 +resnetv2_152x2_bit_teacher_384,88.150,11.850,98.051,1.949,236.34,384,1.000,bicubic,+4.306,+0.933,-28 +ig_resnext101_32x8d,88.146,11.854,97.856,2.144,88.79,224,0.875,bilinear,+5.458,+1.220,+53 +cait_xxs36_384,88.140,11.860,97.908,2.092,17.37,384,1.000,bicubic,+5.946,+1.760,+103 +dm_nfnet_f0,88.125,11.875,97.854,2.146,71.49,256,0.900,bicubic,+4.739,+1.282,+4 +pvt_v2_b3,88.112,11.888,97.779,2.220,45.24,224,0.900,bicubic,+4.986,+1.224,+21 +pvt_v2_b5,88.108,11.892,97.701,2.300,81.96,224,0.900,bicubic,+4.368,+0.989,-22 +xcit_tiny_12_p8_384_dist,88.103,11.897,97.918,2.082,6.71,384,1.000,bicubic,+5.715,+1.694,+77 +swsl_resnext101_32x4d,88.099,11.901,97.967,2.033,44.18,224,0.875,bilinear,+4.869,+1.207,+9 +xception65,88.076,11.924,97.752,2.248,39.92,299,0.940,bicubic,+4.896,+1.160,+11 +swin_s3_base_224,88.048,11.952,97.656,2.344,71.13,224,0.900,bicubic,+4.118,+0.994,-43 +xcit_tiny_24_p8_224_dist,88.041,11.959,97.814,2.186,12.11,224,1.000,bicubic,+5.480,+1.748,+57 +maxvit_tiny_tf_224.in1k,88.020,11.980,97.814,2.186,30.92,224,0.950,bicubic,+4.622,+1.226,-7 +gcvit_tiny,88.003,11.997,97.728,2.272,28.22,224,0.875,bicubic,+4.603,+1.330,-9 +cs3sedarknet_x,87.984,12.016,97.799,2.201,35.40,288,1.000,bicubic,+5.330,+1.445,+43 +eca_nfnet_l0,87.980,12.020,97.871,2.129,24.14,288,1.000,bicubic,+5.400,+1.381,+50 +efficientformer_l3,87.971,12.029,97.709,2.291,31.41,224,0.950,bicubic,+5.421,+1.461,+54 +nfnet_l0,87.967,12.033,97.867,2.133,35.07,288,1.000,bicubic,+5.217,+1.351,+33 +xcit_small_24_p8_224,87.965,12.035,97.581,2.419,47.63,224,1.000,bicubic,+4.127,+0.945,-43 +tf_efficientnet_b4.aa_in1k,87.963,12.037,97.739,2.261,19.34,380,0.922,bicubic,+4.941,+1.439,+17 +coatnet_1_rw_224,87.948,12.052,97.455,2.545,41.72,224,0.950,bicubic,+4.340,+1.067,-26 +resnet101d,87.941,12.059,97.908,2.092,44.57,320,1.000,bicubic,+4.919,+1.462,+14 +regnety_032,87.937,12.063,97.891,2.109,19.44,288,1.000,bicubic,+5.213,+1.467,+29 +mobilevitv2_200_384_in22ft1k,87.926,12.074,97.820,2.180,18.45,384,1.000,bicubic,+4.532,+1.240,-17 +swinv2_cr_small_ns_224,87.920,12.080,97.668,2.332,49.70,224,0.900,bicubic,+4.432,+1.182,-25 +regnety_040,87.915,12.085,97.884,2.116,20.65,288,1.000,bicubic,+4.877,+1.374,+8 +sequencer2d_l,87.915,12.085,97.696,2.304,54.30,224,0.875,bicubic,+4.509,+1.190,-23 +vit_base_patch32_384.augreg_in21k_ft_in1k,87.909,12.091,98.012,1.988,88.30,384,1.000,bicubic,+4.559,+1.176,-16 +twins_svt_large,87.901,12.099,97.581,2.419,99.27,224,0.900,bicubic,+4.223,+0.987,-35 +coatnet_rmlp_1_rw_224,87.892,12.108,97.628,2.372,41.69,224,0.950,bicubic,+4.534,+1.172,-19 +twins_pcpvt_large,87.877,12.123,97.856,2.144,60.99,224,0.900,bicubic,+4.737,+1.258,-7 +regnetz_c16,87.860,12.140,97.818,2.182,13.46,320,0.940,bicubic,+5.342,+1.746,+42 +swin_s3_small_224,87.854,12.146,97.431,2.568,49.74,224,0.900,bicubic,+4.084,+0.981,-51 +maxvit_tiny_rw_224,87.852,12.149,97.643,2.357,29.06,224,0.950,bicubic,+4.347,+1.141,-35 +deit_base_patch16_384,87.845,12.155,97.510,2.490,86.86,384,1.000,bicubic,+4.739,+1.138,-6 +mobilevitv2_175_384_in22ft1k,87.841,12.159,97.726,2.274,14.25,384,1.000,bicubic,+4.899,+1.300,+3 +convnext_nano.in12k_ft_in1k,87.834,12.166,97.888,2.112,15.59,288,1.000,bicubic,+4.976,+1.332,+7 +xcit_small_12_p8_224,87.828,12.172,97.568,2.432,26.21,224,1.000,bicubic,+4.484,+1.088,-23 +flexivit_small.1200ep_in1k,87.815,12.185,97.615,2.385,22.06,240,0.950,bicubic,+5.289,+1.479,+33 +tf_efficientnetv2_b3.in21k_ft_in1k,87.813,12.187,97.893,2.107,14.36,300,0.900,bicubic,+5.141,+1.269,+17 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,87.813,12.187,97.754,2.246,88.22,224,0.900,bicubic,+4.507,+1.224,-25 +flexivit_small.600ep_in1k,87.811,12.189,97.581,2.419,22.06,240,0.950,bicubic,+5.457,+1.495,+46 +maxxvit_rmlp_nano_rw_256,87.802,12.198,97.756,2.244,16.78,256,0.950,bicubic,+4.772,+1.412,-9 +deit3_medium_patch16_224,87.802,12.198,97.649,2.351,38.85,224,0.900,bicubic,+4.722,+1.357,-13 +tresnet_xl_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.746,+1.285,-12 +resnetv2_50x1_bit_distilled,87.787,12.213,97.899,2.101,25.55,224,0.875,bicubic,+4.969,+1.377,0 +convnext_tiny.fb_in1k,87.762,12.238,97.585,2.415,28.59,288,1.000,bicubic,+5.062,+1.449,+8 +twins_pcpvt_base,87.736,12.264,97.726,2.274,43.83,224,0.900,bicubic,+5.028,+1.380,+4 +tresnet_m,87.736,12.264,97.523,2.477,31.39,224,0.875,bilinear,+4.656,+1.405,-17 +mvitv2_tiny,87.721,12.279,97.555,2.445,24.17,224,0.900,bicubic,+5.317,+1.399,+31 +maxvit_rmlp_nano_rw_256,87.719,12.281,97.579,2.421,15.50,256,0.950,bicubic,+4.757,+1.309,-13 +gc_efficientnetv2_rw_t.agc_in1k,87.715,12.285,97.807,2.193,13.68,288,1.000,bicubic,+5.251,+1.509,+26 +resnetv2_101x1_bitm,87.681,12.319,97.940,2.060,44.54,448,1.000,bilinear,+5.349,+1.984,+39 +swin_small_patch4_window7_224,87.664,12.336,97.566,2.434,49.61,224,0.900,bicubic,+4.452,+1.244,-35 +mobilevitv2_150_384_in22ft1k,87.657,12.343,97.649,2.351,10.59,384,1.000,bicubic,+5.063,+1.331,+7 +efficientnetv2_rw_t.ra2_in1k,87.644,12.356,97.688,2.312,13.65,288,1.000,bicubic,+5.296,+1.492,+33 +twins_svt_base,87.638,12.362,97.523,2.477,56.07,224,0.900,bicubic,+4.502,+1.105,-32 +pnasnet5large,87.636,12.364,97.485,2.515,86.06,331,0.911,bicubic,+4.854,+1.445,-9 +cs3edgenet_x,87.619,12.381,97.654,2.346,47.82,288,1.000,bicubic,+4.917,+1.284,-5 +swsl_resnext101_32x16d,87.615,12.386,97.820,2.180,194.03,224,0.875,bilinear,+4.269,+0.974,-48 +flexivit_small.300ep_in1k,87.615,12.386,97.613,2.387,22.06,240,0.950,bicubic,+5.443,+1.589,+50 +swinv2_tiny_window16_256,87.615,12.386,97.562,2.438,28.35,256,0.900,bicubic,+4.805,+1.330,-14 +maxvit_nano_rw_256,87.610,12.390,97.523,2.477,15.45,256,0.950,bicubic,+4.678,+1.301,-23 +jx_nest_base,87.606,12.394,97.521,2.479,67.72,224,0.875,bicubic,+4.054,+1.151,-67 +swsl_resnext50_32x4d,87.600,12.400,97.651,2.349,25.03,224,0.875,bilinear,+5.418,+1.421,+43 +xcit_medium_24_p8_224,87.600,12.400,97.199,2.801,84.32,224,1.000,bicubic,+3.866,+0.805,-80 +sequencer2d_m,87.565,12.435,97.581,2.419,38.31,224,0.875,bicubic,+4.759,+1.313,-19 +tf_efficientnet_b2.ns_jft_in1k,87.557,12.443,97.628,2.372,9.11,260,0.890,bicubic,+5.177,+1.380,+18 +levit_384,87.553,12.447,97.545,2.455,39.13,224,0.900,bicubic,+4.967,+1.529,-5 +ecaresnet50t,87.538,12.462,97.643,2.357,25.57,320,0.950,bicubic,+5.192,+1.505,+20 +vit_base_patch32_clip_224.laion2b_ft_in1k,87.535,12.464,97.553,2.447,88.22,224,0.900,bicubic,+4.954,+1.351,-6 +vit_base_patch16_rpn_224.in1k,87.508,12.492,97.489,2.511,86.54,224,0.900,bicubic,+5.306,+1.493,+33 +pvt_v2_b2_li,87.504,12.496,97.478,2.522,22.55,224,0.900,bicubic,+5.308,+1.374,+33 +edgenext_small,87.501,12.499,97.583,2.417,5.59,320,1.000,bicubic,+5.933,+1.877,+75 +coatnet_bn_0_rw_224,87.499,12.501,97.547,2.453,27.44,224,0.950,bicubic,+5.101,+1.365,+8 +resnetv2_152x2_bit_teacher,87.493,12.507,97.812,2.188,236.34,224,0.875,bicubic,+4.631,+1.244,-33 +jx_nest_small,87.489,12.511,97.517,2.483,38.35,224,0.875,bicubic,+4.368,+1.189,-49 +vit_relpos_base_patch16_clsgap_224.sw_in1k,87.469,12.531,97.525,2.474,86.43,224,0.900,bicubic,+4.707,+1.351,-28 +vit_relpos_base_patch16_224.sw_in1k,87.463,12.537,97.558,2.442,86.43,224,0.900,bicubic,+4.979,+1.416,-3 +resnet152,87.461,12.539,97.402,2.598,60.19,224,0.950,bicubic,+4.639,+1.276,-35 +fbnetv3_g.ra2_in1k,87.452,12.548,97.547,2.453,16.62,288,0.950,bilinear,+5.404,+1.483,+37 +resnext101_64x4d,87.444,12.556,97.442,2.558,83.46,288,1.000,bicubic,+4.296,+1.070,-60 +efficientnet_b3.ra2_in1k,87.435,12.565,97.681,2.319,12.23,320,1.000,bicubic,+5.193,+1.567,+18 +resnet61q,87.435,12.565,97.598,2.402,36.85,288,1.000,bicubic,+4.911,+1.468,-11 +cait_xxs24_384,87.416,12.584,97.619,2.381,12.03,384,1.000,bicubic,+6.450,+1.973,+110 +cs3darknet_x,87.401,12.599,97.607,2.393,35.05,288,1.000,bicubic,+5.173,+1.373,+17 +cs3sedarknet_l,87.397,12.603,97.570,2.430,21.91,288,0.950,bicubic,+5.623,+1.602,+52 +resnet51q,87.395,12.605,97.587,2.413,35.70,288,1.000,bilinear,+5.035,+1.407,-1 +xcit_tiny_24_p8_224,87.380,12.620,97.628,2.372,12.11,224,1.000,bicubic,+5.480,+1.652,+41 +coat_lite_small,87.380,12.620,97.365,2.635,19.84,224,0.900,bicubic,+5.072,+1.515,+6 +tresnet_l_448,87.377,12.623,97.485,2.515,55.99,448,0.875,bilinear,+5.109,+1.509,+9 +sequencer2d_s,87.375,12.625,97.389,2.611,27.65,224,0.875,bicubic,+5.033,+1.359,-1 +pvt_v2_b2,87.373,12.627,97.517,2.483,25.36,224,0.900,bicubic,+5.297,+1.555,+21 +swinv2_cr_small_224,87.371,12.629,97.344,2.656,49.70,224,0.900,bicubic,+4.225,+1.250,-71 +vit_relpos_medium_patch16_cls_224.sw_in1k,87.369,12.631,97.453,2.547,38.76,224,0.900,bicubic,+4.807,+1.283,-25 +nasnetalarge,87.350,12.650,97.417,2.583,88.75,331,0.911,bicubic,+4.730,+1.371,-35 +crossvit_18_dagger_240,87.346,12.655,97.457,2.543,44.27,240,0.875,bicubic,+4.828,+1.097,-22 +crossvit_18_240,87.316,12.684,97.483,2.517,43.27,240,0.875,bicubic,+4.916,+1.429,-16 +resnetv2_101,87.307,12.693,97.323,2.677,44.54,224,0.950,bicubic,+5.277,+1.463,+20 +ecaresnet101d,87.288,12.712,97.562,2.438,44.57,224,0.875,bicubic,+5.116,+1.516,+9 +resnest101e,87.284,12.716,97.560,2.440,48.28,256,0.875,bilinear,+4.394,+1.240,-59 +gcvit_xtiny,87.281,12.719,97.481,2.519,19.98,224,0.875,bicubic,+5.329,+1.515,+24 +pit_s_distilled_224,87.277,12.723,97.500,2.500,24.04,224,0.900,bicubic,+5.281,+1.702,+18 +coatnet_rmlp_nano_rw_224,87.275,12.725,97.440,2.560,15.15,224,0.900,bicubic,+5.211,+1.570,+11 +resnetv2_50d_gn,87.260,12.740,97.513,2.487,25.57,288,0.950,bicubic,+5.444,+1.589,+30 +vit_relpos_medium_patch16_rpn_224.sw_in1k,87.258,12.742,97.442,2.558,38.73,224,0.900,bicubic,+4.960,+1.468,-9 +resnetrs101,87.247,12.753,97.457,2.543,63.62,288,0.940,bicubic,+4.959,+1.519,-9 +coatnext_nano_rw_224,87.239,12.761,97.549,2.451,14.70,224,0.900,bicubic,+5.291,+1.631,+19 +poolformer_m48,87.239,12.761,97.308,2.692,73.47,224,0.950,bicubic,+4.777,+1.350,-30 +mixer_b16_224_miil,87.226,12.774,97.410,2.590,59.88,224,0.875,bilinear,+4.918,+1.694,-14 +xcit_tiny_12_p8_224_dist,87.224,12.776,97.444,2.556,6.71,224,1.000,bicubic,+6.012,+1.844,+63 +tresnet_xl,87.224,12.776,97.400,2.600,78.44,224,0.875,bilinear,+5.170,+1.463,+6 +xcit_tiny_12_p16_384_dist,87.202,12.798,97.466,2.534,6.72,384,1.000,bicubic,+6.262,+2.056,+85 +convit_base,87.200,12.800,97.286,2.714,86.54,224,0.875,bicubic,+4.912,+1.278,-15 +resnetv2_50d_evos,87.194,12.806,97.361,2.639,25.59,288,0.950,bicubic,+5.218,+1.445,+7 +tf_efficientnet_b3.ap_in1k,87.192,12.808,97.380,2.620,12.23,300,0.904,bicubic,+5.370,+1.756,+18 +visformer_small,87.181,12.819,97.323,2.677,40.22,224,0.900,bicubic,+5.075,+1.451,-4 +vit_base_patch32_clip_224.openai_ft_in1k,87.177,12.823,97.463,2.537,88.22,224,0.900,bicubic,+5.247,+1.495,+12 +crossvit_15_dagger_240,87.172,12.828,97.438,2.562,28.21,240,0.875,bicubic,+4.841,+0.920,-26 +vit_srelpos_medium_patch16_224.sw_in1k,87.168,12.832,97.314,2.686,38.74,224,0.900,bicubic,+4.932,+1.380,-18 +convnext_tiny_hnf.a2h_in1k,87.143,12.857,97.293,2.707,28.59,288,1.000,bicubic,+4.553,+1.277,-58 +vit_relpos_medium_patch16_224.sw_in1k,87.141,12.860,97.504,2.496,38.75,224,0.900,bicubic,+4.675,+1.416,-45 +xcit_small_24_p16_224,87.132,12.868,97.259,2.741,47.67,224,1.000,bicubic,+4.552,+1.255,-56 +swin_s3_tiny_224,87.128,12.872,97.303,2.697,28.33,224,0.900,bicubic,+5.006,+1.355,-13 +coatnet_0_rw_224,87.119,12.881,97.224,2.776,27.44,224,0.950,bicubic,+4.729,+1.388,-41 +swinv2_tiny_window8_256,87.076,12.924,97.515,2.485,28.35,256,0.900,bicubic,+5.270,+1.521,+10 +resnet101,87.068,12.932,97.263,2.737,44.55,224,0.950,bicubic,+5.130,+1.509,+2 +mobilevitv2_200_in22ft1k,87.053,12.947,97.429,2.571,18.45,256,0.888,bicubic,+4.729,+1.489,-35 +convit_small,87.053,12.947,97.350,2.650,27.78,224,0.875,bicubic,+5.627,+1.606,+33 +crossvit_15_240,87.051,12.949,97.423,2.577,27.53,240,0.875,bicubic,+5.515,+1.731,+21 +xception41p,87.049,12.951,97.205,2.795,26.91,299,0.940,bicubic,+5.091,+1.411,-6 +tf_efficientnetv2_b3.in1k,87.032,12.968,97.303,2.697,14.36,300,0.904,bicubic,+5.062,+1.521,-8 +regnetz_b16,87.017,12.983,97.429,2.571,9.72,288,0.940,bicubic,+6.301,+1.951,+78 +xcit_small_12_p16_224,87.010,12.990,97.248,2.752,26.25,224,1.000,bicubic,+5.036,+1.432,-12 +deit3_small_patch16_224,87.010,12.990,97.169,2.831,22.06,224,0.900,bicubic,+5.624,+1.719,+32 +jx_nest_tiny,87.008,12.992,97.378,2.622,17.06,224,0.875,bicubic,+5.594,+1.762,+27 +swinv2_cr_tiny_ns_224,87.000,13.000,97.284,2.716,28.33,224,0.900,bicubic,+5.210,+1.460,0 +deit_small_distilled_patch16_224,86.993,13.007,97.316,2.684,22.44,224,0.900,bicubic,+5.793,+1.938,+38 +resmlp_36_distilled_224,86.993,13.007,97.278,2.722,44.69,224,0.875,bicubic,+5.833,+1.790,+40 +coatnet_nano_rw_224,86.989,13.011,97.242,2.759,15.14,224,0.900,bicubic,+5.289,+1.603,+1 +xcit_large_24_p16_224,86.955,13.045,96.921,3.079,189.10,224,1.000,bicubic,+4.059,+1.039,-100 +poolformer_m36,86.948,13.052,97.148,2.852,56.17,224,0.950,bicubic,+4.838,+1.460,-30 +mobilevitv2_175_in22ft1k,86.944,13.056,97.333,2.667,14.25,256,0.888,bicubic,+5.000,+1.541,-15 +xcit_medium_24_p16_224,86.936,13.065,97.101,2.899,84.40,224,1.000,bicubic,+4.300,+1.125,-85 +tnt_s_patch16_224,86.903,13.097,97.368,2.632,23.76,224,0.900,bicubic,+5.385,+1.620,+8 +vit_relpos_small_patch16_224.sw_in1k,86.899,13.101,97.489,2.511,21.98,224,0.900,bicubic,+5.437,+1.661,+13 +vit_small_patch16_224.augreg_in21k_ft_in1k,86.869,13.131,97.613,2.387,22.05,224,0.900,bicubic,+5.467,+1.479,+17 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,86.863,13.137,97.528,2.472,36.43,224,0.900,bicubic,+5.005,+1.506,-16 +ssl_resnext101_32x16d,86.856,13.143,97.517,2.483,194.03,224,0.875,bilinear,+5.013,+1.421,-16 +convmixer_1536_20,86.852,13.148,97.346,2.654,51.63,224,0.960,bicubic,+5.476,+1.732,+18 +rexnet_200,86.846,13.154,97.276,2.724,16.37,224,0.875,bicubic,+5.214,+1.608,-6 +tf_efficientnet_b3.aa_in1k,86.835,13.165,97.297,2.703,12.23,300,0.904,bicubic,+5.199,+1.579,-8 +deit_base_patch16_224,86.829,13.171,97.049,2.951,86.57,224,0.900,bicubic,+4.831,+1.315,-33 +tresnet_m_448,86.820,13.180,97.212,2.788,31.39,448,0.875,bilinear,+5.106,+1.640,-14 +swsl_resnet50,86.807,13.193,97.498,2.502,25.56,224,0.875,bilinear,+5.641,+2.402,+21 +ssl_resnext101_32x8d,86.807,13.193,97.466,2.534,88.79,224,0.875,bilinear,+5.191,+1.428,-8 +tf_efficientnet_lite4.in1k,86.803,13.197,97.263,2.737,13.01,380,0.920,bilinear,+5.267,+1.595,-5 +coat_mini,86.793,13.207,97.162,2.837,10.34,224,0.900,bicubic,+5.525,+1.770,+14 +vit_base_patch16_224.orig_in21k_ft_in1k,86.778,13.223,97.438,2.562,86.57,224,0.900,bicubic,+4.992,+1.316,-22 +resnetaa50,86.778,13.223,97.395,2.605,25.56,288,1.000,bicubic,+5.156,+1.587,-13 +tresnet_l,86.767,13.233,97.271,2.729,55.99,224,0.875,bilinear,+5.279,+1.647,-5 +cs3darknet_l,86.756,13.244,97.466,2.534,21.16,288,0.950,bicubic,+5.860,+1.796,+39 +twins_svt_small,86.756,13.244,97.175,2.825,24.06,224,0.900,bicubic,+5.074,+1.505,-21 +cs3darknet_focus_l,86.743,13.257,97.376,2.624,21.15,288,0.950,bicubic,+5.859,+1.694,+39 +mobilevitv2_150_in22ft1k,86.739,13.261,97.222,2.778,10.59,256,0.888,bicubic,+5.261,+1.548,-8 +crossvit_base_240,86.733,13.267,97.120,2.880,105.03,240,0.875,bicubic,+4.517,+1.290,-62 +levit_256,86.728,13.272,97.259,2.741,18.89,224,0.900,bicubic,+5.218,+1.769,-13 +convnext_nano_ols.d1h_in1k,86.722,13.278,97.049,2.951,15.65,288,1.000,bicubic,+5.112,+1.409,-20 +vit_srelpos_small_patch16_224.sw_in1k,86.699,13.301,97.250,2.750,21.97,224,0.900,bicubic,+5.605,+1.918,+16 +seresnext50_32x4d,86.699,13.301,97.214,2.786,27.56,224,0.875,bicubic,+5.433,+1.594,+4 +crossvit_small_240,86.686,13.314,97.273,2.727,26.86,240,0.875,bicubic,+5.666,+1.814,+20 +pit_b_224,86.686,13.314,96.898,3.102,73.76,224,0.900,bicubic,+4.240,+1.188,-92 +halo2botnet50ts_256,86.681,13.319,97.090,2.910,22.64,256,0.950,bicubic,+4.621,+1.454,-57 +tf_efficientnet_b1.ns_jft_in1k,86.669,13.331,97.378,2.622,7.79,240,0.882,bicubic,+5.281,+1.640,-9 +swin_tiny_patch4_window7_224,86.664,13.336,97.197,2.803,28.29,224,0.900,bicubic,+5.286,+1.657,-8 +gernet_l,86.654,13.346,97.186,2.814,31.08,256,0.875,bilinear,+5.300,+1.650,-7 +wide_resnet50_2,86.647,13.353,97.214,2.786,68.88,224,0.875,bicubic,+5.191,+1.682,-17 +poolformer_s36,86.639,13.361,97.158,2.842,30.86,224,0.900,bicubic,+5.223,+1.712,-16 +efficientnet_el.ra_in1k,86.635,13.366,97.175,2.825,10.59,300,0.904,bicubic,+5.319,+1.649,-9 +resmlp_24_distilled_224,86.622,13.378,97.135,2.865,30.02,224,0.875,bicubic,+5.856,+1.917,+28 +twins_pcpvt_small,86.620,13.380,97.340,2.660,24.11,224,0.900,bicubic,+5.532,+1.698,+7 +nf_resnet50,86.609,13.391,97.293,2.707,25.56,288,0.940,bicubic,+5.947,+1.957,+30 +resnest50d_4s2x40d,86.592,13.408,97.269,2.731,30.42,224,0.875,bicubic,+5.484,+1.711,+1 +efficientnet_b3_pruned.in1k,86.581,13.419,97.190,2.810,9.86,300,0.904,bicubic,+5.723,+1.948,+21 +repvgg_b3,86.566,13.434,97.139,2.861,123.09,224,0.875,bilinear,+6.074,+1.879,+38 +sehalonet33ts,86.564,13.436,97.004,2.995,13.69,256,0.940,bicubic,+5.606,+1.728,+10 +sebotnet33ts_256,86.558,13.442,96.785,3.215,13.70,256,0.940,bicubic,+5.408,+1.611,-7 +xcit_tiny_24_p16_224_dist,86.536,13.464,97.218,2.782,12.12,224,1.000,bicubic,+6.090,+2.000,+43 +convnext_nano.d1h_in1k,86.530,13.470,97.177,2.823,15.59,288,1.000,bicubic,+5.060,+1.519,-31 +vit_small_patch16_384.augreg_in1k,86.496,13.504,97.182,2.818,22.20,384,1.000,bicubic,+5.376,+1.608,-8 +halonet50ts,86.483,13.517,97.152,2.848,22.73,256,0.940,bicubic,+4.839,+1.544,-48 +ssl_resnext101_32x4d,86.479,13.521,97.468,2.532,44.18,224,0.875,bilinear,+5.555,+1.740,+6 +maxvit_rmlp_pico_rw_256,86.479,13.521,97.203,2.797,7.52,256,0.950,bicubic,+5.963,+1.991,+29 +ecaresnet50d,86.470,13.530,97.186,2.814,25.58,224,0.875,bicubic,+5.878,+1.866,+21 +gcresnet50t,86.470,13.530,97.141,2.859,25.90,256,0.900,bicubic,+5.530,+1.687,+2 +gluon_resnet152_v1s,86.468,13.532,97.109,2.891,60.32,224,0.875,bicubic,+5.452,+1.697,-4 +haloregnetz_b,86.464,13.536,96.943,3.057,11.68,224,0.940,bicubic,+5.414,+1.747,-8 +mobilevitv2_200,86.451,13.549,96.972,3.027,18.45,256,0.888,bicubic,+5.315,+1.606,-17 +resnest50d_1s4x24d,86.447,13.553,97.148,2.852,25.68,224,0.875,bicubic,+5.459,+1.826,-6 +resnetv2_50x1_bitm,86.436,13.564,97.602,2.398,25.55,448,1.000,bilinear,+6.094,+1.918,+39 +repvgg_b3g4,86.361,13.639,97.054,2.946,83.83,224,0.875,bilinear,+6.149,+1.944,+51 +darknetaa53,86.359,13.641,97.158,2.842,36.02,288,1.000,bilinear,+5.837,+1.836,+18 +lamhalobotnet50ts_256,86.357,13.643,97.062,2.938,22.57,256,0.950,bicubic,+4.813,+1.558,-53 +darknet53,86.355,13.645,97.115,2.885,41.61,288,1.000,bicubic,+5.821,+1.695,+14 +efficientformer_l1,86.344,13.656,97.024,2.976,12.29,224,0.950,bicubic,+5.842,+2.026,+17 +legacy_senet154,86.342,13.658,96.928,3.072,115.09,224,0.875,bilinear,+5.032,+1.432,-35 +cait_xxs36_224,86.340,13.660,97.111,2.889,17.30,224,1.000,bicubic,+6.590,+2.245,+70 +resnext50_32x4d,86.340,13.660,96.972,3.027,25.03,224,0.950,bicubic,+5.222,+1.641,-25 +gernet_m,86.319,13.681,97.096,2.904,21.14,224,0.875,bilinear,+5.587,+1.912,0 +pit_s_224,86.316,13.684,97.045,2.955,23.46,224,0.900,bicubic,+5.222,+1.475,-24 +mobilevitv2_175,86.316,13.684,96.985,3.015,14.25,256,0.888,bicubic,+5.456,+1.731,-6 +vit_small_patch32_384.augreg_in21k_ft_in1k,86.312,13.688,97.417,2.583,22.92,384,1.000,bicubic,+5.832,+1.819,+12 +efficientnet_b2.ra_in1k,86.304,13.696,96.990,3.010,9.11,288,1.000,bicubic,+5.692,+1.672,0 +gluon_senet154,86.278,13.722,96.949,3.051,115.09,224,0.875,bicubic,+5.044,+1.601,-40 +gcvit_xxtiny,86.242,13.758,97.109,2.891,12.00,224,0.875,bicubic,+6.528,+2.029,+65 +resnest50d,86.240,13.761,97.073,2.927,27.48,224,0.875,bilinear,+5.266,+1.695,-22 +convmixer_768_32,86.229,13.771,97.034,2.966,21.11,224,0.960,bicubic,+6.065,+1.962,+38 +vit_base_patch16_384.augreg_in1k,86.227,13.773,96.968,3.032,86.86,384,1.000,bicubic,+5.125,+1.636,-33 +ecaresnet101d_pruned,86.210,13.790,97.335,2.665,24.88,224,0.875,bicubic,+5.392,+1.707,-13 +efficientnet_el_pruned.in1k,86.192,13.807,97.026,2.974,10.59,300,0.904,bicubic,+5.892,+1.808,+25 +cspdarknet53,86.182,13.818,97.013,2.987,27.64,256,0.887,bilinear,+6.124,+1.929,+40 +inception_v4,86.169,13.831,96.919,3.081,42.68,299,0.875,bicubic,+6.001,+1.951,+32 +rexnet_150,86.154,13.846,97.058,2.942,9.73,224,0.875,bicubic,+5.844,+1.892,+20 +inception_resnet_v2,86.133,13.867,97.043,2.957,55.84,299,0.897,bicubic,+5.675,+1.737,+5 +xcit_tiny_12_p8_224,86.105,13.895,97.084,2.917,6.71,224,1.000,bicubic,+6.411,+2.031,+56 +ssl_resnext50_32x4d,86.086,13.914,97.212,2.788,25.03,224,0.875,bilinear,+5.768,+1.806,+15 +tf_efficientnet_el.in1k,86.084,13.916,96.964,3.036,10.59,300,0.904,bicubic,+5.834,+1.836,+21 +mobilevitv2_150,86.075,13.925,96.849,3.151,10.59,256,0.888,bicubic,+5.699,+1.789,+7 +cspresnext50,86.073,13.927,97.101,2.899,20.57,256,0.887,bilinear,+5.527,+1.781,-12 +convnext_pico_ols.d1_in1k,86.067,13.933,97.017,2.983,9.06,288,1.000,bicubic,+5.603,+1.775,-3 +gluon_resnet101_v1s,86.054,13.946,97.022,2.978,44.67,224,0.875,bicubic,+5.752,+1.862,+13 +lambda_resnet50ts,86.054,13.946,96.736,3.264,21.54,256,0.950,bicubic,+4.888,+0.764,-54 +ecaresnetlight,86.052,13.948,97.069,2.931,30.16,224,0.875,bicubic,+5.590,+1.821,-5 +edgenext_small_rw,86.047,13.953,96.925,3.075,7.83,320,1.000,bicubic,+5.591,+1.734,-4 +poolformer_s24,86.032,13.968,97.028,2.972,21.39,224,0.900,bicubic,+5.716,+1.990,+6 +gluon_seresnext101_32x4d,86.032,13.968,96.977,3.023,48.96,224,0.875,bicubic,+5.128,+1.683,-35 +resnetv2_50,86.032,13.968,96.902,3.098,25.55,224,0.950,bicubic,+5.600,+1.822,-5 +convnext_pico.d1_in1k,86.020,13.980,96.941,3.059,9.05,288,0.950,bicubic,+5.594,+1.882,-5 +resnet50d,86.009,13.991,96.979,3.021,25.58,224,0.875,bicubic,+5.479,+1.819,-20 +seresnet33ts,86.007,13.993,97.011,2.989,19.78,256,0.900,bicubic,+5.655,+1.905,-3 +gcresnext50ts,86.007,13.993,96.966,3.034,15.67,256,0.900,bicubic,+5.427,+1.796,-24 +ecaresnet26t,85.983,14.017,97.041,2.959,16.01,320,0.950,bicubic,+6.129,+1.957,+30 +tf_efficientnet_b2.ap_in1k,85.975,14.025,96.810,3.190,9.11,260,0.890,bicubic,+5.675,+1.782,+3 +gluon_seresnext101_64x4d,85.960,14.040,96.979,3.021,88.23,224,0.875,bicubic,+5.066,+1.671,-42 +vit_base_patch32_224.augreg_in21k_ft_in1k,85.956,14.044,97.130,2.869,88.22,224,0.900,bicubic,+5.231,+1.562,-35 +fbnetv3_d.ra2_in1k,85.924,14.076,97.026,2.974,10.31,256,0.950,bilinear,+6.243,+2.082,+38 +gluon_resnet152_v1d,85.917,14.083,96.812,3.188,60.21,224,0.875,bicubic,+5.443,+1.606,-21 +vit_large_patch32_384.orig_in21k_ft_in1k,85.909,14.091,97.368,2.632,306.63,384,1.000,bicubic,+4.403,+1.276,-93 +tf_efficientnet_b2.aa_in1k,85.902,14.098,96.862,3.139,9.11,260,0.890,bicubic,+5.816,+1.954,+9 +tf_efficientnetv2_b2.in1k,85.900,14.100,96.889,3.111,10.10,260,0.890,bicubic,+5.692,+1.847,+3 +resnet50_gn,85.881,14.119,96.851,3.149,25.56,224,0.940,bicubic,+5.829,+1.905,+11 +vit_base_patch16_224.sam,85.877,14.123,96.697,3.303,86.57,224,0.900,bicubic,+5.635,+1.941,-2 +seresnet50,85.857,14.143,97.004,2.995,28.09,224,0.875,bicubic,+5.583,+1.934,-6 +repvgg_b2g4,85.855,14.145,96.812,3.188,61.76,224,0.875,bilinear,+6.489,+2.124,+45 +gluon_resnet101_v1d,85.849,14.151,96.663,3.337,44.57,224,0.875,bicubic,+5.435,+1.649,-21 +gcresnet33ts,85.815,14.185,96.898,3.102,19.88,256,0.900,bicubic,+5.733,+1.900,+3 +mixnet_xl.ra_in1k,85.798,14.202,96.712,3.288,11.90,224,0.875,bicubic,+5.322,+1.776,-32 +ens_adv_inception_resnet_v2,85.781,14.220,96.761,3.239,55.84,299,0.897,bicubic,+5.799,+1.823,+6 +tf_efficientnet_lite3.in1k,85.755,14.245,96.887,3.113,8.20,300,0.904,bilinear,+5.935,+1.973,+16 +ese_vovnet39b,85.751,14.249,96.891,3.109,24.57,224,0.875,bicubic,+6.431,+2.179,+41 +legacy_seresnext101_32x4d,85.746,14.254,96.757,3.243,48.96,224,0.875,bilinear,+5.518,+1.739,-11 +gluon_resnext101_32x4d,85.746,14.254,96.635,3.365,44.18,224,0.875,bicubic,+5.412,+1.709,-21 +eca_resnet33ts,85.740,14.260,96.900,3.100,19.68,256,0.900,bicubic,+5.662,+1.930,-3 +xcit_tiny_24_p16_224,85.734,14.267,96.938,3.062,12.12,224,1.000,bicubic,+6.290,+2.056,+33 +regnety_320,85.725,14.275,96.725,3.275,145.05,224,0.875,bicubic,+4.915,+1.481,-58 +cspresnet50,85.721,14.279,96.795,3.205,21.62,256,0.887,bilinear,+6.147,+2.083,+23 +resnet50,85.706,14.294,96.494,3.506,25.56,224,0.950,bicubic,+5.332,+1.880,-31 +xception71,85.697,14.303,96.776,3.224,42.34,299,0.903,bicubic,+5.823,+1.854,+1 +resmlp_big_24_224,85.695,14.305,96.426,3.574,129.14,224,0.875,bicubic,+4.667,+1.404,-79 +gluon_resnext101_64x4d,85.693,14.307,96.644,3.356,83.46,224,0.875,bicubic,+5.089,+1.656,-56 +efficientnet_em.ra2_in1k,85.684,14.316,96.938,3.062,6.90,240,0.882,bicubic,+6.432,+2.144,+41 +deit_small_patch16_224,85.678,14.322,96.906,3.094,22.05,224,0.900,bicubic,+5.822,+1.854,-2 +pit_xs_distilled_224,85.657,14.343,96.667,3.333,11.00,224,0.900,bicubic,+6.351,+2.303,+32 +efficientnet_b2_pruned.in1k,85.642,14.358,96.746,3.254,8.31,260,0.890,bicubic,+5.726,+1.890,-8 +dpn107,85.640,14.360,96.729,3.271,86.92,224,0.875,bicubic,+5.484,+1.819,-19 +resmlp_36_224,85.620,14.380,96.795,3.205,44.69,224,0.875,bicubic,+5.850,+1.909,0 +mobilevitv2_125,85.584,14.416,96.665,3.335,7.48,256,0.888,bicubic,+5.900,+1.815,+6 +ecaresnet50d_pruned,85.580,14.420,96.936,3.064,19.94,224,0.875,bicubic,+5.864,+2.056,0 +levit_192,85.580,14.420,96.740,3.260,10.95,224,0.900,bicubic,+5.738,+1.954,-7 +gluon_resnet152_v1c,85.580,14.420,96.646,3.354,60.21,224,0.875,bicubic,+5.670,+1.806,-12 +resnext50d_32x4d,85.569,14.431,96.748,3.252,25.05,224,0.875,bicubic,+5.893,+1.882,+4 +tf_efficientnetv2_b1.in1k,85.561,14.439,96.727,3.273,8.14,240,0.882,bicubic,+6.099,+2.005,+13 +regnety_120,85.543,14.457,96.785,3.215,51.82,224,0.875,bicubic,+5.177,+1.659,-46 +fbnetv3_b.ra2_in1k,85.524,14.476,96.862,3.139,8.60,256,0.950,bilinear,+6.374,+2.116,+36 +regnetx_320,85.524,14.476,96.669,3.331,107.81,224,0.875,bicubic,+5.278,+1.643,-36 +nf_regnet_b1,85.505,14.495,96.791,3.209,10.22,288,0.900,bicubic,+6.213,+2.043,+21 +dpn92,85.494,14.506,96.635,3.365,37.67,224,0.875,bicubic,+5.486,+1.799,-24 +gluon_resnet152_v1b,85.475,14.525,96.550,3.450,60.19,224,0.875,bicubic,+5.789,+1.814,-6 +rexnet_130,85.473,14.527,96.684,3.316,7.56,224,0.875,bicubic,+5.973,+2.002,+2 +resnetrs50,85.462,14.538,96.736,3.264,35.69,224,0.910,bicubic,+5.570,+1.767,-22 +dpn131,85.398,14.602,96.639,3.361,79.25,224,0.875,bicubic,+5.576,+1.929,-17 +convnext_tiny.fb_in22k_ft_in1k,85.390,14.610,96.799,3.200,28.59,288,1.000,bicubic,+6.482,+2.125,+41 +regnetx_160,85.390,14.610,96.637,3.363,54.28,224,0.875,bicubic,+5.534,+1.807,-22 +dla102x2,85.366,14.634,96.629,3.371,41.28,224,0.875,bilinear,+5.918,+1.989,+2 +gmlp_s16_224,85.353,14.646,96.648,3.352,19.42,224,0.875,bicubic,+5.712,+2.050,-9 +botnet26t_256,85.343,14.657,96.629,3.371,12.49,256,0.950,bicubic,+6.071,+2.101,+15 +gluon_seresnext50_32x4d,85.336,14.664,96.667,3.333,27.56,224,0.875,bicubic,+5.418,+1.845,-32 +skresnext50_32x4d,85.313,14.687,96.390,3.610,27.48,224,0.875,bicubic,+5.157,+1.748,-41 +dpn98,85.311,14.689,96.469,3.531,61.57,224,0.875,bicubic,+5.669,+1.841,-12 +gluon_resnet101_v1c,85.304,14.696,96.405,3.595,44.57,224,0.875,bicubic,+5.770,+1.827,-10 +lambda_resnet26t,85.300,14.700,96.718,3.282,10.96,256,0.940,bicubic,+6.204,+2.126,+21 +dpn68b,85.291,14.709,96.464,3.536,12.61,224,0.875,bicubic,+6.076,+2.050,+12 +resnetblur50,85.283,14.717,96.531,3.470,25.56,224,0.875,bicubic,+5.997,+1.892,+5 +resmlp_24_224,85.268,14.732,96.492,3.508,30.02,224,0.875,bicubic,+5.894,+1.946,-6 +convnext_femto_ols.d1_in1k,85.255,14.745,96.770,3.230,5.23,288,0.950,bicubic,+6.321,+2.238,+26 +coat_lite_mini,85.251,14.749,96.680,3.320,11.01,224,0.900,bicubic,+6.163,+2.076,+17 +cait_xxs24_224,85.228,14.773,96.712,3.288,11.96,224,1.000,bicubic,+6.842,+2.402,+55 +resnet33ts,85.228,14.773,96.627,3.373,19.68,256,0.900,bicubic,+6.014,+2.053,+7 +xcit_tiny_12_p16_224_dist,85.208,14.792,96.597,3.403,6.72,224,1.000,bicubic,+6.630,+2.401,+40 +pvt_v2_b1,85.198,14.802,96.622,3.378,14.01,224,0.900,bicubic,+6.504,+2.130,+35 +halonet26t,85.195,14.805,96.462,3.538,12.48,256,0.950,bicubic,+6.096,+2.150,+10 +resnext101_32x8d,85.187,14.813,96.445,3.555,88.79,224,0.875,bilinear,+5.879,+1.927,-10 +gluon_inception_v3,85.183,14.817,96.526,3.474,23.83,299,0.875,bicubic,+6.377,+2.156,+25 +resnet32ts,85.155,14.845,96.624,3.376,17.96,256,0.900,bicubic,+6.151,+2.269,+14 +convnext_femto.d1_in1k,85.151,14.849,96.708,3.292,5.22,288,0.950,bicubic,+6.447,+2.274,+29 +hrnet_w48,85.151,14.849,96.492,3.508,77.47,224,0.875,bilinear,+5.851,+1.980,-10 +gluon_xception65,85.148,14.851,96.597,3.403,39.92,299,0.903,bicubic,+5.433,+1.737,-38 +gluon_resnet101_v1b,85.142,14.858,96.366,3.634,44.55,224,0.875,bicubic,+5.836,+1.842,-14 +eca_halonext26ts,85.131,14.869,96.584,3.416,10.76,256,0.940,bicubic,+5.645,+1.986,-27 +regnetx_120,85.131,14.869,96.477,3.523,46.11,224,0.875,bicubic,+5.535,+1.739,-32 +xception,85.129,14.871,96.471,3.529,22.86,299,0.897,bicubic,+6.077,+2.079,+6 +tf_efficientnet_b1.ap_in1k,85.127,14.873,96.405,3.595,7.79,240,0.882,bicubic,+5.847,+2.099,-13 +eca_botnext26ts_256,85.125,14.875,96.509,3.491,10.59,256,0.950,bicubic,+5.851,+1.895,-13 +hrnet_w64,85.119,14.881,96.744,3.256,128.06,224,0.875,bilinear,+5.645,+2.092,-30 +ssl_resnet50,85.097,14.903,96.866,3.134,25.56,224,0.875,bilinear,+5.875,+2.034,-12 +lambda_resnet26rpt_256,85.095,14.905,96.560,3.440,10.99,256,0.940,bicubic,+6.125,+2.130,+4 +res2net101_26w_4s,85.093,14.907,96.381,3.619,45.21,224,0.875,bilinear,+5.895,+1.949,-10 +tf_efficientnet_cc_b1_8e.in1k,85.063,14.937,96.422,3.578,39.72,240,0.882,bicubic,+5.755,+2.052,-25 +res2net50_26w_8s,85.031,14.969,96.419,3.580,48.40,224,0.875,bilinear,+5.831,+2.052,-13 +xcit_nano_12_p8_384_dist,85.029,14.971,96.629,3.371,3.05,384,1.000,bicubic,+7.209,+2.593,+64 +resnest26d,85.008,14.992,96.637,3.363,17.07,224,0.875,bilinear,+6.530,+2.339,+24 +gluon_resnext50_32x4d,84.995,15.005,96.426,3.574,25.03,224,0.875,bicubic,+5.641,+2.000,-32 +tf_efficientnet_b0.ns_jft_in1k,84.984,15.016,96.503,3.497,5.29,224,0.875,bicubic,+6.326,+2.127,+15 +coat_tiny,84.976,15.024,96.409,3.591,5.50,224,0.900,bicubic,+6.542,+2.371,+25 +dla169,84.920,15.080,96.535,3.465,53.39,224,0.875,bilinear,+6.232,+2.199,+11 +tf_efficientnet_b1.aa_in1k,84.918,15.082,96.364,3.636,7.79,240,0.882,bicubic,+6.092,+2.166,+1 +mobilevitv2_100,84.905,15.095,96.385,3.615,4.90,256,0.888,bicubic,+6.815,+2.221,+41 +legacy_seresnext50_32x4d,84.901,15.099,96.434,3.566,27.56,224,0.875,bilinear,+5.823,+1.998,-14 +hrnet_w44,84.884,15.116,96.434,3.566,67.06,224,0.875,bilinear,+5.988,+2.066,-5 +gluon_resnet50_v1s,84.862,15.138,96.443,3.557,25.68,224,0.875,bicubic,+6.150,+2.205,+3 +regnetx_080,84.862,15.138,96.434,3.566,39.57,224,0.875,bicubic,+5.668,+1.874,-23 +levit_128,84.843,15.157,96.360,3.640,9.21,224,0.900,bicubic,+6.357,+2.350,+11 +gluon_resnet50_v1d,84.832,15.168,96.398,3.602,25.58,224,0.875,bicubic,+5.758,+1.928,-18 +dla60_res2next,84.830,15.170,96.411,3.589,17.03,224,0.875,bilinear,+6.390,+2.259,+14 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,84.828,15.172,96.708,3.292,5.79,384,1.000,bicubic,+6.398,+2.166,+15 +mixnet_l.ft_in1k,84.822,15.178,96.328,3.672,7.33,224,0.875,bicubic,+5.846,+2.146,-17 +tv_resnet152,84.815,15.185,96.225,3.775,60.19,224,0.875,bilinear,+6.503,+2.187,+20 +dla102x,84.813,15.187,96.552,3.448,26.31,224,0.875,bilinear,+6.303,+2.324,+3 +dla60_res2net,84.813,15.187,96.481,3.519,20.85,224,0.875,bilinear,+6.349,+2.275,+8 +pit_xs_224,84.792,15.208,96.492,3.508,10.62,224,0.900,bicubic,+6.610,+2.324,+23 +xception41,84.792,15.208,96.413,3.587,26.97,299,0.903,bicubic,+6.276,+2.135,0 +regnetx_064,84.781,15.219,96.490,3.510,26.21,224,0.875,bicubic,+5.709,+2.032,-26 +hrnet_w40,84.743,15.257,96.554,3.446,57.56,224,0.875,bilinear,+5.823,+2.084,-21 +res2net50_26w_6s,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.156,+2.157,-4 +repvgg_b2,84.724,15.276,96.469,3.531,89.02,224,0.875,bilinear,+5.932,+2.055,-15 +vit_base_patch32_384.augreg_in1k,84.722,15.278,96.319,3.681,88.30,384,1.000,bicubic,+5.962,+2.091,-14 +resmlp_12_distilled_224,84.713,15.287,96.225,3.775,15.35,224,0.875,bicubic,+6.769,+2.667,+29 +cs3darknet_m,84.704,15.296,96.488,3.512,9.31,288,0.950,bicubic,+7.068,+2.474,+41 +legacy_seresnet152,84.704,15.296,96.417,3.583,66.82,224,0.875,bilinear,+6.044,+2.047,-12 +selecsls60b,84.657,15.343,96.300,3.700,32.77,224,0.875,bicubic,+6.245,+2.126,+1 +hrnet_w32,84.651,15.349,96.407,3.593,41.23,224,0.875,bilinear,+6.201,+2.221,-4 +bat_resnext26ts,84.638,15.362,96.272,3.728,10.73,256,0.900,bicubic,+6.396,+2.172,+8 +tf_efficientnetv2_b0.in1k,84.625,15.375,96.274,3.726,7.14,224,0.875,bicubic,+6.269,+2.250,+2 +efficientnet_b1.ft_in1k,84.608,15.392,96.332,3.668,7.79,256,1.000,bicubic,+5.814,+1.990,-25 +regnetx_040,84.600,15.400,96.383,3.617,22.12,224,0.875,bicubic,+6.118,+2.139,-11 +efficientnet_es.ra_in1k,84.591,15.409,96.311,3.689,5.44,224,0.875,bicubic,+6.525,+2.385,+13 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,84.591,15.409,96.005,3.995,119.42,256,0.900,bicubic,+5.111,+1.867,-74 +hrnet_w30,84.572,15.428,96.388,3.612,37.71,224,0.875,bilinear,+6.366,+2.166,+4 +tf_mixnet_l.in1k,84.564,15.437,96.244,3.756,7.33,224,0.875,bicubic,+5.790,+2.246,-28 +wide_resnet101_2,84.557,15.443,96.349,3.651,126.89,224,0.875,bilinear,+5.701,+2.067,-35 +vit_small_patch16_224.augreg_in1k,84.538,15.462,96.276,3.724,22.05,224,0.900,bicubic,+5.692,+1.992,-35 +dla60x,84.523,15.477,96.285,3.715,17.35,224,0.875,bilinear,+6.277,+2.267,-4 +legacy_seresnet101,84.504,15.496,96.330,3.670,49.33,224,0.875,bilinear,+6.122,+2.066,-10 +cs3darknet_focus_m,84.478,15.522,96.419,3.580,9.30,288,0.950,bicubic,+7.200,+2.450,+43 +resnet26t,84.476,15.524,96.217,3.783,16.01,256,0.940,bicubic,+6.594,+2.377,+14 +coat_lite_tiny,84.450,15.550,96.368,3.632,5.72,224,0.900,bicubic,+6.938,+2.452,+31 +tf_efficientnet_em.in1k,84.450,15.550,96.180,3.820,6.90,240,0.882,bicubic,+6.320,+2.136,-1 +repvgg_b1,84.416,15.584,96.221,3.779,57.42,224,0.875,bilinear,+6.050,+2.123,-14 +efficientnet_b1_pruned.in1k,84.393,15.607,96.140,3.860,6.33,240,0.882,bicubic,+6.157,+2.306,-8 +vit_base_patch16_224.augreg_in1k,84.391,15.610,96.046,3.954,86.57,224,0.900,bicubic,+5.237,+1.946,-61 +res2net50_26w_4s,84.365,15.635,96.082,3.918,25.70,224,0.875,bilinear,+6.401,+2.228,+4 +hardcorenas_f,84.326,15.674,96.025,3.975,8.20,224,0.875,bilinear,+6.222,+2.222,-5 +res2net50_14w_8s,84.309,15.691,96.072,3.929,25.06,224,0.875,bilinear,+6.159,+2.224,-8 +selecsls60,84.288,15.712,96.095,3.905,30.67,224,0.875,bicubic,+6.306,+2.267,0 +mobilevit_s,84.271,15.729,96.264,3.736,5.58,256,0.900,bicubic,+5.959,+2.118,-19 +regnetx_032,84.237,15.763,96.247,3.753,15.30,224,0.875,bicubic,+6.065,+2.159,-12 +res2next50,84.226,15.774,95.997,4.003,24.67,224,0.875,bilinear,+5.980,+2.105,-18 +convnext_atto_ols.a2_in1k,84.220,15.780,96.223,3.777,3.70,288,0.950,bicubic,+7.004,+2.543,+32 +gluon_resnet50_v1c,84.207,15.793,96.161,3.839,25.58,224,0.875,bicubic,+6.195,+2.173,-7 +dla102,84.190,15.810,96.206,3.794,33.27,224,0.875,bilinear,+6.158,+2.260,-9 +gcresnext26ts,84.164,15.836,96.086,3.914,10.48,256,0.900,bicubic,+6.350,+2.252,+4 +rexnet_100,84.162,15.838,96.255,3.745,4.80,224,0.875,bicubic,+6.304,+2.385,+1 +seresnext26ts,84.149,15.851,96.074,3.926,10.39,256,0.900,bicubic,+6.283,+2.284,-3 +convnext_atto.d2_in1k,84.147,15.853,96.200,3.800,3.70,288,0.950,bicubic,+7.133,+2.500,+34 +tf_inception_v3,84.136,15.864,95.920,4.080,23.83,299,0.875,bicubic,+6.276,+2.280,-3 +res2net50_48w_2s,84.126,15.874,95.965,4.035,25.29,224,0.875,bilinear,+6.604,+2.411,+10 +resnet34d,84.098,15.902,95.978,4.022,21.82,224,0.875,bicubic,+6.982,+2.596,+26 +tf_efficientnet_lite2.in1k,84.094,15.906,96.069,3.931,6.09,260,0.890,bicubic,+6.626,+2.315,+10 +xcit_tiny_12_p16_224,84.090,15.911,96.234,3.766,6.72,224,1.000,bicubic,+6.969,+2.522,+23 +efficientnet_b0.ra_in1k,84.038,15.962,95.956,4.044,5.29,224,0.875,bicubic,+6.340,+2.424,-3 +poolformer_s12,84.032,15.968,96.161,3.839,11.92,224,0.900,bicubic,+6.802,+2.657,+18 +crossvit_9_dagger_240,84.015,15.985,96.084,3.916,8.78,240,0.875,bicubic,+7.035,+2.474,+27 +hardcorenas_e,83.968,16.032,95.898,4.101,8.07,224,0.875,bilinear,+6.174,+2.204,-8 +gmixer_24_224,83.968,16.032,95.849,4.151,24.72,224,0.875,bicubic,+5.932,+2.185,-23 +tf_efficientnet_cc_b0_8e.in1k,83.966,16.034,96.065,3.935,24.01,224,0.875,bicubic,+6.058,+2.411,-17 +tv_resnext50_32x4d,83.959,16.041,95.960,4.040,25.03,224,0.875,bilinear,+6.339,+2.264,-5 +regnety_016,83.955,16.045,96.005,3.995,11.20,224,0.875,bicubic,+6.093,+2.285,-16 +gluon_resnet50_v1b,83.940,16.060,96.012,3.988,25.56,224,0.875,bicubic,+6.360,+2.296,-3 +densenet161,83.906,16.094,96.010,3.990,28.68,224,0.875,bicubic,+6.548,+2.372,+4 +adv_inception_v3,83.902,16.098,95.935,4.065,23.83,299,0.875,bicubic,+6.320,+2.199,-6 +mobilenetv2_120d.ra_in1k,83.893,16.107,95.909,4.091,5.83,224,0.875,bicubic,+6.609,+2.417,+5 +seresnext26t_32x4d,83.878,16.122,95.931,4.069,16.81,224,0.875,bicubic,+5.892,+2.185,-29 +tv_resnet101,83.848,16.152,95.892,4.108,44.55,224,0.875,bilinear,+6.474,+2.352,-1 +tinynet_a.in1k,83.827,16.173,95.820,4.181,6.19,192,0.875,bicubic,+6.175,+2.284,-16 +inception_v3,83.761,16.239,95.879,4.121,23.83,299,0.875,bicubic,+6.321,+2.403,-5 +hardcorenas_d,83.759,16.241,95.734,4.266,7.50,224,0.875,bilinear,+6.327,+2.250,-5 +seresnext26d_32x4d,83.754,16.246,95.849,4.151,16.81,224,0.875,bicubic,+6.152,+2.241,-15 +xcit_nano_12_p8_224_dist,83.731,16.269,95.960,4.040,3.05,224,1.000,bicubic,+7.407,+2.870,+31 +dla60,83.729,16.271,95.933,4.067,22.04,224,0.875,bilinear,+6.697,+2.615,+8 +eca_resnext26ts,83.701,16.299,95.948,4.052,10.30,256,0.900,bicubic,+6.249,+2.382,-11 +repvgg_b1g4,83.699,16.301,96.020,3.980,39.97,224,0.875,bilinear,+6.105,+2.194,-18 +convmixer_1024_20_ks9_p14,83.682,16.318,95.896,4.104,24.38,224,0.960,bicubic,+6.736,+2.538,+9 +legacy_seresnet50,83.662,16.337,95.973,4.027,28.09,224,0.875,bilinear,+6.032,+2.225,-23 +tf_efficientnet_b0.ap_in1k,83.650,16.350,95.779,4.221,5.29,224,0.875,bicubic,+6.564,+2.523,+1 +skresnet34,83.641,16.359,95.933,4.067,22.28,224,0.875,bicubic,+6.729,+2.611,+9 +tf_efficientnet_cc_b0_4e.in1k,83.639,16.361,95.740,4.260,13.31,224,0.875,bicubic,+6.333,+2.406,-12 +resmlp_12_224,83.571,16.429,95.760,4.240,15.35,224,0.875,bicubic,+6.917,+2.580,+13 +densenet201,83.556,16.444,95.811,4.189,20.01,224,0.875,bicubic,+6.270,+2.333,-13 +mobilenetv3_large_100.miil_in21k_ft_in1k,83.556,16.444,95.452,4.548,5.48,224,0.875,bilinear,+5.640,+2.542,-42 +gernet_s,83.522,16.478,95.794,4.206,8.17,224,0.875,bilinear,+6.606,+2.662,+3 +legacy_seresnext26_32x4d,83.517,16.483,95.719,4.281,16.79,224,0.875,bicubic,+6.413,+2.403,-7 +tf_efficientnet_b0.aa_in1k,83.515,16.485,95.719,4.281,5.29,224,0.875,bicubic,+6.667,+2.491,+2 +mixnet_m.ft_in1k,83.515,16.485,95.689,4.311,5.01,224,0.875,bicubic,+6.255,+2.265,-14 +hrnet_w18,83.500,16.500,95.907,4.093,21.30,224,0.875,bilinear,+6.742,+2.463,+4 +densenetblur121d,83.472,16.527,95.822,4.178,8.00,224,0.875,bicubic,+6.885,+2.630,+9 +resnext26ts,83.464,16.536,95.728,4.272,10.30,256,0.900,bicubic,+6.684,+2.598,+1 +selecsls42b,83.457,16.543,95.745,4.255,32.46,224,0.875,bicubic,+6.283,+2.355,-16 +tf_efficientnet_lite1.in1k,83.344,16.656,95.642,4.358,5.42,240,0.882,bicubic,+6.702,+2.416,+3 +hardcorenas_c,83.342,16.658,95.706,4.294,5.52,224,0.875,bilinear,+6.288,+2.548,-13 +regnetx_016,83.195,16.805,95.740,4.260,9.19,224,0.875,bicubic,+6.245,+2.320,-10 +mobilenetv2_140.ra_in1k,83.182,16.818,95.689,4.311,6.11,224,0.875,bicubic,+6.666,+2.693,+6 +dpn68,83.178,16.822,95.597,4.402,12.61,224,0.875,bicubic,+6.860,+2.620,+8 +tf_efficientnet_es.in1k,83.178,16.822,95.585,4.415,5.44,224,0.875,bicubic,+6.584,+2.383,0 +xcit_nano_12_p16_384_dist,83.176,16.824,95.753,4.247,3.05,384,1.000,bicubic,+7.718,+3.059,+22 +tf_mixnet_m.in1k,83.176,16.824,95.461,4.539,5.01,224,0.875,bicubic,+6.234,+2.309,-12 +ese_vovnet19b_dw,83.109,16.890,95.779,4.221,6.54,224,0.875,bicubic,+6.311,+2.511,-10 +levit_128s,83.069,16.931,95.531,4.469,7.78,224,0.900,bicubic,+6.539,+2.665,-1 +resnet26d,83.050,16.950,95.604,4.396,16.01,224,0.875,bicubic,+6.354,+2.454,-9 +repvgg_a2,83.001,16.999,95.589,4.411,28.21,224,0.875,bilinear,+6.541,+2.585,-1 +tv_resnet50,82.958,17.042,95.467,4.533,25.56,224,0.875,bilinear,+6.820,+2.603,+2 +hardcorenas_b,82.873,17.128,95.392,4.607,5.18,224,0.875,bilinear,+6.335,+2.638,-6 +densenet121,82.823,17.177,95.585,4.415,7.98,224,0.875,bicubic,+7.245,+2.933,+11 +mobilevitv2_075,82.813,17.187,95.576,4.424,2.87,256,0.888,bicubic,+7.191,+2.808,+9 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,82.691,17.309,95.845,4.155,6.36,384,1.000,bicubic,+6.739,+2.585,+1 +densenet169,82.683,17.317,95.600,4.400,14.15,224,0.875,bicubic,+6.776,+2.574,+2 +edgenext_x_small,82.572,17.428,95.461,4.539,2.34,288,1.000,bicubic,+6.883,+2.695,+3 +mixnet_s.ft_in1k,82.525,17.476,95.356,4.644,4.13,224,0.875,bicubic,+6.532,+2.560,-4 +vit_small_patch32_224.augreg_in21k_ft_in1k,82.514,17.486,95.670,4.330,22.88,224,0.900,bicubic,+6.524,+2.398,-4 +regnety_008,82.493,17.508,95.487,4.513,6.26,224,0.875,bicubic,+6.177,+2.421,-8 +efficientnet_lite0.ra_in1k,82.382,17.619,95.279,4.721,4.65,224,0.875,bicubic,+6.898,+2.769,+6 +resnest14d,82.352,17.648,95.339,4.661,10.61,224,0.875,bilinear,+6.846,+2.821,+4 +hardcorenas_a,82.313,17.687,95.294,4.706,5.26,224,0.875,bilinear,+6.397,+2.780,-6 +efficientnet_es_pruned.in1k,82.296,17.704,95.303,4.697,5.44,224,0.875,bicubic,+7.296,+2.855,+15 +mobilenetv3_rw.rmsp_in1k,82.275,17.725,95.234,4.766,5.48,224,0.875,bicubic,+6.641,+2.526,-3 +semnasnet_100.rmsp_in1k,82.251,17.749,95.230,4.770,3.89,224,0.875,bicubic,+6.803,+2.626,+4 +mobilenetv3_large_100.ra_in1k,82.177,17.823,95.196,4.804,5.48,224,0.875,bicubic,+6.410,+2.654,-8 +resnet34,82.138,17.862,95.130,4.870,21.80,224,0.875,bilinear,+7.028,+2.846,+8 +mobilenetv2_110d.ra_in1k,82.070,17.930,95.076,4.923,4.52,224,0.875,bicubic,+7.034,+2.890,+9 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,82.066,17.934,95.489,4.511,5.72,224,0.900,bicubic,+6.612,+2.641,-1 +tf_mixnet_s.in1k,82.038,17.962,95.121,4.879,4.13,224,0.875,bicubic,+6.388,+2.493,-10 +repvgg_b0,82.001,17.999,95.100,4.900,15.82,224,0.875,bilinear,+6.849,+2.682,+1 +deit_tiny_distilled_patch16_224,81.997,18.003,95.141,4.859,5.91,224,0.900,bicubic,+7.487,+3.251,+17 +mixer_b16_224,81.976,18.024,94.449,5.551,59.88,224,0.875,bicubic,+5.376,+2.221,-31 +pit_ti_distilled_224,81.967,18.033,95.145,4.855,5.10,224,0.900,bicubic,+7.437,+3.049,+14 +hrnet_w18_small_v2,81.961,18.039,95.164,4.836,15.60,224,0.875,bilinear,+6.847,+2.748,-1 +tf_efficientnet_lite0.in1k,81.952,18.048,95.168,4.832,4.65,224,0.875,bicubic,+7.122,+2.992,+5 +resnet26,81.944,18.056,95.241,4.759,16.00,224,0.875,bicubic,+6.652,+2.671,-7 +tinynet_b.in1k,81.871,18.129,94.878,5.122,3.73,188,0.875,bicubic,+6.897,+2.690,+1 +tf_mobilenetv3_large_100.in1k,81.848,18.152,95.070,4.930,5.48,224,0.875,bilinear,+6.330,+2.464,-15 +tv_densenet121,81.726,18.274,95.034,4.966,7.98,224,0.875,bicubic,+6.988,+2.884,+3 +regnety_006,81.700,18.300,95.115,4.885,6.06,224,0.875,bicubic,+6.454,+2.583,-10 +dla34,81.658,18.342,94.878,5.122,15.74,224,0.875,bilinear,+7.028,+2.800,+4 +xcit_nano_12_p8_224,81.643,18.357,95.273,4.727,3.05,224,1.000,bicubic,+7.729,+3.101,+12 +crossvit_9_240,81.615,18.385,94.978,5.022,8.55,240,0.875,bicubic,+7.651,+3.010,+10 +mobilevit_xs,81.566,18.434,95.034,4.966,2.32,256,0.900,bicubic,+6.922,+2.682,0 +fbnetc_100.rmsp_in1k,81.559,18.441,94.970,5.030,5.57,224,0.875,bilinear,+6.436,+2.584,-13 +legacy_seresnet34,81.534,18.466,94.899,5.101,21.96,224,0.875,bilinear,+6.726,+2.775,-5 +gluon_resnet34_v1b,81.500,18.500,94.810,5.190,21.80,224,0.875,bicubic,+6.912,+2.820,-1 +regnetx_008,81.485,18.515,95.059,4.941,7.26,224,0.875,bicubic,+6.447,+2.724,-13 +mnasnet_100.rmsp_in1k,81.459,18.541,94.899,5.101,4.38,224,0.875,bicubic,+6.801,+2.785,-6 +vgg19_bn,81.446,18.554,94.763,5.237,143.68,224,0.875,bilinear,+7.232,+2.921,-1 +vit_base_patch32_224.augreg_in1k,81.143,18.857,94.427,5.572,88.22,224,0.900,bicubic,+6.239,+2.649,-12 +convit_tiny,81.126,18.874,95.044,4.955,5.71,224,0.875,bicubic,+8.010,+3.331,+10 +crossvit_tiny_240,81.088,18.912,94.987,5.013,7.01,240,0.875,bicubic,+7.764,+3.071,+6 +spnasnet_100.rmsp_in1k,80.878,19.122,94.526,5.474,4.42,224,0.875,bilinear,+6.794,+2.708,-4 +ghostnet_100,80.699,19.301,94.291,5.709,5.18,224,0.875,bilinear,+6.721,+2.835,-3 +regnety_004,80.659,19.341,94.686,5.314,4.34,224,0.875,bicubic,+6.624,+2.934,-5 +skresnet18,80.637,19.363,94.378,5.622,11.96,224,0.875,bicubic,+7.599,+3.210,+6 +regnetx_006,80.629,19.371,94.524,5.476,6.20,224,0.875,bicubic,+6.777,+2.852,-3 +pit_ti_224,80.605,19.395,94.618,5.383,4.85,224,0.900,bicubic,+7.693,+3.216,+7 +swsl_resnet18,80.575,19.425,94.743,5.256,11.69,224,0.875,bilinear,+7.299,+3.010,+1 +vgg16_bn,80.556,19.444,94.592,5.408,138.37,224,0.875,bilinear,+7.206,+3.086,-3 +semnasnet_075.rmsp_in1k,80.477,19.523,94.321,5.679,2.91,224,0.875,bicubic,+7.503,+3.185,+2 +tv_resnet34,80.389,19.611,94.436,5.564,21.80,224,0.875,bilinear,+7.077,+3.010,-3 +resnet18d,80.387,19.613,94.252,5.748,11.71,224,0.875,bicubic,+8.127,+3.556,+9 +mobilenetv2_100.ra_in1k,80.257,19.743,94.195,5.805,3.50,224,0.875,bicubic,+7.287,+3.179,0 +xcit_nano_12_p16_224_dist,80.212,19.788,94.361,5.639,3.05,224,1.000,bicubic,+7.910,+3.499,+6 +vit_base_patch32_224.sam,80.208,19.792,93.825,6.175,88.22,224,0.900,bicubic,+6.518,+2.811,-11 +ssl_resnet18,80.101,19.899,94.590,5.410,11.69,224,0.875,bilinear,+7.491,+3.174,-1 +tf_mobilenetv3_large_075.in1k,80.093,19.907,94.184,5.816,3.99,224,0.875,bilinear,+6.655,+2.834,-12 +deit_tiny_patch16_224,80.018,19.982,94.449,5.551,5.72,224,0.900,bicubic,+7.850,+3.331,+5 +hrnet_w18_small,79.555,20.445,93.898,6.102,13.19,224,0.875,bilinear,+7.213,+3.220,0 +vgg19,79.480,20.520,93.870,6.130,143.67,224,0.875,bilinear,+7.112,+2.998,-3 +regnetx_004,79.435,20.565,93.853,6.147,5.16,224,0.875,bicubic,+7.039,+3.023,-5 +resnet14t,79.239,20.761,93.606,6.394,10.08,224,0.950,bilinear,+6.889,+3.266,-4 +tf_mobilenetv3_large_minimal_100.in1k,79.222,20.778,93.706,6.294,3.92,224,0.875,bilinear,+6.974,+3.076,-1 +edgenext_xx_small,79.175,20.825,93.821,6.179,1.33,288,1.000,bicubic,+7.309,+3.277,+2 +legacy_seresnet18,79.153,20.847,93.783,6.217,11.78,224,0.875,bicubic,+7.409,+3.449,+3 +vgg16,79.038,20.962,93.646,6.354,138.36,224,0.875,bilinear,+7.444,+3.270,+3 +vgg13_bn,79.006,20.994,93.655,6.345,133.05,224,0.875,bilinear,+7.412,+3.273,+3 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,78.991,21.009,93.902,6.098,6.34,224,0.900,bicubic,+7.203,+3.074,-1 +lcnet_100.ra2_in1k,78.914,21.086,93.550,6.450,2.95,224,0.875,bicubic,+6.800,+3.172,-5 +pvt_v2_b0,78.752,21.248,93.849,6.151,3.67,224,0.900,bicubic,+8.096,+3.641,+3 +tinynet_c.in1k,78.438,21.562,93.140,6.860,2.46,184,0.875,bicubic,+7.206,+3.392,0 +gluon_resnet18_v1b,78.372,21.628,93.136,6.864,11.69,224,0.875,bicubic,+7.536,+3.376,0 +mobilevitv2_050,78.118,21.882,93.573,6.426,1.37,256,0.888,bicubic,+7.978,+3.647,+3 vgg11_bn,77.926,22.074,93.230,6.770,132.87,224,0.875,bilinear,+7.566,+3.428,0 -xcit_nano_12_p16_224,77.900,22.100,93.430,6.570,3.05,224,1.000,bicubic,+7.946,+3.674,+2 -regnety_002,77.411,22.589,92.912,7.088,3.16,224,0.875,bicubic,+7.155,+3.378,-1 -mixer_l16_224,77.287,22.713,90.574,9.426,208.20,224,0.875,bicubic,+5.221,+2.908,-11 -resnet18,77.279,22.721,92.760,7.240,11.69,224,0.875,bilinear,+7.531,+3.676,+1 -vgg13,77.227,22.773,92.689,7.311,133.05,224,0.875,bilinear,+7.301,+3.443,-1 -mobilevit_xxs,76.602,23.398,92.694,7.306,1.27,256,0.900,bicubic,+7.682,+3.748,+1 -vgg11,76.393,23.607,92.154,7.846,132.86,224,0.875,bilinear,+7.365,+3.526,-1 -resnet10t,76.222,23.778,92.224,7.776,5.44,224,0.950,bilinear,+7.914,+4.144,+2 -regnetx_002,76.119,23.881,92.211,7.789,2.68,224,0.875,bicubic,+7.365,+3.655,0 -lcnet_075,76.051,23.949,92.068,7.932,2.36,224,0.875,bicubic,+7.237,+3.704,-2 -dla60x_c,75.618,24.382,92.179,7.821,1.32,224,0.875,bilinear,+7.738,+3.745,+1 -mobilenetv3_small_100,74.911,25.089,91.496,8.504,2.54,224,0.875,bicubic,+7.253,+3.862,+1 -tf_mobilenetv3_small_100,74.717,25.283,91.257,8.743,2.54,224,0.875,bilinear,+6.791,+3.589,-2 -tinynet_d,74.283,25.717,90.926,9.074,2.34,152,0.875,bicubic,+7.321,+3.862,0 -mnasnet_small,73.816,26.184,90.727,9.273,2.03,224,0.875,bicubic,+7.610,+4.221,0 -dla46x_c,73.632,26.368,91.110,8.890,1.07,224,0.875,bilinear,+7.680,+4.124,0 -mobilenetv2_050,73.468,26.532,90.317,9.682,1.97,224,0.875,bicubic,+7.524,+4.237,0 -tf_mobilenetv3_small_075,72.812,27.188,90.038,9.962,2.04,224,0.875,bilinear,+7.100,+3.908,0 -dla46_c,72.611,27.389,90.503,9.497,1.30,224,0.875,bilinear,+7.739,+4.201,+1 -mobilenetv3_small_075,72.323,27.677,89.671,10.329,2.04,224,0.875,bicubic,+7.085,+4.231,-1 -lcnet_050,70.385,29.616,88.821,11.179,1.88,224,0.875,bicubic,+7.291,+4.439,0 -tf_mobilenetv3_small_minimal_100,70.113,29.887,88.505,11.495,2.04,224,0.875,bilinear,+7.213,+4.271,0 -tinynet_e,66.813,33.187,86.276,13.724,2.04,106,0.875,bicubic,+6.957,+4.510,0 -mobilenetv3_small_050,64.671,35.329,84.867,15.133,1.59,224,0.875,bicubic,+6.781,+4.673,0 +xcit_nano_12_p16_224,77.900,22.100,93.430,6.570,3.05,224,1.000,bicubic,+7.946,+3.676,+2 +regnety_002,77.405,22.595,92.914,7.086,3.16,224,0.875,bicubic,+7.153,+3.374,-1 +mixer_l16_224,77.285,22.715,90.582,9.418,208.20,224,0.875,bicubic,+5.227,+2.914,-12 +resnet18,77.276,22.724,92.756,7.244,11.69,224,0.875,bilinear,+7.528,+3.678,+1 +vgg13,77.230,22.770,92.689,7.311,133.05,224,0.875,bilinear,+7.303,+3.444,-1 +mobilevit_xxs,76.595,23.405,92.685,7.315,1.27,256,0.900,bicubic,+7.683,+3.747,+1 +vgg11,76.384,23.616,92.154,7.846,132.86,224,0.875,bilinear,+7.360,+3.526,-1 +resnet10t,76.215,23.785,92.224,7.776,5.44,224,0.950,bilinear,+7.921,+4.146,+2 +regnetx_002,76.124,23.876,92.211,7.789,2.68,224,0.875,bicubic,+7.362,+3.655,0 +lcnet_075.ra2_in1k,76.053,23.947,92.066,7.934,2.36,224,0.875,bicubic,+7.235,+3.696,-2 +dla60x_c,75.637,24.363,92.177,7.823,1.32,224,0.875,bilinear,+7.745,+3.751,+1 +mobilenetv3_small_100.lamb_in1k,74.911,25.089,91.498,8.502,2.54,224,0.875,bicubic,+7.259,+3.862,+1 +tf_mobilenetv3_small_100.in1k,74.717,25.283,91.257,8.743,2.54,224,0.875,bilinear,+6.795,+3.593,-2 +tinynet_d.in1k,74.285,25.715,90.924,9.076,2.34,152,0.875,bicubic,+7.323,+3.858,0 +mnasnet_small.lamb_in1k,73.816,26.184,90.732,9.268,2.03,224,0.875,bicubic,+7.610,+4.224,0 +dla46x_c,73.647,26.353,91.095,8.905,1.07,224,0.875,bilinear,+7.677,+4.115,0 +mobilenetv2_050.lamb_in1k,73.465,26.535,90.320,9.680,1.97,224,0.875,bicubic,+7.523,+4.238,0 +tf_mobilenetv3_small_075.in1k,72.812,27.188,90.036,9.964,2.04,224,0.875,bilinear,+7.096,+3.906,0 +dla46_c,72.603,27.397,90.499,9.501,1.30,224,0.875,bilinear,+7.737,+4.207,+1 +mobilenetv3_small_075.lamb_in1k,72.330,27.670,89.666,10.334,2.04,224,0.875,bicubic,+7.084,+4.230,-1 +lcnet_050.ra2_in1k,70.400,29.601,88.823,11.177,1.88,224,0.875,bicubic,+7.300,+4.443,0 +tf_mobilenetv3_small_minimal_100.in1k,70.111,29.889,88.505,11.495,2.04,224,0.875,bilinear,+7.205,+4.275,0 +tinynet_e.in1k,66.810,33.190,86.274,13.726,2.04,106,0.875,bicubic,+6.954,+4.512,0 +mobilenetv3_small_050.lamb_in1k,64.669,35.331,84.865,15.136,1.59,224,0.875,bicubic,+6.779,+4.671,0 diff --git a/results/results-imagenet.csv b/results/results-imagenet.csv index d475f4da..199dfd6e 100644 --- a/results/results-imagenet.csv +++ b/results/results-imagenet.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation -beit_large_patch16_512,88.602,11.398,98.656,1.344,305.67,512,1.000,bicubic -beit_large_patch16_384,88.406,11.594,98.606,1.394,305.00,384,1.000,bicubic -tf_efficientnet_l2_ns,88.350,11.650,98.650,1.350,480.31,800,0.960,bicubic -tf_efficientnet_l2_ns_475,88.232,11.768,98.546,1.454,480.31,475,0.936,bicubic +eva_giant_patch14_560.m30m_ft_in22k_in1k,89.796,10.204,98.992,1.008,"1,014.45",560,1.000,bicubic +eva_giant_patch14_336.m30m_ft_in22k_in1k,89.568,10.432,98.952,1.048,"1,013.01",336,1.000,bicubic +eva_giant_patch14_336.clip_ft_in1k,89.476,10.524,98.824,1.176,"1,013.01",336,1.000,bicubic +eva_large_patch14_336.in22k_ft_in22k_in1k,89.204,10.796,98.850,1.150,304.53,336,1.000,bicubic +eva_giant_patch14_224.clip_ft_in1k,89.100,10.900,98.716,1.284,"1,012.56",224,1.000,bicubic +eva_large_patch14_336.in22k_ft_in1k,88.664,11.336,98.720,1.280,304.53,336,1.000,bicubic +beit_large_patch16_512.in22k_ft_in22k_in1k,88.598,11.402,98.656,1.344,305.67,512,1.000,bicubic +eva_large_patch14_196.in22k_ft_in22k_in1k,88.586,11.414,98.656,1.344,304.14,196,1.000,bicubic +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,88.574,11.426,98.660,1.340,632.46,336,1.000,bicubic +maxvit_xlarge_tf_512.in21k_ft_in1k,88.538,11.462,98.644,1.356,475.77,512,1.000,bicubic +beit_large_patch16_384.in22k_ft_in22k_in1k,88.404,11.596,98.608,1.392,305.00,384,1.000,bicubic +beitv2_large_patch16_224.in1k_ft_in22k_in1k,88.386,11.614,98.598,1.402,304.43,224,0.950,bicubic +tf_efficientnet_l2.ns_jft_in1k,88.352,11.648,98.650,1.350,480.31,800,0.960,bicubic +maxvit_xlarge_tf_384.in21k_ft_in1k,88.306,11.694,98.544,1.456,475.32,384,1.000,bicubic +vit_large_patch14_clip_336.openai_ft_in12k_in1k,88.266,11.734,98.532,1.468,304.53,336,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,88.246,11.754,98.550,1.450,632.05,224,1.000,bicubic +tf_efficientnet_l2.ns_jft_in1k_475,88.234,11.766,98.546,1.454,480.31,475,0.936,bicubic +maxvit_large_tf_512.in21k_ft_in1k,88.218,11.782,98.598,1.402,212.33,512,1.000,bicubic +maxvit_base_tf_512.in21k_ft_in1k,88.212,11.788,98.532,1.468,119.88,512,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,88.182,11.818,98.572,1.428,304.53,336,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in12k_in1k,88.168,11.832,98.544,1.456,304.20,224,1.000,bicubic +maxvit_large_tf_384.in21k_ft_in1k,87.992,12.008,98.566,1.434,212.03,384,1.000,bicubic +eva_large_patch14_196.in22k_ft_in1k,87.938,12.062,98.492,1.508,304.14,196,1.000,bicubic +maxvit_base_tf_384.in21k_ft_in1k,87.922,12.078,98.542,1.458,119.65,384,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,87.890,12.110,98.410,1.590,304.20,224,1.000,bicubic +vit_large_patch14_clip_224.openai_ft_in1k,87.852,12.148,98.428,1.572,304.20,224,1.000,bicubic +vit_large_patch14_clip_336.laion2b_ft_in1k,87.848,12.152,98.370,1.630,304.53,336,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k_384,87.748,12.252,98.554,1.446,350.20,384,1.000,bicubic deit3_large_patch16_384_in21ft1k,87.716,12.284,98.512,1.488,304.76,384,1.000,bicubic -convnext_xlarge_384_in22ft1k,87.544,12.456,98.486,1.514,350.20,384,1.000,bicubic -beit_large_patch16_224,87.476,12.524,98.304,1.696,304.43,224,0.900,bicubic -swinv2_large_window12to24_192to384_22kft1k,87.456,12.544,98.252,1.748,196.74,384,1.000,bicubic -convnext_large_384_in22ft1k,87.396,12.604,98.366,1.634,197.77,384,1.000,bicubic -deit3_huge_patch14_224_in21ft1k,87.180,12.820,98.260,1.740,632.13,224,1.000,bicubic -swin_large_patch4_window12_384,87.152,12.848,98.240,1.760,196.74,384,1.000,bicubic +vit_huge_patch14_clip_224.laion2b_ft_in1k,87.594,12.406,98.220,1.780,632.05,224,1.000,bicubic +beit_large_patch16_224.in22k_ft_in22k_in1k,87.476,12.524,98.304,1.696,304.43,224,0.900,bicubic +convnext_large.fb_in22k_ft_in1k_384,87.472,12.528,98.386,1.614,197.77,384,1.000,bicubic +swinv2_large_window12to24_192to384_22kft1k,87.458,12.542,98.252,1.748,196.74,384,1.000,bicubic +convnext_xlarge.fb_in22k_ft_in1k,87.338,12.662,98.328,1.672,350.20,288,1.000,bicubic +vit_large_patch14_clip_224.laion2b_ft_in1k,87.292,12.708,98.246,1.754,304.20,224,1.000,bicubic +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,87.218,12.782,98.034,1.966,86.86,384,1.000,bicubic +deit3_huge_patch14_224_in21ft1k,87.184,12.816,98.260,1.740,632.13,224,1.000,bicubic +swin_large_patch4_window12_384,87.148,12.852,98.234,1.766,196.74,384,1.000,bicubic swinv2_base_window12to24_192to384_22kft1k,87.108,12.892,98.236,1.764,87.92,384,1.000,bicubic -vit_large_patch16_384,87.080,12.920,98.300,1.700,304.72,384,1.000,bicubic -volo_d5_512,87.040,12.960,97.968,2.032,296.09,512,1.150,bicubic -convnext_xlarge_in22ft1k,87.002,12.998,98.212,1.788,350.20,224,0.875,bicubic -deit3_large_patch16_224_in21ft1k,86.982,13.018,98.238,1.762,304.37,224,1.000,bicubic -volo_d5_448,86.954,13.046,97.940,2.060,295.91,448,1.150,bicubic -swinv2_large_window12to16_192to256_22kft1k,86.946,13.054,98.110,1.890,196.74,256,0.900,bicubic -tf_efficientnet_b7_ns,86.832,13.168,98.096,1.904,66.35,600,0.949,bicubic -beit_base_patch16_384,86.798,13.202,98.136,1.864,86.74,384,1.000,bicubic -volo_d4_448,86.792,13.208,97.882,2.118,193.41,448,1.150,bicubic -deit3_base_patch16_384_in21ft1k,86.742,13.258,98.112,1.888,86.88,384,1.000,bicubic -convnext_large_in22ft1k,86.636,13.364,98.028,1.972,197.77,224,0.875,bicubic -convnext_base_384_in22ft1k,86.542,13.458,98.190,1.810,88.59,384,1.000,bicubic -volo_d3_448,86.496,13.504,97.710,2.290,86.63,448,1.000,bicubic -cait_m48_448,86.488,13.512,97.750,2.250,356.46,448,1.000,bicubic -tf_efficientnet_b6_ns,86.450,13.550,97.886,2.114,43.04,528,0.942,bicubic -swin_base_patch4_window12_384,86.432,13.568,98.056,1.944,87.90,384,1.000,bicubic -tf_efficientnetv2_xl_in21ft1k,86.420,13.580,97.868,2.132,208.12,512,1.000,bicubic -swin_large_patch4_window7_224,86.320,13.680,97.892,2.108,196.53,224,0.900,bicubic -tf_efficientnetv2_l_in21ft1k,86.304,13.696,97.980,2.020,118.52,480,1.000,bicubic -swinv2_base_window12to16_192to256_22kft1k,86.270,13.730,97.896,2.104,87.92,256,0.900,bicubic -vit_large_r50_s32_384,86.180,13.820,97.920,2.080,329.09,384,1.000,bicubic -dm_nfnet_f6,86.142,13.858,97.730,2.270,438.36,576,0.956,bicubic -tf_efficientnet_b5_ns,86.088,13.912,97.752,2.248,30.39,456,0.934,bicubic -volo_d5_224,86.070,13.930,97.578,2.422,295.46,224,0.960,bicubic +vit_large_patch16_384.augreg_in21k_ft_in1k,87.080,12.920,98.300,1.700,304.72,384,1.000,bicubic +volo_d5_512,87.044,12.956,97.968,2.032,296.09,512,1.150,bicubic +vit_base_patch16_clip_384.openai_ft_in12k_in1k,87.034,12.966,98.180,1.820,86.86,384,0.950,bicubic +convnext_large.fb_in22k_ft_in1k,87.016,12.984,98.206,1.794,197.77,288,1.000,bicubic +deit3_large_patch16_224_in21ft1k,86.978,13.022,98.238,1.762,304.37,224,1.000,bicubic +volo_d5_448,86.954,13.046,97.938,2.062,295.91,448,1.150,bicubic +swinv2_large_window12to16_192to256_22kft1k,86.936,13.064,98.108,1.892,196.74,256,0.900,bicubic +tf_efficientnet_b7.ns_jft_in1k,86.840,13.160,98.094,1.906,66.35,600,0.949,bicubic +tf_efficientnetv2_l.in21k_ft_in1k,86.806,13.194,98.134,1.866,118.52,480,1.000,bicubic +beit_base_patch16_384.in22k_ft_in22k_in1k,86.800,13.200,98.138,1.862,86.74,384,1.000,bicubic +convnext_base.fb_in22k_ft_in1k_384,86.794,13.206,98.264,1.736,88.59,384,1.000,bicubic +volo_d4_448,86.790,13.210,97.882,2.118,193.41,448,1.150,bicubic +tf_efficientnetv2_xl.in21k_ft_in1k,86.748,13.252,98.018,1.982,208.12,512,1.000,bicubic +deit3_base_patch16_384_in21ft1k,86.744,13.256,98.112,1.888,86.88,384,1.000,bicubic +vit_base_patch16_clip_384.laion2b_ft_in1k,86.620,13.380,98.010,1.990,86.86,384,1.000,bicubic +maxvit_base_tf_512.in1k,86.598,13.402,97.920,2.080,119.88,512,1.000,bicubic +maxvit_large_tf_512.in1k,86.518,13.482,97.884,2.116,212.33,512,1.000,bicubic +volo_d3_448,86.494,13.506,97.710,2.290,86.63,448,1.000,bicubic +cait_m48_448,86.484,13.516,97.754,2.246,356.46,448,1.000,bicubic +beitv2_base_patch16_224.in1k_ft_in22k_in1k,86.480,13.520,98.048,1.952,86.53,224,0.900,bicubic +tf_efficientnet_b6.ns_jft_in1k,86.452,13.548,97.882,2.118,43.04,528,0.942,bicubic +swin_base_patch4_window12_384,86.432,13.568,98.058,1.942,87.90,384,1.000,bicubic +swin_large_patch4_window7_224,86.320,13.680,97.896,2.104,196.53,224,0.900,bicubic +maxvit_base_tf_384.in1k,86.294,13.706,97.804,2.196,119.65,384,1.000,bicubic +convnext_base.fb_in22k_ft_in1k,86.280,13.720,98.090,1.910,88.59,288,1.000,bicubic +swinv2_base_window12to16_192to256_22kft1k,86.274,13.726,97.896,2.104,87.92,256,0.900,bicubic +maxvit_large_tf_384.in1k,86.236,13.764,97.690,2.310,212.03,384,1.000,bicubic +vit_base_patch8_224.augreg2_in21k_ft_in1k,86.212,13.788,97.832,2.168,86.58,224,0.900,bicubic +vit_base_patch16_clip_384.openai_ft_in1k,86.206,13.794,97.874,2.126,86.86,384,1.000,bicubic +vit_large_r50_s32_384.augreg_in21k_ft_in1k,86.184,13.816,97.918,2.082,329.09,384,1.000,bicubic +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,86.170,13.830,97.754,2.246,86.57,224,0.950,bicubic +dm_nfnet_f6,86.144,13.856,97.730,2.270,438.36,576,0.956,bicubic +maxvit_small_tf_512.in1k,86.088,13.912,97.758,2.242,69.13,512,1.000,bicubic +tf_efficientnet_b5.ns_jft_in1k,86.088,13.912,97.752,2.248,30.39,456,0.934,bicubic +volo_d5_224,86.068,13.932,97.578,2.422,295.46,224,0.960,bicubic cait_m36_384,86.054,13.946,97.730,2.270,271.22,384,1.000,bicubic -volo_d2_384,86.036,13.964,97.574,2.426,58.87,384,1.000,bicubic -vit_base_patch16_384,86.006,13.994,98.004,1.996,86.86,384,1.000,bicubic -xcit_large_24_p8_384_dist,85.998,14.002,97.684,2.316,188.93,384,1.000,bicubic -volo_d4_224,85.876,14.124,97.468,2.532,192.96,224,0.960,bicubic -vit_large_patch16_224,85.844,14.156,97.822,2.178,304.33,224,0.900,bicubic -convnext_base_in22ft1k,85.824,14.176,97.866,2.134,88.59,224,0.875,bicubic +volo_d2_384,86.036,13.964,97.572,2.428,58.87,384,1.000,bicubic +vit_base_patch16_384.augreg_in21k_ft_in1k,86.006,13.994,98.000,2.000,86.86,384,1.000,bicubic +tf_efficientnetv2_m.in21k_ft_in1k,86.004,13.996,97.942,2.058,54.14,480,1.000,bicubic +xcit_large_24_p8_384_dist,86.000,14.000,97.686,2.314,188.93,384,1.000,bicubic +vit_base_patch16_clip_224.openai_ft_in12k_in1k,85.930,14.070,97.724,2.276,86.57,224,0.950,bicubic +efficientnet_b5.in12k_ft_in1k,85.888,14.112,97.732,2.268,30.39,448,1.000,bicubic +volo_d4_224,85.872,14.128,97.468,2.532,192.96,224,0.960,bicubic +vit_large_patch16_224.augreg_in21k_ft_in1k,85.842,14.158,97.824,2.176,304.33,224,0.900,bicubic xcit_medium_24_p8_384_dist,85.816,14.184,97.592,2.408,84.32,384,1.000,bicubic -dm_nfnet_f5,85.816,14.184,97.486,2.514,377.21,544,0.954,bicubic -deit3_large_patch16_384,85.806,14.194,97.596,2.404,304.76,384,1.000,bicubic -vit_base_patch8_224,85.790,14.210,97.792,2.208,86.58,224,0.900,bicubic -xcit_large_24_p16_384_dist,85.752,14.248,97.538,2.462,189.10,384,1.000,bicubic -convnext_small_384_in22ft1k,85.724,14.276,97.864,2.136,50.22,384,1.000,bicubic -deit3_base_patch16_224_in21ft1k,85.716,14.284,97.744,2.256,86.59,224,1.000,bicubic +dm_nfnet_f5,85.814,14.186,97.488,2.512,377.21,544,0.954,bicubic +deit3_large_patch16_384,85.810,14.190,97.596,2.404,304.76,384,1.000,bicubic +vit_base_patch8_224.augreg_in21k_ft_in1k,85.796,14.204,97.790,2.210,86.58,224,0.900,bicubic +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,85.784,14.216,97.634,2.366,88.34,448,1.000,bicubic +convnext_small.fb_in22k_ft_in1k_384,85.778,14.222,97.892,2.108,50.22,384,1.000,bicubic +xcit_large_24_p16_384_dist,85.754,14.246,97.538,2.462,189.10,384,1.000,bicubic +deit3_base_patch16_224_in21ft1k,85.714,14.286,97.744,2.256,86.59,224,1.000,bicubic dm_nfnet_f4,85.714,14.286,97.520,2.480,316.07,512,0.951,bicubic -tf_efficientnetv2_m_in21ft1k,85.586,14.414,97.746,2.254,54.14,480,1.000,bicubic -xcit_small_24_p8_384_dist,85.554,14.446,97.572,2.428,47.63,384,1.000,bicubic +tf_efficientnetv2_l.in1k,85.670,14.330,97.474,2.526,118.52,480,1.000,bicubic +maxvit_tiny_tf_512.in1k,85.662,14.338,97.580,2.420,31.05,512,1.000,bicubic +flexivit_large.1200ep_in1k,85.644,14.356,97.542,2.458,304.36,240,0.950,bicubic +xcit_small_24_p8_384_dist,85.556,14.444,97.572,2.428,47.63,384,1.000,bicubic +flexivit_large.600ep_in1k,85.538,14.462,97.492,2.508,304.36,240,0.950,bicubic +vit_medium_patch16_gap_384.in12k_ft_in1k,85.536,14.464,97.634,2.366,39.03,384,0.950,bicubic +maxvit_small_tf_384.in1k,85.534,14.466,97.464,2.536,69.02,384,1.000,bicubic dm_nfnet_f3,85.522,14.478,97.462,2.538,254.92,416,0.940,bicubic -tf_efficientnetv2_l,85.488,14.512,97.372,2.628,118.52,480,1.000,bicubic -cait_s36_384,85.460,14.540,97.478,2.522,68.37,384,1.000,bicubic -ig_resnext101_32x48d,85.436,14.564,97.576,2.424,828.41,224,0.875,bilinear -xcit_medium_24_p16_384_dist,85.422,14.578,97.406,2.594,84.40,384,1.000,bicubic +vit_base_patch16_clip_224.laion2b_ft_in1k,85.468,14.532,97.576,2.424,86.57,224,1.000,bicubic +cait_s36_384,85.460,14.540,97.480,2.520,68.37,384,1.000,bicubic +ig_resnext101_32x48d,85.428,14.572,97.572,2.428,828.41,224,0.875,bilinear deit_base_distilled_patch16_384,85.422,14.578,97.332,2.668,87.63,384,1.000,bicubic -volo_d3_224,85.412,14.588,97.280,2.720,86.33,224,0.960,bicubic -xcit_large_24_p8_224_dist,85.398,14.602,97.410,2.590,188.93,224,1.000,bicubic -tf_efficientnet_b8_ap,85.372,14.628,97.294,2.706,87.41,672,0.954,bicubic -tf_efficientnet_b8,85.368,14.632,97.392,2.608,87.41,672,0.954,bicubic -swin_base_patch4_window7_224,85.250,14.750,97.562,2.438,87.77,224,0.900,bicubic +xcit_medium_24_p16_384_dist,85.412,14.588,97.406,2.594,84.40,384,1.000,bicubic +volo_d3_224,85.408,14.592,97.280,2.720,86.33,224,0.960,bicubic +xcit_large_24_p8_224_dist,85.396,14.604,97.410,2.590,188.93,224,1.000,bicubic +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,85.372,14.628,97.664,2.336,88.30,384,1.000,bicubic +tf_efficientnet_b8.ra_in1k,85.370,14.630,97.390,2.610,87.41,672,0.954,bicubic +tf_efficientnet_b8.ap_in1k,85.370,14.630,97.294,2.706,87.41,672,0.954,bicubic +vit_base_patch16_clip_224.openai_ft_in1k,85.280,14.720,97.440,2.560,86.57,224,0.900,bicubic +flexivit_large.300ep_in1k,85.280,14.720,97.406,2.594,304.36,240,0.950,bicubic +convnext_small.fb_in22k_ft_in1k,85.262,14.738,97.684,2.316,50.22,288,1.000,bicubic +swin_base_patch4_window7_224,85.252,14.748,97.562,2.438,87.77,224,0.900,bicubic volo_d1_384,85.250,14.750,97.214,2.786,26.78,384,1.000,bicubic -beit_base_patch16_224,85.228,14.772,97.656,2.344,86.53,224,0.900,bicubic -deit3_huge_patch14_224,85.206,14.794,97.358,2.642,632.13,224,0.900,bicubic -volo_d2_224,85.194,14.806,97.188,2.812,58.68,224,0.960,bicubic -tf_efficientnet_b4_ns,85.160,14.840,97.470,2.530,19.34,380,0.922,bicubic -tf_efficientnet_b7_ap,85.120,14.880,97.252,2.748,66.35,600,0.949,bicubic -ig_resnext101_32x32d,85.100,14.900,97.434,2.566,468.53,224,0.875,bilinear -xcit_small_24_p16_384_dist,85.088,14.912,97.308,2.692,47.67,384,1.000,bicubic -xcit_small_12_p8_384_dist,85.080,14.920,97.280,2.720,26.21,384,1.000,bicubic -deit3_base_patch16_384,85.076,14.924,97.254,2.746,86.88,384,1.000,bicubic -xcit_medium_24_p8_224_dist,85.070,14.930,97.280,2.720,84.32,224,1.000,bicubic -dm_nfnet_f2,85.066,14.934,97.242,2.758,193.78,352,0.920,bicubic -cait_s24_384,85.050,14.950,97.348,2.652,47.06,384,1.000,bicubic -tf_efficientnetv2_m,85.036,14.964,97.278,2.722,54.14,480,1.000,bicubic +mvitv2_large,85.250,14.750,97.196,2.804,217.99,224,0.900,bicubic +beit_base_patch16_224.in22k_ft_in22k_in1k,85.236,14.764,97.656,2.344,86.53,224,0.900,bicubic +vit_base_patch32_clip_384.openai_ft_in12k_in1k,85.212,14.788,97.402,2.598,88.30,384,0.950,bicubic +tf_efficientnetv2_m.in1k,85.208,14.792,97.368,2.632,54.14,480,1.000,bicubic +deit3_huge_patch14_224,85.204,14.796,97.358,2.642,632.13,224,0.900,bicubic +volo_d2_224,85.196,14.804,97.188,2.812,58.68,224,0.960,bicubic +tf_efficientnet_b4.ns_jft_in1k,85.162,14.838,97.470,2.530,19.34,380,0.922,bicubic +tf_efficientnet_b7.ap_in1k,85.120,14.880,97.252,2.748,66.35,600,0.949,bicubic +vit_base_patch16_224.augreg2_in21k_ft_in1k,85.106,14.894,97.534,2.466,86.57,224,0.900,bicubic +maxvit_tiny_tf_384.in1k,85.106,14.894,97.380,2.620,30.98,384,1.000,bicubic +xcit_small_24_p16_384_dist,85.098,14.902,97.310,2.690,47.67,384,1.000,bicubic +ig_resnext101_32x32d,85.094,14.906,97.438,2.562,468.53,224,0.875,bilinear +xcit_small_12_p8_384_dist,85.088,14.912,97.282,2.718,26.21,384,1.000,bicubic +xcit_medium_24_p8_224_dist,85.072,14.928,97.278,2.722,84.32,224,1.000,bicubic +deit3_base_patch16_384,85.072,14.928,97.254,2.746,86.88,384,1.000,bicubic +dm_nfnet_f2,85.064,14.936,97.240,2.760,193.78,352,0.920,bicubic +cait_s24_384,85.046,14.954,97.346,2.654,47.06,384,1.000,bicubic regnetz_e8,85.030,14.970,97.264,2.736,57.70,320,1.000,bicubic resnetrs420,85.008,14.992,97.124,2.876,191.89,416,1.000,bicubic -vit_base_r50_s16_384,84.976,15.024,97.290,2.710,98.95,384,1.000,bicubic -ecaresnet269d,84.974,15.026,97.226,2.774,102.09,352,1.000,bicubic -tf_efficientnet_b7,84.934,15.066,97.206,2.794,66.35,600,0.949,bicubic -xcit_large_24_p16_224_dist,84.920,15.080,97.132,2.868,189.10,224,1.000,bicubic -resnetv2_152x4_bitm,84.918,15.082,97.442,2.558,936.53,480,1.000,bilinear +ecaresnet269d,84.976,15.024,97.226,2.774,102.09,352,1.000,bicubic +vit_base_r50_s16_384.orig_in21k_ft_in1k,84.972,15.028,97.288,2.712,98.95,384,1.000,bicubic +tf_efficientnet_b7.ra_in1k,84.936,15.064,97.204,2.796,66.35,600,0.949,bicubic +maxvit_large_tf_224.in1k,84.926,15.074,96.972,3.028,211.79,224,0.950,bicubic +xcit_large_24_p16_224_dist,84.918,15.082,97.132,2.868,189.10,224,1.000,bicubic +resnetv2_152x4_bitm,84.916,15.084,97.440,2.560,936.53,480,1.000,bilinear xcit_small_24_p8_224_dist,84.876,15.124,97.188,2.812,47.63,224,1.000,bicubic -deit3_small_patch16_384_in21ft1k,84.824,15.176,97.484,2.516,22.21,384,1.000,bicubic -efficientnetv2_rw_m,84.812,15.188,97.146,2.854,53.24,416,1.000,bicubic -tf_efficientnet_b6_ap,84.786,15.214,97.138,2.862,43.04,528,0.942,bicubic -deit3_large_patch16_224,84.762,15.238,97.038,2.962,304.37,224,0.900,bicubic -resnetrs350,84.712,15.288,96.990,3.010,163.96,384,1.000,bicubic -xcit_small_12_p16_384_dist,84.708,15.292,97.116,2.884,26.25,384,1.000,bicubic -eca_nfnet_l2,84.696,15.304,97.264,2.736,56.72,384,1.000,bicubic -dm_nfnet_f1,84.624,15.376,97.098,2.902,132.63,320,0.910,bicubic -swinv2_base_window16_256,84.592,15.408,97.074,2.926,87.92,256,0.900,bicubic -seresnextaa101d_32x8d,84.572,15.428,97.070,2.930,93.59,288,1.000,bicubic -convnext_small_in22ft1k,84.568,15.432,97.396,2.604,50.22,224,0.875,bicubic -vit_base_patch16_224,84.530,15.470,97.296,2.704,86.57,224,0.900,bicubic +maxvit_base_tf_224.in1k,84.860,15.140,96.990,3.010,119.47,224,0.950,bicubic +convnext_large.fb_in1k,84.846,15.154,97.212,2.788,197.77,288,1.000,bicubic +deit3_small_patch16_384_in21ft1k,84.824,15.176,97.486,2.514,22.21,384,1.000,bicubic +efficientnetv2_rw_m.agc_in1k,84.808,15.192,97.148,2.852,53.24,416,1.000,bicubic +tf_efficientnet_b6.ap_in1k,84.788,15.212,97.138,2.862,43.04,528,0.942,bicubic +deit3_large_patch16_224,84.764,15.236,97.038,2.962,304.37,224,0.900,bicubic +resnetrs350,84.720,15.280,96.988,3.012,163.96,384,1.000,bicubic +xcit_small_12_p16_384_dist,84.706,15.294,97.118,2.882,26.25,384,1.000,bicubic +eca_nfnet_l2,84.698,15.302,97.264,2.736,56.72,384,1.000,bicubic +flexivit_base.1200ep_in1k,84.664,15.336,96.992,3.008,86.59,240,0.950,bicubic +maxxvit_rmlp_small_rw_256,84.628,15.372,97.062,2.938,66.01,256,0.950,bicubic +dm_nfnet_f1,84.626,15.374,97.100,2.900,132.63,320,0.910,bicubic +coatnet_rmlp_2_rw_224,84.600,15.400,96.736,3.264,73.88,224,0.950,bicubic +swinv2_base_window16_256,84.594,15.406,97.074,2.926,87.92,256,0.900,bicubic +seresnextaa101d_32x8d,84.568,15.432,97.070,2.930,93.59,288,1.000,bicubic +deit3_medium_patch16_224_in21ft1k,84.560,15.440,97.188,2.812,38.85,224,1.000,bicubic +vit_base_patch16_224.augreg_in21k_ft_in1k,84.532,15.468,97.294,2.706,86.57,224,0.900,bicubic resnest269e,84.518,15.482,96.986,3.014,110.93,416,0.928,bicubic -resnetv2_152x2_bitm,84.510,15.490,97.434,2.566,236.34,448,1.000,bilinear -regnetz_040h,84.496,15.504,97.006,2.994,28.94,320,1.000,bicubic -resnetv2_101x3_bitm,84.444,15.556,97.382,2.618,387.93,448,1.000,bilinear -resnetrs200,84.440,15.560,97.080,2.920,93.21,320,1.000,bicubic -resnetrs270,84.436,15.564,96.974,3.026,129.86,352,1.000,bicubic -vit_large_r50_s32_224,84.430,15.570,97.166,2.834,328.99,224,0.900,bicubic -resmlp_big_24_224_in22ft1k,84.398,15.602,97.118,2.882,129.14,224,0.875,bicubic -xcit_large_24_p8_224,84.392,15.608,96.658,3.342,188.93,224,1.000,bicubic -seresnet152d,84.364,15.636,97.044,2.956,66.84,320,1.000,bicubic -seresnext101d_32x8d,84.362,15.638,96.918,3.082,93.59,288,1.000,bicubic -tf_efficientnetv2_s_in21ft1k,84.296,15.704,97.254,2.746,21.46,384,1.000,bicubic -convnext_large,84.296,15.704,96.894,3.106,197.77,224,0.875,bicubic -swsl_resnext101_32x8d,84.290,15.710,97.182,2.818,88.79,224,0.875,bilinear -xcit_medium_24_p16_224_dist,84.278,15.722,96.940,3.060,84.40,224,1.000,bicubic -vit_base_patch16_224_miil,84.272,15.728,96.802,3.198,86.54,224,0.875,bilinear +flexivit_base.600ep_in1k,84.518,15.482,96.936,3.064,86.59,240,0.950,bicubic +resnetv2_152x2_bitm,84.510,15.490,97.432,2.568,236.34,448,1.000,bilinear +regnetz_040h,84.494,15.506,97.006,2.994,28.94,320,1.000,bicubic +maxvit_rmlp_small_rw_224,84.484,15.516,96.762,3.238,64.90,224,0.900,bicubic +resnetrs200,84.448,15.552,97.082,2.918,93.21,320,1.000,bicubic +gcvit_base,84.448,15.552,96.844,3.156,90.32,224,0.875,bicubic +resnetv2_101x3_bitm,84.440,15.560,97.382,2.618,387.93,448,1.000,bilinear +vit_large_r50_s32_224.augreg_in21k_ft_in1k,84.434,15.566,97.164,2.836,328.99,224,0.900,bicubic +convnext_base.fb_in1k,84.434,15.566,96.972,3.028,88.59,288,1.000,bicubic +resnetrs270,84.434,15.566,96.970,3.030,129.86,352,1.000,bicubic +maxvit_small_tf_224.in1k,84.434,15.566,96.820,3.180,68.93,224,0.950,bicubic +vit_medium_patch16_gap_256.in12k_ft_in1k,84.430,15.570,97.212,2.788,38.86,256,0.950,bicubic +mvitv2_base,84.422,15.578,96.864,3.136,51.47,224,0.900,bicubic +resmlp_big_24_224_in22ft1k,84.394,15.606,97.120,2.880,129.14,224,0.875,bicubic +flexivit_base.300ep_in1k,84.394,15.606,96.882,3.118,86.59,240,0.950,bicubic +xcit_large_24_p8_224,84.392,15.608,96.656,3.344,188.93,224,1.000,bicubic +seresnext101d_32x8d,84.370,15.630,96.916,3.084,93.59,288,1.000,bicubic +seresnet152d,84.362,15.638,97.040,2.960,66.84,320,1.000,bicubic +tf_efficientnetv2_s.in21k_ft_in1k,84.302,15.698,97.252,2.748,21.46,384,1.000,bicubic +swsl_resnext101_32x8d,84.284,15.716,97.176,2.824,88.79,224,0.875,bilinear +xcit_medium_24_p16_224_dist,84.274,15.726,96.940,3.060,84.40,224,1.000,bicubic +vit_base_patch16_224_miil.in21k_ft_in1k,84.268,15.732,96.802,3.198,86.54,224,0.875,bilinear swinv2_base_window8_256,84.262,15.738,96.922,3.078,87.92,256,0.900,bicubic -tf_efficientnet_b5_ap,84.254,15.746,96.978,3.022,30.39,456,0.934,bicubic +tf_efficientnet_b5.ap_in1k,84.252,15.748,96.974,3.026,30.39,456,0.934,bicubic regnetz_040,84.236,15.764,96.932,3.068,27.12,320,1.000,bicubic -xcit_small_12_p8_224_dist,84.230,15.770,96.874,3.126,26.21,224,1.000,bicubic -swinv2_small_window16_256,84.210,15.790,96.870,3.130,49.73,256,0.900,bicubic -seresnext101_32x8d,84.204,15.796,96.874,3.126,93.57,288,1.000,bicubic -crossvit_18_dagger_408,84.194,15.806,96.818,3.182,44.61,408,1.000,bicubic -ig_resnext101_32x16d,84.170,15.830,97.198,2.802,194.03,224,0.875,bilinear -volo_d1_224,84.164,15.836,96.774,3.226,26.63,224,0.960,bicubic -pit_b_distilled_224,84.142,15.858,96.856,3.144,74.79,224,0.900,bicubic -tf_efficientnet_b6,84.108,15.892,96.888,3.112,43.04,528,0.942,bicubic -convnext_tiny_384_in22ft1k,84.076,15.924,97.158,2.842,28.59,384,1.000,bicubic -cait_xs24_384,84.064,15.936,96.890,3.110,26.67,384,1.000,bicubic -regnetz_d8,84.052,15.948,96.996,3.004,23.37,320,1.000,bicubic -regnetz_d8_evos,84.050,15.950,96.996,3.004,23.46,320,0.950,bicubic -vit_small_r26_s32_384,84.048,15.952,97.328,2.672,36.47,384,1.000,bicubic -tf_efficientnet_b3_ns,84.048,15.952,96.912,3.088,12.23,300,0.904,bicubic -regnetz_d32,84.024,15.976,96.868,3.132,27.58,320,0.950,bicubic -resnetv2_50x3_bitm,84.012,15.988,97.126,2.874,217.32,448,1.000,bilinear -eca_nfnet_l1,84.012,15.988,97.032,2.968,41.41,320,1.000,bicubic -resnet200d,83.960,16.040,96.824,3.176,64.69,320,1.000,bicubic -swin_s3_base_224,83.932,16.068,96.660,3.340,71.13,224,0.900,bicubic -regnety_080,83.928,16.072,96.888,3.112,39.18,288,1.000,bicubic -tf_efficientnetv2_s,83.884,16.116,96.698,3.302,21.46,384,1.000,bicubic -xcit_small_24_p16_224_dist,83.870,16.130,96.732,3.268,47.67,224,1.000,bicubic -swinv2_small_window8_256,83.854,16.146,96.642,3.358,49.73,256,0.900,bicubic -resnetv2_152x2_bit_teacher_384,83.844,16.156,97.116,2.884,236.34,384,1.000,bicubic -convnext_base,83.840,16.160,96.750,3.250,88.59,224,0.875,bicubic -xcit_small_24_p8_224,83.840,16.160,96.636,3.364,47.63,224,1.000,bicubic -crossvit_15_dagger_408,83.838,16.162,96.780,3.220,28.50,408,1.000,bicubic -resnest200e,83.828,16.172,96.892,3.108,70.20,320,0.909,bicubic -tf_efficientnet_b5,83.814,16.186,96.748,3.252,30.39,456,0.934,bicubic -efficientnetv2_rw_s,83.810,16.190,96.724,3.276,23.94,384,1.000,bicubic -vit_small_patch16_384,83.800,16.200,97.100,2.900,22.20,384,1.000,bicubic +xcit_small_12_p8_224_dist,84.232,15.768,96.876,3.124,26.21,224,1.000,bicubic +maxvit_rmlp_tiny_rw_256,84.232,15.768,96.778,3.222,29.15,256,0.950,bicubic +vit_base_patch16_384.orig_in21k_ft_in1k,84.210,15.790,97.218,2.782,86.86,384,1.000,bicubic +swinv2_small_window16_256,84.206,15.794,96.870,3.130,49.73,256,0.900,bicubic +crossvit_18_dagger_408,84.196,15.804,96.818,3.182,44.61,408,1.000,bicubic +seresnext101_32x8d,84.192,15.808,96.874,3.126,93.57,288,1.000,bicubic +ig_resnext101_32x16d,84.170,15.830,97.196,2.804,194.03,224,0.875,bilinear +volo_d1_224,84.164,15.836,96.776,3.224,26.63,224,0.960,bicubic +pit_b_distilled_224,84.144,15.856,96.856,3.144,74.79,224,0.900,bicubic +tf_efficientnet_b6.aa_in1k,84.110,15.890,96.886,3.114,43.04,528,0.942,bicubic +convnext_tiny.fb_in22k_ft_in1k_384,84.080,15.920,97.142,2.858,28.59,384,1.000,bicubic +cait_xs24_384,84.062,15.938,96.888,3.112,26.67,384,1.000,bicubic +regnetz_d8,84.050,15.950,96.998,3.002,23.37,320,1.000,bicubic +regnetz_d8_evos,84.050,15.950,96.994,3.006,23.46,320,0.950,bicubic +tf_efficientnet_b3.ns_jft_in1k,84.048,15.952,96.910,3.090,12.23,300,0.904,bicubic +vit_small_r26_s32_384.augreg_in21k_ft_in1k,84.046,15.954,97.328,2.672,36.47,384,1.000,bicubic +regnetz_d32,84.022,15.978,96.866,3.134,27.58,320,0.950,bicubic +resnetv2_50x3_bitm,84.014,15.986,97.124,2.876,217.32,448,1.000,bilinear +eca_nfnet_l1,84.010,15.990,97.028,2.972,41.41,320,1.000,bicubic +resnet200d,83.962,16.038,96.824,3.176,64.69,320,1.000,bicubic +edgenext_base,83.960,16.040,96.768,3.232,18.51,320,1.000,bicubic +regnety_080,83.932,16.068,96.888,3.112,39.18,288,1.000,bicubic +swin_s3_base_224,83.930,16.070,96.662,3.338,71.13,224,0.900,bicubic +tresnet_v2_l,83.902,16.098,96.492,3.508,46.17,224,0.875,bilinear +tf_efficientnetv2_s.in1k,83.894,16.106,96.698,3.302,21.46,384,1.000,bicubic +gcvit_small,83.884,16.116,96.658,3.342,51.09,224,0.875,bicubic +xcit_small_24_p16_224_dist,83.862,16.138,96.728,3.272,47.67,224,1.000,bicubic +swinv2_small_window8_256,83.856,16.144,96.640,3.360,49.73,256,0.900,bicubic +resnetv2_152x2_bit_teacher_384,83.844,16.156,97.118,2.882,236.34,384,1.000,bicubic +crossvit_15_dagger_408,83.838,16.162,96.782,3.218,28.50,408,1.000,bicubic +xcit_small_24_p8_224,83.838,16.162,96.636,3.364,47.63,224,1.000,bicubic +resnest200e,83.832,16.168,96.894,3.106,70.20,320,0.909,bicubic +tf_efficientnet_b5.ra_in1k,83.812,16.188,96.748,3.252,30.39,456,0.934,bicubic +efficientnetv2_rw_s.ra2_in1k,83.808,16.192,96.724,3.276,23.94,384,1.000,bicubic +vit_small_patch16_384.augreg_in21k_ft_in1k,83.802,16.198,97.102,2.898,22.20,384,1.000,bicubic deit3_base_patch16_224,83.792,16.208,96.584,3.416,86.59,224,0.900,bicubic -swin_s3_small_224,83.774,16.226,96.452,3.548,49.74,224,0.900,bicubic -xcit_tiny_24_p8_384_dist,83.746,16.254,96.712,3.288,12.11,384,1.000,bicubic -xcit_medium_24_p8_224,83.738,16.262,96.394,3.606,84.32,224,1.000,bicubic -regnety_064,83.720,16.280,96.726,3.274,30.58,288,1.000,bicubic -resnetrs152,83.714,16.286,96.614,3.386,86.62,320,1.000,bicubic -regnetv_064,83.712,16.288,96.746,3.254,30.58,288,1.000,bicubic -regnety_160,83.692,16.308,96.776,3.224,83.59,288,1.000,bicubic -twins_svt_large,83.680,16.320,96.594,3.406,99.27,224,0.900,bicubic -resnet152d,83.678,16.322,96.740,3.260,60.21,320,1.000,bicubic +swin_s3_small_224,83.770,16.230,96.450,3.550,49.74,224,0.900,bicubic +mvitv2_small,83.768,16.232,96.570,3.430,34.87,224,0.900,bicubic +xcit_tiny_24_p8_384_dist,83.740,16.260,96.712,3.288,12.11,384,1.000,bicubic +pvt_v2_b5,83.740,16.260,96.634,3.366,81.96,224,0.900,bicubic +xcit_medium_24_p8_224,83.734,16.266,96.394,3.606,84.32,224,1.000,bicubic +regnety_064,83.716,16.284,96.720,3.280,30.58,288,1.000,bicubic +pvt_v2_b4,83.716,16.284,96.674,3.326,62.56,224,0.900,bicubic +regnetv_064,83.712,16.288,96.748,3.252,30.58,288,1.000,bicubic +resnetrs152,83.712,16.288,96.614,3.386,86.62,320,1.000,bicubic +convnext_small.fb_in1k,83.706,16.294,96.810,3.190,50.22,288,1.000,bicubic +regnety_160,83.686,16.314,96.776,3.224,83.59,288,1.000,bicubic +resnet152d,83.680,16.320,96.738,3.262,60.21,320,1.000,bicubic +twins_svt_large,83.678,16.322,96.594,3.406,99.27,224,0.900,bicubic +coatnet_1_rw_224,83.608,16.392,96.388,3.612,41.72,224,0.950,bicubic resmlp_big_24_distilled_224,83.588,16.412,96.648,3.352,129.14,224,0.875,bicubic -jx_nest_base,83.554,16.446,96.364,3.636,67.72,224,0.875,bicubic -cs3se_edgenet_x,83.548,16.452,96.666,3.334,50.72,320,1.000,bicubic -swinv2_cr_small_ns_224,83.486,16.514,96.484,3.516,49.70,224,0.900,bicubic -cait_s24_224,83.458,16.542,96.562,3.438,46.92,224,1.000,bicubic -deit3_small_patch16_384,83.428,16.572,96.676,3.324,22.21,384,1.000,bicubic -efficientnet_b4,83.424,16.576,96.598,3.402,19.34,384,1.000,bicubic -sequencer2d_l,83.406,16.594,96.500,3.500,54.30,224,0.875,bicubic -mobilevitv2_200_384_in22ft1k,83.400,16.600,96.582,3.418,18.45,384,1.000,bicubic +jx_nest_base,83.552,16.448,96.370,3.630,67.72,224,0.875,bicubic +cs3se_edgenet_x,83.548,16.452,96.670,3.330,50.72,320,1.000,bicubic +maxvit_tiny_rw_224,83.504,16.496,96.502,3.498,29.06,224,0.950,bicubic +swinv2_cr_small_ns_224,83.488,16.512,96.486,3.514,49.70,224,0.900,bicubic +cait_s24_224,83.452,16.548,96.564,3.436,46.92,224,1.000,bicubic +efficientnet_b4.ra2_in1k,83.428,16.572,96.596,3.404,19.34,384,1.000,bicubic +deit3_small_patch16_384,83.426,16.574,96.676,3.324,22.21,384,1.000,bicubic +sequencer2d_l,83.406,16.594,96.506,3.494,54.30,224,0.875,bicubic +gcvit_tiny,83.400,16.600,96.398,3.602,28.22,224,0.875,bicubic +maxvit_tiny_tf_224.in1k,83.398,16.602,96.588,3.412,30.92,224,0.950,bicubic +mobilevitv2_200_384_in22ft1k,83.394,16.606,96.580,3.420,18.45,384,1.000,bicubic deit_base_distilled_patch16_224,83.388,16.612,96.488,3.512,87.34,224,0.900,bicubic -dm_nfnet_f0,83.384,16.616,96.574,3.426,71.49,256,0.900,bicubic -vit_base_patch32_384,83.352,16.648,96.836,3.164,88.30,384,1.000,bicubic -swsl_resnext101_32x16d,83.350,16.650,96.844,3.156,194.03,224,0.875,bilinear -xcit_small_12_p16_224_dist,83.346,16.654,96.418,3.582,26.25,224,1.000,bicubic -xcit_small_12_p8_224,83.340,16.660,96.480,3.520,26.21,224,1.000,bicubic -tf_efficientnet_b4_ap,83.248,16.752,96.392,3.608,19.34,380,0.922,bicubic -swsl_resnext101_32x4d,83.240,16.760,96.760,3.240,44.18,224,0.875,bilinear -swin_small_patch4_window7_224,83.218,16.782,96.326,3.674,49.61,224,0.900,bicubic -regnetv_040,83.198,16.802,96.664,3.336,20.64,288,1.000,bicubic -xception65,83.174,16.826,96.592,3.408,39.92,299,0.940,bicubic -convnext_small,83.150,16.850,96.430,3.570,50.22,224,0.875,bicubic -resnext101_64x4d,83.144,16.856,96.374,3.626,83.46,288,1.000,bicubic -twins_svt_base,83.138,16.862,96.420,3.580,56.07,224,0.900,bicubic -swinv2_cr_small_224,83.138,16.862,96.098,3.902,49.70,224,0.900,bicubic -twins_pcpvt_large,83.136,16.864,96.604,3.396,60.99,224,0.900,bicubic +dm_nfnet_f0,83.386,16.614,96.572,3.428,71.49,256,0.900,bicubic +efficientformer_l7,83.386,16.614,96.540,3.460,82.23,224,0.950,bicubic +coatnet_rmlp_1_rw_224,83.358,16.642,96.456,3.544,41.69,224,0.950,bicubic +vit_base_patch32_384.augreg_in21k_ft_in1k,83.350,16.650,96.836,3.164,88.30,384,1.000,bicubic +xcit_small_12_p16_224_dist,83.350,16.650,96.414,3.586,26.25,224,1.000,bicubic +swsl_resnext101_32x16d,83.346,16.654,96.846,3.154,194.03,224,0.875,bilinear +xcit_small_12_p8_224,83.344,16.656,96.480,3.520,26.21,224,1.000,bicubic +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,83.306,16.694,96.530,3.470,88.22,224,0.900,bicubic +tf_efficientnet_b4.ap_in1k,83.248,16.752,96.392,3.608,19.34,380,0.922,bicubic +swsl_resnext101_32x4d,83.230,16.770,96.760,3.240,44.18,224,0.875,bilinear +swin_small_patch4_window7_224,83.212,16.788,96.322,3.678,49.61,224,0.900,bicubic +regnetv_040,83.194,16.806,96.660,3.340,20.64,288,1.000,bicubic +xception65,83.180,16.820,96.592,3.408,39.92,299,0.940,bicubic +resnext101_64x4d,83.148,16.852,96.372,3.628,83.46,288,1.000,bicubic +swinv2_cr_small_224,83.146,16.854,96.094,3.906,49.70,224,0.900,bicubic +twins_pcpvt_large,83.140,16.860,96.598,3.402,60.99,224,0.900,bicubic +twins_svt_base,83.136,16.864,96.418,3.582,56.07,224,0.900,bicubic xception65p,83.130,16.870,96.480,3.520,39.82,299,0.940,bicubic -jx_nest_small,83.120,16.880,96.330,3.670,38.35,224,0.875,bicubic -deit_base_patch16_384,83.106,16.894,96.370,3.630,86.86,384,1.000,bicubic -deit3_small_patch16_224_in21ft1k,83.076,16.924,96.776,3.224,22.06,224,1.000,bicubic -tresnet_m,83.074,16.926,96.120,3.880,31.39,224,0.875,bilinear -tresnet_xl_448,83.048,16.952,96.170,3.830,78.44,448,0.875,bilinear -regnety_040,83.036,16.964,96.510,3.490,20.65,288,1.000,bicubic -tf_efficientnet_b4,83.024,16.976,96.300,3.700,19.34,380,0.922,bicubic +pvt_v2_b3,83.126,16.874,96.556,3.444,45.24,224,0.900,bicubic +jx_nest_small,83.120,16.880,96.328,3.672,38.35,224,0.875,bicubic +deit_base_patch16_384,83.106,16.894,96.372,3.628,86.86,384,1.000,bicubic +deit3_medium_patch16_224,83.080,16.920,96.292,3.708,38.85,224,0.900,bicubic +tresnet_m,83.080,16.920,96.118,3.882,31.39,224,0.875,bilinear +deit3_small_patch16_224_in21ft1k,83.070,16.930,96.780,3.220,22.06,224,1.000,bicubic +tresnet_xl_448,83.050,16.950,96.174,3.826,78.44,448,0.875,bilinear +regnety_040,83.038,16.962,96.510,3.490,20.65,288,1.000,bicubic +maxxvit_rmlp_nano_rw_256,83.030,16.970,96.344,3.656,16.78,256,0.950,bicubic resnet101d,83.022,16.978,96.446,3.554,44.57,320,1.000,bicubic -mobilevitv2_175_384_in22ft1k,82.934,17.066,96.430,3.570,14.25,384,1.000,bicubic -convnext_tiny_in22ft1k,82.912,17.088,96.624,3.376,28.59,224,0.875,bicubic -xcit_large_24_p16_224,82.892,17.108,95.878,4.122,189.10,224,1.000,bicubic -resnest101e,82.888,17.112,96.320,3.680,48.28,256,0.875,bilinear -resnetv2_152x2_bit_teacher,82.868,17.132,96.568,3.432,236.34,224,0.875,bicubic -resnetv2_50x1_bit_distilled,82.822,17.178,96.522,3.478,25.55,224,0.875,bicubic -resnet152,82.818,17.182,96.132,3.868,60.19,224,0.950,bicubic -swinv2_tiny_window16_256,82.810,17.190,96.230,3.770,28.35,256,0.900,bicubic -sequencer2d_m,82.808,17.192,96.268,3.732,38.31,224,0.875,bicubic -pnasnet5large,82.782,17.218,96.042,3.958,86.06,331,0.911,bicubic -vit_relpos_base_patch16_clsgap_224,82.760,17.240,96.174,3.826,86.43,224,0.900,bicubic -nfnet_l0,82.752,17.248,96.518,3.482,35.07,288,1.000,bicubic -regnety_032,82.724,17.276,96.422,3.578,19.44,288,1.000,bicubic -cs3edgenet_x,82.722,17.278,96.376,3.624,47.82,288,1.000,bicubic -twins_pcpvt_base,82.708,17.292,96.350,3.650,43.83,224,0.900,bicubic -ig_resnext101_32x8d,82.698,17.302,96.632,3.368,88.79,224,0.875,bilinear -cs3sedarknet_x,82.654,17.346,96.346,3.654,35.40,288,1.000,bicubic -xcit_medium_24_p16_224,82.638,17.362,95.978,4.022,84.40,224,1.000,bicubic -regnetz_c16_evos,82.632,17.368,96.476,3.524,13.49,320,0.950,bicubic -nasnetalarge,82.618,17.382,96.044,3.956,88.75,331,0.911,bicubic -mobilevitv2_150_384_in22ft1k,82.590,17.410,96.316,3.684,10.59,384,1.000,bicubic -levit_384,82.588,17.412,96.018,3.982,39.13,224,0.900,bicubic -xcit_small_24_p16_224,82.584,17.416,96.000,4.000,47.67,224,1.000,bicubic -eca_nfnet_l0,82.578,17.422,96.490,3.510,24.14,288,1.000,bicubic -xcit_tiny_24_p16_384_dist,82.572,17.428,96.288,3.712,12.12,384,1.000,bicubic -vit_relpos_medium_patch16_cls_224,82.562,17.438,96.066,3.934,38.76,224,0.900,bicubic -xcit_tiny_24_p8_224_dist,82.560,17.440,96.168,3.832,12.11,224,1.000,bicubic -regnetz_c16,82.520,17.480,96.360,3.640,13.46,320,0.940,bicubic -crossvit_18_dagger_240,82.520,17.480,96.068,3.932,44.27,240,0.875,bicubic -resnet61q,82.518,17.482,96.130,3.870,36.85,288,1.000,bicubic -vit_relpos_base_patch16_224,82.486,17.514,96.142,3.858,86.43,224,0.900,bicubic -gc_efficientnetv2_rw_t,82.466,17.534,96.298,3.702,13.68,288,1.000,bicubic -vit_relpos_medium_patch16_224,82.462,17.538,96.086,3.914,38.75,224,0.900,bicubic -poolformer_m48,82.460,17.540,95.958,4.042,73.47,224,0.950,bicubic -pit_b_224,82.444,17.556,95.712,4.288,73.76,224,0.900,bicubic -crossvit_18_240,82.398,17.602,96.054,3.946,43.27,240,0.875,bicubic -xcit_tiny_12_p8_384_dist,82.386,17.614,96.222,3.778,6.71,384,1.000,bicubic -tf_efficientnet_b2_ns,82.384,17.616,96.246,3.754,9.11,260,0.890,bicubic -resnet51q,82.358,17.642,96.178,3.822,35.70,288,1.000,bilinear -ecaresnet50t,82.348,17.652,96.138,3.862,25.57,320,0.950,bicubic -efficientnetv2_rw_t,82.344,17.656,96.196,3.804,13.65,288,1.000,bicubic -sequencer2d_s,82.344,17.656,96.034,3.966,27.65,224,0.875,bicubic -mobilevitv2_200_in22ft1k,82.334,17.666,95.938,4.062,18.45,256,0.888,bicubic -resnetv2_101x1_bitm,82.332,17.668,96.516,3.484,44.54,448,1.000,bilinear -crossvit_15_dagger_240,82.326,17.674,95.956,4.044,28.21,240,0.875,bicubic -coat_lite_small,82.304,17.696,95.850,4.150,19.84,224,0.900,bicubic -mixer_b16_224_miil,82.304,17.696,95.720,4.280,59.88,224,0.875,bilinear -vit_relpos_medium_patch16_rpn_224,82.294,17.706,95.972,4.028,38.73,224,0.900,bicubic -convit_base,82.292,17.708,95.938,4.062,86.54,224,0.875,bicubic -resnetrs101,82.284,17.716,96.008,3.992,63.62,288,0.940,bicubic -tresnet_l_448,82.270,17.730,95.980,4.020,55.99,448,0.875,bilinear -efficientnet_b3,82.240,17.760,96.118,3.882,12.23,320,1.000,bicubic -vit_srelpos_medium_patch16_224,82.236,17.764,95.934,4.066,38.74,224,0.900,bicubic -cs3darknet_x,82.224,17.776,96.230,3.770,35.05,288,1.000,bicubic -convnext_tiny_hnf,82.220,17.780,95.866,4.134,28.59,224,0.950,bicubic -crossvit_base_240,82.216,17.784,95.832,4.168,105.03,240,0.875,bicubic -vit_base_patch16_rpn_224,82.200,17.800,95.996,4.004,86.54,224,0.900,bicubic -cait_xxs36_384,82.192,17.808,96.144,3.856,17.37,384,1.000,bicubic -swsl_resnext50_32x4d,82.176,17.824,96.232,3.768,25.03,224,0.875,bilinear -ecaresnet101d,82.170,17.830,96.048,3.952,44.57,224,0.875,bicubic -swin_s3_tiny_224,82.124,17.876,95.950,4.050,28.33,224,0.900,bicubic -visformer_small,82.108,17.892,95.876,4.124,40.22,224,0.900,bicubic -poolformer_m36,82.108,17.892,95.690,4.310,56.17,224,0.950,bicubic -halo2botnet50ts_256,82.068,17.932,95.642,4.358,22.64,256,0.950,bicubic -tresnet_xl,82.062,17.938,95.936,4.064,78.44,224,0.875,bilinear -convnext_tiny,82.062,17.938,95.854,4.146,28.59,224,0.875,bicubic -resnetv2_101,82.046,17.954,95.862,4.138,44.54,224,0.950,bicubic -fbnetv3_g,82.034,17.966,96.066,3.934,16.62,288,0.950,bilinear -pit_s_distilled_224,81.994,18.006,95.796,4.204,24.04,224,0.900,bicubic -deit_base_patch16_224,81.994,18.006,95.732,4.268,86.57,224,0.900,bicubic -resnetv2_50d_evos,81.978,18.022,95.912,4.088,25.59,288,0.950,bicubic -xcit_small_12_p16_224,81.972,18.028,95.812,4.188,26.25,224,1.000,bicubic -xception41p,81.968,18.032,95.794,4.206,26.91,299,0.940,bicubic -tf_efficientnetv2_b3,81.966,18.034,95.782,4.218,14.36,300,0.904,bicubic -mobilevitv2_175_in22ft1k,81.940,18.060,95.790,4.210,14.25,256,0.888,bicubic -resnet101,81.930,18.070,95.766,4.234,44.55,224,0.950,bicubic -xcit_tiny_24_p8_224,81.896,18.104,95.974,4.026,12.11,224,1.000,bicubic -vit_small_r26_s32_224,81.862,18.138,96.022,3.978,36.43,224,0.900,bicubic -ssl_resnext101_32x16d,81.856,18.144,96.096,3.904,194.03,224,0.875,bilinear -resnetv2_50d_gn,81.824,18.176,95.924,4.076,25.57,288,0.950,bicubic -tf_efficientnet_b3_ap,81.824,18.176,95.624,4.376,12.23,300,0.904,bicubic -swinv2_tiny_window8_256,81.810,18.190,95.994,4.006,28.35,256,0.900,bicubic -swinv2_cr_tiny_ns_224,81.786,18.214,95.822,4.178,28.33,224,0.900,bicubic -cs3sedarknet_l,81.776,18.224,95.970,4.030,21.91,288,0.950,bicubic -tresnet_m_448,81.706,18.294,95.572,4.428,31.39,448,0.875,bilinear -twins_svt_small,81.682,18.318,95.666,4.334,24.06,224,0.900,bicubic -halonet50ts,81.652,18.348,95.612,4.388,22.73,256,0.940,bicubic -tf_efficientnet_b3,81.638,18.362,95.718,4.282,12.23,300,0.904,bicubic -rexnet_200,81.628,18.372,95.668,4.332,16.37,224,0.875,bicubic -resnetaa50,81.618,18.382,95.810,4.190,25.56,288,1.000,bicubic -ssl_resnext101_32x8d,81.608,18.392,96.042,3.958,88.79,224,0.875,bilinear -edgenext_small,81.574,18.426,95.714,4.286,5.59,320,1.000,bicubic -lamhalobotnet50ts_256,81.552,18.448,95.504,4.496,22.57,256,0.950,bicubic -crossvit_15_240,81.544,18.456,95.690,4.310,27.53,240,0.875,bicubic -tf_efficientnet_lite4,81.534,18.466,95.666,4.334,13.01,380,0.920,bilinear -tnt_s_patch16_224,81.518,18.482,95.746,4.254,23.76,224,0.900,bicubic -levit_256,81.516,18.484,95.490,4.510,18.89,224,0.900,bicubic -vit_large_patch32_384,81.508,18.492,96.090,3.910,306.63,384,1.000,bicubic -tresnet_l,81.490,18.510,95.626,4.374,55.99,224,0.875,bilinear -convnext_nano,81.476,18.524,95.660,4.340,15.59,288,1.000,bicubic -mobilevitv2_150_in22ft1k,81.470,18.530,95.668,4.332,10.59,256,0.888,bicubic -wide_resnet50_2,81.456,18.544,95.530,4.470,68.88,224,0.875,bicubic -vit_relpos_small_patch16_224,81.454,18.546,95.828,4.172,21.98,224,0.900,bicubic -convit_small,81.428,18.572,95.742,4.258,27.78,224,0.875,bicubic -jx_nest_tiny,81.418,18.582,95.618,4.382,17.06,224,0.875,bicubic -poolformer_s36,81.418,18.582,95.448,4.552,30.86,224,0.900,bicubic -vit_small_patch16_224,81.396,18.604,96.138,3.862,22.05,224,0.900,bicubic -tf_efficientnet_b1_ns,81.386,18.614,95.736,4.264,7.79,240,0.882,bicubic -deit3_small_patch16_224,81.382,18.618,95.450,4.550,22.06,224,0.900,bicubic -swin_tiny_patch4_window7_224,81.376,18.624,95.542,4.458,28.29,224,0.900,bicubic -convmixer_1536_20,81.370,18.630,95.612,4.388,51.63,224,0.960,bicubic -gernet_l,81.350,18.650,95.536,4.464,31.08,256,0.875,bilinear -legacy_senet154,81.308,18.692,95.496,4.504,115.09,224,0.875,bilinear -efficientnet_el,81.306,18.694,95.534,4.466,10.59,300,0.904,bicubic -coat_mini,81.266,18.734,95.392,4.608,10.34,224,0.900,bicubic -seresnext50_32x4d,81.262,18.738,95.628,4.372,27.56,224,0.875,bicubic -gluon_senet154,81.230,18.770,95.346,4.654,115.09,224,0.875,bicubic -xcit_tiny_12_p8_224_dist,81.208,18.792,95.606,4.394,6.71,224,1.000,bicubic -deit_small_distilled_patch16_224,81.208,18.792,95.374,4.626,22.44,224,0.900,bicubic -swsl_resnet50,81.180,18.820,95.980,4.020,25.56,224,0.875,bilinear -resmlp_36_distilled_224,81.156,18.844,95.486,4.514,44.69,224,0.875,bicubic -sebotnet33ts_256,81.154,18.846,95.166,4.834,13.70,256,0.940,bicubic -lambda_resnet50ts,81.152,18.848,95.102,4.898,21.54,256,0.950,bicubic -mobilevitv2_200,81.140,18.860,95.368,4.632,18.45,256,0.888,bicubic -resnest50d_4s2x40d,81.108,18.892,95.562,4.438,30.42,224,0.875,bicubic -vit_srelpos_small_patch16_224,81.098,18.902,95.572,4.428,21.97,224,0.900,bicubic -pit_s_224,81.098,18.902,95.332,4.668,23.46,224,0.900,bicubic -resnext50_32x4d,81.096,18.904,95.326,4.674,25.03,224,0.950,bicubic -twins_pcpvt_small,81.090,18.910,95.642,4.358,24.11,224,0.900,bicubic -haloregnetz_b,81.044,18.956,95.198,4.802,11.68,224,0.940,bicubic -resmlp_big_24_224,81.030,18.970,95.020,4.980,129.14,224,0.875,bicubic -crossvit_small_240,81.016,18.984,95.456,4.544,26.86,240,0.875,bicubic -gluon_resnet152_v1s,81.014,18.986,95.414,4.586,60.32,224,0.875,bicubic -resnest50d_1s4x24d,80.984,19.016,95.324,4.676,25.68,224,0.875,bicubic -resnest50d,80.974,19.026,95.380,4.620,27.48,224,0.875,bilinear -sehalonet33ts,80.972,19.028,95.272,4.728,13.69,256,0.940,bicubic -cait_xxs24_384,80.962,19.038,95.644,4.356,12.03,384,1.000,bicubic -xcit_tiny_12_p16_384_dist,80.942,19.058,95.408,4.592,6.72,384,1.000,bicubic -gcresnet50t,80.934,19.066,95.454,4.546,25.90,256,0.900,bicubic -ssl_resnext101_32x4d,80.924,19.076,95.726,4.274,44.18,224,0.875,bilinear -gluon_seresnext101_32x4d,80.906,19.094,95.296,4.704,48.96,224,0.875,bicubic -cs3darknet_l,80.886,19.114,95.668,4.332,21.16,288,0.950,bicubic -gluon_seresnext101_64x4d,80.880,19.120,95.296,4.704,88.23,224,0.875,bicubic -cs3darknet_focus_l,80.874,19.126,95.692,4.308,21.15,288,0.950,bicubic -mobilevitv2_175,80.862,19.138,95.262,4.738,14.25,256,0.888,bicubic -efficientnet_b3_pruned,80.858,19.142,95.244,4.756,9.86,300,0.904,bicubic -ecaresnet101d_pruned,80.810,19.190,95.628,4.372,24.88,224,0.875,bicubic -regnety_320,80.804,19.196,95.244,4.756,145.05,224,0.875,bicubic -resmlp_24_distilled_224,80.764,19.236,95.222,4.778,30.02,224,0.875,bicubic -gernet_m,80.730,19.270,95.186,4.814,21.14,224,0.875,bilinear -vit_base_patch32_224,80.724,19.276,95.566,4.434,88.22,224,0.900,bicubic -regnetz_b16,80.712,19.288,95.474,4.526,9.72,288,0.940,bicubic -nf_resnet50,80.654,19.346,95.334,4.666,25.56,288,0.940,bicubic -efficientnet_b2,80.616,19.384,95.316,4.684,9.11,288,1.000,bicubic -gluon_resnext101_64x4d,80.604,19.396,94.992,5.008,83.46,224,0.875,bicubic -ecaresnet50d,80.598,19.402,95.318,4.682,25.58,224,0.875,bicubic -gcresnext50ts,80.578,19.422,95.170,4.830,15.67,256,0.900,bicubic -cspresnext50,80.544,19.456,95.324,4.676,20.57,256,0.887,bilinear -darknet53,80.538,19.462,95.420,4.580,41.61,288,1.000,bicubic -resnet50d,80.528,19.472,95.168,4.832,25.58,224,0.875,bicubic -darknetaa53,80.522,19.478,95.326,4.674,36.02,288,1.000,bilinear -repvgg_b3,80.496,19.504,95.264,4.736,123.09,224,0.875,bilinear -vit_small_patch32_384,80.490,19.510,95.600,4.400,22.92,384,1.000,bicubic -mixnet_xl,80.478,19.522,94.934,5.066,11.90,224,0.875,bicubic -gluon_resnet152_v1d,80.476,19.524,95.200,4.800,60.21,224,0.875,bicubic -inception_resnet_v2,80.460,19.540,95.306,4.694,55.84,299,0.897,bicubic -ecaresnetlight,80.456,19.544,95.246,4.754,30.16,224,0.875,bicubic -edgenext_small_rw,80.452,19.548,95.190,4.810,7.83,320,1.000,bicubic -xcit_tiny_24_p16_224_dist,80.448,19.552,95.212,4.788,12.12,224,1.000,bicubic -gluon_resnet101_v1d,80.418,19.582,95.014,4.986,44.57,224,0.875,bicubic -resnetv2_50,80.412,19.588,95.072,4.928,25.55,224,0.950,bicubic -regnety_120,80.376,19.624,95.122,4.878,51.82,224,0.875,bicubic +tf_efficientnet_b4.aa_in1k,83.022,16.978,96.300,3.700,19.34,380,0.922,bicubic +maxvit_rmlp_nano_rw_256,82.962,17.038,96.270,3.730,15.50,256,0.950,bicubic +mobilevitv2_175_384_in22ft1k,82.942,17.058,96.426,3.574,14.25,384,1.000,bicubic +maxvit_nano_rw_256,82.932,17.068,96.222,3.778,15.45,256,0.950,bicubic +xcit_large_24_p16_224,82.896,17.104,95.882,4.118,189.10,224,1.000,bicubic +resnest101e,82.890,17.110,96.320,3.680,48.28,256,0.875,bilinear +resnetv2_152x2_bit_teacher,82.862,17.138,96.568,3.432,236.34,224,0.875,bicubic +convnext_nano.in12k_ft_in1k,82.858,17.142,96.556,3.444,15.59,288,1.000,bicubic +resnet152,82.822,17.178,96.126,3.874,60.19,224,0.950,bicubic +resnetv2_50x1_bit_distilled,82.818,17.182,96.522,3.478,25.55,224,0.875,bicubic +swinv2_tiny_window16_256,82.810,17.190,96.232,3.768,28.35,256,0.900,bicubic +sequencer2d_m,82.806,17.194,96.268,3.732,38.31,224,0.875,bicubic +pnasnet5large,82.782,17.218,96.040,3.960,86.06,331,0.911,bicubic +vit_relpos_base_patch16_clsgap_224.sw_in1k,82.762,17.238,96.174,3.826,86.43,224,0.900,bicubic +nfnet_l0,82.750,17.250,96.516,3.484,35.07,288,1.000,bicubic +regnety_032,82.724,17.276,96.424,3.576,19.44,288,1.000,bicubic +twins_pcpvt_base,82.708,17.292,96.346,3.654,43.83,224,0.900,bicubic +cs3edgenet_x,82.702,17.298,96.370,3.630,47.82,288,1.000,bicubic +convnext_tiny.fb_in1k,82.700,17.300,96.136,3.864,28.59,288,1.000,bicubic +ig_resnext101_32x8d,82.688,17.312,96.636,3.364,88.79,224,0.875,bilinear +tf_efficientnetv2_b3.in21k_ft_in1k,82.672,17.328,96.624,3.376,14.36,300,0.900,bicubic +cs3sedarknet_x,82.654,17.346,96.354,3.646,35.40,288,1.000,bicubic +xcit_medium_24_p16_224,82.636,17.364,95.976,4.024,84.40,224,1.000,bicubic +regnetz_c16_evos,82.630,17.370,96.474,3.526,13.49,320,0.950,bicubic +nasnetalarge,82.620,17.380,96.046,3.954,88.75,331,0.911,bicubic +mobilevitv2_150_384_in22ft1k,82.594,17.406,96.318,3.682,10.59,384,1.000,bicubic +convnext_tiny_hnf.a2h_in1k,82.590,17.410,96.016,3.984,28.59,288,1.000,bicubic +levit_384,82.586,17.414,96.016,3.984,39.13,224,0.900,bicubic +vit_base_patch32_clip_224.laion2b_ft_in1k,82.582,17.418,96.202,3.798,88.22,224,0.900,bicubic +eca_nfnet_l0,82.580,17.420,96.490,3.510,24.14,288,1.000,bicubic +xcit_small_24_p16_224,82.580,17.420,96.004,3.996,47.67,224,1.000,bicubic +xcit_tiny_24_p16_384_dist,82.570,17.430,96.286,3.714,12.12,384,1.000,bicubic +xcit_tiny_24_p8_224_dist,82.562,17.438,96.170,3.830,12.11,224,1.000,bicubic +vit_relpos_medium_patch16_cls_224.sw_in1k,82.562,17.438,96.066,3.934,38.76,224,0.900,bicubic +efficientformer_l3,82.550,17.450,96.248,3.752,31.41,224,0.950,bicubic +flexivit_small.1200ep_in1k,82.526,17.474,96.136,3.864,22.06,240,0.950,bicubic +resnet61q,82.524,17.476,96.130,3.870,36.85,288,1.000,bicubic +regnetz_c16,82.518,17.482,96.360,3.640,13.46,320,0.940,bicubic +crossvit_18_dagger_240,82.518,17.482,96.072,3.928,44.27,240,0.875,bicubic +vit_relpos_base_patch16_224.sw_in1k,82.484,17.516,96.142,3.858,86.43,224,0.900,bicubic +vit_relpos_medium_patch16_224.sw_in1k,82.466,17.534,96.088,3.912,38.75,224,0.900,bicubic +gc_efficientnetv2_rw_t.agc_in1k,82.464,17.536,96.298,3.702,13.68,288,1.000,bicubic +poolformer_m48,82.462,17.538,95.958,4.042,73.47,224,0.950,bicubic +pit_b_224,82.446,17.554,95.710,4.290,73.76,224,0.900,bicubic +mvitv2_tiny,82.404,17.596,96.156,3.844,24.17,224,0.900,bicubic +crossvit_18_240,82.400,17.600,96.054,3.946,43.27,240,0.875,bicubic +coatnet_bn_0_rw_224,82.398,17.602,96.182,3.818,27.44,224,0.950,bicubic +coatnet_0_rw_224,82.390,17.610,95.836,4.164,27.44,224,0.950,bicubic +xcit_tiny_12_p8_384_dist,82.388,17.612,96.224,3.776,6.71,384,1.000,bicubic +tf_efficientnet_b2.ns_jft_in1k,82.380,17.620,96.248,3.752,9.11,260,0.890,bicubic +resnet51q,82.360,17.640,96.180,3.820,35.70,288,1.000,bilinear +flexivit_small.600ep_in1k,82.354,17.646,96.086,3.914,22.06,240,0.950,bicubic +efficientnetv2_rw_t.ra2_in1k,82.348,17.652,96.196,3.804,13.65,288,1.000,bicubic +ecaresnet50t,82.346,17.654,96.138,3.862,25.57,320,0.950,bicubic +sequencer2d_s,82.342,17.658,96.030,3.970,27.65,224,0.875,bicubic +resnetv2_101x1_bitm,82.332,17.668,96.518,3.482,44.54,448,1.000,bilinear +crossvit_15_dagger_240,82.332,17.668,95.956,4.044,28.21,240,0.875,bicubic +mobilevitv2_200_in22ft1k,82.324,17.676,95.940,4.060,18.45,256,0.888,bicubic +coat_lite_small,82.308,17.692,95.850,4.150,19.84,224,0.900,bicubic +mixer_b16_224_miil,82.308,17.692,95.716,4.284,59.88,224,0.875,bilinear +vit_relpos_medium_patch16_rpn_224.sw_in1k,82.298,17.702,95.974,4.026,38.73,224,0.900,bicubic +resnetrs101,82.288,17.712,96.008,3.992,63.62,288,0.940,bicubic +convit_base,82.288,17.712,95.938,4.062,86.54,224,0.875,bicubic +tresnet_l_448,82.268,17.732,95.976,4.024,55.99,448,0.875,bilinear +efficientnet_b3.ra2_in1k,82.242,17.758,96.114,3.886,12.23,320,1.000,bicubic +vit_srelpos_medium_patch16_224.sw_in1k,82.236,17.764,95.934,4.066,38.74,224,0.900,bicubic +cs3darknet_x,82.228,17.772,96.234,3.766,35.05,288,1.000,bicubic +crossvit_base_240,82.216,17.784,95.830,4.170,105.03,240,0.875,bicubic +vit_base_patch16_rpn_224.in1k,82.202,17.798,95.996,4.004,86.54,224,0.900,bicubic +pvt_v2_b2_li,82.196,17.804,96.104,3.896,22.55,224,0.900,bicubic +cait_xxs36_384,82.194,17.806,96.148,3.852,17.37,384,1.000,bicubic +swsl_resnext50_32x4d,82.182,17.818,96.230,3.770,25.03,224,0.875,bilinear +ecaresnet101d,82.172,17.828,96.046,3.954,44.57,224,0.875,bicubic +flexivit_small.300ep_in1k,82.172,17.828,96.024,3.976,22.06,240,0.950,bicubic +swin_s3_tiny_224,82.122,17.878,95.948,4.052,28.33,224,0.900,bicubic +poolformer_m36,82.110,17.890,95.688,4.312,56.17,224,0.950,bicubic +visformer_small,82.106,17.894,95.872,4.128,40.22,224,0.900,bicubic +pvt_v2_b2,82.076,17.924,95.962,4.038,25.36,224,0.900,bicubic +coatnet_rmlp_nano_rw_224,82.064,17.936,95.870,4.130,15.15,224,0.900,bicubic +halo2botnet50ts_256,82.060,17.940,95.636,4.364,22.64,256,0.950,bicubic +tresnet_xl,82.054,17.946,95.936,4.064,78.44,224,0.875,bilinear +fbnetv3_g.ra2_in1k,82.048,17.952,96.064,3.936,16.62,288,0.950,bilinear +resnetv2_101,82.030,17.970,95.860,4.140,44.54,224,0.950,bicubic +deit_base_patch16_224,81.998,18.002,95.734,4.266,86.57,224,0.900,bicubic +pit_s_distilled_224,81.996,18.004,95.798,4.202,24.04,224,0.900,bicubic +resnetv2_50d_evos,81.976,18.024,95.916,4.084,25.59,288,0.950,bicubic +xcit_small_12_p16_224,81.974,18.026,95.816,4.184,26.25,224,1.000,bicubic +tf_efficientnetv2_b3.in1k,81.970,18.030,95.782,4.218,14.36,300,0.904,bicubic +xception41p,81.958,18.042,95.794,4.206,26.91,299,0.940,bicubic +gcvit_xtiny,81.952,18.048,95.966,4.034,19.98,224,0.875,bicubic +coatnext_nano_rw_224,81.948,18.052,95.918,4.082,14.70,224,0.900,bicubic +mobilevitv2_175_in22ft1k,81.944,18.056,95.792,4.208,14.25,256,0.888,bicubic +resnet101,81.938,18.062,95.754,4.246,44.55,224,0.950,bicubic +vit_base_patch32_clip_224.openai_ft_in1k,81.930,18.070,95.968,4.032,88.22,224,0.900,bicubic +xcit_tiny_24_p8_224,81.900,18.100,95.976,4.024,12.11,224,1.000,bicubic +vit_small_r26_s32_224.augreg_in21k_ft_in1k,81.858,18.142,96.022,3.978,36.43,224,0.900,bicubic +ssl_resnext101_32x16d,81.844,18.156,96.096,3.904,194.03,224,0.875,bilinear +tf_efficientnet_b3.ap_in1k,81.822,18.178,95.624,4.376,12.23,300,0.904,bicubic +resnetv2_50d_gn,81.816,18.184,95.924,4.076,25.57,288,0.950,bicubic +swinv2_tiny_window8_256,81.806,18.194,95.994,4.006,28.35,256,0.900,bicubic +swinv2_cr_tiny_ns_224,81.790,18.210,95.824,4.176,28.33,224,0.900,bicubic +vit_base_patch16_224.orig_in21k_ft_in1k,81.786,18.214,96.122,3.878,86.57,224,0.900,bicubic +cs3sedarknet_l,81.774,18.226,95.968,4.032,21.91,288,0.950,bicubic +tresnet_m_448,81.714,18.286,95.572,4.428,31.39,448,0.875,bilinear +coatnet_nano_rw_224,81.700,18.300,95.638,4.362,15.14,224,0.900,bicubic +twins_svt_small,81.682,18.318,95.670,4.330,24.06,224,0.900,bicubic +halonet50ts,81.644,18.356,95.608,4.392,22.73,256,0.940,bicubic +tf_efficientnet_b3.aa_in1k,81.636,18.364,95.718,4.282,12.23,300,0.904,bicubic +rexnet_200,81.632,18.368,95.668,4.332,16.37,224,0.875,bicubic +resnetaa50,81.622,18.378,95.808,4.192,25.56,288,1.000,bicubic +ssl_resnext101_32x8d,81.616,18.384,96.038,3.962,88.79,224,0.875,bilinear +convnext_nano_ols.d1h_in1k,81.610,18.390,95.640,4.360,15.65,288,1.000,bicubic +edgenext_small,81.568,18.432,95.706,4.294,5.59,320,1.000,bicubic +lamhalobotnet50ts_256,81.544,18.456,95.504,4.496,22.57,256,0.950,bicubic +crossvit_15_240,81.536,18.464,95.692,4.308,27.53,240,0.875,bicubic +tf_efficientnet_lite4.in1k,81.536,18.464,95.668,4.332,13.01,380,0.920,bilinear +tnt_s_patch16_224,81.518,18.482,95.748,4.252,23.76,224,0.900,bicubic +levit_256,81.510,18.490,95.490,4.510,18.89,224,0.900,bicubic +vit_large_patch32_384.orig_in21k_ft_in1k,81.506,18.494,96.092,3.908,306.63,384,1.000,bicubic +tresnet_l,81.488,18.512,95.624,4.376,55.99,224,0.875,bilinear +mobilevitv2_150_in22ft1k,81.478,18.522,95.674,4.326,10.59,256,0.888,bicubic +convnext_nano.d1h_in1k,81.470,18.530,95.658,4.342,15.59,288,1.000,bicubic +vit_relpos_small_patch16_224.sw_in1k,81.462,18.538,95.828,4.172,21.98,224,0.900,bicubic +wide_resnet50_2,81.456,18.544,95.532,4.468,68.88,224,0.875,bicubic +convit_small,81.426,18.574,95.744,4.256,27.78,224,0.875,bicubic +poolformer_s36,81.416,18.584,95.446,4.554,30.86,224,0.900,bicubic +jx_nest_tiny,81.414,18.586,95.616,4.384,17.06,224,0.875,bicubic +vit_small_patch16_224.augreg_in21k_ft_in1k,81.402,18.598,96.134,3.866,22.05,224,0.900,bicubic +tf_efficientnet_b1.ns_jft_in1k,81.388,18.612,95.738,4.262,7.79,240,0.882,bicubic +deit3_small_patch16_224,81.386,18.614,95.450,4.550,22.06,224,0.900,bicubic +swin_tiny_patch4_window7_224,81.378,18.622,95.540,4.460,28.29,224,0.900,bicubic +convmixer_1536_20,81.376,18.624,95.614,4.386,51.63,224,0.960,bicubic +gernet_l,81.354,18.646,95.536,4.464,31.08,256,0.875,bilinear +efficientnet_el.ra_in1k,81.316,18.684,95.526,4.474,10.59,300,0.904,bicubic +legacy_senet154,81.310,18.690,95.496,4.504,115.09,224,0.875,bilinear +coat_mini,81.268,18.732,95.392,4.608,10.34,224,0.900,bicubic +seresnext50_32x4d,81.266,18.734,95.620,4.380,27.56,224,0.875,bicubic +gluon_senet154,81.234,18.766,95.348,4.652,115.09,224,0.875,bicubic +xcit_tiny_12_p8_224_dist,81.212,18.788,95.600,4.400,6.71,224,1.000,bicubic +deit_small_distilled_patch16_224,81.200,18.800,95.378,4.622,22.44,224,0.900,bicubic +swsl_resnet50,81.166,18.834,95.972,4.028,25.56,224,0.875,bilinear +lambda_resnet50ts,81.166,18.834,95.096,4.904,21.54,256,0.950,bicubic +resmlp_36_distilled_224,81.160,18.840,95.488,4.512,44.69,224,0.875,bicubic +sebotnet33ts_256,81.150,18.850,95.174,4.826,13.70,256,0.940,bicubic +mobilevitv2_200,81.136,18.864,95.366,4.634,18.45,256,0.888,bicubic +vit_small_patch16_384.augreg_in1k,81.120,18.880,95.574,4.426,22.20,384,1.000,bicubic +resnext50_32x4d,81.118,18.882,95.332,4.668,25.03,224,0.950,bicubic +resnest50d_4s2x40d,81.108,18.892,95.558,4.442,30.42,224,0.875,bicubic +vit_base_patch16_384.augreg_in1k,81.102,18.898,95.332,4.668,86.86,384,1.000,bicubic +vit_srelpos_small_patch16_224.sw_in1k,81.094,18.906,95.570,4.430,21.97,224,0.900,bicubic +pit_s_224,81.094,18.906,95.332,4.668,23.46,224,0.900,bicubic +twins_pcpvt_small,81.088,18.912,95.642,4.358,24.11,224,0.900,bicubic +haloregnetz_b,81.050,18.950,95.196,4.804,11.68,224,0.940,bicubic +resmlp_big_24_224,81.028,18.972,95.022,4.978,129.14,224,0.875,bicubic +crossvit_small_240,81.020,18.980,95.460,4.540,26.86,240,0.875,bicubic +gluon_resnet152_v1s,81.016,18.984,95.412,4.588,60.32,224,0.875,bicubic +resnest50d_1s4x24d,80.988,19.012,95.322,4.678,25.68,224,0.875,bicubic +resnest50d,80.974,19.026,95.378,4.622,27.48,224,0.875,bilinear +cait_xxs24_384,80.966,19.034,95.646,4.354,12.03,384,1.000,bicubic +sehalonet33ts,80.958,19.042,95.276,4.724,13.69,256,0.940,bicubic +gcresnet50t,80.940,19.060,95.454,4.546,25.90,256,0.900,bicubic +xcit_tiny_12_p16_384_dist,80.940,19.060,95.410,4.590,6.72,384,1.000,bicubic +ssl_resnext101_32x4d,80.924,19.076,95.728,4.272,44.18,224,0.875,bilinear +gluon_seresnext101_32x4d,80.904,19.096,95.294,4.706,48.96,224,0.875,bicubic +cs3darknet_l,80.896,19.104,95.670,4.330,21.16,288,0.950,bicubic +gluon_seresnext101_64x4d,80.894,19.106,95.308,4.692,88.23,224,0.875,bicubic +cs3darknet_focus_l,80.884,19.116,95.682,4.318,21.15,288,0.950,bicubic +mobilevitv2_175,80.860,19.140,95.254,4.746,14.25,256,0.888,bicubic +efficientnet_b3_pruned.in1k,80.858,19.142,95.242,4.758,9.86,300,0.904,bicubic +ecaresnet101d_pruned,80.818,19.182,95.628,4.372,24.88,224,0.875,bicubic +regnety_320,80.810,19.190,95.244,4.756,145.05,224,0.875,bicubic +resmlp_24_distilled_224,80.766,19.234,95.218,4.782,30.02,224,0.875,bicubic +gernet_m,80.732,19.268,95.184,4.816,21.14,224,0.875,bilinear +vit_base_patch32_224.augreg_in21k_ft_in1k,80.724,19.276,95.568,4.432,88.22,224,0.900,bicubic +regnetz_b16,80.716,19.284,95.478,4.522,9.72,288,0.940,bicubic +nf_resnet50,80.662,19.338,95.336,4.664,25.56,288,0.940,bicubic +efficientnet_b2.ra_in1k,80.612,19.388,95.318,4.682,9.11,288,1.000,bicubic +gluon_resnext101_64x4d,80.604,19.396,94.988,5.012,83.46,224,0.875,bicubic +ecaresnet50d,80.592,19.408,95.320,4.680,25.58,224,0.875,bicubic +gcresnext50ts,80.580,19.420,95.170,4.830,15.67,256,0.900,bicubic +cspresnext50,80.546,19.454,95.320,4.680,20.57,256,0.887,bilinear +darknet53,80.534,19.466,95.420,4.580,41.61,288,1.000,bicubic +resnet50d,80.530,19.470,95.160,4.840,25.58,224,0.875,bicubic +darknetaa53,80.522,19.478,95.322,4.678,36.02,288,1.000,bilinear +maxvit_rmlp_pico_rw_256,80.516,19.484,95.212,4.788,7.52,256,0.950,bicubic +efficientformer_l1,80.502,19.498,94.998,5.002,12.29,224,0.950,bicubic +repvgg_b3,80.492,19.508,95.260,4.740,123.09,224,0.875,bilinear +vit_small_patch32_384.augreg_in21k_ft_in1k,80.480,19.520,95.598,4.402,22.92,384,1.000,bicubic +mixnet_xl.ra_in1k,80.476,19.524,94.936,5.064,11.90,224,0.875,bicubic +gluon_resnet152_v1d,80.474,19.526,95.206,4.794,60.21,224,0.875,bicubic +convnext_pico_ols.d1_in1k,80.464,19.536,95.242,4.758,9.06,288,1.000,bicubic +ecaresnetlight,80.462,19.538,95.248,4.752,30.16,224,0.875,bicubic +inception_resnet_v2,80.458,19.542,95.306,4.694,55.84,299,0.897,bicubic +edgenext_small_rw,80.456,19.544,95.192,4.808,7.83,320,1.000,bicubic +xcit_tiny_24_p16_224_dist,80.446,19.554,95.218,4.782,12.12,224,1.000,bicubic +resnetv2_50,80.432,19.568,95.080,4.920,25.55,224,0.950,bicubic +convnext_pico.d1_in1k,80.426,19.574,95.058,4.942,9.05,288,0.950,bicubic +gluon_resnet101_v1d,80.414,19.586,95.014,4.986,44.57,224,0.875,bicubic +mobilevitv2_150,80.376,19.624,95.060,4.940,10.59,256,0.888,bicubic resnet50,80.374,19.626,94.614,5.386,25.56,224,0.950,bicubic -mobilevitv2_150,80.368,19.632,95.064,4.936,10.59,256,0.888,bicubic -seresnet33ts,80.354,19.646,95.106,4.894,19.78,256,0.900,bicubic -resnetv2_50x1_bitm,80.342,19.658,95.686,4.314,25.55,448,1.000,bilinear -gluon_resnext101_32x4d,80.340,19.660,94.926,5.074,44.18,224,0.875,bicubic -ssl_resnext50_32x4d,80.326,19.674,95.412,4.588,25.03,224,0.875,bilinear -poolformer_s24,80.316,19.684,95.042,4.958,21.39,224,0.900,bicubic -rexnet_150,80.314,19.686,95.166,4.834,9.73,224,0.875,bicubic -tf_efficientnet_b2_ap,80.302,19.698,95.028,4.972,9.11,260,0.890,bicubic -efficientnet_el_pruned,80.298,19.702,95.214,4.786,10.59,300,0.904,bicubic -gluon_resnet101_v1s,80.298,19.702,95.162,4.838,44.67,224,0.875,bicubic -seresnet50,80.266,19.734,95.070,4.930,28.09,224,0.875,bicubic -tf_efficientnet_el,80.254,19.746,95.128,4.872,10.59,300,0.904,bicubic -regnetx_320,80.244,19.756,95.020,4.980,107.81,224,0.875,bicubic -vit_base_patch16_224_sam,80.244,19.756,94.754,5.246,86.57,224,0.900,bicubic -legacy_seresnext101_32x4d,80.222,19.778,95.014,4.986,48.96,224,0.875,bilinear -repvgg_b3g4,80.216,19.784,95.108,4.892,83.83,224,0.875,bilinear -tf_efficientnetv2_b2,80.208,19.792,95.044,4.956,10.10,260,0.890,bicubic -inception_v4,80.168,19.832,94.964,5.036,42.68,299,0.875,bicubic -dpn107,80.168,19.832,94.906,5.094,86.92,224,0.875,bicubic +regnety_120,80.366,19.634,95.126,4.874,51.82,224,0.875,bicubic +seresnet33ts,80.352,19.648,95.106,4.894,19.78,256,0.900,bicubic +resnetv2_50x1_bitm,80.342,19.658,95.684,4.316,25.55,448,1.000,bilinear +gluon_resnext101_32x4d,80.334,19.666,94.926,5.074,44.18,224,0.875,bicubic +ssl_resnext50_32x4d,80.318,19.682,95.406,4.594,25.03,224,0.875,bilinear +poolformer_s24,80.316,19.684,95.038,4.962,21.39,224,0.900,bicubic +rexnet_150,80.310,19.690,95.166,4.834,9.73,224,0.875,bicubic +gluon_resnet101_v1s,80.302,19.698,95.160,4.840,44.67,224,0.875,bicubic +efficientnet_el_pruned.in1k,80.300,19.700,95.218,4.782,10.59,300,0.904,bicubic +tf_efficientnet_b2.ap_in1k,80.300,19.700,95.028,4.972,9.11,260,0.890,bicubic +seresnet50,80.274,19.726,95.070,4.930,28.09,224,0.875,bicubic +tf_efficientnet_el.in1k,80.250,19.750,95.128,4.872,10.59,300,0.904,bicubic +regnetx_320,80.246,19.754,95.026,4.974,107.81,224,0.875,bicubic +vit_base_patch16_224.sam,80.242,19.758,94.756,5.244,86.57,224,0.900,bicubic +legacy_seresnext101_32x4d,80.228,19.772,95.018,4.982,48.96,224,0.875,bilinear +repvgg_b3g4,80.212,19.788,95.110,4.890,83.83,224,0.875,bilinear +tf_efficientnetv2_b2.in1k,80.208,19.792,95.042,4.958,10.10,260,0.890,bicubic +inception_v4,80.168,19.832,94.968,5.032,42.68,299,0.875,bicubic convmixer_768_32,80.164,19.836,95.072,4.928,21.11,224,0.960,bicubic -skresnext50_32x4d,80.154,19.846,94.646,5.354,27.48,224,0.875,bicubic -tf_efficientnet_b2,80.088,19.912,94.908,5.092,9.11,260,0.890,bicubic -eca_resnet33ts,80.080,19.920,94.972,5.028,19.68,256,0.900,bicubic -gcresnet33ts,80.076,19.924,94.994,5.006,19.88,256,0.900,bicubic -resnet50_gn,80.060,19.940,94.948,5.052,25.56,224,0.940,bicubic -cspdarknet53,80.056,19.944,95.086,4.914,27.64,256,0.887,bilinear -dpn92,80.020,19.980,94.830,5.170,37.67,224,0.875,bicubic -ens_adv_inception_resnet_v2,79.974,20.026,94.942,5.058,55.84,299,0.897,bicubic -efficientnet_b2_pruned,79.918,20.082,94.850,5.150,8.31,260,0.890,bicubic -gluon_resnet152_v1c,79.912,20.088,94.842,5.158,60.21,224,0.875,bicubic -gluon_seresnext50_32x4d,79.912,20.088,94.832,5.168,27.56,224,0.875,bicubic -resnetrs50,79.886,20.114,94.970,5.030,35.69,224,0.910,bicubic -xception71,79.870,20.130,94.924,5.076,42.34,299,0.903,bicubic -deit_small_patch16_224,79.864,20.136,95.048,4.952,22.05,224,0.900,bicubic -regnetx_160,79.854,20.146,94.830,5.170,54.28,224,0.875,bicubic -ecaresnet26t,79.852,20.148,95.084,4.916,16.01,320,0.950,bicubic -levit_192,79.836,20.164,94.790,5.210,10.95,224,0.900,bicubic -dpn131,79.826,20.174,94.708,5.292,79.25,224,0.875,bicubic -tf_efficientnet_lite3,79.818,20.182,94.914,5.086,8.20,300,0.904,bilinear +dpn107,80.156,19.844,94.910,5.090,86.92,224,0.875,bicubic +skresnext50_32x4d,80.156,19.844,94.642,5.358,27.48,224,0.875,bicubic +tf_efficientnet_b2.aa_in1k,80.086,19.914,94.908,5.092,9.11,260,0.890,bicubic +gcresnet33ts,80.082,19.918,94.998,5.002,19.88,256,0.900,bicubic +eca_resnet33ts,80.078,19.922,94.970,5.030,19.68,256,0.900,bicubic +cspdarknet53,80.058,19.942,95.084,4.916,27.64,256,0.887,bilinear +resnet50_gn,80.052,19.948,94.946,5.054,25.56,224,0.940,bicubic +dpn92,80.008,19.992,94.836,5.164,37.67,224,0.875,bicubic +ens_adv_inception_resnet_v2,79.982,20.018,94.938,5.062,55.84,299,0.897,bicubic +gluon_seresnext50_32x4d,79.918,20.082,94.822,5.178,27.56,224,0.875,bicubic +efficientnet_b2_pruned.in1k,79.916,20.084,94.856,5.144,8.31,260,0.890,bicubic +gluon_resnet152_v1c,79.910,20.090,94.840,5.160,60.21,224,0.875,bicubic +resnetrs50,79.892,20.108,94.968,5.032,35.69,224,0.910,bicubic +xception71,79.874,20.126,94.922,5.078,42.34,299,0.903,bicubic +deit_small_patch16_224,79.856,20.144,95.052,4.948,22.05,224,0.900,bicubic +regnetx_160,79.856,20.144,94.830,5.170,54.28,224,0.875,bicubic +ecaresnet26t,79.854,20.146,95.084,4.916,16.01,320,0.950,bicubic +levit_192,79.842,20.158,94.786,5.214,10.95,224,0.900,bicubic +dpn131,79.822,20.178,94.710,5.290,79.25,224,0.875,bicubic +tf_efficientnet_lite3.in1k,79.820,20.180,94.914,5.086,8.20,300,0.904,bilinear resmlp_36_224,79.770,20.230,94.886,5.114,44.69,224,0.875,bicubic -cait_xxs36_224,79.748,20.252,94.868,5.132,17.30,224,1.000,bicubic -gluon_xception65,79.722,20.278,94.860,5.140,39.92,299,0.903,bicubic -ecaresnet50d_pruned,79.718,20.282,94.876,5.124,19.94,224,0.875,bicubic -xcit_tiny_12_p8_224,79.694,20.306,95.048,4.952,6.71,224,1.000,bicubic -mobilevitv2_125,79.682,20.318,94.848,5.152,7.48,256,0.888,bicubic -gluon_resnet152_v1b,79.682,20.318,94.736,5.264,60.19,224,0.875,bicubic -fbnetv3_d,79.680,20.320,94.940,5.060,10.31,256,0.950,bilinear +cait_xxs36_224,79.750,20.250,94.866,5.134,17.30,224,1.000,bicubic +ecaresnet50d_pruned,79.716,20.284,94.880,5.120,19.94,224,0.875,bicubic +gluon_xception65,79.716,20.284,94.860,5.140,39.92,299,0.903,bicubic +gcvit_xxtiny,79.714,20.286,95.080,4.920,12.00,224,0.875,bicubic +xcit_tiny_12_p8_224,79.694,20.306,95.052,4.948,6.71,224,1.000,bicubic +gluon_resnet152_v1b,79.686,20.314,94.736,5.264,60.19,224,0.875,bicubic +mobilevitv2_125,79.684,20.316,94.850,5.150,7.48,256,0.888,bicubic +fbnetv3_d.ra2_in1k,79.680,20.320,94.944,5.056,10.31,256,0.950,bilinear resnext50d_32x4d,79.676,20.324,94.866,5.134,25.05,224,0.875,bicubic -dpn98,79.644,20.356,94.600,5.400,61.57,224,0.875,bicubic -gmlp_s16_224,79.640,20.360,94.624,5.376,19.42,224,0.875,bicubic -regnetx_120,79.592,20.408,94.734,5.266,46.11,224,0.875,bicubic -cspresnet50,79.582,20.418,94.708,5.292,21.62,256,0.887,bilinear -gluon_resnet101_v1c,79.536,20.464,94.578,5.422,44.57,224,0.875,bicubic -rexnet_130,79.502,20.498,94.682,5.318,7.56,224,0.875,bicubic -eca_halonext26ts,79.488,20.512,94.604,5.396,10.76,256,0.940,bicubic -vit_relpos_base_patch32_plus_rpn_256,79.486,20.514,94.140,5.860,119.42,256,0.900,bicubic -hrnet_w64,79.470,20.530,94.654,5.346,128.06,224,0.875,bilinear -tf_efficientnetv2_b1,79.466,20.534,94.722,5.278,8.14,240,0.882,bicubic -xcit_tiny_24_p16_224,79.444,20.556,94.888,5.112,12.12,224,1.000,bicubic -dla102x2,79.442,20.558,94.646,5.354,41.28,224,0.875,bilinear -resmlp_24_224,79.378,20.622,94.546,5.454,30.02,224,0.875,bicubic +gmlp_s16_224,79.642,20.358,94.628,5.372,19.42,224,0.875,bicubic +dpn98,79.642,20.358,94.598,5.402,61.57,224,0.875,bicubic +regnetx_120,79.596,20.404,94.738,5.262,46.11,224,0.875,bicubic +cspresnet50,79.574,20.426,94.712,5.288,21.62,256,0.887,bilinear +gluon_resnet101_v1c,79.534,20.466,94.578,5.422,44.57,224,0.875,bicubic +rexnet_130,79.500,20.500,94.682,5.318,7.56,224,0.875,bicubic +eca_halonext26ts,79.486,20.514,94.598,5.402,10.76,256,0.940,bicubic +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,79.480,20.520,94.138,5.862,119.42,256,0.900,bicubic +hrnet_w64,79.474,20.526,94.652,5.348,128.06,224,0.875,bilinear +tf_efficientnetv2_b1.in1k,79.462,20.538,94.722,5.278,8.14,240,0.882,bicubic +dla102x2,79.448,20.552,94.640,5.360,41.28,224,0.875,bilinear +xcit_tiny_24_p16_224,79.444,20.556,94.882,5.118,12.12,224,1.000,bicubic +resmlp_24_224,79.374,20.626,94.546,5.454,30.02,224,0.875,bicubic repvgg_b2g4,79.366,20.634,94.688,5.312,61.76,224,0.875,bilinear -gluon_resnext50_32x4d,79.360,20.640,94.426,5.574,25.03,224,0.875,bicubic -resnext101_32x8d,79.316,20.684,94.518,5.482,88.79,224,0.875,bilinear -tf_efficientnet_cc_b1_8e,79.314,20.686,94.370,5.630,39.72,240,0.882,bicubic -ese_vovnet39b,79.312,20.688,94.714,5.286,24.57,224,0.875,bicubic -pit_xs_distilled_224,79.308,20.692,94.366,5.634,11.00,224,0.900,bicubic -gluon_resnet101_v1b,79.304,20.696,94.520,5.480,44.55,224,0.875,bicubic -nf_regnet_b1,79.300,20.700,94.754,5.246,10.22,288,0.900,bicubic -hrnet_w48,79.300,20.700,94.514,5.486,77.47,224,0.875,bilinear -resnetblur50,79.294,20.706,94.634,5.366,25.56,224,0.875,bicubic -eca_botnext26ts_256,79.276,20.724,94.616,5.384,10.59,256,0.950,bicubic -tf_efficientnet_b1_ap,79.274,20.726,94.308,5.692,7.79,240,0.882,bicubic -botnet26t_256,79.258,20.742,94.528,5.472,12.49,256,0.950,bicubic -efficientnet_em,79.252,20.748,94.792,5.208,6.90,240,0.882,bicubic -ssl_resnet50,79.224,20.776,94.830,5.170,25.56,224,0.875,bilinear +gluon_resnext50_32x4d,79.354,20.646,94.426,5.574,25.03,224,0.875,bicubic +ese_vovnet39b,79.320,20.680,94.712,5.288,24.57,224,0.875,bicubic +resnext101_32x8d,79.308,20.692,94.518,5.482,88.79,224,0.875,bilinear +tf_efficientnet_cc_b1_8e.in1k,79.308,20.692,94.370,5.630,39.72,240,0.882,bicubic +gluon_resnet101_v1b,79.306,20.694,94.524,5.476,44.55,224,0.875,bicubic +pit_xs_distilled_224,79.306,20.694,94.364,5.636,11.00,224,0.900,bicubic +hrnet_w48,79.300,20.700,94.512,5.488,77.47,224,0.875,bilinear +nf_regnet_b1,79.292,20.708,94.748,5.252,10.22,288,0.900,bicubic +resnetblur50,79.286,20.714,94.638,5.362,25.56,224,0.875,bicubic +tf_efficientnet_b1.ap_in1k,79.280,20.720,94.306,5.694,7.79,240,0.882,bicubic +eca_botnext26ts_256,79.274,20.726,94.614,5.386,10.59,256,0.950,bicubic +botnet26t_256,79.272,20.728,94.528,5.472,12.49,256,0.950,bicubic +efficientnet_em.ra2_in1k,79.252,20.748,94.794,5.206,6.90,240,0.882,bicubic +ssl_resnet50,79.222,20.778,94.832,5.168,25.56,224,0.875,bilinear dpn68b,79.216,20.784,94.414,5.586,12.61,224,0.875,bicubic -resnet33ts,79.208,20.792,94.574,5.426,19.68,256,0.900,bicubic -regnetx_080,79.202,20.798,94.552,5.448,39.57,224,0.875,bicubic -res2net101_26w_4s,79.196,20.804,94.436,5.564,45.21,224,0.875,bilinear -fbnetv3_b,79.142,20.858,94.750,5.250,8.60,256,0.950,bilinear -halonet26t,79.112,20.888,94.314,5.686,12.48,256,0.950,bicubic -lambda_resnet26t,79.098,20.902,94.590,5.410,10.96,256,0.940,bicubic -coat_lite_mini,79.088,20.912,94.608,5.392,11.01,224,0.900,bicubic -legacy_seresnext50_32x4d,79.076,20.924,94.434,5.566,27.56,224,0.875,bilinear -regnetx_064,79.074,20.926,94.460,5.540,26.21,224,0.875,bicubic -gluon_resnet50_v1d,79.070,20.930,94.466,5.534,25.58,224,0.875,bicubic -xception,79.044,20.956,94.394,5.606,22.86,299,0.897,bicubic -resnet32ts,79.014,20.986,94.356,5.644,17.96,256,0.900,bicubic -mixnet_l,78.976,21.024,94.178,5.822,7.33,224,0.875,bicubic -lambda_resnet26rpt_256,78.964,21.036,94.426,5.574,10.99,256,0.940,bicubic -res2net50_26w_8s,78.952,21.048,94.306,5.694,48.40,224,0.875,bilinear -hrnet_w40,78.922,21.078,94.470,5.530,57.56,224,0.875,bilinear -hrnet_w44,78.896,21.104,94.370,5.630,67.06,224,0.875,bilinear -wide_resnet101_2,78.852,21.148,94.288,5.712,126.89,224,0.875,bilinear -tf_efficientnet_b1,78.828,21.172,94.198,5.802,7.79,240,0.882,bicubic +resnet33ts,79.214,20.786,94.574,5.426,19.68,256,0.900,bicubic +res2net50_26w_8s,79.200,20.800,94.368,5.632,48.40,224,0.875,bilinear +res2net101_26w_4s,79.198,20.802,94.432,5.568,45.21,224,0.875,bilinear +regnetx_080,79.194,20.806,94.560,5.440,39.57,224,0.875,bicubic +vit_base_patch16_224.augreg_in1k,79.154,20.846,94.100,5.900,86.57,224,0.900,bicubic +fbnetv3_b.ra2_in1k,79.150,20.850,94.746,5.254,8.60,256,0.950,bilinear +halonet26t,79.100,20.900,94.312,5.688,12.48,256,0.950,bicubic +lambda_resnet26t,79.096,20.904,94.592,5.408,10.96,256,0.940,bicubic +coat_lite_mini,79.088,20.912,94.604,5.396,11.01,224,0.900,bicubic +legacy_seresnext50_32x4d,79.078,20.922,94.436,5.564,27.56,224,0.875,bilinear +gluon_resnet50_v1d,79.074,20.926,94.470,5.530,25.58,224,0.875,bicubic +regnetx_064,79.072,20.928,94.458,5.542,26.21,224,0.875,bicubic +xception,79.052,20.948,94.392,5.608,22.86,299,0.897,bicubic +resnet32ts,79.004,20.996,94.356,5.644,17.96,256,0.900,bicubic +mixnet_l.ft_in1k,78.976,21.024,94.182,5.818,7.33,224,0.875,bicubic +lambda_resnet26rpt_256,78.970,21.030,94.430,5.570,10.99,256,0.940,bicubic +convnext_femto_ols.d1_in1k,78.934,21.066,94.532,5.468,5.23,288,0.950,bicubic +hrnet_w40,78.920,21.080,94.470,5.530,57.56,224,0.875,bilinear +convnext_tiny.fb_in22k_ft_in1k,78.908,21.092,94.674,5.326,28.59,288,1.000,bicubic +hrnet_w44,78.896,21.104,94.368,5.632,67.06,224,0.875,bilinear +wide_resnet101_2,78.856,21.144,94.282,5.718,126.89,224,0.875,bilinear +vit_small_patch16_224.augreg_in1k,78.846,21.154,94.284,5.716,22.05,224,0.900,bicubic +tf_efficientnet_b1.aa_in1k,78.826,21.174,94.198,5.802,7.79,240,0.882,bicubic gluon_inception_v3,78.806,21.194,94.370,5.630,23.83,299,0.875,bicubic -repvgg_b2,78.794,21.206,94.418,5.582,89.02,224,0.875,bilinear -efficientnet_b1,78.788,21.212,94.346,5.654,7.79,256,1.000,bicubic -tf_mixnet_l,78.778,21.222,93.998,6.002,7.33,224,0.875,bicubic -gluon_resnet50_v1s,78.706,21.294,94.238,5.762,25.68,224,0.875,bicubic -dla169,78.682,21.318,94.336,5.664,53.39,224,0.875,bilinear -tf_efficientnet_b0_ns,78.664,21.336,94.376,5.624,5.29,224,0.875,bicubic -legacy_seresnet152,78.652,21.348,94.370,5.630,66.82,224,0.875,bilinear -xcit_tiny_12_p16_224_dist,78.578,21.422,94.198,5.802,6.72,224,1.000,bicubic +efficientnet_b1.ft_in1k,78.794,21.206,94.342,5.658,7.79,256,1.000,bicubic +repvgg_b2,78.792,21.208,94.414,5.586,89.02,224,0.875,bilinear +tf_mixnet_l.in1k,78.774,21.226,93.998,6.002,7.33,224,0.875,bicubic +vit_base_patch32_384.augreg_in1k,78.760,21.240,94.228,5.772,88.30,384,1.000,bicubic +gluon_resnet50_v1s,78.712,21.288,94.238,5.762,25.68,224,0.875,bicubic +convnext_femto.d1_in1k,78.704,21.296,94.434,5.566,5.22,288,0.950,bicubic +pvt_v2_b1,78.694,21.306,94.492,5.508,14.01,224,0.900,bicubic +dla169,78.688,21.312,94.336,5.664,53.39,224,0.875,bilinear +legacy_seresnet152,78.660,21.340,94.370,5.630,66.82,224,0.875,bilinear +tf_efficientnet_b0.ns_jft_in1k,78.658,21.342,94.376,5.624,5.29,224,0.875,bicubic +xcit_tiny_12_p16_224_dist,78.578,21.422,94.196,5.804,6.72,224,1.000,bicubic res2net50_26w_6s,78.570,21.430,94.124,5.876,37.05,224,0.875,bilinear -xception41,78.516,21.484,94.280,5.720,26.97,299,0.903,bicubic -dla102x,78.512,21.488,94.228,5.772,26.31,224,0.875,bilinear -regnetx_040,78.488,21.512,94.238,5.762,22.12,224,0.875,bicubic -resnest26d,78.484,21.516,94.294,5.706,17.07,224,0.875,bilinear -levit_128,78.482,21.518,94.012,5.988,9.21,224,0.900,bicubic -dla60_res2net,78.458,21.542,94.196,5.804,20.85,224,0.875,bilinear -dla60_res2next,78.456,21.544,94.146,5.854,17.03,224,0.875,bilinear -hrnet_w32,78.452,21.548,94.188,5.812,41.23,224,0.875,bilinear -coat_tiny,78.436,21.564,94.038,5.962,5.50,224,0.900,bicubic -vit_tiny_patch16_384,78.430,21.570,94.544,5.456,5.79,384,1.000,bicubic -selecsls60b,78.404,21.596,94.172,5.828,32.77,224,0.875,bicubic -cait_xxs24_224,78.386,21.614,94.308,5.692,11.96,224,1.000,bicubic -legacy_seresnet101,78.380,21.620,94.262,5.738,49.33,224,0.875,bilinear -repvgg_b1,78.368,21.632,94.094,5.906,57.42,224,0.875,bilinear -tf_efficientnetv2_b0,78.352,21.648,94.026,5.974,7.14,224,0.875,bicubic -tv_resnet152,78.320,21.680,94.034,5.966,60.19,224,0.875,bilinear -mobilevit_s,78.310,21.690,94.152,5.848,5.58,256,0.900,bicubic -res2next50,78.258,21.742,93.888,6.112,24.67,224,0.875,bilinear -bat_resnext26ts,78.248,21.752,94.096,5.904,10.73,256,0.900,bicubic -efficientnet_b1_pruned,78.244,21.756,93.834,6.166,6.33,240,0.882,bicubic -dla60x,78.228,21.772,94.024,5.976,17.35,224,0.875,bilinear -hrnet_w30,78.198,21.802,94.224,5.776,37.71,224,0.875,bilinear -pit_xs_224,78.190,21.810,94.166,5.834,10.62,224,0.900,bicubic -regnetx_032,78.184,21.816,94.088,5.912,15.30,224,0.875,bicubic -res2net50_14w_8s,78.144,21.856,93.852,6.148,25.06,224,0.875,bilinear -tf_efficientnet_em,78.126,21.874,94.046,5.954,6.90,240,0.882,bicubic -hardcorenas_f,78.102,21.898,93.802,6.198,8.20,224,0.875,bilinear -mobilevitv2_100,78.086,21.914,94.160,5.840,4.90,256,0.888,bicubic -efficientnet_es,78.058,21.942,93.944,6.056,5.44,224,0.875,bicubic -gmixer_24_224,78.036,21.964,93.670,6.330,24.72,224,0.875,bicubic -dla102,78.028,21.972,93.950,6.050,33.27,224,0.875,bilinear -gluon_resnet50_v1c,78.008,21.992,93.990,6.010,25.58,224,0.875,bicubic -selecsls60,77.984,22.016,93.832,6.168,30.67,224,0.875,bicubic -seresnext26t_32x4d,77.968,22.032,93.748,6.252,16.81,224,0.875,bicubic -res2net50_26w_4s,77.962,22.038,93.852,6.148,25.70,224,0.875,bilinear -resmlp_12_distilled_224,77.946,22.054,93.560,6.440,15.35,224,0.875,bicubic -mobilenetv3_large_100_miil,77.922,22.078,92.920,7.080,5.48,224,0.875,bilinear -tf_efficientnet_cc_b0_8e,77.900,22.100,93.658,6.342,24.01,224,0.875,bicubic -resnet26t,77.864,22.136,93.842,6.158,16.01,256,0.940,bicubic -rexnet_100,77.860,22.140,93.874,6.126,4.80,224,0.875,bicubic -seresnext26ts,77.858,22.142,93.790,6.210,10.39,256,0.900,bicubic -regnety_016,77.856,22.144,93.720,6.280,11.20,224,0.875,bicubic -tf_inception_v3,77.852,22.148,93.640,6.360,23.83,299,0.875,bicubic -xcit_nano_12_p8_384_dist,77.816,22.184,94.046,5.954,3.05,384,1.000,bicubic -gcresnext26ts,77.814,22.186,93.836,6.164,10.48,256,0.900,bicubic -hardcorenas_e,77.786,22.214,93.704,6.296,8.07,224,0.875,bilinear -efficientnet_b0,77.700,22.300,93.532,6.468,5.29,224,0.875,bicubic -tinynet_a,77.648,22.352,93.536,6.464,6.19,192,0.875,bicubic -legacy_seresnet50,77.632,22.368,93.750,6.250,28.09,224,0.875,bilinear -cs3darknet_m,77.626,22.374,94.014,5.986,9.31,288,0.950,bicubic -tv_resnext50_32x4d,77.618,22.382,93.700,6.300,25.03,224,0.875,bilinear -seresnext26d_32x4d,77.606,22.394,93.606,6.394,16.81,224,0.875,bicubic -repvgg_b1g4,77.588,22.412,93.830,6.170,39.97,224,0.875,bilinear -gluon_resnet50_v1b,77.584,22.416,93.720,6.280,25.56,224,0.875,bicubic -adv_inception_v3,77.578,22.422,93.738,6.262,23.83,299,0.875,bicubic -res2net50_48w_2s,77.524,22.476,93.550,6.450,25.29,224,0.875,bilinear -coat_lite_tiny,77.516,22.484,93.914,6.086,5.72,224,0.900,bicubic -tf_efficientnet_lite2,77.466,22.534,93.758,6.242,6.09,260,0.890,bicubic -eca_resnext26ts,77.458,22.542,93.568,6.432,10.30,256,0.900,bicubic -inception_v3,77.438,22.562,93.476,6.524,23.83,299,0.875,bicubic -hardcorenas_d,77.430,22.570,93.484,6.516,7.50,224,0.875,bilinear -tv_resnet101,77.380,22.620,93.544,6.456,44.55,224,0.875,bilinear -densenet161,77.354,22.646,93.636,6.364,28.68,224,0.875,bicubic -tf_efficientnet_cc_b0_4e,77.310,22.690,93.340,6.660,13.31,224,0.875,bicubic -mobilenetv2_120d,77.290,22.710,93.500,6.500,5.83,224,0.875,bicubic -densenet201,77.288,22.712,93.480,6.520,20.01,224,0.875,bicubic -cs3darknet_focus_m,77.282,22.718,93.972,6.028,9.30,288,0.950,bicubic -mixnet_m,77.262,22.738,93.422,6.578,5.01,224,0.875,bicubic -poolformer_s12,77.238,22.762,93.506,6.494,11.92,224,0.900,bicubic -selecsls42b,77.178,22.822,93.392,6.608,32.46,224,0.875,bicubic -xcit_tiny_12_p16_224,77.124,22.876,93.712,6.288,6.72,224,1.000,bicubic +xception41,78.516,21.484,94.278,5.722,26.97,299,0.903,bicubic +dla102x,78.510,21.490,94.228,5.772,26.31,224,0.875,bilinear +levit_128,78.486,21.514,94.010,5.990,9.21,224,0.900,bicubic +regnetx_040,78.482,21.518,94.244,5.756,22.12,224,0.875,bicubic +resnest26d,78.478,21.522,94.298,5.702,17.07,224,0.875,bilinear +dla60_res2net,78.464,21.536,94.206,5.794,20.85,224,0.875,bilinear +hrnet_w32,78.450,21.550,94.186,5.814,41.23,224,0.875,bilinear +dla60_res2next,78.440,21.560,94.152,5.848,17.03,224,0.875,bilinear +coat_tiny,78.434,21.566,94.038,5.962,5.50,224,0.900,bicubic +vit_tiny_patch16_384.augreg_in21k_ft_in1k,78.430,21.570,94.542,5.458,5.79,384,1.000,bicubic +selecsls60b,78.412,21.588,94.174,5.826,32.77,224,0.875,bicubic +cait_xxs24_224,78.386,21.614,94.310,5.690,11.96,224,1.000,bicubic +legacy_seresnet101,78.382,21.618,94.264,5.736,49.33,224,0.875,bilinear +repvgg_b1,78.366,21.634,94.098,5.902,57.42,224,0.875,bilinear +tf_efficientnetv2_b0.in1k,78.356,21.644,94.024,5.976,7.14,224,0.875,bicubic +mobilevit_s,78.312,21.688,94.146,5.854,5.58,256,0.900,bicubic +tv_resnet152,78.312,21.688,94.038,5.962,60.19,224,0.875,bilinear +dla60x,78.246,21.754,94.018,5.982,17.35,224,0.875,bilinear +res2next50,78.246,21.754,93.892,6.108,24.67,224,0.875,bilinear +bat_resnext26ts,78.242,21.758,94.100,5.900,10.73,256,0.900,bicubic +efficientnet_b1_pruned.in1k,78.236,21.764,93.834,6.166,6.33,240,0.882,bicubic +hrnet_w30,78.206,21.794,94.222,5.778,37.71,224,0.875,bilinear +pit_xs_224,78.182,21.818,94.168,5.832,10.62,224,0.900,bicubic +regnetx_032,78.172,21.828,94.088,5.912,15.30,224,0.875,bicubic +res2net50_14w_8s,78.150,21.850,93.848,6.152,25.06,224,0.875,bilinear +tf_efficientnet_em.in1k,78.130,21.870,94.044,5.956,6.90,240,0.882,bicubic +hardcorenas_f,78.104,21.896,93.802,6.198,8.20,224,0.875,bilinear +mobilevitv2_100,78.090,21.910,94.164,5.836,4.90,256,0.888,bicubic +efficientnet_es.ra_in1k,78.066,21.934,93.926,6.074,5.44,224,0.875,bicubic +gmixer_24_224,78.036,21.964,93.664,6.336,24.72,224,0.875,bicubic +dla102,78.032,21.968,93.946,6.054,33.27,224,0.875,bilinear +gluon_resnet50_v1c,78.012,21.988,93.988,6.012,25.58,224,0.875,bicubic +seresnext26t_32x4d,77.986,22.014,93.746,6.254,16.81,224,0.875,bicubic +selecsls60,77.982,22.018,93.828,6.172,30.67,224,0.875,bicubic +res2net50_26w_4s,77.964,22.036,93.854,6.146,25.70,224,0.875,bilinear +resmlp_12_distilled_224,77.944,22.056,93.558,6.442,15.35,224,0.875,bicubic +mobilenetv3_large_100.miil_in21k_ft_in1k,77.916,22.084,92.910,7.090,5.48,224,0.875,bilinear +tf_efficientnet_cc_b0_8e.in1k,77.908,22.092,93.654,6.346,24.01,224,0.875,bicubic +resnet26t,77.882,22.118,93.840,6.160,16.01,256,0.940,bicubic +seresnext26ts,77.866,22.134,93.790,6.210,10.39,256,0.900,bicubic +regnety_016,77.862,22.138,93.720,6.280,11.20,224,0.875,bicubic +tf_inception_v3,77.860,22.140,93.640,6.360,23.83,299,0.875,bicubic +rexnet_100,77.858,22.142,93.870,6.130,4.80,224,0.875,bicubic +xcit_nano_12_p8_384_dist,77.820,22.180,94.036,5.964,3.05,384,1.000,bicubic +gcresnext26ts,77.814,22.186,93.834,6.166,10.48,256,0.900,bicubic +hardcorenas_e,77.794,22.206,93.694,6.306,8.07,224,0.875,bilinear +efficientnet_b0.ra_in1k,77.698,22.302,93.532,6.468,5.29,224,0.875,bicubic +tinynet_a.in1k,77.652,22.348,93.536,6.464,6.19,192,0.875,bicubic +cs3darknet_m,77.636,22.364,94.014,5.986,9.31,288,0.950,bicubic +legacy_seresnet50,77.630,22.370,93.748,6.252,28.09,224,0.875,bilinear +tv_resnext50_32x4d,77.620,22.380,93.696,6.304,25.03,224,0.875,bilinear +seresnext26d_32x4d,77.602,22.398,93.608,6.392,16.81,224,0.875,bicubic +repvgg_b1g4,77.594,22.406,93.826,6.174,39.97,224,0.875,bilinear +adv_inception_v3,77.582,22.418,93.736,6.264,23.83,299,0.875,bicubic +gluon_resnet50_v1b,77.580,22.420,93.716,6.284,25.56,224,0.875,bicubic +res2net50_48w_2s,77.522,22.478,93.554,6.446,25.29,224,0.875,bilinear +coat_lite_tiny,77.512,22.488,93.916,6.084,5.72,224,0.900,bicubic +tf_efficientnet_lite2.in1k,77.468,22.532,93.754,6.246,6.09,260,0.890,bicubic +eca_resnext26ts,77.452,22.548,93.566,6.434,10.30,256,0.900,bicubic +inception_v3,77.440,22.560,93.476,6.524,23.83,299,0.875,bicubic +hardcorenas_d,77.432,22.568,93.484,6.516,7.50,224,0.875,bilinear +tv_resnet101,77.374,22.626,93.540,6.460,44.55,224,0.875,bilinear +densenet161,77.358,22.642,93.638,6.362,28.68,224,0.875,bicubic +tf_efficientnet_cc_b0_4e.in1k,77.306,22.694,93.334,6.666,13.31,224,0.875,bicubic +densenet201,77.286,22.714,93.478,6.522,20.01,224,0.875,bicubic +mobilenetv2_120d.ra_in1k,77.284,22.716,93.492,6.508,5.83,224,0.875,bicubic +cs3darknet_focus_m,77.278,22.722,93.970,6.030,9.30,288,0.950,bicubic +mixnet_m.ft_in1k,77.260,22.740,93.424,6.576,5.01,224,0.875,bicubic +poolformer_s12,77.230,22.770,93.504,6.496,11.92,224,0.900,bicubic +convnext_atto_ols.a2_in1k,77.216,22.784,93.680,6.320,3.70,288,0.950,bicubic +selecsls42b,77.174,22.826,93.390,6.610,32.46,224,0.875,bicubic +xcit_tiny_12_p16_224,77.120,22.880,93.712,6.288,6.72,224,1.000,bicubic resnet34d,77.116,22.884,93.382,6.618,21.82,224,0.875,bicubic legacy_seresnext26_32x4d,77.104,22.896,93.316,6.684,16.79,224,0.875,bicubic -tf_efficientnet_b0_ap,77.088,22.912,93.258,6.742,5.29,224,0.875,bicubic -hardcorenas_c,77.052,22.948,93.160,6.840,5.52,224,0.875,bilinear -dla60,77.022,22.978,93.320,6.680,22.04,224,0.875,bilinear -crossvit_9_dagger_240,76.978,23.022,93.614,6.386,8.78,240,0.875,bicubic -tf_mixnet_m,76.946,23.054,93.152,6.848,5.01,224,0.875,bicubic -regnetx_016,76.942,23.058,93.424,6.576,9.19,224,0.875,bicubic -convmixer_1024_20_ks9_p14,76.942,23.058,93.358,6.642,24.38,224,0.960,bicubic -gernet_s,76.916,23.084,93.134,6.866,8.17,224,0.875,bilinear -skresnet34,76.904,23.096,93.320,6.680,22.28,224,0.875,bicubic -tf_efficientnet_b0,76.840,23.160,93.218,6.782,5.29,224,0.875,bicubic -ese_vovnet19b_dw,76.794,23.206,93.266,6.734,6.54,224,0.875,bicubic -resnext26ts,76.780,23.220,93.132,6.868,10.30,256,0.900,bicubic -hrnet_w18,76.760,23.240,93.444,6.556,21.30,224,0.875,bilinear -resnet26d,76.702,23.298,93.152,6.848,16.01,224,0.875,bicubic -resmlp_12_224,76.656,23.344,93.180,6.820,15.35,224,0.875,bicubic -tf_efficientnet_lite1,76.638,23.362,93.224,6.776,5.42,240,0.882,bicubic -mixer_b16_224,76.610,23.390,92.230,7.770,59.88,224,0.875,bicubic -tf_efficientnet_es,76.598,23.402,93.204,6.796,5.44,224,0.875,bicubic -densenetblur121d,76.580,23.420,93.188,6.812,8.00,224,0.875,bicubic -hardcorenas_b,76.536,23.464,92.754,7.246,5.18,224,0.875,bilinear -levit_128s,76.514,23.486,92.870,7.130,7.78,224,0.900,bicubic -mobilenetv2_140,76.512,23.488,92.998,7.002,6.11,224,0.875,bicubic -repvgg_a2,76.460,23.540,93.010,6.990,28.21,224,0.875,bilinear -xcit_nano_12_p8_224_dist,76.328,23.672,93.094,6.906,3.05,224,1.000,bicubic -regnety_008,76.314,23.686,93.070,6.930,6.26,224,0.875,bicubic -dpn68,76.310,23.690,92.978,7.022,12.61,224,0.875,bicubic -tv_resnet50,76.134,23.866,92.868,7.132,25.56,224,0.875,bilinear -mixnet_s,75.996,24.004,92.800,7.200,4.13,224,0.875,bicubic -vit_small_patch32_224,75.990,24.010,93.268,6.732,22.88,224,0.900,bicubic -vit_tiny_r_s16_p8_384,75.952,24.048,93.262,6.738,6.36,384,1.000,bicubic -hardcorenas_a,75.930,24.070,92.510,7.490,5.26,224,0.875,bilinear -densenet169,75.904,24.096,93.024,6.976,14.15,224,0.875,bicubic -mobilenetv3_large_100,75.776,24.224,92.540,7.460,5.48,224,0.875,bicubic -tf_mixnet_s,75.652,24.348,92.626,7.374,4.13,224,0.875,bicubic -mobilenetv3_rw,75.634,24.366,92.708,7.292,5.48,224,0.875,bicubic -mobilevitv2_075,75.608,24.392,92.758,7.242,2.87,256,0.888,bicubic -densenet121,75.580,24.420,92.648,7.352,7.98,224,0.875,bicubic -tf_mobilenetv3_large_100,75.512,24.488,92.606,7.394,5.48,224,0.875,bilinear -resnest14d,75.508,24.492,92.524,7.476,10.61,224,0.875,bilinear -efficientnet_lite0,75.468,24.532,92.516,7.484,4.65,224,0.875,bicubic -vit_tiny_patch16_224,75.464,24.536,92.844,7.156,5.72,224,0.900,bicubic -xcit_nano_12_p16_384_dist,75.456,24.544,92.690,7.310,3.05,384,1.000,bicubic -semnasnet_100,75.450,24.550,92.600,7.400,3.89,224,0.875,bicubic -resnet26,75.300,24.700,92.580,7.420,16.00,224,0.875,bicubic -regnety_006,75.252,24.748,92.532,7.468,6.06,224,0.875,bicubic -repvgg_b0,75.154,24.846,92.416,7.584,15.82,224,0.875,bilinear -fbnetc_100,75.116,24.884,92.386,7.614,5.57,224,0.875,bilinear -resnet34,75.112,24.888,92.284,7.716,21.80,224,0.875,bilinear -hrnet_w18_small_v2,75.110,24.890,92.416,7.584,15.60,224,0.875,bilinear -mobilenetv2_110d,75.036,24.964,92.192,7.808,4.52,224,0.875,bicubic -regnetx_008,75.034,24.966,92.340,7.660,7.26,224,0.875,bicubic -efficientnet_es_pruned,75.000,25.000,92.442,7.558,5.44,224,0.875,bicubic -tinynet_b,74.974,25.026,92.182,7.818,3.73,188,0.875,bicubic -edgenext_x_small,74.864,25.136,92.300,7.700,2.34,256,0.900,bicubic -tf_efficientnet_lite0,74.832,25.168,92.174,7.826,4.65,224,0.875,bicubic -legacy_seresnet34,74.810,25.190,92.126,7.874,21.96,224,0.875,bilinear -tv_densenet121,74.740,25.260,92.148,7.852,7.98,224,0.875,bicubic -mnasnet_100,74.650,25.350,92.114,7.886,4.38,224,0.875,bicubic -mobilevit_xs,74.634,25.366,92.346,7.654,2.32,256,0.900,bicubic -dla34,74.624,25.376,92.072,7.928,15.74,224,0.875,bilinear -gluon_resnet34_v1b,74.592,25.408,91.988,8.012,21.80,224,0.875,bicubic -pit_ti_distilled_224,74.534,25.466,92.096,7.904,5.10,224,0.900,bicubic -deit_tiny_distilled_patch16_224,74.512,25.488,91.890,8.110,5.91,224,0.900,bicubic -vgg19_bn,74.214,25.786,91.844,8.156,143.68,224,0.875,bilinear -spnasnet_100,74.090,25.910,91.816,8.184,4.42,224,0.875,bilinear -regnety_004,74.024,25.976,91.756,8.244,4.34,224,0.875,bicubic -ghostnet_100,73.980,26.020,91.458,8.542,5.18,224,0.875,bilinear -crossvit_9_240,73.960,26.040,91.964,8.036,8.55,240,0.875,bicubic -xcit_nano_12_p8_224,73.916,26.084,92.168,7.832,3.05,224,1.000,bicubic -regnetx_006,73.856,26.144,91.672,8.328,6.20,224,0.875,bicubic -vit_base_patch32_224_sam,73.692,26.308,91.012,8.988,88.22,224,0.900,bicubic -tf_mobilenetv3_large_075,73.440,26.560,91.348,8.652,3.99,224,0.875,bilinear -vgg16_bn,73.350,26.650,91.504,8.496,138.37,224,0.875,bilinear -crossvit_tiny_240,73.338,26.662,91.914,8.086,7.01,240,0.875,bicubic -tv_resnet34,73.308,26.692,91.424,8.576,21.80,224,0.875,bilinear -swsl_resnet18,73.274,26.726,91.736,8.264,11.69,224,0.875,bilinear -convit_tiny,73.114,26.886,91.720,8.280,5.71,224,0.875,bicubic -skresnet18,73.034,26.966,91.166,8.834,11.96,224,0.875,bicubic -semnasnet_075,72.974,27.026,91.134,8.866,2.91,224,0.875,bicubic -mobilenetv2_100,72.956,27.044,91.010,8.990,3.50,224,0.875,bicubic -pit_ti_224,72.912,27.088,91.406,8.594,4.85,224,0.900,bicubic -ssl_resnet18,72.604,27.396,91.424,8.576,11.69,224,0.875,bilinear -regnetx_004,72.396,27.604,90.838,9.162,5.16,224,0.875,bicubic -vgg19,72.366,27.634,90.872,9.128,143.67,224,0.875,bilinear -resnet14t,72.356,27.644,90.340,9.660,10.08,224,0.950,bilinear -hrnet_w18_small,72.336,27.664,90.680,9.320,13.19,224,0.875,bilinear +tf_efficientnet_b0.ap_in1k,77.086,22.914,93.256,6.744,5.29,224,0.875,bicubic +hardcorenas_c,77.054,22.946,93.158,6.842,5.52,224,0.875,bilinear +dla60,77.032,22.968,93.318,6.682,22.04,224,0.875,bilinear +convnext_atto.d2_in1k,77.014,22.986,93.700,6.300,3.70,288,0.950,bicubic +crossvit_9_dagger_240,76.980,23.020,93.610,6.390,8.78,240,0.875,bicubic +regnetx_016,76.950,23.050,93.420,6.580,9.19,224,0.875,bicubic +convmixer_1024_20_ks9_p14,76.946,23.054,93.358,6.642,24.38,224,0.960,bicubic +tf_mixnet_m.in1k,76.942,23.058,93.152,6.848,5.01,224,0.875,bicubic +gernet_s,76.916,23.084,93.132,6.868,8.17,224,0.875,bilinear +skresnet34,76.912,23.088,93.322,6.678,22.28,224,0.875,bicubic +tf_efficientnet_b0.aa_in1k,76.848,23.152,93.228,6.772,5.29,224,0.875,bicubic +ese_vovnet19b_dw,76.798,23.202,93.268,6.732,6.54,224,0.875,bicubic +resnext26ts,76.780,23.220,93.130,6.870,10.30,256,0.900,bicubic +hrnet_w18,76.758,23.242,93.444,6.556,21.30,224,0.875,bilinear +resnet26d,76.696,23.304,93.150,6.850,16.01,224,0.875,bicubic +resmlp_12_224,76.654,23.346,93.180,6.820,15.35,224,0.875,bicubic +tf_efficientnet_lite1.in1k,76.642,23.358,93.226,6.774,5.42,240,0.882,bicubic +mixer_b16_224,76.600,23.400,92.228,7.772,59.88,224,0.875,bicubic +tf_efficientnet_es.in1k,76.594,23.406,93.202,6.798,5.44,224,0.875,bicubic +densenetblur121d,76.588,23.412,93.192,6.808,8.00,224,0.875,bicubic +hardcorenas_b,76.538,23.462,92.754,7.246,5.18,224,0.875,bilinear +levit_128s,76.530,23.470,92.866,7.134,7.78,224,0.900,bicubic +mobilenetv2_140.ra_in1k,76.516,23.484,92.996,7.004,6.11,224,0.875,bicubic +repvgg_a2,76.460,23.540,93.004,6.996,28.21,224,0.875,bilinear +xcit_nano_12_p8_224_dist,76.324,23.676,93.090,6.910,3.05,224,1.000,bicubic +dpn68,76.318,23.682,92.978,7.022,12.61,224,0.875,bicubic +regnety_008,76.316,23.684,93.066,6.934,6.26,224,0.875,bicubic +tv_resnet50,76.138,23.862,92.864,7.136,25.56,224,0.875,bilinear +mixnet_s.ft_in1k,75.992,24.008,92.796,7.204,4.13,224,0.875,bicubic +vit_small_patch32_224.augreg_in21k_ft_in1k,75.990,24.010,93.272,6.728,22.88,224,0.900,bicubic +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,75.952,24.048,93.260,6.740,6.36,384,1.000,bicubic +hardcorenas_a,75.916,24.084,92.514,7.486,5.26,224,0.875,bilinear +densenet169,75.906,24.094,93.026,6.974,14.15,224,0.875,bicubic +mobilenetv3_large_100.ra_in1k,75.766,24.234,92.542,7.458,5.48,224,0.875,bicubic +edgenext_x_small,75.688,24.312,92.766,7.234,2.34,288,1.000,bicubic +tf_mixnet_s.in1k,75.650,24.350,92.628,7.372,4.13,224,0.875,bicubic +mobilenetv3_rw.rmsp_in1k,75.634,24.366,92.708,7.292,5.48,224,0.875,bicubic +mobilevitv2_075,75.622,24.378,92.768,7.232,2.87,256,0.888,bicubic +densenet121,75.578,24.422,92.652,7.348,7.98,224,0.875,bicubic +tf_mobilenetv3_large_100.in1k,75.518,24.482,92.606,7.394,5.48,224,0.875,bilinear +resnest14d,75.506,24.494,92.518,7.482,10.61,224,0.875,bilinear +efficientnet_lite0.ra_in1k,75.484,24.516,92.510,7.490,4.65,224,0.875,bicubic +xcit_nano_12_p16_384_dist,75.458,24.542,92.694,7.306,3.05,384,1.000,bicubic +vit_tiny_patch16_224.augreg_in21k_ft_in1k,75.454,24.546,92.848,7.152,5.72,224,0.900,bicubic +semnasnet_100.rmsp_in1k,75.448,24.552,92.604,7.396,3.89,224,0.875,bicubic +resnet26,75.292,24.708,92.570,7.430,16.00,224,0.875,bicubic +regnety_006,75.246,24.754,92.532,7.468,6.06,224,0.875,bicubic +repvgg_b0,75.152,24.848,92.418,7.582,15.82,224,0.875,bilinear +fbnetc_100.rmsp_in1k,75.124,24.876,92.386,7.614,5.57,224,0.875,bilinear +hrnet_w18_small_v2,75.114,24.886,92.416,7.584,15.60,224,0.875,bilinear +resnet34,75.110,24.890,92.284,7.716,21.80,224,0.875,bilinear +regnetx_008,75.038,24.962,92.336,7.664,7.26,224,0.875,bicubic +mobilenetv2_110d.ra_in1k,75.036,24.964,92.186,7.814,4.52,224,0.875,bicubic +efficientnet_es_pruned.in1k,75.000,25.000,92.448,7.552,5.44,224,0.875,bicubic +tinynet_b.in1k,74.974,25.026,92.188,7.812,3.73,188,0.875,bicubic +vit_base_patch32_224.augreg_in1k,74.904,25.096,91.778,8.222,88.22,224,0.900,bicubic +tf_efficientnet_lite0.in1k,74.830,25.170,92.176,7.824,4.65,224,0.875,bicubic +legacy_seresnet34,74.808,25.192,92.124,7.876,21.96,224,0.875,bilinear +tv_densenet121,74.738,25.262,92.150,7.850,7.98,224,0.875,bicubic +mnasnet_100.rmsp_in1k,74.658,25.342,92.114,7.886,4.38,224,0.875,bicubic +mobilevit_xs,74.644,25.356,92.352,7.648,2.32,256,0.900,bicubic +dla34,74.630,25.370,92.078,7.922,15.74,224,0.875,bilinear +gluon_resnet34_v1b,74.588,25.412,91.990,8.010,21.80,224,0.875,bicubic +pit_ti_distilled_224,74.530,25.470,92.096,7.904,5.10,224,0.900,bicubic +deit_tiny_distilled_patch16_224,74.510,25.490,91.890,8.110,5.91,224,0.900,bicubic +vgg19_bn,74.214,25.786,91.842,8.158,143.68,224,0.875,bilinear +spnasnet_100.rmsp_in1k,74.084,25.916,91.818,8.182,4.42,224,0.875,bilinear +regnety_004,74.034,25.966,91.752,8.248,4.34,224,0.875,bicubic +ghostnet_100,73.978,26.022,91.456,8.544,5.18,224,0.875,bilinear +crossvit_9_240,73.964,26.036,91.968,8.032,8.55,240,0.875,bicubic +xcit_nano_12_p8_224,73.914,26.086,92.172,7.828,3.05,224,1.000,bicubic +regnetx_006,73.852,26.148,91.672,8.328,6.20,224,0.875,bicubic +vit_base_patch32_224.sam,73.690,26.310,91.014,8.986,88.22,224,0.900,bicubic +tf_mobilenetv3_large_075.in1k,73.438,26.562,91.350,8.650,3.99,224,0.875,bilinear +vgg16_bn,73.350,26.650,91.506,8.494,138.37,224,0.875,bilinear +crossvit_tiny_240,73.324,26.676,91.916,8.084,7.01,240,0.875,bicubic +tv_resnet34,73.312,26.688,91.426,8.574,21.80,224,0.875,bilinear +swsl_resnet18,73.276,26.724,91.734,8.266,11.69,224,0.875,bilinear +convit_tiny,73.116,26.884,91.714,8.286,5.71,224,0.875,bicubic +skresnet18,73.038,26.962,91.168,8.832,11.96,224,0.875,bicubic +semnasnet_075.rmsp_in1k,72.974,27.026,91.136,8.864,2.91,224,0.875,bicubic +mobilenetv2_100.ra_in1k,72.970,27.030,91.016,8.984,3.50,224,0.875,bicubic +pit_ti_224,72.912,27.088,91.402,8.598,4.85,224,0.900,bicubic +ssl_resnet18,72.610,27.390,91.416,8.584,11.69,224,0.875,bilinear +regnetx_004,72.396,27.604,90.830,9.170,5.16,224,0.875,bicubic +vgg19,72.368,27.632,90.872,9.128,143.67,224,0.875,bilinear +resnet14t,72.350,27.650,90.340,9.660,10.08,224,0.950,bilinear +hrnet_w18_small,72.342,27.658,90.678,9.322,13.19,224,0.875,bilinear xcit_nano_12_p16_224_dist,72.302,27.698,90.862,9.138,3.05,224,1.000,bicubic -resnet18d,72.258,27.742,90.688,9.312,11.71,224,0.875,bicubic -tf_mobilenetv3_large_minimal_100,72.250,27.750,90.620,9.380,3.92,224,0.875,bilinear -deit_tiny_patch16_224,72.174,27.826,91.114,8.886,5.72,224,0.900,bicubic -lcnet_100,72.110,27.890,90.378,9.622,2.95,224,0.875,bicubic -mixer_l16_224,72.066,27.934,87.666,12.334,208.20,224,0.875,bicubic -vit_tiny_r_s16_p8_224,71.794,28.206,90.818,9.182,6.34,224,0.900,bicubic -legacy_seresnet18,71.740,28.260,90.330,9.670,11.78,224,0.875,bicubic -vgg13_bn,71.598,28.402,90.376,9.624,133.05,224,0.875,bilinear -vgg16,71.590,28.410,90.382,9.618,138.36,224,0.875,bilinear -tinynet_c,71.228,28.772,89.748,10.252,2.46,184,0.875,bicubic -edgenext_xx_small,71.106,28.894,90.032,9.968,1.33,256,0.900,bicubic -gluon_resnet18_v1b,70.838,29.162,89.762,10.238,11.69,224,0.875,bicubic +resnet18d,72.260,27.740,90.696,9.304,11.71,224,0.875,bicubic +tf_mobilenetv3_large_minimal_100.in1k,72.248,27.752,90.630,9.370,3.92,224,0.875,bilinear +deit_tiny_patch16_224,72.168,27.832,91.118,8.882,5.72,224,0.900,bicubic +lcnet_100.ra2_in1k,72.114,27.886,90.378,9.622,2.95,224,0.875,bicubic +mixer_l16_224,72.058,27.942,87.668,12.332,208.20,224,0.875,bicubic +edgenext_xx_small,71.866,28.134,90.544,9.456,1.33,288,1.000,bicubic +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,71.788,28.212,90.828,9.172,6.34,224,0.900,bicubic +legacy_seresnet18,71.744,28.256,90.334,9.666,11.78,224,0.875,bicubic +vgg16,71.594,28.406,90.382,9.618,138.36,224,0.875,bilinear +vgg13_bn,71.594,28.406,90.376,9.624,133.05,224,0.875,bilinear +tinynet_c.in1k,71.232,28.768,89.748,10.252,2.46,184,0.875,bicubic +gluon_resnet18_v1b,70.836,29.164,89.760,10.240,11.69,224,0.875,bicubic +pvt_v2_b0,70.656,29.344,90.208,9.792,3.67,224,0.900,bicubic vgg11_bn,70.360,29.640,89.802,10.198,132.87,224,0.875,bilinear -regnety_002,70.256,29.744,89.534,10.466,3.16,224,0.875,bicubic -mobilevitv2_050,70.140,29.860,89.930,10.070,1.37,256,0.888,bicubic -xcit_nano_12_p16_224,69.954,30.046,89.756,10.244,3.05,224,1.000,bicubic +regnety_002,70.252,29.748,89.540,10.460,3.16,224,0.875,bicubic +mobilevitv2_050,70.140,29.860,89.926,10.074,1.37,256,0.888,bicubic +xcit_nano_12_p16_224,69.954,30.046,89.754,10.246,3.05,224,1.000,bicubic vgg13,69.926,30.074,89.246,10.754,133.05,224,0.875,bilinear -resnet18,69.748,30.252,89.084,10.916,11.69,224,0.875,bilinear -vgg11,69.028,30.972,88.628,11.372,132.86,224,0.875,bilinear -mobilevit_xxs,68.920,31.080,88.946,11.054,1.27,256,0.900,bicubic -lcnet_075,68.814,31.186,88.364,11.636,2.36,224,0.875,bicubic -regnetx_002,68.754,31.246,88.556,11.444,2.68,224,0.875,bicubic -resnet10t,68.308,31.692,88.080,11.920,5.44,224,0.950,bilinear -tf_mobilenetv3_small_100,67.926,32.074,87.668,12.332,2.54,224,0.875,bilinear -dla60x_c,67.880,32.120,88.434,11.566,1.32,224,0.875,bilinear -mobilenetv3_small_100,67.658,32.342,87.634,12.366,2.54,224,0.875,bicubic -tinynet_d,66.962,33.038,87.064,12.936,2.34,152,0.875,bicubic -mnasnet_small,66.206,33.794,86.506,13.494,2.03,224,0.875,bicubic -dla46x_c,65.952,34.048,86.986,13.014,1.07,224,0.875,bilinear -mobilenetv2_050,65.944,34.056,86.080,13.920,1.97,224,0.875,bicubic -tf_mobilenetv3_small_075,65.712,34.288,86.130,13.870,2.04,224,0.875,bilinear -mobilenetv3_small_075,65.238,34.762,85.440,14.560,2.04,224,0.875,bicubic -dla46_c,64.872,35.128,86.302,13.698,1.30,224,0.875,bilinear -lcnet_050,63.094,36.906,84.382,15.618,1.88,224,0.875,bicubic -tf_mobilenetv3_small_minimal_100,62.900,37.100,84.234,15.766,2.04,224,0.875,bilinear -tinynet_e,59.856,40.144,81.766,18.234,2.04,106,0.875,bicubic -mobilenetv3_small_050,57.890,42.110,80.194,19.806,1.59,224,0.875,bicubic +resnet18,69.748,30.252,89.078,10.922,11.69,224,0.875,bilinear +vgg11,69.024,30.976,88.628,11.372,132.86,224,0.875,bilinear +mobilevit_xxs,68.912,31.088,88.938,11.062,1.27,256,0.900,bicubic +lcnet_075.ra2_in1k,68.818,31.182,88.370,11.630,2.36,224,0.875,bicubic +regnetx_002,68.762,31.238,88.556,11.444,2.68,224,0.875,bicubic +resnet10t,68.294,31.706,88.078,11.922,5.44,224,0.950,bilinear +tf_mobilenetv3_small_100.in1k,67.922,32.078,87.664,12.336,2.54,224,0.875,bilinear +dla60x_c,67.892,32.108,88.426,11.574,1.32,224,0.875,bilinear +mobilenetv3_small_100.lamb_in1k,67.652,32.348,87.636,12.364,2.54,224,0.875,bicubic +tinynet_d.in1k,66.962,33.038,87.066,12.934,2.34,152,0.875,bicubic +mnasnet_small.lamb_in1k,66.206,33.794,86.508,13.492,2.03,224,0.875,bicubic +dla46x_c,65.970,34.030,86.980,13.020,1.07,224,0.875,bilinear +mobilenetv2_050.lamb_in1k,65.942,34.058,86.082,13.918,1.97,224,0.875,bicubic +tf_mobilenetv3_small_075.in1k,65.716,34.284,86.130,13.870,2.04,224,0.875,bilinear +mobilenetv3_small_075.lamb_in1k,65.246,34.754,85.436,14.564,2.04,224,0.875,bicubic +dla46_c,64.866,35.134,86.292,13.708,1.30,224,0.875,bilinear +lcnet_050.ra2_in1k,63.100,36.900,84.380,15.620,1.88,224,0.875,bicubic +tf_mobilenetv3_small_minimal_100.in1k,62.906,37.094,84.230,15.770,2.04,224,0.875,bilinear +tinynet_e.in1k,59.856,40.144,81.762,18.238,2.04,106,0.875,bicubic +mobilenetv3_small_050.lamb_in1k,57.890,42.110,80.194,19.806,1.59,224,0.875,bicubic diff --git a/results/results-imagenetv2-matched-frequency.csv b/results/results-imagenetv2-matched-frequency.csv index 131dd32e..71aa3f5a 100644 --- a/results/results-imagenetv2-matched-frequency.csv +++ b/results/results-imagenetv2-matched-frequency.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff -tf_efficientnet_l2_ns_475,80.460,19.540,95.730,4.270,480.31,475,0.936,bicubic,-7.772,-2.816,+3 -tf_efficientnet_l2_ns,80.250,19.750,95.840,4.160,480.31,800,0.960,bicubic,-8.100,-2.810,+1 -beit_large_patch16_512,79.940,20.060,95.350,4.650,305.67,512,1.000,bicubic,-8.662,-3.306,-2 -beit_large_patch16_384,79.500,20.500,95.170,4.830,305.00,384,1.000,bicubic,-8.906,-3.436,-2 -deit3_huge_patch14_224_in21ft1k,79.160,20.840,94.860,5.140,632.13,224,1.000,bicubic,-8.020,-3.400,+5 -deit3_large_patch16_384_in21ft1k,79.100,20.900,94.880,5.120,304.76,384,1.000,bicubic,-8.616,-3.632,-1 -beit_large_patch16_224,78.820,21.180,94.610,5.390,304.43,224,0.900,bicubic,-8.656,-3.694,0 -deit3_large_patch16_224_in21ft1k,78.630,21.370,94.720,5.280,304.37,224,1.000,bicubic,-8.352,-3.518,+8 -tf_efficientnet_b7_ns,78.520,21.480,94.390,5.610,66.35,600,0.949,bicubic,-8.312,-3.706,+10 -volo_d5_512,77.970,22.030,94.160,5.840,296.09,512,1.150,bicubic,-9.070,-3.808,+4 -vit_large_patch16_384,77.940,22.060,94.440,5.560,304.72,384,1.000,bicubic,-9.140,-3.860,+2 -deit3_base_patch16_384_in21ft1k,77.880,22.120,94.030,5.970,86.88,384,1.000,bicubic,-8.862,-4.082,+10 -volo_d5_448,77.770,22.230,94.050,5.950,295.91,448,1.150,bicubic,-9.184,-3.890,+4 -volo_d4_448,77.750,22.250,93.930,6.070,193.41,448,1.150,bicubic,-9.042,-3.952,+7 -convnext_large_384_in22ft1k,77.730,22.270,94.080,5.920,197.77,384,1.000,bicubic,-9.666,-4.286,-6 -convnext_xlarge_384_in22ft1k,77.710,22.290,94.200,5.800,350.20,384,1.000,bicubic,-9.834,-4.286,-10 -swinv2_large_window12to24_192to384_22kft1k,77.310,22.690,93.930,6.070,196.74,384,1.000,bicubic,-10.146,-4.322,-9 -tf_efficientnet_b6_ns,77.280,22.720,93.890,6.110,43.04,528,0.942,bicubic,-9.170,-3.996,+9 -swinv2_base_window12to24_192to384_22kft1k,77.170,22.830,94.260,5.740,87.92,384,1.000,bicubic,-9.938,-3.976,-7 -volo_d3_448,77.070,22.930,94.110,5.890,86.63,448,1.000,bicubic,-9.426,-3.600,+5 -vit_large_r50_s32_384,77.070,22.930,93.720,6.280,329.09,384,1.000,bicubic,-9.110,-4.200,+12 -tf_efficientnetv2_xl_in21ft1k,77.040,22.960,93.270,6.730,208.12,512,1.000,bicubic,-9.380,-4.598,+7 -swin_large_patch4_window12_384,77.030,22.970,93.750,6.250,196.74,384,1.000,bicubic,-10.122,-4.490,-12 -tf_efficientnetv2_l_in21ft1k,76.940,23.060,93.960,6.040,118.52,480,1.000,bicubic,-9.364,-4.020,+7 -swinv2_large_window12to16_192to256_22kft1k,76.930,23.070,93.540,6.460,196.74,256,0.900,bicubic,-10.016,-4.570,-7 -beit_base_patch16_384,76.900,23.100,93.910,6.090,86.74,384,1.000,bicubic,-9.898,-4.226,-6 -ig_resnext101_32x48d,76.880,23.120,93.310,6.690,828.41,224,0.875,bilinear,-8.556,-4.266,+30 -cait_m48_448,76.870,23.130,93.370,6.630,356.46,448,1.000,bicubic,-9.618,-4.380,-2 -ig_resnext101_32x32d,76.820,23.180,93.200,6.800,468.53,224,0.875,bilinear,-8.280,-4.234,+42 -tf_efficientnet_b5_ns,76.810,23.190,93.580,6.420,30.39,456,0.934,bicubic,-9.278,-4.172,+5 -convnext_xlarge_in22ft1k,76.770,23.230,93.550,6.450,350.20,224,0.875,bicubic,-10.232,-4.662,-16 -deit3_large_patch16_384,76.690,23.310,93.350,6.650,304.76,384,1.000,bicubic,-9.116,-4.246,+14 -xcit_large_24_p8_384_dist,76.620,23.380,93.090,6.910,188.93,384,1.000,bicubic,-9.378,-4.594,+7 -convnext_base_384_in22ft1k,76.580,23.420,93.720,6.280,88.59,384,1.000,bicubic,-9.962,-4.470,-10 -volo_d5_224,76.580,23.420,93.300,6.700,295.46,224,0.960,bicubic,-9.490,-4.278,+1 -deit3_base_patch16_224_in21ft1k,76.540,23.460,93.560,6.440,86.59,224,1.000,bicubic,-9.176,-4.184,+14 -vit_base_patch16_384,76.480,23.520,93.770,6.230,86.86,384,1.000,bicubic,-9.526,-4.234,+2 -swinv2_base_window12to16_192to256_22kft1k,76.430,23.570,93.690,6.310,87.92,256,0.900,bicubic,-9.840,-4.206,-6 -convnext_large_in22ft1k,76.430,23.570,93.470,6.530,197.77,224,0.875,bicubic,-10.206,-4.558,-16 -cait_m36_384,76.330,23.670,93.050,6.950,271.22,384,1.000,bicubic,-9.724,-4.680,-3 -vit_large_patch16_224,76.300,23.700,93.600,6.400,304.33,224,0.900,bicubic,-9.544,-4.222,+1 -swin_base_patch4_window12_384,76.290,23.710,93.320,6.680,87.90,384,1.000,bicubic,-10.142,-4.736,-14 -tf_efficientnetv2_l,76.280,23.720,92.970,7.030,118.52,480,1.000,bicubic,-9.208,-4.402,+12 -swin_large_patch4_window7_224,76.270,23.730,93.410,6.590,196.53,224,0.900,bicubic,-10.050,-4.482,-14 -cait_s36_384,76.210,23.790,92.970,7.030,68.37,384,1.000,bicubic,-9.250,-4.508,+11 -xcit_medium_24_p8_384_dist,76.140,23.860,92.980,7.020,84.32,384,1.000,bicubic,-9.676,-4.612,-2 -dm_nfnet_f6,76.130,23.870,93.110,6.890,438.36,576,0.956,bicubic,-10.012,-4.620,-13 -tf_efficientnet_b7_ap,76.100,23.900,92.970,7.030,66.35,600,0.949,bicubic,-9.020,-4.282,+22 -volo_d2_384,76.090,23.910,93.130,6.870,58.87,384,1.000,bicubic,-9.946,-4.444,-11 -tf_efficientnet_b8_ap,76.080,23.920,92.730,7.270,87.41,672,0.954,bicubic,-9.292,-4.564,+12 -vit_base_patch8_224,76.010,23.990,93.370,6.630,86.58,224,0.900,bicubic,-9.780,-4.422,-4 -volo_d4_224,76.010,23.990,93.010,6.990,192.96,224,0.960,bicubic,-9.866,-4.458,-11 -xcit_large_24_p8_224_dist,75.990,24.010,92.730,7.270,188.93,224,1.000,bicubic,-9.408,-4.680,+8 -tf_efficientnetv2_m_in21ft1k,75.920,24.080,93.280,6.720,54.14,480,1.000,bicubic,-9.666,-4.466,-2 -dm_nfnet_f4,75.850,24.150,92.970,7.030,316.07,512,0.951,bicubic,-9.864,-4.550,-4 -xcit_large_24_p16_384_dist,75.820,24.180,92.750,7.250,189.10,384,1.000,bicubic,-9.932,-4.788,-8 -deit3_huge_patch14_224,75.790,24.210,92.760,7.240,632.13,224,0.900,bicubic,-9.416,-4.598,+10 -xcit_small_24_p8_384_dist,75.770,24.230,92.970,7.030,47.63,384,1.000,bicubic,-9.784,-4.602,-5 -ig_resnext101_32x16d,75.750,24.250,92.880,7.120,194.03,224,0.875,bilinear,-8.420,-4.318,+63 -tf_efficientnet_b4_ns,75.670,24.330,93.050,6.950,19.34,380,0.922,bicubic,-9.490,-4.420,+9 -volo_d1_384,75.620,24.380,93.060,6.940,26.78,384,1.000,bicubic,-9.630,-4.154,+4 -volo_d3_224,75.610,24.390,93.000,7.000,86.33,224,0.960,bicubic,-9.802,-4.280,-2 -convnext_base_in22ft1k,75.580,24.420,93.130,6.870,88.59,224,0.875,bicubic,-10.244,-4.736,-20 -vit_base_r50_s16_384,75.580,24.420,92.790,7.210,98.95,384,1.000,bicubic,-9.396,-4.500,+17 +eva_giant_patch14_336.clip_ft_in1k,82.190,17.810,96.280,3.720,"1,013.01",336,1.000,bicubic,-7.286,-2.544,+2 +eva_giant_patch14_560.m30m_ft_in22k_in1k,82.050,17.950,96.440,3.560,"1,014.45",560,1.000,bicubic,-7.746,-2.552,-1 +eva_giant_patch14_336.m30m_ft_in22k_in1k,81.840,18.160,96.290,3.710,"1,013.01",336,1.000,bicubic,-7.728,-2.662,-1 +eva_giant_patch14_224.clip_ft_in1k,81.590,18.410,96.140,3.860,"1,012.56",224,1.000,bicubic,-7.510,-2.576,+1 +eva_large_patch14_336.in22k_ft_in1k,81.180,18.820,95.880,4.120,304.53,336,1.000,bicubic,-7.484,-2.840,+1 +eva_large_patch14_336.in22k_ft_in22k_in1k,80.940,19.060,96.010,3.990,304.53,336,1.000,bicubic,-8.264,-2.840,-2 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,80.530,19.470,95.500,4.500,304.53,336,1.000,bicubic,-7.736,-3.032,+8 +tf_efficientnet_l2.ns_jft_in1k_475,80.460,19.540,95.730,4.270,480.31,475,0.936,bicubic,-7.774,-2.816,+9 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,80.260,19.740,95.160,4.840,304.43,224,0.950,bicubic,-8.126,-3.438,+3 +tf_efficientnet_l2.ns_jft_in1k,80.250,19.750,95.840,4.160,480.31,800,0.960,bicubic,-8.102,-2.810,+3 +eva_large_patch14_196.in22k_ft_in1k,80.170,19.830,95.450,4.550,304.14,196,1.000,bicubic,-7.768,-3.042,+12 +maxvit_base_tf_512.in21k_ft_in1k,80.160,19.840,95.490,4.510,119.88,512,1.000,bicubic,-8.052,-3.042,+6 +eva_large_patch14_196.in22k_ft_in22k_in1k,80.160,19.840,95.390,4.610,304.14,196,1.000,bicubic,-8.426,-3.266,-4 +maxvit_xlarge_tf_512.in21k_ft_in1k,80.100,19.900,95.490,4.510,475.77,512,1.000,bicubic,-8.438,-3.154,-4 +maxvit_large_tf_512.in21k_ft_in1k,79.990,20.010,95.150,4.850,212.33,512,1.000,bicubic,-8.228,-3.448,+3 +beit_large_patch16_512.in22k_ft_in22k_in1k,79.940,20.060,95.350,4.650,305.67,512,1.000,bicubic,-8.658,-3.306,-9 +maxvit_xlarge_tf_384.in21k_ft_in1k,79.690,20.310,95.160,4.840,475.32,384,1.000,bicubic,-8.616,-3.384,-3 +maxvit_large_tf_384.in21k_ft_in1k,79.600,20.400,95.080,4.920,212.03,384,1.000,bicubic,-8.392,-3.486,+4 +vit_large_patch14_clip_224.openai_ft_in1k,79.590,20.410,94.990,5.010,304.20,224,1.000,bicubic,-8.262,-3.438,+7 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,79.530,20.470,94.990,5.010,632.46,336,1.000,bicubic,-9.044,-3.670,-11 +beit_large_patch16_384.in22k_ft_in22k_in1k,79.500,20.500,95.180,4.820,305.00,384,1.000,bicubic,-8.904,-3.428,-10 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,79.390,20.610,95.070,4.930,304.20,224,1.000,bicubic,-8.778,-3.474,-1 +vit_huge_patch14_clip_224.laion2b_ft_in1k,79.370,20.630,94.920,5.080,632.05,224,1.000,bicubic,-8.224,-3.300,+7 +maxvit_base_tf_384.in21k_ft_in1k,79.340,20.660,95.080,4.920,119.65,384,1.000,bicubic,-8.582,-3.462,0 +vit_large_patch14_clip_336.laion2b_ft_in1k,79.230,20.770,94.980,5.020,304.53,336,1.000,bicubic,-8.618,-3.390,+2 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,79.220,20.780,95.090,4.910,632.05,224,1.000,bicubic,-9.026,-3.460,-10 +deit3_huge_patch14_224_in21ft1k,79.160,20.840,94.860,5.140,632.13,224,1.000,bicubic,-8.024,-3.400,+10 +deit3_large_patch16_384_in21ft1k,79.090,20.910,94.880,5.120,304.76,384,1.000,bicubic,-8.626,-3.632,+1 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,78.990,21.010,94.910,5.090,304.53,336,1.000,bicubic,-9.192,-3.662,-9 +beit_large_patch16_224.in22k_ft_in22k_in1k,78.820,21.180,94.600,5.400,304.43,224,0.900,bicubic,-8.656,-3.704,+1 +deit3_large_patch16_224_in21ft1k,78.630,21.370,94.720,5.280,304.37,224,1.000,bicubic,-8.348,-3.518,+13 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,78.510,21.490,94.640,5.360,304.20,224,1.000,bicubic,-9.380,-3.770,-8 +tf_efficientnet_b7.ns_jft_in1k,78.510,21.490,94.380,5.620,66.35,600,0.949,bicubic,-8.330,-3.714,+15 +vit_large_patch14_clip_224.laion2b_ft_in1k,78.410,21.590,94.570,5.430,304.20,224,1.000,bicubic,-8.882,-3.676,+1 +volo_d5_512,77.970,22.030,94.170,5.830,296.09,512,1.150,bicubic,-9.074,-3.798,+6 +convnext_xlarge.fb_in22k_ft_in1k_384,77.960,22.040,94.460,5.540,350.20,384,1.000,bicubic,-9.788,-4.094,-8 +vit_large_patch16_384.augreg_in21k_ft_in1k,77.940,22.060,94.450,5.550,304.72,384,1.000,bicubic,-9.140,-3.850,+3 +deit3_base_patch16_384_in21ft1k,77.890,22.110,94.030,5.970,86.88,384,1.000,bicubic,-8.854,-4.082,+15 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,77.790,22.210,94.170,5.830,86.86,384,1.000,bicubic,-9.428,-3.864,-3 +volo_d5_448,77.770,22.230,94.050,5.950,295.91,448,1.150,bicubic,-9.184,-3.888,+5 +volo_d4_448,77.750,22.250,93.930,6.070,193.41,448,1.150,bicubic,-9.040,-3.952,+10 +tf_efficientnetv2_xl.in21k_ft_in1k,77.640,22.360,93.970,6.030,208.12,512,1.000,bicubic,-9.108,-4.048,+10 +tf_efficientnetv2_l.in21k_ft_in1k,77.580,22.420,94.280,5.720,118.52,480,1.000,bicubic,-9.226,-3.854,+5 +maxvit_base_tf_512.in1k,77.460,22.540,93.960,6.040,119.88,512,1.000,bicubic,-9.138,-3.960,+11 +convnext_large.fb_in22k_ft_in1k_384,77.420,22.580,94.200,5.800,197.77,384,1.000,bicubic,-10.052,-4.186,-13 +vit_base_patch16_clip_384.laion2b_ft_in1k,77.340,22.660,93.860,6.140,86.86,384,1.000,bicubic,-9.280,-4.150,+8 +swinv2_large_window12to24_192to384_22kft1k,77.310,22.690,93.930,6.070,196.74,384,1.000,bicubic,-10.148,-4.322,-14 +tf_efficientnet_b6.ns_jft_in1k,77.280,22.720,93.890,6.110,43.04,528,0.942,bicubic,-9.172,-3.992,+11 +maxvit_large_tf_512.in1k,77.280,22.720,93.780,6.220,212.33,512,1.000,bicubic,-9.238,-4.104,+8 +swinv2_base_window12to24_192to384_22kft1k,77.180,22.820,94.260,5.740,87.92,384,1.000,bicubic,-9.928,-3.976,-11 +maxvit_large_tf_384.in1k,77.120,22.880,93.460,6.540,212.03,384,1.000,bicubic,-9.116,-4.230,+15 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,77.090,22.910,94.020,5.980,86.53,224,0.900,bicubic,-9.390,-4.028,+7 +volo_d3_448,77.070,22.930,94.110,5.890,86.63,448,1.000,bicubic,-9.424,-3.600,+4 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,77.060,22.940,93.720,6.280,329.09,384,1.000,bicubic,-9.124,-4.198,+15 +swin_large_patch4_window12_384,77.040,22.960,93.750,6.250,196.74,384,1.000,bicubic,-10.108,-4.484,-17 +vit_base_patch16_clip_384.openai_ft_in1k,76.960,23.040,93.750,6.250,86.86,384,1.000,bicubic,-9.246,-4.124,+12 +swinv2_large_window12to16_192to256_22kft1k,76.930,23.070,93.530,6.470,196.74,256,0.900,bicubic,-10.006,-4.578,-11 +beit_base_patch16_384.in22k_ft_in22k_in1k,76.890,23.110,93.910,6.090,86.74,384,1.000,bicubic,-9.910,-4.228,-9 +tf_efficientnetv2_m.in21k_ft_in1k,76.890,23.110,93.650,6.350,54.14,480,1.000,bicubic,-9.114,-4.292,+19 +cait_m48_448,76.870,23.130,93.370,6.630,356.46,448,1.000,bicubic,-9.614,-4.384,-2 +ig_resnext101_32x48d,76.870,23.130,93.310,6.690,828.41,224,0.875,bilinear,-8.558,-4.262,+42 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,76.850,23.150,93.790,6.210,86.86,384,0.950,bicubic,-10.184,-4.390,-20 +ig_resnext101_32x32d,76.840,23.160,93.200,6.800,468.53,224,0.875,bilinear,-8.254,-4.238,+64 +tf_efficientnet_b5.ns_jft_in1k,76.810,23.190,93.580,6.420,30.39,456,0.934,bicubic,-9.278,-4.172,+9 +maxvit_base_tf_384.in1k,76.770,23.230,93.420,6.580,119.65,384,1.000,bicubic,-9.524,-4.384,-2 +convnext_large.fb_in22k_ft_in1k,76.730,23.270,93.710,6.290,197.77,288,1.000,bicubic,-10.286,-4.496,-23 +deit3_large_patch16_384,76.690,23.310,93.350,6.650,304.76,384,1.000,bicubic,-9.120,-4.246,+19 +convnext_base.fb_in22k_ft_in1k_384,76.640,23.360,93.700,6.300,88.59,384,1.000,bicubic,-10.154,-4.564,-18 +xcit_large_24_p8_384_dist,76.630,23.370,93.090,6.910,188.93,384,1.000,bicubic,-9.370,-4.596,+10 +convnext_xlarge.fb_in22k_ft_in1k,76.620,23.380,93.850,6.150,350.20,288,1.000,bicubic,-10.718,-4.478,-36 +volo_d5_224,76.580,23.420,93.300,6.700,295.46,224,0.960,bicubic,-9.488,-4.278,+3 +vit_base_patch8_224.augreg2_in21k_ft_in1k,76.570,23.430,93.330,6.670,86.58,224,0.900,bicubic,-9.642,-4.502,-5 +deit3_base_patch16_224_in21ft1k,76.550,23.450,93.560,6.440,86.59,224,1.000,bicubic,-9.164,-4.184,+18 +vit_base_patch16_384.augreg_in21k_ft_in1k,76.500,23.500,93.750,6.250,86.86,384,1.000,bicubic,-9.506,-4.250,+3 +maxvit_small_tf_512.in1k,76.490,23.510,93.390,6.610,69.13,512,1.000,bicubic,-9.598,-4.368,-3 +swinv2_base_window12to16_192to256_22kft1k,76.450,23.550,93.670,6.330,87.92,256,0.900,bicubic,-9.824,-4.226,-11 +cait_m36_384,76.320,23.680,93.050,6.950,271.22,384,1.000,bicubic,-9.734,-4.680,-2 +vit_large_patch16_224.augreg_in21k_ft_in1k,76.290,23.710,93.600,6.400,304.33,224,0.900,bicubic,-9.552,-4.224,+5 +swin_base_patch4_window12_384,76.280,23.720,93.320,6.680,87.90,384,1.000,bicubic,-10.152,-4.738,-18 +swin_large_patch4_window7_224,76.270,23.730,93.420,6.580,196.53,224,0.900,bicubic,-10.050,-4.476,-18 +cait_s36_384,76.210,23.790,92.970,7.030,68.37,384,1.000,bicubic,-9.250,-4.510,+21 +xcit_medium_24_p8_384_dist,76.140,23.860,92.980,7.020,84.32,384,1.000,bicubic,-9.676,-4.612,+2 +dm_nfnet_f6,76.130,23.870,93.110,6.890,438.36,576,0.956,bicubic,-10.014,-4.620,-12 +flexivit_large.1200ep_in1k,76.100,23.900,93.010,6.990,304.36,240,0.950,bicubic,-9.544,-4.532,+11 +tf_efficientnet_b7.ap_in1k,76.090,23.910,92.970,7.030,66.35,600,0.949,bicubic,-9.030,-4.282,+38 +tf_efficientnet_b8.ap_in1k,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.280,-4.660,+24 +volo_d2_384,76.080,23.920,93.130,6.870,58.87,384,1.000,bicubic,-9.956,-4.442,-11 +maxvit_small_tf_384.in1k,76.070,23.930,92.620,7.380,69.02,384,1.000,bicubic,-9.464,-4.844,+11 +maxvit_tiny_tf_512.in1k,76.050,23.950,93.160,6.840,31.05,512,1.000,bicubic,-9.612,-4.420,+5 +flexivit_large.600ep_in1k,76.040,23.960,92.960,7.040,304.36,240,0.950,bicubic,-9.498,-4.532,+7 +vit_base_patch8_224.augreg_in21k_ft_in1k,76.010,23.990,93.380,6.620,86.58,224,0.900,bicubic,-9.786,-4.410,-4 +tf_efficientnetv2_l.in1k,76.000,24.000,93.080,6.920,118.52,480,1.000,bicubic,-9.670,-4.394,+1 +volo_d4_224,76.000,24.000,93.000,7.000,192.96,224,0.960,bicubic,-9.872,-4.468,-11 +xcit_large_24_p8_224_dist,75.990,24.010,92.730,7.270,188.93,224,1.000,bicubic,-9.406,-4.680,+13 +flexivit_large.300ep_in1k,75.930,24.070,92.650,7.350,304.36,240,0.950,bicubic,-9.350,-4.790,+17 +convnext_base.fb_in22k_ft_in1k,75.910,24.090,93.580,6.420,88.59,288,1.000,bicubic,-10.370,-4.510,-32 +dm_nfnet_f4,75.850,24.150,92.950,7.050,316.07,512,0.951,bicubic,-9.864,-4.570,-5 +xcit_large_24_p16_384_dist,75.820,24.180,92.750,7.250,189.10,384,1.000,bicubic,-9.934,-4.788,-8 +deit3_huge_patch14_224,75.790,24.210,92.760,7.240,632.13,224,0.900,bicubic,-9.414,-4.598,+21 +xcit_small_24_p8_384_dist,75.770,24.230,92.980,7.020,47.63,384,1.000,bicubic,-9.786,-4.592,-4 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,75.740,24.260,92.750,7.250,86.57,224,0.950,bicubic,-10.430,-5.004,-31 +ig_resnext101_32x16d,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.450,-4.286,+89 +efficientnet_b5.in12k_ft_in1k,75.680,24.320,93.040,6.960,30.39,448,1.000,bicubic,-10.208,-4.692,-22 +tf_efficientnet_b4.ns_jft_in1k,75.670,24.330,93.050,6.950,19.34,380,0.922,bicubic,-9.492,-4.420,+18 +vit_medium_patch16_gap_384.in12k_ft_in1k,75.660,24.340,92.970,7.030,39.03,384,0.950,bicubic,-9.876,-4.664,-7 +volo_d1_384,75.610,24.390,93.070,6.930,26.78,384,1.000,bicubic,-9.640,-4.126,+9 +volo_d3_224,75.610,24.390,93.000,7.000,86.33,224,0.960,bicubic,-9.798,-4.280,-1 +vit_base_patch16_clip_224.openai_ft_in1k,75.590,24.410,92.970,7.030,86.57,224,0.900,bicubic,-9.690,-4.436,+3 +vit_base_r50_s16_384.orig_in21k_ft_in1k,75.590,24.410,92.790,7.210,98.95,384,1.000,bicubic,-9.382,-4.498,+27 deit_base_distilled_patch16_384,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.872,-4.832,-6 -tf_efficientnetv2_m,75.520,24.480,92.620,7.380,54.14,480,1.000,bicubic,-9.516,-4.658,+12 -regnetz_e8,75.490,24.510,92.710,7.290,57.70,320,1.000,bicubic,-9.540,-4.554,+12 -cait_s24_384,75.480,24.520,92.600,7.400,47.06,384,1.000,bicubic,-9.570,-4.748,+9 -xcit_medium_24_p8_224_dist,75.470,24.530,92.900,7.100,84.32,224,1.000,bicubic,-9.600,-4.380,+6 -swsl_resnext101_32x8d,75.420,24.580,92.750,7.250,88.79,224,0.875,bilinear,-8.870,-4.432,+42 -tf_efficientnet_b6_ap,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.406,-4.698,+18 -beit_base_patch16_224,75.370,24.630,93.040,6.960,86.53,224,0.900,bicubic,-9.858,-4.616,-6 -volo_d2_224,75.300,24.700,92.510,7.490,58.68,224,0.960,bicubic,-9.894,-4.678,-5 -dm_nfnet_f3,75.200,24.800,92.940,7.060,254.92,416,0.940,bicubic,-10.322,-4.522,-20 -efficientnetv2_rw_m,75.160,24.840,92.570,7.430,53.24,416,1.000,bicubic,-9.652,-4.576,+13 -deit3_large_patch16_224,75.140,24.860,92.280,7.720,304.37,224,0.900,bicubic,-9.622,-4.758,+14 -ecaresnet269d,75.120,24.880,92.840,7.160,102.09,352,1.000,bicubic,-9.854,-4.386,+5 -xcit_medium_24_p16_384_dist,75.110,24.890,92.440,7.560,84.40,384,1.000,bicubic,-10.312,-4.966,-20 -deit3_small_patch16_384_in21ft1k,75.090,24.910,92.800,7.200,22.21,384,1.000,bicubic,-9.734,-4.684,+8 -convnext_small_384_in22ft1k,75.050,24.950,93.010,6.990,50.22,384,1.000,bicubic,-10.674,-4.854,-31 -dm_nfnet_f5,75.010,24.990,92.600,7.400,377.21,544,0.954,bicubic,-10.806,-4.886,-36 -xcit_small_24_p8_224_dist,74.980,25.020,92.300,7.700,47.63,224,1.000,bicubic,-9.896,-4.888,+4 -tf_efficientnet_b8,74.930,25.070,92.320,7.680,87.41,672,0.954,bicubic,-10.438,-5.072,-20 -xcit_small_12_p8_384_dist,74.860,25.140,92.460,7.540,26.21,384,1.000,bicubic,-10.220,-4.820,-11 -eca_nfnet_l2,74.830,25.170,92.650,7.350,56.72,384,1.000,bicubic,-9.866,-4.614,+8 -deit3_base_patch16_384,74.790,25.210,92.240,7.760,86.88,384,1.000,bicubic,-10.286,-5.014,-12 -tf_efficientnet_b7,74.720,25.280,92.220,7.780,66.35,600,0.949,bicubic,-10.214,-4.986,-4 -xcit_large_24_p16_224_dist,74.670,25.330,91.860,8.140,189.10,224,1.000,bicubic,-10.250,-5.272,-4 -dm_nfnet_f2,74.620,25.380,92.250,7.750,193.78,352,0.920,bicubic,-10.446,-4.992,-13 -tf_efficientnet_b5_ap,74.590,25.410,91.990,8.010,30.39,456,0.934,bicubic,-9.664,-4.988,+26 -xcit_small_24_p16_384_dist,74.580,25.420,92.450,7.550,47.67,384,1.000,bicubic,-10.508,-4.858,-19 -dm_nfnet_f1,74.570,25.430,92.260,7.740,132.63,320,0.910,bicubic,-10.054,-4.838,+2 -swin_base_patch4_window7_224,74.540,25.460,92.560,7.440,87.77,224,0.900,bicubic,-10.710,-5.002,-29 -seresnet152d,74.520,25.480,92.080,7.920,66.84,320,1.000,bicubic,-9.844,-4.964,+14 -regnetz_040,74.460,25.540,91.900,8.100,27.12,320,1.000,bicubic,-9.776,-5.032,+22 -resnest200e,74.460,25.540,91.860,8.140,70.20,320,0.909,bicubic,-9.368,-5.032,+49 -tf_efficientnetv2_s_in21ft1k,74.450,25.550,92.500,7.500,21.46,384,1.000,bicubic,-9.846,-4.754,+13 -regnetz_040h,74.440,25.560,92.240,7.760,28.94,320,1.000,bicubic,-10.056,-4.766,+3 -resnetrs200,74.360,25.640,91.940,8.060,93.21,320,1.000,bicubic,-10.080,-5.140,+4 -seresnextaa101d_32x8d,74.320,25.680,91.720,8.280,93.59,288,1.000,bicubic,-10.252,-5.350,-4 -convnext_small_in22ft1k,74.210,25.790,92.550,7.450,50.22,224,0.875,bicubic,-10.358,-4.846,-4 -seresnext101d_32x8d,74.210,25.790,91.860,8.140,93.59,288,1.000,bicubic,-10.152,-5.058,+7 -efficientnetv2_rw_s,74.180,25.820,91.710,8.290,23.94,384,1.000,bicubic,-9.630,-5.014,+44 -resnest269e,74.170,25.830,91.930,8.070,110.93,416,0.928,bicubic,-10.348,-5.056,-5 -cait_xs24_384,74.170,25.830,91.910,8.090,26.67,384,1.000,bicubic,-9.894,-4.980,+22 -pit_b_distilled_224,74.160,25.840,91.660,8.340,74.79,224,0.900,bicubic,-9.982,-5.196,+18 -swsl_resnext101_32x4d,74.140,25.860,91.990,8.010,44.18,224,0.875,bilinear,-9.100,-4.770,+68 -vit_large_r50_s32_224,74.120,25.880,92.380,7.620,328.99,224,0.900,bicubic,-10.310,-4.786,-3 -eca_nfnet_l1,74.120,25.880,92.070,7.930,41.41,320,1.000,bicubic,-9.892,-4.962,+25 -xcit_small_12_p16_384_dist,74.120,25.880,92.070,7.930,26.25,384,1.000,bicubic,-10.588,-5.046,-18 -volo_d1_224,74.120,25.880,92.030,7.970,26.63,224,0.960,bicubic,-10.044,-4.744,+12 -convnext_large,74.070,25.930,91.550,8.450,197.77,224,0.875,bicubic,-10.226,-5.344,-1 -xcit_large_24_p8_224,74.070,25.930,90.890,9.110,188.93,224,1.000,bicubic,-10.322,-5.768,-6 -vit_base_patch16_224_miil,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.232,-5.102,0 -resnetv2_152x4_bitm,74.010,25.990,92.340,7.660,936.53,480,1.000,bilinear,-10.908,-5.102,-30 -swinv2_base_window16_256,74.010,25.990,91.750,8.250,87.92,256,0.900,bicubic,-10.582,-5.324,-21 -vit_base_patch16_224,74.000,26.000,92.470,7.530,86.57,224,0.900,bicubic,-10.530,-4.826,-19 -tf_efficientnetv2_s,74.000,26.000,91.530,8.470,21.46,384,1.000,bicubic,-9.884,-5.168,+20 -swsl_resnext101_32x16d,73.990,26.010,92.180,7.820,194.03,224,0.875,bilinear,-9.360,-4.664,+52 -regnetz_d32,73.970,26.030,91.950,8.050,27.58,320,0.950,bicubic,-10.054,-4.918,+12 -crossvit_18_dagger_408,73.970,26.030,91.410,8.590,44.61,408,1.000,bicubic,-10.224,-5.408,0 -seresnext101_32x8d,73.940,26.060,91.450,8.550,93.57,288,1.000,bicubic,-10.264,-5.424,-2 -resnetv2_152x2_bitm,73.920,26.080,92.670,7.330,236.34,448,1.000,bilinear,-10.590,-4.764,-23 -resnetrs420,73.920,26.080,91.760,8.240,191.89,416,1.000,bicubic,-11.088,-5.364,-44 -xcit_small_12_p8_224_dist,73.920,26.080,91.720,8.280,26.21,224,1.000,bicubic,-10.310,-5.154,-7 -resmlp_big_24_224_in22ft1k,73.900,26.100,91.750,8.250,129.14,224,0.875,bicubic,-10.498,-5.368,-20 -tf_efficientnet_b6,73.890,26.110,91.750,8.250,43.04,528,0.942,bicubic,-10.218,-5.138,-2 -tf_efficientnet_b3_ns,73.880,26.120,91.870,8.130,12.23,300,0.904,bicubic,-10.168,-5.042,+3 -convnext_base,73.870,26.130,91.320,8.680,88.59,224,0.875,bicubic,-9.970,-5.430,+13 -deit3_small_patch16_224_in21ft1k,73.840,26.160,91.960,8.040,22.06,224,1.000,bicubic,-9.236,-4.816,+57 -vit_small_r26_s32_384,73.800,26.200,92.290,7.710,36.47,384,1.000,bicubic,-10.248,-5.038,-1 -regnetz_d8,73.760,26.240,92.020,7.980,23.37,320,1.000,bicubic,-10.292,-4.976,-4 -regnety_080,73.730,26.270,91.790,8.210,39.18,288,1.000,bicubic,-10.198,-5.098,+4 -resnetrs270,73.690,26.310,91.570,8.430,129.86,352,1.000,bicubic,-10.746,-5.404,-30 -resnetv2_101x3_bitm,73.680,26.320,92.470,7.530,387.93,448,1.000,bilinear,-10.764,-4.912,-33 -resnet200d,73.680,26.320,91.570,8.430,64.69,320,1.000,bicubic,-10.280,-5.254,-1 -ig_resnext101_32x8d,73.660,26.340,92.160,7.840,88.79,224,0.875,bilinear,-9.038,-4.472,+71 -xcit_medium_24_p16_224_dist,73.650,26.350,91.580,8.420,84.40,224,1.000,bicubic,-10.628,-5.360,-25 -regnety_064,73.590,26.410,91.350,8.650,30.58,288,1.000,bicubic,-10.130,-5.376,+14 -tf_efficientnet_b5,73.560,26.440,91.460,8.540,30.39,456,0.934,bicubic,-10.254,-5.288,+6 -swinv2_base_window8_256,73.540,26.460,91.520,8.480,87.92,256,0.900,bicubic,-10.722,-5.402,-26 -resnet152d,73.530,26.470,91.230,8.770,60.21,320,1.000,bicubic,-10.148,-5.510,+16 -regnetv_064,73.490,26.510,91.590,8.410,30.58,288,1.000,bicubic,-10.222,-5.156,+12 -deit3_base_patch16_224,73.480,26.520,91.290,8.710,86.59,224,0.900,bicubic,-10.312,-5.294,+5 -sequencer2d_l,73.480,26.520,91.100,8.900,54.30,224,0.875,bicubic,-9.926,-5.400,+21 -xcit_tiny_24_p8_384_dist,73.420,26.580,91.560,8.440,12.11,384,1.000,bicubic,-10.326,-5.152,+5 -resnetrs350,73.400,26.600,91.310,8.690,163.96,384,1.000,bicubic,-11.312,-5.680,-56 -twins_svt_large,73.390,26.610,90.910,9.090,99.27,224,0.900,bicubic,-10.290,-5.684,+9 -regnetz_d8_evos,73.370,26.630,91.640,8.360,23.46,320,0.950,bicubic,-10.680,-5.356,-20 -regnety_160,73.360,26.640,91.700,8.300,83.59,288,1.000,bicubic,-10.332,-5.076,+6 -swin_s3_base_224,73.320,26.680,91.190,8.810,71.13,224,0.900,bicubic,-10.612,-5.470,-15 -efficientnet_b4,73.310,26.690,91.280,8.720,19.34,384,1.000,bicubic,-10.114,-5.318,+13 -vit_small_patch16_384,73.300,26.700,92.000,8.000,22.20,384,1.000,bicubic,-10.500,-5.100,-5 -resmlp_big_24_distilled_224,73.290,26.710,91.160,8.840,129.14,224,0.875,bicubic,-10.298,-5.488,+5 -swinv2_small_window16_256,73.270,26.730,91.270,8.730,49.73,256,0.900,bicubic,-10.940,-5.600,-36 -xcit_small_24_p16_224_dist,73.260,26.740,91.460,8.540,47.67,224,1.000,bicubic,-10.610,-5.272,-17 -deit_base_distilled_patch16_224,73.240,26.760,91.000,9.000,87.34,224,0.900,bicubic,-10.148,-5.488,+11 -resnetrs152,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.514,-5.354,-4 -xcit_medium_24_p8_224,73.150,26.850,90.280,9.720,84.32,224,1.000,bicubic,-10.588,-6.114,-7 -vit_base_patch32_384,73.130,26.870,91.240,8.760,88.30,384,1.000,bicubic,-10.222,-5.596,+10 -jx_nest_base,73.120,26.880,91.060,8.940,67.72,224,0.875,bicubic,-10.434,-5.304,-1 -swinv2_small_window8_256,73.110,26.890,90.930,9.070,49.73,256,0.900,bicubic,-10.744,-5.712,-22 -cs3se_edgenet_x,73.100,26.900,91.260,8.740,50.72,320,1.000,bicubic,-10.448,-5.406,-2 -deit3_small_patch16_384,73.090,26.910,91.240,8.760,22.21,384,1.000,bicubic,-10.338,-5.436,0 -xcit_small_24_p8_224,73.080,26.920,91.140,8.860,47.63,224,1.000,bicubic,-10.760,-5.496,-22 -cait_s24_224,73.070,26.930,91.120,8.880,46.92,224,1.000,bicubic,-10.388,-5.442,-3 -crossvit_15_dagger_408,72.950,27.050,91.090,8.910,28.50,408,1.000,bicubic,-10.888,-5.690,-23 -resnetv2_152x2_bit_teacher_384,72.900,27.100,91.550,8.450,236.34,384,1.000,bicubic,-10.944,-5.566,-27 -regnetv_040,72.880,27.120,91.110,8.890,20.64,288,1.000,bicubic,-10.318,-5.554,+8 -dm_nfnet_f0,72.880,27.120,91.080,8.920,71.49,256,0.900,bicubic,-10.504,-5.494,-1 -tf_efficientnet_b4_ap,72.880,27.120,90.980,9.020,19.34,380,0.922,bicubic,-10.368,-5.412,+3 -convnext_tiny_384_in22ft1k,72.850,27.150,91.560,8.440,28.59,384,1.000,bicubic,-11.226,-5.598,-46 -swinv2_cr_small_ns_224,72.800,27.200,90.800,9.200,49.70,224,0.900,bicubic,-10.686,-5.684,-11 -xception65p,72.790,27.210,90.910,9.090,39.82,299,0.940,bicubic,-10.340,-5.570,+10 -regnety_032,72.760,27.240,90.960,9.040,19.44,288,1.000,bicubic,-9.964,-5.462,+30 -regnety_040,72.720,27.280,90.730,9.270,20.65,288,1.000,bicubic,-10.316,-5.780,+14 -swin_s3_small_224,72.690,27.310,90.560,9.440,49.74,224,0.900,bicubic,-11.084,-5.892,-27 -resnext101_64x4d,72.620,27.380,90.840,9.160,83.46,288,1.000,bicubic,-10.524,-5.534,+2 -xcit_small_12_p8_224,72.620,27.380,90.670,9.330,26.21,224,1.000,bicubic,-10.720,-5.810,-6 -nfnet_l0,72.610,27.390,91.000,9.000,35.07,288,1.000,bicubic,-10.142,-5.518,+24 -pnasnet5large,72.610,27.390,90.510,9.490,86.06,331,0.911,bicubic,-10.172,-5.532,+21 -xception65,72.600,27.400,90.820,9.180,39.92,299,0.940,bicubic,-10.574,-5.772,-4 -resnest101e,72.580,27.420,90.820,9.180,48.28,256,0.875,bilinear,-10.308,-5.500,+13 -twins_pcpvt_large,72.570,27.430,90.700,9.300,60.99,224,0.900,bicubic,-10.566,-5.904,-1 -swsl_resnext50_32x4d,72.560,27.440,90.850,9.150,25.03,224,0.875,bilinear,-9.616,-5.382,+66 -gc_efficientnetv2_rw_t,72.550,27.450,90.830,9.170,13.68,288,1.000,bicubic,-9.916,-5.468,+38 -twins_svt_base,72.550,27.450,90.450,9.550,56.07,224,0.900,bicubic,-10.588,-5.970,-6 -tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.498,-5.860,+1 -deit_base_patch16_384,72.540,27.460,90.270,9.730,86.86,384,1.000,bicubic,-10.566,-6.100,-3 -resnetv2_50x3_bitm,72.520,27.480,91.760,8.240,217.32,448,1.000,bilinear,-11.492,-5.366,-57 -xcit_small_12_p16_224_dist,72.500,27.500,91.110,8.890,26.25,224,1.000,bicubic,-10.846,-5.308,-19 -xcit_tiny_24_p8_224_dist,72.430,27.570,90.920,9.080,12.11,224,1.000,bicubic,-10.130,-5.248,+27 -resnet101d,72.420,27.580,90.650,9.350,44.57,320,1.000,bicubic,-10.602,-5.796,-1 -sequencer2d_m,72.410,27.590,90.710,9.290,38.31,224,0.875,bicubic,-10.398,-5.558,+7 -cs3sedarknet_x,72.380,27.620,91.020,8.980,35.40,288,1.000,bicubic,-10.274,-5.326,+14 -jx_nest_small,72.360,27.640,90.690,9.310,38.35,224,0.875,bicubic,-10.760,-5.640,-11 -convnext_small,72.330,27.670,90.850,9.150,50.22,224,0.875,bicubic,-10.820,-5.580,-18 -regnetz_c16,72.310,27.690,90.820,9.180,13.46,320,0.940,bicubic,-10.210,-5.540,+22 -tf_efficientnet_b4,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.734,-5.710,-8 -tf_efficientnet_b2_ns,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.104,-5.156,+30 -tresnet_m,72.260,27.740,90.230,9.770,31.39,224,0.875,bilinear,-10.814,-5.890,-13 -resnetv2_50x1_bit_distilled,72.250,27.750,91.010,8.990,25.55,224,0.875,bicubic,-10.572,-5.512,-4 -crossvit_18_240,72.250,27.750,90.270,9.730,43.27,240,0.875,bicubic,-10.148,-5.784,+25 -regnetz_c16_evos,72.230,27.770,91.230,8.770,13.49,320,0.950,bicubic,-10.402,-5.246,+7 -nasnetalarge,72.230,27.770,90.460,9.540,88.75,331,0.911,bicubic,-10.388,-5.584,+7 -efficientnetv2_rw_t,72.230,27.770,90.410,9.590,13.65,288,1.000,bicubic,-10.114,-5.786,+27 -cait_xxs36_384,72.200,27.800,90.840,9.160,17.37,384,1.000,bicubic,-9.992,-5.304,+43 -twins_pcpvt_base,72.190,27.810,90.510,9.490,43.83,224,0.900,bicubic,-10.518,-5.840,-1 -crossvit_18_dagger_240,72.130,27.870,90.070,9.930,44.27,240,0.875,bicubic,-10.390,-5.998,+12 -xcit_tiny_24_p16_384_dist,72.080,27.920,90.590,9.410,12.12,384,1.000,bicubic,-10.492,-5.698,+7 -resnet152,72.060,27.940,90.340,9.660,60.19,224,0.950,bicubic,-10.758,-5.792,-12 -mobilevitv2_200_384_in22ft1k,72.000,28.000,90.630,9.370,18.45,384,1.000,bicubic,-11.400,-5.952,-45 -vit_relpos_base_patch16_clsgap_224,72.000,28.000,90.250,9.750,86.43,224,0.900,bicubic,-10.760,-5.924,-10 -vit_relpos_medium_patch16_cls_224,71.990,28.010,90.290,9.710,38.76,224,0.900,bicubic,-10.572,-5.776,+4 -sequencer2d_s,71.940,28.060,90.490,9.510,27.65,224,0.875,bicubic,-10.404,-5.544,+19 -swinv2_cr_small_224,71.880,28.120,90.260,9.740,49.70,224,0.900,bicubic,-11.258,-5.838,-34 -eca_nfnet_l0,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.738,-5.380,-1 -convnext_tiny_in22ft1k,71.830,28.170,90.920,9.080,28.59,224,0.875,bicubic,-11.082,-5.704,-24 -vit_relpos_base_patch16_224,71.830,28.170,90.260,9.740,86.43,224,0.900,bicubic,-10.656,-5.882,+4 -mobilevitv2_175_384_in22ft1k,71.810,28.190,90.780,9.220,14.25,384,1.000,bicubic,-11.124,-5.650,-27 -cs3edgenet_x,71.810,28.190,90.360,9.640,47.82,288,1.000,bicubic,-10.912,-6.016,-15 -swin_small_patch4_window7_224,71.750,28.250,90.240,9.760,49.61,224,0.900,bicubic,-11.468,-6.086,-46 -pit_b_224,71.710,28.290,89.250,10.750,73.76,224,0.900,bicubic,-10.734,-6.462,+4 -xcit_large_24_p16_224,71.700,28.300,89.170,10.830,189.10,224,1.000,bicubic,-11.192,-6.708,-29 -swsl_resnet50,71.690,28.310,90.470,9.530,25.56,224,0.875,bilinear,-9.490,-5.510,+87 -resnet61q,71.670,28.330,90.270,9.730,36.85,288,1.000,bicubic,-10.848,-5.860,-4 -tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.402,-6.306,+30 -tresnet_l_448,71.610,28.390,90.060,9.940,55.99,448,0.875,bilinear,-10.660,-5.920,+15 -convit_base,71.590,28.410,90.160,9.840,86.54,224,0.875,bicubic,-10.702,-5.778,+12 -xcit_tiny_12_p8_384_dist,71.580,28.420,90.710,9.290,6.71,384,1.000,bicubic,-10.806,-5.512,-1 -swinv2_tiny_window16_256,71.570,28.430,90.350,9.650,28.35,256,0.900,bicubic,-11.240,-5.880,-31 -poolformer_m48,71.550,28.450,89.760,10.240,73.47,224,0.950,bicubic,-10.910,-6.198,-6 -fbnetv3_g,71.520,28.480,90.380,9.620,16.62,288,0.950,bilinear,-10.514,-5.686,+27 -crossvit_15_dagger_240,71.520,28.480,89.860,10.140,28.21,240,0.875,bicubic,-10.806,-6.096,+3 -ssl_resnext101_32x8d,71.510,28.490,90.470,9.530,88.79,224,0.875,bilinear,-10.098,-5.572,+48 -efficientnet_b3,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.760,-6.058,+8 -ecaresnet101d,71.470,28.530,90.330,9.670,44.57,224,0.875,bicubic,-10.700,-5.718,+15 -mobilevitv2_150_384_in22ft1k,71.460,28.540,90.420,9.580,10.59,384,1.000,bicubic,-11.130,-5.896,-25 -ssl_resnext101_32x16d,71.430,28.570,90.520,9.480,194.03,224,0.875,bilinear,-10.426,-5.576,+32 -resnet51q,71.420,28.580,90.180,9.820,35.70,288,1.000,bilinear,-10.938,-5.998,-9 -vit_relpos_medium_patch16_224,71.370,28.630,89.950,10.050,38.75,224,0.900,bicubic,-11.092,-6.136,-16 -pit_s_distilled_224,71.360,28.640,89.780,10.220,24.04,224,0.900,bicubic,-10.634,-6.016,+19 -xcit_tiny_24_p8_224,71.330,28.670,90.240,9.760,12.11,224,1.000,bicubic,-10.566,-5.734,+26 -mixer_b16_224_miil,71.310,28.690,89.650,10.350,59.88,224,0.875,bilinear,-10.994,-6.070,-5 -resnetv2_152x2_bit_teacher,71.290,28.710,90.430,9.570,236.34,224,0.875,bicubic,-11.578,-6.138,-48 -resnetv2_101,71.280,28.720,89.940,10.060,44.54,224,0.950,bicubic,-10.766,-5.922,+13 -convnext_tiny_hnf,71.280,28.720,89.400,10.600,28.59,224,0.950,bicubic,-10.940,-6.466,0 -ecaresnet50t,71.260,28.740,90.420,9.580,25.57,320,0.950,bicubic,-11.088,-5.718,-16 -convmixer_1536_20,71.230,28.770,89.440,10.560,51.63,224,0.960,bicubic,-10.140,-6.172,+54 -xcit_small_12_p16_224,71.200,28.800,89.750,10.250,26.25,224,1.000,bicubic,-10.772,-6.062,+14 -deit_base_patch16_224,71.200,28.800,89.200,10.800,86.57,224,0.900,bicubic,-10.794,-6.532,+11 -crossvit_base_240,71.180,28.820,89.840,10.160,105.03,240,0.875,bicubic,-11.036,-5.992,-4 -vit_relpos_medium_patch16_rpn_224,71.170,28.830,90.090,9.910,38.73,224,0.900,bicubic,-11.124,-5.882,-13 -mobilevitv2_200_in22ft1k,71.140,28.860,89.680,10.320,18.45,256,0.888,bicubic,-11.194,-6.258,-19 -resnetv2_50d_evos,71.120,28.880,90.030,9.970,25.59,288,0.950,bicubic,-10.858,-5.882,+8 -swin_s3_tiny_224,71.120,28.880,89.720,10.280,28.33,224,0.900,bicubic,-11.004,-6.230,-3 -halo2botnet50ts_256,71.110,28.890,89.630,10.370,22.64,256,0.950,bicubic,-10.958,-6.012,-1 -cs3darknet_x,71.080,28.920,90.150,9.850,35.05,288,1.000,bicubic,-11.144,-6.080,-12 -cs3sedarknet_l,71.070,28.930,90.350,9.650,21.91,288,0.950,bicubic,-10.706,-5.620,+17 -xcit_small_24_p16_224,71.040,28.960,89.700,10.300,47.67,224,1.000,bicubic,-11.544,-6.300,-45 -xcit_tiny_12_p8_224_dist,71.030,28.970,89.890,10.110,6.71,224,1.000,bicubic,-10.178,-5.716,+49 -xcit_medium_24_p16_224,71.010,28.990,89.520,10.480,84.40,224,1.000,bicubic,-11.628,-6.458,-52 -visformer_small,71.000,29.000,89.450,10.550,40.22,224,0.900,bicubic,-11.108,-6.426,-9 -resnetv2_101x1_bitm,70.990,29.010,91.090,8.910,44.54,448,1.000,bilinear,-11.342,-5.426,-28 -edgenext_small,70.990,29.010,89.870,10.130,5.59,320,1.000,bicubic,-10.584,-5.844,+19 -resnetv2_50d_gn,70.990,29.010,89.770,10.230,25.57,288,0.950,bicubic,-10.834,-6.154,+6 -lamhalobotnet50ts_256,70.990,29.010,89.070,10.930,22.57,256,0.950,bicubic,-10.562,-6.434,+18 -tresnet_m_448,70.990,29.010,88.690,11.310,31.39,448,0.875,bilinear,-10.716,-6.882,+9 -resnest50d_4s2x40d,70.950,29.050,89.720,10.280,30.42,224,0.875,bicubic,-10.158,-5.842,+48 -tnt_s_patch16_224,70.950,29.050,89.600,10.400,23.76,224,0.900,bicubic,-10.568,-6.146,+18 -wide_resnet50_2,70.940,29.060,89.230,10.770,68.88,224,0.875,bicubic,-10.516,-6.300,+23 -convnext_tiny,70.930,29.070,89.750,10.250,28.59,224,0.875,bicubic,-11.132,-6.104,-14 -tf_efficientnet_b3_ap,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.904,-6.194,0 -vit_small_patch16_224,70.910,29.090,90.150,9.850,22.05,224,0.900,bicubic,-10.486,-5.988,+25 -vit_srelpos_medium_patch16_224,70.910,29.090,89.960,10.040,38.74,224,0.900,bicubic,-11.326,-5.974,-30 -vit_base_patch16_rpn_224,70.870,29.130,89.770,10.230,86.54,224,0.900,bicubic,-11.330,-6.226,-27 -resnet101,70.870,29.130,89.510,10.490,44.55,224,0.950,bicubic,-11.060,-6.256,-9 -vit_large_patch32_384,70.860,29.140,90.570,9.430,306.63,384,1.000,bicubic,-10.648,-5.520,+12 -tf_efficientnet_b1_ns,70.860,29.140,90.140,9.860,7.79,240,0.882,bicubic,-10.526,-5.596,+21 -jx_nest_tiny,70.860,29.140,89.940,10.060,17.06,224,0.875,bicubic,-10.558,-5.678,+17 -resnetrs101,70.860,29.140,89.830,10.170,63.62,288,0.940,bicubic,-11.424,-6.178,-39 -rexnet_200,70.850,29.150,89.710,10.290,16.37,224,0.875,bicubic,-10.778,-5.958,-1 -tresnet_l,70.840,29.160,89.630,10.370,55.99,224,0.875,bilinear,-10.650,-5.996,+8 -tf_efficientnetv2_b3,70.830,29.170,89.510,10.490,14.36,300,0.904,bicubic,-11.136,-6.272,-18 -poolformer_m36,70.800,29.200,89.510,10.490,56.17,224,0.950,bicubic,-11.308,-6.180,-30 -coat_lite_small,70.780,29.220,89.580,10.420,19.84,224,0.900,bicubic,-11.524,-6.270,-48 -deit3_small_patch16_224,70.760,29.240,89.440,10.560,22.06,224,0.900,bicubic,-10.622,-6.010,+14 -levit_384,70.760,29.240,89.290,10.710,39.13,224,0.900,bicubic,-11.828,-6.728,-74 -convnext_nano,70.730,29.270,89.350,10.650,15.59,288,1.000,bicubic,-10.746,-6.310,+3 -swinv2_cr_tiny_ns_224,70.720,29.280,89.380,10.620,28.33,224,0.900,bicubic,-11.066,-6.442,-15 -vit_relpos_small_patch16_224,70.710,29.290,90.000,10.000,21.98,224,0.900,bicubic,-10.744,-5.828,+4 -mobilevitv2_175_in22ft1k,70.660,29.340,89.710,10.290,14.25,256,0.888,bicubic,-11.280,-6.080,-25 -tf_efficientnet_b3,70.640,29.360,89.450,10.550,12.23,300,0.904,bicubic,-10.998,-6.268,-13 -gluon_senet154,70.620,29.380,88.920,11.080,115.09,224,0.875,bicubic,-10.610,-6.426,+15 -crossvit_small_240,70.610,29.390,89.360,10.640,26.86,240,0.875,bicubic,-10.406,-6.096,+29 -cait_xxs24_384,70.600,29.400,89.720,10.280,12.03,384,1.000,bicubic,-10.362,-5.924,+33 -convit_small,70.590,29.410,89.580,10.420,27.78,224,0.875,bicubic,-10.838,-6.162,-1 -twins_pcpvt_small,70.560,29.440,89.070,10.930,24.11,224,0.900,bicubic,-10.530,-6.572,+23 -swinv2_tiny_window8_256,70.540,29.460,89.490,10.510,28.35,256,0.900,bicubic,-11.270,-6.504,-25 -ssl_resnext101_32x4d,70.530,29.470,89.760,10.240,44.18,224,0.875,bilinear,-10.394,-5.966,+32 -vit_small_r26_s32_224,70.520,29.480,90.110,9.890,36.43,224,0.900,bicubic,-11.342,-5.912,-31 -deit_small_distilled_patch16_224,70.520,29.480,89.470,10.530,22.44,224,0.900,bicubic,-10.688,-5.904,+9 -legacy_senet154,70.500,29.500,89.010,10.990,115.09,224,0.875,bilinear,-10.808,-6.486,+2 -halonet50ts,70.490,29.510,89.330,10.670,22.73,256,0.940,bicubic,-11.162,-6.282,-25 -regnetz_b16,70.460,29.540,89.540,10.460,9.72,288,0.940,bicubic,-10.252,-5.934,+39 -crossvit_15_240,70.450,29.550,89.530,10.470,27.53,240,0.875,bicubic,-11.094,-6.160,-20 -gluon_seresnext101_64x4d,70.440,29.560,89.360,10.640,88.23,224,0.875,bicubic,-10.440,-5.936,+28 -twins_svt_small,70.440,29.560,89.350,10.650,24.06,224,0.900,bicubic,-11.242,-6.316,-30 -tf_efficientnet_lite4,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.104,-6.556,-22 -resnetaa50,70.410,29.590,89.970,10.030,25.56,288,1.000,bicubic,-11.208,-5.840,-28 -resnest50d,70.410,29.590,88.760,11.240,27.48,224,0.875,bilinear,-10.564,-6.620,+16 -resnest50d_1s4x24d,70.400,29.600,89.240,10.760,25.68,224,0.875,bicubic,-10.584,-6.084,+14 -seresnext50_32x4d,70.390,29.610,89.110,10.890,27.56,224,0.875,bicubic,-10.872,-6.518,-5 -cs3darknet_l,70.370,29.630,89.750,10.250,21.16,288,0.950,bicubic,-10.516,-5.918,+20 -gernet_l,70.360,29.640,88.980,11.020,31.08,256,0.875,bilinear,-10.990,-6.556,-11 -vit_srelpos_small_patch16_224,70.290,29.710,89.580,10.420,21.97,224,0.900,bicubic,-10.808,-5.992,+2 -gluon_resnet152_v1s,70.290,29.710,88.850,11.150,60.32,224,0.875,bicubic,-10.724,-6.564,+8 -repvgg_b3,70.250,29.750,88.740,11.260,123.09,224,0.875,bilinear,-10.246,-6.524,+36 -coat_mini,70.210,29.790,89.450,10.550,10.34,224,0.900,bicubic,-11.056,-5.942,-12 -xception41p,70.200,29.800,89.090,10.910,26.91,299,0.940,bicubic,-11.768,-6.704,-55 -sebotnet33ts_256,70.150,29.850,88.800,11.200,13.70,256,0.940,bicubic,-11.004,-6.366,-7 -efficientnet_el,70.120,29.880,89.290,10.710,10.59,300,0.904,bicubic,-11.186,-6.244,-16 -inception_resnet_v2,70.120,29.880,88.700,11.300,55.84,299,0.897,bicubic,-10.340,-6.606,+35 -resmlp_36_distilled_224,70.110,29.890,89.100,10.900,44.69,224,0.875,bicubic,-11.046,-6.386,-11 -ecaresnet101d_pruned,70.100,29.900,89.580,10.420,24.88,224,0.875,bicubic,-10.710,-6.048,+14 -haloregnetz_b,70.070,29.930,88.870,11.130,11.68,224,0.940,bicubic,-10.974,-6.328,-4 -poolformer_s36,70.030,29.970,89.190,10.810,30.86,224,0.900,bicubic,-11.388,-6.258,-29 -gluon_seresnext101_32x4d,70.030,29.970,88.910,11.090,48.96,224,0.875,bicubic,-10.876,-6.386,+5 -sehalonet33ts,70.020,29.980,88.710,11.290,13.69,256,0.940,bicubic,-10.952,-6.562,-1 -regnety_320,70.010,29.990,88.890,11.110,145.05,224,0.875,bicubic,-10.794,-6.354,+10 -gluon_resnet152_v1d,69.970,30.030,88.490,11.510,60.21,224,0.875,bicubic,-10.506,-6.710,+26 -levit_256,69.950,30.050,89.240,10.760,18.89,224,0.900,bicubic,-11.566,-6.250,-43 -pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.208,-6.402,-14 -ecaresnet50d,69.840,30.160,89.390,10.610,25.58,224,0.875,bicubic,-10.758,-5.928,+14 -mobilevitv2_150_in22ft1k,69.830,30.170,89.160,10.840,10.59,256,0.888,bicubic,-11.640,-6.508,-42 -mobilevitv2_200,69.760,30.240,88.620,11.380,18.45,256,0.888,bicubic,-11.380,-6.748,-20 -ssl_resnext50_32x4d,69.730,30.270,89.430,10.570,25.03,224,0.875,bilinear,-10.596,-5.982,+33 -gluon_resnext101_64x4d,69.710,30.290,88.270,11.730,83.46,224,0.875,bicubic,-10.894,-6.722,+9 -lambda_resnet50ts,69.700,30.300,88.820,11.180,21.54,256,0.950,bicubic,-11.452,-6.282,-24 -xcit_tiny_24_p16_224_dist,69.700,30.300,88.710,11.290,12.12,224,1.000,bicubic,-10.748,-6.502,+21 -xcit_tiny_12_p16_384_dist,69.690,30.310,89.010,10.990,6.72,384,1.000,bicubic,-11.252,-6.398,-11 -resnext50_32x4d,69.680,30.320,88.660,11.340,25.03,224,0.950,bicubic,-11.416,-6.666,-22 -resmlp_24_distilled_224,69.670,30.330,89.050,10.950,30.02,224,0.875,bicubic,-11.094,-6.172,-2 -efficientnet_b3_pruned,69.590,30.410,88.980,11.020,9.86,300,0.904,bicubic,-11.268,-6.264,-6 -gernet_m,69.560,30.440,88.700,11.300,21.14,224,0.875,bilinear,-11.170,-6.486,-3 -nf_resnet50,69.540,30.460,88.730,11.270,25.56,288,0.940,bicubic,-11.114,-6.604,-1 -gcresnext50ts,69.530,30.470,88.840,11.160,15.67,256,0.900,bicubic,-11.048,-6.330,+2 -efficientnet_el_pruned,69.520,30.480,88.930,11.070,10.59,300,0.904,bicubic,-10.778,-6.284,+26 -repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.696,-6.658,+32 -gcresnet50t,69.510,30.490,89.050,10.950,25.90,256,0.900,bicubic,-11.424,-6.404,-19 -ens_adv_inception_resnet_v2,69.510,30.490,88.520,11.480,55.84,299,0.897,bicubic,-10.464,-6.422,+42 -efficientnet_b2,69.490,30.510,88.690,11.310,9.11,288,1.000,bicubic,-11.126,-6.626,-6 -rexnet_150,69.460,30.540,88.980,11.020,9.73,224,0.875,bicubic,-10.854,-6.186,+19 -regnetx_320,69.450,30.550,88.270,11.730,107.81,224,0.875,bicubic,-10.794,-6.750,+24 -swin_tiny_patch4_window7_224,69.440,30.560,89.020,10.980,28.29,224,0.900,bicubic,-11.936,-6.522,-53 -vit_base_patch32_224,69.420,30.580,89.430,10.570,88.22,224,0.900,bicubic,-11.304,-6.136,-13 -cspresnext50,69.420,30.580,88.610,11.390,20.57,256,0.887,bilinear,-11.124,-6.714,-7 -convmixer_768_32,69.400,30.600,88.870,11.130,21.11,224,0.960,bicubic,-10.764,-6.202,+27 -darknet53,69.370,30.630,88.760,11.240,41.61,288,1.000,bicubic,-11.168,-6.660,-8 -legacy_seresnext101_32x4d,69.370,30.630,88.060,11.940,48.96,224,0.875,bilinear,-10.852,-6.954,+20 -inception_v4,69.360,30.640,88.780,11.220,42.68,299,0.875,bicubic,-10.808,-6.184,+22 -ecaresnetlight,69.350,30.650,89.230,10.770,30.16,224,0.875,bicubic,-11.106,-6.016,-3 -resnet50d,69.350,30.650,88.230,11.770,25.58,224,0.875,bicubic,-11.178,-6.938,-11 -cs3darknet_focus_l,69.330,30.670,89.440,10.560,21.15,288,0.950,bicubic,-11.544,-6.252,-28 -xception71,69.320,30.680,88.270,11.730,42.34,299,0.903,bicubic,-10.550,-6.654,+33 -mobilevitv2_175,69.300,30.700,88.940,11.060,14.25,256,0.888,bicubic,-11.562,-6.322,-29 -vit_small_patch32_384,69.290,30.710,89.820,10.180,22.92,384,1.000,bicubic,-11.200,-5.780,-12 -edgenext_small_rw,69.210,30.790,88.760,11.240,7.83,320,1.000,bicubic,-11.242,-6.430,-8 -gluon_xception65,69.160,30.840,88.080,11.920,39.92,299,0.903,bicubic,-10.562,-6.780,+38 -gluon_resnet152_v1c,69.150,30.850,87.870,12.130,60.21,224,0.875,bicubic,-10.762,-6.972,+25 -mixnet_xl,69.110,30.890,88.310,11.690,11.90,224,0.875,bicubic,-11.368,-6.624,-15 -seresnet33ts,69.100,30.900,88.490,11.510,19.78,256,0.900,bicubic,-11.254,-6.616,-5 -tf_efficientnetv2_b2,69.100,30.900,88.220,11.780,10.10,260,0.890,bicubic,-11.108,-6.824,+9 -resnetv2_50,69.070,30.930,88.440,11.560,25.55,224,0.950,bicubic,-11.342,-6.632,-11 -gcresnet33ts,69.010,30.990,88.470,11.530,19.88,256,0.900,bicubic,-11.066,-6.524,+14 -gluon_resnet101_v1d,69.010,30.990,88.100,11.900,44.57,224,0.875,bicubic,-11.408,-6.914,-14 -repvgg_b2g4,69.000,31.000,88.340,11.660,61.76,224,0.875,bilinear,-10.366,-6.348,+50 -seresnet50,68.950,31.050,88.700,11.300,28.09,224,0.875,bicubic,-11.316,-6.370,-2 -gluon_resnext101_32x4d,68.950,31.050,88.370,11.630,44.18,224,0.875,bicubic,-11.390,-6.556,-10 -cspdarknet53,68.930,31.070,88.600,11.400,27.64,256,0.887,bilinear,-11.126,-6.486,+11 -tf_efficientnet_b2_ap,68.930,31.070,88.350,11.650,9.11,260,0.890,bicubic,-11.372,-6.678,-8 -regnety_120,68.870,31.130,88.330,11.670,51.82,224,0.875,bicubic,-11.506,-6.792,-18 -mobilevitv2_150,68.850,31.150,88.080,11.920,10.59,256,0.888,bicubic,-11.518,-6.984,-17 -resnet50_gn,68.840,31.160,88.420,11.580,25.56,224,0.940,bicubic,-11.220,-6.528,+6 -gluon_resnet152_v1b,68.820,31.180,87.720,12.280,60.19,224,0.875,bicubic,-10.862,-7.016,+26 -eca_resnet33ts,68.800,31.200,88.580,11.420,19.68,256,0.900,bicubic,-11.280,-6.392,+2 -gmlp_s16_224,68.760,31.240,88.080,11.920,19.42,224,0.875,bicubic,-10.880,-6.544,+28 -dpn131,68.760,31.240,87.460,12.540,79.25,224,0.875,bicubic,-11.066,-7.248,+15 -poolformer_s24,68.750,31.250,88.210,11.790,21.39,224,0.900,bicubic,-11.566,-6.832,-18 -darknetaa53,68.740,31.260,88.720,11.280,36.02,288,1.000,bilinear,-11.782,-6.606,-37 -tf_efficientnet_b2,68.740,31.260,87.980,12.020,9.11,260,0.890,bicubic,-11.348,-6.928,-4 -resnet50,68.740,31.260,87.680,12.320,25.56,224,0.950,bicubic,-11.634,-6.934,-27 -resnext50d_32x4d,68.730,31.270,88.300,11.700,25.05,224,0.875,bicubic,-10.946,-6.566,+20 -deit_small_patch16_224,68.710,31.290,88.200,11.800,22.05,224,0.900,bicubic,-11.154,-6.848,+5 -gluon_resnet101_v1s,68.710,31.290,87.910,12.090,44.67,224,0.875,bicubic,-11.588,-7.252,-20 -dpn107,68.700,31.300,88.140,11.860,86.92,224,0.875,bicubic,-11.468,-6.766,-12 -gluon_seresnext50_32x4d,68.680,31.320,88.330,11.670,27.56,224,0.875,bicubic,-11.232,-6.502,-1 -hrnet_w64,68.630,31.370,88.050,11.950,128.06,224,0.875,bilinear,-10.840,-6.604,+24 -dpn98,68.600,31.400,87.660,12.340,61.57,224,0.875,bicubic,-11.044,-6.940,+15 -xcit_tiny_12_p8_224,68.570,31.430,88.690,11.310,6.71,224,1.000,bicubic,-11.124,-6.358,+9 -regnetx_160,68.530,31.470,88.440,11.560,54.28,224,0.875,bicubic,-11.324,-6.390,-1 -xcit_tiny_24_p16_224,68.440,31.560,88.290,11.710,12.12,224,1.000,bicubic,-11.004,-6.598,+22 -rexnet_130,68.440,31.560,88.040,11.960,7.56,224,0.875,bicubic,-11.062,-6.642,+16 -tf_efficientnet_el,68.430,31.570,88.210,11.790,10.59,300,0.904,bicubic,-11.824,-6.918,-27 -cspresnet50,68.420,31.580,87.970,12.030,21.62,256,0.887,bilinear,-11.162,-6.738,+12 -cait_xxs36_224,68.400,31.600,88.640,11.360,17.30,224,1.000,bicubic,-11.348,-6.228,0 -ecaresnet50d_pruned,68.400,31.600,88.370,11.630,19.94,224,0.875,bicubic,-11.318,-6.506,+1 -dla102x2,68.380,31.620,87.890,12.110,41.28,224,0.875,bilinear,-11.062,-6.756,+17 -skresnext50_32x4d,68.370,31.630,87.560,12.440,27.48,224,0.875,bicubic,-11.784,-7.086,-23 -ssl_resnet50,68.360,31.640,88.530,11.470,25.56,224,0.875,bilinear,-10.864,-6.300,+31 -fbnetv3_d,68.350,31.650,88.450,11.550,10.31,256,0.950,bilinear,-11.330,-6.490,+1 -efficientnet_b2_pruned,68.320,31.680,88.100,11.900,8.31,260,0.890,bicubic,-11.598,-6.750,-18 -resmlp_big_24_224,68.320,31.680,87.520,12.480,129.14,224,0.875,bicubic,-12.710,-7.500,-90 -gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.050,-7.126,+14 -vit_base_patch16_224_sam,68.270,31.730,87.730,12.270,86.57,224,0.900,bicubic,-11.974,-7.024,-36 -ecaresnet26t,68.230,31.770,88.800,11.200,16.01,320,0.950,bicubic,-11.622,-6.284,-15 -tf_efficientnet_lite3,68.230,31.770,87.740,12.260,8.20,300,0.904,bilinear,-11.588,-7.174,-13 -ese_vovnet39b,68.200,31.800,88.260,11.740,24.57,224,0.875,bicubic,-11.112,-6.454,+13 -fbnetv3_b,68.190,31.810,87.930,12.070,8.60,256,0.950,bilinear,-10.952,-6.820,+27 -regnetx_120,68.170,31.830,87.660,12.340,46.11,224,0.875,bicubic,-11.422,-7.074,-4 -resmlp_36_224,68.060,31.940,88.190,11.810,44.69,224,0.875,bicubic,-11.710,-6.696,-16 -resnetrs50,68.030,31.970,87.710,12.290,35.69,224,0.910,bicubic,-11.856,-7.260,-25 -pit_xs_distilled_224,68.000,32.000,87.720,12.280,11.00,224,0.900,bicubic,-11.308,-6.646,+9 -nf_regnet_b1,67.970,32.030,88.190,11.810,10.22,288,0.900,bicubic,-11.330,-6.564,+10 -dpn92,67.970,32.030,87.540,12.460,37.67,224,0.875,bicubic,-12.050,-7.290,-33 -gluon_resnet50_v1d,67.950,32.050,87.130,12.870,25.58,224,0.875,bicubic,-11.120,-7.336,+26 -resnetv2_50x1_bitm,67.930,32.070,89.290,10.710,25.55,448,1.000,bilinear,-12.412,-6.396,-59 -levit_192,67.900,32.100,87.900,12.100,10.95,224,0.900,bicubic,-11.936,-6.890,-26 -tf_efficientnetv2_b1,67.890,32.110,87.800,12.200,8.14,240,0.882,bicubic,-11.576,-6.922,-6 -regnetx_080,67.880,32.120,86.990,13.010,39.57,224,0.875,bicubic,-11.322,-7.562,+14 -resnext101_32x8d,67.860,32.140,87.490,12.510,88.79,224,0.875,bilinear,-11.456,-7.028,-2 -efficientnet_em,67.840,32.160,88.120,11.880,6.90,240,0.882,bicubic,-11.412,-6.672,+8 -legacy_seresnext50_32x4d,67.840,32.160,87.620,12.380,27.56,224,0.875,bilinear,-11.236,-6.814,+17 -lambda_resnet26t,67.810,32.190,87.780,12.220,10.96,256,0.940,bicubic,-11.288,-6.810,+14 -resmlp_24_224,67.800,32.200,87.600,12.400,30.02,224,0.875,bicubic,-11.578,-6.946,-9 -hrnet_w48,67.770,32.230,87.410,12.590,77.47,224,0.875,bilinear,-11.530,-7.104,-1 -hrnet_w44,67.740,32.260,87.550,12.450,67.06,224,0.875,bilinear,-11.156,-6.820,+22 -tf_efficientnet_b0_ns,67.720,32.280,88.060,11.940,5.29,224,0.875,bicubic,-10.944,-6.316,+30 -coat_lite_mini,67.720,32.280,87.700,12.300,11.01,224,0.900,bicubic,-11.368,-6.908,+10 -eca_botnext26ts_256,67.690,32.310,87.050,12.950,10.59,256,0.950,bicubic,-11.586,-7.566,-3 -xception,67.680,32.320,87.580,12.420,22.86,299,0.897,bicubic,-11.364,-6.814,+12 -regnetx_064,67.670,32.330,87.540,12.460,26.21,224,0.875,bicubic,-11.404,-6.920,+9 -halonet26t,67.620,32.380,87.260,12.740,12.48,256,0.950,bicubic,-11.492,-7.054,+4 -dpn68b,67.610,32.390,87.670,12.330,12.61,224,0.875,bicubic,-11.606,-6.744,-2 -dla169,67.600,32.400,87.550,12.450,53.39,224,0.875,bilinear,-11.082,-6.786,+22 -gluon_inception_v3,67.590,32.410,87.470,12.530,23.83,299,0.875,bicubic,-11.216,-6.900,+16 -gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.956,-7.398,-29 -hrnet_w40,67.550,32.450,87.140,12.860,57.56,224,0.875,bilinear,-11.372,-7.330,+10 -legacy_seresnet152,67.530,32.470,87.400,12.600,66.82,224,0.875,bilinear,-11.122,-6.970,+20 -tf_efficientnet_b1_ap,67.520,32.480,87.760,12.240,7.79,240,0.882,bicubic,-11.754,-6.548,-12 -eca_halonext26ts,67.480,32.520,87.240,12.760,10.76,256,0.940,bicubic,-12.008,-7.364,-31 -efficientnet_b1,67.470,32.530,87.500,12.500,7.79,256,1.000,bicubic,-11.318,-6.846,+12 -gluon_resnet101_v1b,67.470,32.530,87.230,12.770,44.55,224,0.875,bicubic,-11.834,-7.290,-20 -mobilevitv2_125,67.460,32.540,87.570,12.430,7.48,256,0.888,bicubic,-12.222,-7.278,-44 -resnetblur50,67.460,32.540,87.440,12.560,25.56,224,0.875,bicubic,-11.834,-7.194,-19 -tf_efficientnet_cc_b1_8e,67.460,32.540,87.310,12.690,39.72,240,0.882,bicubic,-11.854,-7.060,-26 -res2net101_26w_4s,67.460,32.540,87.010,12.990,45.21,224,0.875,bilinear,-11.736,-7.426,-12 -res2net50_26w_8s,67.430,32.570,87.270,12.730,48.40,224,0.875,bilinear,-11.522,-7.036,-1 -resnet33ts,67.380,32.620,87.590,12.410,19.68,256,0.900,bicubic,-11.828,-6.984,-16 -cait_xxs24_224,67.350,32.650,87.520,12.480,11.96,224,1.000,bicubic,-11.036,-6.788,+23 -regnetx_032,67.290,32.710,87.000,13.000,15.30,224,0.875,bicubic,-10.894,-7.088,+34 -xception41,67.250,32.750,87.210,12.790,26.97,299,0.903,bicubic,-11.266,-7.070,+10 -coat_tiny,67.240,32.760,87.280,12.720,5.50,224,0.900,bicubic,-11.196,-6.758,+17 -resnest26d,67.190,32.810,87.170,12.830,17.07,224,0.875,bilinear,-11.294,-7.124,+11 -repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.634,-7.088,-2 -legacy_seresnet101,67.140,32.860,87.040,12.960,49.33,224,0.875,bilinear,-11.240,-7.222,+18 -botnet26t_256,67.130,32.870,87.530,12.470,12.49,256,0.950,bicubic,-12.128,-6.998,-28 -vit_relpos_base_patch32_plus_rpn_256,67.130,32.870,86.500,13.500,119.42,256,0.900,bicubic,-12.356,-7.640,-47 -dla60x,67.080,32.920,87.180,12.820,17.35,224,0.875,bilinear,-11.148,-6.844,+23 -gluon_resnet50_v1s,67.060,32.940,86.860,13.140,25.68,224,0.875,bicubic,-11.646,-7.378,-4 -tv_resnet152,67.030,32.970,87.550,12.450,60.19,224,0.875,bilinear,-11.290,-6.484,+16 -dla60_res2net,67.030,32.970,87.160,12.840,20.85,224,0.875,bilinear,-11.428,-7.036,+5 -xcit_tiny_12_p16_224_dist,67.010,32.990,87.410,12.590,6.72,224,1.000,bicubic,-11.568,-6.788,-3 -dla102x,66.980,33.020,86.770,13.230,26.31,224,0.875,bilinear,-11.532,-7.458,-1 -lambda_resnet26rpt_256,66.960,33.040,87.130,12.870,10.99,256,0.940,bicubic,-12.004,-7.296,-19 -mixnet_l,66.960,33.040,86.920,13.080,7.33,224,0.875,bicubic,-12.016,-7.258,-21 -pit_xs_224,66.920,33.080,87.290,12.710,10.62,224,0.900,bicubic,-11.270,-6.876,+17 -res2net50_26w_6s,66.920,33.080,86.860,13.140,37.05,224,0.875,bilinear,-11.650,-7.264,-7 -repvgg_b1,66.900,33.100,86.790,13.210,57.42,224,0.875,bilinear,-11.468,-7.304,+6 -tf_efficientnet_b1,66.890,33.110,87.020,12.980,7.79,240,0.882,bicubic,-11.938,-7.178,-19 -xcit_nano_12_p8_384_dist,66.880,33.120,87.110,12.890,3.05,384,1.000,bicubic,-10.936,-6.936,+34 -efficientnet_es,66.870,33.130,86.730,13.270,5.44,224,0.875,bicubic,-11.188,-7.214,+18 -mobilevit_s,66.860,33.140,87.080,12.920,5.58,256,0.900,bicubic,-11.450,-7.072,+5 -resnet32ts,66.850,33.150,87.260,12.740,17.96,256,0.900,bicubic,-12.164,-7.096,-30 -regnetx_040,66.830,33.170,86.740,13.260,22.12,224,0.875,bicubic,-11.658,-7.498,-11 -tf_mixnet_l,66.780,33.220,86.460,13.540,7.33,224,0.875,bicubic,-11.998,-7.538,-21 -hrnet_w32,66.770,33.230,87.310,12.690,41.23,224,0.875,bilinear,-11.682,-6.878,-8 -hrnet_w30,66.770,33.230,86.790,13.210,37.71,224,0.875,bilinear,-11.428,-7.434,+5 -selecsls60b,66.750,33.250,86.530,13.470,32.77,224,0.875,bicubic,-11.654,-7.642,-7 -wide_resnet101_2,66.720,33.280,87.020,12.980,126.89,224,0.875,bilinear,-12.132,-7.268,-30 -tf_efficientnetv2_b0,66.690,33.310,86.710,13.290,7.14,224,0.875,bicubic,-11.662,-7.316,-5 -adv_inception_v3,66.650,33.350,86.540,13.460,23.83,299,0.875,bicubic,-10.928,-7.198,+34 -dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.816,-7.116,-15 -mobilevitv2_100,66.590,33.410,87.020,12.980,4.90,256,0.888,bicubic,-11.496,-7.140,+5 -vit_tiny_patch16_384,66.570,33.430,87.270,12.730,5.79,384,1.000,bicubic,-11.860,-7.274,-14 -levit_128,66.570,33.430,86.750,13.250,9.21,224,0.900,bicubic,-11.912,-7.262,-20 -cs3darknet_m,66.560,33.440,87.180,12.820,9.31,288,0.950,bicubic,-11.066,-6.834,+24 -gluon_resnet50_v1c,66.560,33.440,86.180,13.820,25.58,224,0.875,bicubic,-11.448,-7.810,+5 -dla102,66.520,33.480,86.910,13.090,33.27,224,0.875,bilinear,-11.508,-7.040,+3 -gmixer_24_224,66.430,33.570,86.160,13.840,24.72,224,0.875,bicubic,-11.606,-7.510,+1 -tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.442,-6.980,+13 -bat_resnext26ts,66.380,33.620,86.830,13.170,10.73,256,0.900,bicubic,-11.868,-7.266,-12 -hardcorenas_f,66.380,33.620,86.190,13.810,8.20,224,0.875,bilinear,-11.722,-7.612,-5 -coat_lite_tiny,66.310,33.690,86.980,13.020,5.72,224,0.900,bicubic,-11.206,-6.934,+24 -efficientnet_b0,66.290,33.710,85.960,14.040,5.29,224,0.875,bicubic,-11.410,-7.572,+13 -cs3darknet_focus_m,66.260,33.740,87.090,12.910,9.30,288,0.950,bicubic,-11.022,-6.882,+32 -legacy_seresnet50,66.260,33.740,86.310,13.690,28.09,224,0.875,bilinear,-11.372,-7.440,+13 -selecsls60,66.200,33.800,86.340,13.660,30.67,224,0.875,bicubic,-11.784,-7.492,-4 -tf_efficientnet_em,66.170,33.830,86.360,13.640,6.90,240,0.882,bicubic,-11.956,-7.686,-12 -tf_efficientnet_cc_b0_8e,66.170,33.830,86.220,13.780,24.01,224,0.875,bicubic,-11.730,-7.438,-1 -tv_resnext50_32x4d,66.160,33.840,86.040,13.960,25.03,224,0.875,bilinear,-11.458,-7.660,+11 -inception_v3,66.150,33.850,86.330,13.670,23.83,299,0.875,bicubic,-11.288,-7.146,+19 -resmlp_12_distilled_224,66.130,33.870,86.620,13.380,15.35,224,0.875,bicubic,-11.816,-6.940,-6 -res2net50_26w_4s,66.130,33.870,86.590,13.410,25.70,224,0.875,bilinear,-11.832,-7.262,-8 -regnety_016,66.100,33.900,86.380,13.620,11.20,224,0.875,bicubic,-11.756,-7.340,-2 -efficientnet_b1_pruned,66.080,33.920,86.570,13.430,6.33,240,0.882,bicubic,-12.164,-7.264,-25 -gluon_resnet50_v1b,66.080,33.920,86.260,13.740,25.56,224,0.875,bicubic,-11.504,-7.460,+8 -rexnet_100,66.060,33.940,86.490,13.510,4.80,224,0.875,bicubic,-11.800,-7.384,-7 -tinynet_a,66.020,33.980,85.790,14.210,6.19,192,0.875,bicubic,-11.628,-7.746,0 -res2net50_14w_8s,66.010,33.990,86.250,13.750,25.06,224,0.875,bilinear,-12.134,-7.602,-24 -gcresnext26ts,65.970,34.030,85.910,14.090,10.48,256,0.900,bicubic,-11.844,-7.926,-5 -seresnext26t_32x4d,65.880,34.120,85.670,14.330,16.81,224,0.875,bicubic,-12.088,-8.078,-17 -res2next50,65.850,34.150,85.830,14.170,24.67,224,0.875,bilinear,-12.408,-8.058,-34 -repvgg_b1g4,65.840,34.160,86.110,13.890,39.97,224,0.875,bilinear,-11.748,-7.720,0 -densenet161,65.830,34.170,86.450,13.550,28.68,224,0.875,bicubic,-11.524,-7.186,+9 -hardcorenas_e,65.810,34.190,85.980,14.020,8.07,224,0.875,bilinear,-11.976,-7.724,-9 -resnet34d,65.790,34.210,86.720,13.280,21.82,224,0.875,bicubic,-11.326,-6.662,+16 -xcit_tiny_12_p16_224,65.770,34.230,86.230,13.770,6.72,224,1.000,bicubic,-11.354,-7.482,+14 -eca_resnext26ts,65.770,34.230,85.840,14.160,10.30,256,0.900,bicubic,-11.688,-7.728,+1 -skresnet34,65.740,34.260,85.960,14.040,22.28,224,0.875,bicubic,-11.164,-7.360,+23 -mobilenetv3_large_100_miil,65.740,34.260,85.170,14.830,5.48,224,0.875,bilinear,-12.182,-7.750,-23 -tv_resnet101,65.690,34.310,85.980,14.020,44.55,224,0.875,bilinear,-11.690,-7.564,+1 -seresnext26ts,65.650,34.350,86.150,13.850,10.39,256,0.900,bicubic,-12.208,-7.640,-21 -hardcorenas_d,65.620,34.380,85.470,14.530,7.50,224,0.875,bilinear,-11.810,-8.014,-2 -selecsls42b,65.600,34.400,85.790,14.210,32.46,224,0.875,bicubic,-11.578,-7.602,+6 -poolformer_s12,65.580,34.420,86.130,13.870,11.92,224,0.900,bicubic,-11.658,-7.376,+4 -tf_efficientnet_b0_ap,65.500,34.500,85.580,14.420,5.29,224,0.875,bicubic,-11.588,-7.678,+8 -seresnext26d_32x4d,65.410,34.590,85.960,14.040,16.81,224,0.875,bicubic,-12.196,-7.646,-15 -convmixer_1024_20_ks9_p14,65.410,34.590,85.590,14.410,24.38,224,0.960,bicubic,-11.532,-7.768,+12 -resnet26t,65.400,34.600,86.110,13.890,16.01,256,0.940,bicubic,-12.464,-7.732,-30 -tf_efficientnet_lite2,65.390,34.610,85.990,14.010,6.09,260,0.890,bicubic,-12.076,-7.768,-12 -res2net50_48w_2s,65.370,34.630,85.960,14.040,25.29,224,0.875,bilinear,-12.154,-7.590,-15 -densenetblur121d,65.290,34.710,85.700,14.300,8.00,224,0.875,bicubic,-11.290,-7.488,+20 -densenet201,65.290,34.710,85.670,14.330,20.01,224,0.875,bicubic,-11.998,-7.810,-7 -dla60,65.210,34.790,85.740,14.260,22.04,224,0.875,bilinear,-11.812,-7.580,+2 -crossvit_9_dagger_240,65.200,34.800,86.590,13.410,8.78,240,0.875,bicubic,-11.778,-7.024,+2 -ese_vovnet19b_dw,65.190,34.810,85.460,14.540,6.54,224,0.875,bicubic,-11.604,-7.806,+8 -tf_efficientnet_cc_b0_4e,65.140,34.860,85.160,14.840,13.31,224,0.875,bicubic,-12.170,-8.180,-13 -gernet_s,65.130,34.870,85.520,14.480,8.17,224,0.875,bilinear,-11.786,-7.614,+3 -legacy_seresnext26_32x4d,65.070,34.930,85.640,14.360,16.79,224,0.875,bicubic,-12.034,-7.676,-6 -mobilenetv2_120d,65.000,35.000,85.960,14.040,5.83,224,0.875,bicubic,-12.290,-7.540,-15 -hrnet_w18,64.920,35.080,85.740,14.260,21.30,224,0.875,bilinear,-11.840,-7.704,+5 -hardcorenas_c,64.880,35.120,85.260,14.740,5.52,224,0.875,bilinear,-12.172,-7.900,-7 -densenet169,64.750,35.250,85.260,14.740,14.15,224,0.875,bicubic,-11.154,-7.764,+22 -mixnet_m,64.710,35.290,85.450,14.550,5.01,224,0.875,bicubic,-12.552,-7.972,-16 -resnet26d,64.680,35.320,85.100,14.900,16.01,224,0.875,bicubic,-12.022,-8.052,+2 -resnext26ts,64.590,35.410,85.120,14.880,10.30,256,0.900,bicubic,-12.190,-8.012,-1 -levit_128s,64.590,35.410,84.730,15.270,7.78,224,0.900,bicubic,-11.924,-8.140,+7 -xcit_nano_12_p8_224_dist,64.550,35.450,85.990,14.010,3.05,224,1.000,bicubic,-11.778,-7.104,+9 -repvgg_a2,64.430,35.570,85.130,14.870,28.21,224,0.875,bilinear,-12.030,-7.880,+7 -xcit_nano_12_p16_384_dist,64.420,35.580,85.310,14.690,3.05,384,1.000,bicubic,-11.036,-7.380,+25 -hardcorenas_b,64.410,35.590,84.900,15.100,5.18,224,0.875,bilinear,-12.126,-7.854,+2 -tf_efficientnet_lite1,64.400,35.600,85.490,14.510,5.42,240,0.882,bicubic,-12.238,-7.734,-3 -regnetx_016,64.370,35.630,85.450,14.550,9.19,224,0.875,bicubic,-12.572,-7.974,-14 -resmlp_12_224,64.350,35.650,85.580,14.420,15.35,224,0.875,bicubic,-12.306,-7.600,-6 -tf_efficientnet_b0,64.320,35.680,85.280,14.720,5.29,224,0.875,bicubic,-12.520,-7.938,-12 -tf_mixnet_m,64.270,35.730,85.100,14.900,5.01,224,0.875,bicubic,-12.676,-8.052,-18 -dpn68,64.240,35.760,85.180,14.820,12.61,224,0.875,bicubic,-12.070,-7.798,+2 -tf_efficientnet_es,64.230,35.770,84.740,15.260,5.44,224,0.875,bicubic,-12.368,-8.464,-7 -regnety_008,64.140,35.860,85.280,14.720,6.26,224,0.875,bicubic,-12.174,-7.790,-1 -vit_small_patch32_224,64.070,35.930,85.570,14.430,22.88,224,0.900,bicubic,-11.920,-7.698,+2 -mobilenetv2_140,64.040,35.960,85.040,14.960,6.11,224,0.875,bicubic,-12.472,-7.958,-6 -densenet121,63.740,36.260,84.610,15.390,7.98,224,0.875,bicubic,-11.840,-8.038,+8 -hardcorenas_a,63.710,36.290,84.400,15.600,5.26,224,0.875,bilinear,-12.220,-8.110,+1 -resnest14d,63.610,36.390,84.250,15.750,10.61,224,0.875,bilinear,-11.898,-8.274,+8 -mobilevitv2_075,63.590,36.410,84.950,15.050,2.87,256,0.888,bicubic,-12.018,-7.808,+4 -tf_mixnet_s,63.560,36.440,84.280,15.720,4.13,224,0.875,bicubic,-12.092,-8.346,+1 -resnet26,63.450,36.550,84.250,15.750,16.00,224,0.875,bicubic,-11.850,-8.330,+10 -mixnet_s,63.390,36.610,84.750,15.250,4.13,224,0.875,bicubic,-12.606,-8.050,-7 -mobilenetv3_large_100,63.360,36.640,84.060,15.940,5.48,224,0.875,bicubic,-12.416,-8.480,-3 -vit_tiny_r_s16_p8_384,63.340,36.660,85.270,14.730,6.36,384,1.000,bicubic,-12.612,-7.992,-7 -tv_resnet50,63.340,36.660,84.650,15.350,25.56,224,0.875,bilinear,-12.794,-8.218,-11 -efficientnet_es_pruned,63.300,36.700,84.960,15.040,5.44,224,0.875,bicubic,-11.700,-7.482,+13 -efficientnet_lite0,63.270,36.730,84.440,15.560,4.65,224,0.875,bicubic,-12.198,-8.076,0 -mixer_b16_224,63.250,36.750,83.310,16.690,59.88,224,0.875,bicubic,-13.360,-8.920,-24 -mobilenetv3_rw,63.240,36.760,84.500,15.500,5.48,224,0.875,bicubic,-12.394,-8.208,-7 -semnasnet_100,63.160,36.840,84.540,15.460,3.89,224,0.875,bicubic,-12.290,-8.060,0 -pit_ti_distilled_224,63.140,36.860,83.950,16.050,5.10,224,0.900,bicubic,-11.394,-8.146,+18 -vit_tiny_patch16_224,63.100,36.900,84.850,15.150,5.72,224,0.900,bicubic,-12.364,-7.994,-4 -regnety_006,63.090,36.910,84.260,15.740,6.06,224,0.875,bicubic,-12.162,-8.272,-1 -mobilevit_xs,62.930,37.070,84.830,15.170,2.32,256,0.900,bicubic,-11.704,-7.516,+12 -tv_densenet121,62.930,37.070,84.250,15.750,7.98,224,0.875,bicubic,-11.810,-7.898,+9 -resnet34,62.860,37.140,84.130,15.870,21.80,224,0.875,bilinear,-12.252,-8.154,-1 -mobilenetv2_110d,62.840,37.160,84.500,15.500,4.52,224,0.875,bicubic,-12.196,-7.692,0 -legacy_seresnet34,62.840,37.160,84.220,15.780,21.96,224,0.875,bilinear,-11.970,-7.906,+5 -hrnet_w18_small_v2,62.800,37.200,83.970,16.030,15.60,224,0.875,bilinear,-12.310,-8.446,-3 -deit_tiny_distilled_patch16_224,62.800,37.200,83.920,16.080,5.91,224,0.900,bicubic,-11.712,-7.970,+10 -swsl_resnet18,62.760,37.240,84.300,15.700,11.69,224,0.875,bilinear,-10.514,-7.436,+22 -tinynet_b,62.730,37.270,84.250,15.750,3.73,188,0.875,bicubic,-12.244,-7.932,-2 -repvgg_b0,62.730,37.270,83.880,16.120,15.82,224,0.875,bilinear,-12.424,-8.536,-10 -xcit_nano_12_p8_224,62.570,37.430,84.200,15.800,3.05,224,1.000,bicubic,-11.346,-7.968,+12 -gluon_resnet34_v1b,62.570,37.430,83.990,16.010,21.80,224,0.875,bicubic,-12.022,-7.998,+3 -tf_efficientnet_lite0,62.550,37.450,84.230,15.770,4.65,224,0.875,bicubic,-12.282,-7.944,-4 -regnetx_008,62.490,37.510,84.020,15.980,7.26,224,0.875,bicubic,-12.544,-8.320,-9 -dla34,62.470,37.530,83.900,16.100,15.74,224,0.875,bilinear,-12.154,-8.172,-1 -fbnetc_100,62.470,37.530,83.380,16.620,5.57,224,0.875,bilinear,-12.646,-9.006,-15 -tf_mobilenetv3_large_100,62.450,37.550,83.950,16.050,5.48,224,0.875,bilinear,-13.062,-8.656,-25 -crossvit_9_240,62.270,37.730,84.240,15.760,8.55,240,0.875,bicubic,-11.690,-7.724,+4 -edgenext_x_small,62.160,37.840,84.050,15.950,2.34,256,0.900,bicubic,-12.704,-8.250,-11 -crossvit_tiny_240,62.070,37.930,83.610,16.390,7.01,240,0.875,bicubic,-11.268,-8.304,+8 -mnasnet_100,61.920,38.080,83.690,16.310,4.38,224,0.875,bicubic,-12.730,-8.424,-9 -regnety_004,61.860,38.140,83.410,16.590,4.34,224,0.875,bicubic,-12.164,-8.346,-2 -vgg19_bn,61.850,38.150,83.450,16.550,143.68,224,0.875,bilinear,-12.364,-8.394,-5 -convit_tiny,61.560,38.440,84.120,15.880,5.71,224,0.875,bicubic,-11.554,-7.600,+7 -ssl_resnet18,61.470,38.530,83.300,16.700,11.69,224,0.875,bilinear,-11.134,-8.124,+11 -regnetx_006,61.360,38.640,83.460,16.540,6.20,224,0.875,bicubic,-12.496,-8.212,-2 -spnasnet_100,61.250,38.750,82.790,17.210,4.42,224,0.875,bilinear,-12.840,-9.026,-8 -tv_resnet34,61.190,38.810,82.730,17.270,21.80,224,0.875,bilinear,-12.118,-8.694,+1 -pit_ti_224,60.970,39.030,83.860,16.140,4.85,224,0.900,bicubic,-11.942,-7.546,+6 -skresnet18,60.880,39.120,82.870,17.130,11.96,224,0.875,bicubic,-12.154,-8.296,+2 -ghostnet_100,60.830,39.170,82.370,17.630,5.18,224,0.875,bilinear,-13.150,-9.088,-10 -vgg16_bn,60.760,39.240,82.950,17.050,138.37,224,0.875,bilinear,-12.590,-8.554,-5 -semnasnet_075,60.700,39.300,82.510,17.490,2.91,224,0.875,bicubic,-12.274,-8.624,0 -tf_mobilenetv3_large_075,60.390,39.610,81.940,18.060,3.99,224,0.875,bilinear,-13.050,-9.408,-8 -xcit_nano_12_p16_224_dist,60.260,39.740,82.500,17.500,3.05,224,1.000,bicubic,-12.042,-8.362,+6 -mobilenetv2_100,60.190,39.810,82.220,17.780,3.50,224,0.875,bicubic,-12.766,-8.790,-2 -resnet18d,60.170,39.830,82.300,17.700,11.71,224,0.875,bicubic,-12.088,-8.388,+5 -vit_base_patch32_224_sam,60.010,39.990,81.240,18.760,88.22,224,0.900,bicubic,-13.682,-9.772,-13 -deit_tiny_patch16_224,59.850,40.150,82.680,17.320,5.72,224,0.900,bicubic,-12.324,-8.434,+5 -legacy_seresnet18,59.810,40.190,81.690,18.310,11.78,224,0.875,bicubic,-11.930,-8.640,+8 -vgg19,59.710,40.290,81.450,18.550,143.67,224,0.875,bilinear,-12.656,-9.422,-3 -regnetx_004,59.400,40.600,81.700,18.300,5.16,224,0.875,bicubic,-12.996,-9.138,-5 -vit_tiny_r_s16_p8_224,59.070,40.930,81.770,18.230,6.34,224,0.900,bicubic,-12.724,-9.048,+4 -tf_mobilenetv3_large_minimal_100,59.070,40.930,81.160,18.840,3.92,224,0.875,bilinear,-13.180,-9.460,-1 -vgg13_bn,59.000,41.000,81.080,18.920,133.05,224,0.875,bilinear,-12.598,-9.296,+4 -hrnet_w18_small,58.960,41.040,81.340,18.660,13.19,224,0.875,bilinear,-13.376,-9.340,-6 -lcnet_100,58.880,41.120,81.180,18.820,2.95,224,0.875,bicubic,-13.230,-9.198,-2 -vgg16,58.840,41.160,81.660,18.340,138.36,224,0.875,bilinear,-12.750,-8.722,+2 -xcit_nano_12_p16_224,58.340,41.660,80.880,19.120,3.05,224,1.000,bicubic,-11.614,-8.876,+8 -gluon_resnet18_v1b,58.330,41.670,80.970,19.030,11.69,224,0.875,bicubic,-12.508,-8.792,+3 -edgenext_xx_small,58.170,41.830,81.350,18.650,1.33,256,0.900,bicubic,-12.936,-8.682,+1 -tinynet_c,58.150,41.850,80.290,19.710,2.46,184,0.875,bicubic,-13.078,-9.458,-1 -resnet14t,57.800,42.200,79.910,20.090,10.08,224,0.950,bilinear,-14.556,-10.430,-14 -mobilevitv2_050,57.730,42.270,80.920,19.080,1.37,256,0.888,bicubic,-12.410,-9.010,+2 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,75.500,24.500,92.760,7.240,86.57,224,0.950,bicubic,-10.430,-4.964,-31 +regnetz_e8,75.490,24.510,92.690,7.310,57.70,320,1.000,bicubic,-9.540,-4.574,+21 +cait_s24_384,75.480,24.520,92.600,7.400,47.06,384,1.000,bicubic,-9.566,-4.746,+19 +xcit_medium_24_p8_224_dist,75.470,24.530,92.900,7.100,84.32,224,1.000,bicubic,-9.602,-4.354,+15 +swsl_resnext101_32x8d,75.430,24.570,92.760,7.240,88.79,224,0.875,bilinear,-8.854,-4.416,+64 +vit_base_patch16_224.augreg2_in21k_ft_in1k,75.410,24.590,93.230,6.770,86.57,224,0.900,bicubic,-9.696,-4.150,+8 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,75.410,24.590,92.710,7.290,88.34,448,1.000,bicubic,-10.374,-4.924,-29 +tf_efficientnetv2_m.in1k,75.390,24.610,92.760,7.240,54.14,480,1.000,bicubic,-9.818,-4.608,+1 +tf_efficientnet_b6.ap_in1k,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+27 +beit_base_patch16_224.in22k_ft_in22k_in1k,75.370,24.630,93.040,6.960,86.53,224,0.900,bicubic,-9.866,-4.616,-3 +volo_d2_224,75.300,24.700,92.510,7.490,58.68,224,0.960,bicubic,-9.896,-4.678,0 +mvitv2_large,75.280,24.720,92.340,7.660,217.99,224,0.900,bicubic,-9.970,-4.874,-6 +dm_nfnet_f3,75.210,24.790,92.940,7.060,254.92,416,0.940,bicubic,-10.312,-4.522,-23 +efficientnetv2_rw_m.agc_in1k,75.170,24.830,92.570,7.430,53.24,416,1.000,bicubic,-9.638,-4.578,+21 +convnext_small.fb_in22k_ft_in1k_384,75.150,24.850,93.050,6.950,50.22,384,1.000,bicubic,-10.628,-4.842,-36 +deit3_large_patch16_224,75.140,24.860,92.280,7.720,304.37,224,0.900,bicubic,-9.624,-4.758,+21 +ecaresnet269d,75.120,24.880,92.840,7.160,102.09,352,1.000,bicubic,-9.856,-4.386,+8 +vit_base_patch16_clip_224.laion2b_ft_in1k,75.120,24.880,92.710,7.290,86.57,224,1.000,bicubic,-10.348,-4.866,-27 +xcit_medium_24_p16_384_dist,75.120,24.880,92.440,7.560,84.40,384,1.000,bicubic,-10.292,-4.966,-24 +deit3_small_patch16_384_in21ft1k,75.090,24.910,92.790,7.210,22.21,384,1.000,bicubic,-9.734,-4.696,+14 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,75.080,24.920,92.590,7.410,88.30,384,1.000,bicubic,-10.292,-5.074,-23 +maxvit_tiny_tf_384.in1k,75.010,24.990,92.470,7.530,30.98,384,1.000,bicubic,-10.096,-5.064,-7 +dm_nfnet_f5,75.000,25.000,92.600,7.400,377.21,544,0.954,bicubic,-10.814,-4.888,-48 +xcit_small_24_p8_224_dist,74.980,25.020,92.310,7.690,47.63,224,1.000,bicubic,-9.896,-4.878,+7 +tf_efficientnet_b8.ra_in1k,74.940,25.060,92.310,7.690,87.41,672,0.954,bicubic,-10.430,-4.984,-26 +xcit_small_12_p8_384_dist,74.880,25.120,92.460,7.540,26.21,384,1.000,bicubic,-10.208,-4.822,-8 +eca_nfnet_l2,74.830,25.170,92.650,7.350,56.72,384,1.000,bicubic,-9.868,-4.614,+13 +deit3_base_patch16_384,74.790,25.210,92.240,7.760,86.88,384,1.000,bicubic,-10.282,-5.038,-8 +tf_efficientnet_b7.ra_in1k,74.720,25.280,92.220,7.780,66.35,600,0.949,bicubic,-10.216,-4.984,-2 +deit3_medium_patch16_224_in21ft1k,74.680,25.320,92.480,7.520,38.85,224,1.000,bicubic,-9.880,-4.708,+17 +xcit_large_24_p16_224_dist,74.670,25.330,91.860,8.140,189.10,224,1.000,bicubic,-10.248,-5.272,-2 +convnext_large.fb_in1k,74.640,25.360,91.970,8.030,197.77,288,1.000,bicubic,-10.206,-5.242,+1 +dm_nfnet_f2,74.620,25.380,92.260,7.740,193.78,352,0.920,bicubic,-10.444,-4.980,-12 +xcit_small_24_p16_384_dist,74.610,25.390,92.450,7.550,47.67,384,1.000,bicubic,-10.488,-4.860,-18 +tf_efficientnet_b5.ap_in1k,74.600,25.400,91.990,8.010,30.39,456,0.934,bicubic,-9.652,-4.984,+38 +vit_medium_patch16_gap_256.in12k_ft_in1k,74.580,25.420,91.950,8.050,38.86,256,0.950,bicubic,-9.850,-5.262,+24 +maxvit_large_tf_224.in1k,74.580,25.420,91.700,8.300,211.79,224,0.950,bicubic,-10.346,-5.272,-8 +swin_base_patch4_window7_224,74.570,25.430,92.560,7.440,87.77,224,0.900,bicubic,-10.682,-5.002,-35 +dm_nfnet_f1,74.570,25.430,92.260,7.740,132.63,320,0.910,bicubic,-10.056,-4.840,+5 +maxxvit_rmlp_small_rw_256,74.530,25.470,91.980,8.020,66.01,256,0.950,bicubic,-10.098,-5.082,+2 +convnext_small.fb_in22k_ft_in1k,74.510,25.490,92.700,7.300,50.22,288,1.000,bicubic,-10.752,-4.984,-38 +seresnet152d,74.510,25.490,92.080,7.920,66.84,320,1.000,bicubic,-9.852,-4.960,+25 +gcvit_base,74.500,25.500,91.750,8.250,90.32,224,0.875,bicubic,-9.948,-5.332,+12 +resnest200e,74.480,25.520,91.860,8.140,70.20,320,0.909,bicubic,-9.352,-5.034,+62 +regnetz_040,74.460,25.540,91.890,8.110,27.12,320,1.000,bicubic,-9.776,-5.042,+29 +tf_efficientnetv2_s.in21k_ft_in1k,74.450,25.550,92.510,7.490,21.46,384,1.000,bicubic,-9.852,-4.742,+22 +regnetz_040h,74.430,25.570,92.240,7.760,28.94,320,1.000,bicubic,-10.064,-4.766,+5 +resnetrs200,74.370,25.630,91.940,8.060,93.21,320,1.000,bicubic,-10.078,-4.904,+6 +maxvit_rmlp_small_rw_224,74.360,25.640,91.410,8.590,64.90,224,0.900,bicubic,-10.124,-5.352,+4 +flexivit_base.1200ep_in1k,74.350,25.650,91.800,8.200,86.59,240,0.950,bicubic,-10.314,-5.192,-9 +maxvit_base_tf_224.in1k,74.330,25.670,91.750,8.250,119.47,224,0.950,bicubic,-10.530,-5.240,-19 +seresnextaa101d_32x8d,74.330,25.670,91.720,8.280,93.59,288,1.000,bicubic,-10.238,-5.350,-6 +mvitv2_base,74.240,25.760,91.620,8.380,51.47,224,0.900,bicubic,-10.182,-5.244,+9 +seresnext101d_32x8d,74.210,25.790,91.860,8.140,93.59,288,1.000,bicubic,-10.160,-5.056,+12 +coatnet_rmlp_2_rw_224,74.200,25.800,91.280,8.720,73.88,224,0.950,bicubic,-10.400,-5.456,-11 +resnest269e,74.170,25.830,91.950,8.050,110.93,416,0.928,bicubic,-10.348,-4.986,-8 +efficientnetv2_rw_s.ra2_in1k,74.170,25.830,91.710,8.290,23.94,384,1.000,bicubic,-9.638,-5.014,+52 +cait_xs24_384,74.160,25.840,91.910,8.090,26.67,384,1.000,bicubic,-9.902,-4.978,+28 +pit_b_distilled_224,74.160,25.840,91.680,8.320,74.79,224,0.900,bicubic,-9.984,-5.176,+24 +swsl_resnext101_32x4d,74.140,25.860,91.990,8.010,44.18,224,0.875,bilinear,-9.090,-4.770,+86 +flexivit_base.300ep_in1k,74.140,25.860,91.380,8.620,86.59,240,0.950,bicubic,-10.254,-5.740,+4 +vit_base_patch16_384.orig_in21k_ft_in1k,74.130,25.870,92.360,7.640,86.86,384,1.000,bicubic,-10.080,-4.858,+15 +eca_nfnet_l1,74.120,25.880,92.070,7.930,41.41,320,1.000,bicubic,-9.890,-4.958,+29 +xcit_small_12_p16_384_dist,74.120,25.880,92.070,7.930,26.25,384,1.000,bicubic,-10.586,-5.048,-26 +convnext_base.fb_in1k,74.120,25.880,91.730,8.270,88.59,288,1.000,bicubic,-10.314,-5.090,-5 +volo_d1_224,74.110,25.890,92.030,7.970,26.63,224,0.960,bicubic,-10.054,-4.746,+15 +flexivit_base.600ep_in1k,74.110,25.890,91.750,8.250,86.59,240,0.950,bicubic,-10.408,-5.236,-16 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,74.100,25.900,92.390,7.610,328.99,224,0.900,bicubic,-10.334,-4.582,-11 +xcit_large_24_p8_224,74.060,25.940,90.890,9.110,188.93,224,1.000,bicubic,-10.332,-5.766,-4 +vit_base_patch16_224_miil.in21k_ft_in1k,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.228,-5.102,+1 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,74.030,25.970,92.440,7.560,88.30,384,0.950,bicubic,-11.182,-4.962,-63 +swsl_resnext101_32x16d,74.020,25.980,92.160,7.840,194.03,224,0.875,bilinear,-9.326,-4.686,+71 +resnetv2_152x4_bitm,74.010,25.990,92.340,7.660,936.53,480,1.000,bilinear,-10.906,-5.100,-43 +vit_base_patch16_224.augreg_in21k_ft_in1k,74.000,26.000,92.470,7.530,86.57,224,0.900,bicubic,-10.532,-4.824,-28 +swinv2_base_window16_256,74.000,26.000,91.740,8.260,87.92,256,0.900,bicubic,-10.594,-5.334,-29 +tf_efficientnetv2_s.in1k,74.000,26.000,91.530,8.470,21.46,384,1.000,bicubic,-9.894,-5.168,+24 +regnetz_d32,73.970,26.030,91.950,8.050,27.58,320,0.950,bicubic,-10.052,-4.916,+14 +crossvit_18_dagger_408,73.960,26.040,91.420,8.580,44.61,408,1.000,bicubic,-10.236,-5.398,+1 +seresnext101_32x8d,73.940,26.060,91.450,8.550,93.57,288,1.000,bicubic,-10.252,-5.424,+1 +xcit_small_12_p8_224_dist,73.930,26.070,91.720,8.280,26.21,224,1.000,bicubic,-10.302,-5.058,-5 +resnetv2_152x2_bitm,73.920,26.080,92.670,7.330,236.34,448,1.000,bilinear,-10.590,-4.762,-31 +resnetrs420,73.920,26.080,91.760,8.240,191.89,416,1.000,bicubic,-11.088,-5.364,-57 +tf_efficientnet_b6.aa_in1k,73.900,26.100,91.750,8.250,43.04,528,0.942,bicubic,-10.210,-5.136,+1 +tf_efficientnet_b3.ns_jft_in1k,73.890,26.110,91.870,8.130,12.23,300,0.904,bicubic,-10.158,-5.040,+5 +edgenext_base,73.880,26.120,91.770,8.230,18.51,320,1.000,bicubic,-10.080,-4.998,+10 +resmlp_big_24_224_in22ft1k,73.880,26.120,91.750,8.250,129.14,224,0.875,bicubic,-10.514,-5.132,-23 +deit3_small_patch16_224_in21ft1k,73.850,26.150,91.960,8.040,22.06,224,1.000,bicubic,-9.220,-4.820,+74 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,73.800,26.200,92.300,7.700,36.47,384,1.000,bicubic,-10.246,-5.028,+1 +maxvit_rmlp_tiny_rw_256,73.800,26.200,91.440,8.560,29.15,256,0.950,bicubic,-10.432,-5.436,-12 +maxvit_small_tf_224.in1k,73.780,26.220,91.440,8.560,68.93,224,0.950,bicubic,-10.654,-5.724,-30 +regnetz_d8,73.760,26.240,92.010,7.990,23.37,320,1.000,bicubic,-10.290,-4.988,-5 +regnety_080,73.760,26.240,91.800,8.200,39.18,288,1.000,bicubic,-10.172,-5.088,+5 +resnetrs270,73.710,26.290,91.580,8.420,129.86,352,1.000,bicubic,-10.724,-5.390,-34 +gcvit_small,73.690,26.310,91.240,8.760,51.09,224,0.875,bicubic,-10.194,-5.418,+6 +resnet200d,73.680,26.320,91.570,8.430,64.69,320,1.000,bicubic,-10.282,-5.254,-1 +resnetv2_101x3_bitm,73.670,26.330,92.470,7.530,387.93,448,1.000,bilinear,-10.770,-4.912,-40 +ig_resnext101_32x8d,73.650,26.350,92.190,7.810,88.79,224,0.875,bilinear,-9.038,-4.446,+88 +xcit_medium_24_p16_224_dist,73.650,26.350,91.570,8.430,84.40,224,1.000,bicubic,-10.624,-5.370,-28 +regnety_064,73.580,26.420,91.340,8.660,30.58,288,1.000,bicubic,-10.136,-5.334,+17 +tresnet_v2_l,73.570,26.430,90.960,9.040,46.17,224,0.875,bilinear,-10.332,-5.532,-2 +tf_efficientnet_b5.ra_in1k,73.550,26.450,91.460,8.540,30.39,456,0.934,bicubic,-10.262,-5.288,+6 +swinv2_base_window8_256,73.530,26.470,91.530,8.470,87.92,256,0.900,bicubic,-10.732,-5.392,-30 +resnet152d,73.520,26.480,91.230,8.770,60.21,320,1.000,bicubic,-10.160,-5.508,+19 +cs3se_edgenet_x,73.510,26.490,91.500,8.500,50.72,320,1.000,bicubic,-10.038,-5.170,+23 +regnetv_064,73.480,26.520,91.590,8.410,30.58,288,1.000,bicubic,-10.232,-5.158,+12 +convnext_small.fb_in1k,73.480,26.520,91.330,8.670,50.22,288,1.000,bicubic,-10.226,-5.480,+15 +deit3_base_patch16_224,73.470,26.530,91.290,8.710,86.59,224,0.900,bicubic,-10.322,-5.294,+3 +sequencer2d_l,73.470,26.530,91.100,8.900,54.30,224,0.875,bicubic,-9.936,-5.406,+25 +xcit_tiny_24_p8_384_dist,73.400,26.600,91.570,8.430,12.11,384,1.000,bicubic,-10.340,-5.064,+3 +resnetrs350,73.400,26.600,91.310,8.690,163.96,384,1.000,bicubic,-11.320,-5.678,-71 +twins_svt_large,73.390,26.610,90.910,9.090,99.27,224,0.900,bicubic,-10.288,-5.684,+12 +regnetz_d8_evos,73.380,26.620,91.640,8.360,23.46,320,0.950,bicubic,-10.670,-5.354,-25 +pvt_v2_b4,73.380,26.620,91.080,8.920,62.56,224,0.900,bicubic,-10.336,-5.640,+5 +regnety_160,73.360,26.640,91.690,8.310,83.59,288,1.000,bicubic,-10.326,-5.086,+7 +efficientnet_b4.ra2_in1k,73.320,26.680,91.280,8.720,19.34,384,1.000,bicubic,-10.108,-5.316,+16 +swin_s3_base_224,73.320,26.680,91.180,8.820,71.13,224,0.900,bicubic,-10.610,-5.482,-19 +vit_small_patch16_384.augreg_in21k_ft_in1k,73.290,26.710,91.990,8.010,22.20,384,1.000,bicubic,-10.512,-5.112,-9 +xcit_small_24_p16_224_dist,73.290,26.710,91.450,8.550,47.67,224,1.000,bicubic,-10.572,-5.278,-18 +resmlp_big_24_distilled_224,73.290,26.710,91.160,8.840,129.14,224,0.875,bicubic,-10.298,-5.488,+8 +swinv2_small_window16_256,73.280,26.720,91.270,8.730,49.73,256,0.900,bicubic,-10.926,-5.600,-42 +mvitv2_small,73.270,26.730,91.200,8.800,34.87,224,0.900,bicubic,-10.498,-5.370,-10 +gcvit_tiny,73.270,26.730,90.970,9.030,28.22,224,0.875,bicubic,-10.130,-5.428,+13 +pvt_v2_b5,73.240,26.760,91.090,8.910,81.96,224,0.900,bicubic,-10.500,-5.622,-10 +deit_base_distilled_patch16_224,73.240,26.760,91.000,9.000,87.34,224,0.900,bicubic,-10.148,-5.488,+14 +pvt_v2_b3,73.210,26.790,91.010,8.990,45.24,224,0.900,bicubic,-9.916,-5.546,+31 +resnetrs152,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.512,-5.354,-8 +maxvit_tiny_rw_224,73.200,26.800,90.820,9.180,29.06,224,0.950,bicubic,-10.304,-5.682,+2 +xcit_medium_24_p8_224,73.140,26.860,90.270,9.730,84.32,224,1.000,bicubic,-10.594,-6.124,-13 +vit_base_patch32_384.augreg_in21k_ft_in1k,73.130,26.870,91.240,8.760,88.30,384,1.000,bicubic,-10.220,-5.596,+12 +jx_nest_base,73.110,26.890,91.070,8.930,67.72,224,0.875,bicubic,-10.442,-5.300,-4 +swinv2_small_window8_256,73.110,26.890,90.940,9.060,49.73,256,0.900,bicubic,-10.746,-5.700,-29 +xcit_small_24_p8_224,73.090,26.910,91.150,8.850,47.63,224,1.000,bicubic,-10.748,-5.486,-27 +deit3_small_patch16_384,73.080,26.920,91.230,8.770,22.21,384,1.000,bicubic,-10.346,-5.446,-1 +cait_s24_224,73.070,26.930,91.130,8.870,46.92,224,1.000,bicubic,-10.382,-5.434,-4 +coatnet_rmlp_1_rw_224,73.020,26.980,90.890,9.110,41.69,224,0.950,bicubic,-10.338,-5.566,+5 +crossvit_15_dagger_408,72.950,27.050,91.080,8.920,28.50,408,1.000,bicubic,-10.888,-5.702,-32 +maxvit_tiny_tf_224.in1k,72.910,27.090,90.810,9.190,30.92,224,0.950,bicubic,-10.488,-5.778,-3 +coatnet_1_rw_224,72.910,27.090,90.790,9.210,41.72,224,0.950,bicubic,-10.698,-5.598,-13 +resnetv2_152x2_bit_teacher_384,72.890,27.110,91.550,8.450,236.34,384,1.000,bicubic,-10.954,-5.568,-36 +tf_efficientnet_b4.ap_in1k,72.890,27.110,90.980,9.020,19.34,380,0.922,bicubic,-10.358,-5.412,+6 +regnetv_040,72.880,27.120,91.100,8.900,20.64,288,1.000,bicubic,-10.314,-5.560,+7 +dm_nfnet_f0,72.880,27.120,91.080,8.920,71.49,256,0.900,bicubic,-10.506,-5.492,-3 +swinv2_cr_small_ns_224,72.800,27.200,90.800,9.200,49.70,224,0.900,bicubic,-10.688,-5.686,-14 +xception65p,72.790,27.210,90.910,9.090,39.82,299,0.940,bicubic,-10.340,-5.570,+11 +regnety_032,72.770,27.230,90.950,9.050,19.44,288,1.000,bicubic,-9.954,-5.474,+36 +regnety_040,72.710,27.290,90.720,9.280,20.65,288,1.000,bicubic,-10.328,-5.790,+17 +efficientformer_l7,72.690,27.310,90.810,9.190,82.23,224,0.950,bicubic,-10.696,-5.730,-8 +swin_s3_small_224,72.670,27.330,90.560,9.440,49.74,224,0.900,bicubic,-11.100,-5.890,-37 +nfnet_l0,72.610,27.390,91.010,8.990,35.07,288,1.000,bicubic,-10.140,-5.506,+31 +resnext101_64x4d,72.610,27.390,90.840,9.160,83.46,288,1.000,bicubic,-10.538,-5.532,0 +xcit_small_12_p8_224,72.610,27.390,90.680,9.320,26.21,224,1.000,bicubic,-10.734,-5.800,-8 +pnasnet5large,72.610,27.390,90.510,9.490,86.06,331,0.911,bicubic,-10.172,-5.530,+28 +xception65,72.600,27.400,90.830,9.170,39.92,299,0.940,bicubic,-10.580,-5.762,-3 +cs3sedarknet_x,72.580,27.420,91.050,8.950,35.40,288,1.000,bicubic,-10.074,-5.304,+33 +twins_pcpvt_large,72.580,27.420,90.700,9.300,60.99,224,0.900,bicubic,-10.560,-5.898,-2 +resnest101e,72.570,27.430,90.820,9.180,48.28,256,0.875,bilinear,-10.320,-5.500,+15 +swsl_resnext50_32x4d,72.560,27.440,90.870,9.130,25.03,224,0.875,bilinear,-9.622,-5.360,+80 +twins_svt_base,72.550,27.450,90.460,9.540,56.07,224,0.900,bicubic,-10.586,-5.958,-5 +tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.500,-5.864,+4 +resnetv2_50x3_bitm,72.530,27.470,91.760,8.240,217.32,448,1.000,bilinear,-11.484,-5.364,-70 +gc_efficientnetv2_rw_t.agc_in1k,72.530,27.470,90.820,9.180,13.68,288,1.000,bicubic,-9.934,-5.478,+46 +deit_base_patch16_384,72.530,27.470,90.250,9.750,86.86,384,1.000,bicubic,-10.576,-6.122,-2 +deit3_medium_patch16_224,72.520,27.480,90.780,9.220,38.85,224,0.900,bicubic,-10.560,-5.512,-4 +xcit_small_12_p16_224_dist,72.500,27.500,91.120,8.880,26.25,224,1.000,bicubic,-10.850,-5.294,-23 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,72.500,27.500,90.870,9.130,88.22,224,0.900,bicubic,-10.806,-5.660,-19 +convnext_tiny.fb_in22k_ft_in1k_384,72.490,27.510,91.540,8.460,28.59,384,1.000,bicubic,-11.590,-5.602,-81 +xcit_tiny_24_p8_224_dist,72.440,27.560,90.920,9.080,12.11,224,1.000,bicubic,-10.122,-5.146,+31 +resnet101d,72.410,27.590,90.650,9.350,44.57,320,1.000,bicubic,-10.612,-5.796,-3 +sequencer2d_m,72.400,27.600,90.690,9.310,38.31,224,0.875,bicubic,-10.406,-5.578,+8 +maxxvit_rmlp_nano_rw_256,72.380,27.620,90.750,9.250,16.78,256,0.950,bicubic,-10.650,-5.594,-8 +jx_nest_small,72.380,27.620,90.690,9.310,38.35,224,0.875,bicubic,-10.740,-5.638,-13 +maxvit_rmlp_nano_rw_256,72.380,27.620,90.450,9.550,15.50,256,0.950,bicubic,-10.582,-5.820,-4 +regnetz_c16,72.320,27.680,90.820,9.180,13.46,320,0.940,bicubic,-10.198,-5.252,+30 +tf_efficientnet_b4.aa_in1k,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.732,-5.710,-8 +tf_efficientnet_b2.ns_jft_in1k,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.100,-5.158,+39 +maxvit_nano_rw_256,72.280,27.720,90.550,9.450,15.45,256,0.950,bicubic,-10.652,-5.672,-6 +tresnet_m,72.270,27.730,90.240,9.760,31.39,224,0.875,bilinear,-10.810,-5.878,-17 +resnetv2_50x1_bit_distilled,72.260,27.740,91.010,8.990,25.55,224,0.875,bicubic,-10.558,-5.512,-3 +crossvit_18_240,72.250,27.750,90.270,9.730,43.27,240,0.875,bicubic,-10.150,-5.784,+32 +regnetz_c16_evos,72.240,27.760,91.230,8.770,13.49,320,0.950,bicubic,-10.390,-5.244,+8 +efficientnetv2_rw_t.ra2_in1k,72.240,27.760,90.420,9.580,13.65,288,1.000,bicubic,-10.108,-5.776,+38 +nasnetalarge,72.230,27.770,90.470,9.530,88.75,331,0.911,bicubic,-10.390,-5.576,+8 +cait_xxs36_384,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.004,-5.308,+53 +twins_pcpvt_base,72.180,27.820,90.510,9.490,43.83,224,0.900,bicubic,-10.528,-5.836,-2 +crossvit_18_dagger_240,72.150,27.850,90.070,9.930,44.27,240,0.875,bicubic,-10.368,-6.290,+19 +xcit_tiny_24_p16_384_dist,72.080,27.920,90.580,9.420,12.12,384,1.000,bicubic,-10.490,-5.706,+11 +resnet152,72.070,27.930,90.350,9.650,60.19,224,0.950,bicubic,-10.752,-5.776,-13 +cs3edgenet_x,72.050,27.950,90.370,9.630,47.82,288,1.000,bicubic,-10.652,-6.000,-5 +vit_relpos_base_patch16_clsgap_224.sw_in1k,72.010,27.990,90.250,9.750,86.43,224,0.900,bicubic,-10.752,-5.924,-10 +mobilevitv2_200_384_in22ft1k,72.000,28.000,90.630,9.370,18.45,384,1.000,bicubic,-11.394,-5.950,-55 +efficientformer_l3,72.000,28.000,90.280,9.720,31.41,224,0.950,bicubic,-10.550,-5.968,+10 +vit_relpos_medium_patch16_cls_224.sw_in1k,71.980,28.020,90.290,9.710,38.76,224,0.900,bicubic,-10.582,-5.880,+5 +convnext_tiny.fb_in1k,71.980,28.020,90.210,9.790,28.59,288,1.000,bicubic,-10.720,-5.926,-8 +convnext_tiny_hnf.a2h_in1k,71.980,28.020,89.770,10.230,28.59,288,1.000,bicubic,-10.610,-6.246,-1 +sequencer2d_s,71.940,28.060,90.480,9.520,27.65,224,0.875,bicubic,-10.402,-5.550,+25 +convnext_nano.in12k_ft_in1k,71.890,28.110,91.000,9.000,15.59,288,1.000,bicubic,-10.968,-5.556,-23 +swinv2_cr_small_224,71.890,28.110,90.270,9.730,49.70,224,0.900,bicubic,-11.256,-5.824,-45 +eca_nfnet_l0,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.740,-5.380,-3 +vit_relpos_base_patch16_224.sw_in1k,71.830,28.170,90.260,9.740,86.43,224,0.900,bicubic,-10.654,-5.882,+6 +mobilevitv2_175_384_in22ft1k,71.810,28.190,90.780,9.220,14.25,384,1.000,bicubic,-11.132,-5.646,-32 +flexivit_small.1200ep_in1k,71.740,28.260,90.270,9.730,22.06,240,0.950,bicubic,-10.786,-5.866,0 +swin_small_patch4_window7_224,71.740,28.260,90.240,9.760,49.61,224,0.900,bicubic,-11.472,-6.082,-54 +mvitv2_tiny,71.720,28.280,90.300,9.700,24.17,224,0.900,bicubic,-10.684,-5.856,+6 +flexivit_small.300ep_in1k,71.720,28.280,89.970,10.030,22.06,240,0.950,bicubic,-10.452,-6.054,+36 +swsl_resnet50,71.700,28.300,90.500,9.500,25.56,224,0.875,bilinear,-9.466,-4.596,+99 +pvt_v2_b2_li,71.700,28.300,90.010,9.990,22.55,224,0.900,bicubic,-10.496,-6.094,+29 +pit_b_224,71.700,28.300,89.250,10.750,73.76,224,0.900,bicubic,-10.746,-6.460,+4 +xcit_large_24_p16_224,71.700,28.300,89.170,10.830,189.10,224,1.000,bicubic,-11.196,-6.712,-38 +coatnet_bn_0_rw_224,71.690,28.310,90.380,9.620,27.44,224,0.950,bicubic,-10.708,-5.802,+3 +flexivit_small.600ep_in1k,71.690,28.310,90.160,9.840,22.06,240,0.950,bicubic,-10.664,-5.926,+7 +resnet61q,71.680,28.320,90.270,9.730,36.85,288,1.000,bicubic,-10.844,-5.860,-9 +gcvit_xtiny,71.660,28.340,90.230,9.770,19.98,224,0.875,bicubic,-10.292,-5.736,+43 +tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.394,-6.306,+33 +convit_base,71.600,28.400,90.150,9.850,86.54,224,0.875,bicubic,-10.688,-5.858,+14 +tresnet_l_448,71.600,28.400,90.050,9.950,55.99,448,0.875,bilinear,-10.668,-5.926,+14 +xcit_tiny_12_p8_384_dist,71.580,28.420,90.710,9.290,6.71,384,1.000,bicubic,-10.808,-5.514,-2 +swinv2_tiny_window16_256,71.570,28.430,90.340,9.660,28.35,256,0.900,bicubic,-11.240,-5.892,-41 +poolformer_m48,71.550,28.450,89.760,10.240,73.47,224,0.950,bicubic,-10.912,-6.198,-10 +coatnet_0_rw_224,71.540,28.460,89.420,10.580,27.44,224,0.950,bicubic,-10.850,-6.416,-6 +crossvit_15_dagger_240,71.520,28.480,89.860,10.140,28.21,240,0.875,bicubic,-10.812,-6.658,+2 +ssl_resnext101_32x8d,71.500,28.500,90.460,9.540,88.79,224,0.875,bilinear,-10.116,-5.578,+54 +fbnetv3_g.ra2_in1k,71.500,28.500,90.370,9.630,16.62,288,0.950,bilinear,-10.548,-5.694,+26 +mobilevitv2_150_384_in22ft1k,71.490,28.510,90.420,9.580,10.59,384,1.000,bicubic,-11.104,-5.898,-33 +ecaresnet101d,71.490,28.510,90.330,9.670,44.57,224,0.875,bicubic,-10.682,-5.716,+15 +efficientnet_b3.ra2_in1k,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.762,-6.054,+5 +resnet51q,71.430,28.570,90.180,9.820,35.70,288,1.000,bilinear,-10.930,-6.000,-11 +pvt_v2_b2,71.430,28.570,90.040,9.960,25.36,224,0.900,bicubic,-10.646,-5.922,+17 +ssl_resnext101_32x16d,71.410,28.590,90.560,9.440,194.03,224,0.875,bilinear,-10.434,-5.536,+34 +pit_s_distilled_224,71.380,28.620,89.780,10.220,24.04,224,0.900,bicubic,-10.616,-6.018,+21 +vit_relpos_medium_patch16_224.sw_in1k,71.350,28.650,89.950,10.050,38.75,224,0.900,bicubic,-11.116,-6.138,-24 +xcit_tiny_24_p8_224,71.340,28.660,90.240,9.760,12.11,224,1.000,bicubic,-10.560,-5.736,+29 +vit_base_patch16_224.orig_in21k_ft_in1k,71.330,28.670,90.460,9.540,86.57,224,0.900,bicubic,-10.456,-5.662,+35 +tf_efficientnetv2_b3.in21k_ft_in1k,71.310,28.690,90.760,9.240,14.36,300,0.900,bicubic,-11.362,-5.864,-47 +mixer_b16_224_miil,71.300,28.700,89.650,10.350,59.88,224,0.875,bilinear,-11.008,-6.066,-9 +resnetv2_152x2_bit_teacher,71.290,28.710,90.430,9.570,236.34,224,0.875,bicubic,-11.572,-6.138,-63 +ecaresnet50t,71.280,28.720,90.420,9.580,25.57,320,0.950,bicubic,-11.066,-5.718,-17 +resnetv2_101,71.270,28.730,89.920,10.080,44.54,224,0.950,bicubic,-10.760,-5.940,+11 +convmixer_1536_20,71.240,28.760,89.430,10.570,51.63,224,0.960,bicubic,-10.136,-6.184,+59 +vit_base_patch32_clip_224.laion2b_ft_in1k,71.200,28.800,90.200,9.800,88.22,224,0.900,bicubic,-11.382,-6.002,-45 +xcit_small_12_p16_224,71.200,28.800,89.750,10.250,26.25,224,1.000,bicubic,-10.774,-6.066,+12 +crossvit_base_240,71.190,28.810,89.840,10.160,105.03,240,0.875,bicubic,-11.026,-5.990,-8 +vit_relpos_medium_patch16_rpn_224.sw_in1k,71.170,28.830,90.080,9.920,38.73,224,0.900,bicubic,-11.128,-5.894,-17 +deit_base_patch16_224,71.170,28.830,89.200,10.800,86.57,224,0.900,bicubic,-10.828,-6.534,+7 +mobilevitv2_200_in22ft1k,71.140,28.860,89.700,10.300,18.45,256,0.888,bicubic,-11.184,-6.240,-21 +swin_s3_tiny_224,71.130,28.870,89.720,10.280,28.33,224,0.900,bicubic,-10.992,-6.228,-5 +resnetv2_50d_evos,71.110,28.890,90.030,9.970,25.59,288,0.950,bicubic,-10.866,-5.886,+5 +halo2botnet50ts_256,71.100,28.900,89.610,10.390,22.64,256,0.950,bicubic,-10.960,-6.026,-2 +cs3sedarknet_l,71.080,28.920,90.330,9.670,21.91,288,0.950,bicubic,-10.694,-5.638,+20 +cs3darknet_x,71.070,28.930,90.120,9.880,35.05,288,1.000,bicubic,-11.158,-6.114,-17 +xcit_tiny_12_p8_224_dist,71.040,28.960,89.880,10.120,6.71,224,1.000,bicubic,-10.172,-5.720,+54 +xcit_medium_24_p16_224,71.020,28.980,89.520,10.480,84.40,224,1.000,bicubic,-11.616,-6.456,-63 +resnetv2_101x1_bitm,71.010,28.990,91.090,8.910,44.54,448,1.000,bilinear,-11.322,-4.866,-32 +resnetv2_50d_gn,71.010,28.990,89.770,10.230,25.57,288,0.950,bicubic,-10.806,-6.154,+12 +xcit_small_24_p16_224,71.010,28.990,89.700,10.300,47.67,224,1.000,bicubic,-11.570,-6.304,-59 +visformer_small,71.010,28.990,89.460,10.540,40.22,224,0.900,bicubic,-11.096,-6.412,-12 +lamhalobotnet50ts_256,70.990,29.010,89.060,10.940,22.57,256,0.950,bicubic,-10.554,-6.444,+23 +tresnet_m_448,70.990,29.010,88.680,11.320,31.39,448,0.875,bilinear,-10.724,-6.892,+12 +edgenext_small,70.980,29.020,89.870,10.130,5.59,320,1.000,bicubic,-10.588,-5.836,+20 +resnest50d_4s2x40d,70.950,29.050,89.710,10.290,30.42,224,0.875,bicubic,-10.158,-5.848,+54 +wide_resnet50_2,70.950,29.050,89.230,10.770,68.88,224,0.875,bicubic,-10.506,-6.302,+29 +convnext_nano.d1h_in1k,70.940,29.060,89.420,10.580,15.59,288,1.000,bicubic,-10.530,-6.238,+26 +vit_small_patch16_224.augreg_in21k_ft_in1k,70.930,29.070,90.140,9.860,22.05,224,0.900,bicubic,-10.472,-5.994,+30 +tnt_s_patch16_224,70.930,29.070,89.600,10.400,23.76,224,0.900,bicubic,-10.588,-6.148,+20 +coatnet_nano_rw_224,70.920,29.080,89.710,10.290,15.14,224,0.900,bicubic,-10.780,-5.928,+6 +tf_efficientnet_b3.ap_in1k,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.902,-6.194,-2 +coatnext_nano_rw_224,70.910,29.090,90.250,9.750,14.70,224,0.900,bicubic,-11.038,-5.668,-11 +coatnet_rmlp_nano_rw_224,70.910,29.090,89.920,10.080,15.15,224,0.900,bicubic,-11.154,-5.950,-22 +vit_srelpos_medium_patch16_224.sw_in1k,70.900,29.100,89.960,10.040,38.74,224,0.900,bicubic,-11.336,-5.974,-37 +tf_efficientnet_b1.ns_jft_in1k,70.870,29.130,90.120,9.880,7.79,240,0.882,bicubic,-10.518,-5.618,+25 +vit_base_patch16_rpn_224.in1k,70.870,29.130,89.770,10.230,86.54,224,0.900,bicubic,-11.332,-6.226,-36 +vit_large_patch32_384.orig_in21k_ft_in1k,70.860,29.140,90.570,9.430,306.63,384,1.000,bicubic,-10.646,-5.522,+13 +jx_nest_tiny,70.850,29.150,89.940,10.060,17.06,224,0.875,bicubic,-10.564,-5.676,+20 +resnetrs101,70.840,29.160,89.830,10.170,63.62,288,0.940,bicubic,-11.448,-6.108,-47 +rexnet_200,70.840,29.160,89.700,10.300,16.37,224,0.875,bicubic,-10.792,-5.968,-1 +tresnet_l,70.840,29.160,89.630,10.370,55.99,224,0.875,bilinear,-10.648,-5.994,+9 +resnet101,70.840,29.160,89.510,10.490,44.55,224,0.950,bicubic,-11.098,-6.244,-15 +tf_efficientnetv2_b3.in1k,70.830,29.170,89.500,10.500,14.36,300,0.904,bicubic,-11.140,-6.282,-24 +coat_lite_small,70.800,29.200,89.570,10.430,19.84,224,0.900,bicubic,-11.508,-6.280,-54 +poolformer_m36,70.790,29.210,89.510,10.490,56.17,224,0.950,bicubic,-11.320,-6.178,-38 +deit3_small_patch16_224,70.760,29.240,89.460,10.540,22.06,224,0.900,bicubic,-10.626,-5.990,+15 +levit_384,70.750,29.250,89.300,10.700,39.13,224,0.900,bicubic,-11.836,-6.716,-88 +swinv2_cr_tiny_ns_224,70.720,29.280,89.370,10.630,28.33,224,0.900,bicubic,-11.070,-6.454,-16 +vit_relpos_small_patch16_224.sw_in1k,70.710,29.290,90.000,10.000,21.98,224,0.900,bicubic,-10.752,-5.828,+4 +vit_base_patch32_clip_224.openai_ft_in1k,70.710,29.290,89.830,10.170,88.22,224,0.900,bicubic,-11.220,-6.138,-24 +mobilevitv2_175_in22ft1k,70.650,29.350,89.710,10.290,14.25,256,0.888,bicubic,-11.294,-6.082,-28 +tf_efficientnet_b3.aa_in1k,70.640,29.360,89.440,10.560,12.23,300,0.904,bicubic,-10.996,-6.278,-13 +crossvit_small_240,70.610,29.390,89.360,10.640,26.86,240,0.875,bicubic,-10.410,-6.100,+33 +cait_xxs24_384,70.600,29.400,89.720,10.280,12.03,384,1.000,bicubic,-10.366,-5.926,+36 +gluon_senet154,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.634,-6.428,+14 +convit_small,70.580,29.420,89.580,10.420,27.78,224,0.875,bicubic,-10.846,-6.164,0 +convnext_nano_ols.d1h_in1k,70.570,29.430,89.090,10.910,15.65,288,1.000,bicubic,-11.040,-6.550,-14 +twins_pcpvt_small,70.550,29.450,89.070,10.930,24.11,224,0.900,bicubic,-10.538,-6.572,+25 +swinv2_tiny_window8_256,70.540,29.460,89.500,10.500,28.35,256,0.900,bicubic,-11.266,-6.494,-28 +ssl_resnext101_32x4d,70.530,29.470,89.760,10.240,44.18,224,0.875,bilinear,-10.394,-5.968,+34 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,70.520,29.480,90.110,9.890,36.43,224,0.900,bicubic,-11.338,-5.912,-35 +deit_small_distilled_patch16_224,70.520,29.480,89.470,10.530,22.44,224,0.900,bicubic,-10.680,-5.908,+10 +legacy_senet154,70.500,29.500,89.010,10.990,115.09,224,0.875,bilinear,-10.810,-6.486,+3 +regnetz_b16,70.450,29.550,89.530,10.470,9.72,288,0.940,bicubic,-10.266,-5.948,+42 +twins_svt_small,70.440,29.560,89.360,10.640,24.06,224,0.900,bicubic,-11.242,-6.310,-28 +crossvit_15_240,70.430,29.570,89.530,10.470,27.53,240,0.875,bicubic,-11.106,-6.162,-20 +gluon_seresnext101_64x4d,70.430,29.570,89.350,10.650,88.23,224,0.875,bicubic,-10.464,-5.958,+30 +halonet50ts,70.430,29.570,89.320,10.680,22.73,256,0.940,bicubic,-11.214,-6.288,-30 +tf_efficientnet_lite4.in1k,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.106,-6.558,-22 +resnetaa50,70.420,29.580,90.000,10.000,25.56,288,1.000,bicubic,-11.202,-5.808,-29 +resnest50d,70.410,29.590,88.760,11.240,27.48,224,0.875,bilinear,-10.564,-6.618,+18 +resnest50d_1s4x24d,70.400,29.600,89.220,10.780,25.68,224,0.875,bicubic,-10.588,-6.102,+16 +seresnext50_32x4d,70.400,29.600,89.110,10.890,27.56,224,0.875,bicubic,-10.866,-6.510,-5 +cs3darknet_l,70.360,29.640,89.750,10.250,21.16,288,0.950,bicubic,-10.536,-5.920,+22 +gernet_l,70.350,29.650,88.980,11.020,31.08,256,0.875,bilinear,-11.004,-6.556,-11 +vit_srelpos_small_patch16_224.sw_in1k,70.290,29.710,89.580,10.420,21.97,224,0.900,bicubic,-10.804,-5.752,+4 +gluon_resnet152_v1s,70.290,29.710,88.850,11.150,60.32,224,0.875,bicubic,-10.726,-6.562,+11 +repvgg_b3,70.250,29.750,88.730,11.270,123.09,224,0.875,bilinear,-10.242,-6.530,+40 +coat_mini,70.220,29.780,89.440,10.560,10.34,224,0.900,bicubic,-11.048,-5.952,-12 +xception41p,70.220,29.780,89.090,10.910,26.91,299,0.940,bicubic,-11.738,-6.704,-60 +sebotnet33ts_256,70.180,29.820,88.790,11.210,13.70,256,0.940,bicubic,-10.970,-6.384,-6 +ecaresnet101d_pruned,70.130,29.870,89.590,10.410,24.88,224,0.875,bicubic,-10.688,-6.038,+19 +efficientnet_el.ra_in1k,70.120,29.880,89.290,10.710,10.59,300,0.904,bicubic,-11.196,-6.236,-18 +inception_resnet_v2,70.120,29.880,88.700,11.300,55.84,299,0.897,bicubic,-10.338,-6.606,+40 +resmlp_36_distilled_224,70.090,29.910,89.100,10.900,44.69,224,0.875,bicubic,-11.070,-6.388,-12 +haloregnetz_b,70.090,29.910,88.860,11.140,11.68,224,0.940,bicubic,-10.960,-6.336,-1 +poolformer_s36,70.020,29.980,89.190,10.810,30.86,224,0.900,bicubic,-11.396,-6.256,-30 +sehalonet33ts,70.020,29.980,88.710,11.290,13.69,256,0.940,bicubic,-10.938,-6.566,+3 +gluon_seresnext101_32x4d,70.010,29.990,88.900,11.100,48.96,224,0.875,bicubic,-10.894,-6.394,+6 +regnety_320,70.000,30.000,88.890,11.110,145.05,224,0.875,bicubic,-10.810,-6.354,+12 +levit_256,69.970,30.030,89.250,10.750,18.89,224,0.900,bicubic,-11.540,-6.240,-42 +gluon_resnet152_v1d,69.960,30.040,88.490,11.510,60.21,224,0.875,bicubic,-10.514,-6.716,+29 +pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.204,-6.640,-11 +maxvit_rmlp_pico_rw_256,69.880,30.120,89.270,10.730,7.52,256,0.950,bicubic,-10.636,-5.942,+22 +ecaresnet50d,69.840,30.160,89.400,10.600,25.58,224,0.875,bicubic,-10.752,-5.920,+15 +mobilevitv2_150_in22ft1k,69.820,30.180,89.170,10.830,10.59,256,0.888,bicubic,-11.658,-6.504,-44 +mobilevitv2_200,69.760,30.240,88.610,11.390,18.45,256,0.888,bicubic,-11.376,-6.756,-21 +ssl_resnext50_32x4d,69.710,30.290,89.440,10.560,25.03,224,0.875,bilinear,-10.608,-5.966,+38 +xcit_tiny_24_p16_224_dist,69.700,30.300,88.710,11.290,12.12,224,1.000,bicubic,-10.746,-6.508,+27 +xcit_tiny_12_p16_384_dist,69.690,30.310,89.030,10.970,6.72,384,1.000,bicubic,-11.250,-6.380,-8 +lambda_resnet50ts,69.690,30.310,88.830,11.170,21.54,256,0.950,bicubic,-11.476,-7.142,-27 +resmlp_24_distilled_224,69.680,30.320,89.050,10.950,30.02,224,0.875,bicubic,-11.086,-6.168,0 +gluon_resnext101_64x4d,69.680,30.320,88.270,11.730,83.46,224,0.875,bicubic,-10.924,-6.718,+7 +resnext50_32x4d,69.660,30.340,88.660,11.340,25.03,224,0.950,bicubic,-11.458,-6.672,-26 +efficientnet_b3_pruned.in1k,69.580,30.420,88.980,11.020,9.86,300,0.904,bicubic,-11.278,-6.262,-5 +gcresnext50ts,69.540,30.460,88.850,11.150,15.67,256,0.900,bicubic,-11.040,-6.320,+5 +nf_resnet50,69.540,30.460,88.730,11.270,25.56,288,0.940,bicubic,-11.122,-6.606,0 +gernet_m,69.530,30.470,88.690,11.310,21.14,224,0.875,bilinear,-11.202,-6.494,-5 +ens_adv_inception_resnet_v2,69.530,30.470,88.510,11.490,55.84,299,0.897,bicubic,-10.452,-6.428,+51 +efficientnet_el_pruned.in1k,69.520,30.480,88.930,11.070,10.59,300,0.904,bicubic,-10.780,-6.288,+30 +repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.692,-6.660,+36 +gcresnet50t,69.500,30.500,89.040,10.960,25.90,256,0.900,bicubic,-11.440,-6.414,-21 +efficientnet_b2.ra_in1k,69.500,30.500,88.680,11.320,9.11,288,1.000,bicubic,-11.112,-6.638,-4 +rexnet_150,69.470,30.530,88.980,11.020,9.73,224,0.875,bicubic,-10.840,-6.186,+23 +gcvit_xxtiny,69.470,30.530,88.870,11.130,12.00,224,0.875,bicubic,-10.244,-6.210,+61 +swin_tiny_patch4_window7_224,69.450,30.550,89.020,10.980,28.29,224,0.900,bicubic,-11.928,-6.520,-54 +regnetx_320,69.440,30.560,88.270,11.730,107.81,224,0.875,bicubic,-10.806,-6.756,+27 +cspresnext50,69.430,30.570,88.620,11.380,20.57,256,0.887,bilinear,-11.116,-6.700,-6 +vit_base_patch32_224.augreg_in21k_ft_in1k,69.410,30.590,89.420,10.580,88.22,224,0.900,bicubic,-11.314,-6.148,-14 +convmixer_768_32,69.400,30.600,88.910,11.090,21.11,224,0.960,bicubic,-10.764,-6.162,+30 +darknet53,69.370,30.630,88.770,11.230,41.61,288,1.000,bicubic,-11.164,-6.650,-8 +inception_v4,69.360,30.640,88.780,11.220,42.68,299,0.875,bicubic,-10.808,-6.188,+27 +legacy_seresnext101_32x4d,69.360,30.640,88.070,11.930,48.96,224,0.875,bilinear,-10.868,-6.948,+23 +ecaresnetlight,69.340,30.660,89.220,10.780,30.16,224,0.875,bicubic,-11.122,-6.028,-1 +resnet50d,69.330,30.670,88.220,11.780,25.58,224,0.875,bicubic,-11.200,-6.940,-11 +cs3darknet_focus_l,69.320,30.680,89.440,10.560,21.15,288,0.950,bicubic,-11.564,-6.242,-28 +xception71,69.320,30.680,88.260,11.740,42.34,299,0.903,bicubic,-10.554,-6.662,+37 +vit_small_patch16_384.augreg_in1k,69.310,30.690,89.020,10.980,22.20,384,1.000,bicubic,-11.810,-6.554,-51 +mobilevitv2_175,69.300,30.700,88.940,11.060,14.25,256,0.888,bicubic,-11.560,-6.314,-30 +vit_small_patch32_384.augreg_in21k_ft_in1k,69.290,30.710,89.820,10.180,22.92,384,1.000,bicubic,-11.190,-5.778,-11 +convnext_pico_ols.d1_in1k,69.240,30.760,88.830,11.170,9.06,288,1.000,bicubic,-11.224,-6.412,-9 +edgenext_small_rw,69.230,30.770,88.750,11.250,7.83,320,1.000,bicubic,-11.226,-6.442,-7 +efficientformer_l1,69.220,30.780,88.540,11.460,12.29,224,0.950,bicubic,-11.282,-6.458,-16 +vit_base_patch16_384.augreg_in1k,69.180,30.820,88.380,11.620,86.86,384,1.000,bicubic,-11.922,-6.952,-54 +gluon_xception65,69.160,30.840,88.090,11.910,39.92,299,0.903,bicubic,-10.556,-6.770,+39 +gluon_resnet152_v1c,69.140,30.860,87.870,12.130,60.21,224,0.875,bicubic,-10.770,-6.970,+26 +mixnet_xl.ra_in1k,69.100,30.900,88.310,11.690,11.90,224,0.875,bicubic,-11.376,-6.626,-17 +seresnet33ts,69.090,30.910,88.490,11.510,19.78,256,0.900,bicubic,-11.262,-6.616,-5 +tf_efficientnetv2_b2.in1k,69.090,30.910,88.220,11.780,10.10,260,0.890,bicubic,-11.118,-6.822,+9 +resnetv2_50,69.040,30.960,88.440,11.560,25.55,224,0.950,bicubic,-11.392,-6.640,-13 +gluon_resnet101_v1d,69.010,30.990,88.100,11.900,44.57,224,0.875,bicubic,-11.404,-6.914,-12 +repvgg_b2g4,69.000,31.000,88.360,11.640,61.76,224,0.875,bilinear,-10.366,-6.328,+52 +seresnet50,68.980,31.020,88.710,11.290,28.09,224,0.875,bicubic,-11.294,-6.360,-2 +gcresnet33ts,68.980,31.020,88.470,11.530,19.88,256,0.900,bicubic,-11.102,-6.528,+11 +gluon_resnext101_32x4d,68.960,31.040,88.360,11.640,44.18,224,0.875,bicubic,-11.374,-6.566,-10 +convnext_pico.d1_in1k,68.930,31.070,88.480,11.520,9.05,288,0.950,bicubic,-11.496,-6.578,-18 +tf_efficientnet_b2.ap_in1k,68.920,31.080,88.350,11.650,9.11,260,0.890,bicubic,-11.380,-6.678,-6 +cspdarknet53,68.890,31.110,88.600,11.400,27.64,256,0.887,bilinear,-11.168,-6.484,+8 +mobilevitv2_150,68.870,31.130,88.090,11.910,10.59,256,0.888,bicubic,-11.506,-6.970,-19 +regnety_120,68.850,31.150,88.330,11.670,51.82,224,0.875,bicubic,-11.516,-6.796,-18 +resnet50_gn,68.830,31.170,88.440,11.560,25.56,224,0.940,bicubic,-11.222,-6.506,+6 +gluon_resnet152_v1b,68.820,31.180,87.710,12.290,60.19,224,0.875,bicubic,-10.866,-7.026,+25 +eca_resnet33ts,68.810,31.190,88.580,11.420,19.68,256,0.900,bicubic,-11.268,-6.390,+2 +dpn131,68.770,31.230,87.470,12.530,79.25,224,0.875,bicubic,-11.052,-7.240,+15 +gmlp_s16_224,68.760,31.240,88.090,11.910,19.42,224,0.875,bicubic,-10.882,-6.508,+26 +darknetaa53,68.750,31.250,88.720,11.280,36.02,288,1.000,bilinear,-11.772,-6.602,-41 +tf_efficientnet_b2.aa_in1k,68.750,31.250,87.990,12.010,9.11,260,0.890,bicubic,-11.336,-6.918,-4 +resnext50d_32x4d,68.740,31.260,88.300,11.700,25.05,224,0.875,bicubic,-10.936,-6.566,+21 +poolformer_s24,68.740,31.260,88.220,11.780,21.39,224,0.900,bicubic,-11.576,-6.818,-21 +resnet50,68.730,31.270,87.690,12.310,25.56,224,0.950,bicubic,-11.644,-6.924,-29 +deit_small_patch16_224,68.720,31.280,88.200,11.800,22.05,224,0.900,bicubic,-11.136,-6.852,+4 +gluon_resnet101_v1s,68.710,31.290,87.910,12.090,44.67,224,0.875,bicubic,-11.592,-7.250,-23 +dpn107,68.690,31.310,88.130,11.870,86.92,224,0.875,bicubic,-11.466,-6.780,-12 +gluon_seresnext50_32x4d,68.670,31.330,88.310,11.690,27.56,224,0.875,bicubic,-11.248,-6.512,-4 +hrnet_w64,68.640,31.360,88.050,11.950,128.06,224,0.875,bilinear,-10.834,-6.602,+24 +dpn98,68.590,31.410,87.680,12.320,61.57,224,0.875,bicubic,-11.052,-6.948,+16 +xcit_tiny_12_p8_224,68.560,31.440,88.680,11.320,6.71,224,1.000,bicubic,-11.134,-6.372,+9 +regnetx_160,68.530,31.470,88.450,11.550,54.28,224,0.875,bicubic,-11.326,-6.380,-2 +cspresnet50,68.460,31.540,88.010,11.990,21.62,256,0.887,bilinear,-11.114,-6.702,+15 +rexnet_130,68.450,31.550,88.040,11.960,7.56,224,0.875,bicubic,-11.050,-6.642,+16 +xcit_tiny_24_p16_224,68.430,31.570,88.290,11.710,12.12,224,1.000,bicubic,-11.014,-6.592,+21 +ecaresnet50d_pruned,68.420,31.580,88.370,11.630,19.94,224,0.875,bicubic,-11.296,-6.510,+1 +tf_efficientnet_el.in1k,68.420,31.580,88.210,11.790,10.59,300,0.904,bicubic,-11.830,-6.918,-30 +cait_xxs36_224,68.410,31.590,88.630,11.370,17.30,224,1.000,bicubic,-11.340,-6.236,-2 +ssl_resnet50,68.410,31.590,88.560,11.440,25.56,224,0.875,bilinear,-10.812,-6.272,+33 +skresnext50_32x4d,68.350,31.650,87.570,12.430,27.48,224,0.875,bicubic,-11.806,-7.072,-24 +fbnetv3_d.ra2_in1k,68.330,31.670,88.450,11.550,10.31,256,0.950,bilinear,-11.350,-6.494,+1 +dla102x2,68.330,31.670,87.890,12.110,41.28,224,0.875,bilinear,-11.118,-6.750,+14 +efficientnet_b2_pruned.in1k,68.320,31.680,88.100,11.900,8.31,260,0.890,bicubic,-11.596,-6.756,-18 +resmlp_big_24_224,68.320,31.680,87.520,12.480,129.14,224,0.875,bicubic,-12.708,-7.502,-95 +gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.044,-7.126,+14 +vit_base_patch16_224.sam,68.280,31.720,87.720,12.280,86.57,224,0.900,bicubic,-11.962,-7.036,-37 +ecaresnet26t,68.230,31.770,88.790,11.210,16.01,320,0.950,bicubic,-11.624,-6.294,-16 +tf_efficientnet_lite3.in1k,68.230,31.770,87.740,12.260,8.20,300,0.904,bilinear,-11.590,-7.174,-14 +ese_vovnet39b,68.210,31.790,88.240,11.760,24.57,224,0.875,bicubic,-11.110,-6.472,+11 +fbnetv3_b.ra2_in1k,68.180,31.820,87.930,12.070,8.60,256,0.950,bilinear,-10.970,-6.816,+29 +regnetx_120,68.150,31.850,87.660,12.340,46.11,224,0.875,bicubic,-11.446,-7.078,-4 +resmlp_36_224,68.080,31.920,88.190,11.810,44.69,224,0.875,bicubic,-11.690,-6.696,-17 +resnetrs50,68.030,31.970,87.710,12.290,35.69,224,0.910,bicubic,-11.862,-7.258,-26 +pit_xs_distilled_224,68.020,31.980,87.720,12.280,11.00,224,0.900,bicubic,-11.286,-6.644,+10 +dpn92,67.990,32.010,87.580,12.420,37.67,224,0.875,bicubic,-12.018,-7.256,-33 +nf_regnet_b1,67.960,32.040,88.200,11.800,10.22,288,0.900,bicubic,-11.332,-6.548,+10 +gluon_resnet50_v1d,67.940,32.060,87.130,12.870,25.58,224,0.875,bicubic,-11.134,-7.340,+27 +resnetv2_50x1_bitm,67.920,32.080,89.300,10.700,25.55,448,1.000,bilinear,-12.422,-6.384,-60 +levit_192,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.942,-6.896,-27 +tf_efficientnetv2_b1.in1k,67.890,32.110,87.800,12.200,8.14,240,0.882,bicubic,-11.572,-6.922,-6 +regnetx_080,67.880,32.120,86.990,13.010,39.57,224,0.875,bicubic,-11.314,-7.570,+16 +resnext101_32x8d,67.860,32.140,87.490,12.510,88.79,224,0.875,bilinear,-11.448,-7.028,-1 +efficientnet_em.ra2_in1k,67.840,32.160,88.120,11.880,6.90,240,0.882,bicubic,-11.412,-6.674,+8 +legacy_seresnext50_32x4d,67.840,32.160,87.620,12.380,27.56,224,0.875,bilinear,-11.238,-6.816,+19 +lambda_resnet26t,67.810,32.190,87.780,12.220,10.96,256,0.940,bicubic,-11.286,-6.812,+16 +resmlp_24_224,67.810,32.190,87.610,12.390,30.02,224,0.875,bicubic,-11.564,-6.936,-9 +hrnet_w48,67.770,32.230,87.420,12.580,77.47,224,0.875,bilinear,-11.530,-7.092,-2 +hrnet_w44,67.740,32.260,87.560,12.440,67.06,224,0.875,bilinear,-11.156,-6.808,+25 +coat_lite_mini,67.720,32.280,87.700,12.300,11.01,224,0.900,bicubic,-11.368,-6.904,+13 +tf_efficientnet_b0.ns_jft_in1k,67.710,32.290,88.070,11.930,5.29,224,0.875,bicubic,-10.948,-6.306,+37 +regnetx_064,67.680,32.320,87.520,12.480,26.21,224,0.875,bicubic,-11.392,-6.938,+13 +eca_botnext26ts_256,67.680,32.320,87.060,12.940,10.59,256,0.950,bicubic,-11.594,-7.554,-2 +convnext_femto_ols.d1_in1k,67.670,32.330,87.380,12.620,5.23,288,0.950,bicubic,-11.264,-7.152,+17 +xception,67.650,32.350,87.570,12.430,22.86,299,0.897,bicubic,-11.402,-6.822,+12 +dpn68b,67.630,32.370,87.670,12.330,12.61,224,0.875,bicubic,-11.586,-6.744,-2 +halonet26t,67.620,32.380,87.250,12.750,12.48,256,0.950,bicubic,-11.480,-7.062,+4 +dla169,67.610,32.390,87.590,12.410,53.39,224,0.875,bilinear,-11.078,-6.746,+28 +gluon_inception_v3,67.590,32.410,87.470,12.530,23.83,299,0.875,bicubic,-11.216,-6.900,+19 +gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.954,-7.398,-30 +res2net50_26w_8s,67.570,32.430,87.280,12.720,48.40,224,0.875,bilinear,-11.630,-7.088,-5 +hrnet_w40,67.560,32.440,87.140,12.860,57.56,224,0.875,bilinear,-11.360,-7.330,+10 +tf_efficientnet_b1.ap_in1k,67.520,32.480,87.760,12.240,7.79,240,0.882,bicubic,-11.760,-6.546,-15 +legacy_seresnet152,67.520,32.480,87.390,12.610,66.82,224,0.875,bilinear,-11.140,-6.980,+24 +mobilevitv2_125,67.470,32.530,87.570,12.430,7.48,256,0.888,bicubic,-12.214,-7.280,-44 +efficientnet_b1.ft_in1k,67.470,32.530,87.510,12.490,7.79,256,1.000,bicubic,-11.324,-6.832,+13 +eca_halonext26ts,67.470,32.530,87.230,12.770,10.76,256,0.940,bicubic,-12.016,-7.368,-33 +gluon_resnet101_v1b,67.460,32.540,87.240,12.760,44.55,224,0.875,bicubic,-11.846,-7.284,-24 +tf_efficientnet_cc_b1_8e.in1k,67.450,32.550,87.310,12.690,39.72,240,0.882,bicubic,-11.858,-7.060,-26 +res2net101_26w_4s,67.440,32.560,87.010,12.990,45.21,224,0.875,bilinear,-11.758,-7.422,-13 +resnetblur50,67.430,32.570,87.440,12.560,25.56,224,0.875,bicubic,-11.856,-7.198,-23 +resnet33ts,67.370,32.630,87.580,12.420,19.68,256,0.900,bicubic,-11.844,-6.994,-17 +cait_xxs24_224,67.330,32.670,87.510,12.490,11.96,224,1.000,bicubic,-11.056,-6.800,+29 +regnetx_032,67.290,32.710,87.000,13.000,15.30,224,0.875,bicubic,-10.882,-7.088,+40 +coat_tiny,67.250,32.750,87.340,12.660,5.50,224,0.900,bicubic,-11.184,-6.698,+24 +xception41,67.250,32.750,87.200,12.800,26.97,299,0.903,bicubic,-11.266,-7.078,+15 +convnext_femto.d1_in1k,67.200,32.800,87.510,12.490,5.22,288,0.950,bicubic,-11.504,-6.924,+7 +resnest26d,67.200,32.800,87.170,12.830,17.07,224,0.875,bilinear,-11.278,-7.128,+17 +repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.632,-7.084,0 +legacy_seresnet101,67.160,32.840,87.060,12.940,49.33,224,0.875,bilinear,-11.222,-7.204,+24 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,67.140,32.860,86.490,13.510,119.42,256,0.900,bicubic,-12.340,-7.648,-48 +botnet26t_256,67.130,32.870,87.550,12.450,12.49,256,0.950,bicubic,-12.142,-6.978,-31 +dla60x,67.100,32.900,87.190,12.810,17.35,224,0.875,bilinear,-11.146,-6.828,+25 +gluon_resnet50_v1s,67.060,32.940,86.860,13.140,25.68,224,0.875,bicubic,-11.652,-7.378,-1 +tv_resnet152,67.050,32.950,87.550,12.450,60.19,224,0.875,bilinear,-11.262,-6.488,+22 +xcit_tiny_12_p16_224_dist,67.020,32.980,87.400,12.600,6.72,224,1.000,bicubic,-11.558,-6.796,+2 +dla60_res2net,67.020,32.980,87.160,12.840,20.85,224,0.875,bilinear,-11.444,-7.046,+10 +dla102x,67.010,32.990,86.770,13.230,26.31,224,0.875,bilinear,-11.500,-7.458,+4 +lambda_resnet26rpt_256,66.960,33.040,87.130,12.870,10.99,256,0.940,bicubic,-12.010,-7.300,-19 +mixnet_l.ft_in1k,66.940,33.060,86.910,13.090,7.33,224,0.875,bicubic,-12.036,-7.272,-21 +pit_xs_224,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.262,-6.888,+22 +pvt_v2_b1,66.910,33.090,87.430,12.570,14.01,224,0.900,bicubic,-11.784,-7.062,-7 +res2net50_26w_6s,66.910,33.090,86.860,13.140,37.05,224,0.875,bilinear,-11.660,-7.264,-3 +repvgg_b1,66.900,33.100,86.780,13.220,57.42,224,0.875,bilinear,-11.466,-7.318,+10 +xcit_nano_12_p8_384_dist,66.880,33.120,87.110,12.890,3.05,384,1.000,bicubic,-10.940,-6.926,+37 +tf_efficientnet_b1.aa_in1k,66.880,33.120,87.010,12.990,7.79,240,0.882,bicubic,-11.946,-7.188,-19 +efficientnet_es.ra_in1k,66.880,33.120,86.730,13.270,5.44,224,0.875,bicubic,-11.186,-7.196,+24 +mobilevit_s,66.870,33.130,87.060,12.940,5.58,256,0.900,bicubic,-11.442,-7.086,+8 +resnet32ts,66.860,33.140,87.250,12.750,17.96,256,0.900,bicubic,-12.144,-7.106,-31 +regnetx_040,66.840,33.160,86.730,13.270,22.12,224,0.875,bicubic,-11.642,-7.514,-6 +hrnet_w30,66.780,33.220,86.800,13.200,37.71,224,0.875,bilinear,-11.426,-7.422,+11 +tf_mixnet_l.in1k,66.780,33.220,86.470,13.530,7.33,224,0.875,bicubic,-11.994,-7.528,-21 +selecsls60b,66.760,33.240,86.530,13.470,32.77,224,0.875,bicubic,-11.652,-7.644,-2 +hrnet_w32,66.750,33.250,87.300,12.700,41.23,224,0.875,bilinear,-11.700,-6.886,-7 +wide_resnet101_2,66.730,33.270,87.030,12.970,126.89,224,0.875,bilinear,-12.126,-7.252,-30 +vit_small_patch16_224.augreg_in1k,66.710,33.290,86.710,13.290,22.05,224,0.900,bicubic,-12.136,-7.574,-30 +tf_efficientnetv2_b0.in1k,66.700,33.300,86.710,13.290,7.14,224,0.875,bicubic,-11.656,-7.314,-2 +adv_inception_v3,66.650,33.350,86.530,13.470,23.83,299,0.875,bicubic,-10.932,-7.206,+36 +dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.800,-7.122,-11 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,66.610,33.390,87.260,12.740,5.79,384,1.000,bicubic,-11.820,-7.282,-11 +mobilevitv2_100,66.610,33.390,87.000,13.000,4.90,256,0.888,bicubic,-11.480,-7.164,+8 +cs3darknet_m,66.570,33.430,87.160,12.840,9.31,288,0.950,bicubic,-11.066,-6.854,+27 +gluon_resnet50_v1c,66.560,33.440,86.180,13.820,25.58,224,0.875,bicubic,-11.452,-7.808,+9 +levit_128,66.550,33.450,86.750,13.250,9.21,224,0.900,bicubic,-11.936,-7.260,-21 +dla102,66.540,33.460,86.910,13.090,33.27,224,0.875,bilinear,-11.492,-7.036,+6 +vit_base_patch16_224.augreg_in1k,66.490,33.510,86.250,13.750,86.57,224,0.900,bicubic,-12.664,-7.850,-57 +vit_base_patch32_384.augreg_in1k,66.420,33.580,86.950,13.050,88.30,384,1.000,bicubic,-12.340,-7.278,-36 +gmixer_24_224,66.420,33.580,86.150,13.850,24.72,224,0.875,bicubic,-11.616,-7.514,+3 +tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.450,-6.980,+13 +bat_resnext26ts,66.400,33.600,86.830,13.170,10.73,256,0.900,bicubic,-11.842,-7.270,-10 +hardcorenas_f,66.370,33.630,86.200,13.800,8.20,224,0.875,bilinear,-11.734,-7.602,-4 +coat_lite_tiny,66.290,33.710,86.980,13.020,5.72,224,0.900,bicubic,-11.222,-6.936,+25 +efficientnet_b0.ra_in1k,66.290,33.710,85.960,14.040,5.29,224,0.875,bicubic,-11.408,-7.572,+14 +cs3darknet_focus_m,66.260,33.740,87.090,12.910,9.30,288,0.950,bicubic,-11.018,-6.880,+33 +legacy_seresnet50,66.250,33.750,86.330,13.670,28.09,224,0.875,bilinear,-11.380,-7.418,+15 +selecsls60,66.210,33.790,86.340,13.660,30.67,224,0.875,bicubic,-11.772,-7.488,-2 +tf_efficientnet_em.in1k,66.180,33.820,86.360,13.640,6.90,240,0.882,bicubic,-11.950,-7.684,-11 +tv_resnext50_32x4d,66.180,33.820,86.040,13.960,25.03,224,0.875,bilinear,-11.440,-7.656,+13 +tf_efficientnet_cc_b0_8e.in1k,66.170,33.830,86.240,13.760,24.01,224,0.875,bicubic,-11.738,-7.414,-1 +inception_v3,66.150,33.850,86.330,13.670,23.83,299,0.875,bicubic,-11.290,-7.146,+20 +res2net50_26w_4s,66.140,33.860,86.600,13.400,25.70,224,0.875,bilinear,-11.824,-7.254,-6 +resmlp_12_distilled_224,66.130,33.870,86.630,13.370,15.35,224,0.875,bicubic,-11.814,-6.928,-6 +efficientnet_b1_pruned.in1k,66.090,33.910,86.570,13.430,6.33,240,0.882,bicubic,-12.146,-7.264,-22 +rexnet_100,66.070,33.930,86.490,13.510,4.80,224,0.875,bicubic,-11.788,-7.380,-2 +gluon_resnet50_v1b,66.070,33.930,86.260,13.740,25.56,224,0.875,bicubic,-11.510,-7.456,+11 +regnety_016,66.060,33.940,86.380,13.620,11.20,224,0.875,bicubic,-11.802,-7.340,-5 +res2net50_14w_8s,66.020,33.980,86.250,13.750,25.06,224,0.875,bilinear,-12.130,-7.598,-22 +tinynet_a.in1k,66.010,33.990,85.790,14.210,6.19,192,0.875,bicubic,-11.642,-7.746,0 +gcresnext26ts,65.940,34.060,85.920,14.080,10.48,256,0.900,bicubic,-11.874,-7.914,-4 +seresnext26t_32x4d,65.880,34.120,85.680,14.320,16.81,224,0.875,bicubic,-12.106,-8.066,-17 +repvgg_b1g4,65.850,34.150,86.120,13.880,39.97,224,0.875,bilinear,-11.744,-7.706,+2 +res2next50,65.850,34.150,85.840,14.160,24.67,224,0.875,bilinear,-12.396,-8.052,-33 +densenet161,65.840,34.160,86.450,13.550,28.68,224,0.875,bicubic,-11.518,-7.188,+10 +hardcorenas_e,65.840,34.160,85.980,14.020,8.07,224,0.875,bilinear,-11.954,-7.714,-8 +resnet34d,65.780,34.220,86.710,13.290,21.82,224,0.875,bicubic,-11.336,-6.672,+18 +xcit_tiny_12_p16_224,65.780,34.220,86.220,13.780,6.72,224,1.000,bicubic,-11.340,-7.492,+16 +eca_resnext26ts,65.760,34.240,85.840,14.160,10.30,256,0.900,bicubic,-11.692,-7.726,+2 +mobilenetv3_large_100.miil_in21k_ft_in1k,65.760,34.240,85.200,14.800,5.48,224,0.875,bilinear,-12.156,-7.710,-21 +skresnet34,65.750,34.250,85.960,14.040,22.28,224,0.875,bicubic,-11.162,-7.362,+25 +tv_resnet101,65.690,34.310,85.980,14.020,44.55,224,0.875,bilinear,-11.684,-7.560,+2 +convnext_tiny.fb_in22k_ft_in1k,65.680,34.320,86.610,13.390,28.59,288,1.000,bicubic,-13.228,-8.064,-78 +seresnext26ts,65.660,34.340,86.140,13.860,10.39,256,0.900,bicubic,-12.206,-7.650,-22 +hardcorenas_d,65.630,34.370,85.460,14.540,7.50,224,0.875,bilinear,-11.802,-8.024,-2 +convnext_atto_ols.a2_in1k,65.610,34.390,86.260,13.740,3.70,288,0.950,bicubic,-11.606,-7.420,+6 +selecsls42b,65.610,34.390,85.810,14.190,32.46,224,0.875,bicubic,-11.564,-7.580,+6 +poolformer_s12,65.600,34.400,86.130,13.870,11.92,224,0.900,bicubic,-11.630,-7.374,+3 +tf_efficientnet_b0.ap_in1k,65.490,34.510,85.580,14.420,5.29,224,0.875,bicubic,-11.596,-7.676,+8 +seresnext26d_32x4d,65.410,34.590,85.970,14.030,16.81,224,0.875,bicubic,-12.192,-7.638,-17 +convmixer_1024_20_ks9_p14,65.410,34.590,85.590,14.410,24.38,224,0.960,bicubic,-11.536,-7.768,+13 +resnet26t,65.400,34.600,86.110,13.890,16.01,256,0.940,bicubic,-12.482,-7.730,-31 +tf_efficientnet_lite2.in1k,65.380,34.620,85.990,14.010,6.09,260,0.890,bicubic,-12.088,-7.764,-13 +res2net50_48w_2s,65.350,34.650,85.960,14.040,25.29,224,0.875,bilinear,-12.172,-7.594,-16 +densenet201,65.290,34.710,85.690,14.310,20.01,224,0.875,bicubic,-11.996,-7.788,-8 +densenetblur121d,65.280,34.720,85.710,14.290,8.00,224,0.875,bicubic,-11.308,-7.482,+20 +dla60,65.200,34.800,85.760,14.240,22.04,224,0.875,bilinear,-11.832,-7.558,+2 +crossvit_9_dagger_240,65.190,34.810,86.600,13.400,8.78,240,0.875,bicubic,-11.790,-7.010,+3 +ese_vovnet19b_dw,65.190,34.810,85.470,14.530,6.54,224,0.875,bicubic,-11.608,-7.798,+9 +tf_efficientnet_cc_b0_4e.in1k,65.150,34.850,85.160,14.840,13.31,224,0.875,bicubic,-12.156,-8.174,-14 +gernet_s,65.120,34.880,85.510,14.490,8.17,224,0.875,bilinear,-11.796,-7.622,+4 +legacy_seresnext26_32x4d,65.050,34.950,85.660,14.340,16.79,224,0.875,bicubic,-12.054,-7.656,-6 +mobilenetv2_120d.ra_in1k,65.030,34.970,85.960,14.040,5.83,224,0.875,bicubic,-12.254,-7.532,-15 +convnext_atto.d2_in1k,64.940,35.060,86.230,13.770,3.70,288,0.950,bicubic,-12.074,-7.470,-4 +hrnet_w18,64.920,35.080,85.740,14.260,21.30,224,0.875,bilinear,-11.838,-7.704,+5 +hardcorenas_c,64.860,35.140,85.250,14.750,5.52,224,0.875,bilinear,-12.194,-7.908,-8 +densenet169,64.760,35.240,85.240,14.760,14.15,224,0.875,bicubic,-11.146,-7.786,+22 +mixnet_m.ft_in1k,64.700,35.300,85.450,14.550,5.01,224,0.875,bicubic,-12.560,-7.974,-18 +resnet26d,64.680,35.320,85.120,14.880,16.01,224,0.875,bicubic,-12.016,-8.030,+2 +levit_128s,64.610,35.390,84.730,15.270,7.78,224,0.900,bicubic,-11.920,-8.136,+8 +resnext26ts,64.590,35.410,85.110,14.890,10.30,256,0.900,bicubic,-12.190,-8.020,-2 +xcit_nano_12_p8_224_dist,64.520,35.480,85.980,14.020,3.05,224,1.000,bicubic,-11.804,-7.110,+9 +repvgg_a2,64.450,35.550,85.130,14.870,28.21,224,0.875,bilinear,-12.010,-7.874,+7 +xcit_nano_12_p16_384_dist,64.430,35.570,85.300,14.700,3.05,384,1.000,bicubic,-11.028,-7.394,+25 +hardcorenas_b,64.420,35.580,84.870,15.130,5.18,224,0.875,bilinear,-12.118,-7.884,+2 +regnetx_016,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.570,-7.950,-14 +tf_efficientnet_lite1.in1k,64.380,35.620,85.470,14.530,5.42,240,0.882,bicubic,-12.262,-7.756,-4 +resmlp_12_224,64.350,35.650,85.580,14.420,15.35,224,0.875,bicubic,-12.304,-7.600,-6 +tf_efficientnet_b0.aa_in1k,64.310,35.690,85.280,14.720,5.29,224,0.875,bicubic,-12.538,-7.948,-12 +tf_mixnet_m.in1k,64.270,35.730,85.090,14.910,5.01,224,0.875,bicubic,-12.672,-8.062,-16 +dpn68,64.230,35.770,85.180,14.820,12.61,224,0.875,bicubic,-12.088,-7.798,+1 +tf_efficientnet_es.in1k,64.230,35.770,84.740,15.260,5.44,224,0.875,bicubic,-12.364,-8.462,-7 +regnety_008,64.160,35.840,85.270,14.730,6.26,224,0.875,bicubic,-12.156,-7.796,0 +vit_small_patch32_224.augreg_in21k_ft_in1k,64.070,35.930,85.560,14.440,22.88,224,0.900,bicubic,-11.920,-7.712,+2 +mobilenetv2_140.ra_in1k,64.060,35.940,85.040,14.960,6.11,224,0.875,bicubic,-12.456,-7.956,-6 +densenet121,63.750,36.250,84.590,15.410,7.98,224,0.875,bicubic,-11.828,-8.062,+9 +hardcorenas_a,63.710,36.290,84.400,15.600,5.26,224,0.875,bilinear,-12.206,-8.114,+1 +mobilevitv2_075,63.590,36.410,84.960,15.040,2.87,256,0.888,bicubic,-12.032,-7.808,+6 +resnest14d,63.590,36.410,84.250,15.750,10.61,224,0.875,bilinear,-11.916,-8.268,+8 +tf_mixnet_s.in1k,63.560,36.440,84.270,15.730,4.13,224,0.875,bicubic,-12.090,-8.358,+2 +resnet26,63.470,36.530,84.260,15.740,16.00,224,0.875,bicubic,-11.822,-8.310,+11 +mixnet_s.ft_in1k,63.390,36.610,84.740,15.260,4.13,224,0.875,bicubic,-12.602,-8.056,-7 +mobilenetv3_large_100.ra_in1k,63.360,36.640,84.090,15.910,5.48,224,0.875,bicubic,-12.406,-8.452,-3 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,63.340,36.660,85.280,14.720,6.36,384,1.000,bicubic,-12.612,-7.980,-7 +efficientnet_es_pruned.in1k,63.330,36.670,84.950,15.050,5.44,224,0.875,bicubic,-11.670,-7.498,+15 +tv_resnet50,63.330,36.670,84.640,15.360,25.56,224,0.875,bilinear,-12.808,-8.224,-12 +mixer_b16_224,63.270,36.730,83.310,16.690,59.88,224,0.875,bicubic,-13.330,-8.918,-23 +efficientnet_lite0.ra_in1k,63.240,36.760,84.440,15.560,4.65,224,0.875,bicubic,-12.244,-8.070,0 +mobilenetv3_rw.rmsp_in1k,63.220,36.780,84.510,15.490,5.48,224,0.875,bicubic,-12.414,-8.198,-6 +semnasnet_100.rmsp_in1k,63.150,36.850,84.520,15.480,3.89,224,0.875,bicubic,-12.298,-8.084,0 +pit_ti_distilled_224,63.150,36.850,83.960,16.040,5.10,224,0.900,bicubic,-11.380,-8.136,+20 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,63.110,36.890,84.850,15.150,5.72,224,0.900,bicubic,-12.344,-7.998,-3 +regnety_006,63.110,36.890,84.250,15.750,6.06,224,0.875,bicubic,-12.136,-8.282,+1 +mobilevit_xs,62.940,37.060,84.840,15.160,2.32,256,0.900,bicubic,-11.704,-7.512,+13 +tv_densenet121,62.940,37.060,84.250,15.750,7.98,224,0.875,bicubic,-11.798,-7.900,+10 +resnet34,62.870,37.130,84.140,15.860,21.80,224,0.875,bilinear,-12.240,-8.144,+1 +legacy_seresnet34,62.850,37.150,84.210,15.790,21.96,224,0.875,bilinear,-11.958,-7.914,+7 +mobilenetv2_110d.ra_in1k,62.830,37.170,84.500,15.500,4.52,224,0.875,bicubic,-12.206,-7.686,+1 +edgenext_x_small,62.820,37.180,84.680,15.320,2.34,288,1.000,bicubic,-12.868,-8.086,-18 +deit_tiny_distilled_patch16_224,62.810,37.190,83.930,16.070,5.91,224,0.900,bicubic,-11.700,-7.960,+11 +hrnet_w18_small_v2,62.800,37.200,83.980,16.020,15.60,224,0.875,bilinear,-12.314,-8.436,-5 +swsl_resnet18,62.760,37.240,84.300,15.700,11.69,224,0.875,bilinear,-10.516,-7.434,+22 +tinynet_b.in1k,62.730,37.270,84.250,15.750,3.73,188,0.875,bicubic,-12.244,-7.938,-2 +repvgg_b0,62.720,37.280,83.860,16.140,15.82,224,0.875,bilinear,-12.432,-8.558,-10 +gluon_resnet34_v1b,62.570,37.430,83.990,16.010,21.80,224,0.875,bicubic,-12.018,-8.000,+4 +xcit_nano_12_p8_224,62.560,37.440,84.200,15.800,3.05,224,1.000,bicubic,-11.354,-7.972,+11 +tf_efficientnet_lite0.in1k,62.550,37.450,84.220,15.780,4.65,224,0.875,bicubic,-12.280,-7.956,-4 +regnetx_008,62.490,37.510,84.020,15.980,7.26,224,0.875,bicubic,-12.548,-8.316,-10 +dla34,62.480,37.520,83.910,16.090,15.74,224,0.875,bilinear,-12.150,-8.168,-1 +tf_mobilenetv3_large_100.in1k,62.460,37.540,83.970,16.030,5.48,224,0.875,bilinear,-13.058,-8.636,-24 +fbnetc_100.rmsp_in1k,62.440,37.560,83.380,16.620,5.57,224,0.875,bilinear,-12.684,-9.006,-16 +crossvit_9_240,62.260,37.740,84.270,15.730,8.55,240,0.875,bicubic,-11.704,-7.698,+4 +crossvit_tiny_240,62.070,37.930,83.600,16.400,7.01,240,0.875,bicubic,-11.254,-8.316,+9 +mnasnet_100.rmsp_in1k,61.900,38.100,83.710,16.290,4.38,224,0.875,bicubic,-12.758,-8.404,-8 +regnety_004,61.870,38.130,83.430,16.570,4.34,224,0.875,bicubic,-12.164,-8.322,-1 +vgg19_bn,61.860,38.140,83.450,16.550,143.68,224,0.875,bilinear,-12.354,-8.392,-4 +convit_tiny,61.590,38.410,84.120,15.880,5.71,224,0.875,bicubic,-11.526,-7.594,+8 +ssl_resnet18,61.480,38.520,83.300,16.700,11.69,224,0.875,bilinear,-11.130,-8.116,+12 +regnetx_006,61.350,38.650,83.450,16.550,6.20,224,0.875,bicubic,-12.502,-8.222,-1 +spnasnet_100.rmsp_in1k,61.220,38.780,82.790,17.210,4.42,224,0.875,bilinear,-12.864,-9.028,-7 +tv_resnet34,61.190,38.810,82.710,17.290,21.80,224,0.875,bilinear,-12.122,-8.716,+2 +vit_base_patch32_224.augreg_in1k,61.050,38.950,82.740,17.260,88.22,224,0.900,bicubic,-13.854,-9.038,-20 +pit_ti_224,60.980,39.020,83.860,16.140,4.85,224,0.900,bicubic,-11.932,-7.542,+6 +skresnet18,60.860,39.140,82.880,17.120,11.96,224,0.875,bicubic,-12.178,-8.288,+2 +ghostnet_100,60.830,39.170,82.360,17.640,5.18,224,0.875,bilinear,-13.148,-9.096,-10 +vgg16_bn,60.760,39.240,82.950,17.050,138.37,224,0.875,bilinear,-12.590,-8.556,-5 +semnasnet_075.rmsp_in1k,60.710,39.290,82.510,17.490,2.91,224,0.875,bicubic,-12.264,-8.626,0 +tf_mobilenetv3_large_075.in1k,60.400,39.600,81.950,18.050,3.99,224,0.875,bilinear,-13.038,-9.400,-8 +xcit_nano_12_p16_224_dist,60.240,39.760,82.490,17.510,3.05,224,1.000,bicubic,-12.062,-8.372,+6 +mobilenetv2_100.ra_in1k,60.190,39.810,82.240,17.760,3.50,224,0.875,bicubic,-12.780,-8.776,-2 +resnet18d,60.160,39.840,82.300,17.700,11.71,224,0.875,bicubic,-12.100,-8.396,+5 +vit_base_patch32_224.sam,60.010,39.990,81.230,18.770,88.22,224,0.900,bicubic,-13.680,-9.784,-13 +deit_tiny_patch16_224,59.830,40.170,82.670,17.330,5.72,224,0.900,bicubic,-12.338,-8.448,+5 +legacy_seresnet18,59.800,40.200,81.690,18.310,11.78,224,0.875,bicubic,-11.944,-8.644,+9 +vgg19,59.710,40.290,81.450,18.550,143.67,224,0.875,bilinear,-12.658,-9.422,-3 +regnetx_004,59.410,40.590,81.690,18.310,5.16,224,0.875,bicubic,-12.986,-9.140,-5 +edgenext_xx_small,59.390,40.610,81.820,18.180,1.33,288,1.000,bicubic,-12.476,-8.724,+4 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,59.070,40.930,81.760,18.240,6.34,224,0.900,bicubic,-12.718,-9.068,+3 +tf_mobilenetv3_large_minimal_100.in1k,59.070,40.930,81.150,18.850,3.92,224,0.875,bilinear,-13.178,-9.480,-1 +vgg13_bn,59.000,41.000,81.070,18.930,133.05,224,0.875,bilinear,-12.594,-9.312,+5 +hrnet_w18_small,58.950,41.050,81.340,18.660,13.19,224,0.875,bilinear,-13.392,-9.338,-7 +lcnet_100.ra2_in1k,58.870,41.130,81.180,18.820,2.95,224,0.875,bicubic,-13.244,-9.198,-3 +vgg16,58.830,41.170,81.660,18.340,138.36,224,0.875,bilinear,-12.764,-8.716,+1 +pvt_v2_b0,58.760,41.240,82.130,17.870,3.67,224,0.900,bicubic,-11.896,-8.078,+4 +xcit_nano_12_p16_224,58.350,41.650,80.880,19.120,3.05,224,1.000,bicubic,-11.604,-8.874,+7 +gluon_resnet18_v1b,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.496,-8.790,+1 +tinynet_c.in1k,58.170,41.830,80.280,19.720,2.46,184,0.875,bicubic,-13.062,-9.468,-1 +resnet14t,57.800,42.200,79.920,20.080,10.08,224,0.950,bilinear,-14.550,-10.420,-15 +mobilevitv2_050,57.720,42.280,80.900,19.100,1.37,256,0.888,bicubic,-12.420,-9.026,+2 vgg11_bn,57.410,42.590,80.020,19.980,132.87,224,0.875,bilinear,-12.950,-9.782,-1 -resnet18,57.170,42.830,80.200,19.800,11.69,224,0.875,bilinear,-12.578,-8.884,+3 -mobilevit_xxs,57.150,42.850,79.740,20.260,1.27,256,0.900,bicubic,-11.770,-9.206,+4 -vgg13,57.150,42.850,79.550,20.450,133.05,224,0.875,bilinear,-12.776,-9.696,0 -regnety_002,56.980,43.020,79.850,20.150,3.16,224,0.875,bicubic,-13.276,-9.684,-4 -mixer_l16_224,56.690,43.310,75.990,24.010,208.20,224,0.875,bicubic,-15.376,-11.676,-14 -regnetx_002,56.050,43.950,79.230,20.770,2.68,224,0.875,bicubic,-12.704,-9.326,+2 -dla60x_c,56.030,43.970,78.920,21.080,1.32,224,0.875,bilinear,-11.850,-9.514,+4 -vgg11,55.790,44.210,78.840,21.160,132.86,224,0.875,bilinear,-13.238,-9.788,-3 -resnet10t,55.660,44.340,78.020,21.980,5.44,224,0.950,bilinear,-12.648,-10.060,0 -lcnet_075,55.360,44.640,78.310,21.690,2.36,224,0.875,bicubic,-13.454,-10.054,-3 -mobilenetv3_small_100,54.700,45.300,77.780,22.220,2.54,224,0.875,bicubic,-12.958,-9.854,+1 -tf_mobilenetv3_small_100,54.510,45.490,77.070,22.930,2.54,224,0.875,bilinear,-13.416,-10.598,-2 -tinynet_d,53.420,46.580,76.350,23.650,2.34,152,0.875,bicubic,-13.542,-10.714,0 -mnasnet_small,53.270,46.730,75.890,24.110,2.03,224,0.875,bicubic,-12.936,-10.616,0 -dla46x_c,53.060,46.940,76.850,23.150,1.07,224,0.875,bilinear,-12.892,-10.136,0 -mobilenetv2_050,52.850,47.150,75.420,24.580,1.97,224,0.875,bicubic,-13.094,-10.660,0 -tf_mobilenetv3_small_075,52.160,47.840,75.480,24.520,2.04,224,0.875,bilinear,-13.552,-10.650,0 -dla46_c,52.120,47.880,75.680,24.320,1.30,224,0.875,bilinear,-12.752,-10.622,+1 -mobilenetv3_small_075,51.890,48.110,74.730,25.270,2.04,224,0.875,bicubic,-13.348,-10.710,-1 -lcnet_050,49.980,50.020,73.430,26.570,1.88,224,0.875,bicubic,-13.114,-10.952,0 -tf_mobilenetv3_small_minimal_100,49.490,50.510,73.030,26.970,2.04,224,0.875,bilinear,-13.410,-11.204,0 -tinynet_e,46.700,53.300,70.360,29.640,2.04,106,0.875,bicubic,-13.156,-11.406,0 -mobilenetv3_small_050,44.890,55.110,67.670,32.330,1.59,224,0.875,bicubic,-13.000,-12.524,0 +resnet18,57.170,42.830,80.200,19.800,11.69,224,0.875,bilinear,-12.578,-8.878,+2 +mobilevit_xxs,57.170,42.830,79.740,20.260,1.27,256,0.900,bicubic,-11.742,-9.198,+5 +vgg13,57.150,42.850,79.540,20.460,133.05,224,0.875,bilinear,-12.776,-9.706,0 +regnety_002,57.000,43.000,79.840,20.160,3.16,224,0.875,bicubic,-13.252,-9.700,-4 +mixer_l16_224,56.690,43.310,75.990,24.010,208.20,224,0.875,bicubic,-15.368,-11.678,-15 +regnetx_002,56.050,43.950,79.210,20.790,2.68,224,0.875,bicubic,-12.712,-9.346,+2 +dla60x_c,56.000,44.000,78.930,21.070,1.32,224,0.875,bilinear,-11.892,-9.496,+4 +vgg11,55.800,44.200,78.830,21.170,132.86,224,0.875,bilinear,-13.224,-9.798,-3 +resnet10t,55.670,44.330,78.020,21.980,5.44,224,0.950,bilinear,-12.624,-10.058,0 +lcnet_075.ra2_in1k,55.410,44.590,78.300,21.700,2.36,224,0.875,bicubic,-13.408,-10.070,-3 +mobilenetv3_small_100.lamb_in1k,54.700,45.300,77.770,22.230,2.54,224,0.875,bicubic,-12.952,-9.866,+1 +tf_mobilenetv3_small_100.in1k,54.530,45.470,77.060,22.940,2.54,224,0.875,bilinear,-13.392,-10.604,-2 +tinynet_d.in1k,53.420,46.580,76.350,23.650,2.34,152,0.875,bicubic,-13.542,-10.716,0 +mnasnet_small.lamb_in1k,53.280,46.720,75.890,24.110,2.03,224,0.875,bicubic,-12.926,-10.618,0 +dla46x_c,53.050,46.950,76.870,23.130,1.07,224,0.875,bilinear,-12.920,-10.110,0 +mobilenetv2_050.lamb_in1k,52.850,47.150,75.440,24.560,1.97,224,0.875,bicubic,-13.092,-10.642,0 +tf_mobilenetv3_small_075.in1k,52.160,47.840,75.470,24.530,2.04,224,0.875,bilinear,-13.556,-10.660,0 +dla46_c,52.130,47.870,75.690,24.310,1.30,224,0.875,bilinear,-12.736,-10.602,+1 +mobilenetv3_small_075.lamb_in1k,51.900,48.100,74.730,25.270,2.04,224,0.875,bicubic,-13.346,-10.706,-1 +lcnet_050.ra2_in1k,49.990,50.010,73.450,26.550,1.88,224,0.875,bicubic,-13.110,-10.930,0 +tf_mobilenetv3_small_minimal_100.in1k,49.500,50.500,73.050,26.950,2.04,224,0.875,bilinear,-13.406,-11.180,0 +tinynet_e.in1k,46.700,53.300,70.360,29.640,2.04,106,0.875,bicubic,-13.156,-11.402,0 +mobilenetv3_small_050.lamb_in1k,44.890,55.110,67.670,32.330,1.59,224,0.875,bicubic,-13.000,-12.524,0 diff --git a/results/results-sketch.csv b/results/results-sketch.csv index b9024991..cbcf4f8d 100644 --- a/results/results-sketch.csv +++ b/results/results-sketch.csv @@ -1,669 +1,791 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,crop_pct,interpolation,top1_diff,top5_diff,rank_diff -ig_resnext101_32x48d,58.820,41.180,81.094,18.906,828.41,224,0.875,bilinear,-26.616,-16.482,+56 -ig_resnext101_32x32d,58.382,41.618,80.383,19.617,468.53,224,0.875,bilinear,-26.718,-17.051,+69 -ig_resnext101_32x16d,57.686,42.314,79.909,20.091,194.03,224,0.875,bilinear,-26.484,-17.289,+119 -swsl_resnext101_32x16d,57.464,42.536,80.373,19.627,194.03,224,0.875,bilinear,-25.886,-16.471,+167 -beit_large_patch16_384,56.892,43.108,79.227,20.773,305.00,384,1.000,bicubic,-31.514,-19.379,-3 -beit_large_patch16_512,56.753,43.247,78.897,21.103,305.67,512,1.000,bicubic,-31.849,-19.759,-5 -swsl_resnext101_32x8d,56.431,43.569,78.939,21.061,88.79,224,0.875,bilinear,-27.859,-18.243,+105 -deit3_huge_patch14_224_in21ft1k,55.763,44.237,77.622,22.378,632.13,224,1.000,bicubic,-31.417,-20.638,+2 -beit_large_patch16_224,54.955,45.045,77.606,22.394,304.43,224,0.900,bicubic,-32.521,-20.698,-2 -ig_resnext101_32x8d,54.931,45.069,77.535,22.465,88.79,224,0.875,bilinear,-27.767,-19.097,+198 -deit3_large_patch16_384_in21ft1k,54.878,45.122,77.370,22.630,304.76,384,1.000,bicubic,-32.838,-21.142,-6 -deit3_large_patch16_224_in21ft1k,54.361,45.639,76.563,23.437,304.37,224,1.000,bicubic,-32.621,-21.675,+4 -convnext_xlarge_384_in22ft1k,53.658,46.342,75.895,24.105,350.20,384,1.000,bicubic,-33.886,-22.591,-7 -swsl_resnext101_32x4d,53.601,46.399,76.353,23.648,44.18,224,0.875,bilinear,-29.639,-20.407,+161 -vit_large_patch16_384,52.756,47.244,74.696,25.304,304.72,384,1.000,bicubic,-34.324,-23.604,-2 -convnext_xlarge_in22ft1k,52.565,47.435,74.403,25.597,350.20,224,0.875,bicubic,-34.437,-23.809,-1 -swinv2_large_window12to24_192to384_22kft1k,52.298,47.702,74.411,25.589,196.74,384,1.000,bicubic,-35.158,-23.841,-9 -vit_large_r50_s32_384,52.041,47.959,73.566,26.434,329.09,384,1.000,bicubic,-34.139,-24.354,+15 -vit_large_patch16_224,51.831,48.169,73.692,26.308,304.33,224,0.900,bicubic,-34.013,-24.130,+23 -convnext_large_384_in22ft1k,51.738,48.262,73.896,26.104,197.77,384,1.000,bicubic,-35.658,-24.470,-11 -tf_efficientnet_l2_ns_475,51.489,48.511,73.928,26.072,480.31,475,0.936,bicubic,-36.743,-24.618,-17 -swinv2_base_window12to24_192to384_22kft1k,50.978,49.022,73.311,26.689,87.92,384,1.000,bicubic,-36.130,-24.925,-10 -swinv2_large_window12to16_192to256_22kft1k,50.441,49.559,72.760,27.240,196.74,256,0.900,bicubic,-36.505,-25.350,-5 -swsl_resnext50_32x4d,50.437,49.563,73.356,26.644,25.03,224,0.875,bilinear,-31.739,-22.876,+227 -convnext_base_384_in22ft1k,50.429,49.571,73.562,26.438,88.59,384,1.000,bicubic,-36.113,-24.628,-1 -swin_large_patch4_window12_384,50.402,49.598,72.568,27.432,196.74,384,1.000,bicubic,-36.750,-25.672,-15 -convnext_large_in22ft1k,49.940,50.060,72.206,27.794,197.77,224,0.875,bicubic,-36.696,-25.822,-4 -swsl_resnet50,49.527,50.473,72.324,27.676,25.56,224,0.875,bilinear,-31.653,-23.656,+284 -swin_large_patch4_window7_224,48.991,51.009,71.389,28.611,196.53,224,0.900,bicubic,-37.329,-26.503,+1 -convnext_base_in22ft1k,48.794,51.206,71.941,28.059,88.59,224,0.875,bicubic,-37.030,-25.925,+13 -swinv2_base_window12to16_192to256_22kft1k,48.788,51.212,71.385,28.615,87.92,256,0.900,bicubic,-37.482,-26.511,+1 -beit_base_patch16_384,48.663,51.337,72.084,27.916,86.74,384,1.000,bicubic,-38.135,-26.052,-12 -swin_base_patch4_window12_384,48.543,51.457,71.823,28.177,87.90,384,1.000,bicubic,-37.889,-26.233,-5 -vit_large_r50_s32_224,48.209,51.791,70.872,29.128,328.99,224,0.900,bicubic,-36.221,-26.294,+71 -tf_efficientnet_b7_ns,47.792,52.208,69.626,30.374,66.35,600,0.949,bicubic,-39.040,-28.470,-16 -tf_efficientnet_b6_ns,47.757,52.243,69.966,30.034,43.04,528,0.942,bicubic,-38.693,-27.920,-9 -tf_efficientnetv2_xl_in21ft1k,47.747,52.253,70.119,29.881,208.12,512,1.000,bicubic,-38.673,-27.749,-8 -vit_base_patch8_224,47.741,52.259,70.931,29.069,86.58,224,0.900,bicubic,-38.049,-26.861,+9 -deit3_base_patch16_384_in21ft1k,47.663,52.337,69.750,30.250,86.88,384,1.000,bicubic,-39.079,-28.362,-17 -tf_efficientnet_l2_ns,47.572,52.428,70.021,29.979,480.31,800,0.960,bicubic,-40.778,-28.629,-37 -deit3_base_patch16_224_in21ft1k,47.370,52.630,69.772,30.229,86.59,224,1.000,bicubic,-38.346,-27.972,+9 -tf_efficientnetv2_l_in21ft1k,46.943,53.057,70.308,29.692,118.52,480,1.000,bicubic,-39.361,-27.672,-11 -beit_base_patch16_224,46.242,53.758,69.901,30.099,86.53,224,0.900,bicubic,-38.986,-27.755,+23 -vit_base_patch16_384,45.902,54.098,68.553,31.447,86.86,384,1.000,bicubic,-40.104,-29.451,-5 -convnext_small_384_in22ft1k,45.792,54.208,68.494,31.506,50.22,384,1.000,bicubic,-39.932,-29.370,+4 -tf_efficientnet_b8_ap,45.768,54.232,67.907,32.093,87.41,672,0.954,bicubic,-39.604,-29.387,+16 -tf_efficientnet_b5_ns,45.607,54.393,67.842,32.158,30.39,456,0.934,bicubic,-40.481,-29.910,-12 -tf_efficientnetv2_m_in21ft1k,45.574,54.426,69.135,30.865,54.14,480,1.000,bicubic,-40.012,-28.611,+4 -swin_base_patch4_window7_224,45.564,54.436,68.504,31.496,87.77,224,0.900,bicubic,-39.686,-29.058,+15 -volo_d5_512,44.572,55.428,65.753,34.247,296.09,512,1.150,bicubic,-42.468,-32.215,-36 -cait_m48_448,44.245,55.755,64.653,35.347,356.46,448,1.000,bicubic,-42.243,-33.097,-25 -deit3_large_patch16_384,44.175,55.825,64.853,35.147,304.76,384,1.000,bicubic,-41.631,-32.743,-6 -volo_d5_448,44.098,55.902,65.063,34.937,295.91,448,1.150,bicubic,-42.856,-32.877,-36 -deit3_huge_patch14_224,43.795,56.205,64.348,35.652,632.13,224,0.900,bicubic,-41.411,-33.010,+13 -convnext_small_in22ft1k,43.607,56.393,66.582,33.418,50.22,224,0.875,bicubic,-40.961,-30.814,+42 -deit3_large_patch16_224,43.520,56.480,63.572,36.428,304.37,224,0.900,bicubic,-41.242,-33.466,+34 -vit_base_r50_s16_384,43.518,56.482,66.783,33.217,98.95,384,1.000,bicubic,-41.458,-30.507,+24 -tf_efficientnet_b4_ns,43.446,56.554,65.515,34.485,19.34,380,0.922,bicubic,-41.714,-31.955,+11 -volo_d5_224,43.259,56.741,64.077,35.923,295.46,224,0.960,bicubic,-42.811,-33.501,-23 -vit_base_patch16_224,43.229,56.771,65.710,34.290,86.57,224,0.900,bicubic,-41.301,-31.586,+38 -volo_d4_448,43.139,56.861,64.114,35.886,193.41,448,1.150,bicubic,-43.653,-33.768,-40 -xcit_large_24_p8_384_dist,42.828,57.172,63.403,36.597,188.93,384,1.000,bicubic,-43.170,-34.281,-22 -xcit_large_24_p8_224_dist,42.563,57.437,63.100,36.900,188.93,224,1.000,bicubic,-42.835,-34.310,-2 -tf_efficientnet_b8,42.502,57.498,64.867,35.133,87.41,672,0.954,bicubic,-42.866,-32.525,-1 -cait_m36_384,42.400,57.600,63.326,36.674,271.22,384,1.000,bicubic,-43.654,-34.404,-28 -volo_d4_224,42.284,57.716,63.010,36.990,192.96,224,0.960,bicubic,-43.592,-34.458,-25 -deit3_small_patch16_384_in21ft1k,41.956,58.044,64.550,35.450,22.21,384,1.000,bicubic,-42.868,-32.934,+20 -tf_efficientnet_b7_ap,41.431,58.569,62.870,37.130,66.35,600,0.949,bicubic,-43.689,-34.382,+2 -tf_efficientnet_b7,41.425,58.575,63.020,36.980,66.35,600,0.949,bicubic,-43.509,-34.186,+14 -tf_efficientnet_b5_ap,41.416,58.584,62.084,37.916,30.39,456,0.934,bicubic,-42.838,-34.894,+46 -resnetv2_152x4_bitm,41.308,58.692,64.305,35.695,936.53,480,1.000,bilinear,-43.610,-33.137,+14 -tf_efficientnet_b6_ap,41.099,58.901,62.355,37.645,43.04,528,0.942,bicubic,-43.687,-34.783,+17 -xcit_large_24_p16_384_dist,41.034,58.966,61.241,38.759,189.10,384,1.000,bicubic,-44.718,-36.297,-25 -xcit_large_24_p16_224_dist,40.956,59.044,61.320,38.680,189.10,224,1.000,bicubic,-43.964,-35.812,+10 -tf_efficientnetv2_s_in21ft1k,40.952,59.048,63.851,36.149,21.46,384,1.000,bicubic,-43.344,-33.403,+35 -xcit_medium_24_p8_224_dist,40.494,59.506,60.502,39.498,84.32,224,1.000,bicubic,-44.576,-36.778,-1 -vit_small_r26_s32_384,40.482,59.518,62.740,37.260,36.47,384,1.000,bicubic,-43.566,-34.588,+53 -tf_efficientnet_b4_ap,40.482,59.518,61.721,38.279,19.34,380,0.922,bicubic,-42.766,-34.671,+96 -deit3_base_patch16_224,40.374,59.626,60.186,39.814,86.59,224,0.900,bicubic,-43.418,-36.398,+70 -vit_base_patch16_224_miil,40.170,59.830,60.889,39.111,86.54,224,0.875,bilinear,-44.102,-35.913,+34 -deit3_small_patch16_224_in21ft1k,40.160,59.840,61.866,38.134,22.06,224,1.000,bicubic,-42.916,-34.910,+106 -regnetz_e8,40.146,59.854,61.322,38.678,57.70,320,1.000,bicubic,-44.884,-35.942,-3 -convnext_large,40.119,59.881,60.092,39.908,197.77,224,0.875,bicubic,-44.177,-36.802,+28 -xcit_medium_24_p8_384_dist,40.040,59.960,60.455,39.545,84.32,384,1.000,bicubic,-45.776,-37.137,-40 -xcit_medium_24_p16_384_dist,39.903,60.097,60.115,39.885,84.40,384,1.000,bicubic,-45.519,-37.291,-27 -tf_efficientnetv2_l,39.826,60.174,60.807,39.193,118.52,480,1.000,bicubic,-45.662,-36.565,-31 -dm_nfnet_f3,39.816,60.184,60.610,39.390,254.92,416,0.940,bicubic,-45.706,-36.852,-33 -cait_s36_384,39.755,60.245,60.475,39.525,68.37,384,1.000,bicubic,-45.705,-37.003,-32 -volo_d3_448,39.712,60.288,59.760,40.240,86.63,448,1.000,bicubic,-46.784,-37.950,-64 -efficientnetv2_rw_m,39.673,60.327,59.687,40.313,53.24,416,1.000,bicubic,-45.139,-37.459,-2 -xception65,39.645,60.355,60.907,39.093,39.92,299,0.940,bicubic,-43.529,-35.685,+87 -tf_efficientnet_b3_ns,39.590,60.410,61.451,38.549,12.23,300,0.904,bicubic,-44.458,-35.461,+39 -ecaresnet269d,39.584,60.416,60.343,39.657,102.09,352,1.000,bicubic,-45.390,-36.883,-11 -dm_nfnet_f6,39.578,60.422,60.911,39.089,438.36,576,0.956,bicubic,-46.564,-36.819,-60 -dm_nfnet_f5,39.504,60.496,60.227,39.773,377.21,544,0.954,bicubic,-46.312,-37.259,-50 -volo_d3_224,39.490,60.510,59.871,40.129,86.33,224,0.960,bicubic,-45.922,-37.409,-36 -deit3_base_patch16_384,39.405,60.595,58.946,41.054,86.88,384,1.000,bicubic,-45.671,-38.308,-23 -xcit_small_24_p8_224_dist,39.309,60.691,59.414,40.586,47.63,224,1.000,bicubic,-45.567,-37.774,-12 -xcit_medium_24_p16_224_dist,39.262,60.738,59.463,40.537,84.40,224,1.000,bicubic,-45.016,-37.477,+14 -efficientnet_b4,39.075,60.925,59.606,40.394,19.34,384,1.000,bicubic,-44.349,-36.992,+65 -xcit_small_24_p8_384_dist,38.999,61.001,59.174,40.826,47.63,384,1.000,bicubic,-46.555,-38.398,-48 -resnetv2_152x2_bit_teacher_384,38.977,61.023,62.436,37.564,236.34,384,1.000,bicubic,-44.867,-34.680,+39 -convnext_tiny_384_in22ft1k,38.922,61.078,60.728,39.272,28.59,384,1.000,bicubic,-45.154,-36.430,+23 -vit_base_patch32_384,38.798,61.202,60.327,39.673,88.30,384,1.000,bicubic,-44.554,-36.509,+66 -eca_nfnet_l2,38.661,61.339,59.441,40.559,56.72,384,1.000,bicubic,-46.035,-37.823,-12 -xcit_small_12_p8_384_dist,38.545,61.455,58.803,41.197,26.21,384,1.000,bicubic,-46.535,-38.477,-33 -xcit_small_24_p16_384_dist,38.503,61.497,58.390,41.610,47.67,384,1.000,bicubic,-46.585,-38.918,-35 -convnext_tiny_in22ft1k,38.470,61.530,60.481,39.519,28.59,224,0.875,bicubic,-44.442,-36.143,+86 -xcit_small_12_p8_224_dist,38.370,61.630,58.799,41.201,26.21,224,1.000,bicubic,-45.860,-38.075,+9 -tf_efficientnet_b5,38.358,61.642,59.917,40.083,30.39,456,0.934,bicubic,-45.456,-36.831,+36 -deit_base_distilled_patch16_384,38.256,61.744,57.788,42.212,87.63,384,1.000,bicubic,-47.166,-39.544,-52 -dm_nfnet_f4,38.234,61.766,58.628,41.372,316.07,512,0.951,bicubic,-47.480,-38.892,-61 -convnext_base,38.234,61.766,58.225,41.775,88.59,224,0.875,bicubic,-45.606,-38.525,+29 -xcit_large_24_p8_224,38.118,61.882,57.885,42.115,188.93,224,1.000,bicubic,-46.274,-38.773,-7 -resnetv2_152x2_bitm,37.985,62.015,61.135,38.865,236.34,448,1.000,bilinear,-46.525,-36.299,-15 -cait_s24_384,37.865,62.135,58.079,41.921,47.06,384,1.000,bicubic,-47.185,-39.269,-39 -resnet152d,37.853,62.147,58.356,41.644,60.21,320,1.000,bicubic,-45.825,-38.384,+41 -tf_efficientnetv2_m,37.822,62.178,58.712,41.288,54.14,480,1.000,bicubic,-47.214,-38.566,-40 -resnetrs420,37.753,62.247,58.215,41.785,191.89,416,1.000,bicubic,-47.255,-38.909,-39 -xcit_small_24_p16_224_dist,37.700,62.300,57.358,42.642,47.67,224,1.000,bicubic,-46.170,-39.374,+19 -resnetrs350,37.676,62.324,58.089,41.911,163.96,384,1.000,bicubic,-47.036,-38.901,-30 -xcit_small_12_p16_384_dist,37.582,62.418,57.773,42.227,26.25,384,1.000,bicubic,-47.126,-39.343,-30 -pit_b_distilled_224,37.582,62.418,57.232,42.768,74.79,224,0.900,bicubic,-46.560,-39.624,+1 -resnet200d,37.505,62.495,58.303,41.697,64.69,320,1.000,bicubic,-46.455,-38.521,+11 -resnetv2_152x2_bit_teacher,37.322,62.678,59.406,40.594,236.34,224,0.875,bicubic,-45.546,-37.162,+72 -resnest269e,37.311,62.689,57.470,42.530,110.93,416,0.928,bicubic,-47.207,-39.516,-27 -vit_small_r26_s32_224,37.242,62.758,59.068,40.932,36.43,224,0.900,bicubic,-44.620,-36.954,+143 -resmlp_big_24_224_in22ft1k,37.242,62.758,58.180,41.820,129.14,224,0.875,bicubic,-47.156,-38.938,-22 -cait_s24_224,37.153,62.847,56.729,43.271,46.92,224,1.000,bicubic,-46.305,-39.833,+34 -vit_base_patch32_224,37.081,62.919,59.286,40.714,88.22,224,0.900,bicubic,-43.643,-36.280,+213 -volo_d1_384,37.075,62.925,57.132,42.868,26.78,384,1.000,bicubic,-48.175,-40.082,-66 -convnext_small,37.055,62.945,57.105,42.895,50.22,224,0.875,bicubic,-46.095,-39.325,+47 -tf_efficientnet_b3_ap,37.049,62.951,57.238,42.762,12.23,300,0.904,bicubic,-44.775,-38.386,+140 -efficientnetv2_rw_s,37.049,62.951,56.810,43.190,23.94,384,1.000,bicubic,-46.761,-39.914,+13 -swinv2_base_window16_256,36.992,63.008,56.128,43.872,87.92,256,0.900,bicubic,-47.600,-40.946,-40 -xcit_small_12_p16_224_dist,36.971,63.029,56.733,43.267,26.25,224,1.000,bicubic,-46.375,-39.685,+36 -regnetz_040h,36.965,63.035,57.280,42.720,28.94,320,1.000,bicubic,-47.531,-39.726,-36 -volo_d1_224,36.880,63.120,56.633,43.367,26.63,224,0.960,bicubic,-47.284,-40.141,-15 -seresnet152d,36.788,63.212,56.718,43.282,66.84,320,1.000,bicubic,-47.576,-40.326,-31 -seresnext101d_32x8d,36.637,63.363,56.328,43.672,93.59,288,1.000,bicubic,-47.725,-40.590,-31 -volo_d2_224,36.601,63.399,56.466,43.534,58.68,224,0.960,bicubic,-48.593,-40.722,-73 -xception65p,36.554,63.446,56.423,43.577,39.82,299,0.940,bicubic,-46.576,-40.057,+42 -seresnextaa101d_32x8d,36.527,63.473,56.403,43.597,93.59,288,1.000,bicubic,-48.045,-40.667,-47 -regnetz_d32,36.444,63.556,57.370,42.630,27.58,320,0.950,bicubic,-47.580,-39.498,-12 -cait_xs24_384,36.420,63.580,56.940,43.060,26.67,384,1.000,bicubic,-47.644,-39.950,-18 -volo_d2_384,36.419,63.581,56.313,43.687,58.87,384,1.000,bicubic,-49.617,-41.261,-108 -efficientnet_b3,36.411,63.589,56.843,43.157,12.23,320,1.000,bicubic,-45.829,-39.275,+97 -deit_base_distilled_patch16_224,36.399,63.601,56.621,43.379,87.34,224,0.900,bicubic,-46.989,-39.867,+20 -resnetv2_101x3_bitm,36.385,63.615,59.066,40.934,387.93,448,1.000,bilinear,-48.059,-38.316,-47 -resnetrs270,36.318,63.682,56.566,43.434,129.86,352,1.000,bicubic,-48.118,-40.408,-46 -tresnet_m,36.289,63.711,55.796,44.204,31.39,224,0.875,bilinear,-46.785,-40.324,+37 -mixer_b16_224_miil,36.267,63.733,55.967,44.033,59.88,224,0.875,bilinear,-46.037,-39.753,+87 -deit3_small_patch16_384,36.185,63.815,55.566,44.434,22.21,384,1.000,bicubic,-47.243,-41.110,+11 -tf_efficientnet_b2_ns,36.179,63.821,57.551,42.449,9.11,260,0.890,bicubic,-46.205,-38.695,+76 -resnet152,36.084,63.916,55.566,44.434,60.19,224,0.950,bicubic,-46.734,-40.566,+44 -regnetz_040,36.047,63.953,55.747,44.253,27.12,320,1.000,bicubic,-48.189,-41.185,-39 -ecaresnet101d,36.010,63.990,56.161,43.839,44.57,224,0.875,bicubic,-46.160,-39.887,+95 -dm_nfnet_f2,36.006,63.994,55.458,44.542,193.78,352,0.920,bicubic,-49.060,-41.784,-82 -resnest200e,35.937,64.063,55.841,44.159,70.20,320,0.909,bicubic,-47.891,-41.051,-14 -swsl_resnet18,35.862,64.138,58.457,41.543,11.69,224,0.875,bilinear,-37.412,-33.279,+460 -eca_nfnet_l1,35.823,64.177,55.961,44.039,41.41,320,1.000,bicubic,-48.189,-41.071,-27 -sequencer2d_l,35.819,64.181,55.719,44.281,54.30,224,0.875,bicubic,-47.587,-40.781,+4 -vit_relpos_medium_patch16_cls_224,35.735,64.265,54.923,45.077,38.76,224,0.900,bicubic,-46.827,-41.143,+55 -xcit_small_24_p8_224,35.556,64.444,54.782,45.218,47.63,224,1.000,bicubic,-48.284,-41.854,-21 -xcit_large_24_p16_224,35.524,64.476,54.762,45.238,189.10,224,1.000,bicubic,-47.368,-41.116,+30 -xcit_small_12_p8_224,35.520,64.480,55.505,44.495,26.21,224,1.000,bicubic,-47.820,-40.975,+7 -vit_small_patch16_384,35.473,64.527,57.541,42.459,22.20,384,1.000,bicubic,-48.327,-39.559,-19 -xcit_medium_24_p8_224,35.452,64.548,54.825,45.175,84.32,224,1.000,bicubic,-48.286,-41.569,-16 -swinv2_base_window8_256,35.444,64.556,54.611,45.389,87.92,256,0.900,bicubic,-48.818,-42.311,-54 -swinv2_small_window16_256,35.430,64.570,54.637,45.363,49.73,256,0.900,bicubic,-48.780,-42.233,-51 -resnest101e,35.375,64.625,55.794,44.206,48.28,256,0.875,bilinear,-47.513,-40.526,+25 -convit_base,35.314,64.686,54.931,45.069,86.54,224,0.875,bicubic,-46.978,-41.007,+69 -convnext_tiny_hnf,35.279,64.721,53.849,46.151,28.59,224,0.950,bicubic,-46.941,-42.017,+74 -xcit_tiny_24_p8_224_dist,35.255,64.745,55.254,44.746,12.11,224,1.000,bicubic,-47.305,-40.914,+45 -twins_svt_large,35.088,64.912,54.719,45.281,99.27,224,0.900,bicubic,-48.592,-41.875,-18 -repvgg_b3,35.051,64.949,54.558,45.442,123.09,224,0.875,bilinear,-45.445,-40.706,+178 -repvgg_b3g4,35.039,64.961,54.774,45.226,83.83,224,0.875,bilinear,-45.177,-40.334,+204 -regnetz_d8,34.996,65.004,55.941,44.059,23.37,320,1.000,bicubic,-49.056,-41.055,-50 -dm_nfnet_f1,34.986,65.014,54.110,45.890,132.63,320,0.910,bicubic,-49.638,-42.988,-85 -xcit_tiny_24_p8_384_dist,34.931,65.069,55.151,44.849,12.11,384,1.000,bicubic,-48.815,-41.561,-29 -regnetz_d8_evos,34.894,65.106,55.258,44.742,23.46,320,0.950,bicubic,-49.156,-41.738,-52 -resnet101d,34.870,65.130,54.194,45.806,44.57,320,1.000,bicubic,-48.152,-42.252,+10 -seresnext101_32x8d,34.791,65.209,53.448,46.552,93.57,288,1.000,bicubic,-49.413,-43.426,-63 -resmlp_big_24_distilled_224,34.788,65.213,54.637,45.363,129.14,224,0.875,bicubic,-48.800,-42.011,-25 -swin_s3_base_224,34.788,65.213,53.693,46.307,71.13,224,0.900,bicubic,-49.144,-42.967,-49 -vit_relpos_base_patch16_clsgap_224,34.725,65.275,54.218,45.782,86.43,224,0.900,bicubic,-48.035,-41.956,+17 -vit_base_patch16_rpn_224,34.711,65.289,54.662,45.338,86.54,224,0.900,bicubic,-47.489,-41.334,+62 -sequencer2d_m,34.705,65.295,54.010,45.990,38.31,224,0.875,bicubic,-48.103,-42.258,+13 -deit3_small_patch16_224,34.675,65.325,53.163,46.837,22.06,224,0.900,bicubic,-46.707,-42.287,+112 -vit_large_patch32_384,34.670,65.330,55.731,44.269,306.63,384,1.000,bicubic,-46.838,-40.359,+100 -resnet101,34.658,65.342,54.297,45.703,44.55,224,0.950,bicubic,-47.272,-41.469,+77 -dm_nfnet_f0,34.624,65.376,54.672,45.328,71.49,256,0.900,bicubic,-48.760,-41.902,-23 -vit_relpos_base_patch16_224,34.613,65.387,54.291,45.709,86.43,224,0.900,bicubic,-47.873,-41.851,+30 -ssl_resnext101_32x16d,34.605,65.395,55.937,44.063,194.03,224,0.875,bilinear,-47.251,-40.159,+77 -repvgg_b2g4,34.587,65.413,54.778,45.222,61.76,224,0.875,bilinear,-44.779,-39.910,+232 -resnetv2_101,34.581,65.419,53.153,46.847,44.54,224,0.950,bicubic,-47.465,-42.709,+63 -resnetrs200,34.507,65.493,54.291,45.709,93.21,320,1.000,bicubic,-49.933,-42.789,-94 -resnest50d_4s2x40d,34.357,65.643,54.733,45.267,30.42,224,0.875,bicubic,-46.751,-40.829,+119 -resnetrs152,34.357,65.643,53.562,46.438,86.62,320,1.000,bicubic,-49.357,-43.052,-45 -crossvit_18_dagger_408,34.253,65.747,53.088,46.912,44.61,408,1.000,bicubic,-49.941,-43.730,-79 -xcit_medium_24_p16_224,34.237,65.763,53.165,46.835,84.40,224,1.000,bicubic,-48.401,-42.813,+9 -tf_efficientnet_b1_ns,34.165,65.835,55.495,44.505,7.79,240,0.882,bicubic,-47.221,-40.241,+98 -efficientnetv2_rw_t,34.155,65.845,53.137,46.863,13.65,288,1.000,bicubic,-48.189,-43.059,+30 -twins_pcpvt_large,34.106,65.894,54.126,45.874,60.99,224,0.900,bicubic,-49.030,-42.478,-21 -tf_efficientnet_b4,34.064,65.936,54.196,45.804,19.34,380,0.922,bicubic,-48.960,-42.104,-14 -ssl_resnext101_32x8d,34.029,65.971,55.601,44.399,88.79,224,0.875,bilinear,-47.579,-40.441,+77 -nfnet_l0,34.005,65.995,54.361,45.639,35.07,288,1.000,bicubic,-48.747,-42.157,-3 -xcit_small_24_p16_224,34.005,65.995,53.271,46.729,47.67,224,1.000,bicubic,-48.579,-42.729,+7 -efficientnet_b3_pruned,33.994,66.006,54.106,45.894,9.86,300,0.904,bicubic,-46.864,-41.138,+129 -tf_efficientnet_b6,33.992,66.008,54.542,45.458,43.04,528,0.942,bicubic,-50.116,-42.346,-85 -regnety_160,33.972,66.028,53.540,46.460,83.59,288,1.000,bicubic,-49.720,-43.236,-55 -gc_efficientnetv2_rw_t,33.960,66.040,53.222,46.778,13.68,288,1.000,bicubic,-48.506,-43.076,+12 -pit_s_distilled_224,33.935,66.065,53.267,46.733,24.04,224,0.900,bicubic,-48.059,-42.529,+48 -convnext_tiny,33.838,66.162,53.656,46.344,28.59,224,0.875,bicubic,-48.224,-42.198,+44 -swinv2_cr_small_ns_224,33.836,66.164,52.618,47.382,49.70,224,0.900,bicubic,-49.650,-43.866,-53 -resnext101_64x4d,33.827,66.173,52.172,47.828,83.46,288,1.000,bicubic,-49.317,-44.202,-36 -xcit_small_12_p16_224,33.768,66.232,53.233,46.767,26.25,224,1.000,bicubic,-48.204,-42.579,+47 -swin_s3_small_224,33.701,66.299,52.391,47.609,49.74,224,0.900,bicubic,-50.073,-44.061,-68 -resnetv2_50x3_bitm,33.663,66.337,55.882,44.118,217.32,448,1.000,bilinear,-50.349,-41.244,-86 -swinv2_small_window8_256,33.636,66.364,52.821,47.179,49.73,256,0.900,bicubic,-50.218,-43.821,-80 -resnet51q,33.551,66.448,53.023,46.977,35.70,288,1.000,bilinear,-48.807,-43.155,+10 -xcit_tiny_24_p16_384_dist,33.512,66.488,52.768,47.232,12.12,384,1.000,bicubic,-49.060,-43.520,-5 -vit_relpos_medium_patch16_224,33.500,66.500,52.603,47.397,38.75,224,0.900,bicubic,-48.962,-43.483,+2 -regnety_080,33.469,66.531,52.939,47.061,39.18,288,1.000,bicubic,-50.459,-43.949,-87 -cs3edgenet_x,33.463,66.537,52.939,47.061,47.82,288,1.000,bicubic,-49.259,-43.437,-19 -sequencer2d_s,33.434,66.566,52.404,47.596,27.65,224,0.875,bicubic,-48.910,-43.630,+8 -convmixer_1536_20,33.428,66.572,53.029,46.971,51.63,224,0.960,bicubic,-47.942,-42.583,+76 -regnety_032,33.406,66.594,52.758,47.242,19.44,288,1.000,bicubic,-49.318,-43.664,-23 -crossvit_18_240,33.396,66.604,52.243,47.757,43.27,240,0.875,bicubic,-49.002,-43.811,-1 -vit_srelpos_medium_patch16_224,33.373,66.627,52.453,47.547,38.74,224,0.900,bicubic,-48.863,-43.481,+15 -gernet_l,33.361,66.639,51.909,48.091,31.08,256,0.875,bilinear,-47.989,-43.627,+73 -crossvit_15_dagger_408,33.331,66.669,52.190,47.810,28.50,408,1.000,bicubic,-50.507,-44.590,-88 -crossvit_18_dagger_240,33.284,66.716,52.202,47.798,44.27,240,0.875,bicubic,-49.236,-43.866,-12 -tresnet_xl,33.261,66.739,52.294,47.706,78.44,224,0.875,bilinear,-48.801,-43.642,+23 -jx_nest_base,33.214,66.787,51.809,48.191,67.72,224,0.875,bicubic,-50.340,-44.555,-75 -resnest50d_1s4x24d,33.149,66.851,52.852,47.148,25.68,224,0.875,bicubic,-47.835,-42.472,+90 -vit_relpos_medium_patch16_rpn_224,33.109,66.891,52.347,47.653,38.73,224,0.900,bicubic,-49.185,-43.625,+3 -resnet61q,33.099,66.900,51.758,48.242,36.85,288,1.000,bicubic,-49.419,-44.372,-16 -jx_nest_small,33.048,66.952,51.062,48.938,38.35,224,0.875,bicubic,-50.072,-45.268,-54 -crossvit_base_240,33.037,66.963,51.384,48.616,105.03,240,0.875,bicubic,-49.179,-44.448,+8 -twins_pcpvt_base,33.023,66.977,52.489,47.511,43.83,224,0.900,bicubic,-49.685,-43.861,-34 -xcit_tiny_24_p16_224_dist,32.987,67.013,52.056,47.944,12.12,224,1.000,bicubic,-47.461,-43.156,+119 -rexnet_200,32.980,67.020,52.935,47.065,16.37,224,0.875,bicubic,-48.648,-42.733,+38 -resnest50d,32.976,67.024,52.711,47.289,27.48,224,0.875,bilinear,-47.998,-42.669,+83 -convit_small,32.909,67.091,52.123,47.877,27.78,224,0.875,bicubic,-48.519,-43.619,+51 -crossvit_15_dagger_240,32.907,67.093,51.783,48.217,28.21,240,0.875,bicubic,-49.419,-44.173,-9 -tf_efficientnetv2_s,32.907,67.093,51.730,48.270,21.46,384,1.000,bicubic,-50.977,-44.968,-109 -vit_small_patch16_224,32.877,67.123,53.917,46.083,22.05,224,0.900,bicubic,-48.519,-42.221,+51 -tf_efficientnet_b3,32.862,67.138,52.955,47.045,12.23,300,0.904,bicubic,-48.776,-42.763,+31 -pnasnet5large,32.850,67.150,50.506,49.494,86.06,331,0.911,bicubic,-49.932,-45.536,-48 -twins_svt_base,32.832,67.168,51.563,48.437,56.07,224,0.900,bicubic,-50.306,-44.857,-70 -regnetv_064,32.830,67.170,52.854,47.146,30.58,288,1.000,bicubic,-50.882,-43.892,-97 -regnetz_c16,32.828,67.172,53.750,46.250,13.46,320,0.940,bicubic,-49.692,-42.610,-33 -nasnetalarge,32.773,67.227,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.903,-42 -gernet_m,32.744,67.256,51.907,48.093,21.14,224,0.875,bilinear,-47.986,-43.279,+87 -inception_resnet_v2,32.736,67.264,50.653,49.347,55.84,299,0.897,bicubic,-47.724,-44.653,+102 -gluon_resnet152_v1d,32.732,67.268,51.088,48.912,60.21,224,0.875,bicubic,-47.744,-44.112,+100 -pit_b_224,32.718,67.282,49.854,50.146,73.76,224,0.900,bicubic,-49.726,-45.858,-31 -tf_efficientnet_b2_ap,32.685,67.315,52.237,47.763,9.11,260,0.890,bicubic,-47.617,-42.791,+114 -fbnetv3_g,32.634,67.366,52.888,47.112,16.62,288,0.950,bilinear,-49.400,-43.178,0 -tresnet_l,32.559,67.441,51.139,48.861,55.99,224,0.875,bilinear,-48.931,-44.487,+30 -cait_xxs36_384,32.543,67.457,52.233,47.767,17.37,384,1.000,bicubic,-49.649,-43.911,-12 -regnetz_c16_evos,32.530,67.470,52.923,47.077,13.49,320,0.950,bicubic,-50.102,-43.553,-52 -wide_resnet50_2,32.435,67.565,51.453,48.547,68.88,224,0.875,bicubic,-49.021,-44.077,+30 -gmlp_s16_224,32.420,67.580,51.819,48.181,19.42,224,0.875,bicubic,-47.220,-42.805,+150 -ens_adv_inception_resnet_v2,32.374,67.626,50.419,49.581,55.84,299,0.897,bicubic,-47.600,-44.523,+127 -deit_base_patch16_224,32.363,67.637,50.997,49.003,86.57,224,0.900,bicubic,-49.631,-44.735,-5 -swin_small_patch4_window7_224,32.349,67.651,50.911,49.089,49.61,224,0.900,bicubic,-50.869,-45.415,-92 -gluon_resnet152_v1s,32.335,67.665,50.528,49.472,60.32,224,0.875,bicubic,-48.679,-44.886,+56 -deit_small_distilled_patch16_224,32.286,67.714,52.109,47.891,22.44,224,0.900,bicubic,-48.922,-43.265,+41 -xcit_tiny_24_p8_224,32.278,67.722,51.903,48.097,12.11,224,1.000,bicubic,-49.618,-44.071,-2 -gluon_seresnext101_64x4d,32.196,67.804,50.306,49.694,88.23,224,0.875,bicubic,-48.684,-44.990,+63 -coat_lite_small,32.121,67.879,49.934,50.066,19.84,224,0.900,bicubic,-50.183,-45.916,-35 -gluon_seresnext101_32x4d,32.109,67.891,51.235,48.765,48.96,224,0.875,bicubic,-48.797,-44.061,+59 -deit_base_patch16_384,31.989,68.011,50.549,49.451,86.86,384,1.000,bicubic,-51.117,-45.821,-89 -seresnext50_32x4d,31.971,68.028,51.227,48.773,27.56,224,0.875,bicubic,-49.291,-44.401,+32 -xcit_tiny_12_p8_224_dist,31.938,68.062,51.394,48.606,6.71,224,1.000,bicubic,-49.270,-44.212,+33 -levit_384,31.865,68.135,50.593,49.407,39.13,224,0.900,bicubic,-50.723,-45.425,-64 -resnetrs101,31.850,68.150,51.019,48.981,63.62,288,0.940,bicubic,-50.434,-44.989,-37 -cs3se_edgenet_x,31.797,68.203,50.763,49.237,50.72,320,1.000,bicubic,-51.751,-45.903,-119 -vit_relpos_small_patch16_224,31.787,68.213,50.628,49.372,21.98,224,0.900,bicubic,-49.667,-45.200,+14 -poolformer_m48,31.698,68.302,49.889,50.111,73.47,224,0.950,bicubic,-50.762,-46.069,-56 -tnt_s_patch16_224,31.643,68.357,51.137,48.863,23.76,224,0.900,bicubic,-49.875,-44.609,+5 -eca_nfnet_l0,31.604,68.396,51.608,48.392,24.14,288,1.000,bicubic,-50.974,-44.882,-68 -resnetv2_50x1_bit_distilled,31.567,68.433,51.268,48.732,25.55,224,0.875,bicubic,-51.255,-45.254,-87 -mobilevitv2_200_in22ft1k,31.527,68.473,51.772,48.228,18.45,256,0.888,bicubic,-50.807,-44.166,-51 -xception41p,31.510,68.490,50.374,49.626,26.91,299,0.940,bicubic,-50.458,-45.420,-22 -regnety_064,31.472,68.528,50.524,49.476,30.58,288,1.000,bicubic,-52.248,-46.202,-135 -poolformer_m36,31.449,68.551,50.034,49.966,56.17,224,0.950,bicubic,-50.659,-45.656,-34 -ssl_resnext101_32x4d,31.415,68.585,52.133,47.867,44.18,224,0.875,bilinear,-49.509,-43.593,+42 -inception_v4,31.380,68.620,49.242,50.758,42.68,299,0.875,bicubic,-48.788,-45.722,+92 -rexnet_150,31.372,68.628,51.276,48.724,9.73,224,0.875,bicubic,-48.942,-43.890,+80 -crossvit_15_240,31.339,68.661,50.168,49.832,27.53,240,0.875,bicubic,-50.205,-45.522,-7 -pit_s_224,31.335,68.665,49.665,50.335,23.46,224,0.900,bicubic,-49.763,-45.667,+25 -swinv2_tiny_window16_256,31.303,68.697,49.645,50.355,28.35,256,0.900,bicubic,-51.507,-46.585,-95 -crossvit_small_240,31.284,68.716,50.196,49.804,26.86,240,0.875,bicubic,-49.732,-45.260,+28 -cspresnet50,31.278,68.722,51.221,48.779,21.62,256,0.887,bilinear,-48.304,-43.487,+120 -vit_srelpos_small_patch16_224,31.278,68.722,50.247,49.753,21.97,224,0.900,bicubic,-49.820,-45.325,+20 -cait_xxs36_224,31.264,68.736,50.612,49.388,17.30,224,1.000,bicubic,-48.484,-44.256,+107 -swinv2_cr_small_224,31.262,68.738,48.737,51.263,49.70,224,0.900,bicubic,-51.876,-47.361,-118 -convmixer_768_32,31.250,68.750,50.950,49.050,21.11,224,0.960,bicubic,-48.914,-44.122,+84 -swin_s3_tiny_224,31.239,68.761,49.718,50.282,28.33,224,0.900,bicubic,-50.885,-46.232,-49 -cspresnext50,31.221,68.779,50.885,49.115,20.57,256,0.887,bilinear,-49.323,-44.439,+47 -regnetv_040,31.213,68.787,50.111,49.889,20.64,288,1.000,bicubic,-51.985,-46.553,-127 -coat_mini,31.203,68.797,49.773,50.227,10.34,224,0.900,bicubic,-50.063,-45.619,+2 -xcit_tiny_12_p8_384_dist,31.188,68.812,50.524,49.476,6.71,384,1.000,bicubic,-51.198,-45.698,-77 -ecaresnetlight,31.125,68.875,50.239,49.761,30.16,224,0.875,bicubic,-49.331,-45.007,+52 -gluon_resnet101_v1s,31.119,68.881,49.799,50.201,44.67,224,0.875,bicubic,-49.179,-45.363,+67 -edgenext_small,31.101,68.899,50.129,49.871,5.59,320,1.000,bicubic,-50.473,-45.585,-25 -tf_efficientnet_cc_b0_8e,31.091,68.909,50.775,49.225,24.01,224,0.875,bicubic,-46.809,-42.883,+199 -resmlp_36_distilled_224,31.072,68.928,49.691,50.309,44.69,224,0.875,bicubic,-50.084,-45.795,+2 -ecaresnet50t,31.052,68.948,50.577,49.423,25.57,320,0.950,bicubic,-51.296,-45.561,-80 -ecaresnet50d,31.048,68.952,50.844,49.156,25.58,224,0.875,bicubic,-49.550,-44.474,+35 -cspdarknet53,31.018,68.981,50.394,49.606,27.64,256,0.887,bilinear,-49.038,-44.692,+77 -resnet50d,31.018,68.981,49.808,50.192,25.58,224,0.875,bicubic,-49.510,-45.360,+37 -cs3sedarknet_x,31.015,68.985,50.144,49.856,35.40,288,1.000,bicubic,-51.639,-46.202,-107 -gcresnet50t,31.011,68.989,50.121,49.879,25.90,256,0.900,bicubic,-49.923,-45.333,+14 -gluon_resnext101_64x4d,30.993,69.007,48.553,51.447,83.46,224,0.875,bicubic,-49.611,-46.439,+29 -gluon_resnet152_v1c,30.991,69.009,48.934,51.066,60.21,224,0.875,bicubic,-48.921,-45.908,+76 -twins_svt_small,30.975,69.025,49.223,50.777,24.06,224,0.900,bicubic,-50.707,-46.443,-42 -resnext50_32x4d,30.922,69.078,49.266,50.734,25.03,224,0.950,bicubic,-50.174,-46.060,-1 -ecaresnet101d_pruned,30.903,69.097,50.003,49.997,24.88,224,0.875,bicubic,-49.907,-45.625,+17 -resmlp_24_distilled_224,30.899,69.101,50.178,49.822,30.02,224,0.875,bicubic,-49.865,-45.044,+18 -tf_efficientnet_cc_b1_8e,30.897,69.103,50.080,49.920,39.72,240,0.882,bicubic,-48.417,-44.290,+106 -gluon_resnext101_32x4d,30.885,69.115,48.547,51.453,44.18,224,0.875,bicubic,-49.455,-46.379,+44 -tf_efficientnetv2_b3,30.861,69.139,49.820,50.180,14.36,300,0.904,bicubic,-51.105,-45.962,-60 -tf_efficientnet_lite4,30.830,69.170,50.394,49.606,13.01,380,0.920,bilinear,-50.704,-45.272,-40 -nf_resnet50,30.700,69.300,49.956,50.044,25.56,288,0.940,bicubic,-49.954,-45.378,+17 -dpn107,30.680,69.320,48.812,51.188,86.92,224,0.875,bicubic,-49.488,-46.094,+55 -poolformer_s36,30.678,69.322,49.433,50.567,30.86,224,0.900,bicubic,-50.740,-46.015,-32 -xcit_tiny_24_p16_224,30.675,69.325,50.416,49.584,12.12,224,1.000,bicubic,-48.769,-44.472,+93 -ese_vovnet39b,30.667,69.333,49.879,50.121,24.57,224,0.875,bicubic,-48.645,-44.835,+99 -tresnet_xl_448,30.620,69.380,49.068,50.932,78.44,448,0.875,bilinear,-52.428,-47.102,-144 -gluon_resnet152_v1b,30.610,69.390,48.515,51.485,60.19,224,0.875,bicubic,-49.072,-46.221,+77 -haloregnetz_b,30.606,69.394,49.013,50.987,11.68,224,0.940,bicubic,-50.438,-46.185,-13 -ssl_resnext50_32x4d,30.596,69.404,50.655,49.345,25.03,224,0.875,bilinear,-49.730,-44.757,+34 -dpn68b,30.525,69.475,49.172,50.828,12.61,224,0.875,bicubic,-48.691,-45.242,+105 -gluon_resnet101_v1d,30.521,69.479,47.953,52.047,44.57,224,0.875,bicubic,-49.897,-47.061,+24 -mobilevitv2_200_384_in22ft1k,30.498,69.502,50.567,49.433,18.45,384,1.000,bicubic,-52.902,-46.015,-172 -resnest26d,30.490,69.510,50.667,49.333,17.07,224,0.875,bilinear,-47.994,-43.627,+135 -efficientnet_b2,30.439,69.561,49.693,50.307,9.11,288,1.000,bicubic,-50.177,-45.623,+5 -tf_efficientnet_b1_ap,30.421,69.579,49.559,50.441,7.79,240,0.882,bicubic,-48.853,-44.749,+96 -xcit_tiny_12_p16_384_dist,30.403,69.597,50.127,49.873,6.72,384,1.000,bicubic,-50.539,-45.281,-13 -cs3darknet_x,30.398,69.603,49.195,50.805,35.05,288,1.000,bicubic,-51.826,-47.035,-98 -twins_pcpvt_small,30.384,69.616,49.388,50.612,24.11,224,0.900,bicubic,-50.706,-46.254,-24 -resnetv2_50,30.384,69.616,48.828,51.172,25.55,224,0.950,bicubic,-50.028,-46.244,+17 -visformer_small,30.335,69.665,48.291,51.709,40.22,224,0.900,bicubic,-51.773,-47.585,-93 -pit_xs_distilled_224,30.278,69.722,49.838,50.162,11.00,224,0.900,bicubic,-49.030,-44.528,+84 -regnety_040,30.252,69.748,48.918,51.082,20.65,288,1.000,bicubic,-52.784,-47.592,-159 -mobilevitv2_175_in22ft1k,30.213,69.787,49.024,50.976,14.25,256,0.888,bicubic,-51.727,-46.766,-83 -vit_relpos_base_patch32_plus_rpn_256,30.211,69.789,48.700,51.300,119.42,256,0.900,bicubic,-49.275,-45.440,+70 -convmixer_1024_20_ks9_p14,30.101,69.899,49.934,50.066,24.38,224,0.960,bicubic,-46.841,-43.424,+199 -seresnet50,30.073,69.927,49.288,50.712,28.09,224,0.875,bicubic,-50.193,-45.782,+23 -dpn98,30.061,69.939,48.254,51.746,61.57,224,0.875,bicubic,-49.583,-46.346,+60 -tf_efficientnet_b2,30.030,69.970,49.581,50.419,9.11,260,0.890,bicubic,-50.058,-45.327,+32 -efficientnet_el,30.022,69.978,48.832,51.168,10.59,300,0.904,bicubic,-51.284,-46.702,-50 -dpn131,30.016,69.984,48.128,51.872,79.25,224,0.875,bicubic,-49.810,-46.580,+46 -legacy_senet154,30.005,69.996,48.042,51.958,115.09,224,0.875,bilinear,-51.303,-47.454,-53 -xcit_tiny_12_p16_224_dist,30.001,69.999,49.643,50.357,6.72,224,1.000,bicubic,-48.577,-44.555,+111 -halo2botnet50ts_256,29.985,70.015,48.374,51.626,22.64,256,0.950,bicubic,-52.083,-47.268,-104 -mobilevitv2_150_in22ft1k,29.957,70.043,49.219,50.781,10.59,256,0.888,bicubic,-51.513,-46.449,-68 -dpn92,29.955,70.045,49.176,50.824,37.67,224,0.875,bicubic,-50.065,-45.654,+30 -resnetv2_101x1_bitm,29.896,70.104,51.127,48.873,44.54,448,1.000,bilinear,-52.436,-45.389,-127 -gluon_senet154,29.877,70.123,47.892,52.108,115.09,224,0.875,bicubic,-51.353,-47.454,-55 -xception,29.863,70.137,48.681,51.319,22.86,299,0.897,bicubic,-49.181,-45.713,+88 -adv_inception_v3,29.820,70.180,47.843,52.157,23.83,299,0.875,bicubic,-47.758,-45.895,+160 -cs3sedarknet_l,29.812,70.188,48.985,51.015,21.91,288,0.950,bicubic,-51.964,-46.985,-91 -resnetaa50,29.794,70.206,48.018,51.982,25.56,288,1.000,bicubic,-51.824,-47.792,-86 -gluon_xception65,29.786,70.214,47.765,52.235,39.92,299,0.903,bicubic,-49.936,-47.095,+38 -lamhalobotnet50ts_256,29.745,70.255,48.339,51.661,22.57,256,0.950,bicubic,-51.807,-47.165,-85 -fbnetv3_d,29.737,70.263,49.453,50.547,10.31,256,0.950,bilinear,-49.943,-45.487,+41 -resmlp_36_224,29.696,70.304,48.969,51.031,44.69,224,0.875,bicubic,-50.074,-45.917,+33 -convnext_nano,29.694,70.306,47.930,52.070,15.59,288,1.000,bicubic,-51.782,-47.730,-81 -resnet50,29.631,70.369,46.745,53.255,25.56,224,0.950,bicubic,-50.743,-47.869,-9 -resnetblur50,29.610,70.391,48.254,51.746,25.56,224,0.875,bicubic,-49.684,-46.380,+61 -resnetv2_50d_gn,29.608,70.392,47.792,52.208,25.57,288,0.950,bicubic,-52.216,-48.132,-104 -jx_nest_tiny,29.543,70.457,46.985,53.015,17.06,224,0.875,bicubic,-51.875,-48.633,-80 -resnet50_gn,29.535,70.465,48.301,51.699,25.56,224,0.940,bicubic,-50.525,-46.647,+12 -efficientnet_em,29.476,70.524,48.942,51.058,6.90,240,0.882,bicubic,-49.776,-45.850,+61 -cs3darknet_l,29.470,70.530,48.215,51.785,21.16,288,0.950,bicubic,-51.416,-47.453,-46 -resnext101_32x8d,29.439,70.561,48.488,51.512,88.79,224,0.875,bilinear,-49.877,-46.030,+48 -gcresnext50ts,29.429,70.571,47.902,52.098,15.67,256,0.900,bicubic,-51.149,-47.268,-33 -coat_lite_mini,29.429,70.571,47.729,52.271,11.01,224,0.900,bicubic,-49.659,-46.879,+66 -deit_small_patch16_224,29.423,70.577,48.258,51.742,22.05,224,0.900,bicubic,-50.441,-46.790,+15 -sebotnet33ts_256,29.423,70.577,47.146,52.854,13.70,256,0.940,bicubic,-51.731,-48.020,-71 -ssl_resnet50,29.405,70.595,49.787,50.213,25.56,224,0.875,bilinear,-49.819,-45.043,+55 -nf_regnet_b1,29.391,70.609,49.411,50.589,10.22,288,0.900,bicubic,-49.909,-45.343,+47 -cait_xxs24_384,29.387,70.612,48.747,51.253,12.03,384,1.000,bicubic,-51.575,-46.897,-59 -edgenext_small_rw,29.350,70.650,48.737,51.263,7.83,320,1.000,bicubic,-51.102,-46.453,-29 -resnet34d,29.332,70.668,48.411,51.589,21.82,224,0.875,bicubic,-47.784,-44.971,+153 -swin_tiny_patch4_window7_224,29.332,70.668,47.611,52.389,28.29,224,0.900,bicubic,-52.044,-47.931,-89 -cait_xxs24_224,29.303,70.697,48.527,51.473,11.96,224,1.000,bicubic,-49.083,-45.781,+91 -ecaresnet50d_pruned,29.209,70.791,48.443,51.557,19.94,224,0.875,bicubic,-50.509,-46.433,+15 -poolformer_s24,29.175,70.825,48.062,51.938,21.39,224,0.900,bicubic,-51.141,-46.980,-23 -tresnet_l_448,29.165,70.835,47.226,52.774,55.99,448,0.875,bilinear,-53.105,-48.754,-152 -gluon_inception_v3,29.120,70.880,46.955,53.045,23.83,299,0.875,bicubic,-49.686,-47.415,+66 -eca_resnet33ts,29.105,70.895,48.796,51.204,19.68,256,0.900,bicubic,-50.975,-46.176,-9 -lambda_resnet50ts,29.097,70.903,46.981,53.019,21.54,256,0.950,bicubic,-52.055,-48.121,-83 -xception71,29.040,70.960,47.411,52.589,42.34,299,0.903,bicubic,-50.830,-47.513,-1 -hrnet_w64,28.991,71.010,47.130,52.870,128.06,224,0.875,bilinear,-50.479,-47.524,+22 -xcit_tiny_12_p8_224,28.957,71.043,47.511,52.489,6.71,224,1.000,bicubic,-50.737,-47.537,+8 -regnetz_b16,28.943,71.057,47.246,52.754,9.72,288,0.940,bicubic,-51.769,-48.228,-58 -cs3darknet_focus_l,28.926,71.074,47.629,52.371,21.15,288,0.950,bicubic,-51.948,-48.063,-67 -tf_efficientnet_b1,28.886,71.114,47.498,52.502,7.79,240,0.882,bicubic,-49.942,-46.700,+57 -tf_efficientnet_b0_ns,28.884,71.116,48.997,51.003,5.29,224,0.875,bicubic,-49.780,-45.379,+63 -resnetv2_50d_evos,28.878,71.121,46.672,53.328,25.59,288,0.950,bicubic,-53.100,-49.240,-143 -vit_small_patch32_384,28.875,71.125,48.889,51.111,22.92,384,1.000,bicubic,-51.615,-46.711,-52 -gluon_resnet101_v1b,28.873,71.127,46.389,53.611,44.55,224,0.875,bicubic,-50.431,-48.131,+25 -mobilevitv2_150_384_in22ft1k,28.869,71.131,47.916,52.084,10.59,384,1.000,bicubic,-53.721,-48.400,-196 -skresnext50_32x4d,28.826,71.174,46.487,53.513,27.48,224,0.875,bicubic,-51.328,-48.159,-24 -sehalonet33ts,28.778,71.222,46.582,53.418,13.69,256,0.940,bicubic,-52.194,-48.690,-83 -levit_256,28.751,71.249,46.721,53.279,18.89,224,0.900,bicubic,-52.765,-48.769,-123 -tf_efficientnet_lite3,28.660,71.340,47.346,52.654,8.20,300,0.904,bilinear,-51.158,-47.568,-9 -skresnet34,28.654,71.346,47.953,52.047,22.28,224,0.875,bicubic,-48.250,-45.367,+139 -gluon_seresnext50_32x4d,28.649,71.351,46.442,53.558,27.56,224,0.875,bicubic,-51.263,-48.390,-19 -darknetaa53,28.647,71.353,46.949,53.051,36.02,288,1.000,bilinear,-51.875,-48.377,-63 -hrnet_w40,28.635,71.365,47.452,52.548,57.56,224,0.875,bilinear,-50.287,-47.018,+41 -swinv2_tiny_window8_256,28.611,71.389,46.171,53.829,28.35,256,0.900,bicubic,-53.199,-49.823,-144 -mobilevitv2_175_384_in22ft1k,28.605,71.395,47.126,52.874,14.25,384,1.000,bicubic,-54.329,-49.304,-226 -halonet50ts,28.580,71.420,46.169,53.831,22.73,256,0.940,bicubic,-53.072,-49.443,-141 -tf_efficientnetv2_b0,28.570,71.430,47.075,52.925,7.14,224,0.875,bicubic,-49.782,-46.951,+65 -tv_resnet152,28.531,71.469,47.116,52.884,60.19,224,0.875,bilinear,-49.789,-46.918,+65 -xcit_tiny_12_p16_224,28.519,71.481,47.403,52.597,6.72,224,1.000,bicubic,-48.605,-46.309,+119 -repvgg_b2,28.430,71.570,47.038,52.962,89.02,224,0.875,bilinear,-50.364,-47.380,+39 -hrnet_w48,28.409,71.591,47.586,52.414,77.47,224,0.875,bilinear,-50.891,-46.928,+10 -gluon_resnext50_32x4d,28.379,71.621,45.316,54.684,25.03,224,0.875,bicubic,-50.981,-49.110,+2 -swinv2_cr_tiny_ns_224,28.373,71.626,45.920,54.080,28.33,224,0.900,bicubic,-53.413,-49.902,-152 -efficientnet_b2_pruned,28.362,71.638,47.050,52.950,8.31,260,0.890,bicubic,-51.556,-47.800,-34 -seresnet33ts,28.338,71.662,47.753,52.247,19.78,256,0.900,bicubic,-52.016,-47.353,-62 -tf_efficientnet_b0_ap,28.338,71.662,47.527,52.473,5.29,224,0.875,bicubic,-48.750,-45.731,+115 -dla169,28.322,71.678,47.393,52.607,53.39,224,0.875,bilinear,-50.360,-46.943,+36 -dla102x2,28.315,71.685,46.770,53.230,41.28,224,0.875,bilinear,-51.127,-47.876,-7 -tf_efficientnet_cc_b0_4e,28.313,71.687,47.360,52.640,13.31,224,0.875,bicubic,-48.997,-45.980,+102 -darknet53,28.313,71.687,46.873,53.127,41.61,288,1.000,bicubic,-52.225,-48.547,-83 -mixnet_xl,28.291,71.709,46.700,53.300,11.90,224,0.875,bicubic,-52.187,-48.234,-79 -gluon_resnet50_v1d,28.240,71.760,45.867,54.133,25.58,224,0.875,bicubic,-50.830,-48.599,+16 -wide_resnet101_2,28.112,71.888,46.411,53.589,126.89,224,0.875,bilinear,-50.740,-47.877,+23 -gluon_resnet101_v1c,28.104,71.896,45.959,54.041,44.57,224,0.875,bicubic,-51.432,-48.619,-20 -regnetx_320,28.093,71.907,45.120,54.880,107.81,224,0.875,bicubic,-52.151,-49.900,-61 -densenet161,28.081,71.919,46.639,53.361,28.68,224,0.875,bicubic,-49.273,-46.997,+94 -regnety_320,28.061,71.939,45.452,54.548,145.05,224,0.875,bicubic,-52.743,-49.792,-101 -gernet_s,28.038,71.963,46.733,53.267,8.17,224,0.875,bilinear,-48.878,-46.401,+110 -mobilevitv2_175,28.034,71.966,46.085,53.915,14.25,256,0.888,bicubic,-52.828,-49.177,-106 -efficientnet_el_pruned,28.018,71.982,46.788,53.212,10.59,300,0.904,bicubic,-52.280,-48.426,-70 -levit_192,28.014,71.986,45.872,54.128,10.95,224,0.900,bicubic,-51.822,-48.918,-43 -xception41,27.888,72.112,45.896,54.104,26.97,299,0.903,bicubic,-50.628,-48.384,+26 -regnetx_160,27.817,72.183,45.623,54.377,54.28,224,0.875,bicubic,-52.037,-49.207,-47 -tf_inception_v3,27.778,72.222,45.717,54.283,23.83,299,0.875,bicubic,-50.074,-47.923,+66 -res2net101_26w_4s,27.774,72.226,45.167,54.833,45.21,224,0.875,bilinear,-51.422,-49.269,-4 -tf_efficientnetv2_b1,27.762,72.238,46.574,53.426,8.14,240,0.882,bicubic,-51.704,-48.148,-27 -vit_base_patch16_224_sam,27.709,72.291,45.112,54.888,86.57,224,0.900,bicubic,-52.535,-49.642,-72 -fbnetv3_b,27.672,72.328,46.981,53.019,8.60,256,0.950,bilinear,-51.470,-47.769,-6 -repvgg_b1,27.648,72.352,46.521,53.479,57.42,224,0.875,bilinear,-50.720,-47.573,+32 -mobilevitv2_200,27.629,72.371,45.766,54.234,18.45,256,0.888,bicubic,-53.511,-49.602,-138 -hrnet_w44,27.623,72.377,45.845,54.155,67.06,224,0.875,bilinear,-51.273,-48.525,+4 -gcresnet33ts,27.585,72.415,46.199,53.801,19.88,256,0.900,bicubic,-52.491,-48.795,-67 -inception_v3,27.556,72.444,45.265,54.735,23.83,299,0.875,bicubic,-49.882,-48.211,+74 -resmlp_24_224,27.534,72.466,45.697,54.303,30.02,224,0.875,bicubic,-51.844,-48.849,-32 -pit_xs_224,27.497,72.503,45.904,54.096,10.62,224,0.900,bicubic,-50.693,-48.262,+35 -regnetx_080,27.393,72.607,45.002,54.998,39.57,224,0.875,bicubic,-51.809,-49.550,-16 -hrnet_w30,27.389,72.611,46.548,53.452,37.71,224,0.875,bilinear,-50.809,-47.676,+32 -hrnet_w32,27.369,72.631,45.990,54.010,41.23,224,0.875,bilinear,-51.083,-48.198,+17 -gluon_resnet50_v1s,27.322,72.678,45.224,54.776,25.68,224,0.875,bicubic,-51.384,-49.014,+3 -res2net50_26w_8s,27.310,72.690,44.823,55.177,48.40,224,0.875,bilinear,-51.642,-49.483,-7 -densenet201,27.259,72.741,46.220,53.780,20.01,224,0.875,bicubic,-50.029,-47.260,+72 -densenetblur121d,27.228,72.772,46.293,53.707,8.00,224,0.875,bicubic,-49.352,-46.895,+97 -efficientnet_b1_pruned,27.181,72.819,45.872,54.128,6.33,240,0.882,bicubic,-51.063,-47.962,+24 -tf_efficientnetv2_b2,27.173,72.827,44.572,55.428,10.10,260,0.890,bicubic,-53.035,-50.472,-86 -resnet33ts,27.136,72.865,45.332,54.668,19.68,256,0.900,bicubic,-52.072,-49.242,-26 -resnetrs50,27.098,72.902,45.029,54.971,35.69,224,0.910,bicubic,-52.788,-49.941,-73 -rexnet_130,27.096,72.904,45.941,54.059,7.56,224,0.875,bicubic,-52.406,-48.741,-52 -resnet32ts,27.045,72.955,45.263,54.737,17.96,256,0.900,bicubic,-51.969,-49.093,-18 -dla102x,27.039,72.961,45.485,54.515,26.31,224,0.875,bilinear,-51.473,-48.743,0 -gmixer_24_224,27.033,72.967,44.369,55.631,24.72,224,0.875,bicubic,-51.003,-49.301,+27 -tv_resnet101,26.963,73.037,45.236,54.764,44.55,224,0.875,bilinear,-50.417,-48.308,+58 -regnetx_120,26.870,73.130,44.676,55.324,46.11,224,0.875,bicubic,-52.722,-50.058,-60 -resnext50d_32x4d,26.866,73.134,44.446,55.554,25.05,224,0.875,bicubic,-52.810,-50.420,-64 -rexnet_100,26.831,73.169,45.377,54.623,4.80,224,0.875,bicubic,-51.029,-48.497,+33 -densenet169,26.827,73.173,45.385,54.615,14.15,224,0.875,bicubic,-49.077,-47.639,+97 -tinynet_a,26.817,73.183,45.106,54.894,6.19,192,0.875,bicubic,-50.831,-48.430,+39 -legacy_seresnext101_32x4d,26.815,73.185,43.501,56.499,48.96,224,0.875,bilinear,-53.407,-51.513,-101 -regnetx_064,26.790,73.210,44.919,55.081,26.21,224,0.875,bicubic,-52.284,-49.541,-31 -regnety_120,26.784,73.216,44.442,55.558,51.82,224,0.875,bicubic,-53.592,-50.680,-119 -regnetx_032,26.707,73.293,45.228,54.772,15.30,224,0.875,bicubic,-51.477,-48.860,+11 -densenet121,26.674,73.326,45.890,54.110,7.98,224,0.875,bicubic,-48.906,-46.758,+96 -legacy_seresnet152,26.672,73.328,43.953,56.047,66.82,224,0.875,bilinear,-51.980,-50.417,-17 -efficientnet_es,26.619,73.381,45.122,54.878,5.44,224,0.875,bicubic,-51.439,-48.822,+13 -res2net50_26w_6s,26.597,73.403,43.998,56.002,37.05,224,0.875,bilinear,-51.973,-50.126,-17 -repvgg_b1g4,26.581,73.419,45.086,54.914,39.97,224,0.875,bilinear,-51.007,-48.744,+35 -dla60x,26.554,73.446,45.008,54.992,17.35,224,0.875,bilinear,-51.674,-49.016,+2 -coat_lite_tiny,26.509,73.491,44.646,55.354,5.72,224,0.900,bicubic,-51.007,-49.268,+37 -mobilenetv3_large_100_miil,26.507,73.493,44.491,55.509,5.48,224,0.875,bilinear,-51.415,-48.429,+16 -res2net50_14w_8s,26.483,73.517,44.371,55.629,25.06,224,0.875,bilinear,-51.661,-49.481,+3 -tf_efficientnet_b0,26.477,73.523,45.650,54.350,5.29,224,0.875,bicubic,-50.363,-47.568,+60 -gluon_resnet50_v1b,26.440,73.560,44.043,55.957,25.56,224,0.875,bicubic,-51.144,-49.677,+30 -tf_efficientnet_el,26.357,73.643,44.175,55.825,10.59,300,0.904,bicubic,-53.897,-50.953,-119 -lambda_resnet26t,26.342,73.658,44.412,55.588,10.96,256,0.940,bicubic,-52.756,-50.178,-49 -levit_128,26.328,73.672,44.114,55.886,9.21,224,0.900,bicubic,-52.154,-49.898,-22 -resmlp_big_24_224,26.320,73.680,43.557,56.443,129.14,224,0.875,bicubic,-54.710,-51.463,-176 -resmlp_12_distilled_224,26.306,73.694,44.870,55.130,15.35,224,0.875,bicubic,-51.640,-48.690,+7 -regnetx_040,26.241,73.759,44.442,55.558,22.12,224,0.875,bicubic,-52.247,-49.796,-27 -mobilevitv2_150,26.190,73.810,43.768,56.232,10.59,256,0.888,bicubic,-54.178,-51.296,-136 -crossvit_9_dagger_240,26.175,73.825,44.538,55.462,8.78,240,0.875,bicubic,-50.803,-49.076,+45 -vit_small_patch32_224,26.161,73.839,45.110,54.890,22.88,224,0.900,bicubic,-49.829,-48.158,+69 -dpn68,26.135,73.865,44.228,55.772,12.61,224,0.875,bicubic,-50.175,-48.750,+65 -efficientnet_b1,26.061,73.939,44.076,55.924,7.79,256,1.000,bicubic,-52.727,-50.270,-42 -mobilevitv2_125,26.025,73.975,43.666,56.334,7.48,256,0.888,bicubic,-53.657,-51.182,-97 -lambda_resnet26rpt_256,26.017,73.983,44.182,55.818,10.99,256,0.940,bicubic,-52.947,-50.244,-52 -hrnet_w18,25.988,74.012,44.817,55.183,21.30,224,0.875,bilinear,-50.772,-48.627,+48 -hardcorenas_f,25.941,74.059,44.212,55.788,8.20,224,0.875,bilinear,-52.161,-49.590,-12 -resnet34,25.890,74.110,43.988,56.012,21.80,224,0.875,bilinear,-49.222,-48.296,+81 -tresnet_m_448,25.862,74.138,42.872,57.128,31.39,448,0.875,bilinear,-55.844,-52.700,-235 -resnet26t,25.860,74.140,43.953,56.047,16.01,256,0.940,bicubic,-52.004,-49.889,-3 -res2net50_26w_4s,25.858,74.142,43.155,56.845,25.70,224,0.875,bilinear,-52.104,-50.697,-8 -coat_tiny,25.848,74.152,43.279,56.721,5.50,224,0.900,bicubic,-52.588,-50.759,-35 -hardcorenas_c,25.821,74.179,44.770,55.230,5.52,224,0.875,bilinear,-51.231,-48.390,+30 -gluon_resnet50_v1c,25.780,74.220,43.025,56.975,25.58,224,0.875,bicubic,-52.228,-50.965,-14 -halonet26t,25.766,74.234,43.231,56.769,12.48,256,0.950,bicubic,-53.346,-51.083,-71 -selecsls60,25.727,74.273,44.065,55.935,30.67,224,0.875,bicubic,-52.257,-49.767,-15 -hardcorenas_e,25.664,74.336,43.404,56.596,8.07,224,0.875,bilinear,-52.122,-50.300,-3 -dla60_res2next,25.656,74.344,43.664,56.336,17.03,224,0.875,bilinear,-52.800,-50.482,-43 -dla60_res2net,25.646,74.354,43.583,56.417,20.85,224,0.875,bilinear,-52.812,-50.613,-45 -poolformer_s12,25.636,74.364,44.137,55.863,11.92,224,0.900,bicubic,-51.602,-49.369,+17 -ecaresnet26t,25.540,74.460,43.666,56.334,16.01,320,0.950,bicubic,-54.312,-51.418,-123 -resmlp_12_224,25.520,74.480,44.340,55.660,15.35,224,0.875,bicubic,-51.136,-48.840,+34 -mixnet_l,25.514,74.486,43.463,56.537,7.33,224,0.875,bicubic,-53.462,-50.715,-71 -tf_efficientnet_lite1,25.503,74.497,43.579,56.421,5.42,240,0.882,bicubic,-51.135,-49.645,+33 -cs3darknet_focus_m,25.485,74.515,43.762,56.238,9.30,288,0.950,bicubic,-51.797,-50.210,+10 -bat_resnext26ts,25.467,74.533,43.206,56.794,10.73,256,0.900,bicubic,-52.781,-50.890,-39 -eca_halonext26ts,25.455,74.545,43.194,56.806,10.76,256,0.940,bicubic,-54.033,-51.410,-110 -botnet26t_256,25.455,74.545,42.638,57.362,12.49,256,0.950,bicubic,-53.803,-51.890,-92 -tv_resnext50_32x4d,25.450,74.550,42.781,57.219,25.03,224,0.875,bilinear,-52.168,-50.919,-10 -repvgg_a2,25.434,74.566,43.941,56.059,28.21,224,0.875,bilinear,-51.026,-49.069,+34 -tf_mixnet_l,25.420,74.580,42.538,57.462,7.33,224,0.875,bicubic,-53.358,-51.460,-69 -hardcorenas_b,25.400,74.600,44.192,55.808,5.18,224,0.875,bilinear,-51.136,-48.562,+29 -res2next50,25.387,74.613,42.498,57.502,24.67,224,0.875,bilinear,-52.871,-51.390,-47 -legacy_seresnet101,25.334,74.666,42.823,57.177,49.33,224,0.875,bilinear,-53.046,-51.439,-53 -selecsls60b,25.332,74.668,43.559,56.441,32.77,224,0.875,bicubic,-53.072,-50.613,-56 -hardcorenas_d,25.324,74.676,43.123,56.877,7.50,224,0.875,bilinear,-52.106,-50.361,-7 -dla102,25.320,74.680,43.846,56.154,33.27,224,0.875,bilinear,-52.708,-50.104,-38 -resnetv2_50x1_bitm,25.316,74.684,45.358,54.642,25.55,448,1.000,bilinear,-55.026,-50.328,-173 -resnest14d,25.275,74.725,44.090,55.910,10.61,224,0.875,bilinear,-50.233,-48.434,+41 -legacy_seresnext50_32x4d,25.214,74.786,41.942,58.058,27.56,224,0.875,bilinear,-53.862,-52.492,-93 -mixer_b16_224,25.117,74.883,41.217,58.783,59.88,224,0.875,bicubic,-51.493,-51.013,+17 -efficientnet_b0,25.027,74.973,42.795,57.205,5.29,224,0.875,bicubic,-52.673,-50.737,-27 -res2net50_48w_2s,25.025,74.975,42.206,57.794,25.29,224,0.875,bilinear,-52.499,-51.344,-19 -gluon_resnet34_v1b,24.935,75.065,42.241,57.759,21.80,224,0.875,bicubic,-49.657,-49.747,+58 -mobilenetv2_120d,24.931,75.069,43.051,56.949,5.83,224,0.875,bicubic,-52.359,-50.449,-12 -dla60,24.911,75.089,43.294,56.706,22.04,224,0.875,bilinear,-52.111,-50.026,-2 -eca_botnext26ts_256,24.868,75.132,42.950,57.050,10.59,256,0.950,bicubic,-54.408,-51.666,-113 -regnety_016,24.817,75.183,42.610,57.390,11.20,224,0.875,bicubic,-53.039,-51.110,-38 -xcit_nano_12_p8_224_dist,24.811,75.189,43.072,56.928,3.05,224,1.000,bicubic,-51.517,-50.022,+16 -seresnext26ts,24.689,75.311,43.106,56.894,10.39,256,0.900,bicubic,-53.169,-50.684,-41 -eca_resnext26ts,24.658,75.342,42.850,57.150,10.30,256,0.900,bicubic,-52.800,-50.718,-24 -cs3darknet_m,24.630,75.370,42.970,57.030,9.31,288,0.950,bicubic,-52.996,-51.044,-34 -mobilevitv2_100,24.547,75.453,42.919,57.081,4.90,256,0.888,bicubic,-53.539,-51.241,-57 -tf_efficientnet_lite2,24.528,75.472,42.280,57.720,6.09,260,0.890,bicubic,-52.938,-51.478,-28 -regnetx_016,24.487,75.513,42.510,57.490,9.19,224,0.875,bicubic,-52.455,-50.914,-8 -skresnet18,24.483,75.517,42.540,57.460,11.96,224,0.875,bicubic,-48.551,-48.626,+63 -pit_ti_distilled_224,24.408,75.592,42.734,57.266,5.10,224,0.900,bicubic,-50.126,-49.362,+46 -hardcorenas_a,24.371,75.629,43.292,56.708,5.26,224,0.875,bilinear,-51.559,-49.218,+14 -tf_efficientnet_lite0,24.367,75.633,42.504,57.496,4.65,224,0.875,bicubic,-50.465,-49.670,+37 -tv_resnet50,24.084,75.916,41.313,58.687,25.56,224,0.875,bilinear,-52.050,-51.555,+8 -levit_128s,24.056,75.944,41.005,58.995,7.78,224,0.900,bicubic,-52.458,-51.865,+1 -legacy_seresnet34,24.029,75.971,41.905,58.095,21.96,224,0.875,bilinear,-50.781,-50.221,+35 -xcit_nano_12_p16_384_dist,24.011,75.989,42.327,57.673,3.05,384,1.000,bicubic,-51.445,-50.363,+20 -xcit_nano_12_p8_384_dist,23.956,76.044,41.946,58.054,3.05,384,1.000,bicubic,-53.860,-52.100,-52 -gcresnext26ts,23.950,76.050,41.359,58.641,10.48,256,0.900,bicubic,-53.864,-52.477,-52 -resnet18d,23.933,76.067,42.298,57.702,11.71,224,0.875,bicubic,-48.325,-48.390,+63 -efficientnet_lite0,23.907,76.093,42.084,57.916,4.65,224,0.875,bicubic,-51.561,-50.432,+14 -resnext26ts,23.868,76.132,41.109,58.891,10.30,256,0.900,bicubic,-52.912,-52.023,-15 -tv_densenet121,23.840,76.160,41.921,58.079,7.98,224,0.875,bicubic,-50.900,-50.227,+29 -efficientnet_es_pruned,23.838,76.162,41.989,58.011,5.44,224,0.875,bicubic,-51.162,-50.453,+23 -mobilenetv2_140,23.714,76.286,41.477,58.523,6.11,224,0.875,bicubic,-52.798,-51.521,-8 -mixnet_m,23.714,76.286,41.148,58.852,5.01,224,0.875,bicubic,-53.548,-52.274,-36 -dla34,23.679,76.321,41.539,58.461,15.74,224,0.875,bilinear,-50.945,-50.533,+28 -legacy_seresnet50,23.651,76.349,40.091,59.909,28.09,224,0.875,bilinear,-53.981,-53.659,-57 -ese_vovnet19b_dw,23.528,76.472,41.284,58.716,6.54,224,0.875,bicubic,-53.266,-51.982,-23 -tf_mixnet_m,23.484,76.516,41.001,58.999,5.01,224,0.875,bicubic,-53.462,-52.151,-30 -tv_resnet34,23.469,76.531,41.364,58.636,21.80,224,0.875,bilinear,-49.839,-50.060,+39 -tf_efficientnet_em,23.361,76.639,40.400,59.600,6.90,240,0.882,bicubic,-54.765,-53.646,-84 -selecsls42b,23.355,76.645,40.675,59.325,32.46,224,0.875,bicubic,-53.823,-52.717,-41 -repvgg_b0,23.319,76.681,41.172,58.828,15.82,224,0.875,bilinear,-51.835,-51.244,+7 -xcit_nano_12_p16_224_dist,23.264,76.736,41.382,58.618,3.05,224,1.000,bicubic,-49.038,-49.480,+47 -mobilenetv2_110d,23.076,76.924,40.748,59.252,4.52,224,0.875,bicubic,-51.960,-51.444,+9 -vit_base_patch32_224_sam,23.048,76.952,39.574,60.426,88.22,224,0.900,bicubic,-50.644,-51.438,+29 -tinynet_b,23.023,76.977,40.968,59.032,3.73,188,0.875,bicubic,-51.951,-51.214,+10 -deit_tiny_distilled_patch16_224,22.726,77.274,40.773,59.227,5.91,224,0.900,bicubic,-51.786,-51.117,+19 -mobilenetv3_large_100,22.655,77.345,40.775,59.225,5.48,224,0.875,bicubic,-53.121,-51.765,-12 -mobilenetv3_rw,22.626,77.374,40.380,59.620,5.48,224,0.875,bicubic,-53.008,-52.328,-11 -tf_mobilenetv3_large_100,22.565,77.435,39.761,60.239,5.48,224,0.875,bilinear,-52.947,-52.845,-9 -mobilevit_s,22.476,77.524,38.643,61.357,5.58,256,0.900,bicubic,-55.834,-55.509,-104 -tf_efficientnet_es,22.416,77.585,39.093,60.907,5.44,224,0.875,bicubic,-54.182,-54.111,-31 -xcit_nano_12_p8_224,22.412,77.588,40.657,59.343,3.05,224,1.000,bicubic,-51.504,-51.511,+19 -hrnet_w18_small_v2,22.337,77.663,39.869,60.131,15.60,224,0.875,bilinear,-52.773,-52.547,-2 -convit_tiny,22.276,77.724,39.665,60.335,5.71,224,0.875,bicubic,-50.838,-52.055,+25 -edgenext_x_small,22.199,77.801,39.075,60.925,2.34,256,0.900,bicubic,-52.665,-53.225,+1 -regnety_008,22.119,77.881,38.891,61.109,6.26,224,0.875,bicubic,-54.195,-54.179,-29 -seresnext26t_32x4d,21.983,78.017,38.486,61.514,16.81,224,0.875,bicubic,-55.985,-55.262,-94 -regnety_006,21.981,78.019,38.950,61.050,6.06,224,0.875,bicubic,-53.271,-53.582,-11 -vit_tiny_r_s16_p8_384,21.958,78.042,39.403,60.597,6.36,384,1.000,bicubic,-53.994,-53.859,-27 -regnetx_008,21.942,78.058,38.926,61.074,7.26,224,0.875,bicubic,-53.092,-53.414,-7 -resnet26d,21.907,78.094,38.621,61.379,16.01,224,0.875,bicubic,-54.795,-54.531,-45 -semnasnet_100,21.897,78.103,38.602,61.398,3.89,224,0.875,bicubic,-53.553,-53.998,-17 -pit_ti_224,21.869,78.131,39.543,60.457,4.85,224,0.900,bicubic,-51.043,-51.863,+20 -regnetx_006,21.738,78.263,38.916,61.084,6.20,224,0.875,bicubic,-52.118,-52.756,+8 -vit_tiny_patch16_384,21.714,78.286,39.327,60.673,5.79,384,1.000,bicubic,-56.716,-55.217,-126 -crossvit_9_240,21.688,78.312,39.278,60.722,8.55,240,0.875,bicubic,-52.272,-52.686,+4 -vgg19_bn,21.625,78.374,39.280,60.720,143.68,224,0.875,bilinear,-52.589,-52.564,-1 -ghostnet_100,21.614,78.386,38.696,61.304,5.18,224,0.875,bilinear,-52.366,-52.762,+1 -semnasnet_075,21.570,78.430,38.934,61.066,2.91,224,0.875,bicubic,-51.404,-52.200,+12 -gluon_resnet18_v1b,21.557,78.443,38.887,61.113,11.69,224,0.875,bicubic,-49.281,-50.875,+31 -mobilevitv2_075,21.535,78.465,38.635,61.365,2.87,256,0.888,bicubic,-54.073,-54.123,-33 -fbnetc_100,21.508,78.492,38.158,61.842,5.57,224,0.875,bilinear,-53.608,-54.228,-23 -xcit_nano_12_p16_224,21.437,78.563,39.798,60.202,3.05,224,1.000,bicubic,-48.517,-49.958,+32 -mnasnet_100,21.362,78.638,37.721,62.279,4.38,224,0.875,bicubic,-53.288,-54.393,-14 -lcnet_100,21.290,78.710,38.849,61.151,2.95,224,0.875,bicubic,-50.820,-51.529,+18 -resnet26,21.285,78.715,38.020,61.980,16.00,224,0.875,bicubic,-54.015,-54.560,-30 -ssl_resnet18,21.278,78.722,39.107,60.893,11.69,224,0.875,bilinear,-51.326,-52.317,+7 -mixnet_s,21.256,78.744,38.183,61.817,4.13,224,0.875,bicubic,-54.740,-54.617,-48 -seresnext26d_32x4d,21.250,78.750,37.319,62.681,16.81,224,0.875,bicubic,-56.356,-56.287,-98 -legacy_seresnext26_32x4d,21.091,78.909,37.629,62.371,16.79,224,0.875,bicubic,-56.013,-55.687,-78 -crossvit_tiny_240,21.050,78.950,38.053,61.947,7.01,240,0.875,bicubic,-52.288,-53.861,-5 -regnetx_004,20.898,79.102,37.568,62.432,5.16,224,0.875,bicubic,-51.498,-53.270,+3 -spnasnet_100,20.865,79.135,37.888,62.112,4.42,224,0.875,bilinear,-53.225,-53.928,-16 -legacy_seresnet18,20.841,79.159,37.613,62.387,11.78,224,0.875,bicubic,-50.899,-52.717,+12 -mobilenetv2_100,20.777,79.223,37.764,62.236,3.50,224,0.875,bicubic,-52.179,-53.246,-3 -tf_mixnet_s,20.462,79.538,36.615,63.385,4.13,224,0.875,bicubic,-55.190,-56.011,-50 -vit_tiny_patch16_224,20.458,79.542,37.603,62.397,5.72,224,0.900,bicubic,-55.006,-55.241,-44 -regnety_004,20.415,79.585,37.002,62.998,4.34,224,0.875,bicubic,-53.609,-54.754,-20 -hrnet_w18_small,20.364,79.636,37.089,62.911,13.19,224,0.875,bilinear,-51.972,-53.591,-1 -tf_mobilenetv3_large_075,20.364,79.636,36.770,63.230,3.99,224,0.875,bilinear,-53.076,-54.578,-16 -resnet18,20.224,79.776,37.256,62.744,11.69,224,0.875,bilinear,-49.524,-51.828,+16 -mixer_l16_224,20.169,79.831,32.942,67.058,208.20,224,0.875,bicubic,-51.897,-54.724,+2 -deit_tiny_patch16_224,20.166,79.835,37.560,62.440,5.72,224,0.900,bicubic,-52.008,-53.554,-1 -tf_mobilenetv3_large_minimal_100,20.108,79.891,36.906,63.094,3.92,224,0.875,bilinear,-52.142,-53.714,-3 -vgg16_bn,19.957,80.043,36.303,63.697,138.37,224,0.875,bilinear,-53.393,-55.201,-20 -vit_tiny_r_s16_p8_224,19.324,80.676,36.051,63.949,6.34,224,0.900,bicubic,-52.470,-54.767,-1 -tinynet_c,19.260,80.740,35.988,64.012,2.46,184,0.875,bicubic,-51.968,-53.760,+2 -edgenext_xx_small,18.580,81.420,34.693,65.307,1.33,256,0.900,bicubic,-52.526,-55.339,+2 -mobilevit_xs,18.303,81.697,33.227,66.773,2.32,256,0.900,bicubic,-56.331,-59.119,-38 -lcnet_075,18.161,81.839,34.406,65.594,2.36,224,0.875,bicubic,-50.653,-53.958,+10 -vgg19,17.929,82.071,33.054,66.946,143.67,224,0.875,bilinear,-54.437,-57.818,-15 -vgg13_bn,17.803,82.197,34.039,65.961,133.05,224,0.875,bilinear,-53.795,-56.337,-5 -vgg16,17.540,82.460,32.769,67.231,138.36,224,0.875,bilinear,-54.050,-57.613,-5 -regnety_002,17.458,82.542,32.431,67.569,3.16,224,0.875,bicubic,-52.798,-57.103,-1 -vgg11_bn,17.403,82.597,33.009,66.991,132.87,224,0.875,bilinear,-52.957,-56.793,-3 -mobilevitv2_050,17.302,82.698,32.999,67.001,1.37,256,0.888,bicubic,-52.838,-56.931,-2 -resnet10t,17.281,82.719,33.070,66.930,5.44,224,0.950,bilinear,-51.027,-55.010,+5 -regnetx_002,16.962,83.038,32.223,67.777,2.68,224,0.875,bicubic,-51.792,-56.333,+3 -mobilenetv3_small_100,16.815,83.185,32.535,67.465,2.54,224,0.875,bicubic,-50.843,-55.099,+6 -tinynet_d,16.675,83.325,32.459,67.541,2.34,152,0.875,bicubic,-50.287,-54.605,+6 -mobilenetv2_050,16.675,83.325,31.952,68.048,1.97,224,0.875,bicubic,-49.269,-54.128,+8 -mnasnet_small,16.636,83.364,31.922,68.078,2.03,224,0.875,bicubic,-49.570,-54.584,+5 -resnet14t,16.471,83.529,30.722,69.278,10.08,224,0.950,bilinear,-55.885,-59.618,-26 -dla60x_c,16.320,83.680,31.752,68.249,1.32,224,0.875,bilinear,-51.560,-56.682,0 -tf_mobilenetv3_small_100,16.227,83.772,31.225,68.775,2.54,224,0.875,bilinear,-51.699,-56.443,-2 -vgg13,16.104,83.896,30.983,69.017,133.05,224,0.875,bilinear,-53.822,-58.263,-10 -vgg11,15.730,84.270,30.453,69.547,132.86,224,0.875,bilinear,-53.298,-58.175,-9 -mobilenetv3_small_075,14.954,85.046,29.735,70.265,2.04,224,0.875,bicubic,-50.284,-55.705,+3 -tf_mobilenetv3_small_075,14.948,85.052,29.576,70.424,2.04,224,0.875,bilinear,-50.764,-56.554,+1 -dla46_c,14.671,85.329,29.374,70.626,1.30,224,0.875,bilinear,-50.201,-56.928,+2 -mobilevit_xxs,14.508,85.492,28.670,71.330,1.27,256,0.900,bicubic,-54.412,-60.276,-12 -dla46x_c,14.382,85.618,29.179,70.821,1.07,224,0.875,bilinear,-51.570,-57.807,-4 -lcnet_050,14.306,85.694,28.647,71.353,1.88,224,0.875,bicubic,-48.788,-55.735,0 -tf_mobilenetv3_small_minimal_100,13.958,86.042,27.979,72.022,2.04,224,0.875,bilinear,-48.942,-56.255,0 -tinynet_e,12.669,87.331,26.389,73.611,2.04,106,0.875,bicubic,-47.187,-55.377,0 -mobilenetv3_small_050,11.034,88.966,23.471,76.529,1.59,224,0.875,bicubic,-46.856,-56.723,0 +eva_giant_patch14_336.clip_ft_in1k,71.180,28.820,90.291,9.709,"1,013.01",336,1.000,bicubic,-18.296,-8.533,+2 +eva_giant_patch14_224.clip_ft_in1k,70.559,29.441,90.004,9.996,"1,012.56",224,1.000,bicubic,-18.540,-8.712,+3 +eva_giant_patch14_336.m30m_ft_in22k_in1k,68.056,31.944,87.821,12.179,"1,013.01",336,1.000,bicubic,-21.512,-11.131,-1 +eva_giant_patch14_560.m30m_ft_in22k_in1k,67.486,32.514,87.461,12.539,"1,014.45",560,1.000,bicubic,-22.310,-11.531,-3 +vit_huge_patch14_clip_224.laion2b_ft_in1k,67.409,32.590,87.895,12.105,632.05,224,1.000,bicubic,-20.184,-10.325,+25 +vit_large_patch14_clip_336.laion2b_ft_in1k,65.733,34.267,86.913,13.087,304.53,336,1.000,bicubic,-22.115,-11.457,+21 +vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k,65.321,34.679,86.836,13.164,632.05,224,1.000,bicubic,-22.925,-11.714,+9 +vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k,65.264,34.736,86.763,13.237,632.46,336,1.000,bicubic,-23.310,-11.897,+1 +vit_large_patch14_clip_224.laion2b_ft_in1k,64.808,35.192,86.573,13.427,304.20,224,1.000,bicubic,-22.484,-11.673,+26 +vit_large_patch14_clip_336.openai_ft_in12k_in1k,64.065,35.935,85.903,14.097,304.53,336,1.000,bicubic,-24.201,-12.629,+5 +eva_large_patch14_336.in22k_ft_in1k,63.108,36.892,84.386,15.614,304.53,336,1.000,bicubic,-25.556,-14.334,-5 +vit_large_patch14_clip_224.openai_ft_in1k,62.630,37.370,85.117,14.883,304.20,224,1.000,bicubic,-25.222,-13.311,+14 +vit_large_patch14_clip_224.laion2b_ft_in12k_in1k,62.053,37.947,84.317,15.683,304.20,224,1.000,bicubic,-25.837,-14.093,+12 +vit_large_patch14_clip_336.laion2b_ft_in12k_in1k,61.628,38.372,83.659,16.341,304.53,336,1.000,bicubic,-26.554,-14.913,+6 +vit_large_patch14_clip_224.openai_ft_in12k_in1k,61.402,38.598,83.374,16.626,304.20,224,1.000,bicubic,-26.766,-15.170,+6 +eva_large_patch14_196.in22k_ft_in1k,61.111,38.889,82.774,17.226,304.14,196,1.000,bicubic,-26.827,-15.718,+7 +eva_large_patch14_336.in22k_ft_in22k_in1k,60.938,39.062,82.155,17.845,304.53,336,1.000,bicubic,-28.265,-16.695,-13 +eva_large_patch14_196.in22k_ft_in22k_in1k,59.852,40.148,81.124,18.876,304.14,196,1.000,bicubic,-28.734,-17.532,-10 +ig_resnext101_32x48d,58.810,41.190,81.076,18.924,828.41,224,0.875,bilinear,-26.618,-16.496,+84 +ig_resnext101_32x32d,58.386,41.614,80.381,19.619,468.53,224,0.875,bilinear,-26.708,-17.057,+107 +beitv2_large_patch16_224.in1k_ft_in22k_in1k,58.358,41.642,80.226,19.774,304.43,224,0.950,bicubic,-30.028,-18.372,-9 +ig_resnext101_32x16d,57.690,42.310,79.905,20.095,194.03,224,0.875,bilinear,-26.480,-17.291,+169 +swsl_resnext101_32x16d,57.458,42.542,80.385,19.615,194.03,224,0.875,bilinear,-25.888,-16.461,+230 +beit_large_patch16_384.in22k_ft_in22k_in1k,56.894,43.106,79.229,20.771,305.00,384,1.000,bicubic,-31.510,-19.379,-13 +vit_base_patch16_clip_384.laion2b_ft_in1k,56.875,43.125,79.994,20.006,86.86,384,1.000,bicubic,-29.745,-18.016,+29 +beit_large_patch16_512.in22k_ft_in22k_in1k,56.755,43.245,78.899,21.101,305.67,512,1.000,bicubic,-31.843,-19.757,-19 +swsl_resnext101_32x8d,56.438,43.562,78.944,21.056,88.79,224,0.875,bilinear,-27.846,-18.232,+152 +maxvit_xlarge_tf_384.in21k_ft_in1k,56.207,43.793,78.750,21.250,475.32,384,1.000,bicubic,-32.099,-19.794,-14 +maxvit_xlarge_tf_512.in21k_ft_in1k,56.150,43.850,78.630,21.370,475.77,512,1.000,bicubic,-32.388,-20.014,-19 +maxvit_base_tf_512.in21k_ft_in1k,56.089,43.911,78.599,21.401,119.88,512,1.000,bicubic,-32.123,-19.933,-11 +deit3_huge_patch14_224_in21ft1k,55.764,44.236,77.624,22.376,632.13,224,1.000,bicubic,-31.419,-20.636,+6 +maxvit_base_tf_384.in21k_ft_in1k,55.633,44.367,78.064,21.936,119.65,384,1.000,bicubic,-32.289,-20.478,-8 +vit_base_patch16_clip_224.laion2b_ft_in1k,55.413,44.587,79.049,20.951,86.57,224,1.000,bicubic,-30.055,-18.527,+68 +maxvit_large_tf_512.in21k_ft_in1k,55.171,44.829,77.276,22.724,212.33,512,1.000,bicubic,-33.047,-21.322,-16 +maxvit_large_tf_384.in21k_ft_in1k,55.075,44.925,77.142,22.858,212.03,384,1.000,bicubic,-32.917,-21.424,-13 +convnext_xlarge.fb_in22k_ft_in1k_384,54.965,45.035,76.828,23.172,350.20,384,1.000,bicubic,-32.783,-21.726,-8 +beit_large_patch16_224.in22k_ft_in22k_in1k,54.959,45.041,77.610,22.390,304.43,224,0.900,bicubic,-32.517,-20.694,-6 +ig_resnext101_32x8d,54.918,45.082,77.534,22.466,88.79,224,0.875,bilinear,-27.770,-19.102,+257 +deit3_large_patch16_384_in21ft1k,54.878,45.122,77.372,22.628,304.76,384,1.000,bicubic,-32.838,-21.140,-10 +deit3_large_patch16_224_in21ft1k,54.361,45.639,76.561,23.439,304.37,224,1.000,bicubic,-32.616,-21.677,+4 +swsl_resnext101_32x4d,53.603,46.397,76.347,23.653,44.18,224,0.875,bilinear,-29.627,-20.413,+216 +vit_base_patch16_clip_384.laion2b_ft_in12k_in1k,53.493,46.507,75.667,24.333,86.86,384,1.000,bicubic,-33.725,-22.367,-6 +vit_base_patch16_clip_384.openai_ft_in1k,53.080,46.920,76.653,23.347,86.86,384,1.000,bicubic,-33.126,-21.221,+25 +convnext_large.fb_in22k_ft_in1k_384,52.758,47.242,74.704,25.296,197.77,384,1.000,bicubic,-34.714,-23.682,-12 +vit_large_patch16_384.augreg_in21k_ft_in1k,52.754,47.246,74.696,25.304,304.72,384,1.000,bicubic,-34.326,-23.604,-5 +swinv2_large_window12to24_192to384_22kft1k,52.300,47.700,74.423,25.577,196.74,384,1.000,bicubic,-35.158,-23.829,-13 +convnext_xlarge.fb_in22k_ft_in1k,52.229,47.771,73.953,26.047,350.20,288,1.000,bicubic,-35.109,-24.375,-13 +vit_large_r50_s32_384.augreg_in21k_ft_in1k,52.039,47.961,73.558,26.442,329.09,384,1.000,bicubic,-34.145,-24.360,+21 +vit_large_patch16_224.augreg_in21k_ft_in1k,51.832,48.168,73.694,26.306,304.33,224,0.900,bicubic,-34.010,-24.130,+34 +vit_base_patch16_clip_224.laion2b_ft_in12k_in1k,51.760,48.240,74.635,25.365,86.57,224,0.950,bicubic,-34.410,-23.119,+20 +convnext_base.fb_in22k_ft_in1k_384,51.561,48.439,74.535,25.465,88.59,384,1.000,bicubic,-35.233,-23.729,-1 +tf_efficientnet_l2.ns_jft_in1k_475,51.494,48.506,73.928,26.072,480.31,475,0.936,bicubic,-36.740,-24.618,-35 +vit_base_patch16_clip_384.openai_ft_in12k_in1k,51.153,48.847,74.328,25.672,86.86,384,0.950,bicubic,-35.882,-23.852,-11 +swinv2_base_window12to24_192to384_22kft1k,50.974,49.026,73.318,26.682,87.92,384,1.000,bicubic,-36.134,-24.918,-15 +vit_base_patch16_clip_224.openai_ft_in1k,50.936,49.064,74.855,25.145,86.57,224,0.900,bicubic,-34.344,-22.551,+56 +swinv2_large_window12to16_192to256_22kft1k,50.441,49.559,72.752,27.247,196.74,256,0.900,bicubic,-36.495,-25.356,-10 +swsl_resnext50_32x4d,50.437,49.563,73.368,26.633,25.03,224,0.875,bilinear,-31.745,-22.862,+290 +swin_large_patch4_window12_384,50.404,49.596,72.564,27.436,196.74,384,1.000,bicubic,-36.744,-25.670,-20 +convnext_large.fb_in22k_ft_in1k,49.993,50.007,72.251,27.749,197.77,288,1.000,bicubic,-37.023,-25.955,-16 +tf_efficientnetv2_xl.in21k_ft_in1k,49.734,50.266,72.120,27.880,208.12,512,1.000,bicubic,-37.014,-25.898,-8 +vit_base_patch16_clip_224.openai_ft_in12k_in1k,49.691,50.309,72.878,27.122,86.57,224,0.950,bicubic,-36.240,-24.846,+19 +swsl_resnet50,49.541,50.459,72.334,27.666,25.56,224,0.875,bilinear,-31.625,-22.762,+354 +beitv2_base_patch16_224.in1k_ft_in22k_in1k,49.512,50.488,72.383,27.617,86.53,224,0.900,bicubic,-36.968,-25.665,-4 +vit_base_patch32_clip_224.laion2b_ft_in1k,49.066,50.934,72.578,27.422,88.22,224,0.900,bicubic,-33.516,-23.624,+240 +swin_large_patch4_window7_224,48.991,51.009,71.391,28.609,196.53,224,0.900,bicubic,-37.329,-26.505,-3 +convnext_base.fb_in22k_ft_in1k,48.934,51.066,71.733,28.267,88.59,288,1.000,bicubic,-37.346,-26.357,-2 +swinv2_base_window12to16_192to256_22kft1k,48.796,51.204,71.387,28.613,87.92,256,0.900,bicubic,-37.478,-26.509,-2 +tf_efficientnetv2_l.in21k_ft_in1k,48.745,51.255,71.990,28.010,118.52,480,1.000,bicubic,-38.061,-26.144,-20 +beit_base_patch16_384.in22k_ft_in22k_in1k,48.669,51.331,72.088,27.912,86.74,384,1.000,bicubic,-38.131,-26.050,-20 +swin_base_patch4_window12_384,48.553,51.447,71.813,28.187,87.90,384,1.000,bicubic,-37.879,-26.245,-9 +maxvit_base_tf_512.in1k,48.240,51.760,70.799,29.201,119.88,512,1.000,bicubic,-38.358,-27.121,-16 +vit_large_r50_s32_224.augreg_in21k_ft_in1k,48.203,51.797,70.868,29.132,328.99,224,0.900,bicubic,-36.231,-26.104,+95 +vit_base_patch32_clip_384.laion2b_ft_in12k_in1k,47.944,52.056,70.923,29.077,88.30,384,1.000,bicubic,-37.428,-26.741,+35 +tf_efficientnet_b7.ns_jft_in1k,47.800,52.200,69.640,30.360,66.35,600,0.949,bicubic,-39.040,-28.454,-27 +tf_efficientnet_b6.ns_jft_in1k,47.761,52.239,69.968,30.032,43.04,528,0.942,bicubic,-38.691,-27.914,-15 +vit_base_patch8_224.augreg_in21k_ft_in1k,47.731,52.269,70.921,29.079,86.58,224,0.900,bicubic,-38.065,-26.869,+11 +deit3_base_patch16_384_in21ft1k,47.664,52.336,69.748,30.252,86.88,384,1.000,bicubic,-39.080,-28.364,-24 +vit_base_patch32_clip_448.laion2b_ft_in12k_in1k,47.572,52.428,70.060,29.940,88.34,448,1.000,bicubic,-38.212,-27.574,+10 +tf_efficientnet_l2.ns_jft_in1k,47.570,52.430,70.019,29.981,480.31,800,0.960,bicubic,-40.782,-28.631,-66 +vit_base_patch8_224.augreg2_in21k_ft_in1k,47.501,52.499,70.322,29.678,86.58,224,0.900,bicubic,-38.711,-27.510,-13 +tf_efficientnetv2_m.in21k_ft_in1k,47.466,52.534,70.939,29.061,54.14,480,1.000,bicubic,-38.538,-27.003,-3 +deit3_base_patch16_224_in21ft1k,47.372,52.628,69.773,30.227,86.59,224,1.000,bicubic,-38.342,-27.971,+9 +maxvit_large_tf_512.in1k,47.016,52.984,69.498,30.502,212.33,512,1.000,bicubic,-39.502,-28.386,-27 +convnext_small.fb_in22k_ft_in1k_384,46.861,53.139,69.530,30.470,50.22,384,1.000,bicubic,-38.917,-28.362,+5 +beit_base_patch16_224.in22k_ft_in22k_in1k,46.242,53.758,69.895,30.105,86.53,224,0.900,bicubic,-38.994,-27.761,+32 +vit_base_patch32_clip_384.openai_ft_in12k_in1k,46.236,53.764,69.312,30.688,88.30,384,0.950,bicubic,-38.976,-28.090,+32 +maxvit_base_tf_384.in1k,46.220,53.780,68.528,31.472,119.65,384,1.000,bicubic,-40.074,-29.276,-24 +vit_base_patch16_384.augreg_in21k_ft_in1k,45.894,54.106,68.557,31.443,86.86,384,1.000,bicubic,-40.112,-29.443,-11 +tf_efficientnet_b8.ap_in1k,45.774,54.226,67.911,32.089,87.41,672,0.954,bicubic,-39.596,-29.479,+21 +maxvit_large_tf_384.in1k,45.751,54.249,68.146,31.854,212.03,384,1.000,bicubic,-40.485,-29.544,-24 +vit_base_patch32_clip_224.laion2b_ft_in12k_in1k,45.747,54.253,68.875,31.125,88.22,224,0.900,bicubic,-37.559,-27.655,+164 +tf_efficientnet_b5.ns_jft_in1k,45.615,54.385,67.842,32.158,30.39,456,0.934,bicubic,-40.473,-29.910,-19 +swin_base_patch4_window7_224,45.560,54.440,68.512,31.488,87.77,224,0.900,bicubic,-39.692,-29.050,+21 +mvitv2_large,45.285,54.715,65.195,34.805,217.99,224,0.900,bicubic,-39.965,-32.019,+22 +vit_base_patch16_224.augreg2_in21k_ft_in1k,45.110,54.890,67.425,32.575,86.57,224,0.900,bicubic,-39.996,-29.955,+29 +vit_base_patch32_clip_224.openai_ft_in1k,45.037,54.963,68.459,31.541,88.22,224,0.900,bicubic,-36.893,-27.509,+273 +volo_d5_512,44.572,55.428,65.755,34.245,296.09,512,1.150,bicubic,-42.472,-32.213,-56 +cait_m48_448,44.245,55.755,64.653,35.347,356.46,448,1.000,bicubic,-42.239,-33.102,-40 +deit3_large_patch16_384,44.175,55.825,64.843,35.157,304.76,384,1.000,bicubic,-41.635,-32.753,-13 +volo_d5_448,44.096,55.904,65.065,34.935,295.91,448,1.150,bicubic,-42.858,-32.873,-55 +deit3_huge_patch14_224,43.801,56.199,64.350,35.650,632.13,224,0.900,bicubic,-41.403,-33.008,+19 +convnext_small.fb_in22k_ft_in1k,43.603,56.397,66.448,33.551,50.22,288,1.000,bicubic,-41.659,-31.236,+11 +deit3_large_patch16_224,43.520,56.480,63.574,36.426,304.37,224,0.900,bicubic,-41.244,-33.464,+44 +vit_base_r50_s16_384.orig_in21k_ft_in1k,43.512,56.488,66.785,33.215,98.95,384,1.000,bicubic,-41.460,-30.503,+32 +tf_efficientnet_b4.ns_jft_in1k,43.450,56.550,65.519,34.481,19.34,380,0.922,bicubic,-41.713,-31.951,+17 +deit3_medium_patch16_224_in21ft1k,43.271,56.729,64.892,35.108,38.85,224,1.000,bicubic,-41.289,-32.296,+51 +volo_d5_224,43.261,56.739,64.077,35.923,295.46,224,0.960,bicubic,-42.807,-33.501,-33 +vit_base_patch16_224.augreg_in21k_ft_in1k,43.220,56.780,65.708,34.292,86.57,224,0.900,bicubic,-41.312,-31.586,+50 +volo_d4_448,43.135,56.865,64.114,35.886,193.41,448,1.150,bicubic,-43.655,-33.768,-58 +efficientnet_b5.in12k_ft_in1k,42.858,57.142,65.419,34.581,30.39,448,1.000,bicubic,-43.030,-32.313,-29 +xcit_large_24_p8_384_dist,42.831,57.169,63.403,36.597,188.93,384,1.000,bicubic,-43.169,-34.283,-32 +maxvit_small_tf_512.in1k,42.689,57.311,64.546,35.454,69.13,512,1.000,bicubic,-43.399,-33.212,-40 +xcit_large_24_p8_224_dist,42.567,57.433,63.100,36.900,188.93,224,1.000,bicubic,-42.829,-34.310,-6 +tf_efficientnet_b8.ra_in1k,42.508,57.492,64.857,35.143,87.41,672,0.954,bicubic,-42.862,-32.437,-5 +maxvit_large_tf_224.in1k,42.410,57.590,63.401,36.599,211.79,224,0.950,bicubic,-42.516,-33.571,+23 +cait_m36_384,42.398,57.602,63.324,36.676,271.22,384,1.000,bicubic,-43.656,-34.406,-41 +volo_d4_224,42.284,57.716,63.000,37.000,192.96,224,0.960,bicubic,-43.588,-34.468,-35 +deit3_small_patch16_384_in21ft1k,41.946,58.054,64.548,35.452,22.21,384,1.000,bicubic,-42.878,-32.938,+26 +vit_medium_patch16_gap_384.in12k_ft_in1k,41.895,58.105,63.692,36.308,39.03,384,0.950,bicubic,-43.641,-33.942,-21 +maxvit_tiny_tf_512.in1k,41.852,58.148,63.586,36.414,31.05,512,1.000,bicubic,-43.810,-33.994,-26 +tf_efficientnet_b7.ra_in1k,41.431,58.569,63.017,36.983,66.35,600,0.949,bicubic,-43.505,-34.186,+16 +tf_efficientnet_b7.ap_in1k,41.429,58.571,62.874,37.126,66.35,600,0.949,bicubic,-43.691,-34.378,+1 +tf_efficientnet_b5.ap_in1k,41.418,58.582,62.084,37.916,30.39,456,0.934,bicubic,-42.834,-34.890,+60 +resnetv2_152x4_bitm,41.302,58.698,64.307,35.693,936.53,480,1.000,bilinear,-43.614,-33.133,+16 +tf_efficientnet_b6.ap_in1k,41.099,58.901,62.355,37.645,43.04,528,0.942,bicubic,-43.689,-34.783,+21 +xcit_large_24_p16_384_dist,41.025,58.975,61.239,38.761,189.10,384,1.000,bicubic,-44.729,-36.299,-36 +xcit_large_24_p16_224_dist,40.958,59.042,61.322,38.678,189.10,224,1.000,bicubic,-43.960,-35.810,+12 +tf_efficientnetv2_s.in21k_ft_in1k,40.950,59.050,63.849,36.151,21.46,384,1.000,bicubic,-43.352,-33.403,+50 +tf_efficientnetv2_l.in1k,40.940,59.060,62.000,38.000,118.52,480,1.000,bicubic,-44.730,-35.474,-36 +maxvit_small_tf_384.in1k,40.848,59.152,61.962,38.038,69.02,384,1.000,bicubic,-44.686,-35.502,-31 +maxvit_base_tf_224.in1k,40.781,59.219,61.202,38.798,119.47,224,0.950,bicubic,-44.079,-35.788,+11 +xcit_medium_24_p8_224_dist,40.488,59.512,60.502,39.498,84.32,224,1.000,bicubic,-44.584,-36.752,-3 +tf_efficientnet_b4.ap_in1k,40.484,59.516,61.723,38.277,19.34,380,0.922,bicubic,-42.764,-34.669,+123 +vit_small_r26_s32_384.augreg_in21k_ft_in1k,40.476,59.524,62.736,37.264,36.47,384,1.000,bicubic,-43.570,-34.592,+66 +deit3_base_patch16_224,40.376,59.624,60.186,39.814,86.59,224,0.900,bicubic,-43.416,-36.398,+85 +vit_medium_patch16_gap_256.in12k_ft_in1k,40.274,59.726,61.668,38.332,38.86,256,0.950,bicubic,-44.156,-35.544,+35 +flexivit_large.600ep_in1k,40.260,59.740,60.365,39.635,304.36,240,0.950,bicubic,-45.278,-37.127,-40 +vit_base_patch16_224_miil.in21k_ft_in1k,40.168,59.832,60.887,39.113,86.54,224,0.875,bilinear,-44.100,-35.915,+43 +deit3_small_patch16_224_in21ft1k,40.166,59.834,61.864,38.136,22.06,224,1.000,bicubic,-42.904,-34.916,+132 +regnetz_e8,40.142,59.858,61.330,38.670,57.70,320,1.000,bicubic,-44.888,-35.934,-7 +maxvit_rmlp_small_rw_224,40.115,59.885,59.504,40.496,64.90,224,0.900,bicubic,-44.369,-37.258,+22 +flexivit_large.1200ep_in1k,40.093,59.907,60.638,39.362,304.36,240,0.950,bicubic,-45.551,-36.904,-47 +xcit_medium_24_p8_384_dist,40.040,59.960,60.457,39.543,84.32,384,1.000,bicubic,-45.776,-37.135,-59 +flexivit_large.300ep_in1k,40.009,59.991,59.991,40.009,304.36,240,0.950,bicubic,-45.271,-37.449,-32 +maxvit_tiny_tf_384.in1k,39.977,60.023,60.897,39.103,30.98,384,1.000,bicubic,-45.129,-36.637,-20 +xcit_medium_24_p16_384_dist,39.901,60.099,60.107,39.893,84.40,384,1.000,bicubic,-45.511,-37.299,-41 +dm_nfnet_f3,39.818,60.182,60.610,39.390,254.92,416,0.940,bicubic,-45.704,-36.852,-47 +convnext_tiny.fb_in22k_ft_in1k_384,39.798,60.202,61.534,38.466,28.59,384,1.000,bicubic,-44.282,-35.608,+47 +cait_s36_384,39.765,60.235,60.475,39.525,68.37,384,1.000,bicubic,-45.695,-37.005,-47 +volo_d3_448,39.702,60.298,59.758,40.242,86.63,448,1.000,bicubic,-46.792,-37.952,-93 +efficientnetv2_rw_m.agc_in1k,39.667,60.333,59.687,40.313,53.24,416,1.000,bicubic,-45.141,-37.461,-6 +xception65,39.635,60.365,60.911,39.089,39.92,299,0.940,bicubic,-43.545,-35.681,+108 +ecaresnet269d,39.594,60.406,60.343,39.657,102.09,352,1.000,bicubic,-45.382,-36.883,-18 +tf_efficientnet_b3.ns_jft_in1k,39.584,60.416,61.453,38.547,12.23,300,0.904,bicubic,-44.464,-35.457,+45 +dm_nfnet_f6,39.578,60.422,60.911,39.089,438.36,576,0.956,bicubic,-46.566,-36.819,-84 +dm_nfnet_f5,39.508,60.492,60.227,39.773,377.21,544,0.954,bicubic,-46.306,-37.261,-71 +volo_d3_224,39.488,60.512,59.873,40.127,86.33,224,0.960,bicubic,-45.920,-37.407,-51 +convnext_large.fb_in1k,39.460,60.540,59.192,40.808,197.77,288,1.000,bicubic,-45.386,-38.020,-15 +deit3_base_patch16_384,39.407,60.593,58.940,41.060,86.88,384,1.000,bicubic,-45.665,-38.338,-29 +xcit_small_24_p8_224_dist,39.305,60.695,59.404,40.596,47.63,224,1.000,bicubic,-45.571,-37.784,-19 +xcit_medium_24_p16_224_dist,39.272,60.728,59.457,40.543,84.40,224,1.000,bicubic,-45.002,-37.483,+19 +efficientnet_b4.ra2_in1k,39.079,60.921,59.608,40.392,19.34,384,1.000,bicubic,-44.349,-36.988,+79 +xcit_small_24_p8_384_dist,39.001,60.999,59.172,40.828,47.63,384,1.000,bicubic,-46.555,-38.400,-67 +tresnet_v2_l,38.995,61.005,59.471,40.529,46.17,224,0.875,bilinear,-44.907,-37.021,+44 +resnetv2_152x2_bit_teacher_384,38.979,61.021,62.440,37.560,236.34,384,1.000,bicubic,-44.865,-34.678,+48 +maxvit_small_tf_224.in1k,38.881,61.119,59.174,40.826,68.93,224,0.950,bicubic,-45.553,-37.990,+4 +coatnet_rmlp_2_rw_224,38.843,61.157,58.030,41.970,73.88,224,0.950,bicubic,-45.757,-38.706,-13 +vit_base_patch32_384.augreg_in21k_ft_in1k,38.794,61.206,60.329,39.671,88.30,384,1.000,bicubic,-44.556,-36.507,+83 +tf_efficientnetv2_m.in1k,38.720,61.280,59.809,40.191,54.14,480,1.000,bicubic,-46.488,-37.559,-50 +eca_nfnet_l2,38.664,61.336,59.445,40.555,56.72,384,1.000,bicubic,-46.033,-37.819,-20 +mvitv2_small,38.580,61.420,58.123,41.877,34.87,224,0.900,bicubic,-45.188,-38.447,+51 +xcit_small_12_p8_384_dist,38.549,61.451,58.799,41.201,26.21,384,1.000,bicubic,-46.539,-38.483,-44 +xcit_small_24_p16_384_dist,38.503,61.497,58.384,41.616,47.67,384,1.000,bicubic,-46.595,-38.926,-47 +mvitv2_base,38.456,61.544,57.930,42.070,51.47,224,0.900,bicubic,-45.966,-38.934,-2 +xcit_small_12_p8_224_dist,38.372,61.628,58.791,41.209,26.21,224,1.000,bicubic,-45.860,-37.987,+10 +tf_efficientnet_b5.ra_in1k,38.356,61.644,59.913,40.087,30.39,456,0.934,bicubic,-45.456,-36.835,+41 +deit_base_distilled_patch16_384,38.260,61.740,57.783,42.217,87.63,384,1.000,bicubic,-47.162,-39.549,-73 +dm_nfnet_f4,38.224,61.776,58.626,41.374,316.07,512,0.951,bicubic,-47.490,-38.894,-86 +xcit_large_24_p8_224,38.114,61.886,57.873,42.127,188.93,224,1.000,bicubic,-46.278,-38.783,-4 +vit_base_patch16_384.orig_in21k_ft_in1k,38.099,61.901,60.428,39.572,86.86,384,1.000,bicubic,-46.111,-36.790,+7 +resnetv2_152x2_bitm,37.985,62.015,61.135,38.865,236.34,448,1.000,bilinear,-46.525,-36.297,-20 +pvt_v2_b4,37.941,62.059,58.207,41.793,62.56,224,0.900,bicubic,-45.775,-38.513,+45 +cait_s24_384,37.873,62.127,58.079,41.921,47.06,384,1.000,bicubic,-47.173,-39.267,-51 +resnet152d,37.857,62.143,58.356,41.644,60.21,320,1.000,bicubic,-45.823,-38.382,+48 +resnetrs420,37.747,62.253,58.215,41.785,191.89,416,1.000,bicubic,-47.261,-38.909,-51 +xcit_small_24_p16_224_dist,37.717,62.283,57.360,42.640,47.67,224,1.000,bicubic,-46.145,-39.368,+25 +deit3_medium_patch16_224,37.712,62.288,57.087,42.913,38.85,224,0.900,bicubic,-45.368,-39.205,+82 +resnetrs350,37.676,62.324,58.083,41.917,163.96,384,1.000,bicubic,-47.044,-38.905,-40 +pit_b_distilled_224,37.590,62.410,57.238,42.762,74.79,224,0.900,bicubic,-46.554,-39.618,+4 +xcit_small_12_p16_384_dist,37.576,62.424,57.769,42.231,26.25,384,1.000,bicubic,-47.130,-39.349,-41 +pvt_v2_b5,37.527,62.473,57.262,42.738,81.96,224,0.900,bicubic,-46.213,-39.450,+33 +resnet200d,37.505,62.495,58.297,41.703,64.69,320,1.000,bicubic,-46.457,-38.526,+12 +maxvit_rmlp_tiny_rw_256,37.393,62.607,57.187,42.813,29.15,256,0.950,bicubic,-46.839,-39.689,-7 +resnetv2_152x2_bit_teacher,37.324,62.676,59.390,40.610,236.34,224,0.875,bicubic,-45.538,-37.178,+88 +resnest269e,37.315,62.685,57.468,42.532,110.93,416,0.928,bicubic,-47.203,-39.468,-36 +convnext_base.fb_in1k,37.307,62.693,57.317,42.683,88.59,288,1.000,bicubic,-47.127,-39.503,-28 +resmlp_big_24_224_in22ft1k,37.244,62.756,58.184,41.816,129.14,224,0.875,bicubic,-47.150,-38.699,-24 +vit_small_r26_s32_224.augreg_in21k_ft_in1k,37.234,62.766,59.060,40.940,36.43,224,0.900,bicubic,-44.624,-36.962,+173 +cait_s24_224,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.299,-39.840,+41 +efficientformer_l7,37.126,62.874,56.896,43.104,82.23,224,0.950,bicubic,-46.260,-39.644,+49 +pvt_v2_b3,37.114,62.886,57.331,42.669,45.24,224,0.900,bicubic,-46.012,-39.225,+65 +volo_d1_384,37.083,62.917,57.130,42.870,26.78,384,1.000,bicubic,-48.167,-40.066,-87 +vit_base_patch32_224.augreg_in21k_ft_in1k,37.077,62.923,59.294,40.706,88.22,224,0.900,bicubic,-43.647,-36.274,+246 +tf_efficientnet_b3.ap_in1k,37.055,62.945,57.240,42.760,12.23,300,0.904,bicubic,-44.767,-38.384,+169 +efficientnetv2_rw_s.ra2_in1k,37.049,62.951,56.814,43.186,23.94,384,1.000,bicubic,-46.759,-39.910,+13 +maxvit_tiny_tf_224.in1k,37.016,62.984,56.902,43.098,30.92,224,0.950,bicubic,-46.382,-39.686,+39 +swinv2_base_window16_256,36.996,63.004,56.138,43.862,87.92,256,0.900,bicubic,-47.598,-40.936,-52 +regnetz_040h,36.973,63.027,57.285,42.715,28.94,320,1.000,bicubic,-47.521,-39.721,-46 +xcit_small_12_p16_224_dist,36.973,63.027,56.733,43.267,26.25,224,1.000,bicubic,-46.377,-39.681,+43 +volo_d1_224,36.884,63.116,56.639,43.361,26.63,224,0.960,bicubic,-47.280,-40.137,-18 +seresnet152d,36.790,63.210,56.718,43.282,66.84,320,1.000,bicubic,-47.572,-40.322,-34 +maxxvit_rmlp_small_rw_256,36.705,63.295,56.022,43.978,66.01,256,0.950,bicubic,-47.923,-41.040,-60 +seresnext101d_32x8d,36.641,63.359,56.336,43.664,93.59,288,1.000,bicubic,-47.730,-40.580,-37 +volo_d2_224,36.595,63.405,56.468,43.532,58.68,224,0.960,bicubic,-48.601,-40.720,-93 +xception65p,36.556,63.444,56.429,43.571,39.82,299,0.940,bicubic,-46.574,-40.051,+50 +seresnextaa101d_32x8d,36.527,63.473,56.403,43.597,93.59,288,1.000,bicubic,-48.041,-40.667,-60 +regnetz_d32,36.444,63.556,57.372,42.628,27.58,320,0.950,bicubic,-47.578,-39.494,-16 +efficientnet_b3.ra2_in1k,36.420,63.580,56.845,43.155,12.23,320,1.000,bicubic,-45.822,-39.269,+122 +cait_xs24_384,36.416,63.584,56.944,43.056,26.67,384,1.000,bicubic,-47.645,-39.945,-23 +volo_d2_384,36.416,63.584,56.311,43.689,58.87,384,1.000,bicubic,-49.620,-41.261,-144 +deit_base_distilled_patch16_224,36.397,63.603,56.617,43.383,87.34,224,0.900,bicubic,-46.991,-39.871,+26 +resnetv2_101x3_bitm,36.381,63.619,59.070,40.930,387.93,448,1.000,bilinear,-48.059,-38.312,-56 +gcvit_base,36.371,63.629,55.896,44.104,90.32,224,0.875,bicubic,-48.077,-41.186,-58 +resnetrs270,36.320,63.680,56.562,43.438,129.86,352,1.000,bicubic,-48.114,-40.408,-55 +tresnet_m,36.285,63.715,55.796,44.204,31.39,224,0.875,bilinear,-46.795,-40.322,+45 +mixer_b16_224_miil,36.269,63.731,55.965,44.035,59.88,224,0.875,bilinear,-46.039,-39.751,+109 +convnext_small.fb_in1k,36.251,63.749,55.914,44.086,50.22,288,1.000,bicubic,-47.455,-40.896,+3 +tf_efficientnet_b2.ns_jft_in1k,36.183,63.817,57.551,42.449,9.11,260,0.890,bicubic,-46.197,-38.697,+96 +deit3_small_patch16_384,36.183,63.817,55.564,44.436,22.21,384,1.000,bicubic,-47.243,-41.112,+14 +mvitv2_tiny,36.161,63.839,55.128,44.872,24.17,224,0.900,bicubic,-46.243,-41.028,+90 +resnet152,36.086,63.914,55.550,44.450,60.19,224,0.950,bicubic,-46.736,-40.576,+53 +regnetz_040,36.051,63.949,55.745,44.255,27.12,320,1.000,bicubic,-48.185,-41.187,-48 +ecaresnet101d,36.004,63.996,56.165,43.835,44.57,224,0.875,bicubic,-46.168,-39.881,+114 +dm_nfnet_f2,36.004,63.996,55.456,44.544,193.78,352,0.920,bicubic,-49.060,-41.784,-102 +resnest200e,35.931,64.069,55.849,44.151,70.20,320,0.909,bicubic,-47.901,-41.045,-19 +swsl_resnet18,35.858,64.142,58.455,41.545,11.69,224,0.875,bilinear,-37.418,-33.279,+505 +sequencer2d_l,35.825,64.175,55.712,44.288,54.30,224,0.875,bicubic,-47.581,-40.794,+6 +eca_nfnet_l1,35.823,64.177,55.957,44.043,41.41,320,1.000,bicubic,-48.187,-41.071,-35 +vit_base_patch16_224.orig_in21k_ft_in1k,35.768,64.232,57.390,42.610,86.57,224,0.900,bicubic,-46.018,-38.732,+138 +gcvit_small,35.746,64.254,54.821,45.179,51.09,224,0.875,bicubic,-48.138,-41.837,-30 +vit_relpos_medium_patch16_cls_224.sw_in1k,35.740,64.260,54.918,45.082,38.76,224,0.900,bicubic,-46.822,-41.252,+68 +xcit_small_24_p8_224,35.546,64.454,54.788,45.212,47.63,224,1.000,bicubic,-48.292,-41.848,-27 +xcit_small_12_p8_224,35.520,64.480,55.511,44.489,26.21,224,1.000,bicubic,-47.824,-40.969,+10 +xcit_large_24_p16_224,35.520,64.480,54.760,45.240,189.10,224,1.000,bicubic,-47.376,-41.122,+37 +flexivit_base.1200ep_in1k,35.519,64.481,53.843,46.157,86.59,240,0.950,bicubic,-49.145,-43.149,-94 +vit_small_patch16_384.augreg_in21k_ft_in1k,35.479,64.521,57.549,42.451,22.20,384,1.000,bicubic,-48.323,-39.553,-27 +xcit_medium_24_p8_224,35.446,64.554,54.827,45.173,84.32,224,1.000,bicubic,-48.288,-41.567,-23 +swinv2_small_window16_256,35.446,64.554,54.641,45.359,49.73,256,0.900,bicubic,-48.760,-42.230,-59 +swinv2_base_window8_256,35.444,64.556,54.617,45.383,87.92,256,0.900,bicubic,-48.818,-42.305,-67 +resnest101e,35.373,64.627,55.780,44.220,48.28,256,0.875,bilinear,-47.517,-40.540,+31 +convit_base,35.314,64.686,54.927,45.073,86.54,224,0.875,bicubic,-46.974,-41.081,+87 +efficientformer_l3,35.263,64.737,54.487,45.513,31.41,224,0.950,bicubic,-47.287,-41.761,+58 +xcit_tiny_24_p8_224_dist,35.253,64.747,55.258,44.742,12.11,224,1.000,bicubic,-47.309,-40.809,+55 +edgenext_base,35.208,64.792,55.128,44.872,18.51,320,1.000,bicubic,-48.752,-41.640,-49 +flexivit_base.600ep_in1k,35.137,64.863,53.662,46.338,86.59,240,0.950,bicubic,-49.381,-43.324,-95 +twins_svt_large,35.086,64.914,54.721,45.279,99.27,224,0.900,bicubic,-48.592,-41.873,-23 +repvgg_b3g4,35.043,64.957,54.772,45.228,83.83,224,0.875,bilinear,-45.169,-40.338,+233 +repvgg_b3,35.043,64.957,54.542,45.458,123.09,224,0.875,bilinear,-45.449,-40.718,+205 +regnetz_d8,34.998,65.002,55.939,44.061,23.37,320,1.000,bicubic,-49.052,-41.059,-62 +dm_nfnet_f1,34.990,65.010,54.108,45.892,132.63,320,0.910,bicubic,-49.636,-42.992,-107 +xcit_tiny_24_p8_384_dist,34.925,65.075,55.153,44.847,12.11,384,1.000,bicubic,-48.815,-41.481,-38 +regnetz_d8_evos,34.898,65.103,55.258,44.742,23.46,320,0.950,bicubic,-49.152,-41.736,-64 +resnet101d,34.872,65.128,54.202,45.798,44.57,320,1.000,bicubic,-48.150,-42.244,+12 +coatnet_1_rw_224,34.840,65.159,53.426,46.574,41.72,224,0.950,bicubic,-48.768,-42.962,-30 +swin_s3_base_224,34.797,65.203,53.707,46.293,71.13,224,0.900,bicubic,-49.133,-42.955,-59 +flexivit_base.300ep_in1k,34.797,65.203,53.171,46.829,86.59,240,0.950,bicubic,-49.597,-43.949,-91 +coatnet_rmlp_1_rw_224,34.795,65.205,53.951,46.049,41.69,224,0.950,bicubic,-48.563,-42.505,-17 +maxvit_tiny_rw_224,34.789,65.211,53.347,46.653,29.06,224,0.950,bicubic,-48.715,-43.154,-30 +resmlp_big_24_distilled_224,34.788,65.213,54.637,45.363,129.14,224,0.875,bicubic,-48.800,-42.011,-34 +seresnext101_32x8d,34.788,65.213,53.462,46.538,93.57,288,1.000,bicubic,-49.404,-43.412,-80 +vit_relpos_base_patch16_clsgap_224.sw_in1k,34.728,65.272,54.218,45.782,86.43,224,0.900,bicubic,-48.034,-41.956,+18 +sequencer2d_m,34.709,65.291,53.998,46.002,38.31,224,0.875,bicubic,-48.097,-42.270,+15 +vit_base_patch16_rpn_224.in1k,34.705,65.295,54.658,45.342,86.54,224,0.900,bicubic,-47.497,-41.338,+71 +resnet101,34.681,65.319,54.318,45.682,44.55,224,0.950,bicubic,-47.257,-41.436,+94 +deit3_small_patch16_224,34.677,65.323,53.159,46.841,22.06,224,0.900,bicubic,-46.709,-42.291,+130 +vit_large_patch32_384.orig_in21k_ft_in1k,34.673,65.326,55.729,44.271,306.63,384,1.000,bicubic,-46.833,-40.363,+118 +dm_nfnet_f0,34.618,65.382,54.672,45.328,71.49,256,0.900,bicubic,-48.767,-41.900,-29 +vit_relpos_base_patch16_224.sw_in1k,34.611,65.389,54.287,45.713,86.43,224,0.900,bicubic,-47.873,-41.855,+37 +ssl_resnext101_32x16d,34.603,65.397,55.931,44.069,194.03,224,0.875,bilinear,-47.241,-40.165,+93 +repvgg_b2g4,34.587,65.413,54.782,45.218,61.76,224,0.875,bilinear,-44.779,-39.906,+258 +resnetv2_101,34.583,65.417,53.155,46.845,44.54,224,0.950,bicubic,-47.447,-42.705,+77 +gcvit_tiny,34.567,65.433,53.245,46.755,28.22,224,0.875,bicubic,-48.833,-43.153,-38 +resnetrs200,34.505,65.496,54.283,45.717,93.21,320,1.000,bicubic,-49.943,-42.561,-119 +resnest50d_4s2x40d,34.355,65.645,54.725,45.275,30.42,224,0.875,bicubic,-46.753,-40.833,+139 +resnetrs152,34.355,65.645,53.562,46.438,86.62,320,1.000,bicubic,-49.357,-43.052,-56 +pvt_v2_b2_li,34.308,65.692,54.094,45.906,22.55,224,0.900,bicubic,-47.888,-42.010,+59 +crossvit_18_dagger_408,34.251,65.749,53.092,46.908,44.61,408,1.000,bicubic,-49.945,-43.726,-98 +xcit_medium_24_p16_224,34.243,65.757,53.159,46.841,84.40,224,1.000,bicubic,-48.393,-42.817,+10 +tf_efficientnet_b1.ns_jft_in1k,34.157,65.843,55.489,44.511,7.79,240,0.882,bicubic,-47.231,-40.249,+115 +efficientnetv2_rw_t.ra2_in1k,34.155,65.845,53.131,46.869,13.65,288,1.000,bicubic,-48.193,-43.065,+38 +twins_pcpvt_large,34.111,65.888,54.128,45.872,60.99,224,0.900,bicubic,-49.029,-42.470,-28 +tf_efficientnet_b4.aa_in1k,34.064,65.936,54.198,45.802,19.34,380,0.922,bicubic,-48.958,-42.102,-16 +ssl_resnext101_32x8d,34.017,65.983,55.601,44.399,88.79,224,0.875,bilinear,-47.599,-40.437,+93 +nfnet_l0,34.002,65.999,54.365,45.635,35.07,288,1.000,bicubic,-48.748,-42.151,-4 +tf_efficientnet_b6.aa_in1k,33.998,66.002,54.544,45.456,43.04,528,0.942,bicubic,-50.112,-42.342,-101 +efficientnet_b3_pruned.in1k,33.996,66.004,54.108,45.892,9.86,300,0.904,bicubic,-46.862,-41.134,+148 +xcit_small_24_p16_224,33.996,66.004,53.285,46.715,47.67,224,1.000,bicubic,-48.584,-42.719,+9 +regnety_160,33.976,66.024,53.546,46.454,83.59,288,1.000,bicubic,-49.710,-43.230,-67 +gc_efficientnetv2_rw_t.agc_in1k,33.952,66.048,53.220,46.780,13.68,288,1.000,bicubic,-48.512,-43.078,+18 +pit_s_distilled_224,33.939,66.061,53.265,46.735,24.04,224,0.900,bicubic,-48.057,-42.533,+60 +swinv2_cr_small_ns_224,33.842,66.158,52.618,47.382,49.70,224,0.900,bicubic,-49.646,-43.868,-62 +resnext101_64x4d,33.833,66.168,52.166,47.834,83.46,288,1.000,bicubic,-49.315,-44.206,-41 +xcit_small_12_p16_224,33.776,66.225,53.233,46.767,26.25,224,1.000,bicubic,-48.199,-42.583,+59 +swin_s3_small_224,33.705,66.295,52.396,47.604,49.74,224,0.900,bicubic,-50.065,-44.054,-83 +resnetv2_50x3_bitm,33.660,66.341,55.882,44.118,217.32,448,1.000,bilinear,-50.354,-41.242,-103 +swinv2_small_window8_256,33.646,66.354,52.813,47.187,49.73,256,0.900,bicubic,-50.210,-43.827,-94 +resnet51q,33.563,66.437,53.021,46.979,35.70,288,1.000,bilinear,-48.797,-43.159,+19 +xcit_tiny_24_p16_384_dist,33.510,66.490,52.774,47.226,12.12,384,1.000,bicubic,-49.060,-43.512,-1 +vit_relpos_medium_patch16_224.sw_in1k,33.498,66.502,52.601,47.399,38.75,224,0.900,bicubic,-48.968,-43.487,+7 +regnety_080,33.467,66.533,52.947,47.053,39.18,288,1.000,bicubic,-50.465,-43.941,-104 +cs3edgenet_x,33.451,66.549,52.921,47.079,47.82,288,1.000,bicubic,-49.251,-43.449,-18 +sequencer2d_s,33.426,66.574,52.398,47.602,27.65,224,0.875,bicubic,-48.916,-43.632,+18 +convmixer_1536_20,33.420,66.580,53.027,46.973,51.63,224,0.960,bicubic,-47.956,-42.587,+94 +regnety_032,33.412,66.588,52.754,47.246,19.44,288,1.000,bicubic,-49.312,-43.670,-23 +crossvit_18_240,33.400,66.600,52.241,47.759,43.27,240,0.875,bicubic,-49.000,-43.813,+6 +vit_srelpos_medium_patch16_224.sw_in1k,33.371,66.629,52.461,47.539,38.74,224,0.900,bicubic,-48.865,-43.473,+25 +tf_efficientnetv2_b3.in21k_ft_in1k,33.365,66.635,54.933,45.067,14.36,300,0.900,bicubic,-49.307,-41.691,-21 +gernet_l,33.357,66.643,51.901,48.099,31.08,256,0.875,bilinear,-47.997,-43.635,+90 +crossvit_15_dagger_408,33.331,66.669,52.194,47.806,28.50,408,1.000,bicubic,-50.507,-44.588,-105 +crossvit_18_dagger_240,33.290,66.710,52.198,47.802,44.27,240,0.875,bicubic,-49.228,-44.162,-6 +tresnet_xl,33.257,66.743,52.294,47.706,78.44,224,0.875,bilinear,-48.797,-43.642,+35 +jx_nest_base,33.214,66.787,51.811,48.189,67.72,224,0.875,bicubic,-50.339,-44.559,-86 +convnext_tiny.fb_in1k,33.164,66.836,52.672,47.328,28.59,288,1.000,bicubic,-49.536,-43.464,-29 +resnest50d_1s4x24d,33.147,66.853,52.839,47.161,25.68,224,0.875,bicubic,-47.841,-42.483,+108 +convnext_nano.in12k_ft_in1k,33.119,66.881,53.970,46.030,15.59,288,1.000,bicubic,-49.739,-42.586,-42 +vit_relpos_medium_patch16_rpn_224.sw_in1k,33.103,66.897,52.353,47.647,38.73,224,0.900,bicubic,-49.195,-43.621,+10 +resnet61q,33.097,66.903,51.754,48.246,36.85,288,1.000,bicubic,-49.427,-44.376,-15 +maxxvit_rmlp_nano_rw_256,33.088,66.912,51.854,48.146,16.78,256,0.950,bicubic,-49.942,-44.490,-54 +jx_nest_small,33.042,66.957,51.062,48.938,38.35,224,0.875,bicubic,-50.078,-45.266,-62 +crossvit_base_240,33.033,66.967,51.394,48.606,105.03,240,0.875,bicubic,-49.183,-44.436,+13 +twins_pcpvt_base,33.021,66.979,52.485,47.515,43.83,224,0.900,bicubic,-49.687,-43.861,-39 +pvt_v2_b2,33.015,66.985,52.037,47.963,25.36,224,0.900,bicubic,-49.061,-43.925,+21 +xcit_tiny_24_p16_224_dist,32.989,67.011,52.056,47.944,12.12,224,1.000,bicubic,-47.457,-43.162,+137 +rexnet_200,32.987,67.013,52.939,47.061,16.37,224,0.875,bicubic,-48.645,-42.729,+50 +resnest50d,32.972,67.028,52.713,47.287,27.48,224,0.875,bilinear,-48.002,-42.665,+98 +tf_efficientnetv2_s.in1k,32.915,67.085,51.726,48.274,21.46,384,1.000,bicubic,-50.979,-44.972,-127 +convit_small,32.913,67.087,52.123,47.877,27.78,224,0.875,bicubic,-48.513,-43.621,+63 +crossvit_15_dagger_240,32.903,67.097,51.783,48.217,28.21,240,0.875,bicubic,-49.429,-44.735,-6 +convnext_tiny_hnf.a2h_in1k,32.895,67.105,51.190,48.810,28.59,288,1.000,bicubic,-49.695,-44.826,-37 +vit_small_patch16_224.augreg_in21k_ft_in1k,32.885,67.115,53.923,46.077,22.05,224,0.900,bicubic,-48.517,-42.211,+63 +tf_efficientnet_b3.aa_in1k,32.860,67.140,52.950,47.050,12.23,300,0.904,bicubic,-48.776,-42.768,+42 +pnasnet5large,32.848,67.152,50.500,49.500,86.06,331,0.911,bicubic,-49.934,-45.540,-54 +regnetv_064,32.836,67.164,52.854,47.146,30.58,288,1.000,bicubic,-50.876,-43.894,-115 +twins_svt_base,32.836,67.164,51.559,48.441,56.07,224,0.900,bicubic,-50.300,-44.859,-80 +regnetz_c16,32.821,67.180,53.744,46.256,13.46,320,0.940,bicubic,-49.697,-42.328,-32 +nasnetalarge,32.775,67.225,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.906,-46 +gernet_m,32.740,67.260,51.913,48.087,21.14,224,0.875,bilinear,-47.992,-43.271,+101 +inception_resnet_v2,32.738,67.262,50.648,49.352,55.84,299,0.897,bicubic,-47.720,-44.658,+120 +gluon_resnet152_v1d,32.734,67.266,51.088,48.912,60.21,224,0.875,bicubic,-47.740,-44.118,+116 +pit_b_224,32.718,67.282,49.852,50.148,73.76,224,0.900,bicubic,-49.728,-45.858,-31 +tf_efficientnet_b2.ap_in1k,32.681,67.319,52.239,47.761,9.11,260,0.890,bicubic,-47.619,-42.789,+134 +fbnetv3_g.ra2_in1k,32.630,67.370,52.892,47.108,16.62,288,0.950,bilinear,-49.418,-43.172,+5 +tresnet_l,32.559,67.441,51.139,48.861,55.99,224,0.875,bilinear,-48.929,-44.485,+42 +cait_xxs36_384,32.549,67.451,52.233,47.767,17.37,384,1.000,bicubic,-49.645,-43.915,-8 +regnetz_c16_evos,32.539,67.461,52.915,47.085,13.49,320,0.950,bicubic,-50.091,-43.559,-56 +wide_resnet50_2,32.439,67.561,51.459,48.541,68.88,224,0.875,bicubic,-49.017,-44.073,+43 +gmlp_s16_224,32.418,67.582,51.815,48.185,19.42,224,0.875,bicubic,-47.224,-42.783,+168 +ens_adv_inception_resnet_v2,32.372,67.628,50.427,49.573,55.84,299,0.897,bicubic,-47.610,-44.511,+145 +deit_base_patch16_224,32.363,67.637,51.011,48.989,86.57,224,0.900,bicubic,-49.635,-44.723,0 +maxvit_nano_rw_256,32.357,67.643,50.618,49.382,15.45,256,0.950,bicubic,-50.575,-45.604,-81 +swin_small_patch4_window7_224,32.341,67.659,50.905,49.095,49.61,224,0.900,bicubic,-50.871,-45.417,-103 +gluon_resnet152_v1s,32.331,67.669,50.526,49.474,60.32,224,0.875,bicubic,-48.685,-44.886,+69 +deit_small_distilled_patch16_224,32.284,67.716,52.102,47.898,22.44,224,0.900,bicubic,-48.916,-43.276,+52 +xcit_tiny_24_p8_224,32.274,67.726,51.901,48.099,12.11,224,1.000,bicubic,-49.626,-44.075,+6 +gluon_seresnext101_64x4d,32.205,67.795,50.319,49.681,88.23,224,0.875,bicubic,-48.689,-44.989,+76 +coat_lite_small,32.127,67.873,49.934,50.066,19.84,224,0.900,bicubic,-50.181,-45.916,-32 +gluon_seresnext101_32x4d,32.107,67.893,51.237,48.763,48.96,224,0.875,bicubic,-48.797,-44.057,+72 +flexivit_small.1200ep_in1k,32.087,67.912,50.296,49.704,22.06,240,0.950,bicubic,-50.438,-45.840,-57 +coatnext_nano_rw_224,32.076,67.924,51.019,48.981,14.70,224,0.900,bicubic,-49.872,-44.899,-3 +gcvit_xtiny,32.050,67.950,50.995,49.005,19.98,224,0.875,bicubic,-49.902,-44.971,-5 +deit_base_patch16_384,31.989,68.011,50.547,49.453,86.86,384,1.000,bicubic,-51.117,-45.825,-103 +seresnext50_32x4d,31.985,68.015,51.231,48.769,27.56,224,0.875,bicubic,-49.281,-44.389,+40 +maxvit_rmlp_nano_rw_256,31.966,68.034,50.626,49.374,15.50,256,0.950,bicubic,-50.996,-45.644,-96 +xcit_tiny_12_p8_224_dist,31.944,68.056,51.390,48.610,6.71,224,1.000,bicubic,-49.268,-44.210,+40 +coatnet_bn_0_rw_224,31.883,68.117,51.017,48.983,27.44,224,0.950,bicubic,-50.515,-45.165,-53 +levit_384,31.877,68.123,50.598,49.402,39.13,224,0.900,bicubic,-50.709,-45.418,-73 +resnetrs101,31.858,68.142,51.017,48.983,63.62,288,0.940,bicubic,-50.430,-44.921,-40 +cs3se_edgenet_x,31.803,68.197,50.773,49.227,50.72,320,1.000,bicubic,-51.745,-45.897,-141 +vit_relpos_small_patch16_224.sw_in1k,31.785,68.215,50.622,49.378,21.98,224,0.900,bicubic,-49.677,-45.206,+19 +poolformer_m48,31.702,68.298,49.883,50.117,73.47,224,0.950,bicubic,-50.760,-46.075,-62 +convnext_tiny.fb_in22k_ft_in1k,31.679,68.321,51.785,48.215,28.59,288,1.000,bicubic,-47.229,-42.889,+191 +flexivit_small.600ep_in1k,31.649,68.351,49.366,50.634,22.06,240,0.950,bicubic,-50.705,-46.720,-55 +tnt_s_patch16_224,31.643,68.357,51.143,48.857,23.76,224,0.900,bicubic,-49.875,-44.605,+9 +eca_nfnet_l0,31.612,68.388,51.614,48.386,24.14,288,1.000,bicubic,-50.968,-44.876,-79 +resnetv2_50x1_bit_distilled,31.584,68.416,51.263,48.737,25.55,224,0.875,bicubic,-51.234,-45.259,-100 +coatnet_rmlp_nano_rw_224,31.545,68.455,50.170,49.830,15.15,224,0.900,bicubic,-50.519,-45.700,-32 +xception41p,31.516,68.484,50.374,49.626,26.91,299,0.940,bicubic,-50.442,-45.420,-23 +mobilevitv2_200_in22ft1k,31.510,68.490,51.758,48.242,18.45,256,0.888,bicubic,-50.814,-44.182,-55 +regnety_064,31.474,68.526,50.528,49.472,30.58,288,1.000,bicubic,-52.242,-46.146,-163 +poolformer_m36,31.443,68.557,50.036,49.964,56.17,224,0.950,bicubic,-50.667,-45.652,-39 +flexivit_small.300ep_in1k,31.439,68.561,49.215,50.785,22.06,240,0.950,bicubic,-50.733,-46.809,-42 +ssl_resnext101_32x4d,31.423,68.577,52.121,47.879,44.18,224,0.875,bilinear,-49.501,-43.607,+46 +inception_v4,31.378,68.622,49.244,50.756,42.68,299,0.875,bicubic,-48.790,-45.724,+100 +rexnet_150,31.366,68.634,51.288,48.712,9.73,224,0.875,bicubic,-48.944,-43.878,+88 +crossvit_15_240,31.341,68.659,50.168,49.832,27.53,240,0.875,bicubic,-50.195,-45.524,-5 +efficientformer_l1,31.333,68.667,50.449,49.551,12.29,224,0.950,bicubic,-49.169,-44.549,+65 +pit_s_224,31.333,68.667,49.661,50.339,23.46,224,0.900,bicubic,-49.761,-45.909,+29 +swinv2_tiny_window16_256,31.313,68.687,49.630,50.370,28.35,256,0.900,bicubic,-51.497,-46.602,-112 +vit_srelpos_small_patch16_224.sw_in1k,31.280,68.720,50.243,49.757,21.97,224,0.900,bicubic,-49.814,-45.089,+26 +cait_xxs36_224,31.278,68.722,50.616,49.384,17.30,224,1.000,bicubic,-48.472,-44.250,+116 +crossvit_small_240,31.276,68.724,50.192,49.808,26.86,240,0.875,bicubic,-49.744,-45.268,+29 +cspresnet50,31.270,68.730,51.223,48.777,21.62,256,0.887,bilinear,-48.304,-43.489,+126 +swinv2_cr_small_224,31.256,68.744,48.747,51.253,49.70,224,0.900,bicubic,-51.890,-47.347,-141 +coatnet_0_rw_224,31.250,68.750,48.621,51.379,27.44,224,0.950,bicubic,-51.140,-47.215,-81 +convmixer_768_32,31.248,68.752,50.942,49.058,21.11,224,0.960,bicubic,-48.916,-44.130,+89 +swin_s3_tiny_224,31.242,68.757,49.720,50.280,28.33,224,0.900,bicubic,-50.880,-46.228,-56 +cspresnext50,31.229,68.771,50.889,49.111,20.57,256,0.887,bilinear,-49.317,-44.431,+49 +regnetv_040,31.211,68.789,50.115,49.885,20.64,288,1.000,bicubic,-51.983,-46.545,-149 +coat_mini,31.203,68.797,49.773,50.227,10.34,224,0.900,bicubic,-50.065,-45.619,+2 +xcit_tiny_12_p8_384_dist,31.191,68.809,50.522,49.478,6.71,384,1.000,bicubic,-51.197,-45.702,-86 +ecaresnetlight,31.121,68.879,50.243,49.757,30.16,224,0.875,bicubic,-49.341,-45.005,+56 +gluon_resnet101_v1s,31.115,68.885,49.793,50.207,44.67,224,0.875,bicubic,-49.187,-45.367,+71 +edgenext_small,31.103,68.897,50.131,49.869,5.59,320,1.000,bicubic,-50.465,-45.575,-25 +coatnet_nano_rw_224,31.093,68.907,49.586,50.414,15.14,224,0.900,bicubic,-50.607,-46.052,-34 +tf_efficientnet_cc_b0_8e.in1k,31.087,68.913,50.761,49.239,24.01,224,0.875,bicubic,-46.821,-42.892,+212 +resmlp_36_distilled_224,31.070,68.930,49.683,50.317,44.69,224,0.875,bicubic,-50.090,-45.805,+2 +ecaresnet50d,31.058,68.942,50.848,49.152,25.58,224,0.875,bicubic,-49.534,-44.472,+37 +ecaresnet50t,31.058,68.942,50.577,49.423,25.57,320,0.950,bicubic,-51.288,-45.561,-89 +cs3sedarknet_x,31.028,68.972,50.135,49.865,35.40,288,1.000,bicubic,-51.626,-46.219,-122 +resnet50d,31.020,68.980,49.808,50.192,25.58,224,0.875,bicubic,-49.510,-45.352,+38 +cspdarknet53,31.017,68.984,50.390,49.610,27.64,256,0.887,bilinear,-49.042,-44.694,+79 +gcresnet50t,31.009,68.991,50.123,49.877,25.90,256,0.900,bicubic,-49.931,-45.331,+14 +gluon_resnet152_v1c,30.991,69.009,48.924,51.076,60.21,224,0.875,bicubic,-48.919,-45.916,+83 +gluon_resnext101_64x4d,30.987,69.013,48.549,51.451,83.46,224,0.875,bicubic,-49.617,-46.439,+29 +twins_svt_small,30.985,69.015,49.223,50.777,24.06,224,0.900,bicubic,-50.697,-46.447,-44 +resnext50_32x4d,30.950,69.050,49.270,50.730,25.03,224,0.950,bicubic,-50.168,-46.062,-4 +resmlp_24_distilled_224,30.901,69.099,50.178,49.822,30.02,224,0.875,bicubic,-49.865,-45.040,+20 +tf_efficientnet_cc_b1_8e.in1k,30.899,69.101,50.080,49.920,39.72,240,0.882,bicubic,-48.409,-44.290,+114 +ecaresnet101d_pruned,30.897,69.103,50.013,49.987,24.88,224,0.875,bicubic,-49.921,-45.615,+16 +gluon_resnext101_32x4d,30.877,69.123,48.537,51.463,44.18,224,0.875,bicubic,-49.457,-46.389,+49 +tf_efficientnetv2_b3.in1k,30.861,69.139,49.814,50.186,14.36,300,0.904,bicubic,-51.109,-45.968,-68 +tf_efficientnet_lite4.in1k,30.830,69.170,50.386,49.614,13.01,380,0.920,bilinear,-50.706,-45.282,-41 +nf_resnet50,30.702,69.298,49.958,50.042,25.56,288,0.940,bicubic,-49.960,-45.378,+18 +dpn107,30.678,69.322,48.810,51.190,86.92,224,0.875,bicubic,-49.478,-46.100,+61 +xcit_tiny_24_p16_224,30.677,69.323,50.410,49.590,12.12,224,1.000,bicubic,-48.767,-44.472,+101 +poolformer_s36,30.667,69.333,49.435,50.565,30.86,224,0.900,bicubic,-50.749,-46.011,-35 +ese_vovnet39b,30.657,69.343,49.875,50.125,24.57,224,0.875,bicubic,-48.663,-44.837,+103 +gluon_resnet152_v1b,30.623,69.376,48.521,51.479,60.19,224,0.875,bicubic,-49.063,-46.215,+83 +tresnet_xl_448,30.614,69.386,49.069,50.931,78.44,448,0.875,bilinear,-52.436,-47.105,-167 +ssl_resnext50_32x4d,30.594,69.406,50.657,49.343,25.03,224,0.875,bilinear,-49.724,-44.749,+39 +haloregnetz_b,30.594,69.406,49.009,50.991,11.68,224,0.940,bicubic,-50.456,-46.187,-12 +gluon_resnet101_v1d,30.523,69.477,47.950,52.050,44.57,224,0.875,bicubic,-49.891,-47.064,+31 +dpn68b,30.517,69.483,49.162,50.838,12.61,224,0.875,bicubic,-48.699,-45.252,+110 +mobilevitv2_200_384_in22ft1k,30.498,69.502,50.575,49.425,18.45,384,1.000,bicubic,-52.896,-46.005,-198 +resnest26d,30.490,69.510,50.677,49.323,17.07,224,0.875,bilinear,-47.988,-43.621,+149 +efficientnet_b2.ra_in1k,30.435,69.565,49.698,50.302,9.11,288,1.000,bicubic,-50.177,-45.620,+6 +tf_efficientnet_b1.ap_in1k,30.421,69.579,49.553,50.447,7.79,240,0.882,bicubic,-48.859,-44.753,+101 +cs3darknet_x,30.409,69.591,49.187,50.813,35.05,288,1.000,bicubic,-51.819,-47.047,-106 +xcit_tiny_12_p16_384_dist,30.405,69.595,50.131,49.869,6.72,384,1.000,bicubic,-50.535,-45.279,-12 +resnetv2_50,30.386,69.614,48.834,51.166,25.55,224,0.950,bicubic,-50.046,-46.246,+21 +twins_pcpvt_small,30.382,69.618,49.386,50.614,24.11,224,0.900,bicubic,-50.706,-46.256,-24 +visformer_small,30.329,69.671,48.285,51.715,40.22,224,0.900,bicubic,-51.777,-47.587,-100 +pit_xs_distilled_224,30.278,69.722,49.836,50.164,11.00,224,0.900,bicubic,-49.028,-44.528,+91 +regnety_040,30.254,69.746,48.910,51.090,20.65,288,1.000,bicubic,-52.784,-47.600,-181 +mobilevitv2_175_in22ft1k,30.209,69.791,49.034,50.966,14.25,256,0.888,bicubic,-51.735,-46.758,-88 +vit_relpos_base_patch32_plus_rpn_256.sw_in1k,30.207,69.793,48.700,51.300,119.42,256,0.900,bicubic,-49.273,-45.438,+76 +convmixer_1024_20_ks9_p14,30.105,69.895,49.932,50.068,24.38,224,0.960,bicubic,-46.841,-43.426,+213 +seresnet50,30.077,69.923,49.292,50.708,28.09,224,0.875,bicubic,-50.197,-45.778,+28 +dpn98,30.067,69.933,48.244,51.756,61.57,224,0.875,bicubic,-49.575,-46.384,+67 +tf_efficientnet_b2.aa_in1k,30.026,69.974,49.581,50.419,9.11,260,0.890,bicubic,-50.060,-45.328,+37 +dpn131,30.024,69.976,48.146,51.854,79.25,224,0.875,bicubic,-49.798,-46.564,+52 +efficientnet_el.ra_in1k,30.018,69.982,48.834,51.166,10.59,300,0.904,bicubic,-51.298,-46.692,-53 +legacy_senet154,30.001,69.999,48.034,51.966,115.09,224,0.875,bilinear,-51.309,-47.462,-53 +xcit_tiny_12_p16_224_dist,29.997,70.003,49.641,50.359,6.72,224,1.000,bicubic,-48.581,-44.555,+124 +halo2botnet50ts_256,29.983,70.017,48.388,51.612,22.64,256,0.950,bicubic,-52.077,-47.248,-110 +dpn92,29.953,70.047,49.162,50.838,37.67,224,0.875,bicubic,-50.055,-45.674,+36 +mobilevitv2_150_in22ft1k,29.951,70.049,49.215,50.785,10.59,256,0.888,bicubic,-51.526,-46.459,-71 +resnetv2_101x1_bitm,29.898,70.102,51.121,48.879,44.54,448,1.000,bilinear,-52.434,-44.835,-137 +gluon_senet154,29.877,70.123,47.894,52.106,115.09,224,0.875,bicubic,-51.357,-47.454,-56 +xception,29.865,70.135,48.686,51.314,22.86,299,0.897,bicubic,-49.187,-45.706,+96 +cs3sedarknet_l,29.814,70.186,48.987,51.013,21.91,288,0.950,bicubic,-51.960,-46.981,-94 +adv_inception_v3,29.814,70.186,47.847,52.153,23.83,299,0.875,bicubic,-47.768,-45.889,+172 +resnetaa50,29.810,70.190,48.022,51.978,25.56,288,1.000,bicubic,-51.812,-47.786,-88 +vit_base_patch16_384.augreg_in1k,29.792,70.208,48.333,51.667,86.86,384,1.000,bicubic,-51.310,-46.999,-50 +gluon_xception65,29.784,70.216,47.755,52.245,39.92,299,0.903,bicubic,-49.932,-47.105,+43 +lamhalobotnet50ts_256,29.755,70.245,48.344,51.656,22.57,256,0.950,bicubic,-51.789,-47.160,-87 +fbnetv3_d.ra2_in1k,29.743,70.257,49.472,50.528,10.31,256,0.950,bilinear,-49.937,-45.472,+46 +convnext_nano.d1h_in1k,29.698,70.302,47.920,52.080,15.59,288,1.000,bicubic,-51.772,-47.738,-81 +resmlp_36_224,29.692,70.308,48.969,51.031,44.69,224,0.875,bicubic,-50.078,-45.917,+36 +vit_base_patch32_384.augreg_in1k,29.657,70.343,48.985,51.015,88.30,384,1.000,bicubic,-49.103,-45.243,+101 +resnet50,29.639,70.361,46.729,53.271,25.56,224,0.950,bicubic,-50.735,-47.885,-6 +resnetblur50,29.625,70.375,48.250,51.750,25.56,224,0.875,bicubic,-49.661,-46.388,+65 +resnetv2_50d_gn,29.611,70.388,47.784,52.216,25.57,288,0.950,bicubic,-52.205,-48.140,-109 +jx_nest_tiny,29.543,70.457,46.994,53.006,17.06,224,0.875,bicubic,-51.871,-48.622,-82 +resnet50_gn,29.537,70.463,48.305,51.695,25.56,224,0.940,bicubic,-50.515,-46.641,+16 +efficientnet_em.ra2_in1k,29.486,70.514,48.946,51.054,6.90,240,0.882,bicubic,-49.766,-45.848,+65 +cs3darknet_l,29.474,70.526,48.217,51.783,21.16,288,0.950,bicubic,-51.422,-47.453,-47 +resnext101_32x8d,29.439,70.561,48.486,51.514,88.79,224,0.875,bilinear,-49.869,-46.032,+53 +gcresnext50ts,29.433,70.567,47.904,52.096,15.67,256,0.900,bicubic,-51.147,-47.266,-35 +coat_lite_mini,29.433,70.567,47.724,52.276,11.01,224,0.900,bicubic,-49.655,-46.880,+73 +ssl_resnet50,29.423,70.577,49.781,50.219,25.56,224,0.875,bilinear,-49.799,-45.051,+61 +deit_small_patch16_224,29.421,70.579,48.256,51.744,22.05,224,0.900,bicubic,-50.435,-46.796,+17 +sebotnet33ts_256,29.421,70.579,47.156,52.844,13.70,256,0.940,bicubic,-51.729,-48.018,-74 +nf_regnet_b1,29.390,70.611,49.425,50.575,10.22,288,0.900,bicubic,-49.903,-45.323,+52 +cait_xxs24_384,29.387,70.612,48.753,51.247,12.03,384,1.000,bicubic,-51.578,-46.893,-61 +edgenext_small_rw,29.352,70.648,48.743,51.257,7.83,320,1.000,bicubic,-51.104,-46.449,-27 +swin_tiny_patch4_window7_224,29.334,70.666,47.602,52.398,28.29,224,0.900,bicubic,-52.044,-47.938,-91 +resnet34d,29.328,70.671,48.409,51.591,21.82,224,0.875,bicubic,-47.788,-44.973,+164 +convnext_nano_ols.d1h_in1k,29.317,70.683,47.484,52.516,15.65,288,1.000,bicubic,-52.293,-48.156,-112 +cait_xxs24_224,29.303,70.697,48.535,51.465,11.96,224,1.000,bicubic,-49.083,-45.775,+101 +pvt_v2_b1,29.242,70.758,48.977,51.023,14.01,224,0.900,bicubic,-49.452,-45.515,+83 +maxvit_rmlp_pico_rw_256,29.226,70.774,47.721,52.279,7.52,256,0.950,bicubic,-51.290,-47.491,-42 +gcvit_xxtiny,29.216,70.784,48.372,51.628,12.00,224,0.875,bicubic,-50.498,-46.708,+16 +ecaresnet50d_pruned,29.215,70.785,48.453,51.547,19.94,224,0.875,bicubic,-50.501,-46.427,+13 +poolformer_s24,29.177,70.823,48.069,51.931,21.39,224,0.900,bicubic,-51.139,-46.969,-24 +tresnet_l_448,29.165,70.835,47.232,52.768,55.99,448,0.875,bilinear,-53.103,-48.744,-167 +gluon_inception_v3,29.124,70.876,46.955,53.045,23.83,299,0.875,bicubic,-49.682,-47.415,+70 +lambda_resnet50ts,29.118,70.882,46.973,53.027,21.54,256,0.950,bicubic,-52.048,-48.999,-91 +eca_resnet33ts,29.095,70.905,48.792,51.208,19.68,256,0.900,bicubic,-50.983,-46.178,-10 +xception71,29.047,70.953,47.405,52.595,42.34,299,0.903,bicubic,-50.826,-47.517,-2 +hrnet_w64,28.989,71.011,47.142,52.858,128.06,224,0.875,bilinear,-50.485,-47.510,+22 +xcit_tiny_12_p8_224,28.953,71.047,47.515,52.485,6.71,224,1.000,bicubic,-50.741,-47.537,+8 +regnetz_b16,28.941,71.059,47.246,52.754,9.72,288,0.940,bicubic,-51.775,-48.232,-63 +cs3darknet_focus_l,28.928,71.072,47.633,52.367,21.15,288,0.950,bicubic,-51.956,-48.049,-72 +tf_efficientnet_b0.ns_jft_in1k,28.902,71.098,49.011,50.989,5.29,224,0.875,bicubic,-49.756,-45.365,+72 +tf_efficientnet_b1.aa_in1k,28.886,71.114,47.503,52.497,7.79,240,0.882,bicubic,-49.940,-46.695,+60 +gluon_resnet101_v1b,28.878,71.121,46.389,53.611,44.55,224,0.875,bicubic,-50.427,-48.135,+26 +mobilevitv2_150_384_in22ft1k,28.873,71.127,47.924,52.076,10.59,384,1.000,bicubic,-53.721,-48.394,-217 +vit_small_patch32_384.augreg_in21k_ft_in1k,28.871,71.129,48.887,51.113,22.92,384,1.000,bicubic,-51.609,-46.711,-56 +resnetv2_50d_evos,28.867,71.133,46.672,53.328,25.59,288,0.950,bicubic,-53.109,-49.244,-159 +skresnext50_32x4d,28.818,71.182,46.497,53.503,27.48,224,0.875,bicubic,-51.338,-48.145,-25 +sehalonet33ts,28.778,71.222,46.586,53.414,13.69,256,0.940,bicubic,-52.180,-48.690,-87 +levit_256,28.745,71.255,46.723,53.277,18.89,224,0.900,bicubic,-52.765,-48.767,-130 +tf_efficientnet_lite3.in1k,28.660,71.340,47.354,52.646,8.20,300,0.904,bilinear,-51.160,-47.560,-10 +gluon_seresnext50_32x4d,28.651,71.349,46.436,53.564,27.56,224,0.875,bicubic,-51.267,-48.386,-21 +skresnet34,28.645,71.355,47.953,52.047,22.28,224,0.875,bicubic,-48.267,-45.369,+147 +hrnet_w40,28.641,71.359,47.454,52.546,57.56,224,0.875,bilinear,-50.279,-47.016,+44 +darknetaa53,28.639,71.361,46.945,53.055,36.02,288,1.000,bilinear,-51.883,-48.377,-69 +mobilevitv2_175_384_in22ft1k,28.615,71.385,47.144,52.856,14.25,384,1.000,bicubic,-54.327,-49.282,-251 +swinv2_tiny_window8_256,28.613,71.387,46.177,53.823,28.35,256,0.900,bicubic,-53.193,-49.817,-155 +halonet50ts,28.578,71.422,46.167,53.833,22.73,256,0.940,bicubic,-53.066,-49.441,-149 +tf_efficientnetv2_b0.in1k,28.566,71.434,47.079,52.921,7.14,224,0.875,bicubic,-49.790,-46.945,+72 +tv_resnet152,28.533,71.467,47.118,52.882,60.19,224,0.875,bilinear,-49.779,-46.920,+73 +xcit_tiny_12_p16_224,28.523,71.477,47.403,52.597,6.72,224,1.000,bicubic,-48.597,-46.309,+127 +repvgg_b2,28.427,71.573,47.038,52.962,89.02,224,0.875,bilinear,-50.365,-47.376,+44 +hrnet_w48,28.413,71.587,47.586,52.414,77.47,224,0.875,bilinear,-50.887,-46.926,+9 +swinv2_cr_tiny_ns_224,28.377,71.623,45.920,54.080,28.33,224,0.900,bicubic,-53.413,-49.904,-161 +gluon_resnext50_32x4d,28.375,71.624,45.328,54.672,25.03,224,0.875,bicubic,-50.978,-49.098,+1 +efficientnet_b2_pruned.in1k,28.362,71.638,47.051,52.949,8.31,260,0.890,bicubic,-51.554,-47.805,-34 +tf_efficientnet_b0.ap_in1k,28.346,71.654,47.531,52.469,5.29,224,0.875,bicubic,-48.740,-45.725,+124 +seresnet33ts,28.340,71.660,47.757,52.243,19.78,256,0.900,bicubic,-52.012,-47.349,-64 +darknet53,28.317,71.683,46.873,53.127,41.61,288,1.000,bicubic,-52.218,-48.547,-85 +tf_efficientnet_cc_b0_4e.in1k,28.315,71.685,47.364,52.636,13.31,224,0.875,bicubic,-48.991,-45.970,+109 +dla102x2,28.315,71.685,46.761,53.239,41.28,224,0.875,bilinear,-51.133,-47.879,-8 +dla169,28.313,71.687,47.391,52.609,53.39,224,0.875,bilinear,-50.375,-46.945,+40 +mixnet_xl.ra_in1k,28.287,71.713,46.702,53.298,11.90,224,0.875,bicubic,-52.189,-48.234,-82 +gluon_resnet50_v1d,28.246,71.754,45.878,54.122,25.58,224,0.875,bicubic,-50.828,-48.592,+17 +wide_resnet101_2,28.108,71.892,46.401,53.599,126.89,224,0.875,bilinear,-50.748,-47.881,+26 +gluon_resnet101_v1c,28.104,71.896,45.961,54.039,44.57,224,0.875,bicubic,-51.430,-48.617,-20 +regnetx_320,28.093,71.907,45.126,54.874,107.81,224,0.875,bicubic,-52.153,-49.900,-62 +densenet161,28.081,71.919,46.641,53.359,28.68,224,0.875,bicubic,-49.277,-46.997,+101 +regnety_320,28.059,71.941,45.444,54.556,145.05,224,0.875,bicubic,-52.751,-49.800,-106 +mobilevitv2_175,28.034,71.966,46.093,53.907,14.25,256,0.888,bicubic,-52.826,-49.162,-110 +gernet_s,28.022,71.978,46.723,53.277,8.17,224,0.875,bilinear,-48.894,-46.409,+118 +efficientnet_el_pruned.in1k,28.016,71.984,46.790,53.210,10.59,300,0.904,bicubic,-52.284,-48.428,-71 +levit_192,28.016,71.984,45.880,54.120,10.95,224,0.900,bicubic,-51.826,-48.906,-44 +vit_base_patch16_224.augreg_in1k,27.963,72.037,45.725,54.275,86.57,224,0.900,bicubic,-51.191,-48.375,+1 +xception41,27.888,72.112,45.890,54.110,26.97,299,0.903,bicubic,-50.628,-48.388,+32 +regnetx_160,27.817,72.183,45.617,54.383,54.28,224,0.875,bicubic,-52.039,-49.213,-49 +tf_inception_v3,27.784,72.216,45.721,54.279,23.83,299,0.875,bicubic,-50.076,-47.919,+71 +res2net101_26w_4s,27.768,72.232,45.179,54.821,45.21,224,0.875,bilinear,-51.430,-49.253,-5 +tf_efficientnetv2_b1.in1k,27.760,72.240,46.580,53.420,8.14,240,0.882,bicubic,-51.702,-48.142,-28 +vit_base_patch16_224.sam,27.709,72.291,45.106,54.894,86.57,224,0.900,bicubic,-52.533,-49.650,-74 +fbnetv3_b.ra2_in1k,27.676,72.324,46.989,53.011,8.60,256,0.950,bilinear,-51.474,-47.757,-5 +repvgg_b1,27.656,72.344,46.531,53.469,57.42,224,0.875,bilinear,-50.710,-47.567,+38 +hrnet_w44,27.621,72.379,45.837,54.163,67.06,224,0.875,bilinear,-51.275,-48.531,+7 +mobilevitv2_200,27.615,72.385,45.762,54.238,18.45,256,0.888,bicubic,-53.521,-49.604,-147 +gcresnet33ts,27.591,72.409,46.191,53.809,19.88,256,0.900,bicubic,-52.491,-48.807,-70 +inception_v3,27.556,72.444,45.267,54.733,23.83,299,0.875,bicubic,-49.884,-48.209,+80 +resmlp_24_224,27.521,72.479,45.696,54.304,30.02,224,0.875,bicubic,-51.853,-48.851,-33 +pit_xs_224,27.491,72.509,45.900,54.100,10.62,224,0.900,bicubic,-50.691,-48.268,+41 +regnetx_080,27.405,72.595,45.002,54.998,39.57,224,0.875,bicubic,-51.789,-49.558,-15 +hrnet_w30,27.381,72.619,46.554,53.446,37.71,224,0.875,bilinear,-50.825,-47.668,+38 +hrnet_w32,27.369,72.631,45.994,54.006,41.23,224,0.875,bilinear,-51.081,-48.192,+22 +convnext_pico.d1_in1k,27.354,72.646,45.648,54.352,9.05,288,0.950,bicubic,-53.072,-49.410,-103 +vit_small_patch16_384.augreg_in1k,27.328,72.672,46.114,53.886,22.20,384,1.000,bicubic,-53.792,-49.460,-155 +gluon_resnet50_v1s,27.326,72.674,45.222,54.778,25.68,224,0.875,bicubic,-51.386,-49.016,+5 +convnext_pico_ols.d1_in1k,27.305,72.695,45.644,54.356,9.06,288,1.000,bicubic,-53.159,-49.598,-112 +densenet201,27.265,72.735,46.222,53.778,20.01,224,0.875,bicubic,-50.021,-47.256,+75 +densenetblur121d,27.228,72.772,46.299,53.701,8.00,224,0.875,bicubic,-49.360,-46.893,+103 +efficientnet_b1_pruned.in1k,27.181,72.819,45.872,54.128,6.33,240,0.882,bicubic,-51.055,-47.962,+29 +tf_efficientnetv2_b2.in1k,27.163,72.837,44.570,55.430,10.10,260,0.890,bicubic,-53.045,-50.472,-90 +vit_base_patch32_224.augreg_in1k,27.141,72.859,45.175,54.825,88.22,224,0.900,bicubic,-47.763,-46.603,+136 +resnet33ts,27.134,72.866,45.338,54.662,19.68,256,0.900,bicubic,-52.080,-49.236,-30 +resnetrs50,27.110,72.890,45.029,54.971,35.69,224,0.910,bicubic,-52.782,-49.939,-78 +rexnet_130,27.094,72.906,45.933,54.067,7.56,224,0.875,bicubic,-52.406,-48.749,-56 +res2net50_26w_8s,27.078,72.921,44.428,55.572,48.40,224,0.875,bilinear,-52.122,-49.940,-32 +dla102x,27.061,72.939,45.474,54.526,26.31,224,0.875,bilinear,-51.449,-48.754,+3 +resnet32ts,27.037,72.963,45.253,54.747,17.96,256,0.900,bicubic,-51.967,-49.103,-22 +gmixer_24_224,27.027,72.972,44.361,55.639,24.72,224,0.875,bicubic,-51.008,-49.303,+29 +tv_resnet101,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.411,-48.306,+60 +resnext50d_32x4d,26.876,73.124,44.436,55.564,25.05,224,0.875,bicubic,-52.800,-50.430,-68 +regnetx_120,26.868,73.132,44.682,55.318,46.11,224,0.875,bicubic,-52.728,-50.056,-66 +rexnet_100,26.831,73.169,45.369,54.631,4.80,224,0.875,bicubic,-51.027,-48.501,+38 +densenet169,26.829,73.171,45.373,54.627,14.15,224,0.875,bicubic,-49.077,-47.653,+101 +legacy_seresnext101_32x4d,26.811,73.189,43.497,56.503,48.96,224,0.875,bilinear,-53.417,-51.521,-106 +tinynet_a.in1k,26.807,73.193,45.098,54.902,6.19,192,0.875,bicubic,-50.845,-48.438,+40 +regnety_120,26.788,73.212,44.454,55.546,51.82,224,0.875,bicubic,-53.578,-50.672,-122 +regnetx_064,26.784,73.216,44.927,55.073,26.21,224,0.875,bicubic,-52.288,-49.531,-34 +regnetx_032,26.703,73.297,45.236,54.764,15.30,224,0.875,bicubic,-51.469,-48.852,+13 +legacy_seresnet152,26.676,73.324,43.947,56.053,66.82,224,0.875,bilinear,-51.984,-50.423,-15 +densenet121,26.664,73.336,45.900,54.100,7.98,224,0.875,bicubic,-48.914,-46.752,+100 +efficientnet_es.ra_in1k,26.621,73.379,45.112,54.888,5.44,224,0.875,bicubic,-51.445,-48.814,+15 +res2net50_26w_6s,26.595,73.405,43.990,56.010,37.05,224,0.875,bilinear,-51.975,-50.134,-15 +repvgg_b1g4,26.579,73.421,45.084,54.916,39.97,224,0.875,bilinear,-51.015,-48.742,+37 +dla60x,26.552,73.448,45.023,54.977,17.35,224,0.875,bilinear,-51.694,-48.995,+1 +coat_lite_tiny,26.507,73.493,44.644,55.356,5.72,224,0.900,bicubic,-51.005,-49.272,+39 +tf_efficientnet_b0.aa_in1k,26.485,73.515,45.646,54.354,5.29,224,0.875,bicubic,-50.363,-47.582,+66 +res2net50_14w_8s,26.483,73.517,44.371,55.629,25.06,224,0.875,bilinear,-51.667,-49.477,+5 +mobilenetv3_large_100.miil_in21k_ft_in1k,26.481,73.519,44.473,55.527,5.48,224,0.875,bilinear,-51.435,-48.437,+16 +gluon_resnet50_v1b,26.436,73.564,44.035,55.965,25.56,224,0.875,bicubic,-51.144,-49.681,+33 +tf_efficientnet_el.in1k,26.357,73.643,44.175,55.825,10.59,300,0.904,bicubic,-53.893,-50.953,-125 +lambda_resnet26t,26.348,73.653,44.408,55.592,10.96,256,0.940,bicubic,-52.749,-50.184,-52 +levit_128,26.332,73.668,44.096,55.904,9.21,224,0.900,bicubic,-52.154,-49.914,-22 +resmlp_big_24_224,26.318,73.682,43.556,56.444,129.14,224,0.875,bicubic,-54.710,-51.466,-186 +resmlp_12_distilled_224,26.314,73.686,44.874,55.126,15.35,224,0.875,bicubic,-51.630,-48.684,+9 +regnetx_040,26.243,73.757,44.438,55.562,22.12,224,0.875,bicubic,-52.239,-49.806,-24 +mobilevitv2_150,26.190,73.810,43.764,56.236,10.59,256,0.888,bicubic,-54.186,-51.296,-144 +crossvit_9_dagger_240,26.183,73.817,44.544,55.456,8.78,240,0.875,bicubic,-50.797,-49.066,+49 +vit_small_patch32_224.augreg_in21k_ft_in1k,26.151,73.849,45.104,54.896,22.88,224,0.900,bicubic,-49.839,-48.168,+73 +dpn68,26.129,73.871,44.228,55.772,12.61,224,0.875,bicubic,-50.189,-48.750,+68 +efficientnet_b1.ft_in1k,26.061,73.939,44.080,55.920,7.79,256,1.000,bicubic,-52.733,-50.262,-44 +mobilevitv2_125,26.029,73.971,43.670,56.330,7.48,256,0.888,bicubic,-53.655,-51.180,-101 +lambda_resnet26rpt_256,26.025,73.975,44.188,55.812,10.99,256,0.940,bicubic,-52.945,-50.242,-55 +hrnet_w18,25.986,74.014,44.813,55.187,21.30,224,0.875,bilinear,-50.772,-48.631,+52 +hardcorenas_f,25.951,74.049,44.220,55.780,8.20,224,0.875,bilinear,-52.153,-49.582,-10 +vit_small_patch16_224.augreg_in1k,25.949,74.051,43.988,56.012,22.05,224,0.900,bicubic,-52.897,-50.296,-52 +resnet34,25.888,74.112,43.982,56.018,21.80,224,0.875,bilinear,-49.222,-48.302,+86 +res2net50_26w_4s,25.866,74.134,43.155,56.845,25.70,224,0.875,bilinear,-52.098,-50.699,-5 +resnet26t,25.852,74.148,43.953,56.047,16.01,256,0.940,bicubic,-52.030,-49.888,-2 +tresnet_m_448,25.852,74.148,42.874,57.126,31.39,448,0.875,bilinear,-55.862,-52.698,-252 +coat_tiny,25.843,74.157,43.276,56.724,5.50,224,0.900,bicubic,-52.591,-50.761,-34 +hardcorenas_c,25.815,74.185,44.772,55.228,5.52,224,0.875,bilinear,-51.239,-48.386,+32 +gluon_resnet50_v1c,25.784,74.216,43.031,56.969,25.58,224,0.875,bicubic,-52.228,-50.957,-13 +halonet26t,25.764,74.236,43.229,56.771,12.48,256,0.950,bicubic,-53.336,-51.083,-75 +selecsls60,25.729,74.272,44.065,55.935,30.67,224,0.875,bicubic,-52.254,-49.764,-13 +hardcorenas_e,25.662,74.338,43.412,56.588,8.07,224,0.875,bilinear,-52.132,-50.282,-2 +dla60_res2net,25.652,74.348,43.599,56.401,20.85,224,0.875,bilinear,-52.812,-50.607,-43 +dla60_res2next,25.640,74.360,43.670,56.330,17.03,224,0.875,bilinear,-52.800,-50.482,-42 +poolformer_s12,25.630,74.370,44.137,55.863,11.92,224,0.900,bicubic,-51.600,-49.367,+18 +ecaresnet26t,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.316,-51.424,-130 +resmlp_12_224,25.518,74.482,44.324,55.676,15.35,224,0.875,bicubic,-51.136,-48.856,+37 +convnext_femto.d1_in1k,25.516,74.484,43.683,56.317,5.22,288,0.950,bicubic,-53.188,-50.751,-60 +mixnet_l.ft_in1k,25.512,74.488,43.455,56.545,7.33,224,0.875,bicubic,-53.464,-50.727,-76 +tf_efficientnet_lite1.in1k,25.499,74.501,43.585,56.415,5.42,240,0.882,bicubic,-51.143,-49.641,+35 +eca_halonext26ts,25.479,74.521,43.194,56.806,10.76,256,0.940,bicubic,-54.007,-51.404,-115 +cs3darknet_focus_m,25.475,74.525,43.750,56.250,9.30,288,0.950,bicubic,-51.803,-50.220,+9 +bat_resnext26ts,25.463,74.537,43.210,56.790,10.73,256,0.900,bicubic,-52.779,-50.890,-39 +tv_resnext50_32x4d,25.455,74.545,42.787,57.213,25.03,224,0.875,bilinear,-52.165,-50.909,-9 +botnet26t_256,25.444,74.556,42.636,57.364,12.49,256,0.950,bicubic,-53.828,-51.892,-100 +repvgg_a2,25.436,74.564,43.939,56.061,28.21,224,0.875,bilinear,-51.024,-49.065,+36 +tf_mixnet_l.in1k,25.422,74.578,42.534,57.466,7.33,224,0.875,bicubic,-53.352,-51.464,-72 +hardcorenas_b,25.402,74.598,44.190,55.810,5.18,224,0.875,bilinear,-51.136,-48.564,+31 +res2next50,25.389,74.611,42.508,57.492,24.67,224,0.875,bilinear,-52.857,-51.384,-46 +convnext_femto_ols.d1_in1k,25.387,74.613,43.153,56.847,5.23,288,0.950,bicubic,-53.547,-51.379,-85 +legacy_seresnet101,25.334,74.666,42.825,57.175,49.33,224,0.875,bilinear,-53.048,-51.439,-54 +selecsls60b,25.332,74.668,43.559,56.441,32.77,224,0.875,bicubic,-53.080,-50.615,-57 +resnetv2_50x1_bitm,25.324,74.676,45.359,54.641,25.55,448,1.000,bilinear,-55.018,-50.325,-180 +dla102,25.316,74.684,43.827,56.173,33.27,224,0.875,bilinear,-52.716,-50.119,-39 +hardcorenas_d,25.300,74.700,43.121,56.879,7.50,224,0.875,bilinear,-52.132,-50.363,-10 +resnest14d,25.284,74.716,44.114,55.886,10.61,224,0.875,bilinear,-50.222,-48.404,+43 +legacy_seresnext50_32x4d,25.210,74.790,41.936,58.064,27.56,224,0.875,bilinear,-53.868,-52.500,-99 +mixer_b16_224,25.121,74.879,41.229,58.771,59.88,224,0.875,bicubic,-51.479,-50.999,+18 +res2net50_48w_2s,25.027,74.973,42.208,57.792,25.29,224,0.875,bilinear,-52.495,-51.346,-19 +efficientnet_b0.ra_in1k,25.015,74.985,42.787,57.213,5.29,224,0.875,bicubic,-52.683,-50.745,-29 +gluon_resnet34_v1b,24.939,75.061,42.243,57.757,21.80,224,0.875,bicubic,-49.649,-49.747,+60 +mobilenetv2_120d.ra_in1k,24.937,75.063,43.058,56.942,5.83,224,0.875,bicubic,-52.347,-50.434,-12 +dla60,24.933,75.067,43.296,56.704,22.04,224,0.875,bilinear,-52.099,-50.022,-2 +eca_botnext26ts_256,24.870,75.130,42.946,57.054,10.59,256,0.950,bicubic,-54.404,-51.668,-120 +regnety_016,24.811,75.189,42.616,57.384,11.20,224,0.875,bicubic,-53.051,-51.104,-40 +xcit_nano_12_p8_224_dist,24.803,75.197,43.076,56.924,3.05,224,1.000,bicubic,-51.521,-50.014,+17 +seresnext26ts,24.683,75.317,43.098,56.902,10.39,256,0.900,bicubic,-53.183,-50.692,-43 +eca_resnext26ts,24.660,75.340,42.842,57.158,10.30,256,0.900,bicubic,-52.792,-50.724,-25 +cs3darknet_m,24.630,75.370,42.966,57.034,9.31,288,0.950,bicubic,-53.006,-51.048,-36 +mobilevitv2_100,24.552,75.448,42.911,57.089,4.90,256,0.888,bicubic,-53.538,-51.253,-58 +tf_efficientnet_lite2.in1k,24.530,75.470,42.280,57.720,6.09,260,0.890,bicubic,-52.938,-51.474,-29 +skresnet18,24.483,75.517,42.536,57.464,11.96,224,0.875,bicubic,-48.555,-48.632,+66 +regnetx_016,24.473,75.527,42.514,57.486,9.19,224,0.875,bicubic,-52.477,-50.906,-9 +pit_ti_distilled_224,24.406,75.594,42.730,57.270,5.10,224,0.900,bicubic,-50.124,-49.366,+48 +tf_efficientnet_lite0.in1k,24.373,75.627,42.487,57.513,4.65,224,0.875,bicubic,-50.457,-49.689,+40 +hardcorenas_a,24.369,75.631,43.284,56.716,5.26,224,0.875,bilinear,-51.547,-49.230,+14 +tv_resnet50,24.070,75.930,41.313,58.687,25.56,224,0.875,bilinear,-52.068,-51.551,+9 +levit_128s,24.058,75.942,41.007,58.993,7.78,224,0.900,bicubic,-52.472,-51.859,+2 +legacy_seresnet34,24.027,75.973,41.909,58.091,21.96,224,0.875,bilinear,-50.781,-50.215,+37 +xcit_nano_12_p16_384_dist,24.011,75.989,42.324,57.676,3.05,384,1.000,bicubic,-51.447,-50.370,+21 +xcit_nano_12_p8_384_dist,23.950,76.050,41.940,58.060,3.05,384,1.000,bicubic,-53.870,-52.096,-53 +gcresnext26ts,23.944,76.056,41.353,58.647,10.48,256,0.900,bicubic,-53.870,-52.481,-53 +resnet18d,23.929,76.071,42.302,57.698,11.71,224,0.875,bicubic,-48.331,-48.394,+65 +efficientnet_lite0.ra_in1k,23.909,76.091,42.088,57.912,4.65,224,0.875,bicubic,-51.575,-50.422,+16 +resnext26ts,23.864,76.136,41.107,58.893,10.30,256,0.900,bicubic,-52.916,-52.023,-14 +tv_densenet121,23.844,76.156,41.925,58.075,7.98,224,0.875,bicubic,-50.894,-50.225,+31 +efficientnet_es_pruned.in1k,23.828,76.172,41.995,58.005,5.44,224,0.875,bicubic,-51.172,-50.453,+25 +mobilenetv2_140.ra_in1k,23.712,76.288,41.477,58.523,6.11,224,0.875,bicubic,-52.804,-51.519,-7 +mixnet_m.ft_in1k,23.710,76.290,41.141,58.859,5.01,224,0.875,bicubic,-53.550,-52.284,-37 +dla34,23.669,76.331,41.551,58.449,15.74,224,0.875,bilinear,-50.961,-50.527,+30 +legacy_seresnet50,23.651,76.349,40.091,59.909,28.09,224,0.875,bilinear,-53.978,-53.657,-57 +convnext_atto.d2_in1k,23.591,76.409,41.076,58.924,3.70,288,0.950,bicubic,-53.423,-52.624,-30 +ese_vovnet19b_dw,23.535,76.465,41.288,58.712,6.54,224,0.875,bicubic,-53.263,-51.980,-23 +tf_mixnet_m.in1k,23.484,76.516,40.989,59.011,5.01,224,0.875,bicubic,-53.458,-52.163,-28 +tv_resnet34,23.473,76.527,41.367,58.633,21.80,224,0.875,bilinear,-49.839,-50.059,+40 +tf_efficientnet_em.in1k,23.359,76.641,40.404,59.596,6.90,240,0.882,bicubic,-54.771,-53.640,-86 +selecsls42b,23.357,76.643,40.677,59.323,32.46,224,0.875,bicubic,-53.817,-52.713,-42 +repvgg_b0,23.316,76.684,41.182,58.818,15.82,224,0.875,bilinear,-51.837,-51.236,+8 +xcit_nano_12_p16_224_dist,23.253,76.747,41.376,58.624,3.05,224,1.000,bicubic,-49.049,-49.486,+48 +convnext_atto_ols.a2_in1k,23.131,76.869,40.873,59.127,3.70,288,0.950,bicubic,-54.085,-52.807,-46 +mobilenetv2_110d.ra_in1k,23.066,76.934,40.716,59.284,4.52,224,0.875,bicubic,-51.970,-51.470,+10 +vit_base_patch32_224.sam,23.048,76.952,39.572,60.428,88.22,224,0.900,bicubic,-50.642,-51.442,+29 +tinynet_b.in1k,23.015,76.985,40.975,59.025,3.73,188,0.875,bicubic,-51.959,-51.213,+10 +deit_tiny_distilled_patch16_224,22.718,77.282,40.771,59.229,5.91,224,0.900,bicubic,-51.792,-51.119,+19 +mobilenetv3_large_100.ra_in1k,22.655,77.345,40.781,59.219,5.48,224,0.875,bicubic,-53.111,-51.761,-13 +mobilenetv3_rw.rmsp_in1k,22.630,77.370,40.374,59.626,5.48,224,0.875,bicubic,-53.004,-52.334,-11 +edgenext_x_small,22.598,77.402,39.500,60.500,2.34,288,1.000,bicubic,-53.090,-53.266,-14 +tf_mobilenetv3_large_100.in1k,22.569,77.431,39.767,60.233,5.48,224,0.875,bilinear,-52.949,-52.839,-10 +mobilevit_s,22.468,77.531,38.635,61.365,5.58,256,0.900,bicubic,-55.843,-55.511,-109 +xcit_nano_12_p8_224,22.413,77.587,40.661,59.339,3.05,224,1.000,bicubic,-51.501,-51.511,+18 +tf_efficientnet_es.in1k,22.413,77.587,39.095,60.905,5.44,224,0.875,bicubic,-54.180,-54.107,-33 +hrnet_w18_small_v2,22.337,77.663,39.861,60.139,15.60,224,0.875,bilinear,-52.777,-52.555,-4 +convit_tiny,22.282,77.718,39.669,60.331,5.71,224,0.875,bicubic,-50.834,-52.045,+24 +regnety_008,22.119,77.881,38.900,61.100,6.26,224,0.875,bicubic,-54.197,-54.166,-29 +seresnext26t_32x4d,21.991,78.009,38.482,61.518,16.81,224,0.875,bicubic,-55.995,-55.264,-98 +regnety_006,21.971,78.029,38.955,61.045,6.06,224,0.875,bicubic,-53.275,-53.577,-11 +vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k,21.954,78.046,39.405,60.595,6.36,384,1.000,bicubic,-53.998,-53.855,-28 +regnetx_008,21.940,78.060,38.928,61.072,7.26,224,0.875,bicubic,-53.098,-53.408,-8 +resnet26d,21.907,78.094,38.619,61.381,16.01,224,0.875,bicubic,-54.789,-54.531,-46 +semnasnet_100.rmsp_in1k,21.903,78.097,38.600,61.400,3.89,224,0.875,bicubic,-53.545,-54.004,-17 +pit_ti_224,21.875,78.125,39.541,60.459,4.85,224,0.900,bicubic,-51.037,-51.861,+20 +pvt_v2_b0,21.838,78.162,40.142,59.858,3.67,224,0.900,bicubic,-48.818,-50.066,+38 +regnetx_006,21.738,78.263,38.904,61.096,6.20,224,0.875,bicubic,-52.115,-52.768,+7 +vit_tiny_patch16_384.augreg_in21k_ft_in1k,21.708,78.292,39.329,60.671,5.79,384,1.000,bicubic,-56.722,-55.213,-130 +crossvit_9_240,21.683,78.317,39.270,60.730,8.55,240,0.875,bicubic,-52.281,-52.698,+3 +vgg19_bn,21.628,78.373,39.283,60.717,143.68,224,0.875,bilinear,-52.587,-52.559,-2 +ghostnet_100,21.620,78.380,38.692,61.308,5.18,224,0.875,bilinear,-52.358,-52.764,0 +semnasnet_075.rmsp_in1k,21.572,78.428,38.928,61.072,2.91,224,0.875,bicubic,-51.402,-52.208,+11 +gluon_resnet18_v1b,21.549,78.451,38.869,61.131,11.69,224,0.875,bicubic,-49.287,-50.891,+30 +mobilevitv2_075,21.541,78.459,38.633,61.367,2.87,256,0.888,bicubic,-54.081,-54.135,-34 +fbnetc_100.rmsp_in1k,21.484,78.516,38.161,61.839,5.57,224,0.875,bilinear,-53.640,-54.224,-24 +xcit_nano_12_p16_224,21.437,78.563,39.796,60.204,3.05,224,1.000,bicubic,-48.517,-49.958,+32 +mnasnet_100.rmsp_in1k,21.350,78.650,37.719,62.281,4.38,224,0.875,bicubic,-53.308,-54.395,-15 +resnet26,21.295,78.705,38.018,61.982,16.00,224,0.875,bicubic,-53.997,-54.552,-30 +lcnet_100.ra2_in1k,21.293,78.707,38.837,61.163,2.95,224,0.875,bicubic,-50.821,-51.541,+16 +ssl_resnet18,21.278,78.722,39.113,60.887,11.69,224,0.875,bilinear,-51.332,-52.303,+6 +mixnet_s.ft_in1k,21.254,78.746,38.187,61.813,4.13,224,0.875,bicubic,-54.738,-54.609,-50 +seresnext26d_32x4d,21.252,78.748,37.311,62.689,16.81,224,0.875,bicubic,-56.350,-56.297,-102 +legacy_seresnext26_32x4d,21.093,78.907,37.633,62.367,16.79,224,0.875,bicubic,-56.011,-55.683,-81 +crossvit_tiny_240,21.050,78.950,38.055,61.945,7.01,240,0.875,bicubic,-52.274,-53.861,-6 +regnetx_004,20.898,79.102,37.566,62.434,5.16,224,0.875,bicubic,-51.498,-53.264,+2 +spnasnet_100.rmsp_in1k,20.863,79.137,37.896,62.104,4.42,224,0.875,bilinear,-53.221,-53.922,-17 +legacy_seresnet18,20.837,79.162,37.619,62.381,11.78,224,0.875,bicubic,-50.907,-52.715,+12 +mobilenetv2_100.ra_in1k,20.773,79.227,37.759,62.241,3.50,224,0.875,bicubic,-52.197,-53.257,-4 +tf_mixnet_s.in1k,20.470,79.530,36.607,63.393,4.13,224,0.875,bicubic,-55.180,-56.021,-51 +vit_tiny_patch16_224.augreg_in21k_ft_in1k,20.458,79.542,37.597,62.403,5.72,224,0.900,bicubic,-54.996,-55.251,-44 +regnety_004,20.415,79.585,37.002,62.998,4.34,224,0.875,bicubic,-53.619,-54.750,-21 +hrnet_w18_small,20.368,79.632,37.093,62.907,13.19,224,0.875,bilinear,-51.974,-53.585,-2 +tf_mobilenetv3_large_075.in1k,20.366,79.634,36.764,63.236,3.99,224,0.875,bilinear,-53.072,-54.586,-17 +resnet18,20.228,79.772,37.261,62.739,11.69,224,0.875,bilinear,-49.520,-51.817,+16 +mixer_l16_224,20.173,79.827,32.952,67.048,208.20,224,0.875,bicubic,-51.885,-54.716,+1 +deit_tiny_patch16_224,20.162,79.838,37.546,62.454,5.72,224,0.900,bicubic,-52.007,-53.572,-2 +tf_mobilenetv3_large_minimal_100.in1k,20.122,79.878,36.908,63.092,3.92,224,0.875,bilinear,-52.126,-53.722,-4 +vgg16_bn,19.959,80.041,36.301,63.699,138.37,224,0.875,bilinear,-53.391,-55.205,-21 +vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k,19.334,80.666,36.047,63.953,6.34,224,0.900,bicubic,-52.454,-54.781,-1 +tinynet_c.in1k,19.252,80.748,35.994,64.006,2.46,184,0.875,bicubic,-51.980,-53.754,+2 +edgenext_xx_small,18.876,81.124,35.155,64.845,1.33,288,1.000,bicubic,-52.990,-55.389,-4 +mobilevit_xs,18.295,81.705,33.214,66.787,2.32,256,0.900,bicubic,-56.349,-59.139,-39 +lcnet_075.ra2_in1k,18.167,81.833,34.410,65.590,2.36,224,0.875,bicubic,-50.651,-53.960,+10 +vgg19,17.929,82.071,33.054,66.946,143.67,224,0.875,bilinear,-54.439,-57.818,-16 +vgg13_bn,17.802,82.198,34.039,65.961,133.05,224,0.875,bilinear,-53.792,-56.343,-4 +vgg16,17.540,82.460,32.773,67.227,138.36,224,0.875,bilinear,-54.054,-57.603,-6 +regnety_002,17.450,82.550,32.431,67.569,3.16,224,0.875,bicubic,-52.802,-57.109,-1 +vgg11_bn,17.403,82.597,33.011,66.989,132.87,224,0.875,bilinear,-52.957,-56.791,-3 +mobilevitv2_050,17.300,82.700,33.003,66.997,1.37,256,0.888,bicubic,-52.840,-56.923,-2 +resnet10t,17.279,82.721,33.078,66.922,5.44,224,0.950,bilinear,-51.015,-55.000,+5 +regnetx_002,16.962,83.038,32.225,67.775,2.68,224,0.875,bicubic,-51.800,-56.331,+3 +mobilenetv3_small_100.lamb_in1k,16.807,83.193,32.524,67.476,2.54,224,0.875,bicubic,-50.845,-55.112,+6 +tinynet_d.in1k,16.674,83.326,32.457,67.543,2.34,152,0.875,bicubic,-50.288,-54.609,+6 +mobilenetv2_050.lamb_in1k,16.666,83.334,31.952,68.048,1.97,224,0.875,bicubic,-49.276,-54.130,+8 +mnasnet_small.lamb_in1k,16.634,83.366,31.921,68.079,2.03,224,0.875,bicubic,-49.572,-54.587,+5 +resnet14t,16.467,83.533,30.732,69.268,10.08,224,0.950,bilinear,-55.883,-59.608,-27 +dla60x_c,16.310,83.690,31.761,68.239,1.32,224,0.875,bilinear,-51.582,-56.665,0 +tf_mobilenetv3_small_100.in1k,16.227,83.772,31.223,68.777,2.54,224,0.875,bilinear,-51.694,-56.441,-2 +vgg13,16.100,83.900,30.985,69.015,133.05,224,0.875,bilinear,-53.826,-58.261,-10 +vgg11,15.728,84.272,30.453,69.547,132.86,224,0.875,bilinear,-53.296,-58.175,-9 +mobilenetv3_small_075.lamb_in1k,14.954,85.046,29.739,70.261,2.04,224,0.875,bicubic,-50.292,-55.697,+3 +tf_mobilenetv3_small_075.in1k,14.944,85.056,29.572,70.428,2.04,224,0.875,bilinear,-50.772,-56.558,+1 +dla46_c,14.657,85.343,29.380,70.620,1.30,224,0.875,bilinear,-50.209,-56.912,+2 +mobilevit_xxs,14.508,85.492,28.670,71.330,1.27,256,0.900,bicubic,-54.404,-60.268,-12 +dla46x_c,14.382,85.618,29.191,70.809,1.07,224,0.875,bilinear,-51.588,-57.789,-4 +lcnet_050.ra2_in1k,14.316,85.684,28.649,71.351,1.88,224,0.875,bicubic,-48.785,-55.731,0 +tf_mobilenetv3_small_minimal_100.in1k,13.964,86.036,27.988,72.012,2.04,224,0.875,bilinear,-48.942,-56.242,0 +tinynet_e.in1k,12.671,87.329,26.389,73.611,2.04,106,0.875,bicubic,-47.185,-55.373,0 +mobilenetv3_small_050.lamb_in1k,11.034,88.966,23.473,76.527,1.59,224,0.875,bicubic,-46.856,-56.721,0 diff --git a/tests/test_models.py b/tests/test_models.py index 4c848440..141caabb 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -27,9 +27,7 @@ NON_STD_FILTERS = [ 'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*', 'convit_*', 'levit*', 'visformer*', 'deit*', 'jx_nest_*', 'nest_*', 'xcit_*', 'crossvit_*', 'beit*', 'poolformer_*', 'volo_*', 'sequencer2d_*', 'swinv2_*', 'pvt_v2*', 'mvitv2*', 'gcvit*', 'efficientformer*', - - 'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', 'eva_*' - + 'coatnet*', 'coatnext*', 'maxvit*', 'maxxvit*', 'eva_*', 'flexivit*' ] NUM_NON_STD = len(NON_STD_FILTERS) @@ -40,7 +38,7 @@ if 'GITHUB_ACTIONS' in os.environ: '*efficientnet_l2*', '*resnext101_32x48d', '*in21k', '*152x4_bitm', '*101x3_bitm', '*50x3_bitm', '*nfnet_f3*', '*nfnet_f4*', '*nfnet_f5*', '*nfnet_f6*', '*nfnet_f7*', '*efficientnetv2_xl*', '*resnetrs350*', '*resnetrs420*', 'xcit_large_24_p8*', 'vit_huge*', 'vit_gi*', 'swin*huge*', - 'swin*giant*', 'davit*giant', 'davit*huge'] + 'swin*giant*', 'davit_giant', 'davit_huge', 'convnextv2_huge*'] NON_STD_EXCLUDE_FILTERS = ['vit_huge*', 'vit_gi*', 'swin*giant*', 'eva_giant*'] else: EXCLUDE_FILTERS = [] @@ -131,7 +129,7 @@ def test_model_backward(model_name, batch_size): @pytest.mark.timeout(300) -@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(exclude_filters=NON_STD_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_default_cfgs(model_name, batch_size): """Run a single forward pass with each model""" @@ -193,7 +191,7 @@ def test_model_default_cfgs(model_name, batch_size): @pytest.mark.timeout(300) -@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS, exclude_filters=NON_STD_EXCLUDE_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_default_cfgs_non_std(model_name, batch_size): """Run a single forward pass with each model""" @@ -306,7 +304,7 @@ if 'GITHUB_ACTIONS' in os.environ: # and 'Linux' in platform.system(): @pytest.mark.timeout(120) -@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS)) +@pytest.mark.parametrize('model_name', list_models(exclude_filters=EXCLUDE_FILTERS + EXCLUDE_FEAT_FILTERS, include_tags=True)) @pytest.mark.parametrize('batch_size', [1]) def test_model_forward_features(model_name, batch_size): """Run a single forward pass with each model in feature extraction mode""" diff --git a/timm/data/auto_augment.py b/timm/data/auto_augment.py index a7701b82..b6eacbf5 100644 --- a/timm/data/auto_augment.py +++ b/timm/data/auto_augment.py @@ -741,7 +741,6 @@ class RandAugment: self.ops = ops self.num_layers = num_layers self.choice_weights = choice_weights - print(self.ops, self.choice_weights) def __call__(self, img): # no replacement when using weighted choice diff --git a/timm/data/dataset_factory.py b/timm/data/dataset_factory.py index 757c2e5d..a4c18e39 100644 --- a/timm/data/dataset_factory.py +++ b/timm/data/dataset_factory.py @@ -151,7 +151,7 @@ def create_dataset( elif name.startswith('hfds/'): # NOTE right now, HF datasets default arrow format is a random-access Dataset, # There will be a IterableDataset variant too, TBD - ds = ImageDataset(root, reader=name, split=split, **kwargs) + ds = ImageDataset(root, reader=name, split=split, class_map=class_map, **kwargs) elif name.startswith('tfds/'): ds = IterableImageDataset( root, diff --git a/timm/data/readers/reader_factory.py b/timm/data/readers/reader_factory.py index 58ff56cd..226e3857 100644 --- a/timm/data/readers/reader_factory.py +++ b/timm/data/readers/reader_factory.py @@ -6,7 +6,7 @@ from .reader_image_in_tar import ReaderImageInTar def create_reader(name, root, split='train', **kwargs): name = name.lower() - name = name.split('/', 2) + name = name.split('/', 1) prefix = '' if len(name) > 1: prefix = name[0] diff --git a/timm/data/readers/reader_hfds.py b/timm/data/readers/reader_hfds.py index 901cf4bc..62ae5f4d 100644 --- a/timm/data/readers/reader_hfds.py +++ b/timm/data/readers/reader_hfds.py @@ -13,13 +13,14 @@ try: except ImportError as e: print("Please install Hugging Face datasets package `pip install datasets`.") exit(1) +from .class_map import load_class_map from .reader import Reader -def get_class_labels(info): +def get_class_labels(info, label_key='label'): if 'label' not in info.features: return {} - class_label = info.features['label'] + class_label = info.features[label_key] class_to_idx = {n: class_label.str2int(n) for n in class_label.names} return class_to_idx @@ -32,6 +33,7 @@ class ReaderHfds(Reader): name, split='train', class_map=None, + label_key='label', download=False, ): """ @@ -43,12 +45,17 @@ class ReaderHfds(Reader): name, # 'name' maps to path arg in hf datasets split=split, cache_dir=self.root, # timm doesn't expect hidden cache dir for datasets, specify a path - #use_auth_token=True, ) # leave decode for caller, plus we want easy access to original path names... self.dataset = self.dataset.cast_column('image', datasets.Image(decode=False)) - self.class_to_idx = get_class_labels(self.dataset.info) + self.label_key = label_key + self.remap_class = False + if class_map: + self.class_to_idx = load_class_map(class_map) + self.remap_class = True + else: + self.class_to_idx = get_class_labels(self.dataset.info, self.label_key) self.split_info = self.dataset.info.splits[split] self.num_samples = self.split_info.num_examples @@ -60,7 +67,10 @@ class ReaderHfds(Reader): else: assert 'path' in image and image['path'] image = open(image['path'], 'rb') - return image, item['label'] + label = item[self.label_key] + if self.remap_class: + label = self.class_to_idx[label] + return image, label def __len__(self): return len(self.dataset) diff --git a/timm/layers/__init__.py b/timm/layers/__init__.py index 21c641b6..6b2dabba 100644 --- a/timm/layers/__init__.py +++ b/timm/layers/__init__.py @@ -1,6 +1,7 @@ from .activations import * from .adaptive_avgmax_pool import \ adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d +from .attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding from .blur_pool import BlurPool2d from .classifier import ClassifierHead, create_classifier from .cond_conv2d import CondConv2d, get_condconv_initializer @@ -25,13 +26,18 @@ from .helpers import to_ntuple, to_2tuple, to_3tuple, to_4tuple, make_divisible, from .inplace_abn import InplaceAbn from .linear import Linear from .mixed_conv2d import MixedConv2d -from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp +from .mlp import Mlp, GluMlp, GatedMlp, ConvMlp, GlobalResponseNormMlp from .non_local_attn import NonLocalAttn, BatNonLocalAttn from .norm import GroupNorm, GroupNorm1, LayerNorm, LayerNorm2d -from .norm_act import BatchNormAct2d, GroupNormAct, convert_sync_batchnorm +from .norm_act import BatchNormAct2d, GroupNormAct, GroupNorm1Act, LayerNormAct, LayerNormAct2d,\ + SyncBatchNormAct, convert_sync_batchnorm, FrozenBatchNormAct2d, freeze_batch_norm_2d, unfreeze_batch_norm_2d from .padding import get_padding, get_same_padding, pad_same -from .patch_embed import PatchEmbed +from .patch_embed import PatchEmbed, resample_patch_embed from .pool2d_same import AvgPool2dSame, create_pool2d +from .pos_embed import resample_abs_pos_embed +from .pos_embed_rel import RelPosMlp, RelPosBias, RelPosBiasTf, gen_relative_position_index, gen_relative_log_coords +from .pos_embed_sincos import build_sincos2d_pos_embed, build_fourier_pos_embed, build_rotary_pos_embed, \ + FourierEmbed, RotaryEmbedding from .squeeze_excite import SEModule, SqueezeExcite, EffectiveSEModule, EffectiveSqueezeExcite from .selective_kernel import SelectiveKernel from .separable_conv import SeparableConv2d, SeparableConvNormAct diff --git a/timm/layers/attention_pool2d.py b/timm/layers/attention_pool2d.py index a13a6881..765efa08 100644 --- a/timm/layers/attention_pool2d.py +++ b/timm/layers/attention_pool2d.py @@ -13,7 +13,7 @@ import torch import torch.nn as nn from .helpers import to_2tuple -from .pos_embed import apply_rot_embed, RotaryEmbedding +from .pos_embed_sincos import apply_rot_embed, RotaryEmbedding from .weight_init import trunc_normal_ diff --git a/timm/layers/grn.py b/timm/layers/grn.py new file mode 100644 index 00000000..ae71e013 --- /dev/null +++ b/timm/layers/grn.py @@ -0,0 +1,39 @@ +""" Global Response Normalization Module + +Based on the GRN layer presented in +`ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808 + +This implementation +* works for both NCHW and NHWC tensor layouts +* uses affine param names matching existing torch norm layers +* slightly improves eager mode performance via fused addcmul + +Hacked together by / Copyright 2023 Ross Wightman +""" + +import torch +from torch import nn as nn + + +class GlobalResponseNorm(nn.Module): + """ Global Response Normalization layer + """ + def __init__(self, dim, eps=1e-6, channels_last=True): + super().__init__() + self.eps = eps + if channels_last: + self.spatial_dim = (1, 2) + self.channel_dim = -1 + self.wb_shape = (1, 1, 1, -1) + else: + self.spatial_dim = (2, 3) + self.channel_dim = 1 + self.wb_shape = (1, -1, 1, 1) + + self.weight = nn.Parameter(torch.zeros(dim)) + self.bias = nn.Parameter(torch.zeros(dim)) + + def forward(self, x): + x_g = x.norm(p=2, dim=self.spatial_dim, keepdim=True) + x_n = x_g / (x_g.mean(dim=self.channel_dim, keepdim=True) + self.eps) + return x + torch.addcmul(self.bias.view(self.wb_shape), self.weight.view(self.wb_shape), x * x_n) diff --git a/timm/layers/helpers.py b/timm/layers/helpers.py index 2fa296bc..bc75ef3e 100644 --- a/timm/layers/helpers.py +++ b/timm/layers/helpers.py @@ -10,7 +10,7 @@ import collections.abc def _ntuple(n): def parse(x): if isinstance(x, collections.abc.Iterable) and not isinstance(x, str): - return x + return tuple(x) return tuple(repeat(x, n)) return parse diff --git a/timm/layers/mlp.py b/timm/layers/mlp.py index 91e80a84..d0188291 100644 --- a/timm/layers/mlp.py +++ b/timm/layers/mlp.py @@ -2,25 +2,38 @@ Hacked together by / Copyright 2020 Ross Wightman """ +from functools import partial + from torch import nn as nn +from .grn import GlobalResponseNorm from .helpers import to_2tuple class Mlp(nn.Module): """ MLP as used in Vision Transformer, MLP-Mixer and related networks """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, bias=True, drop=0.): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + bias=True, + drop=0., + use_conv=False, + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features bias = to_2tuple(bias) drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features, out_features, bias=bias[1]) + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def forward(self, x): @@ -36,18 +49,29 @@ class GluMlp(nn.Module): """ MLP w/ GLU style gating See: https://arxiv.org/abs/1612.08083, https://arxiv.org/abs/2002.05202 """ - def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.Sigmoid, bias=True, drop=0.): + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.Sigmoid, + bias=True, + drop=0., + use_conv=False, + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features assert hidden_features % 2 == 0 bias = to_2tuple(bias) drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + self.chunk_dim = 1 if use_conv else -1 - self.fc1 = nn.Linear(in_features, hidden_features, bias=bias[0]) + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) self.act = act_layer() self.drop1 = nn.Dropout(drop_probs[0]) - self.fc2 = nn.Linear(hidden_features // 2, out_features, bias=bias[1]) + self.fc2 = linear_layer(hidden_features // 2, out_features, bias=bias[1]) self.drop2 = nn.Dropout(drop_probs[1]) def init_weights(self): @@ -58,7 +82,7 @@ class GluMlp(nn.Module): def forward(self, x): x = self.fc1(x) - x, gates = x.chunk(2, dim=-1) + x, gates = x.chunk(2, dim=self.chunk_dim) x = x * self.act(gates) x = self.drop1(x) x = self.fc2(x) @@ -70,8 +94,15 @@ class GatedMlp(nn.Module): """ MLP as used in gMLP """ def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, - gate_layer=None, bias=True, drop=0.): + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + gate_layer=None, + bias=True, + drop=0., + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features @@ -104,8 +135,15 @@ class ConvMlp(nn.Module): """ MLP using 1x1 convs that keeps spatial dims """ def __init__( - self, in_features, hidden_features=None, out_features=None, act_layer=nn.ReLU, - norm_layer=None, bias=True, drop=0.): + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.ReLU, + norm_layer=None, + bias=True, + drop=0., + ): super().__init__() out_features = out_features or in_features hidden_features = hidden_features or in_features @@ -124,3 +162,40 @@ class ConvMlp(nn.Module): x = self.drop(x) x = self.fc2(x) return x + + +class GlobalResponseNormMlp(nn.Module): + """ MLP w/ Global Response Norm (see grn.py), nn.Linear or 1x1 Conv2d + """ + def __init__( + self, + in_features, + hidden_features=None, + out_features=None, + act_layer=nn.GELU, + bias=True, + drop=0., + use_conv=False, + ): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + bias = to_2tuple(bias) + drop_probs = to_2tuple(drop) + linear_layer = partial(nn.Conv2d, kernel_size=1) if use_conv else nn.Linear + + self.fc1 = linear_layer(in_features, hidden_features, bias=bias[0]) + self.act = act_layer() + self.drop1 = nn.Dropout(drop_probs[0]) + self.grn = GlobalResponseNorm(hidden_features, channels_last=not use_conv) + self.fc2 = linear_layer(hidden_features, out_features, bias=bias[1]) + self.drop2 = nn.Dropout(drop_probs[1]) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + x = self.drop1(x) + x = self.grn(x) + x = self.fc2(x) + x = self.drop2(x) + return x diff --git a/timm/layers/norm_act.py b/timm/layers/norm_act.py index ff075fbc..5ca21d18 100644 --- a/timm/layers/norm_act.py +++ b/timm/layers/norm_act.py @@ -17,6 +17,7 @@ from typing import Union, List, Optional, Any import torch from torch import nn as nn from torch.nn import functional as F +from torchvision.ops.misc import FrozenBatchNorm2d from .create_act import get_act_layer from .fast_norm import is_fast_norm, fast_group_norm, fast_layer_norm @@ -77,7 +78,7 @@ class BatchNormAct2d(nn.BatchNorm2d): if self.training and self.track_running_stats: # TODO: if statement only here to tell the jit to skip emitting this when it is None if self.num_batches_tracked is not None: # type: ignore[has-type] - self.num_batches_tracked = self.num_batches_tracked + 1 # type: ignore[has-type] + self.num_batches_tracked.add_(1) # type: ignore[has-type] if self.momentum is None: # use cumulative moving average exponential_average_factor = 1.0 / float(self.num_batches_tracked) else: # use exponential moving average @@ -169,6 +170,159 @@ def convert_sync_batchnorm(module, process_group=None): return module_output +class FrozenBatchNormAct2d(torch.nn.Module): + """ + BatchNormAct2d where the batch statistics and the affine parameters are fixed + + Args: + num_features (int): Number of features ``C`` from an expected input of size ``(N, C, H, W)`` + eps (float): a value added to the denominator for numerical stability. Default: 1e-5 + """ + + def __init__( + self, + num_features: int, + eps: float = 1e-5, + apply_act=True, + act_layer=nn.ReLU, + inplace=True, + drop_layer=None, + ): + super().__init__() + self.eps = eps + self.register_buffer("weight", torch.ones(num_features)) + self.register_buffer("bias", torch.zeros(num_features)) + self.register_buffer("running_mean", torch.zeros(num_features)) + self.register_buffer("running_var", torch.ones(num_features)) + + self.drop = drop_layer() if drop_layer is not None else nn.Identity() + act_layer = get_act_layer(act_layer) # string -> nn.Module + if act_layer is not None and apply_act: + act_args = dict(inplace=True) if inplace else {} + self.act = act_layer(**act_args) + else: + self.act = nn.Identity() + + def _load_from_state_dict( + self, + state_dict: dict, + prefix: str, + local_metadata: dict, + strict: bool, + missing_keys: List[str], + unexpected_keys: List[str], + error_msgs: List[str], + ): + num_batches_tracked_key = prefix + "num_batches_tracked" + if num_batches_tracked_key in state_dict: + del state_dict[num_batches_tracked_key] + + super()._load_from_state_dict( + state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs + ) + + def forward(self, x: torch.Tensor) -> torch.Tensor: + # move reshapes to the beginning + # to make it fuser-friendly + w = self.weight.reshape(1, -1, 1, 1) + b = self.bias.reshape(1, -1, 1, 1) + rv = self.running_var.reshape(1, -1, 1, 1) + rm = self.running_mean.reshape(1, -1, 1, 1) + scale = w * (rv + self.eps).rsqrt() + bias = b - rm * scale + x = x * scale + bias + x = self.act(self.drop(x)) + return x + + def __repr__(self) -> str: + return f"{self.__class__.__name__}({self.weight.shape[0]}, eps={self.eps}, act={self.act})" + + +def freeze_batch_norm_2d(module): + """ + Converts all `BatchNorm2d` and `SyncBatchNorm` or `BatchNormAct2d` and `SyncBatchNormAct2d` layers + of provided module into `FrozenBatchNorm2d` or `FrozenBatchNormAct2d` respectively. + + Args: + module (torch.nn.Module): Any PyTorch module. + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + if isinstance(module, (BatchNormAct2d, SyncBatchNormAct)): + res = FrozenBatchNormAct2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + res.drop = module.drop + res.act = module.act + elif isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)): + res = FrozenBatchNorm2d(module.num_features) + res.num_features = module.num_features + res.affine = module.affine + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for name, child in module.named_children(): + new_child = freeze_batch_norm_2d(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + +def unfreeze_batch_norm_2d(module): + """ + Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance + of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked + recursively and submodules are converted in place. + + Args: + module (torch.nn.Module): Any PyTorch module. + + Returns: + torch.nn.Module: Resulting module + + Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 + """ + res = module + if isinstance(module, FrozenBatchNormAct2d): + res = BatchNormAct2d(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + res.drop = module.drop + res.act = module.act + elif isinstance(module, FrozenBatchNorm2d): + res = torch.nn.BatchNorm2d(module.num_features) + if module.affine: + res.weight.data = module.weight.data.clone().detach() + res.bias.data = module.bias.data.clone().detach() + res.running_mean.data = module.running_mean.data + res.running_var.data = module.running_var.data + res.eps = module.eps + else: + for name, child in module.named_children(): + new_child = unfreeze_batch_norm_2d(child) + if new_child is not child: + res.add_module(name, new_child) + return res + + def _num_groups(num_channels, num_groups, group_size): if group_size: assert num_channels % group_size == 0 @@ -179,10 +333,54 @@ def _num_groups(num_channels, num_groups, group_size): class GroupNormAct(nn.GroupNorm): # NOTE num_channel and num_groups order flipped for easier layer swaps / binding of fixed args def __init__( - self, num_channels, num_groups=32, eps=1e-5, affine=True, group_size=None, - apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None): + self, + num_channels, + num_groups=32, + eps=1e-5, + affine=True, + group_size=None, + apply_act=True, + act_layer=nn.ReLU, + inplace=True, + drop_layer=None, + ): super(GroupNormAct, self).__init__( - _num_groups(num_channels, num_groups, group_size), num_channels, eps=eps, affine=affine) + _num_groups(num_channels, num_groups, group_size), + num_channels, + eps=eps, + affine=affine, + ) + self.drop = drop_layer() if drop_layer is not None else nn.Identity() + act_layer = get_act_layer(act_layer) # string -> nn.Module + if act_layer is not None and apply_act: + act_args = dict(inplace=True) if inplace else {} + self.act = act_layer(**act_args) + else: + self.act = nn.Identity() + self._fast_norm = is_fast_norm() + + def forward(self, x): + if self._fast_norm: + x = fast_group_norm(x, self.num_groups, self.weight, self.bias, self.eps) + else: + x = F.group_norm(x, self.num_groups, self.weight, self.bias, self.eps) + x = self.drop(x) + x = self.act(x) + return x + + +class GroupNorm1Act(nn.GroupNorm): + def __init__( + self, + num_channels, + eps=1e-5, + affine=True, + apply_act=True, + act_layer=nn.ReLU, + inplace=True, + drop_layer=None, + ): + super(GroupNorm1Act, self).__init__(1, num_channels, eps=eps, affine=affine) self.drop = drop_layer() if drop_layer is not None else nn.Identity() act_layer = get_act_layer(act_layer) # string -> nn.Module if act_layer is not None and apply_act: @@ -204,8 +402,15 @@ class GroupNormAct(nn.GroupNorm): class LayerNormAct(nn.LayerNorm): def __init__( - self, normalization_shape: Union[int, List[int], torch.Size], eps=1e-5, affine=True, - apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None): + self, + normalization_shape: Union[int, List[int], torch.Size], + eps=1e-5, + affine=True, + apply_act=True, + act_layer=nn.ReLU, + inplace=True, + drop_layer=None, + ): super(LayerNormAct, self).__init__(normalization_shape, eps=eps, elementwise_affine=affine) self.drop = drop_layer() if drop_layer is not None else nn.Identity() act_layer = get_act_layer(act_layer) # string -> nn.Module @@ -228,8 +433,15 @@ class LayerNormAct(nn.LayerNorm): class LayerNormAct2d(nn.LayerNorm): def __init__( - self, num_channels, eps=1e-5, affine=True, - apply_act=True, act_layer=nn.ReLU, inplace=True, drop_layer=None): + self, + num_channels, + eps=1e-5, + affine=True, + apply_act=True, + act_layer=nn.ReLU, + inplace=True, + drop_layer=None, + ): super(LayerNormAct2d, self).__init__(num_channels, eps=eps, elementwise_affine=affine) self.drop = drop_layer() if drop_layer is not None else nn.Identity() act_layer = get_act_layer(act_layer) # string -> nn.Module diff --git a/timm/layers/patch_embed.py b/timm/layers/patch_embed.py index be8740ce..764519f2 100644 --- a/timm/layers/patch_embed.py +++ b/timm/layers/patch_embed.py @@ -2,15 +2,24 @@ A convolution based approach to patchifying a 2D image w/ embedding projection. -Based on the impl in https://github.com/google-research/vision_transformer +Based on code in: + * https://github.com/google-research/vision_transformer + * https://github.com/google-research/big_vision/tree/main/big_vision Hacked together by / Copyright 2020 Ross Wightman """ +import logging +from typing import List + +import torch from torch import nn as nn +import torch.nn.functional as F from .helpers import to_2tuple from .trace_utils import _assert +_logger = logging.getLogger(__name__) + class PatchEmbed(nn.Module): """ 2D Image to Patch Embedding @@ -46,3 +55,130 @@ class PatchEmbed(nn.Module): x = x.flatten(2).transpose(1, 2) # BCHW -> BNC x = self.norm(x) return x + + +def resample_patch_embed( + patch_embed, + new_size: List[int], + interpolation: str = 'bicubic', + antialias: bool = True, + verbose: bool = False, +): + """Resample the weights of the patch embedding kernel to target resolution. + We resample the patch embedding kernel by approximately inverting the effect + of patch resizing. + + Code based on: + https://github.com/google-research/big_vision/blob/b00544b81f8694488d5f36295aeb7972f3755ffe/big_vision/models/proj/flexi/vit.py + + With this resizing, we can for example load a B/8 filter into a B/16 model + and, on 2x larger input image, the result will match. + + Args: + patch_embed: original parameter to be resized. + new_size (tuple(int, int): target shape (height, width)-only. + interpolation (str): interpolation for resize + antialias (bool): use anti-aliasing filter in resize + verbose (bool): log operation + Returns: + Resized patch embedding kernel. + """ + import numpy as np + try: + import functorch + vmap = functorch.vmap + except ImportError: + if hasattr(torch, 'vmap'): + vmap = torch.vmap + else: + assert False, "functorch or a version of torch with vmap is required for FlexiViT resizing." + + assert len(patch_embed.shape) == 4, "Four dimensions expected" + assert len(new_size) == 2, "New shape should only be hw" + old_size = patch_embed.shape[-2:] + if tuple(old_size) == tuple(new_size): + return patch_embed + + if verbose: + _logger.info(f"Resize patch embedding {patch_embed.shape} to {new_size}, w/ {interpolation} interpolation.") + + def resize(x_np, _new_size): + x_tf = torch.Tensor(x_np)[None, None, ...] + x_upsampled = F.interpolate( + x_tf, size=_new_size, mode=interpolation, antialias=antialias)[0, 0, ...].numpy() + return x_upsampled + + def get_resize_mat(_old_size, _new_size): + mat = [] + for i in range(np.prod(_old_size)): + basis_vec = np.zeros(_old_size) + basis_vec[np.unravel_index(i, _old_size)] = 1. + mat.append(resize(basis_vec, _new_size).reshape(-1)) + return np.stack(mat).T + + resize_mat = get_resize_mat(old_size, new_size) + resize_mat_pinv = torch.Tensor(np.linalg.pinv(resize_mat.T)) + + def resample_kernel(kernel): + resampled_kernel = resize_mat_pinv @ kernel.reshape(-1) + return resampled_kernel.reshape(new_size) + + v_resample_kernel = vmap(vmap(resample_kernel, 0, 0), 1, 1) + return v_resample_kernel(patch_embed) + + +# def divs(n, m=None): +# m = m or n // 2 +# if m == 1: +# return [1] +# if n % m == 0: +# return [m] + divs(n, m - 1) +# return divs(n, m - 1) +# +# +# class FlexiPatchEmbed(nn.Module): +# """ 2D Image to Patch Embedding w/ Flexible Patch sizes (FlexiViT) +# FIXME WIP +# """ +# def __init__( +# self, +# img_size=240, +# patch_size=16, +# in_chans=3, +# embed_dim=768, +# base_img_size=240, +# base_patch_size=32, +# norm_layer=None, +# flatten=True, +# bias=True, +# ): +# super().__init__() +# self.img_size = to_2tuple(img_size) +# self.patch_size = to_2tuple(patch_size) +# self.num_patches = 0 +# +# # full range for 240 = (5, 6, 8, 10, 12, 14, 15, 16, 20, 24, 30, 40, 48) +# self.seqhw = (6, 8, 10, 12, 14, 15, 16, 20, 24, 30) +# +# self.base_img_size = to_2tuple(base_img_size) +# self.base_patch_size = to_2tuple(base_patch_size) +# self.base_grid_size = tuple([i // p for i, p in zip(self.base_img_size, self.base_patch_size)]) +# self.base_num_patches = self.base_grid_size[0] * self.base_grid_size[1] +# +# self.flatten = flatten +# self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=self.patch_size, stride=self.patch_size, bias=bias) +# self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity() +# +# def forward(self, x): +# B, C, H, W = x.shape +# +# if self.patch_size == self.base_patch_size: +# weight = self.proj.weight +# else: +# weight = resample_patch_embed(self.proj.weight, self.patch_size) +# patch_size = self.patch_size +# x = F.conv2d(x, weight, bias=self.proj.bias, stride=patch_size) +# if self.flatten: +# x = x.flatten(2).transpose(1, 2) # BCHW -> BNC +# x = self.norm(x) +# return x diff --git a/timm/layers/pos_embed.py b/timm/layers/pos_embed.py index 99a122a0..d0e67521 100644 --- a/timm/layers/pos_embed.py +++ b/timm/layers/pos_embed.py @@ -1,207 +1,52 @@ +""" Position Embedding Utilities + +Hacked together by / Copyright 2022 Ross Wightman +""" +import logging import math from typing import List, Tuple, Optional, Union import torch -from torch import nn as nn - - -def pixel_freq_bands( - num_bands: int, - max_freq: float = 224., - linear_bands: bool = True, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None, -): - if linear_bands: - bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=dtype, device=device) - else: - bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=dtype, device=device) - return bands * torch.pi - - -def inv_freq_bands( - num_bands: int, - temperature: float = 100000., - step: int = 2, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None, -) -> torch.Tensor: - inv_freq = 1. / (temperature ** (torch.arange(0, num_bands, step, dtype=dtype, device=device) / num_bands)) - return inv_freq - - -def build_sincos2d_pos_embed( - feat_shape: List[int], - dim: int = 64, - temperature: float = 10000., - reverse_coord: bool = False, - interleave_sin_cos: bool = False, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None -) -> torch.Tensor: - """ - - Args: - feat_shape: - dim: - temperature: - reverse_coord: stack grid order W, H instead of H, W - interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos - dtype: - device: - - Returns: - - """ - assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding' - pos_dim = dim // 4 - bands = inv_freq_bands(pos_dim, temperature=temperature, step=1, dtype=dtype, device=device) - - if reverse_coord: - feat_shape = feat_shape[::-1] # stack W, H instead of H, W - grid = torch.stack( - torch.meshgrid([torch.arange(s, device=device, dtype=dtype) for s in feat_shape])).flatten(1).transpose(0, 1) - pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0) - # FIXME add support for unflattened spatial dim? - - stack_dim = 2 if interleave_sin_cos else 1 # stack sin, cos, sin, cos instead of sin sin cos cos - pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1) - return pos_emb - - -def build_fourier_pos_embed( - feat_shape: List[int], - bands: Optional[torch.Tensor] = None, - num_bands: int = 64, - max_res: int = 224, - linear_bands: bool = False, - include_grid: bool = False, - concat_out: bool = True, - in_pixels: bool = True, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None, -) -> List[torch.Tensor]: - if bands is None: - if in_pixels: - bands = pixel_freq_bands(num_bands, float(max_res), linear_bands=linear_bands, dtype=dtype, device=device) - else: - bands = inv_freq_bands(num_bands, step=1, dtype=dtype, device=device) - else: - if device is None: - device = bands.device - if dtype is None: - dtype = bands.dtype - - if in_pixels: - grid = torch.stack(torch.meshgrid( - [torch.linspace(-1., 1., steps=s, device=device, dtype=dtype) for s in feat_shape]), dim=-1) - else: - grid = torch.stack(torch.meshgrid( - [torch.arange(s, device=device, dtype=dtype) for s in feat_shape]), dim=-1) - grid = grid.unsqueeze(-1) - pos = grid * bands - - pos_sin, pos_cos = pos.sin(), pos.cos() - out = (grid, pos_sin, pos_cos) if include_grid else (pos_sin, pos_cos) - # FIXME torchscript doesn't like multiple return types, probably need to always cat? - if concat_out: - out = torch.cat(out, dim=-1) - return out - - -class FourierEmbed(nn.Module): - - def __init__(self, max_res: int = 224, num_bands: int = 64, concat_grid=True, keep_spatial=False): - super().__init__() - self.max_res = max_res - self.num_bands = num_bands - self.concat_grid = concat_grid - self.keep_spatial = keep_spatial - self.register_buffer('bands', pixel_freq_bands(max_res, num_bands), persistent=False) - - def forward(self, x): - B, C = x.shape[:2] - feat_shape = x.shape[2:] - emb = build_fourier_pos_embed( - feat_shape, - self.bands, - include_grid=self.concat_grid, - dtype=x.dtype, - device=x.device) - emb = emb.transpose(-1, -2).flatten(len(feat_shape)) - batch_expand = (B,) + (-1,) * (x.ndim - 1) - - # FIXME support nD - if self.keep_spatial: - x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1) - else: - x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1) - x = x.reshape(B, feat_shape.numel(), -1) - - return x - +import torch.nn.functional as F -def rot(x): - return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape) +from .helpers import to_2tuple +_logger = logging.getLogger(__name__) -def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb): - return x * cos_emb + rot(x) * sin_emb - -def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb): - if isinstance(x, torch.Tensor): - x = [x] - return [t * cos_emb + rot(t) * sin_emb for t in x] - - -def apply_rot_embed_split(x: torch.Tensor, emb): - split = emb.shape[-1] // 2 - return x * emb[:, :split] + rot(x) * emb[:, split:] - - -def build_rotary_pos_embed( - feat_shape: List[int], - bands: Optional[torch.Tensor] = None, - dim: int = 64, - max_freq: float = 224, - linear_bands: bool = False, - dtype: torch.dtype = torch.float32, - device: Optional[torch.device] = None, +def resample_abs_pos_embed( + posemb, + new_size: List[int], + old_size: Optional[List[int]] = None, + num_prefix_tokens: int = 1, + interpolation: str = 'bicubic', + antialias: bool = True, + verbose: bool = False, ): - """ - NOTE: shape arg should include spatial dim only - """ - feat_shape = torch.Size(feat_shape) - - sin_emb, cos_emb = build_fourier_pos_embed( - feat_shape, bands=bands, num_bands=dim // 4, max_res=max_freq, linear_bands=linear_bands, - concat_out=False, device=device, dtype=dtype) - N = feat_shape.numel() - sin_emb = sin_emb.reshape(N, -1).repeat_interleave(2, -1) - cos_emb = cos_emb.reshape(N, -1).repeat_interleave(2, -1) - return sin_emb, cos_emb - - -class RotaryEmbedding(nn.Module): - """ Rotary position embedding - - NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not - been well tested, and will likely change. It will be moved to its own file. + # sort out sizes, assume square if old size not provided + new_size = to_2tuple(new_size) + new_ntok = new_size[0] * new_size[1] + if not old_size: + old_size = int(math.sqrt(posemb.shape[1] - num_prefix_tokens)) + old_size = to_2tuple(old_size) + if new_size == old_size: # might not both be same container type + return posemb + + if num_prefix_tokens: + posemb_prefix, posemb = posemb[:, :num_prefix_tokens], posemb[:, num_prefix_tokens:] + else: + posemb_prefix, posemb = None, posemb - The following impl/resources were referenced for this impl: - * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py - * https://blog.eleuther.ai/rotary-embeddings/ - """ - def __init__(self, dim, max_res=224, linear_bands: bool = False): - super().__init__() - self.dim = dim - self.register_buffer('bands', pixel_freq_bands(dim // 4, max_res, linear_bands=linear_bands), persistent=False) + # do the interpolation + posemb = posemb.reshape(1, old_size[0], old_size[1], -1).permute(0, 3, 1, 2) + posemb = F.interpolate(posemb, size=new_size, mode=interpolation, antialias=antialias) + posemb = posemb.permute(0, 2, 3, 1).reshape(1, new_ntok, -1) - def get_embed(self, shape: List[int]): - return build_rotary_pos_embed(shape, self.bands) + if verbose: + _logger.info(f'Resized position embedding: {old_size} to {new_size}.') - def forward(self, x): - # assuming channel-first tensor where spatial dim are >= 2 - sin_emb, cos_emb = self.get_embed(x.shape[2:]) - return apply_rot_embed(x, sin_emb, cos_emb) + # add back extra (class, etc) prefix tokens + if posemb_prefix is not None: + print(posemb_prefix.shape, posemb.shape) + posemb = torch.cat([posemb_prefix, posemb], dim=1) + return posemb diff --git a/timm/layers/pos_embed_rel.py b/timm/layers/pos_embed_rel.py new file mode 100644 index 00000000..2ef25670 --- /dev/null +++ b/timm/layers/pos_embed_rel.py @@ -0,0 +1,283 @@ +""" Relative position embedding modules and functions + +Hacked together by / Copyright 2022 Ross Wightman +""" +import math +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from .mlp import Mlp +from .weight_init import trunc_normal_ + + +def gen_relative_position_index( + q_size: Tuple[int, int], + k_size: Tuple[int, int] = None, + class_token: bool = False) -> torch.Tensor: + # Adapted with significant modifications from Swin / BeiT codebases + # get pair-wise relative position index for each token inside the window + q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1) # 2, Wh, Ww + if k_size is None: + k_coords = q_coords + k_size = q_size + else: + # different q vs k sizes is a WIP + k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1) + relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww + relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2 + _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0) + + if class_token: + # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias + # NOTE not intended or tested with MLP log-coords + max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1])) + num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3 + relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0]) + relative_position_index[0, 0:] = num_relative_distance - 3 + relative_position_index[0:, 0] = num_relative_distance - 2 + relative_position_index[0, 0] = num_relative_distance - 1 + + return relative_position_index.contiguous() + + +class RelPosBias(nn.Module): + """ Relative Position Bias + Adapted from Swin-V1 relative position bias impl, modularized. + """ + + def __init__(self, window_size, num_heads, prefix_tokens=0): + super().__init__() + assert prefix_tokens <= 1 + self.window_size = window_size + self.window_area = window_size[0] * window_size[1] + self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,) + + num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens + self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads)) + self.register_buffer( + "relative_position_index", + gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0), + persistent=False, + ) + + self.init_weights() + + def init_weights(self): + trunc_normal_(self.relative_position_bias_table, std=.02) + + def get_bias(self) -> torch.Tensor: + relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] + # win_h * win_w, win_h * win_w, num_heads + relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1) + return relative_position_bias.unsqueeze(0).contiguous() + + def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): + return attn + self.get_bias() + + +def gen_relative_log_coords( + win_size: Tuple[int, int], + pretrained_win_size: Tuple[int, int] = (0, 0), + mode='swin', +): + assert mode in ('swin', 'cr', 'rw') + # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well + relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32) + relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32) + relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) + relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2 + if mode == 'swin': + if pretrained_win_size[0] > 0: + relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1) + relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1) + else: + relative_coords_table[:, :, 0] /= (win_size[0] - 1) + relative_coords_table[:, :, 1] /= (win_size[1] - 1) + relative_coords_table *= 8 # normalize to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + 1.0 + relative_coords_table.abs()) / math.log2(8) + else: + if mode == 'rw': + # cr w/ window size normalization -> [-1,1] log coords + relative_coords_table[:, :, 0] /= (win_size[0] - 1) + relative_coords_table[:, :, 1] /= (win_size[1] - 1) + relative_coords_table *= 8 # scale to -8, 8 + relative_coords_table = torch.sign(relative_coords_table) * torch.log2( + 1.0 + relative_coords_table.abs()) + relative_coords_table /= math.log2(9) # -> [-1, 1] + else: + # mode == 'cr' + relative_coords_table = torch.sign(relative_coords_table) * torch.log( + 1.0 + relative_coords_table.abs()) + + return relative_coords_table + + +class RelPosMlp(nn.Module): + """ Log-Coordinate Relative Position MLP + Based on ideas presented in Swin-V2 paper (https://arxiv.org/abs/2111.09883) + + This impl covers the 'swin' implementation as well as two timm specific modes ('cr', and 'rw') + """ + def __init__( + self, + window_size, + num_heads=8, + hidden_dim=128, + prefix_tokens=0, + mode='cr', + pretrained_window_size=(0, 0) + ): + super().__init__() + self.window_size = window_size + self.window_area = self.window_size[0] * self.window_size[1] + self.prefix_tokens = prefix_tokens + self.num_heads = num_heads + self.bias_shape = (self.window_area,) * 2 + (num_heads,) + if mode == 'swin': + self.bias_act = nn.Sigmoid() + self.bias_gain = 16 + mlp_bias = (True, False) + elif mode == 'rw': + self.bias_act = nn.Tanh() + self.bias_gain = 4 + mlp_bias = True + else: + self.bias_act = nn.Identity() + self.bias_gain = None + mlp_bias = True + + self.mlp = Mlp( + 2, # x, y + hidden_features=hidden_dim, + out_features=num_heads, + act_layer=nn.ReLU, + bias=mlp_bias, + drop=(0.125, 0.) + ) + + self.register_buffer( + "relative_position_index", + gen_relative_position_index(window_size), + persistent=False) + + # get relative_coords_table + self.register_buffer( + "rel_coords_log", + gen_relative_log_coords(window_size, pretrained_window_size, mode=mode), + persistent=False) + + def get_bias(self) -> torch.Tensor: + relative_position_bias = self.mlp(self.rel_coords_log) + if self.relative_position_index is not None: + relative_position_bias = relative_position_bias.view(-1, self.num_heads)[ + self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH + relative_position_bias = relative_position_bias.view(self.bias_shape) + relative_position_bias = relative_position_bias.permute(2, 0, 1) + relative_position_bias = self.bias_act(relative_position_bias) + if self.bias_gain is not None: + relative_position_bias = self.bias_gain * relative_position_bias + if self.prefix_tokens: + relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0]) + return relative_position_bias.unsqueeze(0).contiguous() + + def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): + return attn + self.get_bias() + + +def generate_lookup_tensor( + length: int, + max_relative_position: Optional[int] = None, +): + """Generate a one_hot lookup tensor to reindex embeddings along one dimension. + + Args: + length: the length to reindex to. + max_relative_position: the maximum relative position to consider. + Relative position embeddings for distances above this threshold + are zeroed out. + Returns: + a lookup Tensor of size [length, length, vocab_size] that satisfies + ret[n,m,v] = 1{m - n + max_relative_position = v}. + """ + if max_relative_position is None: + max_relative_position = length - 1 + # Return the cached lookup tensor, otherwise compute it and cache it. + vocab_size = 2 * max_relative_position + 1 + ret = torch.zeros(length, length, vocab_size) + for i in range(length): + for x in range(length): + v = x - i + max_relative_position + if abs(x - i) > max_relative_position: + continue + ret[i, x, v] = 1 + return ret + + +def reindex_2d_einsum_lookup( + relative_position_tensor, + height: int, + width: int, + height_lookup: torch.Tensor, + width_lookup: torch.Tensor, +) -> torch.Tensor: + """Reindex 2d relative position bias with 2 independent einsum lookups. + + Adapted from: + https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py + + Args: + relative_position_tensor: tensor of shape + [..., vocab_height, vocab_width, ...]. + height: height to reindex to. + width: width to reindex to. + height_lookup: one-hot height lookup + width_lookup: one-hot width lookup + Returns: + reindexed_tensor: a Tensor of shape + [..., height * width, height * width, ...] + """ + reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup) + reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup) + area = height * width + return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area) + + +class RelPosBiasTf(nn.Module): + """ Relative Position Bias Impl (Compatible with Tensorflow MaxViT models) + Adapted from: + https://github.com/google-research/maxvit/blob/2e06a7f1f70c76e64cd3dabe5cd1b8c1a23c9fb7/maxvit/models/attention_utils.py + """ + def __init__(self, window_size, num_heads, prefix_tokens=0): + super().__init__() + assert prefix_tokens <= 1 + self.window_size = window_size + self.window_area = window_size[0] * window_size[1] + self.num_heads = num_heads + + vocab_height = 2 * window_size[0] - 1 + vocab_width = 2 * window_size[1] - 1 + self.bias_shape = (self.num_heads, vocab_height, vocab_width) + self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape)) + self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False) + self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False) + self.init_weights() + + def init_weights(self): + nn.init.normal_(self.relative_position_bias_table, std=.02) + + def get_bias(self) -> torch.Tensor: + # FIXME change to not use one-hot/einsum? + return reindex_2d_einsum_lookup( + self.relative_position_bias_table, + self.window_size[0], + self.window_size[1], + self.height_lookup, + self.width_lookup + ) + + def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): + return attn + self.get_bias() diff --git a/timm/layers/pos_embed_sincos.py b/timm/layers/pos_embed_sincos.py new file mode 100644 index 00000000..5603a5cd --- /dev/null +++ b/timm/layers/pos_embed_sincos.py @@ -0,0 +1,219 @@ +""" Sin-cos, fourier, rotary position embedding modules and functions + +Hacked together by / Copyright 2022 Ross Wightman +""" +import math +from typing import List, Tuple, Optional, Union + +import torch +from torch import nn as nn + + +def pixel_freq_bands( + num_bands: int, + max_freq: float = 224., + linear_bands: bool = True, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +): + if linear_bands: + bands = torch.linspace(1.0, max_freq / 2, num_bands, dtype=dtype, device=device) + else: + bands = 2 ** torch.linspace(0, math.log(max_freq, 2) - 1, num_bands, dtype=dtype, device=device) + return bands * torch.pi + + +def inv_freq_bands( + num_bands: int, + temperature: float = 100000., + step: int = 2, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +) -> torch.Tensor: + inv_freq = 1. / (temperature ** (torch.arange(0, num_bands, step, dtype=dtype, device=device) / num_bands)) + return inv_freq + + +def build_sincos2d_pos_embed( + feat_shape: List[int], + dim: int = 64, + temperature: float = 10000., + reverse_coord: bool = False, + interleave_sin_cos: bool = False, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None +) -> torch.Tensor: + """ + + Args: + feat_shape: + dim: + temperature: + reverse_coord: stack grid order W, H instead of H, W + interleave_sin_cos: sin, cos, sin, cos stack instead of sin, sin, cos, cos + dtype: + device: + + Returns: + + """ + assert dim % 4 == 0, 'Embed dimension must be divisible by 4 for sin-cos 2D position embedding' + pos_dim = dim // 4 + bands = inv_freq_bands(pos_dim, temperature=temperature, step=1, dtype=dtype, device=device) + + if reverse_coord: + feat_shape = feat_shape[::-1] # stack W, H instead of H, W + grid = torch.stack( + torch.meshgrid([torch.arange(s, device=device, dtype=dtype) for s in feat_shape])).flatten(1).transpose(0, 1) + pos2 = grid.unsqueeze(-1) * bands.unsqueeze(0) + # FIXME add support for unflattened spatial dim? + + stack_dim = 2 if interleave_sin_cos else 1 # stack sin, cos, sin, cos instead of sin sin cos cos + pos_emb = torch.stack([torch.sin(pos2), torch.cos(pos2)], dim=stack_dim).flatten(1) + return pos_emb + + +def build_fourier_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + num_bands: int = 64, + max_res: int = 224, + linear_bands: bool = False, + include_grid: bool = False, + concat_out: bool = True, + in_pixels: bool = True, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +) -> List[torch.Tensor]: + if bands is None: + if in_pixels: + bands = pixel_freq_bands(num_bands, float(max_res), linear_bands=linear_bands, dtype=dtype, device=device) + else: + bands = inv_freq_bands(num_bands, step=1, dtype=dtype, device=device) + else: + if device is None: + device = bands.device + if dtype is None: + dtype = bands.dtype + + if in_pixels: + grid = torch.stack(torch.meshgrid( + [torch.linspace(-1., 1., steps=s, device=device, dtype=dtype) for s in feat_shape]), dim=-1) + else: + grid = torch.stack(torch.meshgrid( + [torch.arange(s, device=device, dtype=dtype) for s in feat_shape]), dim=-1) + grid = grid.unsqueeze(-1) + pos = grid * bands + + pos_sin, pos_cos = pos.sin(), pos.cos() + out = (grid, pos_sin, pos_cos) if include_grid else (pos_sin, pos_cos) + # FIXME torchscript doesn't like multiple return types, probably need to always cat? + if concat_out: + out = torch.cat(out, dim=-1) + return out + + +class FourierEmbed(nn.Module): + + def __init__(self, max_res: int = 224, num_bands: int = 64, concat_grid=True, keep_spatial=False): + super().__init__() + self.max_res = max_res + self.num_bands = num_bands + self.concat_grid = concat_grid + self.keep_spatial = keep_spatial + self.register_buffer('bands', pixel_freq_bands(max_res, num_bands), persistent=False) + + def forward(self, x): + B, C = x.shape[:2] + feat_shape = x.shape[2:] + emb = build_fourier_pos_embed( + feat_shape, + self.bands, + include_grid=self.concat_grid, + dtype=x.dtype, + device=x.device) + emb = emb.transpose(-1, -2).flatten(len(feat_shape)) + batch_expand = (B,) + (-1,) * (x.ndim - 1) + + # FIXME support nD + if self.keep_spatial: + x = torch.cat([x, emb.unsqueeze(0).expand(batch_expand).permute(0, 3, 1, 2)], dim=1) + else: + x = torch.cat([x.permute(0, 2, 3, 1), emb.unsqueeze(0).expand(batch_expand)], dim=-1) + x = x.reshape(B, feat_shape.numel(), -1) + + return x + + +def rot(x): + return torch.stack([-x[..., 1::2], x[..., ::2]], -1).reshape(x.shape) + + +def apply_rot_embed(x: torch.Tensor, sin_emb, cos_emb): + return x * cos_emb + rot(x) * sin_emb + + +def apply_rot_embed_list(x: List[torch.Tensor], sin_emb, cos_emb): + if isinstance(x, torch.Tensor): + x = [x] + return [t * cos_emb + rot(t) * sin_emb for t in x] + + +def apply_rot_embed_split(x: torch.Tensor, emb): + split = emb.shape[-1] // 2 + return x * emb[:, :split] + rot(x) * emb[:, split:] + + +def build_rotary_pos_embed( + feat_shape: List[int], + bands: Optional[torch.Tensor] = None, + dim: int = 64, + max_freq: float = 224, + linear_bands: bool = False, + dtype: torch.dtype = torch.float32, + device: Optional[torch.device] = None, +): + """ + NOTE: shape arg should include spatial dim only + """ + feat_shape = torch.Size(feat_shape) + + sin_emb, cos_emb = build_fourier_pos_embed( + feat_shape, + bands=bands, + num_bands=dim // 4, + max_res=max_freq, + linear_bands=linear_bands, + concat_out=False, + device=device, + dtype=dtype, + ) + N = feat_shape.numel() + sin_emb = sin_emb.reshape(N, -1).repeat_interleave(2, -1) + cos_emb = cos_emb.reshape(N, -1).repeat_interleave(2, -1) + return sin_emb, cos_emb + + +class RotaryEmbedding(nn.Module): + """ Rotary position embedding + + NOTE: This is my initial attempt at impl rotary embedding for spatial use, it has not + been well tested, and will likely change. It will be moved to its own file. + + The following impl/resources were referenced for this impl: + * https://github.com/lucidrains/vit-pytorch/blob/6f3a5fcf0bca1c5ec33a35ef48d97213709df4ba/vit_pytorch/rvt.py + * https://blog.eleuther.ai/rotary-embeddings/ + """ + + def __init__(self, dim, max_res=224, linear_bands: bool = False): + super().__init__() + self.dim = dim + self.register_buffer('bands', pixel_freq_bands(dim // 4, max_res, linear_bands=linear_bands), persistent=False) + + def get_embed(self, shape: List[int]): + return build_rotary_pos_embed(shape, self.bands) + + def forward(self, x): + # assuming channel-first tensor where spatial dim are >= 2 + sin_emb, cos_emb = self.get_embed(x.shape[2:]) + return apply_rot_embed(x, sin_emb, cos_emb) diff --git a/timm/models/_builder.py b/timm/models/_builder.py index f634650e..901d7d44 100644 --- a/timm/models/_builder.py +++ b/timm/models/_builder.py @@ -1,5 +1,6 @@ import dataclasses import logging +import os from copy import deepcopy from typing import Optional, Dict, Callable, Any, Tuple @@ -9,7 +10,7 @@ from torch.hub import load_state_dict_from_url from timm.models._features import FeatureListNet, FeatureHookNet from timm.models._features_fx import FeatureGraphNet from timm.models._helpers import load_state_dict -from timm.models._hub import has_hf_hub, download_cached_file, load_state_dict_from_hf +from timm.models._hub import has_hf_hub, download_cached_file, check_cached_file, load_state_dict_from_hf from timm.models._manipulate import adapt_input_conv from timm.models._pretrained import PretrainedCfg from timm.models._prune import adapt_model_from_file @@ -32,6 +33,7 @@ def _resolve_pretrained_source(pretrained_cfg): pretrained_url = pretrained_cfg.get('url', None) pretrained_file = pretrained_cfg.get('file', None) hf_hub_id = pretrained_cfg.get('hf_hub_id', None) + # resolve where to load pretrained weights from load_from = '' pretrained_loc = '' @@ -43,15 +45,20 @@ def _resolve_pretrained_source(pretrained_cfg): else: # default source == timm or unspecified if pretrained_file: + # file load override is the highest priority if set load_from = 'file' pretrained_loc = pretrained_file - elif pretrained_url: - load_from = 'url' - pretrained_loc = pretrained_url - elif hf_hub_id and has_hf_hub(necessary=True): - # hf-hub available as alternate weight source in default_cfg - load_from = 'hf-hub' - pretrained_loc = hf_hub_id + else: + # next, HF hub is prioritized unless a valid cached version of weights exists already + cached_url_valid = check_cached_file(pretrained_url) if pretrained_url else False + if hf_hub_id and has_hf_hub(necessary=True) and not cached_url_valid: + # hf-hub available as alternate weight source in default_cfg + load_from = 'hf-hub' + pretrained_loc = hf_hub_id + elif pretrained_url: + load_from = 'url' + pretrained_loc = pretrained_url + if load_from == 'hf-hub' and pretrained_cfg.get('hf_hub_filename', None): # if a filename override is set, return tuple for location w/ (hub_id, filename) pretrained_loc = pretrained_loc, pretrained_cfg['hf_hub_filename'] @@ -105,7 +112,7 @@ def load_custom_pretrained( pretrained_loc = download_cached_file( pretrained_loc, check_hash=_CHECK_HASH, - progress=_DOWNLOAD_PROGRESS + progress=_DOWNLOAD_PROGRESS, ) if load_fn is not None: @@ -146,12 +153,21 @@ def load_pretrained( state_dict = load_state_dict(pretrained_loc) elif load_from == 'url': _logger.info(f'Loading pretrained weights from url ({pretrained_loc})') - state_dict = load_state_dict_from_url( - pretrained_loc, - map_location='cpu', - progress=_DOWNLOAD_PROGRESS, - check_hash=_CHECK_HASH, - ) + if pretrained_cfg.get('custom_load', False): + pretrained_loc = download_cached_file( + pretrained_loc, + progress=_DOWNLOAD_PROGRESS, + check_hash=_CHECK_HASH, + ) + model.load_pretrained(pretrained_loc) + return + else: + state_dict = load_state_dict_from_url( + pretrained_loc, + map_location='cpu', + progress=_DOWNLOAD_PROGRESS, + check_hash=_CHECK_HASH, + ) elif load_from == 'hf-hub': _logger.info(f'Loading pretrained weights from Hugging Face hub ({pretrained_loc})') if isinstance(pretrained_loc, (list, tuple)): @@ -364,20 +380,14 @@ def build_model_with_cfg( # For classification models, check class attr, then kwargs, then default to 1k, otherwise 0 for feats num_classes_pretrained = 0 if features else getattr(model, 'num_classes', kwargs.get('num_classes', 1000)) if pretrained: - if pretrained_cfg.get('custom_load', False): - load_custom_pretrained( - model, - pretrained_cfg=pretrained_cfg, - ) - else: - load_pretrained( - model, - pretrained_cfg=pretrained_cfg, - num_classes=num_classes_pretrained, - in_chans=kwargs.get('in_chans', 3), - filter_fn=pretrained_filter_fn, - strict=pretrained_strict, - ) + load_pretrained( + model, + pretrained_cfg=pretrained_cfg, + num_classes=num_classes_pretrained, + in_chans=kwargs.get('in_chans', 3), + filter_fn=pretrained_filter_fn, + strict=pretrained_strict, + ) # Wrap the model in a feature extraction module if enabled if features: diff --git a/timm/models/_hub.py b/timm/models/_hub.py index e6b7d558..df1a1ef7 100644 --- a/timm/models/_hub.py +++ b/timm/models/_hub.py @@ -1,3 +1,4 @@ +import hashlib import json import logging import os @@ -67,6 +68,26 @@ def download_cached_file(url, check_hash=True, progress=False): return cached_file +def check_cached_file(url, check_hash=True): + if isinstance(url, (list, tuple)): + url, filename = url + else: + parts = urlparse(url) + filename = os.path.basename(parts.path) + cached_file = os.path.join(get_cache_dir(), filename) + if os.path.exists(cached_file): + if check_hash: + r = HASH_REGEX.search(filename) # r is Optional[Match[str]] + hash_prefix = r.group(1) if r else None + if hash_prefix: + with open(cached_file, 'rb') as f: + hd = hashlib.sha256(f.read()).hexdigest() + if hd[:len(hash_prefix)] != hash_prefix: + return False + return True + return False + + def has_hf_hub(necessary=False): if not _has_hf_hub and necessary: # if no HF Hub module installed, and it is necessary to continue, raise error @@ -90,14 +111,14 @@ def load_cfg_from_json(json_file: Union[str, os.PathLike]): return json.loads(text) -def _download_from_hf(model_id: str, filename: str): +def download_from_hf(model_id: str, filename: str): hf_model_id, hf_revision = hf_split(model_id) return hf_hub_download(hf_model_id, filename, revision=hf_revision) def load_model_config_from_hf(model_id: str): assert has_hf_hub(True) - cached_file = _download_from_hf(model_id, 'config.json') + cached_file = download_from_hf(model_id, 'config.json') hf_config = load_cfg_from_json(cached_file) if 'pretrained_cfg' not in hf_config: @@ -124,34 +145,28 @@ def load_model_config_from_hf(model_id: str): def load_state_dict_from_hf(model_id: str, filename: str = 'pytorch_model.bin'): assert has_hf_hub(True) - cached_file = _download_from_hf(model_id, filename) + cached_file = download_from_hf(model_id, filename) state_dict = torch.load(cached_file, map_location='cpu') return state_dict -def save_for_hf(model, save_directory, model_config=None): - assert has_hf_hub(True) +def save_config_for_hf(model, config_path, model_config=None): model_config = model_config or {} - save_directory = Path(save_directory) - save_directory.mkdir(exist_ok=True, parents=True) - - weights_path = save_directory / 'pytorch_model.bin' - torch.save(model.state_dict(), weights_path) - - config_path = save_directory / 'config.json' hf_config = {} pretrained_cfg = filter_pretrained_cfg(model.pretrained_cfg, remove_source=True, remove_null=True) # set some values at root config level hf_config['architecture'] = pretrained_cfg.pop('architecture') hf_config['num_classes'] = model_config.get('num_classes', model.num_classes) hf_config['num_features'] = model_config.get('num_features', model.num_features) - hf_config['global_pool'] = model_config.get('global_pool', getattr(model, 'global_pool', None)) + global_pool_type = model_config.get('global_pool', getattr(model, 'global_pool', None)) + if isinstance(global_pool_type, str) and global_pool_type: + hf_config['global_pool'] = global_pool_type - if 'label' in model_config: + if 'labels' in model_config: _logger.warning( - "'label' as a config field for timm models is deprecated. Please use 'label_name' and 'display_name'. " + "'labels' as a config field for timm models is deprecated. Please use 'label_name' and 'display_name'. " "Using provided 'label' field as 'label_name'.") - model_config['label_name'] = model_config.pop('label') + model_config['label_name'] = model_config.pop('labels') label_name = model_config.pop('label_name', None) if label_name: @@ -173,6 +188,18 @@ def save_for_hf(model, save_directory, model_config=None): json.dump(hf_config, f, indent=2) +def save_for_hf(model, save_directory, model_config=None): + assert has_hf_hub(True) + save_directory = Path(save_directory) + save_directory.mkdir(exist_ok=True, parents=True) + + weights_path = save_directory / 'pytorch_model.bin' + torch.save(model.state_dict(), weights_path) + + config_path = save_directory / 'config.json' + save_config_for_hf(model, config_path, model_config=model_config) + + def push_to_hf_hub( model, repo_id: str, @@ -182,6 +209,7 @@ def push_to_hf_hub( private: bool = False, create_pr: bool = False, model_config: Optional[dict] = None, + model_card: Optional[dict] = None, ): # Create repo if it doesn't exist yet repo_url = create_repo(repo_id, token=token, private=private, exist_ok=True) @@ -205,9 +233,23 @@ def push_to_hf_hub( # Add readme if it does not exist if not has_readme: + model_card = model_card or {} model_name = repo_id.split('/')[-1] readme_path = Path(tmpdir) / "README.md" - readme_text = f'---\ntags:\n- image-classification\n- timm\nlibrary_tag: timm\n---\n# Model card for {model_name}' + readme_text = "---\n" + readme_text += "tags:\n- image-classification\n- timm\n" + readme_text += "library_tag: timm\n" + readme_text += f"license: {model_card.get('license', 'apache-2.0')}\n" + readme_text += "---\n" + readme_text += f"# Model card for {model_name}\n" + if 'description' in model_card: + readme_text += f"\n{model_card['description']}\n" + if 'details' in model_card: + readme_text += f"\n## Model Details\n" + for k, v in model_card['details'].items(): + readme_text += f"- **{k}:** {v}\n" + if 'citation' in model_card: + readme_text += f"\n## Citation\n```\n{model_card['citation']}```\n" readme_path.write_text(readme_text) # Upload model and return diff --git a/timm/models/_pretrained.py b/timm/models/_pretrained.py index b5ecbc50..dca81eb0 100644 --- a/timm/models/_pretrained.py +++ b/timm/models/_pretrained.py @@ -19,6 +19,7 @@ class PretrainedCfg: source: Optional[str] = None # source of cfg / weight location used (url, file, hf-hub) architecture: Optional[str] = None # architecture variant can be set when not implicit + tag: Optional[str] = None # pretrained tag of source custom_load: bool = False # use custom model specific model.load_pretrained() (ie for npz files) # input / data config @@ -44,9 +45,11 @@ class PretrainedCfg: classifier: Optional[str] = None license: Optional[str] = None - source_url: Optional[str] = None - paper: Optional[str] = None - notes: Optional[str] = None + description: Optional[str] = None + origin_url: Optional[str] = None + paper_name: Optional[str] = None + paper_ids: Optional[Union[str, Tuple[str]]] = None + notes: Optional[Tuple[str]] = None @property def has_weights(self): @@ -62,11 +65,11 @@ class PretrainedCfg: def filter_pretrained_cfg(cfg, remove_source=False, remove_null=True): filtered_cfg = {} - keep_none = {'pool_size', 'first_conv', 'classifier'} # always keep these keys, even if none + keep_null = {'pool_size', 'first_conv', 'classifier'} # always keep these keys, even if none for k, v in cfg.items(): if remove_source and k in {'url', 'file', 'hf_hub_id', 'hf_hub_id', 'hf_hub_filename', 'source'}: continue - if remove_null and v is None and k not in keep_none: + if remove_null and v is None and k not in keep_null: continue filtered_cfg[k] = v return filtered_cfg diff --git a/timm/models/_registry.py b/timm/models/_registry.py index fc7b3437..80eb2e94 100644 --- a/timm/models/_registry.py +++ b/timm/models/_registry.py @@ -7,6 +7,7 @@ import re import sys from collections import defaultdict, deque from copy import deepcopy +from dataclasses import replace from typing import List, Optional, Union, Tuple from ._pretrained import PretrainedCfg, DefaultCfg, split_model_name_tag @@ -20,7 +21,7 @@ _model_to_module = {} # mapping of model names to module names _model_entrypoints = {} # mapping of model names to architecture entrypoint fns _model_has_pretrained = set() # set of model names that have pretrained weight url present _model_default_cfgs = dict() # central repo for model arch -> default cfg objects -_model_pretrained_cfgs = dict() # central repo for model arch + tag -> pretrained cfgs +_model_pretrained_cfgs = dict() # central repo for model arch.tag -> pretrained cfgs _model_with_tags = defaultdict(list) # shortcut to map each model arch to all model + tag names @@ -48,24 +49,31 @@ def register_model(fn): if hasattr(mod, 'default_cfgs') and model_name in mod.default_cfgs: # this will catch all models that have entrypoint matching cfg key, but miss any aliasing # entrypoints or non-matching combos - cfg = mod.default_cfgs[model_name] - if not isinstance(cfg, DefaultCfg): + default_cfg = mod.default_cfgs[model_name] + if not isinstance(default_cfg, DefaultCfg): # new style default cfg dataclass w/ multiple entries per model-arch - assert isinstance(cfg, dict) + assert isinstance(default_cfg, dict) # old style cfg dict per model-arch - cfg = PretrainedCfg(**cfg) - cfg = DefaultCfg(tags=deque(['']), cfgs={'': cfg}) + pretrained_cfg = PretrainedCfg(**default_cfg) + default_cfg = DefaultCfg(tags=deque(['']), cfgs={'': pretrained_cfg}) - for tag_idx, tag in enumerate(cfg.tags): + for tag_idx, tag in enumerate(default_cfg.tags): is_default = tag_idx == 0 - pretrained_cfg = cfg.cfgs[tag] + pretrained_cfg = default_cfg.cfgs[tag] + model_name_tag = '.'.join([model_name, tag]) if tag else model_name + replace_items = dict(architecture=model_name, tag=tag if tag else None) + if pretrained_cfg.hf_hub_id and pretrained_cfg.hf_hub_id == 'timm/': + # auto-complete hub name w/ architecture.tag + replace_items['hf_hub_id'] = pretrained_cfg.hf_hub_id + model_name_tag + pretrained_cfg = replace(pretrained_cfg, **replace_items) + if is_default: _model_pretrained_cfgs[model_name] = pretrained_cfg if pretrained_cfg.has_weights: # add tagless entry if it's default and has weights _model_has_pretrained.add(model_name) + if tag: - model_name_tag = '.'.join([model_name, tag]) _model_pretrained_cfgs[model_name_tag] = pretrained_cfg if pretrained_cfg.has_weights: # add model w/ tag if tag is valid @@ -74,7 +82,7 @@ def register_model(fn): else: _model_with_tags[model_name].append(model_name) # has empty tag (to slowly remove these instances) - _model_default_cfgs[model_name] = cfg + _model_default_cfgs[model_name] = default_cfg return fn @@ -198,15 +206,21 @@ def is_model_pretrained(model_name): return model_name in _model_has_pretrained -def get_pretrained_cfg(model_name): +def get_pretrained_cfg(model_name, allow_unregistered=True): if model_name in _model_pretrained_cfgs: return deepcopy(_model_pretrained_cfgs[model_name]) - raise RuntimeError(f'No pretrained config exists for model {model_name}.') + arch_name, tag = split_model_name_tag(model_name) + if arch_name in _model_default_cfgs: + # if model arch exists, but the tag is wrong, error out + raise RuntimeError(f'Invalid pretrained tag ({tag}) for {arch_name}.') + if allow_unregistered: + # if model arch doesn't exist, it has no pretrained_cfg registered, allow a default to be created + return None + raise RuntimeError(f'Model architecture ({arch_name}) has no pretrained cfg registered.') def get_pretrained_cfg_value(model_name, cfg_key): """ Get a specific model default_cfg value by key. None if key doesn't exist. """ - if model_name in _model_pretrained_cfgs: - return getattr(_model_pretrained_cfgs[model_name], cfg_key, None) - raise RuntimeError(f'No pretrained config exist for model {model_name}.') \ No newline at end of file + cfg = get_pretrained_cfg(model_name, allow_unregistered=False) + return getattr(cfg, cfg_key, None) diff --git a/timm/models/beit.py b/timm/models/beit.py index de71f441..12ec493d 100644 --- a/timm/models/beit.py +++ b/timm/models/beit.py @@ -355,64 +355,76 @@ def _cfg(url='', **kwargs): default_cfgs = generate_default_cfgs({ 'beit_base_patch16_224.in22k_ft_in22k_in1k': _cfg( - url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth'), + url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth', + hf_hub_id='timm/'), 'beit_base_patch16_384.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_384_pt22k_ft22kto1k.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=1.0, ), 'beit_base_patch16_224.in22k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22k.pth', + hf_hub_id='timm/', num_classes=21841, ), 'beit_large_patch16_224.in22k_ft_in22k_in1k': _cfg( - url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth'), + url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22kto1k.pth', + hf_hub_id='timm/'), 'beit_large_patch16_384.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_384_pt22k_ft22kto1k.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=1.0, ), 'beit_large_patch16_512.in22k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_512_pt22k_ft22kto1k.pth', + hf_hub_id='timm/', input_size=(3, 512, 512), crop_pct=1.0, ), 'beit_large_patch16_224.in22k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_large_patch16_224_pt22k_ft22k.pth', + hf_hub_id='timm/', num_classes=21841, ), 'beitv2_base_patch16_224.in1k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21kto1k.pth', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_base_patch16_224.in1k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_base_patch16_224_pt1k_ft21k.pth', - num_classes=21841, - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD + hf_hub_id='timm/', + num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_large_patch16_224.in1k_ft_in22k_in1k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21kto1k.pth', - crop_pct=0.95, - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD + hf_hub_id='timm/', + crop_pct=0.95, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'beitv2_large_patch16_224.in1k_ft_in22k': _cfg( url='https://conversationhub.blob.core.windows.net/beit-share-public/beitv2/beitv2_large_patch16_224_pt1k_ft21k.pth', - num_classes=21841, - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD + hf_hub_id='timm/', + num_classes=21841, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD ), 'eva_giant_patch14_224.clip_ft_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz224_ftcls_89p1.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, ), 'eva_giant_patch14_336.clip_ft_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_clip_vis_enc_sz336_ftcls_89p4.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'), 'eva_giant_patch14_336.m30m_ft_in22k_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_336px_psz14_ema_89p6.pt', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'), 'eva_giant_patch14_560.m30m_ft_in22k_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_560px_psz14_ema_89p7.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_21k_1k_560px_psz14_ema_89p7.pt', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 560, 560), crop_pct=1.0, crop_mode='squash'), }) diff --git a/timm/models/byobnet.py b/timm/models/byobnet.py index 0e5c9c7f..1c7f1137 100644 --- a/timm/models/byobnet.py +++ b/timm/models/byobnet.py @@ -218,7 +218,10 @@ def _rep_vgg_bcfg(d=(4, 6, 16, 1), wf=(1., 1., 1., 1.), groups=0): def interleave_blocks( - types: Tuple[str, str], d, every: Union[int, List[int]] = 1, first: bool = False, **kwargs + types: Tuple[str, str], d, + every: Union[int, List[int]] = 1, + first: bool = False, + **kwargs, ) -> Tuple[ByoBlockCfg]: """ interleave 2 block types in stack """ @@ -962,9 +965,21 @@ class BasicBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), group_size=None, bottle_ratio=1.0, - downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None, - drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + group_size=None, + bottle_ratio=1.0, + downsample='avg', + attn_last=True, + linear_out=False, + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(BasicBlock, self).__init__() layers = layers or LayerFn() mid_chs = make_divisible(out_chs * bottle_ratio) @@ -983,7 +998,7 @@ class BasicBlock(nn.Module): self.act = nn.Identity() if linear_out else layers.act(inplace=True) def init_weights(self, zero_init_last: bool = False): - if zero_init_last and self.shortcut is not None: + if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None: nn.init.zeros_(self.conv2_kxk.bn.weight) for attn in (self.attn, self.attn_last): if hasattr(attn, 'reset_parameters'): @@ -1005,9 +1020,23 @@ class BottleneckBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None, - downsample='avg', attn_last=False, linear_out=False, extra_conv=False, bottle_in=False, - layers: LayerFn = None, drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + bottle_ratio=1., + group_size=None, + downsample='avg', + attn_last=False, + linear_out=False, + extra_conv=False, + bottle_in=False, + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(BottleneckBlock, self).__init__() layers = layers or LayerFn() mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio) @@ -1031,7 +1060,7 @@ class BottleneckBlock(nn.Module): self.act = nn.Identity() if linear_out else layers.act(inplace=True) def init_weights(self, zero_init_last: bool = False): - if zero_init_last and self.shortcut is not None: + if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None: nn.init.zeros_(self.conv3_1x1.bn.weight) for attn in (self.attn, self.attn_last): if hasattr(attn, 'reset_parameters'): @@ -1063,9 +1092,21 @@ class DarkBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None, - downsample='avg', attn_last=True, linear_out=False, layers: LayerFn = None, drop_block=None, - drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + bottle_ratio=1.0, + group_size=None, + downsample='avg', + attn_last=True, + linear_out=False, + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(DarkBlock, self).__init__() layers = layers or LayerFn() mid_chs = make_divisible(out_chs * bottle_ratio) @@ -1085,7 +1126,7 @@ class DarkBlock(nn.Module): self.act = nn.Identity() if linear_out else layers.act(inplace=True) def init_weights(self, zero_init_last: bool = False): - if zero_init_last and self.shortcut is not None: + if zero_init_last and self.shortcut is not None and getattr(self.conv2_kxk.bn, 'weight', None) is not None: nn.init.zeros_(self.conv2_kxk.bn.weight) for attn in (self.attn, self.attn_last): if hasattr(attn, 'reset_parameters'): @@ -1114,9 +1155,21 @@ class EdgeBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None, - downsample='avg', attn_last=False, linear_out=False, layers: LayerFn = None, - drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + bottle_ratio=1.0, + group_size=None, + downsample='avg', + attn_last=False, + linear_out=False, + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(EdgeBlock, self).__init__() layers = layers or LayerFn() mid_chs = make_divisible(out_chs * bottle_ratio) @@ -1135,7 +1188,7 @@ class EdgeBlock(nn.Module): self.act = nn.Identity() if linear_out else layers.act(inplace=True) def init_weights(self, zero_init_last: bool = False): - if zero_init_last and self.shortcut is not None: + if zero_init_last and self.shortcut is not None and getattr(self.conv2_1x1.bn, 'weight', None) is not None: nn.init.zeros_(self.conv2_1x1.bn.weight) for attn in (self.attn, self.attn_last): if hasattr(attn, 'reset_parameters'): @@ -1162,8 +1215,19 @@ class RepVggBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1.0, group_size=None, - downsample='', layers: LayerFn = None, drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + bottle_ratio=1.0, + group_size=None, + downsample='', + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(RepVggBlock, self).__init__() layers = layers or LayerFn() groups = num_groups(group_size, in_chs) @@ -1204,9 +1268,24 @@ class SelfAttnBlock(nn.Module): """ def __init__( - self, in_chs, out_chs, kernel_size=3, stride=1, dilation=(1, 1), bottle_ratio=1., group_size=None, - downsample='avg', extra_conv=False, linear_out=False, bottle_in=False, post_attn_na=True, - feat_size=None, layers: LayerFn = None, drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + kernel_size=3, + stride=1, + dilation=(1, 1), + bottle_ratio=1., + group_size=None, + downsample='avg', + extra_conv=False, + linear_out=False, + bottle_in=False, + post_attn_na=True, + feat_size=None, + layers: LayerFn = None, + drop_block=None, + drop_path_rate=0., + ): super(SelfAttnBlock, self).__init__() assert layers is not None mid_chs = make_divisible((in_chs if bottle_in else out_chs) * bottle_ratio) @@ -1233,7 +1312,7 @@ class SelfAttnBlock(nn.Module): self.act = nn.Identity() if linear_out else layers.act(inplace=True) def init_weights(self, zero_init_last: bool = False): - if zero_init_last and self.shortcut is not None: + if zero_init_last and self.shortcut is not None and getattr(self.conv3_1x1.bn, 'weight', None) is not None: nn.init.zeros_(self.conv3_1x1.bn.weight) if hasattr(self.self_attn, 'reset_parameters'): self.self_attn.reset_parameters() @@ -1274,8 +1353,17 @@ def create_block(block: Union[str, nn.Module], **kwargs): class Stem(nn.Sequential): def __init__( - self, in_chs, out_chs, kernel_size=3, stride=4, pool='maxpool', - num_rep=3, num_act=None, chs_decay=0.5, layers: LayerFn = None): + self, + in_chs, + out_chs, + kernel_size=3, + stride=4, + pool='maxpool', + num_rep=3, + num_act=None, + chs_decay=0.5, + layers: LayerFn = None, + ): super().__init__() assert stride in (2, 4) layers = layers or LayerFn() @@ -1319,7 +1407,14 @@ class Stem(nn.Sequential): assert curr_stride == stride -def create_byob_stem(in_chs, out_chs, stem_type='', pool_type='', feat_prefix='stem', layers: LayerFn = None): +def create_byob_stem( + in_chs, + out_chs, + stem_type='', + pool_type='', + feat_prefix='stem', + layers: LayerFn = None, +): layers = layers or LayerFn() assert stem_type in ('', 'quad', 'quad2', 'tiered', 'deep', 'rep', '7x7', '3x3') if 'quad' in stem_type: @@ -1407,10 +1502,14 @@ def update_block_kwargs(block_kwargs: Dict[str, Any], block_cfg: ByoBlockCfg, mo def create_byob_stages( - cfg: ByoModelCfg, drop_path_rate: float, output_stride: int, stem_feat: Dict[str, Any], + cfg: ByoModelCfg, + drop_path_rate: float, + output_stride: int, + stem_feat: Dict[str, Any], feat_size: Optional[int] = None, layers: Optional[LayerFn] = None, - block_kwargs_fn: Optional[Callable] = update_block_kwargs): + block_kwargs_fn: Optional[Callable] = update_block_kwargs, +): layers = layers or LayerFn() feature_info = [] @@ -1485,12 +1584,38 @@ class ByobNet(nn.Module): Current assumption is that both stem and blocks are in conv-bn-act order (w/ block ending in act). """ def __init__( - self, cfg: ByoModelCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32, - zero_init_last=True, img_size=None, drop_rate=0., drop_path_rate=0.): + self, + cfg: ByoModelCfg, + num_classes=1000, + in_chans=3, + global_pool='avg', + output_stride=32, + img_size=None, + drop_rate=0., + drop_path_rate=0., + zero_init_last=True, + **kwargs, + ): + """ + + Args: + cfg (ByoModelCfg): Model architecture configuration + num_classes (int): Number of classifier classes (default: 1000) + in_chans (int): Number of input channels (default: 3) + global_pool (str): Global pooling type (default: 'avg') + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + img_size (Union[int, Tuple[int]): Image size for fixed image size models (i.e. self-attn) + drop_rate (float): Dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) + zero_init_last (bool): Zero-init last weight of residual path + kwargs (dict): Extra kwargs overlayed onto cfg + """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate self.grad_checkpointing = False + + cfg = replace(cfg, **kwargs) # overlay kwargs onto cfg layers = get_layer_fns(cfg) if cfg.fixed_input_size: assert img_size is not None, 'img_size argument is required for fixed input size model' diff --git a/timm/models/convnext.py b/timm/models/convnext.py index eea5782a..05e29a73 100644 --- a/timm/models/convnext.py +++ b/timm/models/convnext.py @@ -1,25 +1,51 @@ """ ConvNeXt -Paper: `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf - -Original code and weights from https://github.com/facebookresearch/ConvNeXt, original copyright below - -Model defs atto, femto, pico, nano and _ols / _hnf variants are timm specific. +Papers: +* `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf +@Article{liu2022convnet, + author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie}, + title = {A ConvNet for the 2020s}, + journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2022}, +} + +* `ConvNeXt-V2 - Co-designing and Scaling ConvNets with Masked Autoencoders` - https://arxiv.org/abs/2301.00808 +@article{Woo2023ConvNeXtV2, + title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders}, + author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie}, + year={2023}, + journal={arXiv preprint arXiv:2301.00808}, +} + +Original code and weights from: +* https://github.com/facebookresearch/ConvNeXt, original copyright below +* https://github.com/facebookresearch/ConvNeXt-V2, original copyright below + +Model defs atto, femto, pico, nano and _ols / _hnf variants are timm originals. Modifications and additions for timm hacked together by / Copyright 2022, Ross Wightman """ +# ConvNeXt # Copyright (c) Meta Platforms, Inc. and affiliates. # All rights reserved. # This source code is licensed under the MIT license + +# ConvNeXt-V2 +# Copyright (c) Meta Platforms, Inc. and affiliates. +# All rights reserved. +# This source code is licensed under the license found in the +# LICENSE file in the root directory of this source tree (Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)) +# No code was used directly from ConvNeXt-V2, however the weights are CC BY-NC 4.0 so beware if using commercially. + from collections import OrderedDict from functools import partial import torch import torch.nn as nn -from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, ConvMlp, Mlp, LayerNorm2d, LayerNorm, \ - create_conv2d, get_act_layer, make_divisible, to_ntuple +from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, OPENAI_CLIP_MEAN, OPENAI_CLIP_STD +from timm.layers import trunc_normal_, SelectAdaptivePool2d, DropPath, Mlp, GlobalResponseNormMlp, \ + LayerNorm2d, LayerNorm, create_conv2d, get_act_layer, make_divisible, to_ntuple from ._builder import build_model_with_cfg from ._manipulate import named_apply, checkpoint_seq from ._pretrained import generate_default_cfgs @@ -54,6 +80,7 @@ class ConvNeXtBlock(nn.Module): mlp_ratio=4, conv_mlp=False, conv_bias=True, + use_grn=False, ls_init_value=1e-6, act_layer='gelu', norm_layer=None, @@ -64,14 +91,13 @@ class ConvNeXtBlock(nn.Module): act_layer = get_act_layer(act_layer) if not norm_layer: norm_layer = LayerNorm2d if conv_mlp else LayerNorm - mlp_layer = ConvMlp if conv_mlp else Mlp + mlp_layer = partial(GlobalResponseNormMlp if use_grn else Mlp, use_conv=conv_mlp) self.use_conv_mlp = conv_mlp - self.conv_dw = create_conv2d( in_chs, out_chs, kernel_size=kernel_size, stride=stride, dilation=dilation, depthwise=True, bias=conv_bias) self.norm = norm_layer(out_chs) self.mlp = mlp_layer(out_chs, int(mlp_ratio * out_chs), act_layer=act_layer) - self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value > 0 else None + self.gamma = nn.Parameter(ls_init_value * torch.ones(out_chs)) if ls_init_value is not None else None self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity() def forward(self, x): @@ -106,6 +132,7 @@ class ConvNeXtStage(nn.Module): ls_init_value=1.0, conv_mlp=False, conv_bias=True, + use_grn=False, act_layer='gelu', norm_layer=None, norm_layer_cl=None @@ -138,8 +165,9 @@ class ConvNeXtStage(nn.Module): ls_init_value=ls_init_value, conv_mlp=conv_mlp, conv_bias=conv_bias, + use_grn=use_grn, act_layer=act_layer, - norm_layer=norm_layer if conv_mlp else norm_layer_cl + norm_layer=norm_layer if conv_mlp else norm_layer_cl, )) in_chs = out_chs self.blocks = nn.Sequential(*stage_blocks) @@ -156,16 +184,6 @@ class ConvNeXtStage(nn.Module): class ConvNeXt(nn.Module): r""" ConvNeXt A PyTorch impl of : `A ConvNet for the 2020s` - https://arxiv.org/pdf/2201.03545.pdf - - Args: - in_chans (int): Number of input image channels. Default: 3 - num_classes (int): Number of classes for classification head. Default: 1000 - depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3] - dims (tuple(int)): Feature dimension at each stage. Default: [96, 192, 384, 768] - drop_rate (float): Head dropout rate - drop_path_rate (float): Stochastic depth rate. Default: 0. - ls_init_value (float): Init value for Layer Scale. Default: 1e-6. - head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1. """ def __init__( @@ -184,21 +202,50 @@ class ConvNeXt(nn.Module): head_norm_first=False, conv_mlp=False, conv_bias=True, + use_grn=False, act_layer='gelu', norm_layer=None, + norm_eps=None, drop_rate=0., drop_path_rate=0., ): + """ + Args: + in_chans (int): Number of input image channels (default: 3) + num_classes (int): Number of classes for classification head (default: 1000) + global_pool (str): Global pooling type (default: 'avg') + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + depths (tuple(int)): Number of blocks at each stage. (default: [3, 3, 9, 3]) + dims (tuple(int)): Feature dimension at each stage. (default: [96, 192, 384, 768]) + kernel_sizes (Union[int, List[int]]: Depthwise convolution kernel-sizes for each stage (default: 7) + ls_init_value (float): Init value for Layer Scale (default: 1e-6) + stem_type (str): Type of stem (default: 'patch') + patch_size (int): Stem patch size for patch stem (default: 4) + head_init_scale (float): Init scaling value for classifier weights and biases (default: 1) + head_norm_first (bool): Apply normalization before global pool + head (default: False) + conv_mlp (bool): Use 1x1 conv in MLP, improves speed for small networks w/ chan last (default: False) + conv_bias (bool): Use bias layers w/ all convolutions (default: True) + use_grn (bool): Use Global Response Norm (ConvNeXt-V2) in MLP (default: False) + act_layer (Union[str, nn.Module]): Activation Layer + norm_layer (Union[str, nn.Module]): Normalization Layer + drop_rate (float): Head dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth rate (default: 0.) + """ super().__init__() assert output_stride in (8, 16, 32) kernel_sizes = to_ntuple(4)(kernel_sizes) if norm_layer is None: norm_layer = LayerNorm2d norm_layer_cl = norm_layer if conv_mlp else LayerNorm + if norm_eps is not None: + norm_layer = partial(norm_layer, eps=norm_eps) + norm_layer_cl = partial(norm_layer_cl, eps=norm_eps) else: assert conv_mlp,\ 'If a norm_layer is specified, conv MLP must be used so all norm expect rank-4, channels-first input' norm_layer_cl = norm_layer + if norm_eps is not None: + norm_layer_cl = partial(norm_layer_cl, eps=norm_eps) self.num_classes = num_classes self.drop_rate = drop_rate @@ -209,7 +256,7 @@ class ConvNeXt(nn.Module): # NOTE: this stem is a minimal form of ViT PatchEmbed, as used in SwinTransformer w/ patch_size = 4 self.stem = nn.Sequential( nn.Conv2d(in_chans, dims[0], kernel_size=patch_size, stride=patch_size, bias=conv_bias), - norm_layer(dims[0]) + norm_layer(dims[0]), ) stem_stride = patch_size else: @@ -247,9 +294,10 @@ class ConvNeXt(nn.Module): ls_init_value=ls_init_value, conv_mlp=conv_mlp, conv_bias=conv_bias, + use_grn=use_grn, act_layer=act_layer, norm_layer=norm_layer, - norm_layer_cl=norm_layer_cl + norm_layer_cl=norm_layer_cl, )) prev_chs = out_chs # NOTE feature_info use currently assumes stage 0 == stride 1, rest are stride 2 @@ -334,7 +382,15 @@ def checkpoint_filter_fn(state_dict, model): return state_dict # non-FB checkpoint if 'model' in state_dict: state_dict = state_dict['model'] + out_dict = {} + if 'visual.trunk.stem.0.weight' in state_dict: + out_dict = {k.replace('visual.trunk.', ''): v for k, v in state_dict.items() if k.startswith('visual.trunk.')} + if 'visual.head.proj.weight' in state_dict: + out_dict['head.fc.weight'] = state_dict['visual.head.proj.weight'] + out_dict['head.fc.bias'] = torch.zeros(state_dict['visual.head.proj.weight'].shape[0]) + return out_dict + import re for k, v in state_dict.items(): k = k.replace('downsample_layers.0.', 'stem.') @@ -342,6 +398,10 @@ def checkpoint_filter_fn(state_dict, model): k = re.sub(r'downsample_layers.([0-9]+).([0-9]+)', r'stages.\1.downsample.\2', k) k = k.replace('dwconv', 'conv_dw') k = k.replace('pwconv', 'mlp.fc') + if 'grn' in k: + k = k.replace('grn.beta', 'mlp.grn.bias') + k = k.replace('grn.gamma', 'mlp.grn.weight') + v = v.reshape(v.shape[-1]) k = k.replace('head.', 'head.fc.') if k.startswith('norm.'): k = k.replace('norm', 'head.norm') @@ -349,10 +409,16 @@ def checkpoint_filter_fn(state_dict, model): model_shape = model.state_dict()[k].shape v = v.reshape(model_shape) out_dict[k] = v + return out_dict def _create_convnext(variant, pretrained=False, **kwargs): + if kwargs.get('pretrained_cfg', '') == 'fcmae': + # NOTE fcmae pretrained weights have no classifier or final norm-layer (`head.norm`) + # This is workaround loading with num_classes=0 w/o removing norm-layer. + kwargs.setdefault('pretrained_strict', False) + model = build_model_with_cfg( ConvNeXt, variant, pretrained, pretrained_filter_fn=checkpoint_filter_fn, @@ -361,7 +427,6 @@ def _create_convnext(variant, pretrained=False, **kwargs): return model - def _cfg(url='', **kwargs): return { 'url': url, @@ -373,92 +438,295 @@ def _cfg(url='', **kwargs): } +def _cfgv2(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.875, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'stem.0', 'classifier': 'head.fc', + 'license': 'cc-by-nc-4.0', 'paper_ids': 'arXiv:2301.00808', + 'paper_name': 'ConvNeXt-V2: Co-designing and Scaling ConvNets with Masked Autoencoders', + 'origin_url': 'https://github.com/facebookresearch/ConvNeXt-V2', + **kwargs + } + + default_cfgs = generate_default_cfgs({ # timm specific variants - 'convnext_atto.timm_in1k': _cfg( + 'convnext_atto.d2_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_d2-01bb0f51.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=0.95), - 'convnext_atto_ols.timm_in1k': _cfg( + 'convnext_atto_ols.a2_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_atto_ols_a2-78d1c8f3.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=0.95), - 'convnext_femto.timm_in1k': _cfg( + 'convnext_femto.d1_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_d1-d71d5b4c.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=0.95), - 'convnext_femto_ols.timm_in1k': _cfg( + 'convnext_femto_ols.d1_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_femto_ols_d1-246bf2ed.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=0.95), - 'convnext_pico.timm_in1k': _cfg( + 'convnext_pico.d1_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_d1-10ad7f0d.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=0.95), - 'convnext_pico_ols.timm_in1k': _cfg( + 'convnext_pico_ols.d1_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_pico_ols_d1-611f0ca7.pth', + hf_hub_id='timm/', + crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnext_nano.in12k_ft_in1k': _cfg( + hf_hub_id='timm/', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), - 'convnext_nano.timm_in1k': _cfg( + 'convnext_nano.d1h_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_d1h-7eb4bdea.pth', + hf_hub_id='timm/', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), - 'convnext_nano_ols.timm_in1k': _cfg( + 'convnext_nano_ols.d1h_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_nano_ols_d1h-ae424a9a.pth', + hf_hub_id='timm/', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), - 'convnext_tiny_hnf.timm_in1k': _cfg( + 'convnext_tiny_hnf.a2h_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-rsb-weights/convnext_tiny_hnf_a2h-ab7e9df2.pth', + hf_hub_id='timm/', crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnext_tiny.in12k_ft_in1k': _cfg( + hf_hub_id='timm/', + crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnext_small.in12k_ft_in1k': _cfg( + hf_hub_id='timm/', + crop_pct=0.95, test_input_size=(3, 288, 288), test_crop_pct=1.0), + + 'convnext_nano.in12k': _cfg( + hf_hub_id='timm/', + crop_pct=0.95, num_classes=11821), + 'convnext_tiny.in12k': _cfg( + hf_hub_id='timm/', + crop_pct=0.95, num_classes=11821), + 'convnext_small.in12k': _cfg( + hf_hub_id='timm/', + crop_pct=0.95, num_classes=11821), 'convnext_tiny.fb_in1k': _cfg( url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth", + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_small.fb_in1k': _cfg( url="https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth", + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_base.fb_in1k': _cfg( url="https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth", + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_large.fb_in1k': _cfg( url="https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth", + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_xlarge.untrained': _cfg(), + 'convnext_xxlarge.untrained': _cfg(), 'convnext_tiny.fb_in22k_ft_in1k': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_224.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_small.fb_in22k_ft_in1k': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_224.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_base.fb_in22k_ft_in1k': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_large.fb_in22k_ft_in1k': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_xlarge.fb_in22k_ft_in1k': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth', + hf_hub_id='timm/', test_input_size=(3, 288, 288), test_crop_pct=1.0), 'convnext_tiny.fb_in22k_ft_in1k_384': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_1k_384.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'convnext_small..fb_in22k_ft_in1k_384': _cfg( + 'convnext_small.fb_in22k_ft_in1k_384': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_1k_384.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), 'convnext_base.fb_in22k_ft_in1k_384': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), 'convnext_large.fb_in22k_ft_in1k_384': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), 'convnext_xlarge.fb_in22k_ft_in1k_384': _cfg( url='https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth', + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + + 'convnext_tiny.fb_in22k': _cfg( + url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", + hf_hub_id='timm/', + num_classes=21841), + 'convnext_small.fb_in22k': _cfg( + url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", + hf_hub_id='timm/', + num_classes=21841), + 'convnext_base.fb_in22k': _cfg( + url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", + hf_hub_id='timm/', + num_classes=21841), + 'convnext_large.fb_in22k': _cfg( + url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", + hf_hub_id='timm/', + num_classes=21841), + 'convnext_xlarge.fb_in22k': _cfg( + url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", + hf_hub_id='timm/', + num_classes=21841), + + 'convnextv2_nano.fcmae_ft_in22k_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_nano.fcmae_ft_in22k_in1k_384': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt', + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_tiny.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_tiny.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_base.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_base.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_large.fcmae_ft_in22k_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_large.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt", + hf_hub_id='timm/', input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_huge.fcmae_ft_in22k_in1k_384': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt", + hf_hub_id='timm/', + input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'convnextv2_huge.fcmae_ft_in22k_in1k_512': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt", + hf_hub_id='timm/', + input_size=(3, 512, 512), pool_size=(15, 15), crop_pct=1.0, crop_mode='squash'), + + 'convnextv2_atto.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_femto.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_pico.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=0.95), + 'convnextv2_nano.fcmae_ft_in1k': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt', + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_tiny.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_base.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_large.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), + 'convnextv2_huge.fcmae_ft_in1k': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt", + hf_hub_id='timm/', + test_input_size=(3, 288, 288), test_crop_pct=1.0), - 'convnext_tiny_in22k.fb_in22k': _cfg( - url="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_22k_224.pth", num_classes=21841), - 'convnext_small_in22k.fb_in22k': _cfg( - url="https://dl.fbaipublicfiles.com/convnext/convnext_small_22k_224.pth", num_classes=21841), - 'convnext_base_in22k.fb_in22k': _cfg( - url="https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth", num_classes=21841), - 'convnext_large_in22k.fb_in22k': _cfg( - url="https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth", num_classes=21841), - 'convnext_xlarge_in22k.fb_in22k': _cfg( - url="https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth", num_classes=21841), + 'convnextv2_atto.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_atto_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_femto.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_femto_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_pico.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_pico_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_nano.fcmae': _cfgv2( + url='https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_nano_1k_224_fcmae.pt', + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_tiny.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_tiny_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_base.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_base_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_large.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_large_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + 'convnextv2_huge.fcmae': _cfgv2( + url="https://dl.fbaipublicfiles.com/convnext/convnextv2/pt_only/convnextv2_huge_1k_224_fcmae.pt", + hf_hub_id='timm/', + num_classes=0), + + 'convnextv2_small.untrained': _cfg(), + + # CLIP based weights, original image tower weights and fine-tunes + 'convnext_base.clip_laion2b': _cfg( + hf_hub_id='laion/CLIP-convnext_base_w-laion2B-s13B-b82K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + input_size=(3, 256, 256), crop_pct=1.0, num_classes=640), + 'convnext_base.clip_laion2b_augreg': _cfg( + hf_hub_id='laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + input_size=(3, 256, 256), crop_pct=1.0, num_classes=640), + 'convnext_base.clip_laiona': _cfg( + hf_hub_id='laion/CLIP-convnext_base_w-laion_aesthetic-s13B-b82K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + input_size=(3, 256, 256), crop_pct=1.0, num_classes=640), + 'convnext_base.clip_laiona_320': _cfg( + hf_hub_id='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + input_size=(3, 320, 320), crop_pct=1.0, num_classes=640), + 'convnext_base.clip_laiona_augreg_320': _cfg( + hf_hub_id='laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K-augreg', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + input_size=(3, 320, 320), crop_pct=1.0, num_classes=640), }) @@ -576,3 +844,82 @@ def convnext_xlarge(pretrained=False, **kwargs): model_args = dict(depths=[3, 3, 27, 3], dims=[256, 512, 1024, 2048], **kwargs) model = _create_convnext('convnext_xlarge', pretrained=pretrained, **model_args) return model + + +@register_model +def convnext_xxlarge(pretrained=False, **kwargs): + model_args = dict(depths=[3, 4, 30, 3], dims=[384, 768, 1536, 3072], **kwargs) + model = _create_convnext('convnext_xxlarge', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_atto(pretrained=False, **kwargs): + # timm femto variant (NOTE: still tweaking depths, will vary between 3-4M param, current is 3.7M + model_args = dict( + depths=(2, 2, 6, 2), dims=(40, 80, 160, 320), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_atto', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_femto(pretrained=False, **kwargs): + # timm femto variant + model_args = dict( + depths=(2, 2, 6, 2), dims=(48, 96, 192, 384), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_femto', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_pico(pretrained=False, **kwargs): + # timm pico variant + model_args = dict( + depths=(2, 2, 6, 2), dims=(64, 128, 256, 512), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_pico', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_nano(pretrained=False, **kwargs): + # timm nano variant with standard stem and head + model_args = dict( + depths=(2, 2, 8, 2), dims=(80, 160, 320, 640), use_grn=True, ls_init_value=None, conv_mlp=True, **kwargs) + model = _create_convnext('convnextv2_nano', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_tiny(pretrained=False, **kwargs): + model_args = dict( + depths=(3, 3, 9, 3), dims=(96, 192, 384, 768), use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_tiny', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_small(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[96, 192, 384, 768], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_small', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_base(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[128, 256, 512, 1024], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_base', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_large(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[192, 384, 768, 1536], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_large', pretrained=pretrained, **model_args) + return model + + +@register_model +def convnextv2_huge(pretrained=False, **kwargs): + model_args = dict(depths=[3, 3, 27, 3], dims=[352, 704, 1408, 2816], use_grn=True, ls_init_value=None, **kwargs) + model = _create_convnext('convnextv2_huge', pretrained=pretrained, **model_args) + return model \ No newline at end of file diff --git a/timm/models/cspnet.py b/timm/models/cspnet.py index 280f929e..26ec54d9 100644 --- a/timm/models/cspnet.py +++ b/timm/models/cspnet.py @@ -12,7 +12,7 @@ Reference impl via darknet cfg files at https://github.com/WongKinYiu/CrossStage Hacked together by / Copyright 2020 Ross Wightman """ -from dataclasses import dataclass, asdict +from dataclasses import dataclass, asdict, replace from functools import partial from typing import Any, Dict, Optional, Tuple, Union @@ -518,7 +518,7 @@ class CrossStage(nn.Module): cross_linear=False, block_dpr=None, block_fn=BottleneckBlock, - **block_kwargs + **block_kwargs, ): super(CrossStage, self).__init__() first_dilation = first_dilation or dilation @@ -558,7 +558,7 @@ class CrossStage(nn.Module): bottle_ratio=bottle_ratio, groups=groups, drop_path=block_dpr[i] if block_dpr is not None else 0., - **block_kwargs + **block_kwargs, )) prev_chs = block_out_chs @@ -597,7 +597,7 @@ class CrossStage3(nn.Module): cross_linear=False, block_dpr=None, block_fn=BottleneckBlock, - **block_kwargs + **block_kwargs, ): super(CrossStage3, self).__init__() first_dilation = first_dilation or dilation @@ -635,7 +635,7 @@ class CrossStage3(nn.Module): bottle_ratio=bottle_ratio, groups=groups, drop_path=block_dpr[i] if block_dpr is not None else 0., - **block_kwargs + **block_kwargs, )) prev_chs = block_out_chs @@ -668,7 +668,7 @@ class DarkStage(nn.Module): avg_down=False, block_fn=BottleneckBlock, block_dpr=None, - **block_kwargs + **block_kwargs, ): super(DarkStage, self).__init__() first_dilation = first_dilation or dilation @@ -715,7 +715,7 @@ def create_csp_stem( padding='', act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - aa_layer=None + aa_layer=None, ): stem = nn.Sequential() feature_info = [] @@ -738,7 +738,7 @@ def create_csp_stem( stride=conv_stride, padding=padding if i == 0 else '', act_layer=act_layer, - norm_layer=norm_layer + norm_layer=norm_layer, )) stem_stride *= conv_stride prev_chs = chs @@ -800,7 +800,7 @@ def create_csp_stages( cfg: CspModelCfg, drop_path_rate: float, output_stride: int, - stem_feat: Dict[str, Any] + stem_feat: Dict[str, Any], ): cfg_dict = asdict(cfg.stages) num_stages = len(cfg.stages.depth) @@ -868,12 +868,27 @@ class CspNet(nn.Module): global_pool='avg', drop_rate=0., drop_path_rate=0., - zero_init_last=True + zero_init_last=True, + **kwargs, ): + """ + Args: + cfg (CspModelCfg): Model architecture configuration + in_chans (int): Number of input channels (default: 3) + num_classes (int): Number of classifier classes (default: 1000) + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + global_pool (str): Global pooling type (default: 'avg') + drop_rate (float): Dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) + zero_init_last (bool): Zero-init last weight of residual path + kwargs (dict): Extra kwargs overlayed onto cfg + """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate assert output_stride in (8, 16, 32) + + cfg = replace(cfg, **kwargs) # overlay kwargs onto cfg layer_args = dict( act_layer=cfg.act_layer, norm_layer=cfg.norm_layer, diff --git a/timm/models/davit.py b/timm/models/davit.py index 0ccd2ae0..f57cc5ae 100644 --- a/timm/models/davit.py +++ b/timm/models/davit.py @@ -12,6 +12,7 @@ DaViT model defs and weights adapted from https://github.com/dingmyu/davit, orig # All rights reserved. # This source code is licensed under the MIT license +from collections import OrderedDict import itertools import torch @@ -20,7 +21,7 @@ import torch.nn.functional as F from torch import Tensor from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import DropPath, to_2tuple, trunc_normal_, ClassifierHead, Mlp +from timm.layers import DropPath, to_2tuple, trunc_normal_, SelectAdaptivePool2d, Mlp # ClassifierHead from ._builder import build_model_with_cfg from ._features import FeatureInfo from ._features_fx import register_notrace_function @@ -407,7 +408,11 @@ class DaViTStage(nn.Module): stage_blocks.append(nn.Sequential(*dual_attention_block)) self.blocks = nn.Sequential(*stage_blocks) - + + @torch.jit.ignore + def set_grad_checkpointing(self, enable=True): + self.grad_checkpointing = enable + def forward(self, x : Tensor): x = self.patch_embed(x) if self.grad_checkpointing and not torch.jit.is_scripting(): @@ -455,7 +460,8 @@ class DaViT(nn.Module): drop_rate=0., attn_drop_rate=0., num_classes=1000, - global_pool='avg' + global_pool='avg', + head_norm_first=False, ): super().__init__() @@ -503,11 +509,19 @@ class DaViT(nn.Module): stages.append(stage) self.feature_info += [dict(num_chs=self.embed_dims[stage_id], reduction=2, module=f'stages.{stage_id}')] - self.stages = nn.Sequential(*stages) - self.norms = norm_layer(self.num_features) - self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=drop_rate) + # if head_norm_first == true, norm -> global pool -> fc ordering, like most other nets + # otherwise pool -> norm -> fc, the default DaViT order, similar to ConvNeXt + # FIXME generalize this structure to ClassifierHead + self.norm_pre = norm_layer(self.num_features) if head_norm_first else nn.Identity() + self.head = nn.Sequential(OrderedDict([ + ('global_pool', SelectAdaptivePool2d(pool_type=global_pool)), + ('norm', nn.Identity() if head_norm_first else norm_layer(self.num_features)), + ('flatten', nn.Flatten(1) if global_pool else nn.Identity()), + ('drop', nn.Dropout(self.drop_rate)), + ('fc', nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity())])) + self.apply(self._init_weights) def _init_weights(self, m): @@ -522,40 +536,44 @@ class DaViT(nn.Module): @torch.jit.ignore def set_grad_checkpointing(self, enable=True): self.grad_checkpointing = enable + for stage in self.stages: + stage.set_grad_checkpointing(enable=enable) @torch.jit.ignore def get_classifier(self): return self.head.fc def reset_classifier(self, num_classes, global_pool=None): - self.num_classes = num_classes - if global_pool is None: - global_pool = self.head.global_pool.pool_type - self.head = ClassifierHead(self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate) + if global_pool is not None: + self.head.global_pool = SelectAdaptivePool2d(pool_type=global_pool) + self.head.flatten = nn.Flatten(1) if global_pool else nn.Identity() + self.head.fc = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity() def forward_features(self, x): x = self.patch_embed(x) - x = self.stages(x) - # take final feature and norm - x = self.norms(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) - #H, W = sizes[-1] - #x = x.view(-1, H, W, self.embed_dims[-1]).permute(0, 3, 1, 2).contiguous() + if self.grad_checkpointing and not torch.jit.is_scripting(): + x = checkpoint_seq(self.stages, x) + else: + x = self.stages(x) + x = self.norm_pre(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) return x def forward_head(self, x, pre_logits: bool = False): - return self.head(x, pre_logits=pre_logits) + x = self.head.global_pool(x) + x = self.head.norm(x.permute(0, 2, 3, 1)).permute(0, 3, 1, 2) + x = self.head.flatten(x) + x = self.head.drop(x) + return x if pre_logits else self.head.fc(x) - def forward_classifier(self, x): + def forward(self, x): x = self.forward_features(x) x = self.forward_head(x) return x - - def forward(self, x): - return self.forward_classifier(x) + def checkpoint_filter_fn(state_dict, model): """ Remap MSFT checkpoints -> timm """ - if 'head.norm.weight' in state_dict: + if 'head' in state_dict: return state_dict # non-MSFT checkpoint if 'state_dict' in state_dict: @@ -569,6 +587,7 @@ def checkpoint_filter_fn(state_dict, model): k = re.sub(r'main_blocks.([0-9]+)', r'stages.\1.blocks', k) k = k.replace('stages.0.patch_embed', 'patch_embed') k = k.replace('head.', 'head.fc.') + k = k.replace('norms.', 'head.norm.') k = k.replace('cpe.0', 'cpe1') k = k.replace('cpe.1', 'cpe2') out_dict[k] = v @@ -577,8 +596,6 @@ def checkpoint_filter_fn(state_dict, model): def _create_davit(variant, pretrained=False, **kwargs): - - default_out_indices = tuple(i for i, _ in enumerate(kwargs.get('depths', (1, 1, 3, 1)))) out_indices = kwargs.pop('out_indices', default_out_indices) @@ -594,11 +611,11 @@ def _create_davit(variant, pretrained=False, **kwargs): -def _cfg(url='', **kwargs): # not sure how this should be set up +def _cfg(url='', **kwargs): return { 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), - 'crop_pct': 0.875, 'interpolation': 'bilinear', + 'crop_pct': 0.850, 'interpolation': 'bicubic', 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, 'first_conv': 'patch_embed.proj', 'classifier': 'head.fc', **kwargs diff --git a/timm/models/densenet.py b/timm/models/densenet.py index e731f7b0..ccbb491c 100644 --- a/timm/models/densenet.py +++ b/timm/models/densenet.py @@ -12,7 +12,7 @@ import torch.utils.checkpoint as cp from torch.jit.annotations import List from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import BatchNormAct2d, create_norm_act_layer, BlurPool2d, create_classifier +from timm.layers import BatchNormAct2d, get_norm_act_layer, BlurPool2d, create_classifier from ._builder import build_model_with_cfg from ._manipulate import MATCH_PREV_GROUP from ._registry import register_model @@ -115,8 +115,15 @@ class DenseBlock(nn.ModuleDict): _version = 2 def __init__( - self, num_layers, num_input_features, bn_size, growth_rate, norm_layer=BatchNormAct2d, - drop_rate=0., memory_efficient=False): + self, + num_layers, + num_input_features, + bn_size, + growth_rate, + norm_layer=BatchNormAct2d, + drop_rate=0., + memory_efficient=False, + ): super(DenseBlock, self).__init__() for i in range(num_layers): layer = DenseLayer( @@ -165,12 +172,25 @@ class DenseNet(nn.Module): """ def __init__( - self, growth_rate=32, block_config=(6, 12, 24, 16), num_classes=1000, in_chans=3, global_pool='avg', - bn_size=4, stem_type='', norm_layer=BatchNormAct2d, aa_layer=None, drop_rate=0, - memory_efficient=False, aa_stem_only=True): + self, + growth_rate=32, + block_config=(6, 12, 24, 16), + num_classes=1000, + in_chans=3, + global_pool='avg', + bn_size=4, + stem_type='', + act_layer='relu', + norm_layer='batchnorm2d', + aa_layer=None, + drop_rate=0, + memory_efficient=False, + aa_stem_only=True, + ): self.num_classes = num_classes self.drop_rate = drop_rate super(DenseNet, self).__init__() + norm_layer = get_norm_act_layer(norm_layer, act_layer=act_layer) # Stem deep_stem = 'deep' in stem_type # 3x3 deep stem @@ -226,8 +246,11 @@ class DenseNet(nn.Module): dict(num_chs=num_features, reduction=current_stride, module='features.' + module_name)] current_stride *= 2 trans = DenseTransition( - num_input_features=num_features, num_output_features=num_features // 2, - norm_layer=norm_layer, aa_layer=transition_aa_layer) + num_input_features=num_features, + num_output_features=num_features // 2, + norm_layer=norm_layer, + aa_layer=transition_aa_layer, + ) self.features.add_module(f'transition{i + 1}', trans) num_features = num_features // 2 @@ -322,8 +345,8 @@ def densenetblur121d(pretrained=False, **kwargs): `"Densely Connected Convolutional Networks" ` """ model = _create_densenet( - 'densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, stem_type='deep', - aa_layer=BlurPool2d, **kwargs) + 'densenetblur121d', growth_rate=32, block_config=(6, 12, 24, 16), pretrained=pretrained, + stem_type='deep', aa_layer=BlurPool2d, **kwargs) return model @@ -382,11 +405,9 @@ def densenet264(pretrained=False, **kwargs): def densenet264d_iabn(pretrained=False, **kwargs): r"""Densenet-264 model with deep stem and Inplace-ABN """ - def norm_act_fn(num_features, **kwargs): - return create_norm_act_layer('iabn', num_features, act_layer='leaky_relu', **kwargs) model = _create_densenet( 'densenet264d_iabn', growth_rate=48, block_config=(6, 12, 64, 48), stem_type='deep', - norm_layer=norm_act_fn, pretrained=pretrained, **kwargs) + norm_layer='iabn', act_layer='leaky_relu', pretrained=pretrained, **kwargs) return model diff --git a/timm/models/dpn.py b/timm/models/dpn.py index 87bd918f..29a7a7e8 100644 --- a/timm/models/dpn.py +++ b/timm/models/dpn.py @@ -15,7 +15,7 @@ import torch.nn as nn import torch.nn.functional as F from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier +from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier, get_norm_act_layer from ._builder import build_model_with_cfg from ._registry import register_model @@ -33,6 +33,7 @@ def _cfg(url='', **kwargs): default_cfgs = { + 'dpn48b': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD), 'dpn68': _cfg( url='https://github.com/rwightman/pytorch-dpn-pretrained/releases/download/v0.1/dpn68-66bebafa7.pth'), 'dpn68b': _cfg( @@ -82,7 +83,16 @@ class BnActConv2d(nn.Module): class DualPathBlock(nn.Module): def __init__( - self, in_chs, num_1x1_a, num_3x3_b, num_1x1_c, inc, groups, block_type='normal', b=False): + self, + in_chs, + num_1x1_a, + num_3x3_b, + num_1x1_c, + inc, + groups, + block_type='normal', + b=False, + ): super(DualPathBlock, self).__init__() self.num_1x1_c = num_1x1_c self.inc = inc @@ -167,16 +177,31 @@ class DualPathBlock(nn.Module): class DPN(nn.Module): def __init__( - self, small=False, num_init_features=64, k_r=96, groups=32, global_pool='avg', - b=False, k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), output_stride=32, - num_classes=1000, in_chans=3, drop_rate=0., fc_act_layer=nn.ELU): + self, + k_sec=(3, 4, 20, 3), + inc_sec=(16, 32, 24, 128), + k_r=96, + groups=32, + num_classes=1000, + in_chans=3, + output_stride=32, + global_pool='avg', + small=False, + num_init_features=64, + b=False, + drop_rate=0., + norm_layer='batchnorm2d', + act_layer='relu', + fc_act_layer='elu', + ): super(DPN, self).__init__() self.num_classes = num_classes self.drop_rate = drop_rate self.b = b assert output_stride == 32 # FIXME look into dilation support - norm_layer = partial(BatchNormAct2d, eps=.001) - fc_norm_layer = partial(BatchNormAct2d, eps=.001, act_layer=fc_act_layer, inplace=False) + + norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=act_layer), eps=.001) + fc_norm_layer = partial(get_norm_act_layer(norm_layer, act_layer=fc_act_layer), eps=.001, inplace=False) bw_factor = 1 if small else 4 blocks = OrderedDict() @@ -291,49 +316,57 @@ def _create_dpn(variant, pretrained=False, **kwargs): **kwargs) +@register_model +def dpn48b(pretrained=False, **kwargs): + model_kwargs = dict( + small=True, num_init_features=10, k_r=128, groups=32, + b=True, k_sec=(3, 4, 6, 3), inc_sec=(16, 32, 32, 64), act_layer='silu') + return _create_dpn('dpn48b', pretrained=pretrained, **dict(model_kwargs, **kwargs)) + + @register_model def dpn68(pretrained=False, **kwargs): model_kwargs = dict( small=True, num_init_features=10, k_r=128, groups=32, - k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64), **kwargs) - return _create_dpn('dpn68', pretrained=pretrained, **model_kwargs) + k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64)) + return _create_dpn('dpn68', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model def dpn68b(pretrained=False, **kwargs): model_kwargs = dict( small=True, num_init_features=10, k_r=128, groups=32, - b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64), **kwargs) - return _create_dpn('dpn68b', pretrained=pretrained, **model_kwargs) + b=True, k_sec=(3, 4, 12, 3), inc_sec=(16, 32, 32, 64)) + return _create_dpn('dpn68b', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model def dpn92(pretrained=False, **kwargs): model_kwargs = dict( num_init_features=64, k_r=96, groups=32, - k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128), **kwargs) - return _create_dpn('dpn92', pretrained=pretrained, **model_kwargs) + k_sec=(3, 4, 20, 3), inc_sec=(16, 32, 24, 128)) + return _create_dpn('dpn92', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model def dpn98(pretrained=False, **kwargs): model_kwargs = dict( num_init_features=96, k_r=160, groups=40, - k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128), **kwargs) - return _create_dpn('dpn98', pretrained=pretrained, **model_kwargs) + k_sec=(3, 6, 20, 3), inc_sec=(16, 32, 32, 128)) + return _create_dpn('dpn98', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model def dpn131(pretrained=False, **kwargs): model_kwargs = dict( num_init_features=128, k_r=160, groups=40, - k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128), **kwargs) - return _create_dpn('dpn131', pretrained=pretrained, **model_kwargs) + k_sec=(4, 8, 28, 3), inc_sec=(16, 32, 32, 128)) + return _create_dpn('dpn131', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model def dpn107(pretrained=False, **kwargs): model_kwargs = dict( num_init_features=128, k_r=200, groups=50, - k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128), **kwargs) - return _create_dpn('dpn107', pretrained=pretrained, **model_kwargs) + k_sec=(4, 8, 20, 3), inc_sec=(20, 64, 64, 128)) + return _create_dpn('dpn107', pretrained=pretrained, **dict(model_kwargs, **kwargs)) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index a1324ae3..a3866fec 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -50,410 +50,12 @@ from ._efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficie round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT from ._features import FeatureInfo, FeatureHooks from ._manipulate import checkpoint_seq +from ._pretrained import generate_default_cfgs from ._registry import register_model __all__ = ['EfficientNet', 'EfficientNetFeatures'] -def _cfg(url='', **kwargs): - return { - 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), - 'crop_pct': 0.875, 'interpolation': 'bicubic', - 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, - 'first_conv': 'conv_stem', 'classifier': 'classifier', - **kwargs - } - - -default_cfgs = { - 'mnasnet_050': _cfg(url=''), - 'mnasnet_075': _cfg(url=''), - 'mnasnet_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth'), - 'mnasnet_140': _cfg(url=''), - - 'semnasnet_050': _cfg(url=''), - 'semnasnet_075': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth'), - 'semnasnet_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth'), - 'semnasnet_140': _cfg(url=''), - 'mnasnet_small': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth'), - - 'mobilenetv2_035': _cfg( - url=''), - 'mobilenetv2_050': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth', - interpolation='bicubic', - ), - 'mobilenetv2_075': _cfg( - url=''), - 'mobilenetv2_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth'), - 'mobilenetv2_110d': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth'), - 'mobilenetv2_120d': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth'), - 'mobilenetv2_140': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth'), - - 'fbnetc_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth', - interpolation='bilinear'), - 'spnasnet_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth', - interpolation='bilinear'), - - # NOTE experimenting with alternate attention - 'efficientnet_b0': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth'), - 'efficientnet_b1': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth', - test_input_size=(3, 256, 256), crop_pct=1.0), - 'efficientnet_b2': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth', - input_size=(3, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), crop_pct=1.0), - 'efficientnet_b3': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth', - input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), - 'efficientnet_b4': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth', - input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0), - 'efficientnet_b5': _cfg( - url='', input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), - 'efficientnet_b6': _cfg( - url='', input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), - 'efficientnet_b7': _cfg( - url='', input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), - 'efficientnet_b8': _cfg( - url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), - 'efficientnet_l2': _cfg( - url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961), - - # FIXME experimental - 'efficientnet_b0_gn': _cfg( - url=''), - 'efficientnet_b0_g8_gn': _cfg( - url=''), - 'efficientnet_b0_g16_evos': _cfg( - url=''), - 'efficientnet_b3_gn': _cfg( - url='', - input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), - 'efficientnet_b3_g8_gn': _cfg( - url='', - input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), - - 'efficientnet_es': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth'), - 'efficientnet_em': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth', - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'efficientnet_el': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el-3b455510.pth', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - - 'efficientnet_es_pruned': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_pruned75-1b7248cf.pth'), - 'efficientnet_el_pruned': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el_pruned70-ef2a2ccf.pth', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - - 'efficientnet_cc_b0_4e': _cfg(url=''), - 'efficientnet_cc_b0_8e': _cfg(url=''), - 'efficientnet_cc_b1_8e': _cfg(url='', input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - - 'efficientnet_lite0': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth'), - 'efficientnet_lite1': _cfg( - url='', - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'efficientnet_lite2': _cfg( - url='', - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), - 'efficientnet_lite3': _cfg( - url='', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - 'efficientnet_lite4': _cfg( - url='', input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), - - 'efficientnet_b1_pruned': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb1_pruned-bea43a3a.pth', - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'efficientnet_b2_pruned': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb2_pruned-08c1b27c.pth', - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'efficientnet_b3_pruned': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb3_pruned-59ecf72d.pth', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - - 'efficientnetv2_rw_t': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth', - input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), - 'gc_efficientnetv2_rw_t': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth', - input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), - 'efficientnetv2_rw_s': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth', - input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), - 'efficientnetv2_rw_m': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth', - input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), - - 'efficientnetv2_s': _cfg( - url='', - input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), - 'efficientnetv2_m': _cfg( - url='', - input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), - 'efficientnetv2_l': _cfg( - url='', - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), - 'efficientnetv2_xl': _cfg( - url='', - input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0), - - 'tf_efficientnet_b0': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', - input_size=(3, 224, 224)), - 'tf_efficientnet_b1': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth', - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'tf_efficientnet_b2': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth', - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), - 'tf_efficientnet_b3': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - 'tf_efficientnet_b4': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth', - input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), - 'tf_efficientnet_b5': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth', - input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), - 'tf_efficientnet_b6': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth', - input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), - 'tf_efficientnet_b7': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth', - input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), - 'tf_efficientnet_b8': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth', - input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), - - 'tf_efficientnet_b0_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)), - 'tf_efficientnet_b1_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'tf_efficientnet_b2_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), - 'tf_efficientnet_b3_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - 'tf_efficientnet_b4_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), - 'tf_efficientnet_b5_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), - 'tf_efficientnet_b6_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), - 'tf_efficientnet_b7_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), - 'tf_efficientnet_b8_ap': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), - - 'tf_efficientnet_b0_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth', - input_size=(3, 224, 224)), - 'tf_efficientnet_b1_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth', - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'tf_efficientnet_b2_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth', - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), - 'tf_efficientnet_b3_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth', - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - 'tf_efficientnet_b4_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth', - input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), - 'tf_efficientnet_b5_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth', - input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), - 'tf_efficientnet_b6_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth', - input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), - 'tf_efficientnet_b7_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth', - input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), - 'tf_efficientnet_l2_ns_475': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth', - input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936), - 'tf_efficientnet_l2_ns': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth', - input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96), - - 'tf_efficientnet_es': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 224, 224), ), - 'tf_efficientnet_em': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - 'tf_efficientnet_el': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), - - 'tf_efficientnet_cc_b0_4e': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_efficientnet_cc_b0_8e': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_efficientnet_cc_b1_8e': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), - - 'tf_efficientnet_lite0': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res - ), - 'tf_efficientnet_lite1': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, - interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res - ), - 'tf_efficientnet_lite2': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, - interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res - ), - 'tf_efficientnet_lite3': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, interpolation='bilinear'), - 'tf_efficientnet_lite4': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.920, interpolation='bilinear'), - - 'tf_efficientnetv2_s': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s-eb54923e.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m-cc09e0cd.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'tf_efficientnetv2_l': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l-d664b728.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - - 'tf_efficientnetv2_s_in21ft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_in21ft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'tf_efficientnetv2_l_in21ft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'tf_efficientnetv2_xl_in21ft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21ft1k-06c35c48.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), - input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - - 'tf_efficientnetv2_s_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, - input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), - 'tf_efficientnetv2_m_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'tf_efficientnetv2_l_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, - input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - 'tf_efficientnetv2_xl_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21k-fd7e8abf.pth', - mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, - input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), - - 'tf_efficientnetv2_b0': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth', - input_size=(3, 192, 192), test_input_size=(3, 224, 224), pool_size=(6, 6)), - 'tf_efficientnetv2_b1': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b1-be6e41b0.pth', - input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882), - 'tf_efficientnetv2_b2': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth', - input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890), - 'tf_efficientnetv2_b3': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth', - input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904), - - 'mixnet_s': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth'), - 'mixnet_m': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth'), - 'mixnet_l': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth'), - 'mixnet_xl': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth'), - 'mixnet_xxl': _cfg(), - - 'tf_mixnet_s': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth'), - 'tf_mixnet_m': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth'), - 'tf_mixnet_l': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'), - - "tinynet_a": _cfg( - input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86) - url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth'), - "tinynet_b": _cfg( - input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84) - url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth'), - "tinynet_c": _cfg( - input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825) - url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth'), - "tinynet_d": _cfg( - input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68) - url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth'), - "tinynet_e": _cfg( - input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475) - url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth'), -} - - class EfficientNet(nn.Module): """ EfficientNet @@ -471,9 +73,23 @@ class EfficientNet(nn.Module): """ def __init__( - self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32, fix_stem=False, - output_stride=32, pad_type='', round_chs_fn=round_channels, act_layer=None, norm_layer=None, - se_layer=None, drop_rate=0., drop_path_rate=0., global_pool='avg'): + self, + block_args, + num_classes=1000, + num_features=1280, + in_chans=3, + stem_size=32, + fix_stem=False, + output_stride=32, + pad_type='', + round_chs_fn=round_channels, + act_layer=None, + norm_layer=None, + se_layer=None, + drop_rate=0., + drop_path_rate=0., + global_pool='avg' + ): super(EfficientNet, self).__init__() act_layer = act_layer or nn.ReLU norm_layer = norm_layer or nn.BatchNorm2d @@ -492,8 +108,14 @@ class EfficientNet(nn.Module): # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( - output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn, - act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate) + output_stride=output_stride, + pad_type=pad_type, + round_chs_fn=round_chs_fn, + act_layer=act_layer, + norm_layer=norm_layer, + se_layer=se_layer, + drop_path_rate=drop_path_rate, + ) self.blocks = nn.Sequential(*builder(stem_size, block_args)) self.feature_info = builder.features head_chs = builder.in_chs @@ -567,9 +189,22 @@ class EfficientNetFeatures(nn.Module): """ def __init__( - self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3, - stem_size=32, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels, - act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.): + self, + block_args, + out_indices=(0, 1, 2, 3, 4), + feature_location='bottleneck', + in_chans=3, + stem_size=32, + fix_stem=False, + output_stride=32, + pad_type='', + round_chs_fn=round_channels, + act_layer=None, + norm_layer=None, + se_layer=None, + drop_rate=0., + drop_path_rate=0. + ): super(EfficientNetFeatures, self).__init__() act_layer = act_layer or nn.ReLU norm_layer = norm_layer or nn.BatchNorm2d @@ -585,9 +220,15 @@ class EfficientNetFeatures(nn.Module): # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( - output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn, - act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate, - feature_location=feature_location) + output_stride=output_stride, + pad_type=pad_type, + round_chs_fn=round_chs_fn, + act_layer=act_layer, + norm_layer=norm_layer, + se_layer=se_layer, + drop_path_rate=drop_path_rate, + feature_location=feature_location, + ) self.blocks = nn.Sequential(*builder(stem_size, block_args)) self.feature_info = FeatureInfo(builder.features, out_indices) self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices} @@ -1233,23 +874,518 @@ def _gen_tinynet( return model -@register_model -def mnasnet_050(pretrained=False, **kwargs): - """ MNASNet B1, depth multiplier of 0.5. """ - model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs) - return model - +def _cfg(url='', **kwargs): + return { + 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.875, 'interpolation': 'bicubic', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'conv_stem', 'classifier': 'classifier', + **kwargs + } -@register_model -def mnasnet_075(pretrained=False, **kwargs): - """ MNASNet B1, depth multiplier of 0.75. """ - model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs) - return model +default_cfgs = generate_default_cfgs({ + 'mnasnet_050.untrained': _cfg(), + 'mnasnet_075.untrained': _cfg(), + 'mnasnet_100.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_b1-74cb7081.pth', + hf_hub_id='timm/'), + 'mnasnet_140.untrained': _cfg(), + + 'semnasnet_050.untrained': _cfg(), + 'semnasnet_075.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/semnasnet_075-18710866.pth', + hf_hub_id='timm/'), + 'semnasnet_100.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_a1-d9418771.pth', + hf_hub_id='timm/'), + 'semnasnet_140.untrained': _cfg(), + 'mnasnet_small.lamb_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mnasnet_small_lamb-aff75073.pth', + hf_hub_id='timm/'), + + 'mobilenetv2_035.untrained': _cfg(), + 'mobilenetv2_050.lamb_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_050-3d30d450.pth', + hf_hub_id='timm/', + interpolation='bicubic', + ), + 'mobilenetv2_075.untrained': _cfg(), + 'mobilenetv2_100.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_100_ra-b33bc2c4.pth', + hf_hub_id='timm/'), + 'mobilenetv2_110d.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_110d_ra-77090ade.pth', + hf_hub_id='timm/'), + 'mobilenetv2_120d.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_120d_ra-5987e2ed.pth', + hf_hub_id='timm/'), + 'mobilenetv2_140.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv2_140_ra-21a4e913.pth', + hf_hub_id='timm/'), + + 'fbnetc_100.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetc_100-c345b898.pth', + hf_hub_id='timm/', + interpolation='bilinear'), + 'spnasnet_100.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/spnasnet_100-048bc3f4.pth', + hf_hub_id='timm/', + interpolation='bilinear'), -@register_model -def mnasnet_100(pretrained=False, **kwargs): - """ MNASNet B1, depth multiplier of 1.0. """ + # NOTE experimenting with alternate attention + 'efficientnet_b0.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b0_ra-3dd342df.pth', + hf_hub_id='timm/'), + 'efficientnet_b1.ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b1-533bc792.pth', + hf_hub_id='timm/', + test_input_size=(3, 256, 256), crop_pct=1.0), + 'efficientnet_b2.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b2_ra-bcdf34b7.pth', + hf_hub_id='timm/', + input_size=(3, 256, 256), pool_size=(8, 8), test_input_size=(3, 288, 288), crop_pct=1.0), + 'efficientnet_b3.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b3_ra2-cf984f9c.pth', + hf_hub_id='timm/', + input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), + 'efficientnet_b4.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_b4_ra2_320-7eb33cd5.pth', + hf_hub_id='timm/', + input_size=(3, 320, 320), pool_size=(10, 10), test_input_size=(3, 384, 384), crop_pct=1.0), + 'efficientnet_b5.in12k_ft_in1k': _cfg( + hf_hub_id='timm/', + input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0, crop_mode='squash'), + 'efficientnet_b5.in12k': _cfg( + hf_hub_id='timm/', + input_size=(3, 416, 416), pool_size=(13, 13), crop_pct=0.95, num_classes=11821), + 'efficientnet_b6.untrained': _cfg( + url='', input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), + 'efficientnet_b7.untrained': _cfg( + url='', input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), + 'efficientnet_b8.untrained': _cfg( + url='', input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), + 'efficientnet_l2.untrained': _cfg( + url='', input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.961), + + # FIXME experimental + 'efficientnet_b0_gn.untrained': _cfg(), + 'efficientnet_b0_g8_gn.untrained': _cfg(), + 'efficientnet_b0_g16_evos.untrained': _cfg(), + 'efficientnet_b3_gn.untrained': _cfg( + input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), + 'efficientnet_b3_g8_gn.untrained': _cfg( + input_size=(3, 288, 288), pool_size=(9, 9), test_input_size=(3, 320, 320), crop_pct=1.0), + + 'efficientnet_es.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_ra-f111e99c.pth', + hf_hub_id='timm/'), + 'efficientnet_em.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_em_ra2-66250f76.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'efficientnet_el.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el-3b455510.pth', + hf_hub_id='timm/', + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + + 'efficientnet_es_pruned.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_es_pruned75-1b7248cf.pth', + hf_hub_id='timm/'), + 'efficientnet_el_pruned.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_el_pruned70-ef2a2ccf.pth', + hf_hub_id='timm/', + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + + 'efficientnet_cc_b0_4e.untrained': _cfg(), + 'efficientnet_cc_b0_8e.untrained': _cfg(), + 'efficientnet_cc_b1_8e.untrained': _cfg(input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + + 'efficientnet_lite0.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_lite0_ra-37913777.pth', + hf_hub_id='timm/'), + 'efficientnet_lite1.untrained': _cfg( + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'efficientnet_lite2.untrained': _cfg( + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), + 'efficientnet_lite3.untrained': _cfg( + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + 'efficientnet_lite4.untrained': _cfg( + input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), + + 'efficientnet_b1_pruned.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb1_pruned-bea43a3a.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), pool_size=(8, 8), + crop_pct=0.882, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'efficientnet_b2_pruned.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb2_pruned-08c1b27c.pth', + hf_hub_id='timm/', + input_size=(3, 260, 260), pool_size=(9, 9), + crop_pct=0.890, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'efficientnet_b3_pruned.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/effnetb3_pruned-59ecf72d.pth', + hf_hub_id='timm/', + input_size=(3, 300, 300), pool_size=(10, 10), + crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + + 'efficientnetv2_rw_t.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_t_agc-3620981a.pth', + hf_hub_id='timm/', + input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), + 'gc_efficientnetv2_rw_t.agc_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gc_efficientnetv2_rw_t_agc-927a0bde.pth', + hf_hub_id='timm/', + input_size=(3, 224, 224), test_input_size=(3, 288, 288), pool_size=(7, 7), crop_pct=1.0), + 'efficientnetv2_rw_s.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth', + hf_hub_id='timm/', + input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), + 'efficientnetv2_rw_m.agc_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnetv2_rw_m_agc-3d90cb1e.pth', + hf_hub_id='timm/', + input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), + + 'efficientnetv2_s.untrained': _cfg( + input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), + 'efficientnetv2_m.untrained': _cfg( + input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0), + 'efficientnetv2_l.untrained': _cfg( + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0), + 'efficientnetv2_xl.untrained': _cfg( + input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0), + + 'tf_efficientnet_b0.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth', + hf_hub_id='timm/', + input_size=(3, 224, 224)), + 'tf_efficientnet_b1.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_aa-ea7a6ee0.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'tf_efficientnet_b2.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_aa-60c94f97.pth', + hf_hub_id='timm/', + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), + 'tf_efficientnet_b3.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_aa-84b4657e.pth', + hf_hub_id='timm/', + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + 'tf_efficientnet_b4.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_aa-818f208c.pth', + hf_hub_id='timm/', + input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), + 'tf_efficientnet_b5.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ra-9a3e5369.pth', + hf_hub_id='timm/', + input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), + 'tf_efficientnet_b6.aa_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_aa-80ba17e4.pth', + hf_hub_id='timm/', + input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), + 'tf_efficientnet_b7.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ra-6c08e654.pth', + hf_hub_id='timm/', + input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), + 'tf_efficientnet_b8.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ra-572d5dd9.pth', + hf_hub_id='timm/', + input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), + + 'tf_efficientnet_b0.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ap-f262efe1.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, input_size=(3, 224, 224)), + 'tf_efficientnet_b1.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ap-44ef0a3d.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'tf_efficientnet_b2.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ap-2f8e7636.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), + 'tf_efficientnet_b3.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ap-aad25bdd.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + 'tf_efficientnet_b4.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ap-dedb23e6.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), + 'tf_efficientnet_b5.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ap-9e82fae8.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), + 'tf_efficientnet_b6.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ap-4ffb161f.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), + 'tf_efficientnet_b7.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ap-ddb28fec.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), + 'tf_efficientnet_b8.ap_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b8_ap-00e169fa.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 672, 672), pool_size=(21, 21), crop_pct=0.954), + + 'tf_efficientnet_b0.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_ns-c0e6a31c.pth', + hf_hub_id='timm/', + input_size=(3, 224, 224)), + 'tf_efficientnet_b1.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b1_ns-99dd0c41.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'tf_efficientnet_b2.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b2_ns-00306e48.pth', + hf_hub_id='timm/', + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890), + 'tf_efficientnet_b3.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b3_ns-9d44bf68.pth', + hf_hub_id='timm/', + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + 'tf_efficientnet_b4.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b4_ns-d6313a46.pth', + hf_hub_id='timm/', + input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.922), + 'tf_efficientnet_b5.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b5_ns-6f26d0cf.pth', + hf_hub_id='timm/', + input_size=(3, 456, 456), pool_size=(15, 15), crop_pct=0.934), + 'tf_efficientnet_b6.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b6_ns-51548356.pth', + hf_hub_id='timm/', + input_size=(3, 528, 528), pool_size=(17, 17), crop_pct=0.942), + 'tf_efficientnet_b7.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b7_ns-1dbc32de.pth', + hf_hub_id='timm/', + input_size=(3, 600, 600), pool_size=(19, 19), crop_pct=0.949), + 'tf_efficientnet_l2.ns_jft_in1k_475': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns_475-bebbd00a.pth', + hf_hub_id='timm/', + input_size=(3, 475, 475), pool_size=(15, 15), crop_pct=0.936), + 'tf_efficientnet_l2.ns_jft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_l2_ns-df73bb44.pth', + hf_hub_id='timm/', + input_size=(3, 800, 800), pool_size=(25, 25), crop_pct=0.96), + + 'tf_efficientnet_es.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_es-ca1afbfe.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 224, 224), ), + 'tf_efficientnet_em.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_em-e78cfe58.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + 'tf_efficientnet_el.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_el-5143854e.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904), + + 'tf_efficientnet_cc_b0_4e.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_4e-4362b6b2.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_efficientnet_cc_b0_8e.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b0_8e-66184a25.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_efficientnet_cc_b1_8e.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_cc_b1_8e-f7c79ae1.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882), + + 'tf_efficientnet_lite0.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite0-0aa007d2.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res + ), + 'tf_efficientnet_lite1.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite1-bde8b488.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 240, 240), pool_size=(8, 8), crop_pct=0.882, + interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res + ), + 'tf_efficientnet_lite2.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite2-dcccb7df.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 260, 260), pool_size=(9, 9), crop_pct=0.890, + interpolation='bicubic', # should be bilinear but bicubic better match for TF bilinear at low res + ), + 'tf_efficientnet_lite3.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite3-b733e338.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, interpolation='bilinear'), + 'tf_efficientnet_lite4.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_lite4-741542c3.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.920, interpolation='bilinear'), + + 'tf_efficientnetv2_s.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s-eb54923e.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), + 'tf_efficientnetv2_m.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m-cc09e0cd.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'tf_efficientnetv2_l.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l-d664b728.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + + 'tf_efficientnetv2_s.in21k_ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), + 'tf_efficientnetv2_m.in21k_ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'tf_efficientnetv2_l.in21k_ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'tf_efficientnetv2_xl.in21k_ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21ft1k-06c35c48.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), + input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + + 'tf_efficientnetv2_s.in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, + input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0), + 'tf_efficientnetv2_m.in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'tf_efficientnetv2_l.in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, + input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + 'tf_efficientnetv2_xl.in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_xl_in21k-fd7e8abf.pth', + hf_hub_id='timm/', + mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843, + input_size=(3, 384, 384), test_input_size=(3, 512, 512), pool_size=(12, 12), crop_pct=1.0, crop_mode='squash'), + + 'tf_efficientnetv2_b0.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth', + hf_hub_id='timm/', + input_size=(3, 192, 192), test_input_size=(3, 224, 224), pool_size=(6, 6)), + 'tf_efficientnetv2_b1.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b1-be6e41b0.pth', + hf_hub_id='timm/', + input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882), + 'tf_efficientnetv2_b2.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth', + hf_hub_id='timm/', + input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890), + 'tf_efficientnetv2_b3.in21k_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.9, crop_mode='squash'), + 'tf_efficientnetv2_b3.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904), + 'tf_efficientnetv2_b3.in21k': _cfg( + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, num_classes=21843, + input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904), + + 'mixnet_s.ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth', + hf_hub_id='timm/'), + 'mixnet_m.ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_m-4647fc68.pth', + hf_hub_id='timm/'), + 'mixnet_l.ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_l-5a9a2ed8.pth', + hf_hub_id='timm/'), + 'mixnet_xl.ra_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_xl_ra-aac3c00c.pth', + hf_hub_id='timm/'), + 'mixnet_xxl.untrained': _cfg(), + + 'tf_mixnet_s.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_s-89d3354b.pth', + hf_hub_id='timm/'), + 'tf_mixnet_m.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_m-0f4d8805.pth', + hf_hub_id='timm/'), + 'tf_mixnet_l.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth', + hf_hub_id='timm/'), + + "tinynet_a.in1k": _cfg( + input_size=(3, 192, 192), pool_size=(6, 6), # int(224 * 0.86) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_a.pth', + hf_hub_id='timm/'), + "tinynet_b.in1k": _cfg( + input_size=(3, 188, 188), pool_size=(6, 6), # int(224 * 0.84) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_b.pth', + hf_hub_id='timm/'), + "tinynet_c.in1k": _cfg( + input_size=(3, 184, 184), pool_size=(6, 6), # int(224 * 0.825) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_c.pth', + hf_hub_id='timm/'), + "tinynet_d.in1k": _cfg( + input_size=(3, 152, 152), pool_size=(5, 5), # int(224 * 0.68) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_d.pth', + hf_hub_id='timm/'), + "tinynet_e.in1k": _cfg( + input_size=(3, 106, 106), pool_size=(4, 4), # int(224 * 0.475) + url='https://github.com/huawei-noah/CV-Backbones/releases/download/v1.2.0/tinynet_e.pth', + hf_hub_id='timm/'), +}) + + +@register_model +def mnasnet_050(pretrained=False, **kwargs): + """ MNASNet B1, depth multiplier of 0.5. """ + model = _gen_mnasnet_b1('mnasnet_050', 0.5, pretrained=pretrained, **kwargs) + return model + + +@register_model +def mnasnet_075(pretrained=False, **kwargs): + """ MNASNet B1, depth multiplier of 0.75. """ + model = _gen_mnasnet_b1('mnasnet_075', 0.75, pretrained=pretrained, **kwargs) + return model + + +@register_model +def mnasnet_100(pretrained=False, **kwargs): + """ MNASNet B1, depth multiplier of 1.0. """ model = _gen_mnasnet_b1('mnasnet_100', 1.0, pretrained=pretrained, **kwargs) return model @@ -1830,199 +1966,13 @@ def tf_efficientnet_b8(pretrained=False, **kwargs): @register_model -def tf_efficientnet_b0_ap(pretrained=False, **kwargs): - """ EfficientNet-B0 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b0_ap', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b1_ap(pretrained=False, **kwargs): - """ EfficientNet-B1 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b1_ap', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b2_ap(pretrained=False, **kwargs): - """ EfficientNet-B2 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b2_ap', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b3_ap(pretrained=False, **kwargs): - """ EfficientNet-B3 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b3_ap', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b4_ap(pretrained=False, **kwargs): - """ EfficientNet-B4 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b4_ap', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b5_ap(pretrained=False, **kwargs): - """ EfficientNet-B5 AdvProp. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b5_ap', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b6_ap(pretrained=False, **kwargs): - """ EfficientNet-B6 AdvProp. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b6_ap', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b7_ap(pretrained=False, **kwargs): - """ EfficientNet-B7 AdvProp. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b7_ap', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b8_ap(pretrained=False, **kwargs): - """ EfficientNet-B8 AdvProp. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b8_ap', channel_multiplier=2.2, depth_multiplier=3.6, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b0_ns(pretrained=False, **kwargs): - """ EfficientNet-B0 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b0_ns', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b1_ns(pretrained=False, **kwargs): - """ EfficientNet-B1 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b1_ns', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b2_ns(pretrained=False, **kwargs): - """ EfficientNet-B2 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b2_ns', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b3_ns(pretrained=False, **kwargs): - """ EfficientNet-B3 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b3_ns', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b4_ns(pretrained=False, **kwargs): - """ EfficientNet-B4 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b4_ns', channel_multiplier=1.4, depth_multiplier=1.8, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b5_ns(pretrained=False, **kwargs): - """ EfficientNet-B5 NoisyStudent. Tensorflow compatible variant """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b5_ns', channel_multiplier=1.6, depth_multiplier=2.2, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b6_ns(pretrained=False, **kwargs): - """ EfficientNet-B6 NoisyStudent. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b6_ns', channel_multiplier=1.8, depth_multiplier=2.6, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_b7_ns(pretrained=False, **kwargs): - """ EfficientNet-B7 NoisyStudent. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_b7_ns', channel_multiplier=2.0, depth_multiplier=3.1, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_l2_ns_475(pretrained=False, **kwargs): - """ EfficientNet-L2 NoisyStudent @ 475x475. Tensorflow compatible variant """ - # NOTE for train, drop_rate should be 0.5 - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnet( - 'tf_efficientnet_l2_ns_475', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnet_l2_ns(pretrained=False, **kwargs): +def tf_efficientnet_l2(pretrained=False, **kwargs): """ EfficientNet-L2 NoisyStudent. Tensorflow compatible variant """ # NOTE for train, drop_rate should be 0.5 kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' model = _gen_efficientnet( - 'tf_efficientnet_l2_ns', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) + 'tf_efficientnet_l2', channel_multiplier=4.3, depth_multiplier=5.3, pretrained=pretrained, **kwargs) return model @@ -2146,7 +2096,6 @@ def tf_efficientnet_lite4(pretrained=False, **kwargs): return model - @register_model def tf_efficientnetv2_s(pretrained=False, **kwargs): """ EfficientNet-V2 Small. Tensorflow compatible variant """ @@ -2175,82 +2124,12 @@ def tf_efficientnetv2_l(pretrained=False, **kwargs): @register_model -def tf_efficientnetv2_s_in21ft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21ft1k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_m_in21ft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_l_in21ft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21ft1k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_xl_in21ft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Xtra-Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21ft1k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_s_in21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_s('tf_efficientnetv2_s_in21k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_m_in21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_m('tf_efficientnetv2_m_in21k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_l_in21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant - """ - kwargs['bn_eps'] = BN_EPS_TF_DEFAULT - kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_l('tf_efficientnetv2_l_in21k', pretrained=pretrained, **kwargs) - return model - - -@register_model -def tf_efficientnetv2_xl_in21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Xtra-Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant +def tf_efficientnetv2_xl(pretrained=False, **kwargs): + """ EfficientNet-V2 Xtra-Large. Tensorflow compatible variant """ kwargs['bn_eps'] = BN_EPS_TF_DEFAULT kwargs['pad_type'] = 'same' - model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl_in21k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_xl('tf_efficientnetv2_xl', pretrained=pretrained, **kwargs) return model diff --git a/timm/models/layers/__init__.py b/timm/models/layers/__init__.py index 97e70563..dd5b27d9 100644 --- a/timm/models/layers/__init__.py +++ b/timm/models/layers/__init__.py @@ -2,6 +2,7 @@ from timm.layers.activations import * from timm.layers.adaptive_avgmax_pool import \ adaptive_avgmax_pool2d, select_adaptive_pool2d, AdaptiveAvgMaxPool2d, SelectAdaptivePool2d +from timm.layers.attention_pool2d import AttentionPool2d, RotAttentionPool2d, RotaryEmbedding from timm.layers.blur_pool import BlurPool2d from timm.layers.classifier import ClassifierHead, create_classifier from timm.layers.cond_conv2d import CondConv2d, get_condconv_initializer diff --git a/timm/models/maxxvit.py b/timm/models/maxxvit.py index 1e2666e5..dd424078 100644 --- a/timm/models/maxxvit.py +++ b/timm/models/maxxvit.py @@ -47,16 +47,15 @@ import torch from torch import nn from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD -from timm.layers import Mlp, ConvMlp, DropPath, ClassifierHead, trunc_normal_tf_, LayerNorm -from timm.layers import SelectAdaptivePool2d, create_pool2d -from timm.layers import create_attn, get_act_layer, get_norm_layer, get_norm_act_layer, create_conv2d -from timm.layers import to_2tuple, extend_tuple, make_divisible, _assert +from timm.layers import Mlp, ConvMlp, DropPath, ClassifierHead, LayerNorm, SelectAdaptivePool2d +from timm.layers import create_attn, get_act_layer, get_norm_layer, get_norm_act_layer, create_conv2d, create_pool2d +from timm.layers import trunc_normal_tf_, to_2tuple, extend_tuple, make_divisible, _assert +from timm.layers import RelPosMlp, RelPosBias, RelPosBiasTf from ._builder import build_model_with_cfg from ._features_fx import register_notrace_function from ._manipulate import named_apply, checkpoint_seq from ._pretrained import generate_default_cfgs from ._registry import register_model -from .vision_transformer_relpos import RelPosMlp, RelPosBias # FIXME move these to common location __all__ = ['MaxxVitCfg', 'MaxxVitConvCfg', 'MaxxVitTransformerCfg', 'MaxxVit'] @@ -1076,93 +1075,6 @@ def cfg_window_size(cfg: MaxxVitTransformerCfg, img_size: Tuple[int, int]): return cfg -def generate_lookup_tensor( - length: int, - max_relative_position: Optional[int] = None, -): - """Generate a one_hot lookup tensor to reindex embeddings along one dimension. - Args: - length: the length to reindex to. - max_relative_position: the maximum relative position to consider. - Relative position embeddings for distances above this threshold - are zeroed out. - Returns: - a lookup Tensor of size [length, length, vocab_size] that satisfies - ret[n,m,v] = 1{m - n + max_relative_position = v}. - """ - if max_relative_position is None: - max_relative_position = length - 1 - # Return the cached lookup tensor, otherwise compute it and cache it. - vocab_size = 2 * max_relative_position + 1 - ret = torch.zeros(length, length, vocab_size) - for i in range(length): - for x in range(length): - v = x - i + max_relative_position - if abs(x - i) > max_relative_position: - continue - ret[i, x, v] = 1 - return ret - - -def reindex_2d_einsum_lookup( - relative_position_tensor, - height: int, - width: int, - height_lookup: torch.Tensor, - width_lookup: torch.Tensor, -) -> torch.Tensor: - """Reindex 2d relative position bias with 2 independent einsum lookups. - Args: - relative_position_tensor: tensor of shape - [..., vocab_height, vocab_width, ...]. - height: height to reindex to. - width: width to reindex to. - height_lookup: one-hot height lookup - width_lookup: one-hot width lookup - Returns: - reindexed_tensor: a Tensor of shape - [..., height * width, height * width, ...] - """ - reindexed_tensor = torch.einsum('nhw,ixh->nixw', relative_position_tensor, height_lookup) - reindexed_tensor = torch.einsum('nixw,jyw->nijxy', reindexed_tensor, width_lookup) - area = height * width - return reindexed_tensor.reshape(relative_position_tensor.shape[0], area, area) - - -class RelPosBiasTf(nn.Module): - - def __init__(self, window_size, num_heads, prefix_tokens=0): - super().__init__() - assert prefix_tokens <= 1 - self.window_size = window_size - self.window_area = window_size[0] * window_size[1] - self.num_heads = num_heads - - vocab_height = 2 * window_size[0] - 1 - vocab_width = 2 * window_size[1] - 1 - self.bias_shape = (self.num_heads, vocab_height, vocab_width) - self.relative_position_bias_table = nn.Parameter(torch.zeros(self.bias_shape)) - self.register_buffer('height_lookup', generate_lookup_tensor(window_size[0]), persistent=False) - self.register_buffer('width_lookup', generate_lookup_tensor(window_size[1]), persistent=False) - self.init_weights() - - def init_weights(self): - nn.init.normal_(self.relative_position_bias_table, std=.02) - - def get_bias(self) -> torch.Tensor: - # FIXME change to not use one-hot/einsum? - return reindex_2d_einsum_lookup( - self.relative_position_bias_table, - self.window_size[0], - self.window_size[1], - self.height_lookup, - self.width_lookup - ) - - def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): - return attn + self.get_bias() - - class NormMlpHead(nn.Module): def __init__( @@ -1204,6 +1116,26 @@ class NormMlpHead(nn.Module): return x +def _overlay_kwargs(cfg: MaxxVitCfg, **kwargs): + transformer_kwargs = {} + conv_kwargs = {} + base_kwargs = {} + for k, v in kwargs.items(): + if k.startswith('transformer_'): + transformer_kwargs[k.replace('transformer_', '')] = v + elif k.startswith('conv_'): + conv_kwargs[k.replace('conv_', '')] = v + else: + base_kwargs[k] = v + cfg = replace( + cfg, + transformer_cfg=replace(cfg.transformer_cfg, **transformer_kwargs), + conv_cfg=replace(cfg.conv_cfg, **conv_kwargs), + **base_kwargs + ) + return cfg + + class MaxxVit(nn.Module): """ CoaTNet + MaxVit base model. @@ -1218,10 +1150,13 @@ class MaxxVit(nn.Module): num_classes: int = 1000, global_pool: str = 'avg', drop_rate: float = 0., - drop_path_rate: float = 0. + drop_path_rate: float = 0., + **kwargs, ): super().__init__() img_size = to_2tuple(img_size) + if kwargs: + cfg = _overlay_kwargs(cfg, **kwargs) transformer_cfg = cfg_window_size(cfg.transformer_cfg, img_size) self.num_classes = num_classes self.global_pool = global_pool @@ -1745,6 +1680,26 @@ model_cfgs = dict( init_values=1e-6, ), ), + maxvit_rmlp_base_rw_224=MaxxVitCfg( + embed_dim=(96, 192, 384, 768), + depths=(2, 6, 14, 2), + block_type=('M',) * 4, + stem_width=(32, 64), + head_hidden_size=768, + **_rw_max_cfg( + rel_pos_type='mlp', + ), + ), + maxvit_rmlp_base_rw_384=MaxxVitCfg( + embed_dim=(96, 192, 384, 768), + depths=(2, 6, 14, 2), + block_type=('M',) * 4, + stem_width=(32, 64), + head_hidden_size=768, + **_rw_max_cfg( + rel_pos_type='mlp', + ), + ), maxvit_tiny_pm_256=MaxxVitCfg( embed_dim=(64, 128, 256, 512), @@ -1927,6 +1882,12 @@ default_cfgs = generate_default_cfgs({ 'maxvit_rmlp_small_rw_256': _cfg( url='', input_size=(3, 256, 256), pool_size=(8, 8)), + 'maxvit_rmlp_base_rw_224': _cfg( + url='', + ), + 'maxvit_rmlp_base_rw_384': _cfg( + url='', + input_size=(3, 384, 384), pool_size=(12, 12)), 'maxvit_tiny_pm_256': _cfg(url='', input_size=(3, 256, 256), pool_size=(8, 8)), @@ -2156,6 +2117,16 @@ def maxvit_rmlp_small_rw_256(pretrained=False, **kwargs): return _create_maxxvit('maxvit_rmlp_small_rw_256', pretrained=pretrained, **kwargs) +@register_model +def maxvit_rmlp_base_rw_224(pretrained=False, **kwargs): + return _create_maxxvit('maxvit_rmlp_base_rw_224', pretrained=pretrained, **kwargs) + + +@register_model +def maxvit_rmlp_base_rw_384(pretrained=False, **kwargs): + return _create_maxxvit('maxvit_rmlp_base_rw_384', pretrained=pretrained, **kwargs) + + @register_model def maxvit_tiny_pm_256(pretrained=False, **kwargs): return _create_maxxvit('maxvit_tiny_pm_256', pretrained=pretrained, **kwargs) diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py index cf4f268d..e1da91a2 100644 --- a/timm/models/mobilenetv3.py +++ b/timm/models/mobilenetv3.py @@ -21,93 +21,12 @@ from ._efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficie round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT from ._features import FeatureInfo, FeatureHooks from ._manipulate import checkpoint_seq +from ._pretrained import generate_default_cfgs from ._registry import register_model __all__ = ['MobileNetV3', 'MobileNetV3Features'] -def _cfg(url='', **kwargs): - return { - 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), - 'crop_pct': 0.875, 'interpolation': 'bilinear', - 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, - 'first_conv': 'conv_stem', 'classifier': 'classifier', - **kwargs - } - - -default_cfgs = { - 'mobilenetv3_large_075': _cfg(url=''), - 'mobilenetv3_large_100': _cfg( - interpolation='bicubic', - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth'), - 'mobilenetv3_large_100_miil': _cfg( - interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth'), - 'mobilenetv3_large_100_miil_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth', - interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221), - - 'mobilenetv3_small_050': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth', - interpolation='bicubic'), - 'mobilenetv3_small_075': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth', - interpolation='bicubic'), - 'mobilenetv3_small_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth', - interpolation='bicubic'), - - 'mobilenetv3_rw': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth', - interpolation='bicubic'), - - 'tf_mobilenetv3_large_075': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_mobilenetv3_large_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_mobilenetv3_large_minimal_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_mobilenetv3_small_075': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_mobilenetv3_small_100': _cfg( - url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - 'tf_mobilenetv3_small_minimal_100': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), - - 'fbnetv3_b': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth', - test_input_size=(3, 256, 256), crop_pct=0.95), - 'fbnetv3_d': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth', - test_input_size=(3, 256, 256), crop_pct=0.95), - 'fbnetv3_g': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth', - input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95, pool_size=(8, 8)), - - "lcnet_035": _cfg(), - "lcnet_050": _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth', - interpolation='bicubic', - ), - "lcnet_075": _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth', - interpolation='bicubic', - ), - "lcnet_100": _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth', - interpolation='bicubic', - ), - "lcnet_150": _cfg(), -} - - class MobileNetV3(nn.Module): """ MobiletNet-V3 @@ -124,9 +43,24 @@ class MobileNetV3(nn.Module): """ def __init__( - self, block_args, num_classes=1000, in_chans=3, stem_size=16, fix_stem=False, num_features=1280, - head_bias=True, pad_type='', act_layer=None, norm_layer=None, se_layer=None, se_from_exp=True, - round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'): + self, + block_args, + num_classes=1000, + in_chans=3, + stem_size=16, + fix_stem=False, + num_features=1280, + head_bias=True, + pad_type='', + act_layer=None, + norm_layer=None, + se_layer=None, + se_from_exp=True, + round_chs_fn=round_channels, + drop_rate=0., + drop_path_rate=0., + global_pool='avg', + ): super(MobileNetV3, self).__init__() act_layer = act_layer or nn.ReLU norm_layer = norm_layer or nn.BatchNorm2d @@ -145,8 +79,15 @@ class MobileNetV3(nn.Module): # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( - output_stride=32, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp, - act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate) + output_stride=32, + pad_type=pad_type, + round_chs_fn=round_chs_fn, + se_from_exp=se_from_exp, + act_layer=act_layer, + norm_layer=norm_layer, + se_layer=se_layer, + drop_path_rate=drop_path_rate, + ) self.blocks = nn.Sequential(*builder(stem_size, block_args)) self.feature_info = builder.features head_chs = builder.in_chs @@ -225,9 +166,23 @@ class MobileNetV3Features(nn.Module): """ def __init__( - self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3, - stem_size=16, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels, - se_from_exp=True, act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.): + self, + block_args, + out_indices=(0, 1, 2, 3, 4), + feature_location='bottleneck', + in_chans=3, + stem_size=16, + fix_stem=False, + output_stride=32, + pad_type='', + round_chs_fn=round_channels, + se_from_exp=True, + act_layer=None, + norm_layer=None, + se_layer=None, + drop_rate=0., + drop_path_rate=0., + ): super(MobileNetV3Features, self).__init__() act_layer = act_layer or nn.ReLU norm_layer = norm_layer or nn.BatchNorm2d @@ -243,9 +198,16 @@ class MobileNetV3Features(nn.Module): # Middle stages (IR/ER/DS Blocks) builder = EfficientNetBuilder( - output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn, se_from_exp=se_from_exp, - act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, - drop_path_rate=drop_path_rate, feature_location=feature_location) + output_stride=output_stride, + pad_type=pad_type, + round_chs_fn=round_chs_fn, + se_from_exp=se_from_exp, + act_layer=act_layer, + norm_layer=norm_layer, + se_layer=se_layer, + drop_path_rate=drop_path_rate, + feature_location=feature_location, + ) self.blocks = nn.Sequential(*builder(stem_size, block_args)) self.feature_info = FeatureInfo(builder.features, out_indices) self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices} @@ -286,7 +248,9 @@ def _create_mnv3(variant, pretrained=False, **kwargs): kwargs_filter = ('num_classes', 'num_features', 'head_conv', 'head_bias', 'global_pool') model_cls = MobileNetV3Features model = build_model_with_cfg( - model_cls, variant, pretrained, + model_cls, + variant, + pretrained, pretrained_strict=not features_only, kwargs_filter=kwargs_filter, **kwargs) @@ -567,6 +531,110 @@ def _gen_lcnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs): return model +def _cfg(url='', **kwargs): + return { + 'url': url, 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7), + 'crop_pct': 0.875, 'interpolation': 'bilinear', + 'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD, + 'first_conv': 'conv_stem', 'classifier': 'classifier', + **kwargs + } + + +default_cfgs = generate_default_cfgs({ + 'mobilenetv3_large_075.untrained': _cfg(url=''), + 'mobilenetv3_large_100.ra_in1k': _cfg( + interpolation='bicubic', + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_large_100_ra-f55367f5.pth', + hf_hub_id='timm/'), + 'mobilenetv3_large_100.miil_in21k_ft_in1k': _cfg( + interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), + origin_url='https://github.com/Alibaba-MIIL/ImageNet21K', + paper_ids='arXiv:2104.10972v4', + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_1k_miil_78_0-66471c13.pth', + hf_hub_id='timm/'), + 'mobilenetv3_large_100.miil_in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/mobilenetv3_large_100_in21k_miil-d71cc17b.pth', + hf_hub_id='timm/', + origin_url='https://github.com/Alibaba-MIIL/ImageNet21K', + paper_ids='arXiv:2104.10972v4', + interpolation='bilinear', mean=(0., 0., 0.), std=(1., 1., 1.), num_classes=11221), + + 'mobilenetv3_small_050.lamb_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_050_lambc-4b7bbe87.pth', + hf_hub_id='timm/', + interpolation='bicubic'), + 'mobilenetv3_small_075.lamb_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_075_lambc-384766db.pth', + hf_hub_id='timm/', + interpolation='bicubic'), + 'mobilenetv3_small_100.lamb_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_small_100_lamb-266a294c.pth', + hf_hub_id='timm/', + interpolation='bicubic'), + + 'mobilenetv3_rw.rmsp_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth', + interpolation='bicubic'), + + 'tf_mobilenetv3_large_075.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_mobilenetv3_large_100.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_100-427764d5.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_mobilenetv3_large_minimal_100.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_minimal_100-8596ae28.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_mobilenetv3_small_075.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_075-da427f52.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_mobilenetv3_small_100.in1k': _cfg( + url= 'https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_100-37f49e2b.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + 'tf_mobilenetv3_small_minimal_100.in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_small_minimal_100-922a7843.pth', + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD), + + 'fbnetv3_b.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_b_224-ead5d2a1.pth', + hf_hub_id='timm/', + test_input_size=(3, 256, 256), crop_pct=0.95), + 'fbnetv3_d.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_d_224-c98bce42.pth', + hf_hub_id='timm/', + test_input_size=(3, 256, 256), crop_pct=0.95), + 'fbnetv3_g.ra2_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/fbnetv3_g_240-0b1df83b.pth', + hf_hub_id='timm/', + input_size=(3, 240, 240), test_input_size=(3, 288, 288), crop_pct=0.95, pool_size=(8, 8)), + + "lcnet_035.untrained": _cfg(), + "lcnet_050.ra2_in1k": _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_050-f447553b.pth', + hf_hub_id='timm/', + interpolation='bicubic', + ), + "lcnet_075.ra2_in1k": _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_075-318cad2c.pth', + hf_hub_id='timm/', + interpolation='bicubic', + ), + "lcnet_100.ra2_in1k": _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/lcnet_100-a929038c.pth', + hf_hub_id='timm/', + interpolation='bicubic', + ), + "lcnet_150.untrained": _cfg(), +}) + + @register_model def mobilenetv3_large_075(pretrained=False, **kwargs): """ MobileNet V3 """ @@ -581,24 +649,6 @@ def mobilenetv3_large_100(pretrained=False, **kwargs): return model -@register_model -def mobilenetv3_large_100_miil(pretrained=False, **kwargs): - """ MobileNet V3 - Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K - """ - model = _gen_mobilenet_v3('mobilenetv3_large_100_miil', 1.0, pretrained=pretrained, **kwargs) - return model - - -@register_model -def mobilenetv3_large_100_miil_in21k(pretrained=False, **kwargs): - """ MobileNet V3, 21k pretraining - Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K - """ - model = _gen_mobilenet_v3('mobilenetv3_large_100_miil_in21k', 1.0, pretrained=pretrained, **kwargs) - return model - - @register_model def mobilenetv3_small_050(pretrained=False, **kwargs): """ MobileNet V3 """ diff --git a/timm/models/mobilevit.py b/timm/models/mobilevit.py index 3d2ae84a..8e8f4428 100644 --- a/timm/models/mobilevit.py +++ b/timm/models/mobilevit.py @@ -266,9 +266,16 @@ class MobileVitBlock(nn.Module): self.transformer = nn.Sequential(*[ TransformerBlock( - transformer_dim, mlp_ratio=mlp_ratio, num_heads=num_heads, qkv_bias=True, - attn_drop=attn_drop, drop=drop, drop_path=drop_path_rate, - act_layer=layers.act, norm_layer=transformer_norm_layer) + transformer_dim, + mlp_ratio=mlp_ratio, + num_heads=num_heads, + qkv_bias=True, + attn_drop=attn_drop, + drop=drop, + drop_path=drop_path_rate, + act_layer=layers.act, + norm_layer=transformer_norm_layer, + ) for _ in range(transformer_depth) ]) self.norm = transformer_norm_layer(transformer_dim) diff --git a/timm/models/nfnet.py b/timm/models/nfnet.py index 48f91b35..f9a90ab3 100644 --- a/timm/models/nfnet.py +++ b/timm/models/nfnet.py @@ -17,7 +17,7 @@ Status: Hacked together by / copyright Ross Wightman, 2021. """ from collections import OrderedDict -from dataclasses import dataclass +from dataclasses import dataclass, replace from functools import partial from typing import Tuple, Optional @@ -159,11 +159,25 @@ class NfCfg: def _nfres_cfg( - depths, channels=(256, 512, 1024, 2048), group_size=None, act_layer='relu', attn_layer=None, attn_kwargs=None): + depths, + channels=(256, 512, 1024, 2048), + group_size=None, + act_layer='relu', + attn_layer=None, + attn_kwargs=None, +): attn_kwargs = attn_kwargs or {} cfg = NfCfg( - depths=depths, channels=channels, stem_type='7x7_pool', stem_chs=64, bottle_ratio=0.25, - group_size=group_size, act_layer=act_layer, attn_layer=attn_layer, attn_kwargs=attn_kwargs) + depths=depths, + channels=channels, + stem_type='7x7_pool', + stem_chs=64, + bottle_ratio=0.25, + group_size=group_size, + act_layer=act_layer, + attn_layer=attn_layer, + attn_kwargs=attn_kwargs, + ) return cfg @@ -171,28 +185,70 @@ def _nfreg_cfg(depths, channels=(48, 104, 208, 440)): num_features = 1280 * channels[-1] // 440 attn_kwargs = dict(rd_ratio=0.5) cfg = NfCfg( - depths=depths, channels=channels, stem_type='3x3', group_size=8, width_factor=0.75, bottle_ratio=2.25, - num_features=num_features, reg=True, attn_layer='se', attn_kwargs=attn_kwargs) + depths=depths, + channels=channels, + stem_type='3x3', + group_size=8, + width_factor=0.75, + bottle_ratio=2.25, + num_features=num_features, + reg=True, + attn_layer='se', + attn_kwargs=attn_kwargs, + ) return cfg def _nfnet_cfg( - depths, channels=(256, 512, 1536, 1536), group_size=128, bottle_ratio=0.5, feat_mult=2., - act_layer='gelu', attn_layer='se', attn_kwargs=None): + depths, + channels=(256, 512, 1536, 1536), + group_size=128, + bottle_ratio=0.5, + feat_mult=2., + act_layer='gelu', + attn_layer='se', + attn_kwargs=None, +): num_features = int(channels[-1] * feat_mult) attn_kwargs = attn_kwargs if attn_kwargs is not None else dict(rd_ratio=0.5) cfg = NfCfg( - depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=group_size, - bottle_ratio=bottle_ratio, extra_conv=True, num_features=num_features, act_layer=act_layer, - attn_layer=attn_layer, attn_kwargs=attn_kwargs) + depths=depths, + channels=channels, + stem_type='deep_quad', + stem_chs=128, + group_size=group_size, + bottle_ratio=bottle_ratio, + extra_conv=True, + num_features=num_features, + act_layer=act_layer, + attn_layer=attn_layer, + attn_kwargs=attn_kwargs, + ) return cfg -def _dm_nfnet_cfg(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', skipinit=True): +def _dm_nfnet_cfg( + depths, + channels=(256, 512, 1536, 1536), + act_layer='gelu', + skipinit=True, +): cfg = NfCfg( - depths=depths, channels=channels, stem_type='deep_quad', stem_chs=128, group_size=128, - bottle_ratio=0.5, extra_conv=True, gamma_in_act=True, same_padding=True, skipinit=skipinit, - num_features=int(channels[-1] * 2.0), act_layer=act_layer, attn_layer='se', attn_kwargs=dict(rd_ratio=0.5)) + depths=depths, + channels=channels, + stem_type='deep_quad', + stem_chs=128, + group_size=128, + bottle_ratio=0.5, + extra_conv=True, + gamma_in_act=True, + same_padding=True, + skipinit=skipinit, + num_features=int(channels[-1] * 2.0), + act_layer=act_layer, + attn_layer='se', + attn_kwargs=dict(rd_ratio=0.5), + ) return cfg @@ -278,7 +334,14 @@ def act_with_gamma(act_type, gamma: float = 1.): class DownsampleAvg(nn.Module): def __init__( - self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, conv_layer=ScaledStdConv2d): + self, + in_chs, + out_chs, + stride=1, + dilation=1, + first_dilation=None, + conv_layer=ScaledStdConv2d, + ): """ AvgPool Downsampling as in 'D' ResNet variants. Support for dilation.""" super(DownsampleAvg, self).__init__() avg_stride = stride if dilation == 1 else 1 @@ -299,9 +362,26 @@ class NormFreeBlock(nn.Module): """ def __init__( - self, in_chs, out_chs=None, stride=1, dilation=1, first_dilation=None, - alpha=1.0, beta=1.0, bottle_ratio=0.25, group_size=None, ch_div=1, reg=True, extra_conv=False, - skipinit=False, attn_layer=None, attn_gain=2.0, act_layer=None, conv_layer=None, drop_path_rate=0.): + self, + in_chs, + out_chs=None, + stride=1, + dilation=1, + first_dilation=None, + alpha=1.0, + beta=1.0, + bottle_ratio=0.25, + group_size=None, + ch_div=1, + reg=True, + extra_conv=False, + skipinit=False, + attn_layer=None, + attn_gain=2.0, + act_layer=None, + conv_layer=None, + drop_path_rate=0., + ): super().__init__() first_dilation = first_dilation or dilation out_chs = out_chs or in_chs @@ -316,7 +396,13 @@ class NormFreeBlock(nn.Module): if in_chs != out_chs or stride != 1 or dilation != first_dilation: self.downsample = DownsampleAvg( - in_chs, out_chs, stride=stride, dilation=dilation, first_dilation=first_dilation, conv_layer=conv_layer) + in_chs, + out_chs, + stride=stride, + dilation=dilation, + first_dilation=first_dilation, + conv_layer=conv_layer, + ) else: self.downsample = None @@ -452,14 +538,33 @@ class NormFreeNet(nn.Module): for what it is/does. Approx 8-10% throughput loss. """ def __init__( - self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32, - drop_rate=0., drop_path_rate=0. + self, + cfg: NfCfg, + num_classes=1000, + in_chans=3, + global_pool='avg', + output_stride=32, + drop_rate=0., + drop_path_rate=0., + **kwargs, ): + """ + Args: + cfg (NfCfg): Model architecture configuration + num_classes (int): Number of classifier classes (default: 1000) + in_chans (int): Number of input channels (default: 3) + global_pool (str): Global pooling type (default: 'avg') + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + drop_rate (float): Dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) + kwargs (dict): Extra kwargs overlayed onto cfg + """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate self.grad_checkpointing = False + cfg = replace(cfg, **kwargs) assert cfg.act_layer in _nonlin_gamma, f"Please add non-linearity constants for activation ({cfg.act_layer})." conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d if cfg.gamma_in_act: @@ -472,7 +577,12 @@ class NormFreeNet(nn.Module): stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div) self.stem, stem_stride, stem_feat = create_stem( - in_chans, stem_chs, cfg.stem_type, conv_layer=conv_layer, act_layer=act_layer) + in_chans, + stem_chs, + cfg.stem_type, + conv_layer=conv_layer, + act_layer=act_layer, + ) self.feature_info = [stem_feat] drop_path_rates = [x.tolist() for x in torch.linspace(0, drop_path_rate, sum(cfg.depths)).split(cfg.depths)] diff --git a/timm/models/regnet.py b/timm/models/regnet.py index e1cc821b..9d2528f6 100644 --- a/timm/models/regnet.py +++ b/timm/models/regnet.py @@ -14,7 +14,7 @@ Weights from original impl have been modified Hacked together by / Copyright 2020 Ross Wightman """ import math -from dataclasses import dataclass +from dataclasses import dataclass, replace from functools import partial from typing import Optional, Union, Callable @@ -237,7 +237,15 @@ def downsample_avg(in_chs, out_chs, kernel_size=1, stride=1, dilation=1, norm_la def create_shortcut( - downsample_type, in_chs, out_chs, kernel_size, stride, dilation=(1, 1), norm_layer=None, preact=False): + downsample_type, + in_chs, + out_chs, + kernel_size, + stride, + dilation=(1, 1), + norm_layer=None, + preact=False, +): assert downsample_type in ('avg', 'conv1x1', '', None) if in_chs != out_chs or stride != 1 or dilation[0] != dilation[1]: dargs = dict(stride=stride, dilation=dilation[0], norm_layer=norm_layer, preact=preact) @@ -259,9 +267,21 @@ class Bottleneck(nn.Module): """ def __init__( - self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25, - downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + stride=1, + dilation=(1, 1), + bottle_ratio=1, + group_size=1, + se_ratio=0.25, + downsample='conv1x1', + linear_out=False, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + drop_block=None, + drop_path_rate=0., + ): super(Bottleneck, self).__init__() act_layer = get_act_layer(act_layer) bottleneck_chs = int(round(out_chs * bottle_ratio)) @@ -307,9 +327,21 @@ class PreBottleneck(nn.Module): """ def __init__( - self, in_chs, out_chs, stride=1, dilation=(1, 1), bottle_ratio=1, group_size=1, se_ratio=0.25, - downsample='conv1x1', linear_out=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - drop_block=None, drop_path_rate=0.): + self, + in_chs, + out_chs, + stride=1, + dilation=(1, 1), + bottle_ratio=1, + group_size=1, + se_ratio=0.25, + downsample='conv1x1', + linear_out=False, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + drop_block=None, + drop_path_rate=0., + ): super(PreBottleneck, self).__init__() norm_act_layer = get_norm_act_layer(norm_layer, act_layer) bottleneck_chs = int(round(out_chs * bottle_ratio)) @@ -353,8 +385,16 @@ class RegStage(nn.Module): """Stage (sequence of blocks w/ the same output shape).""" def __init__( - self, depth, in_chs, out_chs, stride, dilation, - drop_path_rates=None, block_fn=Bottleneck, **block_kwargs): + self, + depth, + in_chs, + out_chs, + stride, + dilation, + drop_path_rates=None, + block_fn=Bottleneck, + **block_kwargs, + ): super(RegStage, self).__init__() self.grad_checkpointing = False @@ -367,8 +407,13 @@ class RegStage(nn.Module): name = "b{}".format(i + 1) self.add_module( name, block_fn( - block_in_chs, out_chs, stride=block_stride, dilation=block_dilation, - drop_path_rate=dpr, **block_kwargs) + block_in_chs, + out_chs, + stride=block_stride, + dilation=block_dilation, + drop_path_rate=dpr, + **block_kwargs, + ) ) first_dilation = dilation @@ -389,12 +434,35 @@ class RegNet(nn.Module): """ def __init__( - self, cfg: RegNetCfg, in_chans=3, num_classes=1000, output_stride=32, global_pool='avg', - drop_rate=0., drop_path_rate=0., zero_init_last=True): + self, + cfg: RegNetCfg, + in_chans=3, + num_classes=1000, + output_stride=32, + global_pool='avg', + drop_rate=0., + drop_path_rate=0., + zero_init_last=True, + **kwargs, + ): + """ + + Args: + cfg (RegNetCfg): Model architecture configuration + in_chans (int): Number of input channels (default: 3) + num_classes (int): Number of classifier classes (default: 1000) + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + global_pool (str): Global pooling type (default: 'avg') + drop_rate (float): Dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) + zero_init_last (bool): Zero-init last weight of residual path + kwargs (dict): Extra kwargs overlayed onto cfg + """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate assert output_stride in (8, 16, 32) + cfg = replace(cfg, **kwargs) # update cfg with extra passed kwargs # Construct the stem stem_width = cfg.stem_width @@ -461,8 +529,12 @@ class RegNet(nn.Module): dict(zip(arg_names, params)) for params in zip(stage_widths, stage_strides, stage_dilations, stage_depths, stage_br, stage_gs, stage_dpr)] common_args = dict( - downsample=cfg.downsample, se_ratio=cfg.se_ratio, linear_out=cfg.linear_out, - act_layer=cfg.act_layer, norm_layer=cfg.norm_layer) + downsample=cfg.downsample, + se_ratio=cfg.se_ratio, + linear_out=cfg.linear_out, + act_layer=cfg.act_layer, + norm_layer=cfg.norm_layer, + ) return per_stage_args, common_args @torch.jit.ignore @@ -518,7 +590,6 @@ def _init_weights(module, name='', zero_init_last=False): def _filter_fn(state_dict): - """ convert patch embedding weight from manual patchify + linear proj to conv""" if 'classy_state_dict' in state_dict: import re state_dict = state_dict['classy_state_dict']['base_model']['model'] diff --git a/timm/models/res2net.py b/timm/models/res2net.py index 4724df2a..29a49953 100644 --- a/timm/models/res2net.py +++ b/timm/models/res2net.py @@ -51,9 +51,21 @@ class Bottle2neck(nn.Module): expansion = 4 def __init__( - self, inplanes, planes, stride=1, downsample=None, - cardinality=1, base_width=26, scale=4, dilation=1, first_dilation=None, - act_layer=nn.ReLU, norm_layer=None, attn_layer=None, **_): + self, + inplanes, + planes, + stride=1, + downsample=None, + cardinality=1, + base_width=26, + scale=4, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=None, + attn_layer=None, + **_, + ): super(Bottle2neck, self).__init__() self.scale = scale self.is_first = stride > 1 or downsample is not None @@ -89,7 +101,8 @@ class Bottle2neck(nn.Module): self.downsample = downsample def zero_init_last(self): - nn.init.zeros_(self.bn3.weight) + if getattr(self.bn3, 'weight', None) is not None: + nn.init.zeros_(self.bn3.weight) def forward(self, x): shortcut = x @@ -143,8 +156,8 @@ def res2net50_26w_4s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4), **kwargs) - return _create_res2net('res2net50_26w_4s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=4)) + return _create_res2net('res2net50_26w_4s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -154,8 +167,8 @@ def res2net101_26w_4s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4), **kwargs) - return _create_res2net('res2net101_26w_4s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 23, 3], base_width=26, block_args=dict(scale=4)) + return _create_res2net('res2net101_26w_4s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -165,8 +178,8 @@ def res2net50_26w_6s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6), **kwargs) - return _create_res2net('res2net50_26w_6s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=6)) + return _create_res2net('res2net50_26w_6s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -176,8 +189,8 @@ def res2net50_26w_8s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8), **kwargs) - return _create_res2net('res2net50_26w_8s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=26, block_args=dict(scale=8)) + return _create_res2net('res2net50_26w_8s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -187,8 +200,8 @@ def res2net50_48w_2s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2), **kwargs) - return _create_res2net('res2net50_48w_2s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=48, block_args=dict(scale=2)) + return _create_res2net('res2net50_48w_2s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -198,8 +211,8 @@ def res2net50_14w_8s(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8), **kwargs) - return _create_res2net('res2net50_14w_8s', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=14, block_args=dict(scale=8)) + return _create_res2net('res2net50_14w_8s', pretrained, **dict(model_args, **kwargs)) @register_model @@ -209,5 +222,5 @@ def res2next50(pretrained=False, **kwargs): pretrained (bool): If True, returns a model pre-trained on ImageNet """ model_args = dict( - block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4), **kwargs) - return _create_res2net('res2next50', pretrained, **model_args) + block=Bottle2neck, layers=[3, 4, 6, 3], base_width=4, cardinality=8, block_args=dict(scale=4)) + return _create_res2net('res2next50', pretrained, **dict(model_args, **kwargs)) diff --git a/timm/models/resnest.py b/timm/models/resnest.py index 3b001c7b..38303f9c 100644 --- a/timm/models/resnest.py +++ b/timm/models/resnest.py @@ -57,10 +57,27 @@ class ResNestBottleneck(nn.Module): expansion = 4 def __init__( - self, inplanes, planes, stride=1, downsample=None, - radix=1, cardinality=1, base_width=64, avd=False, avd_first=False, is_first=False, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + self, + inplanes, + planes, + stride=1, + downsample=None, + radix=1, + cardinality=1, + base_width=64, + avd=False, + avd_first=False, + is_first=False, + reduce_first=1, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + attn_layer=None, + aa_layer=None, + drop_block=None, + drop_path=None, + ): super(ResNestBottleneck, self).__init__() assert reduce_first == 1 # not supported assert attn_layer is None # not supported @@ -103,7 +120,8 @@ class ResNestBottleneck(nn.Module): self.downsample = downsample def zero_init_last(self): - nn.init.zeros_(self.bn3.weight) + if getattr(self.bn3, 'weight', None) is not None: + nn.init.zeros_(self.bn3.weight) def forward(self, x): shortcut = x @@ -145,8 +163,8 @@ def resnest14d(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[1, 1, 1, 1], stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest14d', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest14d', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -156,8 +174,8 @@ def resnest26d(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[2, 2, 2, 2], stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest26d', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest26d', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -168,8 +186,8 @@ def resnest50d(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 4, 6, 3], stem_type='deep', stem_width=32, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest50d', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest50d', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -180,8 +198,8 @@ def resnest101e(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 4, 23, 3], stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest101e', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest101e', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -192,8 +210,8 @@ def resnest200e(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 24, 36, 3], stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest200e', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest200e', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -204,8 +222,8 @@ def resnest269e(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 30, 48, 8], stem_type='deep', stem_width=64, avg_down=True, base_width=64, cardinality=1, - block_args=dict(radix=2, avd=True, avd_first=False), **kwargs) - return _create_resnest('resnest269e', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=2, avd=True, avd_first=False)) + return _create_resnest('resnest269e', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -215,8 +233,8 @@ def resnest50d_4s2x40d(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 4, 6, 3], stem_type='deep', stem_width=32, avg_down=True, base_width=40, cardinality=2, - block_args=dict(radix=4, avd=True, avd_first=True), **kwargs) - return _create_resnest('resnest50d_4s2x40d', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=4, avd=True, avd_first=True)) + return _create_resnest('resnest50d_4s2x40d', pretrained=pretrained, **dict(model_kwargs, **kwargs)) @register_model @@ -226,5 +244,5 @@ def resnest50d_1s4x24d(pretrained=False, **kwargs): model_kwargs = dict( block=ResNestBottleneck, layers=[3, 4, 6, 3], stem_type='deep', stem_width=32, avg_down=True, base_width=24, cardinality=4, - block_args=dict(radix=1, avd=True, avd_first=True), **kwargs) - return _create_resnest('resnest50d_1s4x24d', pretrained=pretrained, **model_kwargs) + block_args=dict(radix=1, avd=True, avd_first=True)) + return _create_resnest('resnest50d_1s4x24d', pretrained=pretrained, **dict(model_kwargs, **kwargs)) diff --git a/timm/models/resnet.py b/timm/models/resnet.py index 50849017..200280b3 100644 --- a/timm/models/resnet.py +++ b/timm/models/resnet.py @@ -16,7 +16,7 @@ import torch.nn.functional as F from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD from timm.layers import DropBlock2d, DropPath, AvgPool2dSame, BlurPool2d, GroupNorm, create_attn, get_attn, \ - create_classifier + get_act_layer, get_norm_layer, create_classifier from ._builder import build_model_with_cfg from ._manipulate import checkpoint_seq from ._registry import register_model, model_entrypoint @@ -337,9 +337,23 @@ class BasicBlock(nn.Module): expansion = 1 def __init__( - self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + self, + inplanes, + planes, + stride=1, + downsample=None, + cardinality=1, + base_width=64, + reduce_first=1, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + attn_layer=None, + aa_layer=None, + drop_block=None, + drop_path=None, + ): super(BasicBlock, self).__init__() assert cardinality == 1, 'BasicBlock only supports cardinality of 1' @@ -370,7 +384,8 @@ class BasicBlock(nn.Module): self.drop_path = drop_path def zero_init_last(self): - nn.init.zeros_(self.bn2.weight) + if getattr(self.bn2, 'weight', None) is not None: + nn.init.zeros_(self.bn2.weight) def forward(self, x): shortcut = x @@ -402,9 +417,23 @@ class Bottleneck(nn.Module): expansion = 4 def __init__( - self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + self, + inplanes, + planes, + stride=1, + downsample=None, + cardinality=1, + base_width=64, + reduce_first=1, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + attn_layer=None, + aa_layer=None, + drop_block=None, + drop_path=None, + ): super(Bottleneck, self).__init__() width = int(math.floor(planes * (base_width / 64)) * cardinality) @@ -437,7 +466,8 @@ class Bottleneck(nn.Module): self.drop_path = drop_path def zero_init_last(self): - nn.init.zeros_(self.bn3.weight) + if getattr(self.bn3, 'weight', None) is not None: + nn.init.zeros_(self.bn3.weight) def forward(self, x): shortcut = x @@ -470,7 +500,14 @@ class Bottleneck(nn.Module): def downsample_conv( - in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None): + in_channels, + out_channels, + kernel_size, + stride=1, + dilation=1, + first_dilation=None, + norm_layer=None, +): norm_layer = norm_layer or nn.BatchNorm2d kernel_size = 1 if stride == 1 and dilation == 1 else kernel_size first_dilation = (first_dilation or dilation) if kernel_size > 1 else 1 @@ -484,7 +521,14 @@ def downsample_conv( def downsample_avg( - in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None): + in_channels, + out_channels, + kernel_size, + stride=1, + dilation=1, + first_dilation=None, + norm_layer=None, +): norm_layer = norm_layer or nn.BatchNorm2d avg_stride = stride if dilation == 1 else 1 if stride == 1 and dilation == 1: @@ -508,8 +552,18 @@ def drop_blocks(drop_prob=0.): def make_blocks( - block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32, - down_kernel_size=1, avg_down=False, drop_block_rate=0., drop_path_rate=0., **kwargs): + block_fn, + channels, + block_repeats, + inplanes, + reduce_first=1, + output_stride=32, + down_kernel_size=1, + avg_down=False, + drop_block_rate=0., + drop_path_rate=0., + **kwargs, +): stages = [] feature_info = [] net_num_blocks = sum(block_repeats) @@ -528,8 +582,14 @@ def make_blocks( downsample = None if stride != 1 or inplanes != planes * block_fn.expansion: down_kwargs = dict( - in_channels=inplanes, out_channels=planes * block_fn.expansion, kernel_size=down_kernel_size, - stride=stride, dilation=dilation, first_dilation=prev_dilation, norm_layer=kwargs.get('norm_layer')) + in_channels=inplanes, + out_channels=planes * block_fn.expansion, + kernel_size=down_kernel_size, + stride=stride, + dilation=dilation, + first_dilation=prev_dilation, + norm_layer=kwargs.get('norm_layer'), + ) downsample = downsample_avg(**down_kwargs) if avg_down else downsample_conv(**down_kwargs) block_kwargs = dict(reduce_first=reduce_first, dilation=dilation, drop_block=db, **kwargs) @@ -581,44 +641,72 @@ class ResNet(nn.Module): SENet-154 - 3 layer deep 3x3 stem (same as v1c-v1s), stem_width = 64, cardinality=64, reduction by 2 on width of first bottleneck convolution, 3x3 downsample convs after first block - - Parameters - ---------- - block : Block, class for the residual block. Options are BasicBlockGl, BottleneckGl. - layers : list of int, number of layers in each block - num_classes : int, default 1000, number of classification classes. - in_chans : int, default 3, number of input (color) channels. - output_stride : int, default 32, output stride of the network, 32, 16, or 8. - global_pool : str, Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' - cardinality : int, default 1, number of convolution groups for 3x3 conv in Bottleneck. - base_width : int, default 64, factor determining bottleneck channels. `planes * base_width / 64 * cardinality` - stem_width : int, default 64, number of channels in stem convolutions - stem_type : str, default '' - The type of stem: - * '', default - a single 7x7 conv with a width of stem_width - * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2 - * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2 - block_reduce_first : int, default 1 - Reduction factor for first convolution output width of residual blocks, 1 for all archs except senets, where 2 - down_kernel_size : int, default 1, kernel size of residual block downsample path, 1x1 for most, 3x3 for senets - avg_down : bool, default False, use average pooling for projection skip connection between stages/downsample. - act_layer : nn.Module, activation layer - norm_layer : nn.Module, normalization layer - aa_layer : nn.Module, anti-aliasing layer - drop_rate : float, default 0. Dropout probability before classifier, for training """ def __init__( - self, block, layers, num_classes=1000, in_chans=3, output_stride=32, global_pool='avg', - cardinality=1, base_width=64, stem_width=64, stem_type='', replace_stem_pool=False, block_reduce_first=1, - down_kernel_size=1, avg_down=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, aa_layer=None, - drop_rate=0.0, drop_path_rate=0., drop_block_rate=0., zero_init_last=True, block_args=None): + self, + block, + layers, + num_classes=1000, + in_chans=3, + output_stride=32, + global_pool='avg', + cardinality=1, + base_width=64, + stem_width=64, + stem_type='', + replace_stem_pool=False, + block_reduce_first=1, + down_kernel_size=1, + avg_down=False, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + aa_layer=None, + drop_rate=0.0, + drop_path_rate=0., + drop_block_rate=0., + zero_init_last=True, + block_args=None, + ): + """ + Args: + block (nn.Module): class for the residual block. Options are BasicBlock, Bottleneck. + layers (List[int]) : number of layers in each block + num_classes (int): number of classification classes (default 1000) + in_chans (int): number of input (color) channels. (default 3) + output_stride (int): output stride of the network, 32, 16, or 8. (default 32) + global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg') + cardinality (int): number of convolution groups for 3x3 conv in Bottleneck. (default 1) + base_width (int): bottleneck channels factor. `planes * base_width / 64 * cardinality` (default 64) + stem_width (int): number of channels in stem convolutions (default 64) + stem_type (str): The type of stem (default ''): + * '', default - a single 7x7 conv with a width of stem_width + * 'deep' - three 3x3 convolution layers of widths stem_width, stem_width, stem_width * 2 + * 'deep_tiered' - three 3x3 conv layers of widths stem_width//4 * 3, stem_width, stem_width * 2 + replace_stem_pool (bool): replace stem max-pooling layer with a 3x3 stride-2 convolution + block_reduce_first (int): Reduction factor for first convolution output width of residual blocks, + 1 for all archs except senets, where 2 (default 1) + down_kernel_size (int): kernel size of residual block downsample path, + 1x1 for most, 3x3 for senets (default: 1) + avg_down (bool): use avg pooling for projection skip connection between stages/downsample (default False) + act_layer (str, nn.Module): activation layer + norm_layer (str, nn.Module): normalization layer + aa_layer (nn.Module): anti-aliasing layer + drop_rate (float): Dropout probability before classifier, for training (default 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default 0.) + drop_block_rate (float): Drop block rate (default 0.) + zero_init_last (bool): zero-init the last weight in residual path (usually last BN affine weight) + block_args (dict): Extra kwargs to pass through to block module + """ super(ResNet, self).__init__() block_args = block_args or dict() assert output_stride in (8, 16, 32) self.num_classes = num_classes self.drop_rate = drop_rate self.grad_checkpointing = False + + act_layer = get_act_layer(act_layer) + norm_layer = get_norm_layer(norm_layer) # Stem deep_stem = 'deep' in stem_type @@ -663,10 +751,23 @@ class ResNet(nn.Module): # Feature Blocks channels = [64, 128, 256, 512] stage_modules, stage_feature_info = make_blocks( - block, channels, layers, inplanes, cardinality=cardinality, base_width=base_width, - output_stride=output_stride, reduce_first=block_reduce_first, avg_down=avg_down, - down_kernel_size=down_kernel_size, act_layer=act_layer, norm_layer=norm_layer, aa_layer=aa_layer, - drop_block_rate=drop_block_rate, drop_path_rate=drop_path_rate, **block_args) + block, + channels, + layers, + inplanes, + cardinality=cardinality, + base_width=base_width, + output_stride=output_stride, + reduce_first=block_reduce_first, + avg_down=avg_down, + down_kernel_size=down_kernel_size, + act_layer=act_layer, + norm_layer=norm_layer, + aa_layer=aa_layer, + drop_block_rate=drop_block_rate, + drop_path_rate=drop_path_rate, + **block_args, + ) for stage in stage_modules: self.add_module(*stage) # layer1, layer2, etc self.feature_info.extend(stage_feature_info) @@ -687,9 +788,6 @@ class ResNet(nn.Module): for n, m in self.named_modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') - elif isinstance(m, nn.BatchNorm2d): - nn.init.ones_(m.weight) - nn.init.zeros_(m.bias) if zero_init_last: for m in self.modules(): if hasattr(m, 'zero_init_last'): @@ -747,77 +845,72 @@ def _create_resnet(variant, pretrained=False, **kwargs): def resnet10t(pretrained=False, **kwargs): """Constructs a ResNet-10-T model. """ - model_args = dict( - block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs) - return _create_resnet('resnet10t', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True) + return _create_resnet('resnet10t', pretrained, **dict(model_args, **kwargs)) @register_model def resnet14t(pretrained=False, **kwargs): """Constructs a ResNet-14-T model. """ - model_args = dict( - block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs) - return _create_resnet('resnet14t', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[1, 1, 1, 1], stem_width=32, stem_type='deep_tiered', avg_down=True) + return _create_resnet('resnet14t', pretrained, **dict(model_args, **kwargs)) @register_model def resnet18(pretrained=False, **kwargs): """Constructs a ResNet-18 model. """ - model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs) - return _create_resnet('resnet18', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2]) + return _create_resnet('resnet18', pretrained, **dict(model_args, **kwargs)) @register_model def resnet18d(pretrained=False, **kwargs): """Constructs a ResNet-18-D model. """ - model_args = dict( - block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet18d', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet18d', pretrained, **dict(model_args, **kwargs)) @register_model def resnet34(pretrained=False, **kwargs): """Constructs a ResNet-34 model. """ - model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('resnet34', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3]) + return _create_resnet('resnet34', pretrained, **dict(model_args, **kwargs)) @register_model def resnet34d(pretrained=False, **kwargs): """Constructs a ResNet-34-D model. """ - model_args = dict( - block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet34d', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet34d', pretrained, **dict(model_args, **kwargs)) @register_model def resnet26(pretrained=False, **kwargs): """Constructs a ResNet-26 model. """ - model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], **kwargs) - return _create_resnet('resnet26', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2]) + return _create_resnet('resnet26', pretrained, **dict(model_args, **kwargs)) @register_model def resnet26t(pretrained=False, **kwargs): """Constructs a ResNet-26-T model. """ - model_args = dict( - block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs) - return _create_resnet('resnet26t', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep_tiered', avg_down=True) + return _create_resnet('resnet26t', pretrained, **dict(model_args, **kwargs)) @register_model def resnet26d(pretrained=False, **kwargs): """Constructs a ResNet-26-D model. """ - model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet26d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet26d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -825,83 +918,79 @@ def resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model. """ model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('resnet50', pretrained, **model_args) + return _create_resnet('resnet50', pretrained, **dict(model_args, **kwargs)) @register_model def resnet50d(pretrained=False, **kwargs) -> ResNet: """Constructs a ResNet-50-D model. """ - model_args = dict( - block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet50d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet50d', pretrained, **dict(model_args, **kwargs)) @register_model def resnet50t(pretrained=False, **kwargs): """Constructs a ResNet-50-T model. """ - model_args = dict( - block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True, **kwargs) - return _create_resnet('resnet50t', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True) + return _create_resnet('resnet50t', pretrained, **dict(model_args, **kwargs)) @register_model def resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs) - return _create_resnet('resnet101', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3]) + return _create_resnet('resnet101', pretrained, **dict(model_args, **kwargs)) @register_model def resnet101d(pretrained=False, **kwargs): """Constructs a ResNet-101-D model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet101d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet101d', pretrained, **dict(model_args, **kwargs)) @register_model def resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model. """ - model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs) - return _create_resnet('resnet152', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3]) + return _create_resnet('resnet152', pretrained, **dict(model_args, **kwargs)) @register_model def resnet152d(pretrained=False, **kwargs): """Constructs a ResNet-152-D model. """ - model_args = dict( - block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet152d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet152d', pretrained, **dict(model_args, **kwargs)) @register_model def resnet200(pretrained=False, **kwargs): """Constructs a ResNet-200 model. """ - model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], **kwargs) - return _create_resnet('resnet200', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3]) + return _create_resnet('resnet200', pretrained, **dict(model_args, **kwargs)) @register_model def resnet200d(pretrained=False, **kwargs): """Constructs a ResNet-200-D model. """ - model_args = dict( - block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnet200d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnet200d', pretrained, **dict(model_args, **kwargs)) @register_model def tv_resnet34(pretrained=False, **kwargs): """Constructs a ResNet-34 model with original Torchvision weights. """ - model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('tv_resnet34', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3]) + return _create_resnet('tv_resnet34', pretrained, **dict(model_args, **kwargs)) @register_model @@ -909,23 +998,23 @@ def tv_resnet50(pretrained=False, **kwargs): """Constructs a ResNet-50 model with original Torchvision weights. """ model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('tv_resnet50', pretrained, **model_args) + return _create_resnet('tv_resnet50', pretrained, **dict(model_args, **kwargs)) @register_model def tv_resnet101(pretrained=False, **kwargs): """Constructs a ResNet-101 model w/ Torchvision pretrained weights. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], **kwargs) - return _create_resnet('tv_resnet101', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3]) + return _create_resnet('tv_resnet101', pretrained, **dict(model_args, **kwargs)) @register_model def tv_resnet152(pretrained=False, **kwargs): """Constructs a ResNet-152 model w/ Torchvision pretrained weights. """ - model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], **kwargs) - return _create_resnet('tv_resnet152', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3]) + return _create_resnet('tv_resnet152', pretrained, **dict(model_args, **kwargs)) @register_model @@ -936,8 +1025,8 @@ def wide_resnet50_2(pretrained=False, **kwargs): convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048 channels, and in Wide ResNet-50-2 has 2048-1024-2048. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], base_width=128, **kwargs) - return _create_resnet('wide_resnet50_2', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], base_width=128) + return _create_resnet('wide_resnet50_2', pretrained, **dict(model_args, **kwargs)) @register_model @@ -947,8 +1036,8 @@ def wide_resnet101_2(pretrained=False, **kwargs): which is twice larger in every block. The number of channels in outer 1x1 convolutions is the same. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], base_width=128, **kwargs) - return _create_resnet('wide_resnet101_2', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], base_width=128) + return _create_resnet('wide_resnet101_2', pretrained, **dict(model_args, **kwargs)) @register_model @@ -963,8 +1052,8 @@ def resnet50_gn(pretrained=False, **kwargs): def resnext50_32x4d(pretrained=False, **kwargs): """Constructs a ResNeXt50-32x4d model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('resnext50_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4) + return _create_resnet('resnext50_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -973,40 +1062,40 @@ def resnext50d_32x4d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnext50d_32x4d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnext50d_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model def resnext101_32x4d(pretrained=False, **kwargs): """Constructs a ResNeXt-101 32x4d model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('resnext101_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4) + return _create_resnet('resnext101_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model def resnext101_32x8d(pretrained=False, **kwargs): """Constructs a ResNeXt-101 32x8d model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) - return _create_resnet('resnext101_32x8d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8) + return _create_resnet('resnext101_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model def resnext101_64x4d(pretrained=False, **kwargs): """Constructs a ResNeXt101-64x4d model. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4, **kwargs) - return _create_resnet('resnext101_64x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=64, base_width=4) + return _create_resnet('resnext101_64x4d', pretrained, **dict(model_args, **kwargs)) @register_model def tv_resnext50_32x4d(pretrained=False, **kwargs): """Constructs a ResNeXt50-32x4d model with original Torchvision weights. """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('tv_resnext50_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4) + return _create_resnet('tv_resnext50_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1016,8 +1105,8 @@ def ig_resnext101_32x8d(pretrained=False, **kwargs): `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) - return _create_resnet('ig_resnext101_32x8d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8) + return _create_resnet('ig_resnext101_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1027,8 +1116,8 @@ def ig_resnext101_32x16d(pretrained=False, **kwargs): `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) - return _create_resnet('ig_resnext101_32x16d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16) + return _create_resnet('ig_resnext101_32x16d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1038,8 +1127,8 @@ def ig_resnext101_32x32d(pretrained=False, **kwargs): `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=32, **kwargs) - return _create_resnet('ig_resnext101_32x32d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=32) + return _create_resnet('ig_resnext101_32x32d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1049,8 +1138,8 @@ def ig_resnext101_32x48d(pretrained=False, **kwargs): `"Exploring the Limits of Weakly Supervised Pretraining" `_ Weights from https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=48, **kwargs) - return _create_resnet('ig_resnext101_32x48d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=48) + return _create_resnet('ig_resnext101_32x48d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1059,8 +1148,8 @@ def ssl_resnet18(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs) - return _create_resnet('ssl_resnet18', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2]) + return _create_resnet('ssl_resnet18', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1070,7 +1159,7 @@ def ssl_resnet50(pretrained=False, **kwargs): Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('ssl_resnet50', pretrained, **model_args) + return _create_resnet('ssl_resnet50', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1079,8 +1168,8 @@ def ssl_resnext50_32x4d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('ssl_resnext50_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4) + return _create_resnet('ssl_resnext50_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1089,8 +1178,8 @@ def ssl_resnext101_32x4d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('ssl_resnext101_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4) + return _create_resnet('ssl_resnext101_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1099,8 +1188,8 @@ def ssl_resnext101_32x8d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) - return _create_resnet('ssl_resnext101_32x8d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8) + return _create_resnet('ssl_resnext101_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1109,8 +1198,8 @@ def ssl_resnext101_32x16d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) - return _create_resnet('ssl_resnext101_32x16d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16) + return _create_resnet('ssl_resnext101_32x16d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1120,8 +1209,8 @@ def swsl_resnet18(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], **kwargs) - return _create_resnet('swsl_resnet18', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2]) + return _create_resnet('swsl_resnet18', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1132,7 +1221,7 @@ def swsl_resnet50(pretrained=False, **kwargs): Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], **kwargs) - return _create_resnet('swsl_resnet50', pretrained, **model_args) + return _create_resnet('swsl_resnet50', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1142,8 +1231,8 @@ def swsl_resnext50_32x4d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('swsl_resnext50_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4) + return _create_resnet('swsl_resnext50_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1153,8 +1242,8 @@ def swsl_resnext101_32x4d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, **kwargs) - return _create_resnet('swsl_resnext101_32x4d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4) + return _create_resnet('swsl_resnext101_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1164,8 +1253,8 @@ def swsl_resnext101_32x8d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, **kwargs) - return _create_resnet('swsl_resnext101_32x8d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8) + return _create_resnet('swsl_resnext101_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1175,8 +1264,8 @@ def swsl_resnext101_32x16d(pretrained=False, **kwargs): `"Billion-scale Semi-Supervised Learning for Image Classification" `_ Weights from https://github.com/facebookresearch/semi-supervised-ImageNet1K-models/ """ - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16, **kwargs) - return _create_resnet('swsl_resnext101_32x16d', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=16) + return _create_resnet('swsl_resnext101_32x16d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1187,8 +1276,8 @@ def ecaresnet26t(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[2, 2, 2, 2], stem_width=32, - stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet26t', pretrained, **model_args) + stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet26t', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1197,8 +1286,8 @@ def ecaresnet50d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet50d', pretrained, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet50d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1208,8 +1297,8 @@ def ecaresnet50d_pruned(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet50d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs)) @register_model @@ -1219,8 +1308,8 @@ def ecaresnet50t(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, - stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet50t', pretrained, **model_args) + stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet50t', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1229,8 +1318,8 @@ def ecaresnetlight(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[1, 1, 11, 3], stem_width=32, avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnetlight', pretrained, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnetlight', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1239,8 +1328,8 @@ def ecaresnet101d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet101d', pretrained, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet101d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1250,8 +1339,8 @@ def ecaresnet101d_pruned(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet101d_pruned', pretrained, pruned=True, **dict(model_args, **kwargs)) @register_model @@ -1260,8 +1349,8 @@ def ecaresnet200d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet200d', pretrained, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet200d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1270,8 +1359,8 @@ def ecaresnet269d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnet269d', pretrained, **model_args) + block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnet269d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1282,8 +1371,8 @@ def ecaresnext26t_32x4d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, - stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnext26t_32x4d', pretrained, **model_args) + stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnext26t_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1294,54 +1383,54 @@ def ecaresnext50t_32x4d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, - stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca'), **kwargs) - return _create_resnet('ecaresnext50t_32x4d', pretrained, **model_args) + stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='eca')) + return _create_resnet('ecaresnext50t_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet18(pretrained=False, **kwargs): - model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet18', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], block_args=dict(attn_layer='se')) + return _create_resnet('seresnet18', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet34(pretrained=False, **kwargs): - model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet34', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se')) + return _create_resnet('seresnet34', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet50(pretrained=False, **kwargs): - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet50', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], block_args=dict(attn_layer='se')) + return _create_resnet('seresnet50', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet50t(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', avg_down=True, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet50t', pretrained, **model_args) + block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep_tiered', + avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnet50t', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet101(pretrained=False, **kwargs): - model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet101', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 23, 3], block_args=dict(attn_layer='se')) + return _create_resnet('seresnet101', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet152(pretrained=False, **kwargs): - model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet152', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 8, 36, 3], block_args=dict(attn_layer='se')) + return _create_resnet('seresnet152', pretrained, **dict(model_args, **kwargs)) @register_model def seresnet152d(pretrained=False, **kwargs): model_args = dict( - block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet152d', pretrained, **model_args) + block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', + avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnet152d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1349,9 +1438,9 @@ def seresnet200d(pretrained=False, **kwargs): """Constructs a ResNet-200-D model with SE attn. """ model_args = dict( - block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet200d', pretrained, **model_args) + block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', + avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnet200d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1359,9 +1448,9 @@ def seresnet269d(pretrained=False, **kwargs): """Constructs a ResNet-269-D model with SE attn. """ model_args = dict( - block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnet269d', pretrained, **model_args) + block=Bottleneck, layers=[3, 30, 48, 8], stem_width=32, stem_type='deep', + avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnet269d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1372,8 +1461,8 @@ def seresnext26d_32x4d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, - stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext26d_32x4d', pretrained, **model_args) + stem_type='deep', avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnext26d_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1384,8 +1473,8 @@ def seresnext26t_32x4d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[2, 2, 2, 2], cardinality=32, base_width=4, stem_width=32, - stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext26t_32x4d', pretrained, **model_args) + stem_type='deep_tiered', avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnext26t_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1401,24 +1490,24 @@ def seresnext26tn_32x4d(pretrained=False, **kwargs): def seresnext50_32x4d(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], cardinality=32, base_width=4, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext50_32x4d', pretrained, **model_args) + block_args=dict(attn_layer='se')) + return _create_resnet('seresnext50_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model def seresnext101_32x4d(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=4, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext101_32x4d', pretrained, **model_args) + block_args=dict(attn_layer='se')) + return _create_resnet('seresnext101_32x4d', pretrained, **dict(model_args, **kwargs)) @register_model def seresnext101_32x8d(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext101_32x8d', pretrained, **model_args) + block_args=dict(attn_layer='se')) + return _create_resnet('seresnext101_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1426,32 +1515,32 @@ def seresnext101d_32x8d(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, stem_width=32, stem_type='deep', avg_down=True, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnext101d_32x8d', pretrained, **model_args) + block_args=dict(attn_layer='se')) + return _create_resnet('seresnext101d_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model def senet154(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 8, 36, 3], cardinality=64, base_width=4, stem_type='deep', - down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('senet154', pretrained, **model_args) + down_kernel_size=3, block_reduce_first=2, block_args=dict(attn_layer='se')) + return _create_resnet('senet154', pretrained, **dict(model_args, **kwargs)) @register_model def resnetblur18(pretrained=False, **kwargs): """Constructs a ResNet-18 model with blur anti-aliasing """ - model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], aa_layer=BlurPool2d, **kwargs) - return _create_resnet('resnetblur18', pretrained, **model_args) + model_args = dict(block=BasicBlock, layers=[2, 2, 2, 2], aa_layer=BlurPool2d) + return _create_resnet('resnetblur18', pretrained, **dict(model_args, **kwargs)) @register_model def resnetblur50(pretrained=False, **kwargs): """Constructs a ResNet-50 model with blur anti-aliasing """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d, **kwargs) - return _create_resnet('resnetblur50', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d) + return _create_resnet('resnetblur50', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1460,8 +1549,8 @@ def resnetblur50d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=BlurPool2d, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnetblur50d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnetblur50d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1470,16 +1559,25 @@ def resnetblur101d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=BlurPool2d, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnetblur101d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnetblur101d', pretrained, **dict(model_args, **kwargs)) + + +@register_model +def resnetaa34d(pretrained=False, **kwargs): + """Constructs a ResNet-34-D model w/ avgpool anti-aliasing + """ + model_args = dict( + block=BasicBlock, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnetaa34d', pretrained, **dict(model_args, **kwargs)) @register_model def resnetaa50(pretrained=False, **kwargs): """Constructs a ResNet-50 model with avgpool anti-aliasing """ - model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, **kwargs) - return _create_resnet('resnetaa50', pretrained, **model_args) + model_args = dict(block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d) + return _create_resnet('resnetaa50', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1488,8 +1586,8 @@ def resnetaa50d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnetaa50d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnetaa50d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1498,8 +1596,8 @@ def resnetaa101d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], aa_layer=nn.AvgPool2d, - stem_width=32, stem_type='deep', avg_down=True, **kwargs) - return _create_resnet('resnetaa101d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True) + return _create_resnet('resnetaa101d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1508,8 +1606,8 @@ def seresnetaa50d(pretrained=False, **kwargs): """ model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], aa_layer=nn.AvgPool2d, - stem_width=32, stem_type='deep', avg_down=True, block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnetaa50d', pretrained, **model_args) + stem_width=32, stem_type='deep', avg_down=True, block_args=dict(attn_layer='se')) + return _create_resnet('seresnetaa50d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1519,8 +1617,8 @@ def seresnextaa101d_32x8d(pretrained=False, **kwargs): model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], cardinality=32, base_width=8, stem_width=32, stem_type='deep', avg_down=True, aa_layer=nn.AvgPool2d, - block_args=dict(attn_layer='se'), **kwargs) - return _create_resnet('seresnextaa101d_32x8d', pretrained, **model_args) + block_args=dict(attn_layer='se')) + return _create_resnet('seresnextaa101d_32x8d', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1532,8 +1630,8 @@ def resnetrs50(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[3, 4, 6, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs50', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs50', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1545,8 +1643,8 @@ def resnetrs101(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[3, 4, 23, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs101', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs101', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1558,8 +1656,8 @@ def resnetrs152(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[3, 8, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs152', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs152', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1571,8 +1669,8 @@ def resnetrs200(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[3, 24, 36, 3], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs200', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs200', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1584,8 +1682,8 @@ def resnetrs270(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[4, 29, 53, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs270', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs270', pretrained, **dict(model_args, **kwargs)) @@ -1598,8 +1696,8 @@ def resnetrs350(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[4, 36, 72, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs350', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs350', pretrained, **dict(model_args, **kwargs)) @register_model @@ -1611,5 +1709,5 @@ def resnetrs420(pretrained=False, **kwargs): attn_layer = partial(get_attn('se'), rd_ratio=0.25) model_args = dict( block=Bottleneck, layers=[4, 44, 87, 4], stem_width=32, stem_type='deep', replace_stem_pool=True, - avg_down=True, block_args=dict(attn_layer=attn_layer), **kwargs) - return _create_resnet('resnetrs420', pretrained, **model_args) + avg_down=True, block_args=dict(attn_layer=attn_layer)) + return _create_resnet('resnetrs420', pretrained, **dict(model_args, **kwargs)) diff --git a/timm/models/resnetv2.py b/timm/models/resnetv2.py index f8c4298b..41e29e12 100644 --- a/timm/models/resnetv2.py +++ b/timm/models/resnetv2.py @@ -37,7 +37,7 @@ import torch.nn as nn from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.layers import GroupNormAct, BatchNormAct2d, EvoNorm2dB0, EvoNorm2dS0, FilterResponseNormTlu2d, \ - ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d + ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d, get_act_layer, get_norm_act_layer from ._builder import build_model_with_cfg from ._manipulate import checkpoint_seq, named_apply, adapt_input_conv from ._registry import register_model @@ -155,8 +155,20 @@ class PreActBottleneck(nn.Module): """ def __init__( - self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1, - act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.): + self, + in_chs, + out_chs=None, + bottle_ratio=0.25, + stride=1, + dilation=1, + first_dilation=None, + groups=1, + act_layer=None, + conv_layer=None, + norm_layer=None, + proj_layer=None, + drop_path_rate=0., + ): super().__init__() first_dilation = first_dilation or dilation conv_layer = conv_layer or StdConv2d @@ -202,8 +214,20 @@ class Bottleneck(nn.Module): """Non Pre-activation bottleneck block, equiv to V1.5/V1b Bottleneck. Used for ViT. """ def __init__( - self, in_chs, out_chs=None, bottle_ratio=0.25, stride=1, dilation=1, first_dilation=None, groups=1, - act_layer=None, conv_layer=None, norm_layer=None, proj_layer=None, drop_path_rate=0.): + self, + in_chs, + out_chs=None, + bottle_ratio=0.25, + stride=1, + dilation=1, + first_dilation=None, + groups=1, + act_layer=None, + conv_layer=None, + norm_layer=None, + proj_layer=None, + drop_path_rate=0., + ): super().__init__() first_dilation = first_dilation or dilation act_layer = act_layer or nn.ReLU @@ -229,7 +253,8 @@ class Bottleneck(nn.Module): self.act3 = act_layer(inplace=True) def zero_init_last(self): - nn.init.zeros_(self.norm3.weight) + if getattr(self.norm3, 'weight', None) is not None: + nn.init.zeros_(self.norm3.weight) def forward(self, x): # shortcut branch @@ -251,8 +276,16 @@ class Bottleneck(nn.Module): class DownsampleConv(nn.Module): def __init__( - self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, preact=True, - conv_layer=None, norm_layer=None): + self, + in_chs, + out_chs, + stride=1, + dilation=1, + first_dilation=None, + preact=True, + conv_layer=None, + norm_layer=None, + ): super(DownsampleConv, self).__init__() self.conv = conv_layer(in_chs, out_chs, 1, stride=stride) self.norm = nn.Identity() if preact else norm_layer(out_chs, apply_act=False) @@ -263,8 +296,16 @@ class DownsampleConv(nn.Module): class DownsampleAvg(nn.Module): def __init__( - self, in_chs, out_chs, stride=1, dilation=1, first_dilation=None, - preact=True, conv_layer=None, norm_layer=None): + self, + in_chs, + out_chs, + stride=1, + dilation=1, + first_dilation=None, + preact=True, + conv_layer=None, + norm_layer=None, + ): """ AvgPool Downsampling as in 'D' ResNet variants. This is not in RegNet space but I might experiment.""" super(DownsampleAvg, self).__init__() avg_stride = stride if dilation == 1 else 1 @@ -283,9 +324,22 @@ class DownsampleAvg(nn.Module): class ResNetStage(nn.Module): """ResNet Stage.""" def __init__( - self, in_chs, out_chs, stride, dilation, depth, bottle_ratio=0.25, groups=1, - avg_down=False, block_dpr=None, block_fn=PreActBottleneck, - act_layer=None, conv_layer=None, norm_layer=None, **block_kwargs): + self, + in_chs, + out_chs, + stride, + dilation, + depth, + bottle_ratio=0.25, + groups=1, + avg_down=False, + block_dpr=None, + block_fn=PreActBottleneck, + act_layer=None, + conv_layer=None, + norm_layer=None, + **block_kwargs, + ): super(ResNetStage, self).__init__() first_dilation = 1 if dilation in (1, 2) else 2 layer_kwargs = dict(act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer) @@ -296,9 +350,18 @@ class ResNetStage(nn.Module): drop_path_rate = block_dpr[block_idx] if block_dpr else 0. stride = stride if block_idx == 0 else 1 self.blocks.add_module(str(block_idx), block_fn( - prev_chs, out_chs, stride=stride, dilation=dilation, bottle_ratio=bottle_ratio, groups=groups, - first_dilation=first_dilation, proj_layer=proj_layer, drop_path_rate=drop_path_rate, - **layer_kwargs, **block_kwargs)) + prev_chs, + out_chs, + stride=stride, + dilation=dilation, + bottle_ratio=bottle_ratio, + groups=groups, + first_dilation=first_dilation, + proj_layer=proj_layer, + drop_path_rate=drop_path_rate, + **layer_kwargs, + **block_kwargs, + )) prev_chs = out_chs first_dilation = dilation proj_layer = None @@ -313,8 +376,13 @@ def is_stem_deep(stem_type): def create_resnetv2_stem( - in_chs, out_chs=64, stem_type='', preact=True, - conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32)): + in_chs, + out_chs=64, + stem_type='', + preact=True, + conv_layer=StdConv2d, + norm_layer=partial(GroupNormAct, num_groups=32), +): stem = OrderedDict() assert stem_type in ('', 'fixed', 'same', 'deep', 'deep_fixed', 'deep_same', 'tiered') @@ -357,20 +425,62 @@ class ResNetV2(nn.Module): """ def __init__( - self, layers, channels=(256, 512, 1024, 2048), - num_classes=1000, in_chans=3, global_pool='avg', output_stride=32, - width_factor=1, stem_chs=64, stem_type='', avg_down=False, preact=True, - act_layer=nn.ReLU, conv_layer=StdConv2d, norm_layer=partial(GroupNormAct, num_groups=32), - drop_rate=0., drop_path_rate=0., zero_init_last=False): + self, + layers, + channels=(256, 512, 1024, 2048), + num_classes=1000, + in_chans=3, + global_pool='avg', + output_stride=32, + width_factor=1, + stem_chs=64, + stem_type='', + avg_down=False, + preact=True, + act_layer=nn.ReLU, + norm_layer=partial(GroupNormAct, num_groups=32), + conv_layer=StdConv2d, + drop_rate=0., + drop_path_rate=0., + zero_init_last=False, + ): + """ + Args: + layers (List[int]) : number of layers in each block + channels (List[int]) : number of channels in each block: + num_classes (int): number of classification classes (default 1000) + in_chans (int): number of input (color) channels. (default 3) + global_pool (str): Global pooling type. One of 'avg', 'max', 'avgmax', 'catavgmax' (default 'avg') + output_stride (int): output stride of the network, 32, 16, or 8. (default 32) + width_factor (int): channel (width) multiplication factor + stem_chs (int): stem width (default: 64) + stem_type (str): stem type (default: '' == 7x7) + avg_down (bool): average pooling in residual downsampling (default: False) + preact (bool): pre-activiation (default: True) + act_layer (Union[str, nn.Module]): activation layer + norm_layer (Union[str, nn.Module]): normalization layer + conv_layer (nn.Module): convolution module + drop_rate: classifier dropout rate (default: 0.) + drop_path_rate: stochastic depth rate (default: 0.) + zero_init_last: zero-init last weight in residual path (default: False) + """ super().__init__() self.num_classes = num_classes self.drop_rate = drop_rate wf = width_factor + norm_layer = get_norm_act_layer(norm_layer, act_layer=act_layer) + act_layer = get_act_layer(act_layer) self.feature_info = [] stem_chs = make_div(stem_chs * wf) self.stem = create_resnetv2_stem( - in_chans, stem_chs, stem_type, preact, conv_layer=conv_layer, norm_layer=norm_layer) + in_chans, + stem_chs, + stem_type, + preact, + conv_layer=conv_layer, + norm_layer=norm_layer, + ) stem_feat = ('stem.conv3' if is_stem_deep(stem_type) else 'stem.conv') if preact else 'stem.norm' self.feature_info.append(dict(num_chs=stem_chs, reduction=2, module=stem_feat)) @@ -387,8 +497,18 @@ class ResNetV2(nn.Module): dilation *= stride stride = 1 stage = ResNetStage( - prev_chs, out_chs, stride=stride, dilation=dilation, depth=d, avg_down=avg_down, - act_layer=act_layer, conv_layer=conv_layer, norm_layer=norm_layer, block_dpr=bdpr, block_fn=block_fn) + prev_chs, + out_chs, + stride=stride, + dilation=dilation, + depth=d, + avg_down=avg_down, + act_layer=act_layer, + conv_layer=conv_layer, + norm_layer=norm_layer, + block_dpr=bdpr, + block_fn=block_fn, + ) prev_chs = out_chs curr_stride *= stride self.feature_info += [dict(num_chs=prev_chs, reduction=curr_stride, module=f'stages.{stage_idx}')] @@ -626,86 +746,83 @@ def resnetv2_152x2_bit_teacher_384(pretrained=False, **kwargs): @register_model def resnetv2_50(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50', pretrained=pretrained, - layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs) + model_args = dict(layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d) + return _create_resnetv2('resnetv2_50', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_50d(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50d', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_50d', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_50t(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50t', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, - stem_type='tiered', avg_down=True, **kwargs) + stem_type='tiered', avg_down=True) + return _create_resnetv2('resnetv2_50t', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_101(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_101', pretrained=pretrained, - layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs) + model_args = dict(layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d) + return _create_resnetv2('resnetv2_101', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_101d(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_101d', pretrained=pretrained, + model_args = dict( layers=[3, 4, 23, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_101d', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_152(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_152', pretrained=pretrained, - layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, **kwargs) + model_args = dict(layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d) + return _create_resnetv2('resnetv2_152', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_152d(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_152d', pretrained=pretrained, + model_args = dict( layers=[3, 8, 36, 3], conv_layer=create_conv2d, norm_layer=BatchNormAct2d, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_152d', pretrained=pretrained, **dict(model_args, **kwargs)) # Experimental configs (may change / be removed) @register_model def resnetv2_50d_gn(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50d_gn', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=GroupNormAct, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_50d_gn', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_50d_evob(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50d_evob', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=EvoNorm2dB0, - stem_type='deep', avg_down=True, zero_init_last=True, **kwargs) + stem_type='deep', avg_down=True, zero_init_last=True) + return _create_resnetv2('resnetv2_50d_evob', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_50d_evos(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50d_evos', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=EvoNorm2dS0, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_50d_evos', pretrained=pretrained, **dict(model_args, **kwargs)) @register_model def resnetv2_50d_frn(pretrained=False, **kwargs): - return _create_resnetv2( - 'resnetv2_50d_frn', pretrained=pretrained, + model_args = dict( layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=FilterResponseNormTlu2d, - stem_type='deep', avg_down=True, **kwargs) + stem_type='deep', avg_down=True) + return _create_resnetv2('resnetv2_50d_frn', pretrained=pretrained, **dict(model_args, **kwargs)) diff --git a/timm/models/sknet.py b/timm/models/sknet.py index 5a29b9a4..425bd7c2 100644 --- a/timm/models/sknet.py +++ b/timm/models/sknet.py @@ -47,9 +47,24 @@ class SelectiveKernelBasic(nn.Module): expansion = 1 def __init__( - self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, - sk_kwargs=None, reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, - norm_layer=nn.BatchNorm2d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + self, + inplanes, + planes, + stride=1, + downsample=None, + cardinality=1, + base_width=64, + sk_kwargs=None, + reduce_first=1, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + attn_layer=None, + aa_layer=None, + drop_block=None, + drop_path=None, + ): super(SelectiveKernelBasic, self).__init__() sk_kwargs = sk_kwargs or {} @@ -71,7 +86,8 @@ class SelectiveKernelBasic(nn.Module): self.drop_path = drop_path def zero_init_last(self): - nn.init.zeros_(self.conv2.bn.weight) + if getattr(self.conv2.bn, 'weight', None) is not None: + nn.init.zeros_(self.conv2.bn.weight) def forward(self, x): shortcut = x @@ -92,9 +108,24 @@ class SelectiveKernelBottleneck(nn.Module): expansion = 4 def __init__( - self, inplanes, planes, stride=1, downsample=None, cardinality=1, base_width=64, sk_kwargs=None, - reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, - attn_layer=None, aa_layer=None, drop_block=None, drop_path=None): + self, + inplanes, + planes, + stride=1, + downsample=None, + cardinality=1, + base_width=64, + sk_kwargs=None, + reduce_first=1, + dilation=1, + first_dilation=None, + act_layer=nn.ReLU, + norm_layer=nn.BatchNorm2d, + attn_layer=None, + aa_layer=None, + drop_block=None, + drop_path=None, + ): super(SelectiveKernelBottleneck, self).__init__() sk_kwargs = sk_kwargs or {} @@ -115,7 +146,8 @@ class SelectiveKernelBottleneck(nn.Module): self.drop_path = drop_path def zero_init_last(self): - nn.init.zeros_(self.conv3.bn.weight) + if getattr(self.conv3.bn, 'weight', None) is not None: + nn.init.zeros_(self.conv3.bn.weight) def forward(self, x): shortcut = x diff --git a/timm/models/vision_transformer.py b/timm/models/vision_transformer.py index 5b93628f..8ffb1200 100644 --- a/timm/models/vision_transformer.py +++ b/timm/models/vision_transformer.py @@ -8,14 +8,18 @@ A PyTorch implement of Vision Transformers as described in: `How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers` - https://arxiv.org/abs/2106.10270 -The official jax code is released and available at https://github.com/google-research/vision_transformer +`FlexiViT: One Model for All Patch Sizes` + - https://arxiv.org/abs/2212.08013 + +The official jax code is released and available at + * https://github.com/google-research/vision_transformer + * https://github.com/google-research/big_vision Acknowledgments: -* The paper authors for releasing code and weights, thanks! -* I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch ... check it out -for some einops/einsum fun -* Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT -* Bert reference code checks against Huggingface Transformers and Tensorflow Bert + * The paper authors for releasing code and weights, thanks! + * I fixed my class token impl based on Phil Wang's https://github.com/lucidrains/vit-pytorch + * Simple transformer style inspired by Andrej Karpathy's https://github.com/karpathy/minGPT + * Bert reference code checks against Huggingface Transformers and Tensorflow Bert Hacked together by / Copyright 2020, Ross Wightman """ @@ -23,7 +27,7 @@ import logging import math from collections import OrderedDict from functools import partial -from typing import Optional +from typing import Optional, List import torch import torch.nn as nn @@ -32,7 +36,8 @@ import torch.utils.checkpoint from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD, \ OPENAI_CLIP_MEAN, OPENAI_CLIP_STD -from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_ +from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_, resample_patch_embed, \ + resample_abs_pos_embed from ._builder import build_model_with_cfg from ._manipulate import named_apply, checkpoint_seq, adapt_input_conv from ._pretrained import generate_default_cfgs @@ -449,6 +454,39 @@ def get_init_weights_vit(mode='jax', head_bias: float = 0.): return init_weights_vit_timm +def resize_pos_embed( + posemb, + posemb_new, + num_prefix_tokens=1, + gs_new=(), + interpolation='bicubic', + antialias=False, +): + """ Rescale the grid of position embeddings when loading from state_dict. + + *DEPRECATED* This function is being deprecated in favour of resample_abs_pos_embed + + Adapted from: + https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 + """ + ntok_new = posemb_new.shape[1] + if num_prefix_tokens: + posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:] + ntok_new -= num_prefix_tokens + else: + posemb_prefix, posemb_grid = posemb[:, :0], posemb[0] + gs_old = int(math.sqrt(len(posemb_grid))) + if not len(gs_new): # backwards compatibility + gs_new = [int(math.sqrt(ntok_new))] * 2 + assert len(gs_new) >= 2 + _logger.info(f'Resized position embedding: {posemb.shape} ({[gs_old, gs_old]}) to {posemb_new.shape} ({gs_new}).') + posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) + posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode=interpolation, antialias=antialias, align_corners=False) + posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) + posemb = torch.cat([posemb_prefix, posemb_grid], dim=1) + return posemb + + @torch.no_grad() def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''): """ Load weights from .npz checkpoints for official Google Brain Flax implementation @@ -468,8 +506,15 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = return torch.from_numpy(w) w = np.load(checkpoint_path) - if not prefix and 'opt/target/embedding/kernel' in w: - prefix = 'opt/target/' + interpolation = 'bilinear' + antialias = False + big_vision = False + if not prefix: + if 'opt/target/embedding/kernel' in w: + prefix = 'opt/target/' + elif 'params/embedding/kernel' in w: + prefix = 'params/' + big_vision = True if hasattr(model.patch_embed, 'backbone'): # hybrid @@ -495,17 +540,33 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = else: embed_conv_w = adapt_input_conv( model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel'])) + if embed_conv_w.shape[-2:] != model.patch_embed.proj.weight.shape[-2:]: + embed_conv_w = resample_patch_embed( + embed_conv_w, + model.patch_embed.proj.weight.shape[-2:], + interpolation=interpolation, + antialias=antialias, + verbose=True, + ) + model.patch_embed.proj.weight.copy_(embed_conv_w) model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias'])) if model.cls_token is not None: model.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False)) - pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) + if big_vision: + pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False) + else: + pos_embed_w = _n2p(w[f'{prefix}Transformer/posembed_input/pos_embedding'], t=False) if pos_embed_w.shape != model.pos_embed.shape: - pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights + old_shape = pos_embed_w.shape + num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1) + pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights pos_embed_w, - model.pos_embed, - getattr(model, 'num_prefix_tokens', 1), - model.patch_embed.grid_size + new_size=model.patch_embed.grid_size, + num_prefix_tokens=num_prefix_tokens, + interpolation=interpolation, + antialias=antialias, + verbose=True, ) model.pos_embed.copy_(pos_embed_w) model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale'])) @@ -517,9 +578,10 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = # if isinstance(getattr(model.pre_logits, 'fc', None), nn.Linear) and f'{prefix}pre_logits/bias' in w: # model.pre_logits.fc.weight.copy_(_n2p(w[f'{prefix}pre_logits/kernel'])) # model.pre_logits.fc.bias.copy_(_n2p(w[f'{prefix}pre_logits/bias'])) + mha_sub, b_sub, ln1_sub = (0, 0, 1) if big_vision else (1, 3, 2) for i, block in enumerate(model.blocks.children()): block_prefix = f'{prefix}Transformer/encoderblock_{i}/' - mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/' + mha_prefix = block_prefix + f'MultiHeadDotProductAttention_{mha_sub}/' block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale'])) block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias'])) block.attn.qkv.weight.copy_(torch.cat([ @@ -529,32 +591,10 @@ def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1)) block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias'])) for r in range(2): - getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/kernel'])) - getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_3/Dense_{r}/bias'])) - block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/scale'])) - block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_2/bias'])) - - -def resize_pos_embed(posemb, posemb_new, num_prefix_tokens=1, gs_new=()): - # Rescale the grid of position embeddings when loading from state_dict. Adapted from - # https://github.com/google-research/vision_transformer/blob/00883dd691c63a6830751563748663526e811cee/vit_jax/checkpoint.py#L224 - _logger.info('Resized position embedding: %s to %s', posemb.shape, posemb_new.shape) - ntok_new = posemb_new.shape[1] - if num_prefix_tokens: - posemb_prefix, posemb_grid = posemb[:, :num_prefix_tokens], posemb[0, num_prefix_tokens:] - ntok_new -= num_prefix_tokens - else: - posemb_prefix, posemb_grid = posemb[:, :0], posemb[0] - gs_old = int(math.sqrt(len(posemb_grid))) - if not len(gs_new): # backwards compatibility - gs_new = [int(math.sqrt(ntok_new))] * 2 - assert len(gs_new) >= 2 - _logger.info('Position embedding grid-size from %s to %s', [gs_old, gs_old], gs_new) - posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2) - posemb_grid = F.interpolate(posemb_grid, size=gs_new, mode='bicubic', align_corners=False) - posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_new[0] * gs_new[1], -1) - posemb = torch.cat([posemb_prefix, posemb_grid], dim=1) - return posemb + getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel'])) + getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias'])) + block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/scale'])) + block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/bias'])) def _convert_openai_clip(state_dict, model): @@ -591,7 +631,13 @@ def _convert_openai_clip(state_dict, model): return out_dict -def checkpoint_filter_fn(state_dict, model, adapt_layer_scale=False): +def checkpoint_filter_fn( + state_dict, + model, + adapt_layer_scale=False, + interpolation='bicubic', + antialias=True, +): """ convert patch embedding weight from manual patchify + linear proj to conv""" import re out_dict = {} @@ -603,17 +649,30 @@ def checkpoint_filter_fn(state_dict, model, adapt_layer_scale=False): return _convert_openai_clip(state_dict, model) for k, v in state_dict.items(): - if 'patch_embed.proj.weight' in k and len(v.shape) < 4: - # For old models that I trained prior to conv based patchification + if 'patch_embed.proj.weight' in k: O, I, H, W = model.patch_embed.proj.weight.shape - v = v.reshape(O, -1, H, W) + if len(v.shape) < 4: + # For old models that I trained prior to conv based patchification + O, I, H, W = model.patch_embed.proj.weight.shape + v = v.reshape(O, -1, H, W) + if v.shape[-1] != W or v.shape[-2] != H: + v = resample_patch_embed( + v, + (H, W), + interpolation=interpolation, + antialias=antialias, + verbose=True, + ) elif k == 'pos_embed' and v.shape[1] != model.pos_embed.shape[1]: # To resize pos embedding when using model at different size from pretrained weights - v = resize_pos_embed( + num_prefix_tokens = 0 if getattr(model, 'no_embed_class', False) else getattr(model, 'num_prefix_tokens', 1) + v = resample_abs_pos_embed( v, - model.pos_embed, - 0 if getattr(model, 'no_embed_class') else getattr(model, 'num_prefix_tokens', 1), - model.patch_embed.grid_size + new_size=model.patch_embed.grid_size, + num_prefix_tokens=num_prefix_tokens, + interpolation=interpolation, + antialias=antialias, + verbose=True, ) elif adapt_layer_scale and 'gamma_' in k: # remap layer-scale gamma into sub-module (deit3 models) @@ -638,70 +697,104 @@ def _cfg(url='', **kwargs): default_cfgs = generate_default_cfgs({ + # re-finetuned augreg 21k FT on in1k weights + 'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg( + hf_hub_id='timm/'), + 'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg(), + 'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg( + hf_hub_id='timm/'), + # How to train your ViT (augreg) weights, pretrained on 21k FT on in1k 'vit_tiny_patch16_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_tiny_patch16_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_small_patch32_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_small_patch32_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_small_patch16_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_small_patch16_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_base_patch32_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_base_patch32_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_light1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_base_patch16_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_base_patch16_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_base_patch8_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_large_patch16_224.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_large_patch16_384.augreg_in21k_ft_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), - # re-finetuned augreg 21k FT on in1k weights - 'vit_base_patch16_224.augreg2_in21k_ft_in1k': _cfg( - file='b16_augreg-a-8.pth'), - 'vit_base_patch16_384.augreg2_in21k_ft_in1k': _cfg( - url=''), - 'vit_base_patch8_224.augreg2_in21k_ft_in1k': _cfg( - url=''), - # patch models (weights from official Google JAX impl) pretrained on in21k FT on in1k 'vit_base_patch16_224.orig_in21k_ft_in1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth'), + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth', + hf_hub_id='timm/'), 'vit_base_patch16_384.orig_in21k_ft_in1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth'), + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth', + hf_hub_id='timm/', + input_size=(3, 384, 384), crop_pct=1.0), 'vit_large_patch32_384.orig_in21k_ft_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth', + hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=1.0), - # How to train your ViT (augreg) weights trained on in1k + # How to train your ViT (augreg) weights trained on in1k only + 'vit_small_patch16_224.augreg_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', + custom_load=True), + 'vit_small_patch16_384.augreg_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/S_16-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', + custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), + 'vit_base_patch32_224.augreg_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', + custom_load=True), + 'vit_base_patch32_384.augreg_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/B_32-i1k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', + custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_base_patch16_224.augreg_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', custom_load=True), 'vit_base_patch16_384.augreg_in1k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_16-i1k-300ep-lr_0.001-aug_strong2-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', custom_load=True, input_size=(3, 384, 384), crop_pct=1.0), 'vit_large_patch14_224.untrained': _cfg(url=''), @@ -709,229 +802,244 @@ default_cfgs = generate_default_cfgs({ 'vit_giant_patch14_224.untrained': _cfg(url=''), 'vit_gigantic_patch14_224.untrained': _cfg(url=''), - # patch models, imagenet21k (weights from official Google JAX impl) - 'vit_large_patch32_224.v1_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth', - num_classes=21843), - 'vit_huge_patch14_224.v1_in21k': _cfg( + 'vit_large_patch32_224.orig_in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch32_224_in21k-9046d2e7.pth', + hf_hub_id='timm/', + num_classes=21843), + 'vit_huge_patch14_224.orig_in21k': _cfg( url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz', - hf_hub_id='timm/vit_huge_patch14_224_in21k', + hf_hub_id='timm/', custom_load=True, num_classes=21843), # How to train your ViT (augreg) weights, pretrained on in21k 'vit_tiny_patch16_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_small_patch32_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_32-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_small_patch16_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/S_16-i21k-300ep-lr_0.001-aug_light1-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_base_patch32_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_base_patch16_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_base_patch8_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/B_8-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), 'vit_large_patch16_224.augreg_in21k': _cfg( url='https://storage.googleapis.com/vit_models/augreg/L_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1.npz', + hf_hub_id='timm/', custom_load=True, num_classes=21843), # SAM trained models (https://arxiv.org/abs/2106.01548) 'vit_base_patch32_224.sam': _cfg( - url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz', custom_load=True), + url='https://storage.googleapis.com/vit_models/sam/ViT-B_32.npz', custom_load=True, + hf_hub_id='timm/'), 'vit_base_patch16_224.sam': _cfg( - url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz', custom_load=True), + url='https://storage.googleapis.com/vit_models/sam/ViT-B_16.npz', custom_load=True, + hf_hub_id='timm/'), # DINO pretrained - https://arxiv.org/abs/2104.14294 (no classifier head, for fine-tune only) 'vit_small_patch16_224.dino': _cfg( url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall16_pretrain/dino_deitsmall16_pretrain.pth', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0), 'vit_small_patch8_224.dino': _cfg( url='https://dl.fbaipublicfiles.com/dino/dino_deitsmall8_pretrain/dino_deitsmall8_pretrain.pth', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0), 'vit_base_patch16_224.dino': _cfg( url='https://dl.fbaipublicfiles.com/dino/dino_vitbase16_pretrain/dino_vitbase16_pretrain.pth', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0), 'vit_base_patch8_224.dino': _cfg( url='https://dl.fbaipublicfiles.com/dino/dino_vitbase8_pretrain/dino_vitbase8_pretrain.pth', + hf_hub_id='timm/', mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, num_classes=0), - # ViT ImageNet-21K-P pretraining by MILL 'vit_base_patch16_224_miil.in21k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_in21k_miil-887286df.pth', + hf_hub_id='timm/', mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear', num_classes=11221), 'vit_base_patch16_224_miil.in21k_ft_in1k': _cfg( url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tresnet/vit_base_patch16_224_1k_miil_84_4-2deb18e3.pth', + hf_hub_id='timm/', mean=(0., 0., 0.), std=(1., 1., 1.), crop_pct=0.875, interpolation='bilinear'), - # custom timm variants + # Custom timm variants 'vit_base_patch16_rpn_224.in1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth'), + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_base_patch16_rpn_224-sw-3b07e89d.pth', + hf_hub_id='timm/'), 'vit_medium_patch16_gap_240.in12k': _cfg( - hf_hub_id='timm/vit_medium_patch16_gap_240.in12k', + hf_hub_id='timm/', input_size=(3, 240, 240), crop_pct=0.95, num_classes=11821), 'vit_medium_patch16_gap_256.in12k_ft_in1k': _cfg( - hf_hub_id='timm/vit_medium_patch16_gap_256.in12k_ft_in1k', + hf_hub_id='timm/', input_size=(3, 256, 256), crop_pct=0.95), 'vit_medium_patch16_gap_384.in12k_ft_in1k': _cfg( - hf_hub_id='timm/vit_medium_patch16_gap_384.in12k_ft_in1k', + hf_hub_id='timm/', input_size=(3, 384, 384), crop_pct=0.95, crop_mode='squash'), 'vit_base_patch16_gap_224': _cfg(), # CLIP pretrained image tower and related fine-tuned weights - 'vit_base_patch32_clip_224.laion2b': _cfg( - hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K', - hf_hub_filename='open_clip_pytorch_model.bin', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), - 'vit_base_patch16_clip_224.laion2b': _cfg( - #hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K', - hf_hub_filename='open_clip_pytorch_model.bin', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512), - 'vit_large_patch14_clip_224.laion2b': _cfg( - hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K', - hf_hub_filename='open_clip_pytorch_model.bin', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=768), - 'vit_huge_patch14_clip_224.laion2b': _cfg( - hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K', - hf_hub_filename='open_clip_pytorch_model.bin', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024), - 'vit_giant_patch14_clip_224.laion2b': _cfg( - hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K', - hf_hub_filename='open_clip_pytorch_model.bin', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024), - - 'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), - 'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.laion2b_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), - 'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_384.laion2b_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, - crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'), - 'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.laion2b_ft_in1k', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0), - 'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_336.laion2b_ft_in1k', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, - crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), - 'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg( - hf_hub_id='timm/vit_huge_patch14_clip_224.laion2b_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), - 'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg( - hf_hub_id='', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, - crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), - 'vit_base_patch32_clip_224.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), 'vit_base_patch32_clip_384.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_384.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 384, 384)), 'vit_base_patch32_clip_448.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_448.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 448, 448)), 'vit_base_patch16_clip_224.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95), 'vit_base_patch16_clip_384.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_384.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'), 'vit_large_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0), 'vit_large_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_336.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), 'vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_huge_patch14_clip_224.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), 'vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_huge_patch14_clip_336.laion2b_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), - 'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg( - #hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), - 'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.laion2b_ft_in12k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), - 'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.laion2b_ft_in12k', - mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821), - 'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg( - hf_hub_id='timm/vit_huge_patch14_clip_224.laion2b_ft_in12k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821), - - 'vit_base_patch32_clip_224.openai': _cfg( - hf_hub_id='timm/clip_vit_base_patch32_224.openai', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), - 'vit_base_patch16_clip_224.openai': _cfg( - hf_hub_id='timm/clip_vit_base_patch16_224.openai', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), - 'vit_large_patch14_clip_224.openai': _cfg( - hf_hub_id='timm/clip_vit_large_patch14_224.openai', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768), - - 'vit_base_patch32_clip_224.openai_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), - 'vit_base_patch16_clip_224.openai_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.openai_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), - 'vit_base_patch16_clip_384.openai_ft_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_384.openai_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, - crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'), - 'vit_large_patch14_clip_224.openai_ft_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.openai_ft_in1k', - mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), - 'vit_base_patch32_clip_224.openai_ft_in12k_in1k': _cfg( - #hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k', + # hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k_in1k', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), 'vit_base_patch32_clip_384.openai_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch32_clip_384.openai_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'), 'vit_base_patch16_clip_224.openai_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.openai_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95), 'vit_base_patch16_clip_384.openai_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_384.openai_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=0.95, input_size=(3, 384, 384), crop_mode='squash'), 'vit_large_patch14_clip_224.openai_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.openai_ft_in12k_in1k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), 'vit_large_patch14_clip_336.openai_ft_in12k_in1k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_336.openai_ft_in12k_in1k', + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), + + 'vit_base_patch32_clip_224.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), + 'vit_base_patch16_clip_224.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), + 'vit_base_patch16_clip_384.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'), + 'vit_large_patch14_clip_224.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0), + 'vit_large_patch14_clip_336.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, + crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), + 'vit_huge_patch14_clip_224.laion2b_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), + 'vit_huge_patch14_clip_336.laion2b_ft_in1k': _cfg( + hf_hub_id='', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, input_size=(3, 336, 336), crop_mode='squash'), + 'vit_base_patch32_clip_224.openai_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), + 'vit_base_patch16_clip_224.openai_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD), + 'vit_base_patch16_clip_384.openai_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, + crop_pct=1.0, input_size=(3, 384, 384), crop_mode='squash'), + 'vit_large_patch14_clip_224.openai_ft_in1k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0), + + 'vit_base_patch32_clip_224.laion2b_ft_in12k': _cfg( + #hf_hub_id='timm/vit_base_patch32_clip_224.laion2b_ft_in12k', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), + 'vit_base_patch16_clip_224.laion2b_ft_in12k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), + 'vit_large_patch14_clip_224.laion2b_ft_in12k': _cfg( + hf_hub_id='timm/', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=11821), + 'vit_huge_patch14_clip_224.laion2b_ft_in12k': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821), + 'vit_base_patch32_clip_224.openai_ft_in12k': _cfg( - #hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k', + # hf_hub_id='timm/vit_base_patch32_clip_224.openai_ft_in12k', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), 'vit_base_patch16_clip_224.openai_ft_in12k': _cfg( - hf_hub_id='timm/vit_base_patch16_clip_224.openai_ft_in12k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=11821), 'vit_large_patch14_clip_224.openai_ft_in12k': _cfg( - hf_hub_id='timm/vit_large_patch14_clip_224.openai_ft_in12k', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=11821), + 'vit_base_patch32_clip_224.laion2b': _cfg( + hf_hub_id='laion/CLIP-ViT-B-32-laion2B-s34B-b79K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), + 'vit_base_patch16_clip_224.laion2b': _cfg( + # hf_hub_id='laion/CLIP-ViT-B-16-laion2B-s34B-b88K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=512), + 'vit_large_patch14_clip_224.laion2b': _cfg( + hf_hub_id='laion/CLIP-ViT-L-14-laion2B-s32B-b82K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD, crop_pct=1.0, num_classes=768), + 'vit_huge_patch14_clip_224.laion2b': _cfg( + hf_hub_id='laion/CLIP-ViT-H-14-laion2B-s32B-b79K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024), + 'vit_giant_patch14_clip_224.laion2b': _cfg( + hf_hub_id='laion/CLIP-ViT-g-14-laion2B-s12B-b42K', + hf_hub_filename='open_clip_pytorch_model.bin', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=1024), + + 'vit_base_patch32_clip_224.openai': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), + 'vit_base_patch16_clip_224.openai': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, num_classes=512), + 'vit_large_patch14_clip_224.openai': _cfg( + hf_hub_id='timm/', + mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, crop_pct=1.0, num_classes=768), + # experimental (may be removed) 'vit_base_patch32_plus_256': _cfg(url='', input_size=(3, 256, 256), crop_pct=0.95), 'vit_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240), crop_pct=0.95), @@ -942,21 +1050,81 @@ default_cfgs = generate_default_cfgs({ # EVA fine-tuned weights from MAE style MIM - EVA-CLIP target pretrain # https://github.com/baaivision/EVA/blob/7ecf2c0a370d97967e86d047d7af9188f78d2df3/eva/README.md#eva-l-learning-better-mim-representations-from-eva-clip 'eva_large_patch14_196.in22k_ft_in22k_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_21k_to_1k_ft_88p6.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_21k_to_1k_ft_88p6.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 196, 196), crop_pct=1.0), 'eva_large_patch14_336.in22k_ft_in22k_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_21k_to_1k_ft_89p2.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_21k_to_1k_ft_89p2.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'), 'eva_large_patch14_196.in22k_ft_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_1k_ft_88p0.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_196px_1k_ft_88p0.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 196, 196), crop_pct=1.0), 'eva_large_patch14_336.in22k_ft_in1k': _cfg( - hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_1k_ft_88p65.pt', + # hf_hub_id='BAAI/EVA', hf_hub_filename='eva_l_psz14_336px_1k_ft_88p65.pt', + hf_hub_id='timm/', mean=OPENAI_CLIP_MEAN, std=OPENAI_CLIP_STD, input_size=(3, 336, 336), crop_pct=1.0, crop_mode='squash'), + + 'flexivit_small.1200ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_small.600ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_600ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_small.300ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_s_i1k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + + 'flexivit_base.1200ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_base.600ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_600ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_base.300ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i1k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_base.1000ep_in21k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_1000ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843), + 'flexivit_base.300ep_in21k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_b_i21k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843), + + 'flexivit_large.1200ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_large.600ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_600ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + 'flexivit_large.300ep_in1k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/flexivit_l_i1k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95), + + 'flexivit_base.patch16_in21k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/vit_b16_i21k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843), + 'flexivit_base.patch30_in21k': _cfg( + url='https://storage.googleapis.com/big_vision/flexivit/vit_b30_i21k_300ep.npz', custom_load=True, + hf_hub_id='timm/', + input_size=(3, 240, 240), crop_pct=0.95, num_classes=21843), }) @@ -964,9 +1132,16 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs): if kwargs.get('features_only', None): raise RuntimeError('features_only not implemented for Vision Transformer models.') + if 'flexi' in variant: + # FIXME Google FlexiViT pretrained models have a strong preference for bilinear patch / embed + # interpolation, other pretrained models resize better w/ anti-aliased bicubic interpolation. + _filter_fn = partial(checkpoint_filter_fn, interpolation='bilinear', antialias=False) + else: + _filter_fn = checkpoint_filter_fn + return build_model_with_cfg( VisionTransformer, variant, pretrained, - pretrained_filter_fn=checkpoint_filter_fn, + pretrained_filter_fn=_filter_fn, **kwargs, ) @@ -975,8 +1150,8 @@ def _create_vision_transformer(variant, pretrained=False, **kwargs): def vit_tiny_patch16_224(pretrained=False, **kwargs): """ ViT-Tiny (Vit-Ti/16) """ - model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs) - model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3) + model = _create_vision_transformer('vit_tiny_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -984,8 +1159,8 @@ def vit_tiny_patch16_224(pretrained=False, **kwargs): def vit_tiny_patch16_384(pretrained=False, **kwargs): """ ViT-Tiny (Vit-Ti/16) @ 384x384. """ - model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs) - model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3) + model = _create_vision_transformer('vit_tiny_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -993,8 +1168,8 @@ def vit_tiny_patch16_384(pretrained=False, **kwargs): def vit_small_patch32_224(pretrained=False, **kwargs): """ ViT-Small (ViT-S/32) """ - model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs) - model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6) + model = _create_vision_transformer('vit_small_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1002,8 +1177,8 @@ def vit_small_patch32_224(pretrained=False, **kwargs): def vit_small_patch32_384(pretrained=False, **kwargs): """ ViT-Small (ViT-S/32) at 384x384. """ - model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs) - model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6) + model = _create_vision_transformer('vit_small_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1011,8 +1186,8 @@ def vit_small_patch32_384(pretrained=False, **kwargs): def vit_small_patch16_224(pretrained=False, **kwargs): """ ViT-Small (ViT-S/16) """ - model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs) - model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6) + model = _create_vision_transformer('vit_small_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1020,8 +1195,8 @@ def vit_small_patch16_224(pretrained=False, **kwargs): def vit_small_patch16_384(pretrained=False, **kwargs): """ ViT-Small (ViT-S/16) """ - model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs) - model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6) + model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1029,8 +1204,8 @@ def vit_small_patch16_384(pretrained=False, **kwargs): def vit_small_patch8_224(pretrained=False, **kwargs): """ ViT-Small (ViT-S/8) """ - model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs) - model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6) + model = _create_vision_transformer('vit_small_patch8_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1039,8 +1214,8 @@ def vit_base_patch32_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12) + model = _create_vision_transformer('vit_base_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1049,8 +1224,8 @@ def vit_base_patch32_384(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=768, depth=12, num_heads=12) + model = _create_vision_transformer('vit_base_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1059,8 +1234,8 @@ def vit_base_patch16_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12) + model = _create_vision_transformer('vit_base_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1069,8 +1244,8 @@ def vit_base_patch16_384(pretrained=False, **kwargs): """ ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12) + model = _create_vision_transformer('vit_base_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1079,8 +1254,8 @@ def vit_base_patch8_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/8) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12, **kwargs) - model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=8, embed_dim=768, depth=12, num_heads=12) + model = _create_vision_transformer('vit_base_patch8_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1088,8 +1263,8 @@ def vit_base_patch8_224(pretrained=False, **kwargs): def vit_large_patch32_224(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). No pretrained weights. """ - model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16) + model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1098,8 +1273,8 @@ def vit_large_patch32_384(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=1024, depth=24, num_heads=16) + model = _create_vision_transformer('vit_large_patch32_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1108,8 +1283,8 @@ def vit_large_patch16_224(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 224x224, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16) + model = _create_vision_transformer('vit_large_patch16_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1118,8 +1293,8 @@ def vit_large_patch16_384(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929). ImageNet-1k weights fine-tuned from in21k @ 384x384, source https://github.com/google-research/vision_transformer. """ - model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16) + model = _create_vision_transformer('vit_large_patch16_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1127,8 +1302,8 @@ def vit_large_patch16_384(pretrained=False, **kwargs): def vit_large_patch14_224(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/14) """ - model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16) + model = _create_vision_transformer('vit_large_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1136,8 +1311,8 @@ def vit_large_patch14_224(pretrained=False, **kwargs): def vit_huge_patch14_224(pretrained=False, **kwargs): """ ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929). """ - model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16) + model = _create_vision_transformer('vit_huge_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1145,8 +1320,8 @@ def vit_huge_patch14_224(pretrained=False, **kwargs): def vit_giant_patch14_224(pretrained=False, **kwargs): """ ViT-Giant (little-g) model (ViT-g/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560 """ - model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16) + model = _create_vision_transformer('vit_giant_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1154,8 +1329,9 @@ def vit_giant_patch14_224(pretrained=False, **kwargs): def vit_gigantic_patch14_224(pretrained=False, **kwargs): """ ViT-Gigantic (big-G) model (ViT-G/14) from `Scaling Vision Transformers` - https://arxiv.org/abs/2106.04560 """ - model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16, **kwargs) - model = _create_vision_transformer('vit_gigantic_patch14_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1664, mlp_ratio=64/13, depth=48, num_heads=16) + model = _create_vision_transformer( + 'vit_gigantic_patch14_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1164,8 +1340,9 @@ def vit_base_patch16_224_miil(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929). Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K """ - model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, **kwargs) - model = _create_vision_transformer('vit_base_patch16_224_miil', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False) + model = _create_vision_transformer( + 'vit_base_patch16_224_miil', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1175,8 +1352,9 @@ def vit_medium_patch16_gap_240(pretrained=False, **kwargs): """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False, - global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs) - model = _create_vision_transformer('vit_medium_patch16_gap_240', pretrained=pretrained, **model_kwargs) + global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False) + model = _create_vision_transformer( + 'vit_medium_patch16_gap_240', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1186,8 +1364,9 @@ def vit_medium_patch16_gap_256(pretrained=False, **kwargs): """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False, - global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs) - model = _create_vision_transformer('vit_medium_patch16_gap_256', pretrained=pretrained, **model_kwargs) + global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False) + model = _create_vision_transformer( + 'vit_medium_patch16_gap_256', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1197,8 +1376,9 @@ def vit_medium_patch16_gap_384(pretrained=False, **kwargs): """ model_kwargs = dict( patch_size=16, embed_dim=512, depth=12, num_heads=8, class_token=False, - global_pool=kwargs.get('global_pool', 'avg'), qkv_bias=False, init_values=1e-6, fc_norm=False, **kwargs) - model = _create_vision_transformer('vit_medium_patch16_gap_384', pretrained=pretrained, **model_kwargs) + global_pool='avg', qkv_bias=False, init_values=1e-6, fc_norm=False) + model = _create_vision_transformer( + 'vit_medium_patch16_gap_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1207,9 +1387,9 @@ def vit_base_patch16_gap_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/o class token, w/ avg-pool @ 256x256 """ model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, - global_pool=kwargs.get('global_pool', 'avg'), fc_norm=False, **kwargs) - model = _create_vision_transformer('vit_base_patch16_gap_224', pretrained=pretrained, **model_kwargs) + patch_size=16, embed_dim=768, depth=12, num_heads=16, class_token=False, global_pool='avg', fc_norm=False) + model = _create_vision_transformer( + 'vit_base_patch16_gap_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1218,8 +1398,9 @@ def vit_base_patch32_clip_224(pretrained=False, **kwargs): """ ViT-B/32 CLIP image tower @ 224x224 """ model_kwargs = dict( - patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_base_patch32_clip_224', pretrained=pretrained, **model_kwargs) + patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_base_patch32_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1228,8 +1409,9 @@ def vit_base_patch32_clip_384(pretrained=False, **kwargs): """ ViT-B/32 CLIP image tower @ 384x384 """ model_kwargs = dict( - patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_base_patch32_clip_384', pretrained=pretrained, **model_kwargs) + patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_base_patch32_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1238,8 +1420,9 @@ def vit_base_patch32_clip_448(pretrained=False, **kwargs): """ ViT-B/32 CLIP image tower @ 448x448 """ model_kwargs = dict( - patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_base_patch32_clip_448', pretrained=pretrained, **model_kwargs) + patch_size=32, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_base_patch32_clip_448', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1247,9 +1430,9 @@ def vit_base_patch32_clip_448(pretrained=False, **kwargs): def vit_base_patch16_clip_224(pretrained=False, **kwargs): """ ViT-B/16 CLIP image tower """ - model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_base_patch16_clip_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_base_patch16_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1257,9 +1440,9 @@ def vit_base_patch16_clip_224(pretrained=False, **kwargs): def vit_base_patch16_clip_384(pretrained=False, **kwargs): """ ViT-B/16 CLIP image tower @ 384x384 """ - model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_base_patch16_clip_384', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_base_patch16_clip_384', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1267,9 +1450,9 @@ def vit_base_patch16_clip_384(pretrained=False, **kwargs): def vit_large_patch14_clip_224(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/14) CLIP image tower """ - model_kwargs = dict( - patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_large_patch14_clip_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_large_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1277,9 +1460,9 @@ def vit_large_patch14_clip_224(pretrained=False, **kwargs): def vit_large_patch14_clip_336(pretrained=False, **kwargs): """ ViT-Large model (ViT-L/14) CLIP image tower @ 336x336 """ - model_kwargs = dict( - patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_large_patch14_clip_336', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_large_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1287,9 +1470,9 @@ def vit_large_patch14_clip_336(pretrained=False, **kwargs): def vit_huge_patch14_clip_224(pretrained=False, **kwargs): """ ViT-Huge model (ViT-H/14) CLIP image tower. """ - model_kwargs = dict( - patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_huge_patch14_clip_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_huge_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1297,9 +1480,9 @@ def vit_huge_patch14_clip_224(pretrained=False, **kwargs): def vit_huge_patch14_clip_336(pretrained=False, **kwargs): """ ViT-Huge model (ViT-H/14) CLIP image tower @ 336x336 """ - model_kwargs = dict( - patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_huge_patch14_clip_336', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1280, depth=32, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_huge_patch14_clip_336', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1309,9 +1492,9 @@ def vit_giant_patch14_clip_224(pretrained=False, **kwargs): Pretrained weights from CLIP image tower. """ model_kwargs = dict( - patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, - pre_norm=True, norm_layer=nn.LayerNorm, **kwargs) - model = _create_vision_transformer('vit_giant_patch14_clip_224', pretrained=pretrained, **model_kwargs) + patch_size=14, embed_dim=1408, mlp_ratio=48/11, depth=40, num_heads=16, pre_norm=True, norm_layer=nn.LayerNorm) + model = _create_vision_transformer( + 'vit_giant_patch14_clip_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1321,8 +1504,9 @@ def vit_giant_patch14_clip_224(pretrained=False, **kwargs): def vit_base_patch32_plus_256(pretrained=False, **kwargs): """ ViT-Base (ViT-B/32+) """ - model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs) - model = _create_vision_transformer('vit_base_patch32_plus_256', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=32, embed_dim=896, depth=12, num_heads=14, init_values=1e-5) + model = _create_vision_transformer( + 'vit_base_patch32_plus_256', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1330,8 +1514,9 @@ def vit_base_patch32_plus_256(pretrained=False, **kwargs): def vit_base_patch16_plus_240(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16+) """ - model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5, **kwargs) - model = _create_vision_transformer('vit_base_patch16_plus_240', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=896, depth=12, num_heads=14, init_values=1e-5) + model = _create_vision_transformer( + 'vit_base_patch16_plus_240', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1340,9 +1525,10 @@ def vit_base_patch16_rpn_224(pretrained=False, **kwargs): """ ViT-Base (ViT-B/16) w/ residual post-norm """ model_kwargs = dict( - patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, class_token=False, - block_fn=ResPostBlock, global_pool=kwargs.pop('global_pool', 'avg'), **kwargs) - model = _create_vision_transformer('vit_base_patch16_rpn_224', pretrained=pretrained, **model_kwargs) + patch_size=16, embed_dim=768, depth=12, num_heads=12, qkv_bias=False, init_values=1e-5, + class_token=False, block_fn=ResPostBlock, global_pool='avg') + model = _create_vision_transformer( + 'vit_base_patch16_rpn_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1352,8 +1538,9 @@ def vit_small_patch16_36x1_224(pretrained=False, **kwargs): Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795 Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow. """ - model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5, **kwargs) - model = _create_vision_transformer('vit_small_patch16_36x1_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=384, depth=36, num_heads=6, init_values=1e-5) + model = _create_vision_transformer( + 'vit_small_patch16_36x1_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1364,8 +1551,9 @@ def vit_small_patch16_18x2_224(pretrained=False, **kwargs): Paper focuses on 24x2 + 48x1 for 'Small' width but those are extremely slow. """ model_kwargs = dict( - patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock, **kwargs) - model = _create_vision_transformer('vit_small_patch16_18x2_224', pretrained=pretrained, **model_kwargs) + patch_size=16, embed_dim=384, depth=18, num_heads=6, init_values=1e-5, block_fn=ParallelBlock) + model = _create_vision_transformer( + 'vit_small_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @@ -1374,25 +1562,51 @@ def vit_base_patch16_18x2_224(pretrained=False, **kwargs): """ ViT-Base w/ LayerScale + 18 x 2 (36 block parallel) config. Experimental, may remove. Based on `Three things everyone should know about Vision Transformers` - https://arxiv.org/abs/2203.09795 """ - model_kwargs = dict( - patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock, **kwargs) - model = _create_vision_transformer('vit_base_patch16_18x2_224', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=16, embed_dim=768, depth=18, num_heads=12, init_values=1e-5, block_fn=ParallelBlock) + model = _create_vision_transformer( + 'vit_base_patch16_18x2_224', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @register_model def eva_large_patch14_196(pretrained=False, **kwargs): """ EVA-large model https://arxiv.org/abs/2211.07636 /via MAE MIM pretrain""" - model_kwargs = dict( - patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg', **kwargs) - model = _create_vision_transformer('eva_large_patch14_196', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg') + model = _create_vision_transformer( + 'eva_large_patch14_196', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model @register_model def eva_large_patch14_336(pretrained=False, **kwargs): """ EVA-large model https://arxiv.org/abs/2211.07636 via MAE MIM pretrain""" - model_kwargs = dict( - patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg', **kwargs) - model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **model_kwargs) + model_kwargs = dict(patch_size=14, embed_dim=1024, depth=24, num_heads=16, global_pool='avg') + model = _create_vision_transformer('eva_large_patch14_336', pretrained=pretrained, **dict(model_kwargs, **kwargs)) + return model + + +@register_model +def flexivit_small(pretrained=False, **kwargs): + """ FlexiViT-Small + """ + model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, no_embed_class=True) + model = _create_vision_transformer('flexivit_small', pretrained=pretrained, **dict(model_kwargs, **kwargs)) + return model + + +@register_model +def flexivit_base(pretrained=False, **kwargs): + """ FlexiViT-Base + """ + model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, no_embed_class=True) + model = _create_vision_transformer('flexivit_base', pretrained=pretrained, **dict(model_kwargs, **kwargs)) + return model + + +@register_model +def flexivit_large(pretrained=False, **kwargs): + """ FlexiViT-Large + """ + model_kwargs = dict(patch_size=16, embed_dim=1024, depth=24, num_heads=16, no_embed_class=True) + model = _create_vision_transformer('flexivit_large', pretrained=pretrained, **dict(model_kwargs, **kwargs)) return model diff --git a/timm/models/vision_transformer_hybrid.py b/timm/models/vision_transformer_hybrid.py index cfdd0a0e..bec7989c 100644 --- a/timm/models/vision_transformer_hybrid.py +++ b/timm/models/vision_transformer_hybrid.py @@ -27,72 +27,6 @@ from .resnetv2 import ResNetV2, create_resnetv2_stem from .vision_transformer import _create_vision_transformer -def _cfg(url='', **kwargs): - return { - 'url': url, - 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, - 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, - 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), - 'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head', - **kwargs - } - - -default_cfgs = generate_default_cfgs({ - # hybrid in-1k models (weights from official JAX impl where they exist) - 'vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', - custom_load=True, - first_conv='patch_embed.backbone.conv'), - 'vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', - first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0, custom_load=True), - 'vit_small_r26_s32_224.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz', - custom_load=True, - ), - 'vit_small_r26_s32_384.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', - input_size=(3, 384, 384), crop_pct=1.0, custom_load=True), - 'vit_base_r26_s32_224.untrained': _cfg(), - 'vit_base_r50_s16_384.v1_in21k_ft_in1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth', - input_size=(3, 384, 384), crop_pct=1.0), - 'vit_large_r50_s32_224.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz', - custom_load=True, - ), - 'vit_large_r50_s32_384.augreg_in21k_ft_in1k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz', - input_size=(3, 384, 384), crop_pct=1.0, custom_load=True, - ), - - # hybrid in-21k models (weights from official Google JAX impl where they exist) - 'vit_tiny_r_s16_p8_224.augreg_in21k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz', - num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv', custom_load=True), - 'vit_small_r26_s32_224.augreg_in21k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz', - num_classes=21843, crop_pct=0.9, custom_load=True), - 'vit_base_r50_s16_224.v1_in21k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth', - num_classes=21843, crop_pct=0.9), - 'vit_large_r50_s32_224.augreg_in21k': _cfg( - url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz', - num_classes=21843, crop_pct=0.9, custom_load=True), - - # hybrid models (using timm resnet backbones) - 'vit_small_resnet26d_224': _cfg( - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), - 'vit_small_resnet50d_s16_224': _cfg( - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), - 'vit_base_resnet26d_224': _cfg( - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), - 'vit_base_resnet50d_224': _cfg( - mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), -}) - - class HybridEmbed(nn.Module): """ CNN Feature Map Embedding Extract feature map from CNN, flatten, project to embedding dim. @@ -166,6 +100,83 @@ def _resnetv2(layers=(3, 4, 9), **kwargs): return backbone +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': (0.5, 0.5, 0.5), 'std': (0.5, 0.5, 0.5), + 'first_conv': 'patch_embed.backbone.stem.conv', 'classifier': 'head', + **kwargs + } + + +default_cfgs = generate_default_cfgs({ + # hybrid in-1k models (weights from official JAX impl where they exist) + 'vit_tiny_r_s16_p8_224.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', + custom_load=True, + first_conv='patch_embed.backbone.conv'), + 'vit_tiny_r_s16_p8_384.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', + first_conv='patch_embed.backbone.conv', input_size=(3, 384, 384), crop_pct=1.0, custom_load=True), + 'vit_small_r26_s32_224.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_light0-wd_0.03-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.03-res_224.npz', + hf_hub_id='timm/', + custom_load=True, + ), + 'vit_small_r26_s32_384.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.03-res_384.npz', + hf_hub_id='timm/', + input_size=(3, 384, 384), crop_pct=1.0, custom_load=True), + 'vit_base_r26_s32_224.untrained': _cfg(), + 'vit_base_r50_s16_384.orig_in21k_ft_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth', + hf_hub_id='timm/', + input_size=(3, 384, 384), crop_pct=1.0), + 'vit_large_r50_s32_224.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.1-sd_0.1--imagenet2012-steps_20k-lr_0.01-res_224.npz', + hf_hub_id='timm/', + custom_load=True, + ), + 'vit_large_r50_s32_384.augreg_in21k_ft_in1k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0--imagenet2012-steps_20k-lr_0.01-res_384.npz', + hf_hub_id='timm/', + input_size=(3, 384, 384), crop_pct=1.0, custom_load=True, + ), + + # hybrid in-21k models (weights from official Google JAX impl where they exist) + 'vit_tiny_r_s16_p8_224.augreg_in21k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R_Ti_16-i21k-300ep-lr_0.001-aug_none-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', + num_classes=21843, crop_pct=0.9, first_conv='patch_embed.backbone.conv', custom_load=True), + 'vit_small_r26_s32_224.augreg_in21k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R26_S_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.03-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', + num_classes=21843, crop_pct=0.9, custom_load=True), + 'vit_base_r50_s16_224.orig_in21k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_224_in21k-6f7c7740.pth', + hf_hub_id='timm/', + num_classes=21843, crop_pct=0.9), + 'vit_large_r50_s32_224.augreg_in21k': _cfg( + url='https://storage.googleapis.com/vit_models/augreg/R50_L_32-i21k-300ep-lr_0.001-aug_medium2-wd_0.1-do_0.0-sd_0.0.npz', + hf_hub_id='timm/', + num_classes=21843, crop_pct=0.9, custom_load=True), + + # hybrid models (using timm resnet backbones) + 'vit_small_resnet26d_224.untrained': _cfg( + mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), + 'vit_small_resnet50d_s16_224.untrained': _cfg( + mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), + 'vit_base_resnet26d_224.untrained': _cfg( + mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), + 'vit_base_resnet50d_224.untrained': _cfg( + mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, first_conv='patch_embed.backbone.conv1.0'), +}) + + @register_model def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs): """ R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224. diff --git a/timm/models/vision_transformer_relpos.py b/timm/models/vision_transformer_relpos.py index 1a7c2f40..a7cf3e53 100644 --- a/timm/models/vision_transformer_relpos.py +++ b/timm/models/vision_transformer_relpos.py @@ -11,12 +11,12 @@ from typing import Optional, Tuple import torch import torch.nn as nn -import torch.nn.functional as F from torch.utils.checkpoint import checkpoint from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD -from timm.layers import PatchEmbed, Mlp, DropPath, trunc_normal_ +from timm.layers import PatchEmbed, Mlp, DropPath, RelPosMlp, RelPosBias from ._builder import build_model_with_cfg +from ._pretrained import generate_default_cfgs from ._registry import register_model __all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint fn to this @@ -24,216 +24,6 @@ __all__ = ['VisionTransformerRelPos'] # model_registry will add each entrypoint _logger = logging.getLogger(__name__) -def _cfg(url='', **kwargs): - return { - 'url': url, - 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, - 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, - 'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, - 'first_conv': 'patch_embed.proj', 'classifier': 'head', - **kwargs - } - - -default_cfgs = { - 'vit_relpos_base_patch32_plus_rpn_256': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth', - input_size=(3, 256, 256)), - 'vit_relpos_base_patch16_plus_240': _cfg(url='', input_size=(3, 240, 240)), - - 'vit_relpos_small_patch16_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth'), - 'vit_relpos_medium_patch16_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth'), - 'vit_relpos_base_patch16_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth'), - - 'vit_srelpos_small_patch16_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth'), - 'vit_srelpos_medium_patch16_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth'), - - 'vit_relpos_medium_patch16_cls_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth'), - 'vit_relpos_base_patch16_cls_224': _cfg( - url=''), - 'vit_relpos_base_patch16_clsgap_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth'), - - 'vit_relpos_small_patch16_rpn_224': _cfg(url=''), - 'vit_relpos_medium_patch16_rpn_224': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth'), - 'vit_relpos_base_patch16_rpn_224': _cfg(url=''), -} - - -def gen_relative_position_index( - q_size: Tuple[int, int], - k_size: Tuple[int, int] = None, - class_token: bool = False) -> torch.Tensor: - # Adapted with significant modifications from Swin / BeiT codebases - # get pair-wise relative position index for each token inside the window - q_coords = torch.stack(torch.meshgrid([torch.arange(q_size[0]), torch.arange(q_size[1])])).flatten(1) # 2, Wh, Ww - if k_size is None: - k_coords = q_coords - k_size = q_size - else: - # different q vs k sizes is a WIP - k_coords = torch.stack(torch.meshgrid([torch.arange(k_size[0]), torch.arange(k_size[1])])).flatten(1) - relative_coords = q_coords[:, :, None] - k_coords[:, None, :] # 2, Wh*Ww, Wh*Ww - relative_coords = relative_coords.permute(1, 2, 0) # Wh*Ww, Wh*Ww, 2 - _, relative_position_index = torch.unique(relative_coords.view(-1, 2), return_inverse=True, dim=0) - - if class_token: - # handle cls to token & token 2 cls & cls to cls as per beit for rel pos bias - # NOTE not intended or tested with MLP log-coords - max_size = (max(q_size[0], k_size[0]), max(q_size[1], k_size[1])) - num_relative_distance = (2 * max_size[0] - 1) * (2 * max_size[1] - 1) + 3 - relative_position_index = F.pad(relative_position_index, [1, 0, 1, 0]) - relative_position_index[0, 0:] = num_relative_distance - 3 - relative_position_index[0:, 0] = num_relative_distance - 2 - relative_position_index[0, 0] = num_relative_distance - 1 - - return relative_position_index.contiguous() - - -def gen_relative_log_coords( - win_size: Tuple[int, int], - pretrained_win_size: Tuple[int, int] = (0, 0), - mode='swin', -): - assert mode in ('swin', 'cr', 'rw') - # as per official swin-v2 impl, supporting timm specific 'cr' and 'rw' log coords as well - relative_coords_h = torch.arange(-(win_size[0] - 1), win_size[0], dtype=torch.float32) - relative_coords_w = torch.arange(-(win_size[1] - 1), win_size[1], dtype=torch.float32) - relative_coords_table = torch.stack(torch.meshgrid([relative_coords_h, relative_coords_w])) - relative_coords_table = relative_coords_table.permute(1, 2, 0).contiguous() # 2*Wh-1, 2*Ww-1, 2 - if mode == 'swin': - if pretrained_win_size[0] > 0: - relative_coords_table[:, :, 0] /= (pretrained_win_size[0] - 1) - relative_coords_table[:, :, 1] /= (pretrained_win_size[1] - 1) - else: - relative_coords_table[:, :, 0] /= (win_size[0] - 1) - relative_coords_table[:, :, 1] /= (win_size[1] - 1) - relative_coords_table *= 8 # normalize to -8, 8 - relative_coords_table = torch.sign(relative_coords_table) * torch.log2( - 1.0 + relative_coords_table.abs()) / math.log2(8) - else: - if mode == 'rw': - # cr w/ window size normalization -> [-1,1] log coords - relative_coords_table[:, :, 0] /= (win_size[0] - 1) - relative_coords_table[:, :, 1] /= (win_size[1] - 1) - relative_coords_table *= 8 # scale to -8, 8 - relative_coords_table = torch.sign(relative_coords_table) * torch.log2( - 1.0 + relative_coords_table.abs()) - relative_coords_table /= math.log2(9) # -> [-1, 1] - else: - # mode == 'cr' - relative_coords_table = torch.sign(relative_coords_table) * torch.log( - 1.0 + relative_coords_table.abs()) - - return relative_coords_table - - -class RelPosMlp(nn.Module): - def __init__( - self, - window_size, - num_heads=8, - hidden_dim=128, - prefix_tokens=0, - mode='cr', - pretrained_window_size=(0, 0) - ): - super().__init__() - self.window_size = window_size - self.window_area = self.window_size[0] * self.window_size[1] - self.prefix_tokens = prefix_tokens - self.num_heads = num_heads - self.bias_shape = (self.window_area,) * 2 + (num_heads,) - if mode == 'swin': - self.bias_act = nn.Sigmoid() - self.bias_gain = 16 - mlp_bias = (True, False) - elif mode == 'rw': - self.bias_act = nn.Tanh() - self.bias_gain = 4 - mlp_bias = True - else: - self.bias_act = nn.Identity() - self.bias_gain = None - mlp_bias = True - - self.mlp = Mlp( - 2, # x, y - hidden_features=hidden_dim, - out_features=num_heads, - act_layer=nn.ReLU, - bias=mlp_bias, - drop=(0.125, 0.) - ) - - self.register_buffer( - "relative_position_index", - gen_relative_position_index(window_size), - persistent=False) - - # get relative_coords_table - self.register_buffer( - "rel_coords_log", - gen_relative_log_coords(window_size, pretrained_window_size, mode=mode), - persistent=False) - - def get_bias(self) -> torch.Tensor: - relative_position_bias = self.mlp(self.rel_coords_log) - if self.relative_position_index is not None: - relative_position_bias = relative_position_bias.view(-1, self.num_heads)[ - self.relative_position_index.view(-1)] # Wh*Ww,Wh*Ww,nH - relative_position_bias = relative_position_bias.view(self.bias_shape) - relative_position_bias = relative_position_bias.permute(2, 0, 1) - relative_position_bias = self.bias_act(relative_position_bias) - if self.bias_gain is not None: - relative_position_bias = self.bias_gain * relative_position_bias - if self.prefix_tokens: - relative_position_bias = F.pad(relative_position_bias, [self.prefix_tokens, 0, self.prefix_tokens, 0]) - return relative_position_bias.unsqueeze(0).contiguous() - - def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): - return attn + self.get_bias() - - -class RelPosBias(nn.Module): - - def __init__(self, window_size, num_heads, prefix_tokens=0): - super().__init__() - assert prefix_tokens <= 1 - self.window_size = window_size - self.window_area = window_size[0] * window_size[1] - self.bias_shape = (self.window_area + prefix_tokens,) * 2 + (num_heads,) - - num_relative_distance = (2 * window_size[0] - 1) * (2 * window_size[1] - 1) + 3 * prefix_tokens - self.relative_position_bias_table = nn.Parameter(torch.zeros(num_relative_distance, num_heads)) - self.register_buffer( - "relative_position_index", - gen_relative_position_index(self.window_size, class_token=prefix_tokens > 0), - persistent=False, - ) - - self.init_weights() - - def init_weights(self): - trunc_normal_(self.relative_position_bias_table, std=.02) - - def get_bias(self) -> torch.Tensor: - relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)] - # win_h * win_w, win_h * win_w, num_heads - relative_position_bias = relative_position_bias.view(self.bias_shape).permute(2, 0, 1) - return relative_position_bias.unsqueeze(0).contiguous() - - def forward(self, attn, shared_rel_pos: Optional[torch.Tensor] = None): - return attn + self.get_bias() - - class RelPosAttention(nn.Module): def __init__(self, dim, num_heads=8, qkv_bias=False, rel_pos_cls=None, attn_drop=0., proj_drop=0.): super().__init__() @@ -513,6 +303,57 @@ def _create_vision_transformer_relpos(variant, pretrained=False, **kwargs): return model +def _cfg(url='', **kwargs): + return { + 'url': url, + 'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None, + 'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True, + 'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD, + 'first_conv': 'patch_embed.proj', 'classifier': 'head', + **kwargs + } + + +default_cfgs = generate_default_cfgs({ + 'vit_relpos_base_patch32_plus_rpn_256.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_replos_base_patch32_plus_rpn_256-sw-dd486f51.pth', + hf_hub_id='timm/', + input_size=(3, 256, 256)), + 'vit_relpos_base_patch16_plus_240.untrained': _cfg(url='', input_size=(3, 240, 240)), + + 'vit_relpos_small_patch16_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_small_patch16_224-sw-ec2778b4.pth', + hf_hub_id='timm/'), + 'vit_relpos_medium_patch16_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_224-sw-11c174af.pth', + hf_hub_id='timm/'), + 'vit_relpos_base_patch16_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_224-sw-49049aed.pth', + hf_hub_id='timm/'), + + 'vit_srelpos_small_patch16_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_small_patch16_224-sw-6cdb8849.pth', + hf_hub_id='timm/'), + 'vit_srelpos_medium_patch16_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_srelpos_medium_patch16_224-sw-ad702b8c.pth', + hf_hub_id='timm/'), + + 'vit_relpos_medium_patch16_cls_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_cls_224-sw-cfe8e259.pth', + hf_hub_id='timm/'), + 'vit_relpos_base_patch16_cls_224.untrained': _cfg(), + 'vit_relpos_base_patch16_clsgap_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_base_patch16_gapcls_224-sw-1a341d6c.pth', + hf_hub_id='timm/'), + + 'vit_relpos_small_patch16_rpn_224.untrained': _cfg(), + 'vit_relpos_medium_patch16_rpn_224.sw_in1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-tpu-weights/vit_relpos_medium_patch16_rpn_224-sw-5d2befd8.pth', + hf_hub_id='timm/'), + 'vit_relpos_base_patch16_rpn_224.untrained': _cfg(), +}) + + @register_model def vit_relpos_base_patch32_plus_rpn_256(pretrained=False, **kwargs): """ ViT-Base (ViT-B/32+) w/ relative log-coord position and residual post-norm, no class token diff --git a/timm/models/vovnet.py b/timm/models/vovnet.py index bf0e4f89..8aea5802 100644 --- a/timm/models/vovnet.py +++ b/timm/models/vovnet.py @@ -181,8 +181,18 @@ class SequentialAppendList(nn.Sequential): class OsaBlock(nn.Module): def __init__( - self, in_chs, mid_chs, out_chs, layer_per_block, residual=False, - depthwise=False, attn='', norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path=None): + self, + in_chs, + mid_chs, + out_chs, + layer_per_block, + residual=False, + depthwise=False, + attn='', + norm_layer=BatchNormAct2d, + act_layer=nn.ReLU, + drop_path=None, + ): super(OsaBlock, self).__init__() self.residual = residual @@ -232,9 +242,20 @@ class OsaBlock(nn.Module): class OsaStage(nn.Module): def __init__( - self, in_chs, mid_chs, out_chs, block_per_stage, layer_per_block, downsample=True, - residual=True, depthwise=False, attn='ese', norm_layer=BatchNormAct2d, act_layer=nn.ReLU, - drop_path_rates=None): + self, + in_chs, + mid_chs, + out_chs, + block_per_stage, + layer_per_block, + downsample=True, + residual=True, + depthwise=False, + attn='ese', + norm_layer=BatchNormAct2d, + act_layer=nn.ReLU, + drop_path_rates=None, + ): super(OsaStage, self).__init__() self.grad_checkpointing = False @@ -270,16 +291,38 @@ class OsaStage(nn.Module): class VovNet(nn.Module): def __init__( - self, cfg, in_chans=3, num_classes=1000, global_pool='avg', drop_rate=0., stem_stride=4, - output_stride=32, norm_layer=BatchNormAct2d, act_layer=nn.ReLU, drop_path_rate=0.): - """ VovNet (v2) + self, + cfg, + in_chans=3, + num_classes=1000, + global_pool='avg', + output_stride=32, + norm_layer=BatchNormAct2d, + act_layer=nn.ReLU, + drop_rate=0., + drop_path_rate=0., + **kwargs, + ): + """ + Args: + cfg (dict): Model architecture configuration + in_chans (int): Number of input channels (default: 3) + num_classes (int): Number of classifier classes (default: 1000) + global_pool (str): Global pooling type (default: 'avg') + output_stride (int): Output stride of network, one of (8, 16, 32) (default: 32) + norm_layer (Union[str, nn.Module]): normalization layer + act_layer (Union[str, nn.Module]): activation layer + drop_rate (float): Dropout rate (default: 0.) + drop_path_rate (float): Stochastic depth drop-path rate (default: 0.) + kwargs (dict): Extra kwargs overlayed onto cfg """ super(VovNet, self).__init__() self.num_classes = num_classes self.drop_rate = drop_rate - assert stem_stride in (4, 2) assert output_stride == 32 # FIXME support dilation + cfg = dict(cfg, **kwargs) + stem_stride = cfg.get("stem_stride", 4) stem_chs = cfg["stem_chs"] stage_conv_chs = cfg["stage_conv_chs"] stage_out_chs = cfg["stage_out_chs"] @@ -307,9 +350,15 @@ class VovNet(nn.Module): for i in range(4): # num_stages downsample = stem_stride == 2 or i > 0 # first stage has no stride/downsample if stem_stride is 4 stages += [OsaStage( - in_ch_list[i], stage_conv_chs[i], stage_out_chs[i], block_per_stage[i], layer_per_block, - downsample=downsample, drop_path_rates=stage_dpr[i], **stage_args) - ] + in_ch_list[i], + stage_conv_chs[i], + stage_out_chs[i], + block_per_stage[i], + layer_per_block, + downsample=downsample, + drop_path_rates=stage_dpr[i], + **stage_args, + )] self.num_features = stage_out_chs[i] current_stride *= 2 if downsample else 1 self.feature_info += [dict(num_chs=self.num_features, reduction=current_stride, module=f'stages.{i}')] @@ -324,7 +373,6 @@ class VovNet(nn.Module): elif isinstance(m, nn.Linear): nn.init.zeros_(m.bias) - @torch.jit.ignore def group_matcher(self, coarse=False): return dict( diff --git a/timm/utils/__init__.py b/timm/utils/__init__.py index a9ff0c78..7727adff 100644 --- a/timm/utils/__init__.py +++ b/timm/utils/__init__.py @@ -8,7 +8,7 @@ from .distributed import distribute_bn, reduce_tensor, init_distributed_device,\ from .jit import set_jit_legacy, set_jit_fuser from .log import setup_default_logging, FormatterNoInfo from .metrics import AverageMeter, accuracy -from .misc import natural_key, add_bool_arg +from .misc import natural_key, add_bool_arg, ParseKwargs from .model import unwrap_model, get_state_dict, freeze, unfreeze from .model_ema import ModelEma, ModelEmaV2 from .random import random_seed diff --git a/timm/utils/misc.py b/timm/utils/misc.py index 39c0097c..326a50f7 100644 --- a/timm/utils/misc.py +++ b/timm/utils/misc.py @@ -2,6 +2,8 @@ Hacked together by / Copyright 2020 Ross Wightman """ +import argparse +import ast import re @@ -16,3 +18,15 @@ def add_bool_arg(parser, name, default=False, help=''): group.add_argument('--' + name, dest=dest_name, action='store_true', help=help) group.add_argument('--no-' + name, dest=dest_name, action='store_false', help=help) parser.set_defaults(**{dest_name: default}) + + +class ParseKwargs(argparse.Action): + def __call__(self, parser, namespace, values, option_string=None): + kw = {} + for value in values: + key, value = value.split('=') + try: + kw[key] = ast.literal_eval(value) + except ValueError: + kw[key] = str(value) # fallback to string (avoid need to escape on command line) + setattr(namespace, self.dest, kw) diff --git a/timm/utils/model.py b/timm/utils/model.py index b95c4539..d74ee5b7 100644 --- a/timm/utils/model.py +++ b/timm/utils/model.py @@ -7,6 +7,8 @@ import fnmatch import torch from torchvision.ops.misc import FrozenBatchNorm2d +from timm.layers import BatchNormAct2d, SyncBatchNormAct, FrozenBatchNormAct2d,\ + freeze_batch_norm_2d, unfreeze_batch_norm_2d from .model_ema import ModelEma @@ -100,70 +102,6 @@ def extract_spp_stats( return hook.stats -def freeze_batch_norm_2d(module): - """ - Converts all `BatchNorm2d` and `SyncBatchNorm` layers of provided module into `FrozenBatchNorm2d`. If `module` is - itself an instance of either `BatchNorm2d` or `SyncBatchNorm`, it is converted into `FrozenBatchNorm2d` and - returned. Otherwise, the module is walked recursively and submodules are converted in place. - - Args: - module (torch.nn.Module): Any PyTorch module. - - Returns: - torch.nn.Module: Resulting module - - Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 - """ - res = module - if isinstance(module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)): - res = FrozenBatchNorm2d(module.num_features) - res.num_features = module.num_features - res.affine = module.affine - if module.affine: - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data - res.running_var.data = module.running_var.data - res.eps = module.eps - else: - for name, child in module.named_children(): - new_child = freeze_batch_norm_2d(child) - if new_child is not child: - res.add_module(name, new_child) - return res - - -def unfreeze_batch_norm_2d(module): - """ - Converts all `FrozenBatchNorm2d` layers of provided module into `BatchNorm2d`. If `module` is itself and instance - of `FrozenBatchNorm2d`, it is converted into `BatchNorm2d` and returned. Otherwise, the module is walked - recursively and submodules are converted in place. - - Args: - module (torch.nn.Module): Any PyTorch module. - - Returns: - torch.nn.Module: Resulting module - - Inspired by https://github.com/pytorch/pytorch/blob/a5895f85be0f10212791145bfedc0261d364f103/torch/nn/modules/batchnorm.py#L762 - """ - res = module - if isinstance(module, FrozenBatchNorm2d): - res = torch.nn.BatchNorm2d(module.num_features) - if module.affine: - res.weight.data = module.weight.data.clone().detach() - res.bias.data = module.bias.data.clone().detach() - res.running_mean.data = module.running_mean.data - res.running_var.data = module.running_var.data - res.eps = module.eps - else: - for name, child in module.named_children(): - new_child = unfreeze_batch_norm_2d(child) - if new_child is not child: - res.add_module(name, new_child) - return res - - def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, mode='freeze'): """ Freeze or unfreeze parameters of the specified modules and those of all their hierarchical descendants. This is @@ -179,7 +117,12 @@ def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, """ assert mode in ["freeze", "unfreeze"], '`mode` must be one of "freeze" or "unfreeze"' - if isinstance(root_module, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)): + if isinstance(root_module, ( + torch.nn.modules.batchnorm.BatchNorm2d, + torch.nn.modules.batchnorm.SyncBatchNorm, + BatchNormAct2d, + SyncBatchNormAct, + )): # Raise assertion here because we can't convert it in place raise AssertionError( "You have provided a batch norm layer as the `root module`. Please use " @@ -213,13 +156,18 @@ def _freeze_unfreeze(root_module, submodules=[], include_bn_running_stats=True, # It's possible that `m` is a type of BatchNorm in itself, in which case `unfreeze_batch_norm_2d` won't # convert it in place, but will return the converted result. In this case `res` holds the converted # result and we may try to re-assign the named module - if isinstance(m, (torch.nn.modules.batchnorm.BatchNorm2d, torch.nn.modules.batchnorm.SyncBatchNorm)): + if isinstance(m, ( + torch.nn.modules.batchnorm.BatchNorm2d, + torch.nn.modules.batchnorm.SyncBatchNorm, + BatchNormAct2d, + SyncBatchNormAct, + )): _add_submodule(root_module, n, res) # Unfreeze batch norm else: res = unfreeze_batch_norm_2d(m) # Ditto. See note above in mode == 'freeze' branch - if isinstance(m, FrozenBatchNorm2d): + if isinstance(m, (FrozenBatchNorm2d, FrozenBatchNormAct2d)): _add_submodule(root_module, n, res) diff --git a/timm/version.py b/timm/version.py index 0716d38a..b285df69 100644 --- a/timm/version.py +++ b/timm/version.py @@ -1 +1 @@ -__version__ = '0.8.1dev0' +__version__ = '0.8.6dev0' diff --git a/train.py b/train.py index e51d7c90..9f450ab8 100755 --- a/train.py +++ b/train.py @@ -89,56 +89,58 @@ parser.add_argument('--data-dir', metavar='DIR', parser.add_argument('--dataset', metavar='NAME', default='', help='dataset type + name ("/") (default: ImageFolder or ImageTar if empty)') group.add_argument('--train-split', metavar='NAME', default='train', - help='dataset train split (default: train)') + help='dataset train split (default: train)') group.add_argument('--val-split', metavar='NAME', default='validation', - help='dataset validation split (default: validation)') + help='dataset validation split (default: validation)') group.add_argument('--dataset-download', action='store_true', default=False, - help='Allow download of dataset for torch/ and tfds/ datasets that support it.') + help='Allow download of dataset for torch/ and tfds/ datasets that support it.') group.add_argument('--class-map', default='', type=str, metavar='FILENAME', - help='path to class to idx mapping file (default: "")') + help='path to class to idx mapping file (default: "")') # Model parameters group = parser.add_argument_group('Model parameters') group.add_argument('--model', default='resnet50', type=str, metavar='MODEL', - help='Name of model to train (default: "resnet50")') + help='Name of model to train (default: "resnet50")') group.add_argument('--pretrained', action='store_true', default=False, - help='Start with pretrained version of specified network (if avail)') + help='Start with pretrained version of specified network (if avail)') group.add_argument('--initial-checkpoint', default='', type=str, metavar='PATH', - help='Initialize model from this checkpoint (default: none)') + help='Initialize model from this checkpoint (default: none)') group.add_argument('--resume', default='', type=str, metavar='PATH', - help='Resume full model and optimizer state from checkpoint (default: none)') + help='Resume full model and optimizer state from checkpoint (default: none)') group.add_argument('--no-resume-opt', action='store_true', default=False, - help='prevent resume of optimizer state when resuming model') + help='prevent resume of optimizer state when resuming model') group.add_argument('--num-classes', type=int, default=None, metavar='N', - help='number of label classes (Model default if None)') + help='number of label classes (Model default if None)') group.add_argument('--gp', default=None, type=str, metavar='POOL', - help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.') + help='Global pool type, one of (fast, avg, max, avgmax, avgmaxc). Model default if None.') group.add_argument('--img-size', type=int, default=None, metavar='N', - help='Image size (default: None => model default)') + help='Image size (default: None => model default)') group.add_argument('--in-chans', type=int, default=None, metavar='N', - help='Image input channels (default: None => 3)') + help='Image input channels (default: None => 3)') group.add_argument('--input-size', default=None, nargs=3, type=int, - metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') + metavar='N N N', + help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') group.add_argument('--crop-pct', default=None, type=float, - metavar='N', help='Input image center crop percent (for validation only)') + metavar='N', help='Input image center crop percent (for validation only)') group.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', - help='Override mean pixel value of dataset') + help='Override mean pixel value of dataset') group.add_argument('--std', type=float, nargs='+', default=None, metavar='STD', - help='Override std deviation of dataset') + help='Override std deviation of dataset') group.add_argument('--interpolation', default='', type=str, metavar='NAME', - help='Image resize interpolation type (overrides model)') + help='Image resize interpolation type (overrides model)') group.add_argument('-b', '--batch-size', type=int, default=128, metavar='N', - help='Input batch size for training (default: 128)') + help='Input batch size for training (default: 128)') group.add_argument('-vb', '--validation-batch-size', type=int, default=None, metavar='N', - help='Validation batch size override (default: None)') + help='Validation batch size override (default: None)') group.add_argument('--channels-last', action='store_true', default=False, - help='Use channels_last memory layout') + help='Use channels_last memory layout') group.add_argument('--fuser', default='', type=str, - help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") + help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") group.add_argument('--grad-checkpointing', action='store_true', default=False, - help='Enable gradient checkpointing through model blocks/stages') + help='Enable gradient checkpointing through model blocks/stages') group.add_argument('--fast-norm', default=False, action='store_true', - help='enable experimental fast-norm') + help='enable experimental fast-norm') +group.add_argument('--model-kwargs', nargs='*', default={}, action=utils.ParseKwargs) scripting_group = group.add_mutually_exclusive_group() scripting_group.add_argument('--torchscript', dest='torchscript', action='store_true', @@ -151,199 +153,200 @@ scripting_group.add_argument('--aot-autograd', default=False, action='store_true # Optimizer parameters group = parser.add_argument_group('Optimizer parameters') group.add_argument('--opt', default='sgd', type=str, metavar='OPTIMIZER', - help='Optimizer (default: "sgd")') + help='Optimizer (default: "sgd")') group.add_argument('--opt-eps', default=None, type=float, metavar='EPSILON', - help='Optimizer Epsilon (default: None, use opt default)') + help='Optimizer Epsilon (default: None, use opt default)') group.add_argument('--opt-betas', default=None, type=float, nargs='+', metavar='BETA', - help='Optimizer Betas (default: None, use opt default)') + help='Optimizer Betas (default: None, use opt default)') group.add_argument('--momentum', type=float, default=0.9, metavar='M', - help='Optimizer momentum (default: 0.9)') + help='Optimizer momentum (default: 0.9)') group.add_argument('--weight-decay', type=float, default=2e-5, - help='weight decay (default: 2e-5)') + help='weight decay (default: 2e-5)') group.add_argument('--clip-grad', type=float, default=None, metavar='NORM', - help='Clip gradient norm (default: None, no clipping)') + help='Clip gradient norm (default: None, no clipping)') group.add_argument('--clip-mode', type=str, default='norm', - help='Gradient clipping mode. One of ("norm", "value", "agc")') + help='Gradient clipping mode. One of ("norm", "value", "agc")') group.add_argument('--layer-decay', type=float, default=None, - help='layer-wise learning rate decay (default: None)') + help='layer-wise learning rate decay (default: None)') +group.add_argument('--opt-kwargs', nargs='*', default={}, action=utils.ParseKwargs) # Learning rate schedule parameters group = parser.add_argument_group('Learning rate schedule parameters') group.add_argument('--sched', type=str, default='cosine', metavar='SCHEDULER', - help='LR scheduler (default: "step"') + help='LR scheduler (default: "step"') group.add_argument('--sched-on-updates', action='store_true', default=False, - help='Apply LR scheduler step on update instead of epoch end.') + help='Apply LR scheduler step on update instead of epoch end.') group.add_argument('--lr', type=float, default=None, metavar='LR', - help='learning rate, overrides lr-base if set (default: None)') + help='learning rate, overrides lr-base if set (default: None)') group.add_argument('--lr-base', type=float, default=0.1, metavar='LR', - help='base learning rate: lr = lr_base * global_batch_size / base_size') + help='base learning rate: lr = lr_base * global_batch_size / base_size') group.add_argument('--lr-base-size', type=int, default=256, metavar='DIV', - help='base learning rate batch size (divisor, default: 256).') + help='base learning rate batch size (divisor, default: 256).') group.add_argument('--lr-base-scale', type=str, default='', metavar='SCALE', - help='base learning rate vs batch_size scaling ("linear", "sqrt", based on opt if empty)') + help='base learning rate vs batch_size scaling ("linear", "sqrt", based on opt if empty)') group.add_argument('--lr-noise', type=float, nargs='+', default=None, metavar='pct, pct', - help='learning rate noise on/off epoch percentages') + help='learning rate noise on/off epoch percentages') group.add_argument('--lr-noise-pct', type=float, default=0.67, metavar='PERCENT', - help='learning rate noise limit percent (default: 0.67)') + help='learning rate noise limit percent (default: 0.67)') group.add_argument('--lr-noise-std', type=float, default=1.0, metavar='STDDEV', - help='learning rate noise std-dev (default: 1.0)') + help='learning rate noise std-dev (default: 1.0)') group.add_argument('--lr-cycle-mul', type=float, default=1.0, metavar='MULT', - help='learning rate cycle len multiplier (default: 1.0)') + help='learning rate cycle len multiplier (default: 1.0)') group.add_argument('--lr-cycle-decay', type=float, default=0.5, metavar='MULT', - help='amount to decay each learning rate cycle (default: 0.5)') + help='amount to decay each learning rate cycle (default: 0.5)') group.add_argument('--lr-cycle-limit', type=int, default=1, metavar='N', - help='learning rate cycle limit, cycles enabled if > 1') + help='learning rate cycle limit, cycles enabled if > 1') group.add_argument('--lr-k-decay', type=float, default=1.0, - help='learning rate k-decay for cosine/poly (default: 1.0)') + help='learning rate k-decay for cosine/poly (default: 1.0)') group.add_argument('--warmup-lr', type=float, default=1e-5, metavar='LR', - help='warmup learning rate (default: 1e-5)') + help='warmup learning rate (default: 1e-5)') group.add_argument('--min-lr', type=float, default=0, metavar='LR', - help='lower lr bound for cyclic schedulers that hit 0 (default: 0)') + help='lower lr bound for cyclic schedulers that hit 0 (default: 0)') group.add_argument('--epochs', type=int, default=300, metavar='N', - help='number of epochs to train (default: 300)') + help='number of epochs to train (default: 300)') group.add_argument('--epoch-repeats', type=float, default=0., metavar='N', - help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).') + help='epoch repeat multiplier (number of times to repeat dataset epoch per train epoch).') group.add_argument('--start-epoch', default=None, type=int, metavar='N', - help='manual epoch number (useful on restarts)') + help='manual epoch number (useful on restarts)') group.add_argument('--decay-milestones', default=[90, 180, 270], type=int, nargs='+', metavar="MILESTONES", - help='list of decay epoch indices for multistep lr. must be increasing') + help='list of decay epoch indices for multistep lr. must be increasing') group.add_argument('--decay-epochs', type=float, default=90, metavar='N', - help='epoch interval to decay LR') + help='epoch interval to decay LR') group.add_argument('--warmup-epochs', type=int, default=5, metavar='N', - help='epochs to warmup LR, if scheduler supports') + help='epochs to warmup LR, if scheduler supports') group.add_argument('--warmup-prefix', action='store_true', default=False, - help='Exclude warmup period from decay schedule.'), + help='Exclude warmup period from decay schedule.'), group.add_argument('--cooldown-epochs', type=int, default=0, metavar='N', - help='epochs to cooldown LR at min_lr, after cyclic schedule ends') + help='epochs to cooldown LR at min_lr, after cyclic schedule ends') group.add_argument('--patience-epochs', type=int, default=10, metavar='N', - help='patience epochs for Plateau LR scheduler (default: 10)') + help='patience epochs for Plateau LR scheduler (default: 10)') group.add_argument('--decay-rate', '--dr', type=float, default=0.1, metavar='RATE', - help='LR decay rate (default: 0.1)') + help='LR decay rate (default: 0.1)') # Augmentation & regularization parameters group = parser.add_argument_group('Augmentation and regularization parameters') group.add_argument('--no-aug', action='store_true', default=False, - help='Disable all training augmentation, override other train aug args') + help='Disable all training augmentation, override other train aug args') group.add_argument('--scale', type=float, nargs='+', default=[0.08, 1.0], metavar='PCT', - help='Random resize scale (default: 0.08 1.0)') -group.add_argument('--ratio', type=float, nargs='+', default=[3./4., 4./3.], metavar='RATIO', - help='Random resize aspect ratio (default: 0.75 1.33)') + help='Random resize scale (default: 0.08 1.0)') +group.add_argument('--ratio', type=float, nargs='+', default=[3. / 4., 4. / 3.], metavar='RATIO', + help='Random resize aspect ratio (default: 0.75 1.33)') group.add_argument('--hflip', type=float, default=0.5, - help='Horizontal flip training aug probability') + help='Horizontal flip training aug probability') group.add_argument('--vflip', type=float, default=0., - help='Vertical flip training aug probability') + help='Vertical flip training aug probability') group.add_argument('--color-jitter', type=float, default=0.4, metavar='PCT', - help='Color jitter factor (default: 0.4)') + help='Color jitter factor (default: 0.4)') group.add_argument('--aa', type=str, default=None, metavar='NAME', - help='Use AutoAugment policy. "v0" or "original". (default: None)'), + help='Use AutoAugment policy. "v0" or "original". (default: None)'), group.add_argument('--aug-repeats', type=float, default=0, - help='Number of augmentation repetitions (distributed training only) (default: 0)') + help='Number of augmentation repetitions (distributed training only) (default: 0)') group.add_argument('--aug-splits', type=int, default=0, - help='Number of augmentation splits (default: 0, valid: 0 or >=2)') + help='Number of augmentation splits (default: 0, valid: 0 or >=2)') group.add_argument('--jsd-loss', action='store_true', default=False, - help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.') + help='Enable Jensen-Shannon Divergence + CE loss. Use with `--aug-splits`.') group.add_argument('--bce-loss', action='store_true', default=False, - help='Enable BCE loss w/ Mixup/CutMix use.') + help='Enable BCE loss w/ Mixup/CutMix use.') group.add_argument('--bce-target-thresh', type=float, default=None, - help='Threshold for binarizing softened BCE targets (default: None, disabled)') + help='Threshold for binarizing softened BCE targets (default: None, disabled)') group.add_argument('--reprob', type=float, default=0., metavar='PCT', - help='Random erase prob (default: 0.)') + help='Random erase prob (default: 0.)') group.add_argument('--remode', type=str, default='pixel', - help='Random erase mode (default: "pixel")') + help='Random erase mode (default: "pixel")') group.add_argument('--recount', type=int, default=1, - help='Random erase count (default: 1)') + help='Random erase count (default: 1)') group.add_argument('--resplit', action='store_true', default=False, - help='Do not random erase first (clean) augmentation split') + help='Do not random erase first (clean) augmentation split') group.add_argument('--mixup', type=float, default=0.0, - help='mixup alpha, mixup enabled if > 0. (default: 0.)') + help='mixup alpha, mixup enabled if > 0. (default: 0.)') group.add_argument('--cutmix', type=float, default=0.0, - help='cutmix alpha, cutmix enabled if > 0. (default: 0.)') + help='cutmix alpha, cutmix enabled if > 0. (default: 0.)') group.add_argument('--cutmix-minmax', type=float, nargs='+', default=None, - help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)') + help='cutmix min/max ratio, overrides alpha and enables cutmix if set (default: None)') group.add_argument('--mixup-prob', type=float, default=1.0, - help='Probability of performing mixup or cutmix when either/both is enabled') + help='Probability of performing mixup or cutmix when either/both is enabled') group.add_argument('--mixup-switch-prob', type=float, default=0.5, - help='Probability of switching to cutmix when both mixup and cutmix enabled') + help='Probability of switching to cutmix when both mixup and cutmix enabled') group.add_argument('--mixup-mode', type=str, default='batch', - help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') + help='How to apply mixup/cutmix params. Per "batch", "pair", or "elem"') group.add_argument('--mixup-off-epoch', default=0, type=int, metavar='N', - help='Turn off mixup after this epoch, disabled if 0 (default: 0)') + help='Turn off mixup after this epoch, disabled if 0 (default: 0)') group.add_argument('--smoothing', type=float, default=0.1, - help='Label smoothing (default: 0.1)') + help='Label smoothing (default: 0.1)') group.add_argument('--train-interpolation', type=str, default='random', - help='Training interpolation (random, bilinear, bicubic default: "random")') + help='Training interpolation (random, bilinear, bicubic default: "random")') group.add_argument('--drop', type=float, default=0.0, metavar='PCT', - help='Dropout rate (default: 0.)') + help='Dropout rate (default: 0.)') group.add_argument('--drop-connect', type=float, default=None, metavar='PCT', - help='Drop connect rate, DEPRECATED, use drop-path (default: None)') + help='Drop connect rate, DEPRECATED, use drop-path (default: None)') group.add_argument('--drop-path', type=float, default=None, metavar='PCT', - help='Drop path rate (default: None)') + help='Drop path rate (default: None)') group.add_argument('--drop-block', type=float, default=None, metavar='PCT', - help='Drop block rate (default: None)') + help='Drop block rate (default: None)') # Batch norm parameters (only works with gen_efficientnet based models currently) group = parser.add_argument_group('Batch norm parameters', 'Only works with gen_efficientnet based models currently.') group.add_argument('--bn-momentum', type=float, default=None, - help='BatchNorm momentum override (if not None)') + help='BatchNorm momentum override (if not None)') group.add_argument('--bn-eps', type=float, default=None, - help='BatchNorm epsilon override (if not None)') + help='BatchNorm epsilon override (if not None)') group.add_argument('--sync-bn', action='store_true', - help='Enable NVIDIA Apex or Torch synchronized BatchNorm.') + help='Enable NVIDIA Apex or Torch synchronized BatchNorm.') group.add_argument('--dist-bn', type=str, default='reduce', - help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")') + help='Distribute BatchNorm stats between nodes after each epoch ("broadcast", "reduce", or "")') group.add_argument('--split-bn', action='store_true', - help='Enable separate BN layers per augmentation split.') + help='Enable separate BN layers per augmentation split.') # Model Exponential Moving Average group = parser.add_argument_group('Model exponential moving average parameters') group.add_argument('--model-ema', action='store_true', default=False, - help='Enable tracking moving average of model weights') + help='Enable tracking moving average of model weights') group.add_argument('--model-ema-force-cpu', action='store_true', default=False, - help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.') + help='Force ema to be tracked on CPU, rank=0 node only. Disables EMA validation.') group.add_argument('--model-ema-decay', type=float, default=0.9998, - help='decay factor for model weights moving average (default: 0.9998)') + help='decay factor for model weights moving average (default: 0.9998)') # Misc group = parser.add_argument_group('Miscellaneous parameters') group.add_argument('--seed', type=int, default=42, metavar='S', - help='random seed (default: 42)') + help='random seed (default: 42)') group.add_argument('--worker-seeding', type=str, default='all', - help='worker seed mode (default: all)') + help='worker seed mode (default: all)') group.add_argument('--log-interval', type=int, default=50, metavar='N', - help='how many batches to wait before logging training status') + help='how many batches to wait before logging training status') group.add_argument('--recovery-interval', type=int, default=0, metavar='N', - help='how many batches to wait before writing recovery checkpoint') + help='how many batches to wait before writing recovery checkpoint') group.add_argument('--checkpoint-hist', type=int, default=10, metavar='N', - help='number of checkpoints to keep (default: 10)') + help='number of checkpoints to keep (default: 10)') group.add_argument('-j', '--workers', type=int, default=4, metavar='N', - help='how many training processes to use (default: 4)') + help='how many training processes to use (default: 4)') group.add_argument('--save-images', action='store_true', default=False, - help='save images of input bathes every log interval for debugging') + help='save images of input bathes every log interval for debugging') group.add_argument('--amp', action='store_true', default=False, - help='use NVIDIA Apex AMP or Native AMP for mixed precision training') + help='use NVIDIA Apex AMP or Native AMP for mixed precision training') group.add_argument('--amp-dtype', default='float16', type=str, - help='lower precision AMP dtype (default: float16)') + help='lower precision AMP dtype (default: float16)') group.add_argument('--amp-impl', default='native', type=str, - help='AMP impl to use, "native" or "apex" (default: native)') + help='AMP impl to use, "native" or "apex" (default: native)') group.add_argument('--no-ddp-bb', action='store_true', default=False, - help='Force broadcast buffers for native DDP to off.') + help='Force broadcast buffers for native DDP to off.') group.add_argument('--pin-mem', action='store_true', default=False, - help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') + help='Pin CPU memory in DataLoader for more efficient (sometimes) transfer to GPU.') group.add_argument('--no-prefetcher', action='store_true', default=False, - help='disable fast prefetcher') + help='disable fast prefetcher') group.add_argument('--output', default='', type=str, metavar='PATH', - help='path to output folder (default: none, current dir)') + help='path to output folder (default: none, current dir)') group.add_argument('--experiment', default='', type=str, metavar='NAME', - help='name of train experiment, name of sub-folder for output') + help='name of train experiment, name of sub-folder for output') group.add_argument('--eval-metric', default='top1', type=str, metavar='EVAL_METRIC', - help='Best metric (default: "top1"') + help='Best metric (default: "top1"') group.add_argument('--tta', type=int, default=0, metavar='N', - help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)') + help='Test/inference time augmentation (oversampling) factor. 0=None (default: 0)') group.add_argument("--local_rank", default=0, type=int) group.add_argument('--use-multi-epochs-loader', action='store_true', default=False, - help='use the multi-epochs-loader to save time at the beginning of every epoch') + help='use the multi-epochs-loader to save time at the beginning of every epoch') group.add_argument('--log-wandb', action='store_true', default=False, - help='log training and validation metrics to wandb') + help='log training and validation metrics to wandb') def _parse_args(): @@ -371,8 +374,6 @@ def main(): torch.backends.cuda.matmul.allow_tf32 = True torch.backends.cudnn.benchmark = True - if args.data and not args.data_dir: - args.data_dir = args.data args.prefetcher = not args.no_prefetcher device = utils.init_distributed_device(args) if args.distributed: @@ -383,14 +384,6 @@ def main(): _logger.info(f'Training with a single process on 1 device ({args.device}).') assert args.rank >= 0 - if utils.is_primary(args) and args.log_wandb: - if has_wandb: - wandb.init(project=args.experiment, config=args) - else: - _logger.warning( - "You've requested to log metrics to wandb but package not found. " - "Metrics not being logged to wandb, try `pip install wandb`") - # resolve AMP arguments based on PyTorch / Apex availability use_amp = None amp_dtype = torch.float16 @@ -432,6 +425,7 @@ def main(): bn_eps=args.bn_eps, scriptable=args.torchscript, checkpoint_path=args.initial_checkpoint, + **args.model_kwargs, ) if args.num_classes is None: assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.' @@ -504,7 +498,11 @@ def main(): f'Learning rate ({args.lr}) calculated from base learning rate ({args.lr_base}) ' f'and global batch size ({global_batch_size}) with {args.lr_base_scale} scaling.') - optimizer = create_optimizer_v2(model, **optimizer_kwargs(cfg=args)) + optimizer = create_optimizer_v2( + model, + **optimizer_kwargs(cfg=args), + **args.opt_kwargs, + ) # setup automatic mixed-precision (AMP) loss scaling and op casting amp_autocast = suppress # do nothing @@ -559,6 +557,8 @@ def main(): # NOTE: EMA model does not need to be wrapped by DDP # create the train and eval datasets + if args.data and not args.data_dir: + args.data_dir = args.data dataset_train = create_dataset( args.dataset, root=args.data_dir, @@ -712,6 +712,14 @@ def main(): with open(os.path.join(output_dir, 'args.yaml'), 'w') as f: f.write(args_text) + if utils.is_primary(args) and args.log_wandb: + if has_wandb: + wandb.init(project=args.experiment, config=args) + else: + _logger.warning( + "You've requested to log metrics to wandb but package not found. " + "Metrics not being logged to wandb, try `pip install wandb`") + # setup learning rate schedule and starting epoch updates_per_epoch = len(loader_train) lr_scheduler, num_epochs = create_scheduler_v2( diff --git a/validate.py b/validate.py index 4669fbac..b606103d 100755 --- a/validate.py +++ b/validate.py @@ -26,7 +26,7 @@ from timm.data import create_dataset, create_loader, resolve_data_config, RealLa from timm.layers import apply_test_time_pool, set_fast_norm from timm.models import create_model, load_checkpoint, is_model, list_models from timm.utils import accuracy, AverageMeter, natural_key, setup_default_logging, set_jit_fuser, \ - decay_batch_step, check_batch_size_retry + decay_batch_step, check_batch_size_retry, ParseKwargs try: from apex import amp @@ -71,6 +71,8 @@ parser.add_argument('-b', '--batch-size', default=256, type=int, metavar='N', help='mini-batch size (default: 256)') parser.add_argument('--img-size', default=None, type=int, metavar='N', help='Input image dimension, uses model default if empty') +parser.add_argument('--in-chans', type=int, default=None, metavar='N', + help='Image input channels (default: None => 3)') parser.add_argument('--input-size', default=None, nargs=3, type=int, metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') parser.add_argument('--use-train-size', action='store_true', default=False, @@ -123,6 +125,8 @@ parser.add_argument('--fuser', default='', type=str, help="Select jit fuser. One of ('', 'te', 'old', 'nvfuser')") parser.add_argument('--fast-norm', default=False, action='store_true', help='enable experimental fast-norm') +parser.add_argument('--model-kwargs', nargs='*', default={}, action=ParseKwargs) + scripting_group = parser.add_mutually_exclusive_group() scripting_group.add_argument('--torchscript', default=False, action='store_true', @@ -181,13 +185,20 @@ def validate(args): set_fast_norm() # create model + in_chans = 3 + if args.in_chans is not None: + in_chans = args.in_chans + elif args.input_size is not None: + in_chans = args.input_size[0] + model = create_model( args.model, pretrained=args.pretrained, num_classes=args.num_classes, - in_chans=3, + in_chans=in_chans, global_pool=args.gp, scriptable=args.torchscript, + **args.model_kwargs, ) if args.num_classes is None: assert hasattr(model, 'num_classes'), 'Model must have `num_classes` attr if not set on cmd line/config.' @@ -232,8 +243,9 @@ def validate(args): criterion = nn.CrossEntropyLoss().to(device) + root_dir = args.data or args.data_dir dataset = create_dataset( - root=args.data, + root=root_dir, name=args.dataset, split=args.split, download=args.dataset_download, @@ -389,7 +401,7 @@ def main(): if args.model == 'all': # validate all models in a list of names with pretrained checkpoints args.pretrained = True - model_names = list_models(pretrained=True, exclude_filters=['*_in21k', '*_in22k', '*_dino']) + model_names = list_models('convnext*', pretrained=True, exclude_filters=['*_in21k', '*_in22k', '*in12k', '*_dino', '*fcmae']) model_cfgs = [(n, '') for n in model_names] elif not is_model(args.model): # model name doesn't exist, try as wildcard filter