* TinyNet models added by [rsomani95](https://github.com/rsomani95)
* TinyNet models added by [rsomani95](https://github.com/rsomani95)
* LCNet added via MobileNetV3 architecture
* 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))
* 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):
* 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)
* 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
## 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.
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.
* `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))
* 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):
* 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)
* 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
### July 12, 2021
* Add XCiT models from [official facebook impl](https://github.com/facebookresearch/xcit). Contributed by [Alexander Soare](https://github.com/alexander-soare)
* Add XCiT models from [official facebook impl](https://github.com/facebookresearch/xcit). Contributed by [Alexander Soare](https://github.com/alexander-soare)
* 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 🎄☃
* 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:
* 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
* 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
* 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:
* 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):
* (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
### July 27, 2022
* All runtime benchmark and validation result csv files are up-to-date!
* 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)
* TinyNet models added by [rsomani95](https://github.com/rsomani95)
* LCNet added via MobileNetV3 architecture
* 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))
* 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):
* 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)
* 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.
Ross Wightman
2 years ago