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Merge branch 'vit_and_bit_test_fixes' into bits_and_tpu

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Ross Wightman 3 years ago
parent 847b4af144 20a2be14c3
commit 56ed0a0b63
41 changed files with 2895 additions and 2118 deletions
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39
README.md
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@ -23,6 +23,34 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
## What's New
### June 23, 2021
* Reproduce gMLP model training, `gmlp_s16_224` trained to 79.6 top-1, matching [paper](https://arxiv.org/abs/2105.08050).
### 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
* Add ResMLP and gMLP MLP vision models to the existing MLP Mixer impl
@ -163,17 +191,6 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
* 320x320 val, 1.0 crop - 84.36
* Update [results files](results/)
### 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)
## Introduction

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@ -1,5 +1,17 @@
# Archived Changes
### 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.

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@ -1,5 +1,11 @@
# Recent Changes
### 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
@ -122,104 +128,3 @@
* 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 features 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

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results/results-imagenet-a-clean.csv
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@ -5,6 +5,7 @@ swin_large_patch4_window12_384,98.040,1.960,99.690,0.310,196.74,384,1.000,bicubi
tf_efficientnet_b7_ns,97.910,2.090,99.720,0.280,66.35,600,0.949,bicubic
swin_base_patch4_window12_384,97.890,2.110,99.710,0.290,87.90,384,1.000,bicubic
dm_nfnet_f6,97.730,2.270,99.580,0.420,438.36,576,0.956,bicubic
tf_efficientnetv2_l_in21ft1k,97.700,2.300,99.670,0.330,118.52,480,1.000,bicubic
swin_large_patch4_window7_224,97.650,2.350,99.580,0.420,196.53,224,0.900,bicubic
tf_efficientnet_b6_ns,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
@ -13,16 +14,19 @@ dm_nfnet_f4,97.570,2.430,99.520,0.480,316.07,512,0.951,bicubic
tf_efficientnet_b5_ns,97.500,2.500,99.630,0.370,30.39,456,0.934,bicubic
resnetv2_152x4_bitm,97.490,2.510,99.600,0.400,936.53,480,1.000,bilinear
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
cait_m36_384,97.400,2.600,99.510,0.490,271.22,384,1.000,bicubic
dm_nfnet_f3,97.360,2.640,99.580,0.420,254.92,416,0.940,bicubic
ig_resnext101_32x32d,97.360,2.640,99.680,0.320,468.53,224,0.875,bilinear
cait_s36_384,97.330,2.670,99.530,0.470,68.37,384,1.000,bicubic
tf_efficientnetv2_l,97.280,2.720,99.550,0.450,118.52,480,1.000,bicubic
swin_base_patch4_window7_224,97.250,2.750,99.530,0.470,87.77,224,0.900,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_b8,97.200,2.800,99.500,0.500,87.41,672,0.954,bicubic
tf_efficientnet_b7_ap,97.200,2.800,99.540,0.460,66.35,600,0.949,bicubic
swsl_resnext101_32x8d,97.200,2.800,99.570,0.430,88.79,224,0.875,bilinear
vit_base_r50_s16_384,97.180,2.820,99.560,0.440,98.95,384,1.000,bicubic
resnetv2_152x2_bitm,97.150,2.850,99.590,0.410,236.34,480,1.000,bilinear
tf_efficientnetv2_m,97.140,2.860,99.410,0.590,54.14,480,1.000,bicubic
tf_efficientnet_b8_ap,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic
vit_large_patch16_384,97.110,2.890,99.640,0.360,304.72,384,1.000,bicubic
ecaresnet269d,97.080,2.920,99.470,0.530,102.09,352,1.000,bicubic
@ -30,6 +34,7 @@ tf_efficientnet_b6_ap,97.080,2.920,99.620,0.380,43.04,528,0.942,bicubic
cait_s24_384,97.070,2.930,99.430,0.570,47.06,384,1.000,bicubic
resnetv2_101x3_bitm,97.050,2.950,99.520,0.480,387.93,480,1.000,bilinear
tf_efficientnet_b7,97.010,2.990,99.520,0.480,66.35,600,0.949,bicubic
efficientnetv2_rw_m,96.980,3.020,99.540,0.460,53.24,416,1.000,bicubic
dm_nfnet_f2,96.960,3.040,99.450,0.550,193.78,352,0.920,bicubic
vit_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
@ -39,18 +44,19 @@ ig_resnext101_32x16d,96.820,3.180,99.590,0.410,194.03,224,0.875,bilinear
resnetv2_50x3_bitm,96.770,3.230,99.430,0.570,217.32,480,1.000,bilinear
seresnet152d,96.770,3.230,99.450,0.550,66.84,320,1.000,bicubic
resnetrs350,96.760,3.240,99.370,0.630,163.96,384,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,96.730,3.270,99.420,0.580,21.46,384,1.000,bicubic
resnet200d,96.720,3.280,99.330,0.670,64.69,320,1.000,bicubic
eca_nfnet_l1,96.700,3.300,99.270,0.730,41.41,320,1.000,bicubic
vit_base_patch16_384,96.700,3.300,99.510,0.490,86.86,384,1.000,bicubic
eca_nfnet_l1,96.700,3.300,99.270,0.730,41.41,320,1.000,bicubic
resnetrs270,96.690,3.310,99.350,0.650,129.86,352,1.000,bicubic
tf_efficientnet_b5_ap,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_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
resnest200e,96.610,3.390,99.350,0.650,70.20,320,0.909,bicubic
swsl_resnext101_32x16d,96.600,3.400,99.520,0.480,194.03,224,0.875,bilinear
resnetrs152,96.580,3.420,99.240,0.760,86.62,320,1.000,bicubic
cait_xs24_384,96.550,3.450,99.420,0.580,26.67,384,1.000,bicubic
efficientnet_v2s,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic
efficientnetv2_rw_s,96.540,3.460,99.360,0.640,23.94,384,1.000,bicubic
resnetrs200,96.530,3.470,99.350,0.650,93.21,320,1.000,bicubic
resnest269e,96.520,3.480,99.350,0.650,110.93,416,0.928,bicubic
vit_base_patch16_224_miil,96.460,3.540,99.300,0.700,86.54,224,0.875,bilinear
@ -60,15 +66,19 @@ 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_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_efficientnetv2_s,96.340,3.660,99.200,0.800,21.46,384,1.000,bicubic
ig_resnext101_32x8d,96.320,3.680,99.430,0.570,88.79,224,0.875,bilinear
resnet101d,96.290,3.710,99.230,0.770,44.57,320,1.000,bicubic
twins_svt_large,96.270,3.730,99.170,0.830,99.27,224,0.900,bicubic
twins_svt_base,96.160,3.840,99.060,0.940,56.07,224,0.900,bicubic
tf_efficientnet_b4_ap,96.160,3.840,99.280,0.720,19.34,380,0.922,bicubic
efficientnet_b4,96.150,3.850,99.200,0.800,19.34,384,1.000,bicubic
twins_pcpvt_large,96.150,3.850,99.180,0.820,60.99,224,0.900,bicubic
vit_deit_base_patch16_384,96.150,3.850,99.140,0.860,86.86,384,1.000,bicubic
dm_nfnet_f0,96.140,3.860,99.240,0.760,71.49,256,0.900,bicubic
vit_deit_base_distilled_patch16_224,96.090,3.910,99.190,0.810,87.34,224,0.900,bicubic
nfnet_l0,96.090,3.910,99.260,0.740,35.07,288,1.000,bicubic
resnetv2_101x1_bitm,96.090,3.910,99.300,0.700,44.54,480,1.000,bilinear
vit_deit_base_distilled_patch16_224,96.090,3.910,99.190,0.810,87.34,224,0.900,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
vit_large_patch16_224,95.950,4.050,99.240,0.760,304.33,224,0.900,bicubic
@ -77,31 +87,42 @@ 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
swsl_resnext50_32x4d,95.870,4.130,99.250,0.750,25.03,224,0.875,bilinear
resnest101e,95.860,4.140,99.210,0.790,48.28,256,0.875,bilinear
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_384,95.850,4.150,99.090,0.910,17.37,384,1.000,bicubic
vit_large_patch32_384,95.830,4.170,99.150,0.850,306.63,384,1.000,bicubic
vit_base_patch32_384,95.810,4.190,99.150,0.850,88.30,384,1.000,bicubic
ssl_resnext101_32x16d,95.800,4.200,99.180,0.820,194.03,224,0.875,bilinear
twins_pcpvt_base,95.790,4.210,99.130,0.870,43.83,224,0.900,bicubic
tf_efficientnet_b2_ns,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic
tresnet_m,95.720,4.280,99.030,0.970,31.39,224,0.875,bilinear
efficientnet_b3,95.710,4.290,99.040,0.960,12.23,320,1.000,bicubic
pnasnet5large,95.710,4.290,98.920,1.080,86.06,331,0.911,bicubic
efficientnet_b3,95.710,4.290,99.040,0.960,12.23,320,1.000,bicubic
nasnetalarge,95.680,4.320,98.930,1.070,88.75,331,0.911,bicubic
pit_b_224,95.640,4.360,98.660,1.340,73.76,224,0.900,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
ecaresnet50t,95.510,4.490,99.120,0.880,25.57,320,0.950,bicubic
visformer_small,95.490,4.510,98.900,1.100,40.22,224,0.900,bicubic
ssl_resnext101_32x8d,95.470,4.530,99.110,0.890,88.79,224,0.875,bilinear
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
vit_deit_base_patch16_224,95.440,4.560,98.840,1.160,86.57,224,0.900,bicubic
ssl_resnext101_32x4d,95.440,4.560,99.130,0.870,44.18,224,0.875,bilinear
resnetrs101,95.430,4.570,99.030,0.970,63.62,288,0.940,bicubic
swsl_resnet50,95.410,4.590,99.290,0.710,25.56,224,0.875,bilinear
vit_base_patch16_224,95.330,4.670,99.000,1.000,86.57,224,0.900,bicubic
tf_efficientnet_b3_ap,95.320,4.680,98.900,1.100,12.23,300,0.904,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_384,95.260,4.740,98.960,1.040,12.03,384,1.000,bicubic
pit_s_distilled_224,95.240,4.760,99.050,0.950,24.04,224,0.900,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,95.170,4.830,99.110,0.890,7.79,240,0.882,bicubic
tf_efficientnetv2_b3,95.160,4.840,98.820,1.180,14.36,300,0.904,bicubic
swin_tiny_patch4_window7_224,95.140,4.860,98.850,1.150,28.29,224,0.900,bicubic
efficientnet_el,95.120,4.880,98.990,1.010,10.59,300,0.904,bicubic
gernet_l,95.090,4.910,98.900,1.100,31.08,256,0.875,bilinear
@ -113,7 +134,9 @@ 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
tf_efficientnet_b3,95.010,4.990,98.910,1.090,12.23,300,0.904,bicubic
levit_256,95.010,4.990,98.890,1.110,18.89,224,0.900,bicubic
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_200,94.940,5.060,99.010,0.990,16.37,224,0.875,bicubic
gluon_seresnext101_64x4d,94.930,5.070,98.830,1.170,88.23,224,0.875,bicubic
@ -137,8 +160,8 @@ nf_resnet50,94.590,5.410,98.810,1.190,25.56,288,0.940,bicubic
pit_s_224,94.590,5.410,98.710,1.290,23.46,224,0.900,bicubic
repvgg_b3,94.570,5.430,98.780,1.220,123.09,224,0.875,bilinear
seresnet50,94.550,5.450,98.750,1.250,28.09,224,0.875,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
regnety_320,94.540,5.460,98.850,1.150,145.05,224,0.875,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
tf_efficientnet_b2_ap,94.490,5.510,98.620,1.380,9.11,260,0.890,bicubic
@ -147,9 +170,10 @@ 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
regnetx_320,94.460,5.540,98.740,1.260,107.81,224,0.875,bicubic
ssl_resnet50,94.450,5.550,98.920,1.080,25.56,224,0.875,bilinear
tf_efficientnetv2_b2,94.420,5.580,98.570,1.430,10.10,260,0.890,bicubic
tf_efficientnet_el,94.410,5.590,98.710,1.290,10.59,300,0.904,bicubic
vit_deit_small_patch16_224,94.400,5.600,98.690,1.310,22.05,224,0.900,bicubic
efficientnet_el_pruned,94.400,5.600,98.740,1.260,10.59,300,0.904,bicubic
vit_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
@ -160,8 +184,8 @@ resnetrs50,94.310,5.690,98.640,1.360,35.69,224,0.910,bicubic
xception71,94.280,5.720,98.640,1.360,42.34,299,0.903,bicubic
resnet50d,94.260,5.740,98.720,1.280,25.58,224,0.875,bicubic
skresnext50_32x4d,94.260,5.740,98.460,1.540,27.48,224,0.875,bicubic
cait_xxs36_224,94.260,5.740,98.720,1.280,17.30,224,1.000,bicubic
gluon_xception65,94.260,5.740,98.570,1.430,39.92,299,0.903,bicubic
cait_xxs36_224,94.260,5.740,98.720,1.280,17.30,224,1.000,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
ecaresnet50d_pruned,94.220,5.780,98.730,1.270,19.94,224,0.875,bicubic
@ -169,6 +193,7 @@ gluon_resnet101_v1d,94.220,5.780,98.550,1.450,44.57,224,0.875,bicubic
tf_efficientnet_lite3,94.200,5.800,98.640,1.360,8.20,300,0.904,bilinear
mixnet_xl,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
regnety_080,94.170,5.830,98.680,1.320,39.18,224,0.875,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
@ -185,6 +210,7 @@ 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
resnetblur50,93.960,6.040,98.590,1.410,25.56,224,0.875,bicubic
dla102x2,93.950,6.050,98.490,1.510,41.28,224,0.875,bilinear
tf_efficientnetv2_b1,93.940,6.060,98.620,1.380,8.14,240,0.882,bicubic
hrnet_w48,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear
rexnet_130,93.900,6.100,98.400,1.600,7.56,224,0.875,bicubic
tf_efficientnet_cc_b1_8e,93.900,6.100,98.260,1.740,39.72,240,0.882,bicubic
@ -204,33 +230,35 @@ res2net101_26w_4s,93.750,6.250,98.310,1.690,45.21,224,0.875,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
wide_resnet101_2,93.720,6.280,98.540,1.460,126.89,224,0.875,bilinear
tf_efficientnet_b1_ap,93.690,6.310,98.360,1.640,7.79,240,0.882,bicubic
dpn68b,93.690,6.310,98.510,1.490,12.61,224,0.875,bicubic
tf_efficientnet_b1_ap,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
tf_efficientnet_b0_ns,93.630,6.370,98.640,1.360,5.29,224,0.875,bicubic
gluon_resnet50_v1s,93.620,6.380,98.460,1.540,25.68,224,0.875,bicubic
cait_xxs24_224,93.600,6.400,98.440,1.560,11.96,224,1.000,bicubic
coat_tiny,93.590,6.410,98.430,1.570,5.50,224,0.900,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
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
tf_efficientnet_b1,93.500,6.500,98.360,1.640,7.79,240,0.882,bicubic
repvgg_b2,93.500,6.500,98.730,1.270,89.02,224,0.875,bilinear
tf_efficientnet_b1,93.500,6.500,98.360,1.640,7.79,240,0.882,bicubic
hrnet_w40,93.490,6.510,98.580,1.420,57.56,224,0.875,bilinear
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
gluon_inception_v3,93.460,6.540,98.570,1.430,23.83,299,0.875,bicubic
mixnet_l,93.450,6.550,98.220,1.780,7.33,224,0.875,bicubic
xception41,93.430,6.570,98.430,1.570,26.97,299,0.903,bicubic
res2net50_26w_6s,93.410,6.590,98.280,1.720,37.05,224,0.875,bilinear
legacy_seresnet152,93.400,6.600,98.350,1.650,66.82,224,0.875,bilinear
dla169,93.340,6.660,98.600,1.400,53.39,224,0.875,bilinear
resnest26d,93.330,6.670,98.630,1.370,17.07,224,0.875,bilinear
levit_128,93.340,6.660,98.380,1.620,9.21,224,0.900,bicubic
repvgg_b1,93.330,6.670,98.510,1.490,57.42,224,0.875,bilinear
resnest26d,93.330,6.670,98.630,1.370,17.07,224,0.875,bilinear
tf_inception_v3,93.320,6.680,98.030,1.970,23.83,299,0.875,bicubic
tf_mixnet_l,93.310,6.690,98.030,1.970,7.33,224,0.875,bicubic
selecsls60b,93.300,6.700,98.280,1.720,32.77,224,0.875,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
efficientnet_b1,93.250,6.750,98.290,1.710,7.79,256,1.000,bicubic
coat_lite_tiny,93.240,6.760,98.260,1.740,5.72,224,0.900,bicubic
@ -241,6 +269,7 @@ efficientnet_es,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_224,93.110,6.890,98.310,1.690,10.62,224,0.900,bicubic
tf_efficientnetv2_b0,93.110,6.890,98.390,1.610,7.14,224,0.875,bicubic
dla102,93.060,6.940,98.540,1.460,33.27,224,0.875,bilinear
gluon_resnet50_v1c,93.030,6.970,98.390,1.610,25.58,224,0.875,bicubic
regnety_016,93.030,6.970,98.360,1.640,11.20,224,0.875,bicubic
@ -271,8 +300,8 @@ tf_efficientnet_cc_b0_4e,92.590,7.410,98.080,1.920,13.31,224,0.875,bicubic
hardcorenas_e,92.570,7.430,98.110,1.890,8.07,224,0.875,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_26w_4s,92.500,7.500,98.060,1.940,25.70,224,0.875,bilinear
densenet161,92.500,7.500,98.290,1.710,28.68,224,0.875,bicubic
res2net50_26w_4s,92.500,7.500,98.060,1.940,25.70,224,0.875,bilinear
mixnet_m,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,92.400,7.600,98.050,1.950,5.83,224,0.875,bicubic
@ -291,6 +320,7 @@ resnet26d,92.070,7.930,97.960,2.040,16.01,224,0.875,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,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
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
@ -326,10 +356,11 @@ dla34,90.760,9.240,97.660,2.340,15.74,224,0.875,bilinear
fbnetc_100,90.700,9.300,97.210,2.790,5.57,224,0.875,bilinear
vit_deit_tiny_distilled_patch16_224,90.700,9.300,97.570,2.430,5.91,224,0.900,bicubic
swsl_resnet18,90.690,9.310,97.700,2.300,11.69,224,0.875,bilinear
convit_tiny,90.630,9.370,97.740,2.260,5.71,224,0.875,bicubic
mnasnet_100,90.510,9.490,97.470,2.530,4.38,224,0.875,bicubic
regnety_004,90.500,9.500,97.540,2.460,4.34,224,0.875,bicubic
spnasnet_100,90.350,9.650,97.190,2.810,4.42,224,0.875,bilinear
regnetx_006,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic
spnasnet_100,90.350,9.650,97.190,2.810,4.42,224,0.875,bilinear
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

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
5 tf_efficientnet_b7_ns 97.910 2.090 99.720 0.280 66.35 600 0.949 bicubic
6 swin_base_patch4_window12_384 97.890 2.110 99.710 0.290 87.90 384 1.000 bicubic
7 dm_nfnet_f6 97.730 2.270 99.580 0.420 438.36 576 0.956 bicubic
8 tf_efficientnetv2_l_in21ft1k 97.700 2.300 99.670 0.330 118.52 480 1.000 bicubic
9 swin_large_patch4_window7_224 97.650 2.350 99.580 0.420 196.53 224 0.900 bicubic
10 tf_efficientnet_b6_ns 97.630 2.370 99.580 0.420 43.04 528 0.942 bicubic
11 ig_resnext101_32x48d 97.620 2.380 99.700 0.300 828.41 224 0.875 bilinear
14 tf_efficientnet_b5_ns 97.500 2.500 99.630 0.370 30.39 456 0.934 bicubic
15 resnetv2_152x4_bitm 97.490 2.510 99.600 0.400 936.53 480 1.000 bilinear
16 cait_m48_448 97.480 2.520 99.550 0.450 356.46 448 1.000 bicubic
17 tf_efficientnetv2_m_in21ft1k 97.480 2.520 99.530 0.470 54.14 480 1.000 bicubic
18 cait_m36_384 97.400 2.600 99.510 0.490 271.22 384 1.000 bicubic
19 dm_nfnet_f3 97.360 2.640 99.580 0.420 254.92 416 0.940 bicubic
20 ig_resnext101_32x32d 97.360 2.640 99.680 0.320 468.53 224 0.875 bilinear
21 cait_s36_384 97.330 2.670 99.530 0.470 68.37 384 1.000 bicubic
22 tf_efficientnetv2_l 97.280 2.720 99.550 0.450 118.52 480 1.000 bicubic
23 swin_base_patch4_window7_224 97.250 2.750 99.530 0.470 87.77 224 0.900 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
24 tf_efficientnet_b8 97.200 2.800 99.500 0.500 87.41 672 0.954 bicubic
25 tf_efficientnet_b7_ap 97.200 2.800 99.540 0.460 66.35 600 0.949 bicubic
26 swsl_resnext101_32x8d 97.200 2.800 99.570 0.430 88.79 224 0.875 bilinear
27 vit_base_r50_s16_384 97.180 2.820 99.560 0.440 98.95 384 1.000 bicubic
28 resnetv2_152x2_bitm 97.150 2.850 99.590 0.410 236.34 480 1.000 bilinear
29 tf_efficientnetv2_m 97.140 2.860 99.410 0.590 54.14 480 1.000 bicubic
30 tf_efficientnet_b8_ap 97.110 2.890 99.660 0.340 87.41 672 0.954 bicubic
31 vit_large_patch16_384 97.110 2.890 99.640 0.360 304.72 384 1.000 bicubic
32 ecaresnet269d 97.080 2.920 99.470 0.530 102.09 352 1.000 bicubic
34 cait_s24_384 97.070 2.930 99.430 0.570 47.06 384 1.000 bicubic
35 resnetv2_101x3_bitm 97.050 2.950 99.520 0.480 387.93 480 1.000 bilinear
36 tf_efficientnet_b7 97.010 2.990 99.520 0.480 66.35 600 0.949 bicubic
37 efficientnetv2_rw_m 96.980 3.020 99.540 0.460 53.24 416 1.000 bicubic
38 dm_nfnet_f2 96.960 3.040 99.450 0.550 193.78 352 0.920 bicubic
39 vit_deit_base_distilled_patch16_384 96.960 3.040 99.480 0.520 87.63 384 1.000 bicubic
40 tf_efficientnet_b4_ns 96.950 3.050 99.580 0.420 19.34 380 0.922 bicubic
44 resnetv2_50x3_bitm 96.770 3.230 99.430 0.570 217.32 480 1.000 bilinear
45 seresnet152d 96.770 3.230 99.450 0.550 66.84 320 1.000 bicubic
46 resnetrs350 96.760 3.240 99.370 0.630 163.96 384 1.000 bicubic
47 tf_efficientnetv2_s_in21ft1k 96.730 3.270 99.420 0.580 21.46 384 1.000 bicubic
48 resnet200d 96.720 3.280 99.330 0.670 64.69 320 1.000 bicubic
eca_nfnet_l1 96.700 3.300 99.270 0.730 41.41 320 1.000 bicubic
49 vit_base_patch16_384 96.700 3.300 99.510 0.490 86.86 384 1.000 bicubic
50 eca_nfnet_l1 96.700 3.300 99.270 0.730 41.41 320 1.000 bicubic
51 resnetrs270 96.690 3.310 99.350 0.650 129.86 352 1.000 bicubic
tf_efficientnet_b5_ap 96.680 3.320 99.460 0.540 30.39 456 0.934 bicubic
52 pit_b_distilled_224 96.680 3.320 99.350 0.650 74.79 224 0.900 bicubic
53 tf_efficientnet_b5_ap 96.680 3.320 99.460 0.540 30.39 456 0.934 bicubic
54 tf_efficientnet_b6 96.670 3.330 99.370 0.630 43.04 528 0.942 bicubic
55 resnest200e 96.610 3.390 99.350 0.650 70.20 320 0.909 bicubic
56 swsl_resnext101_32x16d 96.600 3.400 99.520 0.480 194.03 224 0.875 bilinear
57 resnetrs152 96.580 3.420 99.240 0.760 86.62 320 1.000 bicubic
58 cait_xs24_384 96.550 3.450 99.420 0.580 26.67 384 1.000 bicubic
59 efficientnet_v2s efficientnetv2_rw_s 96.540 3.460 99.360 0.640 23.94 384 1.000 bicubic
60 resnetrs200 96.530 3.470 99.350 0.650 93.21 320 1.000 bicubic
61 resnest269e 96.520 3.480 99.350 0.650 110.93 416 0.928 bicubic
62 vit_base_patch16_224_miil 96.460 3.540 99.300 0.700 86.54 224 0.875 bilinear
66 resnet152d 96.360 3.640 99.390 0.610 60.21 320 1.000 bicubic
67 regnety_160 96.350 3.650 99.330 0.670 83.59 288 1.000 bicubic
68 tf_efficientnet_b5 96.350 3.650 99.310 0.690 30.39 456 0.934 bicubic
69 tf_efficientnetv2_s 96.340 3.660 99.200 0.800 21.46 384 1.000 bicubic
70 ig_resnext101_32x8d 96.320 3.680 99.430 0.570 88.79 224 0.875 bilinear
71 resnet101d 96.290 3.710 99.230 0.770 44.57 320 1.000 bicubic
72 twins_svt_large 96.270 3.730 99.170 0.830 99.27 224 0.900 bicubic
73 twins_svt_base 96.160 3.840 99.060 0.940 56.07 224 0.900 bicubic
74 tf_efficientnet_b4_ap 96.160 3.840 99.280 0.720 19.34 380 0.922 bicubic
75 efficientnet_b4 96.150 3.850 99.200 0.800 19.34 384 1.000 bicubic
76 twins_pcpvt_large 96.150 3.850 99.180 0.820 60.99 224 0.900 bicubic
77 vit_deit_base_patch16_384 96.150 3.850 99.140 0.860 86.86 384 1.000 bicubic
78 dm_nfnet_f0 96.140 3.860 99.240 0.760 71.49 256 0.900 bicubic
vit_deit_base_distilled_patch16_224 96.090 3.910 99.190 0.810 87.34 224 0.900 bicubic
79 nfnet_l0 96.090 3.910 99.260 0.740 35.07 288 1.000 bicubic
80 resnetv2_101x1_bitm 96.090 3.910 99.300 0.700 44.54 480 1.000 bilinear
81 vit_deit_base_distilled_patch16_224 96.090 3.910 99.190 0.810 87.34 224 0.900 bicubic
82 regnety_032 95.970 4.030 99.190 0.810 19.44 288 1.000 bicubic
83 tresnet_xl_448 95.970 4.030 99.130 0.870 78.44 448 0.875 bilinear
84 vit_large_patch16_224 95.950 4.050 99.240 0.760 304.33 224 0.900 bicubic
87 tf_efficientnet_b4 95.900 4.100 99.170 0.830 19.34 380 0.922 bicubic
88 swsl_resnext50_32x4d 95.870 4.130 99.250 0.750 25.03 224 0.875 bilinear
89 resnest101e 95.860 4.140 99.210 0.790 48.28 256 0.875 bilinear
90 resnet51q 95.860 4.140 99.120 0.880 35.70 288 1.000 bilinear
91 tresnet_l_448 95.860 4.140 99.120 0.880 55.99 448 0.875 bilinear
92 cait_xxs36_384 95.850 4.150 99.090 0.910 17.37 384 1.000 bicubic
93 vit_large_patch32_384 95.830 4.170 99.150 0.850 306.63 384 1.000 bicubic
94 vit_base_patch32_384 95.810 4.190 99.150 0.850 88.30 384 1.000 bicubic
95 ssl_resnext101_32x16d 95.800 4.200 99.180 0.820 194.03 224 0.875 bilinear
96 twins_pcpvt_base 95.790 4.210 99.130 0.870 43.83 224 0.900 bicubic
97 tf_efficientnet_b2_ns 95.770 4.230 99.120 0.880 9.11 260 0.890 bicubic
98 tresnet_m 95.720 4.280 99.030 0.970 31.39 224 0.875 bilinear
efficientnet_b3 95.710 4.290 99.040 0.960 12.23 320 1.000 bicubic
99 pnasnet5large 95.710 4.290 98.920 1.080 86.06 331 0.911 bicubic
100 efficientnet_b3 95.710 4.290 99.040 0.960 12.23 320 1.000 bicubic
101 nasnetalarge 95.680 4.320 98.930 1.070 88.75 331 0.911 bicubic
102 pit_b_224 95.640 4.360 98.660 1.340 73.76 224 0.900 bicubic
103 convit_base 95.550 4.450 98.870 1.130 86.54 224 0.875 bicubic
104 coat_lite_small 95.540 4.460 98.860 1.140 19.84 224 0.900 bicubic
105 ecaresnet101d 95.530 4.470 99.130 0.870 44.57 224 0.875 bicubic
106 levit_384 95.530 4.470 99.050 0.950 39.13 224 0.900 bicubic
107 ecaresnet50t 95.510 4.490 99.120 0.880 25.57 320 0.950 bicubic
108 visformer_small 95.490 4.510 98.900 1.100 40.22 224 0.900 bicubic
109 ssl_resnext101_32x8d 95.470 4.530 99.110 0.890 88.79 224 0.875 bilinear
ssl_resnext101_32x4d 95.440 4.560 99.130 0.870 44.18 224 0.875 bilinear
110 tresnet_xl 95.440 4.560 99.050 0.950 78.44 224 0.875 bilinear
111 vit_deit_base_patch16_224 95.440 4.560 98.840 1.160 86.57 224 0.900 bicubic
112 ssl_resnext101_32x4d 95.440 4.560 99.130 0.870 44.18 224 0.875 bilinear
113 resnetrs101 95.430 4.570 99.030 0.970 63.62 288 0.940 bicubic
114 swsl_resnet50 95.410 4.590 99.290 0.710 25.56 224 0.875 bilinear
115 vit_base_patch16_224 95.330 4.670 99.000 1.000 86.57 224 0.900 bicubic
116 tf_efficientnet_b3_ap 95.320 4.680 98.900 1.100 12.23 300 0.904 bicubic
117 mixer_b16_224_miil 95.300 4.700 98.880 1.120 59.88 224 0.875 bilinear
118 tresnet_l 95.290 4.710 99.010 0.990 55.99 224 0.875 bilinear
119 cait_xxs24_384 95.260 4.740 98.960 1.040 12.03 384 1.000 bicubic
120 pit_s_distilled_224 95.240 4.760 99.050 0.950 24.04 224 0.900 bicubic
121 twins_pcpvt_small 95.210 4.790 98.880 1.120 24.11 224 0.900 bicubic
122 convit_small 95.200 4.800 98.900 1.100 27.78 224 0.875 bicubic
123 twins_svt_small 95.200 4.800 98.880 1.120 24.06 224 0.900 bicubic
124 tf_efficientnet_b1_ns 95.170 4.830 99.110 0.890 7.79 240 0.882 bicubic
125 tf_efficientnetv2_b3 95.160 4.840 98.820 1.180 14.36 300 0.904 bicubic
126 swin_tiny_patch4_window7_224 95.140 4.860 98.850 1.150 28.29 224 0.900 bicubic
127 efficientnet_el 95.120 4.880 98.990 1.010 10.59 300 0.904 bicubic
128 gernet_l 95.090 4.910 98.900 1.100 31.08 256 0.875 bilinear
134 seresnext50_32x4d 95.040 4.960 98.880 1.120 27.56 224 0.875 bicubic
135 tnt_s_patch16_224 95.040 4.960 98.830 1.170 23.76 224 0.900 bicubic
136 tf_efficientnet_b3 95.010 4.990 98.910 1.090 12.23 300 0.904 bicubic
137 levit_256 95.010 4.990 98.890 1.110 18.89 224 0.900 bicubic
138 tresnet_m_448 94.990 5.010 98.980 1.020 31.39 448 0.875 bilinear
139 coat_mini 94.970 5.030 98.780 1.220 10.34 224 0.900 bicubic
140 resnest50d_4s2x40d 94.960 5.040 99.070 0.930 30.42 224 0.875 bicubic
141 rexnet_200 94.940 5.060 99.010 0.990 16.37 224 0.875 bicubic
142 gluon_seresnext101_64x4d 94.930 5.070 98.830 1.170 88.23 224 0.875 bicubic
160 pit_s_224 94.590 5.410 98.710 1.290 23.46 224 0.900 bicubic
161 repvgg_b3 94.570 5.430 98.780 1.220 123.09 224 0.875 bilinear
162 seresnet50 94.550 5.450 98.750 1.250 28.09 224 0.875 bicubic
regnety_320 94.540 5.460 98.850 1.150 145.05 224 0.875 bicubic
163 inception_resnet_v2 94.540 5.460 98.790 1.210 55.84 299 0.897 bicubic
164 regnety_320 94.540 5.460 98.850 1.150 145.05 224 0.875 bicubic
165 gluon_resnext101_32x4d 94.530 5.470 98.630 1.370 44.18 224 0.875 bicubic
166 repvgg_b3g4 94.520 5.480 98.970 1.030 83.83 224 0.875 bilinear
167 tf_efficientnet_b2_ap 94.490 5.510 98.620 1.380 9.11 260 0.890 bicubic
170 rexnet_150 94.480 5.520 98.790 1.210 9.73 224 0.875 bicubic
171 regnetx_320 94.460 5.540 98.740 1.260 107.81 224 0.875 bicubic
172 ssl_resnet50 94.450 5.550 98.920 1.080 25.56 224 0.875 bilinear
173 tf_efficientnetv2_b2 94.420 5.580 98.570 1.430 10.10 260 0.890 bicubic
174 tf_efficientnet_el 94.410 5.590 98.710 1.290 10.59 300 0.904 bicubic
vit_deit_small_patch16_224 94.400 5.600 98.690 1.310 22.05 224 0.900 bicubic
175 efficientnet_el_pruned 94.400 5.600 98.740 1.260 10.59 300 0.904 bicubic
176 vit_deit_small_patch16_224 94.400 5.600 98.690 1.310 22.05 224 0.900 bicubic
177 inception_v4 94.380 5.620 98.580 1.420 42.68 299 0.875 bicubic
178 legacy_seresnext101_32x4d 94.370 5.630 98.650 1.350 48.96 224 0.875 bilinear
179 tf_efficientnet_b2 94.360 5.640 98.610 1.390 9.11 260 0.890 bicubic
184 xception71 94.280 5.720 98.640 1.360 42.34 299 0.903 bicubic
185 resnet50d 94.260 5.740 98.720 1.280 25.58 224 0.875 bicubic
186 skresnext50_32x4d 94.260 5.740 98.460 1.540 27.48 224 0.875 bicubic
cait_xxs36_224 94.260 5.740 98.720 1.280 17.30 224 1.000 bicubic
187 gluon_xception65 94.260 5.740 98.570 1.430 39.92 299 0.903 bicubic
188 cait_xxs36_224 94.260 5.740 98.720 1.280 17.30 224 1.000 bicubic
189 regnetx_120 94.240 5.760 98.650 1.350 46.11 224 0.875 bicubic
190 dpn92 94.230 5.770 98.730 1.270 37.67 224 0.875 bicubic
191 ecaresnet50d_pruned 94.220 5.780 98.730 1.270 19.94 224 0.875 bicubic
193 tf_efficientnet_lite3 94.200 5.800 98.640 1.360 8.20 300 0.904 bilinear
194 mixnet_xl 94.190 5.810 98.340 1.660 11.90 224 0.875 bicubic
195 resnext50d_32x4d 94.180 5.820 98.570 1.430 25.05 224 0.875 bicubic
196 levit_192 94.170 5.830 98.540 1.460 10.95 224 0.900 bicubic
197 regnety_080 94.170 5.830 98.680 1.320 39.18 224 0.875 bicubic
198 gluon_resnet152_v1c 94.160 5.840 98.640 1.360 60.21 224 0.875 bicubic
199 ens_adv_inception_resnet_v2 94.160 5.840 98.600 1.400 55.84 299 0.897 bicubic
210 hrnet_w64 94.010 5.990 98.610 1.390 128.06 224 0.875 bilinear
211 resnetblur50 93.960 6.040 98.590 1.410 25.56 224 0.875 bicubic
212 dla102x2 93.950 6.050 98.490 1.510 41.28 224 0.875 bilinear
213 tf_efficientnetv2_b1 93.940 6.060 98.620 1.380 8.14 240 0.882 bicubic
214 hrnet_w48 93.920 6.080 98.610 1.390 77.47 224 0.875 bilinear
215 rexnet_130 93.900 6.100 98.400 1.600 7.56 224 0.875 bicubic
216 tf_efficientnet_cc_b1_8e 93.900 6.100 98.260 1.740 39.72 240 0.882 bicubic
230 cspresnet50 93.740 6.260 98.640 1.360 21.62 256 0.887 bilinear
231 legacy_seresnext50_32x4d 93.730 6.270 98.580 1.420 27.56 224 0.875 bilinear
232 wide_resnet101_2 93.720 6.280 98.540 1.460 126.89 224 0.875 bilinear
tf_efficientnet_b1_ap 93.690 6.310 98.360 1.640 7.79 240 0.882 bicubic
233 dpn68b 93.690 6.310 98.510 1.490 12.61 224 0.875 bicubic
234 tf_efficientnet_b1_ap 93.690 6.310 98.360 1.640 7.79 240 0.882 bicubic
235 gluon_resnet101_v1c 93.670 6.330 98.420 1.580 44.57 224 0.875 bicubic
236 tf_efficientnet_b0_ns 93.630 6.370 98.640 1.360 5.29 224 0.875 bicubic
237 gluon_resnet50_v1s 93.620 6.380 98.460 1.540 25.68 224 0.875 bicubic
238 cait_xxs24_224 93.600 6.400 98.440 1.560 11.96 224 1.000 bicubic
239 coat_tiny 93.590 6.410 98.430 1.570 5.50 224 0.900 bicubic
240 regnetx_040 93.560 6.440 98.540 1.460 22.12 224 0.875 bicubic
241 hrnet_w44 93.550 6.450 98.700 1.300 67.06 224 0.875 bilinear
242 res2net50_26w_8s 93.540 6.460 98.260 1.740 48.40 224 0.875 bilinear
243 hrnet_w32 93.530 6.470 98.450 1.550 41.23 224 0.875 bilinear
244 dla102x 93.520 6.480 98.510 1.490 26.31 224 0.875 bilinear
tf_efficientnet_b1 93.500 6.500 98.360 1.640 7.79 240 0.882 bicubic
245 repvgg_b2 93.500 6.500 98.730 1.270 89.02 224 0.875 bilinear
246 tf_efficientnet_b1 93.500 6.500 98.360 1.640 7.79 240 0.882 bicubic
247 hrnet_w40 93.490 6.510 98.580 1.420 57.56 224 0.875 bilinear
gluon_inception_v3 93.460 6.540 98.570 1.430 23.83 299 0.875 bicubic
248 xception 93.460 6.540 98.530 1.470 22.86 299 0.897 bicubic
249 gluon_inception_v3 93.460 6.540 98.570 1.430 23.83 299 0.875 bicubic
250 mixnet_l 93.450 6.550 98.220 1.780 7.33 224 0.875 bicubic
251 xception41 93.430 6.570 98.430 1.570 26.97 299 0.903 bicubic
252 res2net50_26w_6s 93.410 6.590 98.280 1.720 37.05 224 0.875 bilinear
253 legacy_seresnet152 93.400 6.600 98.350 1.650 66.82 224 0.875 bilinear
254 dla169 93.340 6.660 98.600 1.400 53.39 224 0.875 bilinear
255 resnest26d levit_128 93.330 93.340 6.670 6.660 98.630 98.380 1.370 1.620 17.07 9.21 224 0.875 0.900 bilinear bicubic
256 repvgg_b1 93.330 6.670 98.510 1.490 57.42 224 0.875 bilinear
257 resnest26d 93.330 6.670 98.630 1.370 17.07 224 0.875 bilinear
258 tf_inception_v3 93.320 6.680 98.030 1.970 23.83 299 0.875 bicubic
259 tf_mixnet_l 93.310 6.690 98.030 1.970 7.33 224 0.875 bicubic
selecsls60b 93.300 6.700 98.280 1.720 32.77 224 0.875 bicubic
260 tv_resnet152 93.300 6.700 98.390 1.610 60.19 224 0.875 bilinear
261 selecsls60b 93.300 6.700 98.280 1.720 32.77 224 0.875 bicubic
262 legacy_seresnet101 93.280 6.720 98.510 1.490 49.33 224 0.875 bilinear
263 efficientnet_b1 93.250 6.750 98.290 1.710 7.79 256 1.000 bicubic
264 coat_lite_tiny 93.240 6.760 98.260 1.740 5.72 224 0.900 bicubic
269 dla60x 93.120 6.880 98.510 1.490 17.35 224 0.875 bilinear
270 regnetx_032 93.120 6.880 98.390 1.610 15.30 224 0.875 bicubic
271 pit_xs_224 93.110 6.890 98.310 1.690 10.62 224 0.900 bicubic
272 tf_efficientnetv2_b0 93.110 6.890 98.390 1.610 7.14 224 0.875 bicubic
273 dla102 93.060 6.940 98.540 1.460 33.27 224 0.875 bilinear
274 gluon_resnet50_v1c 93.030 6.970 98.390 1.610 25.58 224 0.875 bicubic
275 regnety_016 93.030 6.970 98.360 1.640 11.20 224 0.875 bicubic
300 hardcorenas_e 92.570 7.430 98.110 1.890 8.07 224 0.875 bilinear
301 res2net50_48w_2s 92.550 7.450 98.080 1.920 25.29 224 0.875 bilinear
302 gluon_resnet50_v1b 92.540 7.460 98.170 1.830 25.56 224 0.875 bicubic
res2net50_26w_4s 92.500 7.500 98.060 1.940 25.70 224 0.875 bilinear
303 densenet161 92.500 7.500 98.290 1.710 28.68 224 0.875 bicubic
304 res2net50_26w_4s 92.500 7.500 98.060 1.940 25.70 224 0.875 bilinear
305 mixnet_m 92.430 7.570 97.870 2.130 5.01 224 0.875 bicubic
306 hardcorenas_d 92.400 7.600 98.070 1.930 7.50 224 0.875 bilinear
307 mobilenetv2_120d 92.400 7.600 98.050 1.950 5.83 224 0.875 bicubic
320 hardcorenas_c 92.020 7.980 97.840 2.160 5.52 224 0.875 bilinear
321 dpn68 92.010 7.990 98.050 1.950 12.61 224 0.875 bicubic
322 tf_efficientnet_es 91.980 8.020 97.860 2.140 5.44 224 0.875 bicubic
323 levit_128s 91.970 8.030 98.060 1.940 7.78 224 0.900 bicubic
324 repvgg_a2 91.940 8.060 98.150 1.850 28.21 224 0.875 bilinear
325 densenet169 91.930 8.070 98.100 1.900 14.15 224 0.875 bicubic
326 densenetblur121d 91.910 8.090 98.070 1.930 8.00 224 0.875 bicubic
356 fbnetc_100 90.700 9.300 97.210 2.790 5.57 224 0.875 bilinear
357 vit_deit_tiny_distilled_patch16_224 90.700 9.300 97.570 2.430 5.91 224 0.900 bicubic
358 swsl_resnet18 90.690 9.310 97.700 2.300 11.69 224 0.875 bilinear
359 convit_tiny 90.630 9.370 97.740 2.260 5.71 224 0.875 bicubic
360 mnasnet_100 90.510 9.490 97.470 2.530 4.38 224 0.875 bicubic
361 regnety_004 90.500 9.500 97.540 2.460 4.34 224 0.875 bicubic
spnasnet_100 90.350 9.650 97.190 2.810 4.42 224 0.875 bilinear
362 regnetx_006 90.350 9.650 97.430 2.570 6.20 224 0.875 bicubic
363 spnasnet_100 90.350 9.650 97.190 2.810 4.42 224 0.875 bilinear
364 ssl_resnet18 90.220 9.780 97.550 2.450 11.69 224 0.875 bilinear
365 vgg16_bn 90.090 9.910 97.370 2.630 138.37 224 0.875 bilinear
366 vgg19_bn 90.080 9.920 97.580 2.420 143.68 224 0.875 bilinear

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results/results-imagenet-a.csv
Unescape View File

@ -3,344 +3,375 @@ tf_efficientnet_l2_ns,84.760,15.240,96.147,3.853,480.31,800,0.960,bicubic,-13.79
tf_efficientnet_l2_ns_475,83.373,16.627,95.453,4.547,480.31,475,0.936,bicubic,-15.127,-4.377,0
swin_large_patch4_window12_384,69.627,30.373,89.560,10.440,196.74,384,1.000,bicubic,-28.413,-10.130,0
tf_efficientnet_b7_ns,67.040,32.960,88.667,11.333,66.35,600,0.949,bicubic,-30.870,-11.053,0
swin_base_patch4_window12_384,64.480,35.520,87.493,12.507,87.90,384,1.000,bicubic,-33.410,-12.217,0
tf_efficientnetv2_l_in21ft1k,66.333,33.667,87.853,12.147,118.52,480,1.000,bicubic,-31.367,-11.817,+2
swin_base_patch4_window12_384,64.480,35.520,87.493,12.507,87.90,384,1.000,bicubic,-33.410,-12.217,-1
cait_m48_448,62.373,37.627,86.453,13.547,356.46,448,1.000,bicubic,-35.107,-13.097,+8
tf_efficientnet_b6_ns,62.267,37.733,85.173,14.827,43.04,528,0.942,bicubic,-35.363,-14.407,+1
dm_nfnet_f6,62.253,37.747,84.667,15.333,438.36,576,0.956,bicubic,-35.477,-14.913,-2
dm_nfnet_f6,62.253,37.747,84.667,15.333,438.36,576,0.956,bicubic,-35.477,-14.913,-3
dm_nfnet_f5,61.587,38.413,84.027,15.973,377.21,544,0.954,bicubic,-36.013,-15.523,+1
ig_resnext101_32x48d,61.013,38.987,83.347,16.653,828.41,224,0.875,bilinear,-36.607,-16.353,-1
swin_large_patch4_window7_224,60.893,39.107,85.840,14.160,196.53,224,0.900,bicubic,-36.757,-13.740,-4
resnetv2_152x4_bitm,60.733,39.267,83.600,16.400,936.53,480,1.000,bilinear,-36.757,-16.000,+1
dm_nfnet_f4,60.720,39.280,83.427,16.573,316.07,512,0.951,bicubic,-36.850,-16.093,-2
tf_efficientnet_b5_ns,60.320,39.680,84.493,15.507,30.39,456,0.934,bicubic,-37.180,-15.137,-2
tf_efficientnetv2_m_in21ft1k,61.387,38.613,85.413,14.587,54.14,480,1.000,bicubic,-36.093,-14.117,+5
ig_resnext101_32x48d,61.013,38.987,83.347,16.653,828.41,224,0.875,bilinear,-36.607,-16.353,-2
swin_large_patch4_window7_224,60.893,39.107,85.840,14.160,196.53,224,0.900,bicubic,-36.757,-13.740,-5
resnetv2_152x4_bitm,60.733,39.267,83.600,16.400,936.53,480,1.000,bilinear,-36.757,-16.000,0
dm_nfnet_f4,60.720,39.280,83.427,16.573,316.07,512,0.951,bicubic,-36.850,-16.093,-3
tf_efficientnet_b5_ns,60.320,39.680,84.493,15.507,30.39,456,0.934,bicubic,-37.180,-15.137,-3
dm_nfnet_f3,58.373,41.627,82.360,17.640,254.92,416,0.940,bicubic,-38.987,-17.220,+1
ig_resnext101_32x32d,58.093,41.907,80.653,19.347,468.53,224,0.875,bilinear,-39.267,-19.027,+1
cait_m36_384,57.840,42.160,84.813,15.187,271.22,384,1.000,bicubic,-39.560,-14.697,-2
resnetv2_152x2_bitm,54.973,45.027,82.813,17.187,236.34,480,1.000,bilinear,-42.177,-16.777,+6
vit_base_r50_s16_384,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.553,-18.347,+4
resnetv2_152x2_bitm,54.973,45.027,82.813,17.187,236.34,480,1.000,bilinear,-42.177,-16.777,+7
vit_base_r50_s16_384,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.553,-18.347,+5
cait_s36_384,54.413,45.587,81.360,18.640,68.37,384,1.000,bicubic,-42.917,-18.170,-2
vit_large_patch16_384,53.867,46.133,80.320,19.680,304.72,384,1.000,bicubic,-43.243,-19.320,+5
resnetv2_101x3_bitm,53.813,46.187,81.093,18.907,387.93,480,1.000,bilinear,-43.237,-18.427,+8
ig_resnext101_32x16d,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.753,-22.683,+14
vit_large_patch16_384,53.867,46.133,80.320,19.680,304.72,384,1.000,bicubic,-43.243,-19.320,+7
resnetv2_101x3_bitm,53.813,46.187,81.093,18.907,387.93,480,1.000,bilinear,-43.237,-18.427,+10
tf_efficientnetv2_l,53.187,46.813,79.133,20.867,118.52,480,1.000,bicubic,-44.093,-20.417,-4
ig_resnext101_32x16d,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.753,-22.683,+16
swin_base_patch4_window7_224,51.453,48.547,79.973,20.027,87.77,224,0.900,bicubic,-45.797,-19.557,-5
tf_efficientnet_b4_ns,51.213,48.787,79.187,20.813,19.34,380,0.922,bicubic,-45.737,-20.393,+9
swsl_resnext101_32x8d,51.187,48.813,78.240,21.760,88.79,224,0.875,bilinear,-46.013,-21.330,-6
dm_nfnet_f2,50.773,49.227,78.013,21.987,193.78,352,0.920,bicubic,-46.187,-21.437,+5
vit_base_patch16_384,50.613,49.387,78.200,21.800,86.86,384,1.000,bicubic,-46.087,-21.310,+15
cait_s24_384,49.733,50.267,78.733,21.267,47.06,384,1.000,bicubic,-47.337,-20.697,0
vit_deit_base_distilled_patch16_384,49.333,50.667,79.253,20.747,87.63,384,1.000,bicubic,-47.627,-20.227,+3
tf_efficientnet_b8,48.947,51.053,77.240,22.760,87.41,672,0.954,bicubic,-48.253,-22.260,-9
resnest269e,48.187,51.813,74.333,25.667,110.93,416,0.928,bicubic,-48.333,-25.017,+22
resnetv2_50x3_bitm,47.787,52.213,77.627,22.373,217.32,480,1.000,bilinear,-48.983,-21.803,+5
tf_efficientnet_b4_ns,51.213,48.787,79.187,20.813,19.34,380,0.922,bicubic,-45.737,-20.393,+11
swsl_resnext101_32x8d,51.187,48.813,78.240,21.760,88.79,224,0.875,bilinear,-46.013,-21.260,-4
dm_nfnet_f2,50.773,49.227,78.013,21.987,193.78,352,0.920,bicubic,-46.187,-21.437,+7
vit_base_patch16_384,50.613,49.387,78.200,21.800,86.86,384,1.000,bicubic,-46.087,-21.070,+17
cait_s24_384,49.733,50.267,78.733,21.267,47.06,384,1.000,bicubic,-47.337,-20.697,+1
vit_deit_base_distilled_patch16_384,49.333,50.667,79.253,20.747,87.63,384,1.000,bicubic,-47.627,-20.227,+5
tf_efficientnet_b8,48.947,51.053,77.240,22.760,87.41,672,0.954,bicubic,-48.253,-22.330,-11
tf_efficientnetv2_s_in21ft1k,48.507,51.493,77.880,22.120,21.46,384,1.000,bicubic,-48.223,-21.540,+11
resnest269e,48.187,51.813,74.333,25.667,110.93,416,0.928,bicubic,-48.333,-25.017,+24
resnetv2_50x3_bitm,47.787,52.213,77.627,22.373,217.32,480,1.000,bilinear,-48.983,-21.803,+6
tf_efficientnet_b8_ap,46.893,53.107,76.507,23.493,87.41,672,0.954,bicubic,-50.217,-23.153,-9
dm_nfnet_f1,46.600,53.400,74.773,25.227,132.63,320,0.910,bicubic,-50.320,-24.617,0
swsl_resnext101_32x16d,46.200,53.800,72.200,27.800,194.03,224,0.875,bilinear,-50.400,-27.320,+13
ecaresnet269d,45.893,54.107,75.133,24.867,102.09,352,1.000,bicubic,-51.187,-24.337,-10
tf_efficientnet_b7_ap,45.373,54.627,74.213,25.787,66.35,600,0.949,bicubic,-51.827,-25.327,-17
ig_resnext101_32x8d,45.320,54.680,70.867,29.133,88.79,224,0.875,bilinear,-51.000,-28.563,+23
dm_nfnet_f1,46.600,53.400,74.773,25.227,132.63,320,0.910,bicubic,-50.320,-24.617,+1
efficientnetv2_rw_m,46.280,53.720,75.707,24.293,53.24,416,1.000,bicubic,-50.700,-23.833,-4
swsl_resnext101_32x16d,46.200,53.800,72.200,27.800,194.03,224,0.875,bilinear,-50.400,-27.320,+14
ecaresnet269d,45.893,54.107,75.133,24.867,102.09,352,1.000,bicubic,-51.187,-24.337,-11
tf_efficientnetv2_m,45.533,54.467,74.533,25.467,54.14,480,1.000,bicubic,-51.607,-24.877,-15
tf_efficientnet_b7_ap,45.373,54.627,74.213,25.787,66.35,600,0.949,bicubic,-51.827,-25.327,-20
ig_resnext101_32x8d,45.320,54.680,70.867,29.133,88.79,224,0.875,bilinear,-51.000,-28.563,+24
resnest200e,44.147,55.853,73.467,26.533,70.20,320,0.909,bicubic,-52.463,-25.883,+8
cait_xs24_384,43.947,56.053,75.187,24.813,26.67,384,1.000,bicubic,-52.603,-24.233,+10
tresnet_xl_448,43.480,56.520,72.453,27.547,78.44,448,0.875,bilinear,-52.490,-26.677,+30
resnetrs420,43.147,56.853,70.453,29.547,191.89,416,1.000,bicubic,-53.763,-29.007,-7
tf_efficientnet_b7,42.960,57.040,73.133,26.867,66.35,600,0.949,bicubic,-54.050,-26.387,-13
tresnet_xl_448,43.480,56.520,72.453,27.547,78.44,448,0.875,bilinear,-52.490,-26.677,+34
resnetrs420,43.147,56.853,70.453,29.547,191.89,416,1.000,bicubic,-53.763,-29.007,-8
tf_efficientnet_b7,42.960,57.040,73.133,26.867,66.35,600,0.949,bicubic,-54.050,-26.387,-15
swsl_resnext101_32x4d,41.560,58.440,71.760,28.240,44.18,224,0.875,bilinear,-54.860,-27.710,+11
tf_efficientnet_b6_ap,40.800,59.200,71.627,28.373,43.04,528,0.942,bicubic,-56.280,-27.993,-18
tresnet_l_448,40.200,59.800,69.893,30.107,55.99,448,0.875,bilinear,-55.660,-29.227,+32
vit_deit_base_patch16_384,40.173,59.827,70.760,29.240,86.86,384,1.000,bicubic,-55.977,-28.380,+18
resnetrs350,39.960,60.040,68.907,31.093,163.96,384,1.000,bicubic,-56.800,-30.463,-9
resnetv2_101x1_bitm,39.307,60.693,71.493,28.507,44.54,480,1.000,bilinear,-56.783,-27.697,+20
vit_large_patch32_384,38.933,61.067,68.920,31.080,306.63,384,1.000,bicubic,-56.897,-30.230,+30
tf_efficientnet_b6_ap,40.800,59.200,71.627,28.373,43.04,528,0.942,bicubic,-56.280,-27.993,-20
tresnet_l_448,40.200,59.800,69.893,30.107,55.99,448,0.875,bilinear,-55.660,-29.227,+37
vit_deit_base_patch16_384,40.173,59.827,70.760,29.240,86.86,384,1.000,bicubic,-55.977,-28.380,+22
resnetrs350,39.960,60.040,68.907,31.093,163.96,384,1.000,bicubic,-56.800,-30.463,-10
resnetv2_101x1_bitm,39.307,60.693,71.493,28.507,44.54,480,1.000,bilinear,-56.783,-27.807,+23
vit_large_patch32_384,38.933,61.067,68.920,31.080,306.63,384,1.000,bicubic,-56.897,-30.230,+35
resnet200d,38.147,61.853,68.613,31.387,64.69,320,1.000,bicubic,-58.573,-30.717,-11
eca_nfnet_l1,38.107,61.893,71.293,28.707,41.41,320,1.000,bicubic,-58.593,-27.977,-11
seresnet152d,37.640,62.360,69.480,30.520,66.84,320,1.000,bicubic,-59.130,-29.970,-15
efficientnet_v2s,36.787,63.213,68.320,31.680,23.94,384,1.000,bicubic,-59.753,-31.040,-3
regnety_160,36.747,63.253,69.107,30.893,83.59,288,1.000,bicubic,-59.603,-30.223,+4
cait_xxs36_384,36.227,63.773,67.800,32.200,17.37,384,1.000,bicubic,-59.623,-31.290,+23
pit_b_distilled_224,35.627,64.373,69.120,30.880,74.79,224,0.900,bicubic,-61.053,-30.340,-12
tf_efficientnet_b3_ns,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.577,-2
vit_large_patch16_224,35.493,64.507,64.427,35.573,304.33,224,0.900,bicubic,-60.457,-34.813,+13
tf_efficientnet_b6,35.213,64.787,67.720,32.280,43.04,528,0.942,bicubic,-61.457,-31.650,-14
resnetrs270,35.013,64.987,65.480,34.520,129.86,352,1.000,bicubic,-61.677,-33.870,-18
tf_efficientnet_b5_ap,34.787,65.213,67.493,32.507,30.39,456,0.934,bicubic,-61.893,-31.857,-18
vit_base_patch16_224_miil,34.507,65.493,65.000,35.000,86.54,224,0.875,bilinear,-61.953,-34.300,-9
resnet152d,34.320,65.680,65.907,34.093,60.21,320,1.000,bicubic,-62.040,-33.483,-6
tresnet_m_448,34.107,65.893,64.493,35.507,31.39,448,0.875,bilinear,-60.883,-34.487,+49
vit_base_patch32_384,33.613,66.387,65.240,34.760,88.30,384,1.000,bicubic,-62.197,-33.910,+15
pit_b_224,33.173,66.827,62.320,37.680,73.76,224,0.900,bicubic,-62.467,-36.340,+21
swsl_resnext50_32x4d,33.013,66.987,65.067,34.933,25.03,224,0.875,bilinear,-62.857,-34.183,+8
ssl_resnext101_32x16d,32.600,67.400,64.000,36.000,194.03,224,0.875,bilinear,-63.200,-35.180,+13
swin_small_patch4_window7_224,32.600,67.400,65.440,34.560,49.61,224,0.900,bicubic,-63.310,-33.580,+4
vit_base_patch16_224,32.053,67.947,61.573,38.427,86.57,224,0.900,bicubic,-63.277,-37.427,+26
tf_efficientnet_b5,31.840,68.160,65.293,34.707,30.39,456,0.934,bicubic,-64.510,-34.017,-12
resnest101e,31.413,68.587,64.360,35.640,48.28,256,0.875,bilinear,-64.447,-34.850,+4
dm_nfnet_f0,31.280,68.720,63.347,36.653,71.49,256,0.900,bicubic,-64.860,-35.893,-8
cait_s24_224,31.200,68.800,64.560,35.440,46.92,224,1.000,bicubic,-65.180,-34.590,-18
eca_nfnet_l1,38.107,61.893,71.293,28.707,41.41,320,1.000,bicubic,-58.593,-28.217,-10
seresnet152d,37.640,62.360,69.480,30.520,66.84,320,1.000,bicubic,-59.130,-29.970,-16
twins_svt_large,37.200,62.800,69.227,30.773,99.27,224,0.900,bicubic,-59.070,-29.943,+10
efficientnetv2_rw_s,36.787,63.213,68.320,31.680,23.94,384,1.000,bicubic,-59.753,-31.040,-4
regnety_160,36.747,63.253,69.107,30.893,83.59,288,1.000,bicubic,-59.603,-30.223,+3
cait_xxs36_384,36.227,63.773,67.800,32.200,17.37,384,1.000,bicubic,-59.623,-31.290,+27
pit_b_distilled_224,35.627,64.373,69.120,30.880,74.79,224,0.900,bicubic,-61.053,-30.230,-14
tf_efficientnet_b3_ns,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.577,-3
vit_large_patch16_224,35.493,64.507,64.427,35.573,304.33,224,0.900,bicubic,-60.457,-34.813,+16
tf_efficientnet_b6,35.213,64.787,67.720,32.280,43.04,528,0.942,bicubic,-61.457,-31.650,-15
resnetrs270,35.013,64.987,65.480,34.520,129.86,352,1.000,bicubic,-61.677,-33.870,-19
tf_efficientnet_b5_ap,34.787,65.213,67.493,32.507,30.39,456,0.934,bicubic,-61.893,-31.967,-18
vit_base_patch16_224_miil,34.507,65.493,65.000,35.000,86.54,224,0.875,bilinear,-61.953,-34.300,-10
resnet152d,34.320,65.680,65.907,34.093,60.21,320,1.000,bicubic,-62.040,-33.483,-7
tresnet_m_448,34.107,65.893,64.493,35.507,31.39,448,0.875,bilinear,-60.883,-34.487,+64
vit_base_patch32_384,33.613,66.387,65.240,34.760,88.30,384,1.000,bicubic,-62.197,-33.910,+19
twins_pcpvt_large,33.387,66.613,67.933,32.067,60.99,224,0.900,bicubic,-62.763,-31.247,0
pit_b_224,33.173,66.827,62.320,37.680,73.76,224,0.900,bicubic,-62.467,-36.340,+25
twins_svt_base,33.173,66.827,65.773,34.227,56.07,224,0.900,bicubic,-62.987,-33.507,-5
swsl_resnext50_32x4d,33.013,66.987,65.067,34.933,25.03,224,0.875,bilinear,-62.857,-34.183,+9
ssl_resnext101_32x16d,32.600,67.400,64.000,36.000,194.03,224,0.875,bilinear,-63.200,-35.180,+15
swin_small_patch4_window7_224,32.600,67.400,65.440,34.560,49.61,224,0.900,bicubic,-63.310,-33.580,+5
vit_base_patch16_224,32.053,67.947,61.573,38.427,86.57,224,0.900,bicubic,-63.277,-37.427,+33
tf_efficientnet_b5,31.840,68.160,65.293,34.707,30.39,456,0.934,bicubic,-64.510,-34.017,-15
resnest101e,31.413,68.587,64.360,35.640,48.28,256,0.875,bilinear,-64.447,-34.850,+5
dm_nfnet_f0,31.280,68.720,63.347,36.653,71.49,256,0.900,bicubic,-64.860,-35.893,-7
cait_s24_224,31.200,68.800,64.560,35.440,46.92,224,1.000,bicubic,-65.180,-34.590,-21
efficientnet_b4,30.867,69.133,64.600,35.400,19.34,384,1.000,bicubic,-65.283,-34.600,-12
resnetrs200,30.773,69.227,63.320,36.680,93.21,320,1.000,bicubic,-65.757,-36.030,-25
cait_xxs24_384,30.027,69.973,63.933,36.067,12.03,384,1.000,bicubic,-65.233,-35.027,+22
swsl_resnet50,29.867,70.133,63.853,36.147,25.56,224,0.875,bilinear,-65.543,-35.437,+17
vit_deit_base_distilled_patch16_224,29.600,70.400,64.453,35.547,87.34,224,0.900,bicubic,-66.490,-34.807,-13
ssl_resnext101_32x8d,29.040,70.960,60.973,39.027,88.79,224,0.875,bilinear,-66.430,-38.137,+10
resnet101d,28.987,71.013,62.053,37.947,44.57,320,1.000,bicubic,-67.303,-37.177,-20
resnetrs152,28.920,71.080,60.520,39.480,86.62,320,1.000,bicubic,-67.660,-38.720,-34
vit_deit_base_patch16_224,27.440,72.560,58.893,41.107,86.57,224,0.900,bicubic,-68.000,-39.947,+10
resnetv2_50x1_bitm,27.347,72.653,63.547,36.453,25.55,480,1.000,bilinear,-67.703,-35.613,+24
nfnet_l0,26.493,73.507,61.987,38.013,35.07,288,1.000,bicubic,-69.597,-37.313,-18
tf_efficientnet_b4,26.293,73.707,60.107,39.893,19.34,380,0.922,bicubic,-69.607,-39.063,-12
tf_efficientnet_b4_ap,26.240,73.760,60.227,39.773,19.34,380,0.922,bicubic,-69.920,-39.053,-25
regnety_032,26.213,73.787,60.987,39.013,19.44,288,1.000,bicubic,-69.757,-38.203,-19
ecaresnet50t,26.133,73.867,60.027,39.973,25.57,320,0.950,bicubic,-69.377,-39.093,0
ecaresnet101d,26.027,73.973,58.987,41.013,44.57,224,0.875,bicubic,-69.503,-40.143,-2
eca_nfnet_l0,25.013,74.987,60.360,39.640,24.14,288,1.000,bicubic,-70.917,-38.850,-19
tnt_s_patch16_224,24.733,75.267,58.187,41.813,23.76,224,0.900,bicubic,-70.307,-40.643,+19
ssl_resnext101_32x4d,24.173,75.827,57.413,42.587,44.18,224,0.875,bilinear,-71.267,-41.717,-2
tf_efficientnet_b2_ns,24.013,75.987,57.293,42.707,9.11,260,0.890,bicubic,-71.757,-41.827,-12
nasnetalarge,23.493,76.507,55.027,44.973,88.75,331,0.911,bicubic,-72.187,-43.903,-9
pnasnet5large,23.333,76.667,53.640,46.360,86.06,331,0.911,bicubic,-72.377,-45.280,-11
efficientnet_b3,23.213,76.787,55.960,44.040,12.23,320,1.000,bicubic,-72.497,-43.080,-13
pit_s_distilled_224,22.360,77.640,57.120,42.880,24.04,224,0.900,bicubic,-72.880,-41.930,+2
tresnet_m,21.680,78.320,53.840,46.160,31.39,224,0.875,bilinear,-74.040,-45.190,-16
swin_tiny_patch4_window7_224,21.173,78.827,55.973,44.027,28.29,224,0.900,bicubic,-73.967,-42.877,+2
pit_s_224,21.080,78.920,53.573,46.427,23.46,224,0.900,bicubic,-73.510,-45.137,+33
resnetrs101,20.893,79.107,52.813,47.187,63.62,288,0.940,bicubic,-74.537,-46.217,-8
vit_deit_small_distilled_patch16_224,20.707,79.293,55.133,44.867,22.44,224,0.900,bicubic,-74.003,-43.897,+23
resnest50d_4s2x40d,20.387,79.613,52.800,47.200,30.42,224,0.875,bicubic,-74.573,-46.270,+10
ssl_resnext50_32x4d,20.000,80.000,53.613,46.387,25.03,224,0.875,bilinear,-74.870,-45.267,+15
tresnet_xl,19.640,80.360,53.133,46.867,78.44,224,0.875,bilinear,-75.800,-45.917,-14
gluon_senet154,19.307,80.693,47.533,52.467,115.09,224,0.875,bicubic,-75.613,-51.227,+10
rexnet_200,19.227,80.773,52.720,47.280,16.37,224,0.875,bicubic,-75.713,-46.290,+7
repvgg_b3,19.107,80.893,50.253,49.747,123.09,224,0.875,bilinear,-75.463,-48.527,+26
resnetrs200,30.773,69.227,63.320,36.680,93.21,320,1.000,bicubic,-65.757,-36.030,-28
cait_xxs24_384,30.027,69.973,63.933,36.067,12.03,384,1.000,bicubic,-65.233,-35.027,+30
twins_pcpvt_base,29.960,70.040,64.587,35.413,43.83,224,0.900,bicubic,-65.830,-34.543,+6
swsl_resnet50,29.867,70.133,63.853,36.147,25.56,224,0.875,bilinear,-65.543,-35.437,+23
vit_deit_base_distilled_patch16_224,29.600,70.400,64.453,35.547,87.34,224,0.900,bicubic,-66.490,-34.737,-11
convit_base,29.520,70.480,61.787,38.213,86.54,224,0.875,bicubic,-66.030,-37.083,+10
ssl_resnext101_32x8d,29.040,70.960,60.973,39.027,88.79,224,0.875,bilinear,-66.430,-38.137,+15
tf_efficientnetv2_s,29.040,70.960,61.213,38.787,21.46,384,1.000,bicubic,-67.300,-37.987,-26
resnet101d,28.987,71.013,62.053,37.947,44.57,320,1.000,bicubic,-67.303,-37.177,-25
resnetrs152,28.920,71.080,60.520,39.480,86.62,320,1.000,bicubic,-67.660,-38.720,-40
coat_lite_small,27.547,72.453,58.547,41.453,19.84,224,0.900,bicubic,-67.993,-40.313,+6
vit_deit_base_patch16_224,27.440,72.560,58.893,41.107,86.57,224,0.900,bicubic,-68.000,-40.157,+12
resnetv2_50x1_bitm,27.347,72.653,63.547,36.453,25.55,480,1.000,bilinear,-67.703,-35.613,+32
nfnet_l0,26.493,73.507,61.987,38.013,35.07,288,1.000,bicubic,-69.597,-37.273,-22
tf_efficientnet_b4,26.293,73.707,60.107,39.893,19.34,380,0.922,bicubic,-69.607,-39.063,-15
tf_efficientnet_b4_ap,26.240,73.760,60.227,39.773,19.34,380,0.922,bicubic,-69.920,-38.833,-29
regnety_032,26.213,73.787,60.987,39.013,19.44,288,1.000,bicubic,-69.757,-38.203,-22
ecaresnet50t,26.133,73.867,60.027,39.973,25.57,320,0.950,bicubic,-69.377,-39.093,+2
ecaresnet101d,26.027,73.973,58.987,41.013,44.57,224,0.875,bicubic,-69.503,-40.143,-1
visformer_small,25.840,74.160,58.907,41.093,40.22,224,0.900,bicubic,-69.650,-39.993,+1
coat_mini,25.520,74.480,57.693,42.307,10.34,224,0.900,bicubic,-69.450,-41.087,+31
convit_small,25.093,74.907,57.280,42.720,27.78,224,0.875,bicubic,-70.107,-41.620,+13
eca_nfnet_l0,25.013,74.987,60.360,39.640,24.14,288,1.000,bicubic,-70.917,-38.850,-25
tnt_s_patch16_224,24.733,75.267,58.187,41.813,23.76,224,0.900,bicubic,-70.307,-40.643,+24
ssl_resnext101_32x4d,24.173,75.827,57.413,42.587,44.18,224,0.875,bilinear,-71.267,-41.427,0
twins_svt_small,24.133,75.867,57.147,42.853,24.06,224,0.900,bicubic,-71.067,-41.733,+10
tf_efficientnet_b2_ns,24.013,75.987,57.293,42.707,9.11,260,0.890,bicubic,-71.757,-41.827,-17
nasnetalarge,23.493,76.507,55.027,44.973,88.75,331,0.911,bicubic,-72.187,-43.903,-14
levit_384,23.440,76.560,56.387,43.613,39.13,224,0.900,bicubic,-72.090,-42.663,-10
pnasnet5large,23.333,76.667,53.640,46.360,86.06,331,0.911,bicubic,-72.377,-45.400,-18
efficientnet_b3,23.213,76.787,55.960,44.040,12.23,320,1.000,bicubic,-72.497,-42.960,-18
twins_pcpvt_small,22.720,77.280,56.853,43.147,24.11,224,0.900,bicubic,-72.490,-42.027,+2
pit_s_distilled_224,22.360,77.640,57.120,42.880,24.04,224,0.900,bicubic,-72.880,-41.930,0
tresnet_m,21.680,78.320,53.840,46.160,31.39,224,0.875,bilinear,-74.040,-45.190,-23
swin_tiny_patch4_window7_224,21.173,78.827,55.973,44.027,28.29,224,0.900,bicubic,-73.967,-42.877,+4
pit_s_224,21.080,78.920,53.573,46.427,23.46,224,0.900,bicubic,-73.510,-45.137,+37
resnet51q,20.960,79.040,55.720,44.280,35.70,288,1.000,bilinear,-74.900,-43.400,-34
resnetrs101,20.893,79.107,52.813,47.187,63.62,288,0.940,bicubic,-74.537,-46.217,-12
vit_deit_small_distilled_patch16_224,20.707,79.293,55.133,44.867,22.44,224,0.900,bicubic,-74.003,-43.897,+26
resnest50d_4s2x40d,20.387,79.613,52.800,47.200,30.42,224,0.875,bicubic,-74.573,-46.270,+13
ssl_resnext50_32x4d,20.000,80.000,53.613,46.387,25.03,224,0.875,bilinear,-74.870,-45.267,+18
tresnet_xl,19.640,80.360,53.133,46.867,78.44,224,0.875,bilinear,-75.800,-45.997,-19
gluon_senet154,19.307,80.693,47.533,52.467,115.09,224,0.875,bicubic,-75.613,-51.227,+13
rexnet_200,19.227,80.773,52.720,47.280,16.37,224,0.875,bicubic,-75.713,-46.290,+10
levit_256,19.200,80.800,50.067,49.933,18.89,224,0.900,bicubic,-75.810,-48.843,+5
repvgg_b3,19.107,80.893,50.253,49.747,123.09,224,0.875,bilinear,-75.463,-48.527,+28
legacy_senet154,19.053,80.947,47.947,52.053,115.09,224,0.875,bilinear,-76.017,-50.883,-3
vit_deit_small_patch16_224,18.907,81.093,51.413,48.587,22.05,224,0.900,bicubic,-75.493,-47.327,+36
gluon_seresnext101_64x4d,18.907,81.093,49.187,50.813,88.23,224,0.875,bicubic,-76.023,-49.643,+5
tf_efficientnet_b1_ns,18.693,81.307,51.667,48.333,7.79,240,0.882,bicubic,-76.477,-47.443,-12
seresnext50_32x4d,18.360,81.640,50.973,49.027,27.56,224,0.875,bicubic,-76.680,-47.907,-4
cait_xxs36_224,18.253,81.747,49.427,50.573,17.30,224,1.000,bicubic,-76.007,-49.293,+45
ecaresnet50d,18.227,81.773,51.880,48.120,25.58,224,0.875,bicubic,-76.403,-47.010,+13
tf_efficientnet_lite4,18.133,81.867,50.707,49.293,13.01,380,0.920,bilinear,-76.757,-48.313,+2
resnest50d_1s4x24d,17.693,82.307,49.800,50.200,25.68,224,0.875,bicubic,-77.057,-49.180,+5
gluon_seresnext101_32x4d,17.373,82.627,46.373,53.627,48.96,224,0.875,bicubic,-77.547,-52.437,-1
resnest50d,17.373,82.627,50.707,49.293,27.48,224,0.875,bilinear,-77.457,-48.173,+1
efficientnet_el,17.347,82.653,49.987,50.013,10.59,300,0.904,bicubic,-77.773,-49.003,-18
inception_v4,17.267,82.733,45.920,54.080,42.68,299,0.875,bicubic,-77.113,-52.660,+28
tf_efficientnet_b3_ap,17.187,82.813,49.680,50.320,12.23,300,0.904,bicubic,-78.133,-49.220,-26
tf_efficientnet_b3,17.000,83.000,49.267,50.733,12.23,300,0.904,bicubic,-78.010,-49.643,-12
xception71,17.000,83.000,45.520,54.480,42.34,299,0.903,bicubic,-77.280,-53.120,+32
gluon_resnext101_64x4d,16.853,83.147,44.213,55.787,83.46,224,0.875,bicubic,-77.817,-54.437,+1
tresnet_l,16.600,83.400,49.920,50.080,55.99,224,0.875,bilinear,-78.690,-49.090,-29
mixer_b16_224_miil,19.053,80.947,51.227,48.773,59.88,224,0.875,bilinear,-76.247,-47.653,-18
vit_deit_small_patch16_224,18.907,81.093,51.413,48.587,22.05,224,0.900,bicubic,-75.493,-47.277,+39
gluon_seresnext101_64x4d,18.907,81.093,49.187,50.813,88.23,224,0.875,bicubic,-76.023,-49.643,+6
tf_efficientnet_b1_ns,18.693,81.307,51.667,48.333,7.79,240,0.882,bicubic,-76.477,-47.443,-14
seresnext50_32x4d,18.360,81.640,50.973,49.027,27.56,224,0.875,bicubic,-76.680,-47.907,-5
cait_xxs36_224,18.253,81.747,49.427,50.573,17.30,224,1.000,bicubic,-76.007,-49.033,+48
ecaresnet50d,18.227,81.773,51.893,48.107,25.58,224,0.875,bicubic,-76.403,-46.997,+14
tf_efficientnet_lite4,18.133,81.867,50.707,49.293,13.01,380,0.920,bilinear,-76.757,-48.313,+3
resnest50d_1s4x24d,17.693,82.307,49.800,50.200,25.68,224,0.875,bicubic,-77.057,-49.180,+6
gluon_seresnext101_32x4d,17.373,82.627,46.373,53.627,48.96,224,0.875,bicubic,-77.547,-52.437,0
resnest50d,17.373,82.627,50.707,49.293,27.48,224,0.875,bilinear,-77.457,-48.173,+2
efficientnet_el,17.347,82.653,49.987,50.013,10.59,300,0.904,bicubic,-77.773,-49.003,-19
inception_v4,17.267,82.733,45.920,54.080,42.68,299,0.875,bicubic,-77.113,-52.660,+30
tf_efficientnet_b3_ap,17.187,82.813,49.680,50.320,12.23,300,0.904,bicubic,-78.133,-49.220,-32
xception71,17.000,83.000,45.520,54.480,42.34,299,0.903,bicubic,-77.280,-53.120,+34
tf_efficientnet_b3,17.000,83.000,49.267,50.733,12.23,300,0.904,bicubic,-78.010,-49.623,-13
gluon_resnext101_64x4d,16.853,83.147,44.213,55.787,83.46,224,0.875,bicubic,-77.817,-54.437,+2
tf_efficientnetv2_b3,16.667,83.333,48.680,51.320,14.36,300,0.904,bicubic,-78.493,-50.140,-27
tresnet_l,16.600,83.400,49.920,50.080,55.99,224,0.875,bilinear,-78.690,-49.090,-35
gluon_resnet152_v1d,16.573,83.427,44.280,55.720,60.21,224,0.875,bicubic,-78.167,-54.460,-4
gluon_resnet152_v1s,16.573,83.427,44.533,55.467,60.32,224,0.875,bicubic,-78.467,-54.397,-20
inception_resnet_v2,16.573,83.427,44.960,55.040,55.84,299,0.897,bicubic,-77.967,-53.890,+8
gluon_xception65,16.440,83.560,46.027,53.973,39.92,299,0.903,bicubic,-77.820,-52.433,+30
gernet_l,16.373,83.627,47.213,52.787,31.08,256,0.875,bilinear,-78.717,-51.687,-28
wide_resnet50_2,16.280,83.720,48.347,51.653,68.88,224,0.875,bicubic,-78.800,-50.623,-27
ens_adv_inception_resnet_v2,16.240,83.760,43.640,56.360,55.84,299,0.897,bicubic,-77.920,-55.000,+37
gluon_resnet152_v1s,16.573,83.427,44.533,55.467,60.32,224,0.875,bicubic,-78.467,-54.397,-22
inception_resnet_v2,16.573,83.427,44.960,55.040,55.84,299,0.897,bicubic,-77.967,-53.830,+7
gluon_xception65,16.440,83.560,46.027,53.973,39.92,299,0.903,bicubic,-77.820,-52.693,+30
gernet_l,16.373,83.627,47.213,52.787,31.08,256,0.875,bilinear,-78.717,-51.687,-30
wide_resnet50_2,16.280,83.720,48.347,51.653,68.88,224,0.875,bicubic,-78.800,-50.623,-29
ens_adv_inception_resnet_v2,16.240,83.760,43.640,56.360,55.84,299,0.897,bicubic,-77.920,-55.000,+39
repvgg_b3g4,16.213,83.787,47.653,52.347,83.83,224,0.875,bilinear,-78.307,-51.317,+5
xception65,16.027,83.973,43.773,56.227,39.92,299,0.903,bicubic,-77.733,-54.597,+62
xception65,16.027,83.973,43.773,56.227,39.92,299,0.903,bicubic,-77.733,-54.597,+65
ssl_resnet50,15.960,84.040,49.467,50.533,25.56,224,0.875,bilinear,-78.490,-49.453,+9
regnety_320,15.627,84.373,44.827,55.173,145.05,224,0.875,bicubic,-78.913,-53.963,-1
ecaresnet101d_pruned,15.600,84.400,48.027,51.973,24.88,224,0.875,bicubic,-79.480,-50.953,-34
ecaresnet26t,15.467,84.533,47.920,52.080,16.01,320,0.950,bicubic,-78.843,-50.800,+15
regnety_320,15.627,84.373,44.827,55.173,145.05,224,0.875,bicubic,-78.913,-54.023,0
ecaresnet101d_pruned,15.600,84.400,48.027,51.973,24.88,224,0.875,bicubic,-79.480,-50.953,-36
ecaresnet26t,15.467,84.533,47.920,52.080,16.01,320,0.950,bicubic,-78.843,-50.800,+16
coat_tiny,15.413,84.587,45.600,54.400,5.50,224,0.900,bicubic,-78.177,-52.830,+72
skresnext50_32x4d,15.373,84.627,44.493,55.507,27.48,224,0.875,bicubic,-78.887,-54.077,+18
cait_xxs24_224,15.160,84.840,44.960,55.040,11.96,224,1.000,bicubic,-78.440,-53.480,+67
ecaresnetlight,15.160,84.840,45.827,54.173,30.16,224,0.875,bicubic,-79.610,-52.973,-21
rexnet_150,14.720,85.280,46.907,53.093,9.73,224,0.875,bicubic,-79.760,-51.883,0
cait_xxs24_224,15.160,84.840,44.960,55.040,11.96,224,1.000,bicubic,-78.440,-53.480,+69
ecaresnetlight,15.160,84.840,45.827,54.173,30.16,224,0.875,bicubic,-79.610,-52.973,-22
levit_192,14.893,85.107,44.920,55.080,10.95,224,0.900,bicubic,-79.277,-53.620,+25
rexnet_150,14.720,85.280,46.907,53.093,9.73,224,0.875,bicubic,-79.760,-51.883,-2
coat_lite_mini,14.507,85.493,44.507,55.493,11.01,224,0.900,bicubic,-79.553,-54.053,+35
efficientnet_el_pruned,14.480,85.520,46.120,53.880,10.59,300,0.904,bicubic,-79.920,-52.570,+3
efficientnet_b2,14.440,85.560,46.080,53.920,9.11,288,1.000,bicubic,-80.170,-52.630,-15
legacy_seresnext101_32x4d,14.147,85.853,42.973,57.027,48.96,224,0.875,bilinear,-80.223,-55.677,+3
seresnet50,14.147,85.853,45.467,54.533,28.09,224,0.875,bicubic,-80.403,-53.283,-13
gernet_m,14.013,85.987,46.067,53.933,21.14,224,0.875,bilinear,-80.607,-52.793,-19
gluon_resnext101_32x4d,13.867,86.133,41.653,58.347,44.18,224,0.875,bicubic,-80.663,-56.977,-12
gluon_seresnext50_32x4d,13.600,86.400,43.760,56.240,27.56,224,0.875,bicubic,-80.740,-54.850,+1
repvgg_b2g4,13.440,86.560,43.787,56.213,61.76,224,0.875,bilinear,-80.420,-54.803,+38
efficientnet_el_pruned,14.480,85.520,46.120,53.880,10.59,300,0.904,bicubic,-79.920,-52.620,+1
efficientnet_b2,14.440,85.560,46.080,53.920,9.11,288,1.000,bicubic,-80.170,-52.630,-17
legacy_seresnext101_32x4d,14.147,85.853,42.973,57.027,48.96,224,0.875,bilinear,-80.223,-55.677,+2
seresnet50,14.147,85.853,45.467,54.533,28.09,224,0.875,bicubic,-80.403,-53.283,-15
gernet_m,14.013,85.987,46.067,53.933,21.14,224,0.875,bilinear,-80.607,-52.793,-21
gluon_resnext101_32x4d,13.867,86.133,41.653,58.347,44.18,224,0.875,bicubic,-80.663,-56.977,-14
gluon_seresnext50_32x4d,13.600,86.400,43.760,56.240,27.56,224,0.875,bicubic,-80.740,-54.850,0
repvgg_b2g4,13.440,86.560,43.787,56.213,61.76,224,0.875,bilinear,-80.420,-54.803,+39
ese_vovnet39b,13.320,86.680,43.813,56.187,24.57,224,0.875,bicubic,-80.770,-54.847,+24
regnetx_320,13.307,86.693,40.720,59.280,107.81,224,0.875,bicubic,-81.153,-58.020,-10
pit_xs_distilled_224,13.240,86.760,44.573,55.427,11.00,224,0.900,bicubic,-80.570,-54.097,+39
efficientnet_b3_pruned,13.173,86.827,45.213,54.787,9.86,300,0.904,bicubic,-81.457,-53.547,-27
gluon_resnet101_v1d,13.160,86.840,41.493,58.507,44.57,224,0.875,bicubic,-81.060,-57.057,+7
mixnet_xl,13.120,86.880,43.253,56.747,11.90,224,0.875,bicubic,-81.070,-55.087,+8
regnetx_320,13.307,86.693,40.720,59.280,107.81,224,0.875,bicubic,-81.153,-58.020,-12
pit_xs_distilled_224,13.240,86.760,44.573,55.427,11.00,224,0.900,bicubic,-80.570,-54.097,+40
efficientnet_b3_pruned,13.173,86.827,45.213,54.787,9.86,300,0.904,bicubic,-81.457,-53.547,-29
gluon_resnet101_v1d,13.160,86.840,41.493,58.507,44.57,224,0.875,bicubic,-81.060,-57.057,+6
mixnet_xl,13.120,86.880,43.253,56.747,11.90,224,0.875,bicubic,-81.070,-55.087,+7
nf_regnet_b1,13.027,86.973,44.413,55.587,10.22,288,0.900,bicubic,-81.103,-54.217,+15
pit_xs_224,12.813,87.187,42.840,57.160,10.62,224,0.900,bicubic,-80.297,-55.470,+79
gluon_inception_v3,12.640,87.360,40.493,59.507,23.83,299,0.875,bicubic,-80.820,-58.077,+56
coat_lite_tiny,12.520,87.480,41.160,58.840,5.72,224,0.900,bicubic,-80.720,-57.100,+70
regnety_120,12.427,87.573,42.200,57.800,51.82,224,0.875,bicubic,-82.053,-56.610,-21
efficientnet_em,12.360,87.640,43.880,56.120,6.90,240,0.882,bicubic,-81.480,-54.930,+27
nf_resnet50,12.320,87.680,46.760,53.240,25.56,288,0.940,bicubic,-82.270,-52.050,-33
vit_small_patch16_224,12.147,87.853,40.320,59.680,48.75,224,0.900,bicubic,-80.613,-57.610,+89
pit_xs_224,12.813,87.187,42.840,57.160,10.62,224,0.900,bicubic,-80.297,-55.470,+82
gluon_inception_v3,12.640,87.360,40.493,59.507,23.83,299,0.875,bicubic,-80.820,-58.037,+59
coat_lite_tiny,12.520,87.480,41.160,58.840,5.72,224,0.900,bicubic,-80.720,-57.100,+73
regnety_120,12.427,87.573,42.200,57.800,51.82,224,0.875,bicubic,-82.053,-56.610,-23
efficientnet_em,12.360,87.640,43.880,56.120,6.90,240,0.882,bicubic,-81.480,-54.930,+28
nf_resnet50,12.320,87.680,46.760,53.240,25.56,288,0.940,bicubic,-82.270,-52.050,-35
vit_small_patch16_224,12.147,87.853,40.320,59.680,48.75,224,0.900,bicubic,-80.613,-57.610,+93
hrnet_w64,12.027,87.973,40.787,59.213,128.06,224,0.875,bilinear,-81.983,-57.823,+14
cspdarknet53,12.013,87.987,43.253,56.747,27.64,256,0.887,bilinear,-82.647,-55.547,-41
gluon_resnet101_v1s,11.880,88.120,40.973,59.027,44.67,224,0.875,bicubic,-82.840,-57.847,-45
resnet50d,11.693,88.307,42.453,57.547,25.58,224,0.875,bicubic,-82.567,-56.267,-13
dpn92,11.627,88.373,40.267,59.733,37.67,224,0.875,bicubic,-82.603,-58.463,-9
xception41,11.600,88.400,39.133,60.867,26.97,299,0.903,bicubic,-81.830,-59.297,+48
cspdarknet53,12.013,87.987,43.253,56.747,27.64,256,0.887,bilinear,-82.647,-55.547,-43
gluon_resnet101_v1s,11.880,88.120,40.973,59.027,44.67,224,0.875,bicubic,-82.840,-57.847,-47
resnet50d,11.693,88.307,42.453,57.547,25.58,224,0.875,bicubic,-82.567,-56.267,-14
dpn92,11.627,88.373,40.267,59.733,37.67,224,0.875,bicubic,-82.603,-58.463,-10
xception41,11.600,88.400,39.133,60.867,26.97,299,0.903,bicubic,-81.830,-59.297,+50
dla102x2,11.573,88.427,41.293,58.707,41.28,224,0.875,bilinear,-82.377,-57.197,+10
regnety_080,11.413,88.587,40.613,59.387,39.18,224,0.875,bicubic,-82.757,-58.067,-6
efficientnet_b2_pruned,11.360,88.640,42.027,57.973,8.31,260,0.890,bicubic,-82.780,-56.503,-3
tf_efficientnet_el,11.333,88.667,42.040,57.960,10.59,300,0.904,bicubic,-83.077,-56.670,-30
gluon_resnet152_v1c,11.093,88.907,37.120,62.880,60.21,224,0.875,bicubic,-83.067,-61.480,-8
levit_128,11.427,88.573,40.267,59.733,9.21,224,0.900,bicubic,-81.913,-58.113,+52
regnety_080,11.413,88.587,40.613,59.387,39.18,224,0.875,bicubic,-82.757,-58.067,-7
efficientnet_b2_pruned,11.360,88.640,42.027,57.973,8.31,260,0.890,bicubic,-82.780,-56.503,-4
tf_efficientnet_el,11.333,88.667,42.040,57.960,10.59,300,0.904,bicubic,-83.077,-56.670,-32
gluon_resnet152_v1c,11.093,88.907,37.120,62.880,60.21,224,0.875,bicubic,-83.067,-61.480,-9
dpn107,11.080,88.920,38.693,61.307,86.92,224,0.875,bicubic,-83.230,-59.787,-27
hrnet_w48,11.080,88.920,40.320,59.680,77.47,224,0.875,bilinear,-82.840,-58.290,+5
dpn107,11.080,88.920,38.693,61.307,86.92,224,0.875,bicubic,-83.230,-59.787,-25
ecaresnet50d_pruned,11.027,88.973,41.947,58.053,19.94,224,0.875,bicubic,-83.193,-56.783,-17
adv_inception_v3,11.013,88.987,36.720,63.280,23.83,299,0.875,bicubic,-81.867,-61.420,+68
tf_efficientnet_b0_ns,10.933,89.067,40.067,59.933,5.29,224,0.875,bicubic,-82.697,-58.573,+24
tf_inception_v3,10.840,89.160,36.853,63.147,23.83,299,0.875,bicubic,-82.480,-61.177,+43
resnext50_32x4d,10.800,89.200,40.307,59.693,25.03,224,0.875,bicubic,-83.300,-58.043,-8
dpn131,10.787,89.213,37.200,62.800,79.25,224,0.875,bicubic,-83.223,-61.520,-5
tf_efficientnet_b2_ap,10.533,89.467,40.107,59.893,9.11,260,0.890,bicubic,-83.957,-58.513,-46
resnext50d_32x4d,10.413,89.587,39.733,60.267,25.05,224,0.875,bicubic,-83.767,-58.837,-20
rexnet_130,10.400,89.600,41.547,58.453,7.56,224,0.875,bicubic,-83.500,-56.853,-3
ecaresnet50d_pruned,11.027,88.973,41.947,58.053,19.94,224,0.875,bicubic,-83.193,-56.783,-19
tf_efficientnetv2_b2,11.027,88.973,39.760,60.240,10.10,260,0.890,bicubic,-83.393,-58.810,-38
adv_inception_v3,11.013,88.987,36.720,63.280,23.83,299,0.875,bicubic,-81.867,-61.420,+70
tf_efficientnet_b0_ns,10.933,89.067,40.067,59.933,5.29,224,0.875,bicubic,-82.697,-58.573,+23
tf_inception_v3,10.840,89.160,36.853,63.147,23.83,299,0.875,bicubic,-82.480,-61.177,+44
resnext50_32x4d,10.800,89.200,40.307,59.693,25.03,224,0.875,bicubic,-83.300,-58.043,-10
dpn131,10.787,89.213,37.200,62.800,79.25,224,0.875,bicubic,-83.223,-61.520,-7
tf_efficientnet_b2_ap,10.533,89.467,40.107,59.893,9.11,260,0.890,bicubic,-83.957,-58.513,-50
resnext50d_32x4d,10.413,89.587,39.733,60.267,25.05,224,0.875,bicubic,-83.767,-58.837,-23
rexnet_130,10.400,89.600,41.547,58.453,7.56,224,0.875,bicubic,-83.500,-56.853,-4
hrnet_w44,10.320,89.680,39.507,60.493,67.06,224,0.875,bilinear,-83.230,-59.193,+21
resnext101_32x8d,10.187,89.813,37.827,62.173,88.79,224,0.875,bilinear,-83.643,-60.753,+2
regnetx_160,10.147,89.853,38.000,62.000,54.28,224,0.875,bicubic,-83.973,-60.750,-16
dpn98,10.133,89.867,36.587,63.413,61.57,224,0.875,bicubic,-83.997,-61.983,-19
cspresnext50,10.120,89.880,40.373,59.627,20.57,224,0.875,bilinear,-84.360,-58.307,-52
legacy_seresnext50_32x4d,10.107,89.893,39.200,60.800,27.56,224,0.875,bilinear,-83.623,-59.380,+7
resnetrs50,10.093,89.907,37.507,62.493,35.69,224,0.910,bicubic,-84.217,-61.133,-40
inception_v3,10.027,89.973,35.227,64.773,23.83,299,0.875,bicubic,-82.693,-62.743,+63
efficientnet_b1,10.013,89.987,37.547,62.453,7.79,256,1.000,bicubic,-83.237,-60.743,+34
xception,9.987,90.013,38.027,61.973,22.86,299,0.897,bicubic,-83.473,-60.503,+20
regnety_064,9.947,90.053,39.067,60.933,30.58,224,0.875,bicubic,-84.203,-59.663,-28
dpn68b,9.787,90.213,38.053,61.947,12.61,224,0.875,bicubic,-83.903,-60.307,+4
gluon_resnet152_v1b,9.747,90.253,36.067,63.933,60.19,224,0.875,bicubic,-84.333,-62.383,-23
tf_efficientnet_lite3,9.667,90.333,39.000,61.000,8.20,300,0.904,bilinear,-84.533,-59.640,-37
tf_efficientnet_b2,9.653,90.347,38.880,61.120,9.11,260,0.890,bicubic,-84.707,-59.730,-52
tf_efficientnet_cc_b1_8e,9.573,90.427,36.773,63.227,39.72,240,0.882,bicubic,-84.327,-61.487,-18
res2net101_26w_4s,9.520,90.480,35.027,64.973,45.21,224,0.875,bilinear,-84.230,-63.283,-6
resnext101_32x8d,10.187,89.813,37.827,62.173,88.79,224,0.875,bilinear,-83.643,-60.753,+1
regnetx_160,10.147,89.853,38.000,62.000,54.28,224,0.875,bicubic,-83.973,-60.750,-18
dpn98,10.133,89.867,36.587,63.413,61.57,224,0.875,bicubic,-83.997,-61.983,-21
cspresnext50,10.120,89.880,40.373,59.627,20.57,224,0.875,bilinear,-84.360,-58.307,-56
legacy_seresnext50_32x4d,10.107,89.893,39.200,60.800,27.56,224,0.875,bilinear,-83.623,-59.380,+6
resnetrs50,10.093,89.907,37.507,62.493,35.69,224,0.910,bicubic,-84.217,-61.133,-43
inception_v3,10.027,89.973,35.227,64.773,23.83,299,0.875,bicubic,-82.693,-62.743,+65
efficientnet_b1,10.013,89.987,37.547,62.453,7.79,256,1.000,bicubic,-83.237,-60.743,+35
xception,9.987,90.013,38.027,61.973,22.86,299,0.897,bicubic,-83.473,-60.543,+19
regnety_064,9.947,90.053,39.067,60.933,30.58,224,0.875,bicubic,-84.203,-59.663,-30
dpn68b,9.787,90.213,38.053,61.947,12.61,224,0.875,bicubic,-83.903,-60.457,+2
gluon_resnet152_v1b,9.747,90.253,36.067,63.933,60.19,224,0.875,bicubic,-84.333,-62.383,-25
tf_efficientnet_lite3,9.667,90.333,39.000,61.000,8.20,300,0.904,bilinear,-84.533,-59.640,-40
tf_efficientnet_b2,9.653,90.347,38.880,61.120,9.11,260,0.890,bicubic,-84.707,-59.730,-55
tf_efficientnet_cc_b1_8e,9.573,90.427,36.773,63.227,39.72,240,0.882,bicubic,-84.327,-61.487,-19
res2net101_26w_4s,9.520,90.480,35.027,64.973,45.21,224,0.875,bilinear,-84.230,-63.283,-7
legacy_seresnet152,9.347,90.653,37.413,62.587,66.82,224,0.875,bilinear,-84.053,-60.937,+16
cspresnet50,9.253,90.747,39.640,60.360,21.62,256,0.887,bilinear,-84.487,-59.000,-7
cspresnet50,9.253,90.747,39.640,60.360,21.62,256,0.887,bilinear,-84.487,-59.000,-8
hrnet_w40,9.227,90.773,36.893,63.107,57.56,224,0.875,bilinear,-84.263,-61.687,+8
regnetx_120,9.187,90.813,37.200,62.800,46.11,224,0.875,bicubic,-85.053,-61.450,-48
seresnext26d_32x4d,9.147,90.853,36.840,63.160,16.81,224,0.875,bicubic,-83.553,-61.310,+50
resnest26d,9.080,90.920,37.853,62.147,17.07,224,0.875,bilinear,-84.250,-60.657,+13
regnety_040,9.000,91.000,37.053,62.947,20.65,224,0.875,bicubic,-84.860,-61.597,-23
gluon_resnext50_32x4d,8.947,91.053,36.333,63.667,25.03,224,0.875,bicubic,-84.863,-62.057,-18
rexnet_100,8.893,91.107,36.373,63.627,4.80,224,0.875,bicubic,-84.137,-61.817,+29
seresnext26t_32x4d,8.893,91.107,36.907,63.093,16.81,224,0.875,bicubic,-83.927,-61.463,+37
regnetx_120,9.187,90.813,37.200,62.800,46.11,224,0.875,bicubic,-85.053,-61.450,-51
seresnext26d_32x4d,9.147,90.853,36.840,63.160,16.81,224,0.875,bicubic,-83.553,-61.310,+52
resnest26d,9.080,90.920,37.853,62.147,17.07,224,0.875,bilinear,-84.250,-60.777,+15
regnety_040,9.000,91.000,37.053,62.947,20.65,224,0.875,bicubic,-84.860,-61.597,-24
gluon_resnext50_32x4d,8.947,91.053,36.333,63.667,25.03,224,0.875,bicubic,-84.863,-62.057,-19
rexnet_100,8.893,91.107,36.373,63.627,4.80,224,0.875,bicubic,-84.137,-61.817,+31
seresnext26t_32x4d,8.893,91.107,36.907,63.093,16.81,224,0.875,bicubic,-83.927,-61.463,+39
mixnet_l,8.853,91.147,36.187,63.813,7.33,224,0.875,bicubic,-84.597,-62.033,+3
mobilenetv3_large_100_miil,8.840,91.160,32.973,67.027,5.48,224,0.875,bilinear,-83.420,-64.667,+63
dla169,8.640,91.360,36.040,63.960,53.39,224,0.875,bilinear,-84.700,-62.560,+5
hrnet_w30,8.613,91.387,37.040,62.960,37.71,224,0.875,bilinear,-84.587,-61.370,+14
mixer_b16_224,8.600,91.400,29.413,70.587,59.88,224,0.875,bicubic,-83.270,-67.837,+74
legacy_seresnet101,8.533,91.467,36.013,63.987,49.33,224,0.875,bilinear,-84.747,-62.497,+9
tf_efficientnet_b1_ap,8.453,91.547,35.253,64.747,7.79,240,0.882,bicubic,-85.237,-63.257,-19
repvgg_b2,8.427,91.573,36.467,63.533,89.02,224,0.875,bilinear,-85.073,-61.893,-8
resnetblur50,8.240,91.760,37.400,62.600,25.56,224,0.875,bicubic,-85.720,-61.190,-42
dla102x,8.200,91.800,37.013,62.987,26.31,224,0.875,bilinear,-85.320,-61.497,-12
hrnet_w32,8.040,91.960,37.507,62.493,41.23,224,0.875,bilinear,-85.490,-60.943,-14
res2net50_26w_8s,8.000,92.000,33.853,66.147,48.40,224,0.875,bilinear,-85.540,-64.407,-16
gluon_resnet101_v1c,7.987,92.013,33.360,66.640,44.57,224,0.875,bicubic,-85.683,-65.060,-23
gluon_resnet50_v1d,7.920,92.080,35.000,65.000,25.58,224,0.875,bicubic,-85.850,-63.390,-33
dla60_res2next,7.787,92.213,34.987,65.013,17.03,224,0.875,bilinear,-85.393,-63.423,+5
densenetblur121d,7.720,92.280,34.733,65.267,8.00,224,0.875,bicubic,-84.190,-63.337,+61
vit_deit_tiny_distilled_patch16_224,7.707,92.293,33.560,66.440,5.91,224,0.900,bicubic,-82.993,-64.010,+91
dla60_res2net,7.560,92.440,34.627,65.373,20.85,224,0.875,bilinear,-85.620,-63.793,+1
efficientnet_b1_pruned,7.440,92.560,34.533,65.467,6.33,240,0.882,bicubic,-85.330,-63.507,+20
wide_resnet101_2,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.360,-64.393,-33
regnetx_064,7.333,92.667,34.373,65.627,26.21,224,0.875,bicubic,-86.557,-64.257,-49
vit_deit_tiny_patch16_224,7.307,92.693,30.707,69.293,5.72,224,0.900,bicubic,-82.363,-66.743,+99
hardcorenas_e,7.240,92.760,33.293,66.707,8.07,224,0.875,bilinear,-85.330,-64.817,+29
gluon_resnet101_v1b,7.227,92.773,32.773,67.227,44.55,224,0.875,bicubic,-86.523,-65.607,-41
efficientnet_b0,7.213,92.787,34.013,65.987,5.29,224,0.875,bicubic,-85.477,-64.057,+21
gluon_resnet50_v1s,7.213,92.787,33.507,66.493,25.68,224,0.875,bicubic,-86.407,-64.953,-34
tf_mixnet_l,7.147,92.853,31.613,68.387,7.33,224,0.875,bicubic,-86.163,-66.417,-15
tf_efficientnet_b1,7.133,92.867,33.040,66.960,7.79,240,0.882,bicubic,-86.367,-65.690,-29
tf_efficientnet_cc_b0_8e,7.120,92.880,31.787,68.213,24.01,224,0.875,bicubic,-85.710,-66.393,+7
hardcorenas_f,6.827,93.173,34.093,65.907,8.20,224,0.875,bilinear,-86.123,-64.067,+2
selecsls60b,6.733,93.267,33.267,66.733,32.77,224,0.875,bicubic,-86.567,-65.013,-19
ese_vovnet19b_dw,6.733,93.267,33.413,66.587,6.54,224,0.875,bicubic,-85.557,-64.677,+32
efficientnet_es,6.707,93.293,33.840,66.160,5.44,224,0.875,bicubic,-86.433,-64.580,-12
res2net50_26w_6s,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.717,-66.627,-28
legacy_seresnext26_32x4d,6.627,93.373,33.253,66.747,16.79,224,0.875,bicubic,-86.013,-64.877,+14
mixnet_m,6.627,93.373,32.053,67.947,5.01,224,0.875,bicubic,-85.803,-65.817,+21
mobilenetv3_large_100_miil,8.840,91.160,32.973,67.027,5.48,224,0.875,bilinear,-83.420,-64.667,+64
convit_tiny,8.840,91.160,34.360,65.640,5.71,224,0.875,bicubic,-81.790,-63.380,+111
levit_128s,8.653,91.347,33.107,66.893,7.78,224,0.900,bicubic,-83.317,-64.953,+73
dla169,8.640,91.360,36.040,63.960,53.39,224,0.875,bilinear,-84.700,-62.560,+3
hrnet_w30,8.613,91.387,37.040,62.960,37.71,224,0.875,bilinear,-84.587,-61.370,+13
mixer_b16_224,8.600,91.400,29.413,70.587,59.88,224,0.875,bicubic,-83.270,-67.837,+75
legacy_seresnet101,8.533,91.467,36.013,63.987,49.33,224,0.875,bilinear,-84.747,-62.497,+8
tf_efficientnet_b1_ap,8.453,91.547,35.253,64.747,7.79,240,0.882,bicubic,-85.237,-63.107,-21
repvgg_b2,8.427,91.573,36.467,63.533,89.02,224,0.875,bilinear,-85.073,-62.263,-11
resnetblur50,8.240,91.760,37.400,62.600,25.56,224,0.875,bicubic,-85.720,-61.190,-46
dla102x,8.200,91.800,37.013,62.987,26.31,224,0.875,bilinear,-85.320,-61.497,-14
hrnet_w32,8.040,91.960,37.507,62.493,41.23,224,0.875,bilinear,-85.490,-60.943,-16
res2net50_26w_8s,8.000,92.000,33.853,66.147,48.40,224,0.875,bilinear,-85.540,-64.407,-18
gluon_resnet101_v1c,7.987,92.013,33.360,66.640,44.57,224,0.875,bicubic,-85.683,-65.060,-26
gluon_resnet50_v1d,7.920,92.080,35.000,65.000,25.58,224,0.875,bicubic,-85.850,-63.390,-36
dla60_res2next,7.787,92.213,34.987,65.013,17.03,224,0.875,bilinear,-85.393,-63.423,+4
densenetblur121d,7.720,92.280,34.733,65.267,8.00,224,0.875,bicubic,-84.190,-63.337,+62
vit_deit_tiny_distilled_patch16_224,7.707,92.293,33.560,66.440,5.91,224,0.900,bicubic,-82.993,-64.010,+92
tf_efficientnetv2_b1,7.693,92.307,34.653,65.347,8.14,240,0.882,bicubic,-86.247,-63.967,-53
dla60_res2net,7.560,92.440,34.627,65.373,20.85,224,0.875,bilinear,-85.620,-63.793,-1
efficientnet_b1_pruned,7.440,92.560,34.533,65.467,6.33,240,0.882,bicubic,-85.330,-63.507,+19
wide_resnet101_2,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.360,-64.393,-37
regnetx_064,7.333,92.667,34.373,65.627,26.21,224,0.875,bicubic,-86.557,-64.257,-53
vit_deit_tiny_patch16_224,7.307,92.693,30.707,69.293,5.72,224,0.900,bicubic,-82.363,-66.743,+100
hardcorenas_e,7.240,92.760,33.293,66.707,8.07,224,0.875,bilinear,-85.330,-64.817,+28
gluon_resnet101_v1b,7.227,92.773,32.773,67.227,44.55,224,0.875,bicubic,-86.523,-65.607,-45
efficientnet_b0,7.213,92.787,34.013,65.987,5.29,224,0.875,bicubic,-85.477,-64.057,+20
gluon_resnet50_v1s,7.213,92.787,33.507,66.493,25.68,224,0.875,bicubic,-86.407,-64.953,-38
tf_mixnet_l,7.147,92.853,31.613,68.387,7.33,224,0.875,bicubic,-86.163,-66.417,-17
tf_efficientnet_b1,7.133,92.867,33.040,66.960,7.79,240,0.882,bicubic,-86.367,-65.320,-31
tf_efficientnet_cc_b0_8e,7.120,92.880,31.787,68.213,24.01,224,0.875,bicubic,-85.710,-66.393,+6
hardcorenas_f,6.827,93.173,34.093,65.907,8.20,224,0.875,bilinear,-86.123,-64.067,+1
ese_vovnet19b_dw,6.733,93.267,33.413,66.587,6.54,224,0.875,bicubic,-85.557,-64.677,+31
selecsls60b,6.733,93.267,33.267,66.733,32.77,224,0.875,bicubic,-86.567,-65.123,-20
efficientnet_es,6.707,93.293,33.840,66.160,5.44,224,0.875,bicubic,-86.433,-64.580,-14
res2net50_26w_6s,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.717,-66.627,-31
pit_ti_distilled_224,6.627,93.373,30.760,69.240,5.10,224,0.900,bicubic,-84.273,-66.940,+66
skresnet34,6.480,93.520,31.547,68.453,22.28,224,0.875,bicubic,-85.910,-66.603,+22
repvgg_b1,6.467,93.533,33.827,66.173,57.42,224,0.875,bilinear,-86.863,-64.803,-29
hardcorenas_d,6.440,93.560,32.213,67.787,7.50,224,0.875,bilinear,-85.960,-65.857,+18
dla60x,6.427,93.573,34.080,65.920,17.35,224,0.875,bilinear,-86.693,-64.430,-19
resnet34d,6.400,93.600,31.493,68.507,21.82,224,0.875,bicubic,-86.280,-66.817,+5
regnetx_080,6.307,93.693,32.320,67.680,39.57,224,0.875,bicubic,-87.563,-66.200,-70
mixnet_m,6.627,93.373,32.053,67.947,5.01,224,0.875,bicubic,-85.803,-65.817,+20
legacy_seresnext26_32x4d,6.627,93.373,33.253,66.747,16.79,224,0.875,bicubic,-86.013,-64.877,+13
skresnet34,6.480,93.520,31.547,68.453,22.28,224,0.875,bicubic,-85.910,-66.603,+21
repvgg_b1,6.467,93.533,33.827,66.173,57.42,224,0.875,bilinear,-86.863,-64.683,-32
hardcorenas_d,6.440,93.560,32.213,67.787,7.50,224,0.875,bilinear,-85.960,-65.857,+17
dla60x,6.427,93.573,34.080,65.920,17.35,224,0.875,bilinear,-86.693,-64.430,-21
resnet34d,6.400,93.600,31.493,68.507,21.82,224,0.875,bicubic,-86.280,-66.817,+4
regnetx_080,6.307,93.693,32.320,67.680,39.57,224,0.875,bicubic,-87.563,-66.200,-74
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,-14
pit_ti_224,6.120,93.880,30.227,69.773,4.85,224,0.900,bicubic,-83.820,-67.223,+72
tv_resnet152,6.040,93.960,32.053,67.947,60.19,224,0.875,bilinear,-87.260,-66.337,-33
regnetx_040,5.973,94.027,31.547,68.453,22.12,224,0.875,bicubic,-87.587,-66.993,-54
tf_efficientnet_cc_b0_4e,5.973,94.027,29.600,70.400,13.31,224,0.875,bicubic,-86.617,-68.480,+2
resnet50,5.933,94.067,29.093,70.907,25.56,224,0.875,bicubic,-87.877,-69.317,-72
dla102,5.880,94.120,32.707,67.293,33.27,224,0.875,bilinear,-87.180,-65.833,-26
legacy_seresnet50,6.187,93.813,32.653,67.347,28.09,224,0.875,bilinear,-86.773,-65.537,-15
pit_ti_224,6.120,93.880,30.227,69.773,4.85,224,0.900,bicubic,-83.820,-67.223,+73
tv_resnet152,6.040,93.960,32.053,67.947,60.19,224,0.875,bilinear,-87.260,-66.227,-36
tf_efficientnet_cc_b0_4e,5.973,94.027,29.600,70.400,13.31,224,0.875,bicubic,-86.617,-68.480,+1
regnetx_040,5.973,94.027,31.547,68.453,22.12,224,0.875,bicubic,-87.587,-66.993,-57
resnet50,5.933,94.067,29.093,70.907,25.56,224,0.875,bicubic,-87.877,-69.317,-76
tf_efficientnetv2_b0,5.893,94.107,30.773,69.227,7.14,224,0.875,bicubic,-87.217,-67.617,-28
dla102,5.880,94.120,32.707,67.293,33.27,224,0.875,bilinear,-87.180,-65.833,-28
mixer_l16_224,5.867,94.133,18.533,81.467,208.20,224,0.875,bicubic,-81.283,-74.987,+84
regnety_016,5.680,94.320,30.413,69.587,11.20,224,0.875,bicubic,-87.350,-67.947,-26
selecsls60,5.653,94.347,32.507,67.493,30.67,224,0.875,bicubic,-87.377,-65.793,-25
hardcorenas_c,5.640,94.360,30.400,69.600,5.52,224,0.875,bilinear,-86.380,-67.440,+17
res2next50,5.627,94.373,30.867,69.133,24.67,224,0.875,bilinear,-87.213,-67.313,-21
hrnet_w18,5.493,94.507,30.960,69.040,21.30,224,0.875,bilinear,-86.827,-67.280,+5
resnest14d,5.480,94.520,28.547,71.453,10.61,224,0.875,bilinear,-86.240,-69.323,+26
tf_efficientnet_lite2,5.360,94.640,30.907,69.093,6.09,260,0.890,bicubic,-87.290,-67.323,-11
tf_efficientnet_em,5.347,94.653,31.107,68.893,6.90,240,0.882,bicubic,-87.583,-67.083,-27
gernet_s,5.307,94.693,30.133,69.867,8.17,224,0.875,bilinear,-86.833,-68.057,+9
tf_efficientnet_b0_ap,5.307,94.693,28.813,71.187,5.29,224,0.875,bicubic,-86.893,-69.207,+6
densenet121,5.293,94.707,29.907,70.093,7.98,224,0.875,bicubic,-86.277,-68.123,+22
repvgg_b1g4,5.293,94.707,30.813,69.187,39.97,224,0.875,bilinear,-87.687,-67.617,-34
res2net50_26w_4s,5.160,94.840,29.360,70.640,25.70,224,0.875,bilinear,-87.340,-68.930,-10
tf_mixnet_m,5.080,94.920,28.147,71.853,5.01,224,0.875,bicubic,-87.250,-69.743,-5
tf_efficientnet_b0,5.067,94.933,28.800,71.200,5.29,224,0.875,bicubic,-87.183,-69.200,-2
mobilenetv3_large_100,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.253,-69.523,+23
res2net50_14w_8s,5.040,94.960,28.773,71.227,25.06,224,0.875,bilinear,-87.700,-69.407,-27
hardcorenas_b,4.947,95.053,28.120,71.880,5.18,224,0.875,bilinear,-86.823,-69.660,+12
mixnet_s,4.907,95.093,28.573,71.427,4.13,224,0.875,bicubic,-86.923,-69.117,+9
mobilenetv3_rw,4.907,95.093,29.853,70.147,5.48,224,0.875,bicubic,-86.303,-67.807,+21
gluon_resnet50_v1c,4.893,95.107,28.147,71.853,25.58,224,0.875,bicubic,-88.137,-70.243,-47
hardcorenas_a,4.867,95.133,28.093,71.907,5.26,224,0.875,bilinear,-86.483,-69.767,+15
regnetx_032,4.853,95.147,30.280,69.720,15.30,224,0.875,bicubic,-88.267,-68.110,-52
tv_resnext50_32x4d,4.840,95.160,30.307,69.693,25.03,224,0.875,bilinear,-87.900,-67.963,-33
tv_resnet101,4.707,95.293,29.333,70.667,44.55,224,0.875,bilinear,-88.103,-68.917,-39
densenet161,4.693,95.307,29.547,70.453,28.68,224,0.875,bicubic,-87.807,-68.513,-22
selecsls42b,4.667,95.333,28.587,71.413,32.46,224,0.875,bicubic,-87.613,-69.563,-15
tf_efficientnet_lite1,4.613,95.387,28.387,71.613,5.42,240,0.882,bicubic,-88.007,-69.693,-30
mobilenetv2_120d,4.533,95.467,29.280,70.720,5.83,224,0.875,bicubic,-87.867,-68.770,-22
efficientnet_es_pruned,4.187,95.813,26.520,73.480,5.44,224,0.875,bicubic,-86.993,-71.230,+13
fbnetc_100,4.133,95.867,25.933,74.067,5.57,224,0.875,bilinear,-86.567,-71.277,+24
densenet201,4.120,95.880,27.547,72.453,20.01,224,0.875,bicubic,-88.630,-70.683,-43
gluon_resnet50_v1b,4.120,95.880,26.933,73.067,25.56,224,0.875,bicubic,-88.420,-71.237,-31
resnet26d,4.040,95.960,28.520,71.480,16.01,224,0.875,bicubic,-88.030,-69.440,-15
semnasnet_100,3.960,96.040,26.947,73.053,3.89,224,0.875,bicubic,-87.320,-70.613,+4
repvgg_a2,3.947,96.053,27.267,72.733,28.21,224,0.875,bilinear,-87.993,-70.883,-13
tf_mixnet_s,3.880,96.120,25.253,74.747,4.13,224,0.875,bicubic,-87.630,-72.367,-3
dpn68,3.867,96.133,26.080,73.920,12.61,224,0.875,bicubic,-88.143,-71.970,-17
tf_efficientnet_es,3.827,96.173,26.107,73.893,5.44,224,0.875,bicubic,-88.153,-71.753,-17
regnety_008,3.813,96.187,27.133,72.867,6.26,224,0.875,bicubic,-87.937,-71.047,-9
dla60,3.773,96.227,27.933,72.067,22.04,224,0.875,bilinear,-88.457,-70.177,-26
regnety_016,5.680,94.320,30.413,69.587,11.20,224,0.875,bicubic,-87.350,-67.947,-28
selecsls60,5.653,94.347,32.507,67.493,30.67,224,0.875,bicubic,-87.377,-65.793,-27
hardcorenas_c,5.640,94.360,30.400,69.600,5.52,224,0.875,bilinear,-86.380,-67.440,+15
res2next50,5.627,94.373,30.867,69.133,24.67,224,0.875,bilinear,-87.213,-67.313,-23
hrnet_w18,5.493,94.507,30.960,69.040,21.30,224,0.875,bilinear,-86.827,-67.280,+3
resnest14d,5.480,94.520,28.547,71.453,10.61,224,0.875,bilinear,-86.240,-69.323,+25
tf_efficientnet_lite2,5.360,94.640,30.907,69.093,6.09,260,0.890,bicubic,-87.290,-67.323,-13
tf_efficientnet_em,5.347,94.653,31.107,68.893,6.90,240,0.882,bicubic,-87.583,-67.083,-29
gernet_s,5.307,94.693,30.133,69.867,8.17,224,0.875,bilinear,-86.833,-68.057,+7
tf_efficientnet_b0_ap,5.307,94.693,28.813,71.187,5.29,224,0.875,bicubic,-86.893,-69.207,+4
densenet121,5.293,94.707,29.907,70.093,7.98,224,0.875,bicubic,-86.277,-68.123,+21
repvgg_b1g4,5.293,94.707,30.813,69.187,39.97,224,0.875,bilinear,-87.687,-67.617,-36
res2net50_26w_4s,5.160,94.840,29.360,70.640,25.70,224,0.875,bilinear,-87.340,-68.700,-11
tf_mixnet_m,5.080,94.920,28.147,71.853,5.01,224,0.875,bicubic,-87.250,-69.743,-7
mobilenetv3_large_100,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.253,-69.523,+22
tf_efficientnet_b0,5.067,94.933,28.800,71.200,5.29,224,0.875,bicubic,-87.183,-69.200,-4
res2net50_14w_8s,5.040,94.960,28.773,71.227,25.06,224,0.875,bilinear,-87.700,-69.407,-29
hardcorenas_b,4.947,95.053,28.120,71.880,5.18,224,0.875,bilinear,-86.823,-69.660,+11
mobilenetv3_rw,4.907,95.093,29.853,70.147,5.48,224,0.875,bicubic,-86.303,-67.807,+20
mixnet_s,4.907,95.093,28.573,71.427,4.13,224,0.875,bicubic,-86.923,-69.117,+8
gluon_resnet50_v1c,4.893,95.107,28.147,71.853,25.58,224,0.875,bicubic,-88.137,-70.243,-49
hardcorenas_a,4.867,95.133,28.093,71.907,5.26,224,0.875,bilinear,-86.483,-69.767,+14
regnetx_032,4.853,95.147,30.280,69.720,15.30,224,0.875,bicubic,-88.267,-68.110,-55
tv_resnext50_32x4d,4.840,95.160,30.307,69.693,25.03,224,0.875,bilinear,-87.900,-67.963,-35
tv_resnet101,4.707,95.293,29.333,70.667,44.55,224,0.875,bilinear,-88.103,-68.917,-41
densenet161,4.693,95.307,29.547,70.453,28.68,224,0.875,bicubic,-87.807,-68.743,-25
selecsls42b,4.667,95.333,28.587,71.413,32.46,224,0.875,bicubic,-87.613,-69.563,-17
tf_efficientnet_lite1,4.613,95.387,28.387,71.613,5.42,240,0.882,bicubic,-88.007,-69.693,-32
mobilenetv2_120d,4.533,95.467,29.280,70.720,5.83,224,0.875,bicubic,-87.867,-68.770,-24
efficientnet_es_pruned,4.187,95.813,26.520,73.480,5.44,224,0.875,bicubic,-86.993,-71.230,+12
fbnetc_100,4.133,95.867,25.933,74.067,5.57,224,0.875,bilinear,-86.567,-71.277,+23
gluon_resnet50_v1b,4.120,95.880,26.933,73.067,25.56,224,0.875,bicubic,-88.420,-71.237,-33
densenet201,4.120,95.880,27.547,72.453,20.01,224,0.875,bicubic,-88.630,-70.683,-45
resnet26d,4.040,95.960,28.520,71.480,16.01,224,0.875,bicubic,-88.030,-69.440,-17
semnasnet_100,3.960,96.040,26.947,73.053,3.89,224,0.875,bicubic,-87.320,-70.613,+3
repvgg_a2,3.947,96.053,27.267,72.733,28.21,224,0.875,bilinear,-87.993,-70.883,-14
tf_mixnet_s,3.880,96.120,25.253,74.747,4.13,224,0.875,bicubic,-87.630,-72.367,-4
dpn68,3.867,96.133,26.080,73.920,12.61,224,0.875,bicubic,-88.143,-71.970,-19
tf_efficientnet_es,3.827,96.173,26.107,73.893,5.44,224,0.875,bicubic,-88.153,-71.753,-19
regnety_008,3.813,96.187,27.133,72.867,6.26,224,0.875,bicubic,-87.937,-71.047,-10
dla60,3.773,96.227,27.933,72.067,22.04,224,0.875,bilinear,-88.457,-70.177,-28
ssl_resnet18,3.747,96.253,25.427,74.573,11.69,224,0.875,bilinear,-86.473,-72.123,+20
mobilenetv2_140,3.720,96.280,26.747,73.253,6.11,224,0.875,bicubic,-88.110,-71.113,-14
densenet169,3.707,96.293,25.613,74.387,14.15,224,0.875,bicubic,-88.223,-72.487,-20
regnetx_016,3.627,96.373,26.293,73.707,9.19,224,0.875,bicubic,-88.543,-71.917,-28
res2net50_48w_2s,3.587,96.413,26.613,73.387,25.29,224,0.875,bilinear,-88.963,-71.467,-45
spnasnet_100,3.547,96.453,24.293,75.707,4.42,224,0.875,bilinear,-86.803,-73.137,+13
tf_mobilenetv3_large_100,3.547,96.453,25.053,74.947,5.48,224,0.875,bilinear,-87.693,-72.607,-8
regnety_006,3.467,96.533,24.893,75.107,6.06,224,0.875,bicubic,-87.903,-72.817,-13
legacy_seresnet34,3.333,96.667,23.800,76.200,21.96,224,0.875,bilinear,-87.557,-73.780,+2
efficientnet_lite0,3.253,96.747,25.867,74.133,4.65,224,0.875,bicubic,-87.887,-71.763,-7
dla34,3.227,96.773,23.573,76.427,15.74,224,0.875,bilinear,-87.533,-74.087,+2
mobilenetv2_140,3.720,96.280,26.747,73.253,6.11,224,0.875,bicubic,-88.110,-71.113,-15
densenet169,3.707,96.293,25.613,74.387,14.15,224,0.875,bicubic,-88.223,-72.487,-21
regnetx_016,3.627,96.373,26.293,73.707,9.19,224,0.875,bicubic,-88.543,-71.917,-30
res2net50_48w_2s,3.587,96.413,26.613,73.387,25.29,224,0.875,bilinear,-88.963,-71.467,-47
spnasnet_100,3.547,96.453,24.293,75.707,4.42,224,0.875,bilinear,-86.803,-72.897,+14
tf_mobilenetv3_large_100,3.547,96.453,25.053,74.947,5.48,224,0.875,bilinear,-87.693,-72.607,-9
regnety_006,3.467,96.533,24.893,75.107,6.06,224,0.875,bicubic,-87.903,-72.817,-14
legacy_seresnet34,3.333,96.667,23.800,76.200,21.96,224,0.875,bilinear,-87.557,-73.780,+1
efficientnet_lite0,3.253,96.747,25.867,74.133,4.65,224,0.875,bicubic,-87.887,-71.763,-8
dla34,3.227,96.773,23.573,76.427,15.74,224,0.875,bilinear,-87.533,-74.087,+1
ghostnet_100,3.227,96.773,24.853,75.147,5.18,224,0.875,bilinear,-86.793,-72.517,+12
regnety_004,3.200,96.800,22.653,77.347,4.34,224,0.875,bicubic,-87.300,-74.887,+5
mobilenetv2_110d,3.173,96.827,24.587,75.413,4.52,224,0.875,bicubic,-87.777,-72.963,-5
mobilenetv2_110d,3.173,96.827,24.587,75.413,4.52,224,0.875,bicubic,-87.777,-72.963,-6
mnasnet_100,3.120,96.880,24.227,75.773,4.38,224,0.875,bicubic,-87.390,-73.243,+2
tf_efficientnet_lite0,3.080,96.920,22.907,77.093,4.65,224,0.875,bicubic,-87.960,-74.683,-9
tf_efficientnet_lite0,3.080,96.920,22.907,77.093,4.65,224,0.875,bicubic,-87.960,-74.683,-10
skresnet18,3.013,96.987,22.800,77.200,11.96,224,0.875,bicubic,-86.647,-74.430,+12
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,-15
resnet34,2.920,97.080,23.680,76.320,21.80,224,0.875,bilinear,-88.210,-73.940,-16
tf_mobilenetv3_large_075,2.867,97.133,21.573,78.427,3.99,224,0.875,bilinear,-86.813,-75.637,+7
hrnet_w18_small_v2,2.720,97.280,23.693,76.307,15.60,224,0.875,bilinear,-88.470,-74.207,-20
gluon_resnet34_v1b,2.667,97.333,21.680,78.320,21.80,224,0.875,bicubic,-88.293,-75.950,-14
regnetx_008,2.653,97.347,22.453,77.547,7.26,224,0.875,bicubic,-88.397,-75.257,-17
hrnet_w18_small_v2,2.720,97.280,23.693,76.307,15.60,224,0.875,bilinear,-88.470,-74.207,-21
gluon_resnet34_v1b,2.667,97.333,21.680,78.320,21.80,224,0.875,bicubic,-88.293,-75.950,-15
regnetx_008,2.653,97.347,22.453,77.547,7.26,224,0.875,bicubic,-88.397,-75.257,-18
vgg16_bn,2.653,97.347,23.773,76.227,138.37,224,0.875,bilinear,-87.437,-73.597,-2
vgg16,2.640,97.360,20.427,79.573,138.36,224,0.875,bilinear,-85.910,-76.363,+13
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,-75.043,-15
repvgg_b0,2.547,97.453,24.013,75.987,15.82,224,0.875,bilinear,-88.883,-73.977,-34
regnetx_006,2.507,97.493,20.653,79.347,6.20,224,0.875,bicubic,-87.843,-76.537,-9
tv_densenet121,2.560,97.440,22.667,77.333,7.98,224,0.875,bicubic,-88.330,-75.043,-16
repvgg_b0,2.547,97.453,24.013,75.987,15.82,224,0.875,bilinear,-88.883,-73.977,-35
regnetx_006,2.507,97.493,20.653,79.347,6.20,224,0.875,bicubic,-87.843,-76.777,-10
legacy_seresnet18,2.493,97.507,20.080,79.920,11.78,224,0.875,bicubic,-86.387,-76.900,+6
resnet26,2.480,97.520,22.987,77.013,16.00,224,0.875,bicubic,-88.630,-74.753,-26
resnet26,2.480,97.520,22.987,77.013,16.00,224,0.875,bicubic,-88.630,-74.753,-27
mobilenetv2_100,2.147,97.853,19.907,80.093,3.50,224,0.875,bicubic,-87.453,-77.233,-2
regnety_002,2.147,97.853,18.880,81.120,3.16,224,0.875,bicubic,-85.233,-77.710,+9
vgg19,2.107,97.893,20.733,79.267,143.67,224,0.875,bilinear,-86.933,-76.137,-1
@ -361,4 +392,4 @@ 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,-2
resnet18,1.160,98.840,16.213,83.787,11.69,224,0.875,bilinear,-86.230,-80.077,-9
tf_mobilenetv3_small_minimal_100,1.013,98.987,11.493,88.507,2.04,224,0.875,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,-67
tv_resnet50,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.880,-83.587,-68

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
3 tf_efficientnet_l2_ns_475 83.373 16.627 95.453 4.547 480.31 475 0.936 bicubic -15.127 -4.377 0
4 swin_large_patch4_window12_384 69.627 30.373 89.560 10.440 196.74 384 1.000 bicubic -28.413 -10.130 0
5 tf_efficientnet_b7_ns 67.040 32.960 88.667 11.333 66.35 600 0.949 bicubic -30.870 -11.053 0
6 swin_base_patch4_window12_384 tf_efficientnetv2_l_in21ft1k 64.480 66.333 35.520 33.667 87.493 87.853 12.507 12.147 87.90 118.52 384 480 1.000 bicubic -33.410 -31.367 -12.217 -11.817 0 +2
7 swin_base_patch4_window12_384 64.480 35.520 87.493 12.507 87.90 384 1.000 bicubic -33.410 -12.217 -1
8 cait_m48_448 62.373 37.627 86.453 13.547 356.46 448 1.000 bicubic -35.107 -13.097 +8
9 tf_efficientnet_b6_ns 62.267 37.733 85.173 14.827 43.04 528 0.942 bicubic -35.363 -14.407 +1
10 dm_nfnet_f6 62.253 37.747 84.667 15.333 438.36 576 0.956 bicubic -35.477 -14.913 -2 -3
11 dm_nfnet_f5 61.587 38.413 84.027 15.973 377.21 544 0.954 bicubic -36.013 -15.523 +1
12 ig_resnext101_32x48d tf_efficientnetv2_m_in21ft1k 61.013 61.387 38.987 38.613 83.347 85.413 16.653 14.587 828.41 54.14 224 480 0.875 1.000 bilinear bicubic -36.607 -36.093 -16.353 -14.117 -1 +5
13 swin_large_patch4_window7_224 ig_resnext101_32x48d 60.893 61.013 39.107 38.987 85.840 83.347 14.160 16.653 196.53 828.41 224 0.900 0.875 bicubic bilinear -36.757 -36.607 -13.740 -16.353 -4 -2
14 resnetv2_152x4_bitm swin_large_patch4_window7_224 60.733 60.893 39.267 39.107 83.600 85.840 16.400 14.160 936.53 196.53 480 224 1.000 0.900 bilinear bicubic -36.757 -16.000 -13.740 +1 -5
15 dm_nfnet_f4 resnetv2_152x4_bitm 60.720 60.733 39.280 39.267 83.427 83.600 16.573 16.400 316.07 936.53 512 480 0.951 1.000 bicubic bilinear -36.850 -36.757 -16.093 -16.000 -2 0
16 tf_efficientnet_b5_ns dm_nfnet_f4 60.320 60.720 39.680 39.280 84.493 83.427 15.507 16.573 30.39 316.07 456 512 0.934 0.951 bicubic -37.180 -36.850 -15.137 -16.093 -2 -3
17 tf_efficientnet_b5_ns 60.320 39.680 84.493 15.507 30.39 456 0.934 bicubic -37.180 -15.137 -3
18 dm_nfnet_f3 58.373 41.627 82.360 17.640 254.92 416 0.940 bicubic -38.987 -17.220 +1
19 ig_resnext101_32x32d 58.093 41.907 80.653 19.347 468.53 224 0.875 bilinear -39.267 -19.027 +1
20 cait_m36_384 57.840 42.160 84.813 15.187 271.22 384 1.000 bicubic -39.560 -14.697 -2
21 resnetv2_152x2_bitm 54.973 45.027 82.813 17.187 236.34 480 1.000 bilinear -42.177 -16.777 +6 +7
22 vit_base_r50_s16_384 54.627 45.373 81.213 18.787 98.95 384 1.000 bicubic -42.553 -18.347 +4 +5
23 cait_s36_384 54.413 45.587 81.360 18.640 68.37 384 1.000 bicubic -42.917 -18.170 -2
24 vit_large_patch16_384 53.867 46.133 80.320 19.680 304.72 384 1.000 bicubic -43.243 -19.320 +5 +7
25 resnetv2_101x3_bitm 53.813 46.187 81.093 18.907 387.93 480 1.000 bilinear -43.237 -18.427 +8 +10
26 ig_resnext101_32x16d tf_efficientnetv2_l 53.067 53.187 46.933 46.813 76.907 79.133 23.093 20.867 194.03 118.52 224 480 0.875 1.000 bilinear bicubic -43.753 -44.093 -22.683 -20.417 +14 -4
27 ig_resnext101_32x16d 53.067 46.933 76.907 23.093 194.03 224 0.875 bilinear -43.753 -22.683 +16
28 swin_base_patch4_window7_224 51.453 48.547 79.973 20.027 87.77 224 0.900 bicubic -45.797 -19.557 -5
29 tf_efficientnet_b4_ns 51.213 48.787 79.187 20.813 19.34 380 0.922 bicubic -45.737 -20.393 +9 +11
30 swsl_resnext101_32x8d 51.187 48.813 78.240 21.760 88.79 224 0.875 bilinear -46.013 -21.330 -21.260 -6 -4
31 dm_nfnet_f2 50.773 49.227 78.013 21.987 193.78 352 0.920 bicubic -46.187 -21.437 +5 +7
32 vit_base_patch16_384 50.613 49.387 78.200 21.800 86.86 384 1.000 bicubic -46.087 -21.310 -21.070 +15 +17
33 cait_s24_384 49.733 50.267 78.733 21.267 47.06 384 1.000 bicubic -47.337 -20.697 0 +1
34 vit_deit_base_distilled_patch16_384 49.333 50.667 79.253 20.747 87.63 384 1.000 bicubic -47.627 -20.227 +3 +5
35 tf_efficientnet_b8 48.947 51.053 77.240 22.760 87.41 672 0.954 bicubic -48.253 -22.260 -22.330 -9 -11
36 resnest269e tf_efficientnetv2_s_in21ft1k 48.187 48.507 51.813 51.493 74.333 77.880 25.667 22.120 110.93 21.46 416 384 0.928 1.000 bicubic -48.333 -48.223 -25.017 -21.540 +22 +11
37 resnetv2_50x3_bitm resnest269e 47.787 48.187 52.213 51.813 77.627 74.333 22.373 25.667 217.32 110.93 480 416 1.000 0.928 bilinear bicubic -48.983 -48.333 -21.803 -25.017 +5 +24
38 resnetv2_50x3_bitm 47.787 52.213 77.627 22.373 217.32 480 1.000 bilinear -48.983 -21.803 +6
39 tf_efficientnet_b8_ap 46.893 53.107 76.507 23.493 87.41 672 0.954 bicubic -50.217 -23.153 -9
40 dm_nfnet_f1 46.600 53.400 74.773 25.227 132.63 320 0.910 bicubic -50.320 -24.617 0 +1
41 swsl_resnext101_32x16d efficientnetv2_rw_m 46.200 46.280 53.800 53.720 72.200 75.707 27.800 24.293 194.03 53.24 224 416 0.875 1.000 bilinear bicubic -50.400 -50.700 -27.320 -23.833 +13 -4
42 ecaresnet269d swsl_resnext101_32x16d 45.893 46.200 54.107 53.800 75.133 72.200 24.867 27.800 102.09 194.03 352 224 1.000 0.875 bicubic bilinear -51.187 -50.400 -24.337 -27.320 -10 +14
43 tf_efficientnet_b7_ap ecaresnet269d 45.373 45.893 54.627 54.107 74.213 75.133 25.787 24.867 66.35 102.09 600 352 0.949 1.000 bicubic -51.827 -51.187 -25.327 -24.337 -17 -11
44 ig_resnext101_32x8d tf_efficientnetv2_m 45.320 45.533 54.680 54.467 70.867 74.533 29.133 25.467 88.79 54.14 224 480 0.875 1.000 bilinear bicubic -51.000 -51.607 -28.563 -24.877 +23 -15
45 tf_efficientnet_b7_ap 45.373 54.627 74.213 25.787 66.35 600 0.949 bicubic -51.827 -25.327 -20
46 ig_resnext101_32x8d 45.320 54.680 70.867 29.133 88.79 224 0.875 bilinear -51.000 -28.563 +24
47 resnest200e 44.147 55.853 73.467 26.533 70.20 320 0.909 bicubic -52.463 -25.883 +8
48 cait_xs24_384 43.947 56.053 75.187 24.813 26.67 384 1.000 bicubic -52.603 -24.233 +10
49 tresnet_xl_448 43.480 56.520 72.453 27.547 78.44 448 0.875 bilinear -52.490 -26.677 +30 +34
50 resnetrs420 43.147 56.853 70.453 29.547 191.89 416 1.000 bicubic -53.763 -29.007 -7 -8
51 tf_efficientnet_b7 42.960 57.040 73.133 26.867 66.35 600 0.949 bicubic -54.050 -26.387 -13 -15
52 swsl_resnext101_32x4d 41.560 58.440 71.760 28.240 44.18 224 0.875 bilinear -54.860 -27.710 +11
53 tf_efficientnet_b6_ap 40.800 59.200 71.627 28.373 43.04 528 0.942 bicubic -56.280 -27.993 -18 -20
54 tresnet_l_448 40.200 59.800 69.893 30.107 55.99 448 0.875 bilinear -55.660 -29.227 +32 +37
55 vit_deit_base_patch16_384 40.173 59.827 70.760 29.240 86.86 384 1.000 bicubic -55.977 -28.380 +18 +22
56 resnetrs350 39.960 60.040 68.907 31.093 163.96 384 1.000 bicubic -56.800 -30.463 -9 -10
57 resnetv2_101x1_bitm 39.307 60.693 71.493 28.507 44.54 480 1.000 bilinear -56.783 -27.697 -27.807 +20 +23
58 vit_large_patch32_384 38.933 61.067 68.920 31.080 306.63 384 1.000 bicubic -56.897 -30.230 +30 +35
59 resnet200d 38.147 61.853 68.613 31.387 64.69 320 1.000 bicubic -58.573 -30.717 -11
60 eca_nfnet_l1 38.107 61.893 71.293 28.707 41.41 320 1.000 bicubic -58.593 -27.977 -28.217 -11 -10
61 seresnet152d 37.640 62.360 69.480 30.520 66.84 320 1.000 bicubic -59.130 -29.970 -15 -16
62 efficientnet_v2s twins_svt_large 36.787 37.200 63.213 62.800 68.320 69.227 31.680 30.773 23.94 99.27 384 224 1.000 0.900 bicubic -59.753 -59.070 -31.040 -29.943 -3 +10
63 regnety_160 efficientnetv2_rw_s 36.747 36.787 63.253 63.213 69.107 68.320 30.893 31.680 83.59 23.94 288 384 1.000 bicubic -59.603 -59.753 -30.223 -31.040 +4 -4
64 cait_xxs36_384 regnety_160 36.227 36.747 63.773 63.253 67.800 69.107 32.200 30.893 17.37 83.59 384 288 1.000 bicubic -59.623 -59.603 -31.290 -30.223 +23 +3
65 pit_b_distilled_224 cait_xxs36_384 35.627 36.227 64.373 63.773 69.120 67.800 30.880 32.200 74.79 17.37 224 384 0.900 1.000 bicubic -61.053 -59.623 -30.340 -31.290 -12 +27
66 tf_efficientnet_b3_ns pit_b_distilled_224 35.520 35.627 64.480 64.373 67.773 69.120 32.227 30.880 12.23 74.79 300 224 0.904 0.900 bicubic -60.870 -61.053 -31.577 -30.230 -2 -14
67 vit_large_patch16_224 tf_efficientnet_b3_ns 35.493 35.520 64.507 64.480 64.427 67.773 35.573 32.227 304.33 12.23 224 300 0.900 0.904 bicubic -60.457 -60.870 -34.813 -31.577 +13 -3
68 tf_efficientnet_b6 vit_large_patch16_224 35.213 35.493 64.787 64.507 67.720 64.427 32.280 35.573 43.04 304.33 528 224 0.942 0.900 bicubic -61.457 -60.457 -31.650 -34.813 -14 +16
69 resnetrs270 tf_efficientnet_b6 35.013 35.213 64.987 64.787 65.480 67.720 34.520 32.280 129.86 43.04 352 528 1.000 0.942 bicubic -61.677 -61.457 -33.870 -31.650 -18 -15
70 tf_efficientnet_b5_ap resnetrs270 34.787 35.013 65.213 64.987 67.493 65.480 32.507 34.520 30.39 129.86 456 352 0.934 1.000 bicubic -61.893 -61.677 -31.857 -33.870 -18 -19
71 vit_base_patch16_224_miil tf_efficientnet_b5_ap 34.507 34.787 65.493 65.213 65.000 67.493 35.000 32.507 86.54 30.39 224 456 0.875 0.934 bilinear bicubic -61.953 -61.893 -34.300 -31.967 -9 -18
72 resnet152d vit_base_patch16_224_miil 34.320 34.507 65.680 65.493 65.907 65.000 34.093 35.000 60.21 86.54 320 224 1.000 0.875 bicubic bilinear -62.040 -61.953 -33.483 -34.300 -6 -10
73 tresnet_m_448 resnet152d 34.107 34.320 65.893 65.680 64.493 65.907 35.507 34.093 31.39 60.21 448 320 0.875 1.000 bilinear bicubic -60.883 -62.040 -34.487 -33.483 +49 -7
74 vit_base_patch32_384 tresnet_m_448 33.613 34.107 66.387 65.893 65.240 64.493 34.760 35.507 88.30 31.39 384 448 1.000 0.875 bicubic bilinear -62.197 -60.883 -33.910 -34.487 +15 +64
75 pit_b_224 vit_base_patch32_384 33.173 33.613 66.827 66.387 62.320 65.240 37.680 34.760 73.76 88.30 224 384 0.900 1.000 bicubic -62.467 -62.197 -36.340 -33.910 +21 +19
76 swsl_resnext50_32x4d twins_pcpvt_large 33.013 33.387 66.987 66.613 65.067 67.933 34.933 32.067 25.03 60.99 224 0.875 0.900 bilinear bicubic -62.857 -62.763 -34.183 -31.247 +8 0
77 ssl_resnext101_32x16d pit_b_224 32.600 33.173 67.400 66.827 64.000 62.320 36.000 37.680 194.03 73.76 224 0.875 0.900 bilinear bicubic -63.200 -62.467 -35.180 -36.340 +13 +25
78 swin_small_patch4_window7_224 twins_svt_base 32.600 33.173 67.400 66.827 65.440 65.773 34.560 34.227 49.61 56.07 224 0.900 bicubic -63.310 -62.987 -33.580 -33.507 +4 -5
79 vit_base_patch16_224 swsl_resnext50_32x4d 32.053 33.013 67.947 66.987 61.573 65.067 38.427 34.933 86.57 25.03 224 0.900 0.875 bicubic bilinear -63.277 -62.857 -37.427 -34.183 +26 +9
80 tf_efficientnet_b5 ssl_resnext101_32x16d 31.840 32.600 68.160 67.400 65.293 64.000 34.707 36.000 30.39 194.03 456 224 0.934 0.875 bicubic bilinear -64.510 -63.200 -34.017 -35.180 -12 +15
81 resnest101e swin_small_patch4_window7_224 31.413 32.600 68.587 67.400 64.360 65.440 35.640 34.560 48.28 49.61 256 224 0.875 0.900 bilinear bicubic -64.447 -63.310 -34.850 -33.580 +4 +5
82 dm_nfnet_f0 vit_base_patch16_224 31.280 32.053 68.720 67.947 63.347 61.573 36.653 38.427 71.49 86.57 256 224 0.900 bicubic -64.860 -63.277 -35.893 -37.427 -8 +33
83 cait_s24_224 tf_efficientnet_b5 31.200 31.840 68.800 68.160 64.560 65.293 35.440 34.707 46.92 30.39 224 456 1.000 0.934 bicubic -65.180 -64.510 -34.590 -34.017 -18 -15
84 resnest101e 31.413 68.587 64.360 35.640 48.28 256 0.875 bilinear -64.447 -34.850 +5
85 dm_nfnet_f0 31.280 68.720 63.347 36.653 71.49 256 0.900 bicubic -64.860 -35.893 -7
86 cait_s24_224 31.200 68.800 64.560 35.440 46.92 224 1.000 bicubic -65.180 -34.590 -21
87 efficientnet_b4 30.867 69.133 64.600 35.400 19.34 384 1.000 bicubic -65.283 -34.600 -12
88 resnetrs200 30.773 69.227 63.320 36.680 93.21 320 1.000 bicubic -65.757 -36.030 -25 -28
89 cait_xxs24_384 30.027 69.973 63.933 36.067 12.03 384 1.000 bicubic -65.233 -35.027 +22 +30
90 swsl_resnet50 twins_pcpvt_base 29.867 29.960 70.133 70.040 63.853 64.587 36.147 35.413 25.56 43.83 224 0.875 0.900 bilinear bicubic -65.543 -65.830 -35.437 -34.543 +17 +6
91 vit_deit_base_distilled_patch16_224 swsl_resnet50 29.600 29.867 70.400 70.133 64.453 63.853 35.547 36.147 87.34 25.56 224 0.900 0.875 bicubic bilinear -66.490 -65.543 -34.807 -35.437 -13 +23
92 ssl_resnext101_32x8d vit_deit_base_distilled_patch16_224 29.040 29.600 70.960 70.400 60.973 64.453 39.027 35.547 88.79 87.34 224 0.875 0.900 bilinear bicubic -66.430 -66.490 -38.137 -34.737 +10 -11
93 resnet101d convit_base 28.987 29.520 71.013 70.480 62.053 61.787 37.947 38.213 44.57 86.54 320 224 1.000 0.875 bicubic -67.303 -66.030 -37.177 -37.083 -20 +10
94 resnetrs152 ssl_resnext101_32x8d 28.920 29.040 71.080 70.960 60.520 60.973 39.480 39.027 86.62 88.79 320 224 1.000 0.875 bicubic bilinear -67.660 -66.430 -38.720 -38.137 -34 +15
95 vit_deit_base_patch16_224 tf_efficientnetv2_s 27.440 29.040 72.560 70.960 58.893 61.213 41.107 38.787 86.57 21.46 224 384 0.900 1.000 bicubic -68.000 -67.300 -39.947 -37.987 +10 -26
96 resnetv2_50x1_bitm resnet101d 27.347 28.987 72.653 71.013 63.547 62.053 36.453 37.947 25.55 44.57 480 320 1.000 bilinear bicubic -67.703 -67.303 -35.613 -37.177 +24 -25
97 nfnet_l0 resnetrs152 26.493 28.920 73.507 71.080 61.987 60.520 38.013 39.480 35.07 86.62 288 320 1.000 bicubic -69.597 -67.660 -37.313 -38.720 -18 -40
98 tf_efficientnet_b4 coat_lite_small 26.293 27.547 73.707 72.453 60.107 58.547 39.893 41.453 19.34 19.84 380 224 0.922 0.900 bicubic -69.607 -67.993 -39.063 -40.313 -12 +6
99 tf_efficientnet_b4_ap vit_deit_base_patch16_224 26.240 27.440 73.760 72.560 60.227 58.893 39.773 41.107 19.34 86.57 380 224 0.922 0.900 bicubic -69.920 -68.000 -39.053 -40.157 -25 +12
100 regnety_032 resnetv2_50x1_bitm 26.213 27.347 73.787 72.653 60.987 63.547 39.013 36.453 19.44 25.55 288 480 1.000 bicubic bilinear -69.757 -67.703 -38.203 -35.613 -19 +32
101 ecaresnet50t nfnet_l0 26.133 26.493 73.867 73.507 60.027 61.987 39.973 38.013 25.57 35.07 320 288 0.950 1.000 bicubic -69.377 -69.597 -39.093 -37.273 0 -22
102 ecaresnet101d tf_efficientnet_b4 26.027 26.293 73.973 73.707 58.987 60.107 41.013 39.893 44.57 19.34 224 380 0.875 0.922 bicubic -69.503 -69.607 -40.143 -39.063 -2 -15
103 eca_nfnet_l0 tf_efficientnet_b4_ap 25.013 26.240 74.987 73.760 60.360 60.227 39.640 39.773 24.14 19.34 288 380 1.000 0.922 bicubic -70.917 -69.920 -38.850 -38.833 -19 -29
104 tnt_s_patch16_224 regnety_032 24.733 26.213 75.267 73.787 58.187 60.987 41.813 39.013 23.76 19.44 224 288 0.900 1.000 bicubic -70.307 -69.757 -40.643 -38.203 +19 -22
105 ssl_resnext101_32x4d ecaresnet50t 24.173 26.133 75.827 73.867 57.413 60.027 42.587 39.973 44.18 25.57 224 320 0.875 0.950 bilinear bicubic -71.267 -69.377 -41.717 -39.093 -2 +2
106 tf_efficientnet_b2_ns ecaresnet101d 24.013 26.027 75.987 73.973 57.293 58.987 42.707 41.013 9.11 44.57 260 224 0.890 0.875 bicubic -71.757 -69.503 -41.827 -40.143 -12 -1
107 nasnetalarge visformer_small 23.493 25.840 76.507 74.160 55.027 58.907 44.973 41.093 88.75 40.22 331 224 0.911 0.900 bicubic -72.187 -69.650 -43.903 -39.993 -9 +1
108 pnasnet5large coat_mini 23.333 25.520 76.667 74.480 53.640 57.693 46.360 42.307 86.06 10.34 331 224 0.911 0.900 bicubic -72.377 -69.450 -45.280 -41.087 -11 +31
109 efficientnet_b3 convit_small 23.213 25.093 76.787 74.907 55.960 57.280 44.040 42.720 12.23 27.78 320 224 1.000 0.875 bicubic -72.497 -70.107 -43.080 -41.620 -13 +13
110 pit_s_distilled_224 eca_nfnet_l0 22.360 25.013 77.640 74.987 57.120 60.360 42.880 39.640 24.04 24.14 224 288 0.900 1.000 bicubic -72.880 -70.917 -41.930 -38.850 +2 -25
111 tresnet_m tnt_s_patch16_224 21.680 24.733 78.320 75.267 53.840 58.187 46.160 41.813 31.39 23.76 224 0.875 0.900 bilinear bicubic -74.040 -70.307 -45.190 -40.643 -16 +24
112 swin_tiny_patch4_window7_224 ssl_resnext101_32x4d 21.173 24.173 78.827 75.827 55.973 57.413 44.027 42.587 28.29 44.18 224 0.900 0.875 bicubic bilinear -73.967 -71.267 -42.877 -41.427 +2 0
113 pit_s_224 twins_svt_small 21.080 24.133 78.920 75.867 53.573 57.147 46.427 42.853 23.46 24.06 224 0.900 bicubic -73.510 -71.067 -45.137 -41.733 +33 +10
114 resnetrs101 tf_efficientnet_b2_ns 20.893 24.013 79.107 75.987 52.813 57.293 47.187 42.707 63.62 9.11 288 260 0.940 0.890 bicubic -74.537 -71.757 -46.217 -41.827 -8 -17
115 vit_deit_small_distilled_patch16_224 nasnetalarge 20.707 23.493 79.293 76.507 55.133 55.027 44.867 44.973 22.44 88.75 224 331 0.900 0.911 bicubic -74.003 -72.187 -43.897 -43.903 +23 -14
116 resnest50d_4s2x40d levit_384 20.387 23.440 79.613 76.560 52.800 56.387 47.200 43.613 30.42 39.13 224 0.875 0.900 bicubic -74.573 -72.090 -46.270 -42.663 +10 -10
117 ssl_resnext50_32x4d pnasnet5large 20.000 23.333 80.000 76.667 53.613 53.640 46.387 46.360 25.03 86.06 224 331 0.875 0.911 bilinear bicubic -74.870 -72.377 -45.267 -45.400 +15 -18
118 tresnet_xl efficientnet_b3 19.640 23.213 80.360 76.787 53.133 55.960 46.867 44.040 78.44 12.23 224 320 0.875 1.000 bilinear bicubic -75.800 -72.497 -45.917 -42.960 -14 -18
119 gluon_senet154 twins_pcpvt_small 19.307 22.720 80.693 77.280 47.533 56.853 52.467 43.147 115.09 24.11 224 0.875 0.900 bicubic -75.613 -72.490 -51.227 -42.027 +10 +2
120 rexnet_200 pit_s_distilled_224 19.227 22.360 80.773 77.640 52.720 57.120 47.280 42.880 16.37 24.04 224 0.875 0.900 bicubic -75.713 -72.880 -46.290 -41.930 +7 0
121 repvgg_b3 tresnet_m 19.107 21.680 80.893 78.320 50.253 53.840 49.747 46.160 123.09 31.39 224 0.875 bilinear -75.463 -74.040 -48.527 -45.190 +26 -23
122 swin_tiny_patch4_window7_224 21.173 78.827 55.973 44.027 28.29 224 0.900 bicubic -73.967 -42.877 +4
123 pit_s_224 21.080 78.920 53.573 46.427 23.46 224 0.900 bicubic -73.510 -45.137 +37
124 resnet51q 20.960 79.040 55.720 44.280 35.70 288 1.000 bilinear -74.900 -43.400 -34
125 resnetrs101 20.893 79.107 52.813 47.187 63.62 288 0.940 bicubic -74.537 -46.217 -12
126 vit_deit_small_distilled_patch16_224 20.707 79.293 55.133 44.867 22.44 224 0.900 bicubic -74.003 -43.897 +26
127 resnest50d_4s2x40d 20.387 79.613 52.800 47.200 30.42 224 0.875 bicubic -74.573 -46.270 +13
128 ssl_resnext50_32x4d 20.000 80.000 53.613 46.387 25.03 224 0.875 bilinear -74.870 -45.267 +18
129 tresnet_xl 19.640 80.360 53.133 46.867 78.44 224 0.875 bilinear -75.800 -45.997 -19
130 gluon_senet154 19.307 80.693 47.533 52.467 115.09 224 0.875 bicubic -75.613 -51.227 +13
131 rexnet_200 19.227 80.773 52.720 47.280 16.37 224 0.875 bicubic -75.713 -46.290 +10
132 levit_256 19.200 80.800 50.067 49.933 18.89 224 0.900 bicubic -75.810 -48.843 +5
133 repvgg_b3 19.107 80.893 50.253 49.747 123.09 224 0.875 bilinear -75.463 -48.527 +28
134 legacy_senet154 19.053 80.947 47.947 52.053 115.09 224 0.875 bilinear -76.017 -50.883 -3
135 vit_deit_small_patch16_224 mixer_b16_224_miil 18.907 19.053 81.093 80.947 51.413 51.227 48.587 48.773 22.05 59.88 224 0.900 0.875 bicubic bilinear -75.493 -76.247 -47.327 -47.653 +36 -18
136 gluon_seresnext101_64x4d vit_deit_small_patch16_224 18.907 81.093 49.187 51.413 50.813 48.587 88.23 22.05 224 0.875 0.900 bicubic -76.023 -75.493 -49.643 -47.277 +5 +39
137 tf_efficientnet_b1_ns gluon_seresnext101_64x4d 18.693 18.907 81.307 81.093 51.667 49.187 48.333 50.813 7.79 88.23 240 224 0.882 0.875 bicubic -76.477 -76.023 -47.443 -49.643 -12 +6
138 seresnext50_32x4d tf_efficientnet_b1_ns 18.360 18.693 81.640 81.307 50.973 51.667 49.027 48.333 27.56 7.79 224 240 0.875 0.882 bicubic -76.680 -76.477 -47.907 -47.443 -4 -14
139 cait_xxs36_224 seresnext50_32x4d 18.253 18.360 81.747 81.640 49.427 50.973 50.573 49.027 17.30 27.56 224 1.000 0.875 bicubic -76.007 -76.680 -49.293 -47.907 +45 -5
140 ecaresnet50d cait_xxs36_224 18.227 18.253 81.773 81.747 51.880 49.427 48.120 50.573 25.58 17.30 224 0.875 1.000 bicubic -76.403 -76.007 -47.010 -49.033 +13 +48
141 tf_efficientnet_lite4 ecaresnet50d 18.133 18.227 81.867 81.773 50.707 51.893 49.293 48.107 13.01 25.58 380 224 0.920 0.875 bilinear bicubic -76.757 -76.403 -48.313 -46.997 +2 +14
142 resnest50d_1s4x24d tf_efficientnet_lite4 17.693 18.133 82.307 81.867 49.800 50.707 50.200 49.293 25.68 13.01 224 380 0.875 0.920 bicubic bilinear -77.057 -76.757 -49.180 -48.313 +5 +3
143 gluon_seresnext101_32x4d resnest50d_1s4x24d 17.373 17.693 82.627 82.307 46.373 49.800 53.627 50.200 48.96 25.68 224 0.875 bicubic -77.547 -77.057 -52.437 -49.180 -1 +6
144 resnest50d gluon_seresnext101_32x4d 17.373 82.627 50.707 46.373 49.293 53.627 27.48 48.96 224 0.875 bilinear bicubic -77.457 -77.547 -48.173 -52.437 +1 0
145 efficientnet_el resnest50d 17.347 17.373 82.653 82.627 49.987 50.707 50.013 49.293 10.59 27.48 300 224 0.904 0.875 bicubic bilinear -77.773 -77.457 -49.003 -48.173 -18 +2
146 inception_v4 efficientnet_el 17.267 17.347 82.733 82.653 45.920 49.987 54.080 50.013 42.68 10.59 299 300 0.875 0.904 bicubic -77.113 -77.773 -52.660 -49.003 +28 -19
147 tf_efficientnet_b3_ap inception_v4 17.187 17.267 82.813 82.733 49.680 45.920 50.320 54.080 12.23 42.68 300 299 0.904 0.875 bicubic -78.133 -77.113 -49.220 -52.660 -26 +30
148 tf_efficientnet_b3 tf_efficientnet_b3_ap 17.000 17.187 83.000 82.813 49.267 49.680 50.733 50.320 12.23 300 0.904 bicubic -78.010 -78.133 -49.643 -49.220 -12 -32
149 xception71 17.000 83.000 45.520 54.480 42.34 299 0.903 bicubic -77.280 -53.120 +32 +34
150 gluon_resnext101_64x4d tf_efficientnet_b3 16.853 17.000 83.147 83.000 44.213 49.267 55.787 50.733 83.46 12.23 224 300 0.875 0.904 bicubic -77.817 -78.010 -54.437 -49.623 +1 -13
151 tresnet_l gluon_resnext101_64x4d 16.600 16.853 83.400 83.147 49.920 44.213 50.080 55.787 55.99 83.46 224 0.875 bilinear bicubic -78.690 -77.817 -49.090 -54.437 -29 +2
152 tf_efficientnetv2_b3 16.667 83.333 48.680 51.320 14.36 300 0.904 bicubic -78.493 -50.140 -27
153 tresnet_l 16.600 83.400 49.920 50.080 55.99 224 0.875 bilinear -78.690 -49.090 -35
154 gluon_resnet152_v1d 16.573 83.427 44.280 55.720 60.21 224 0.875 bicubic -78.167 -54.460 -4
155 gluon_resnet152_v1s 16.573 83.427 44.533 55.467 60.32 224 0.875 bicubic -78.467 -54.397 -20 -22
156 inception_resnet_v2 16.573 83.427 44.960 55.040 55.84 299 0.897 bicubic -77.967 -53.890 -53.830 +8 +7
157 gluon_xception65 16.440 83.560 46.027 53.973 39.92 299 0.903 bicubic -77.820 -52.433 -52.693 +30
158 gernet_l 16.373 83.627 47.213 52.787 31.08 256 0.875 bilinear -78.717 -51.687 -28 -30
159 wide_resnet50_2 16.280 83.720 48.347 51.653 68.88 224 0.875 bicubic -78.800 -50.623 -27 -29
160 ens_adv_inception_resnet_v2 16.240 83.760 43.640 56.360 55.84 299 0.897 bicubic -77.920 -55.000 +37 +39
161 repvgg_b3g4 16.213 83.787 47.653 52.347 83.83 224 0.875 bilinear -78.307 -51.317 +5
162 xception65 16.027 83.973 43.773 56.227 39.92 299 0.903 bicubic -77.733 -54.597 +62 +65
163 ssl_resnet50 15.960 84.040 49.467 50.533 25.56 224 0.875 bilinear -78.490 -49.453 +9
164 regnety_320 15.627 84.373 44.827 55.173 145.05 224 0.875 bicubic -78.913 -53.963 -54.023 -1 0
165 ecaresnet101d_pruned 15.600 84.400 48.027 51.973 24.88 224 0.875 bicubic -79.480 -50.953 -34 -36
166 ecaresnet26t 15.467 84.533 47.920 52.080 16.01 320 0.950 bicubic -78.843 -50.800 +15 +16
167 coat_tiny 15.413 84.587 45.600 54.400 5.50 224 0.900 bicubic -78.177 -52.830 +72
168 skresnext50_32x4d 15.373 84.627 44.493 55.507 27.48 224 0.875 bicubic -78.887 -54.077 +18
169 cait_xxs24_224 15.160 84.840 44.960 55.040 11.96 224 1.000 bicubic -78.440 -53.480 +67 +69
170 ecaresnetlight 15.160 84.840 45.827 54.173 30.16 224 0.875 bicubic -79.610 -52.973 -21 -22
171 rexnet_150 levit_192 14.720 14.893 85.280 85.107 46.907 44.920 53.093 55.080 9.73 10.95 224 0.875 0.900 bicubic -79.760 -79.277 -51.883 -53.620 0 +25
172 rexnet_150 14.720 85.280 46.907 53.093 9.73 224 0.875 bicubic -79.760 -51.883 -2
173 coat_lite_mini 14.507 85.493 44.507 55.493 11.01 224 0.900 bicubic -79.553 -54.053 +35
174 efficientnet_el_pruned 14.480 85.520 46.120 53.880 10.59 300 0.904 bicubic -79.920 -52.570 -52.620 +3 +1
175 efficientnet_b2 14.440 85.560 46.080 53.920 9.11 288 1.000 bicubic -80.170 -52.630 -15 -17
176 legacy_seresnext101_32x4d 14.147 85.853 42.973 57.027 48.96 224 0.875 bilinear -80.223 -55.677 +3 +2
177 seresnet50 14.147 85.853 45.467 54.533 28.09 224 0.875 bicubic -80.403 -53.283 -13 -15
178 gernet_m 14.013 85.987 46.067 53.933 21.14 224 0.875 bilinear -80.607 -52.793 -19 -21
179 gluon_resnext101_32x4d 13.867 86.133 41.653 58.347 44.18 224 0.875 bicubic -80.663 -56.977 -12 -14
180 gluon_seresnext50_32x4d 13.600 86.400 43.760 56.240 27.56 224 0.875 bicubic -80.740 -54.850 +1 0
181 repvgg_b2g4 13.440 86.560 43.787 56.213 61.76 224 0.875 bilinear -80.420 -54.803 +38 +39
182 ese_vovnet39b 13.320 86.680 43.813 56.187 24.57 224 0.875 bicubic -80.770 -54.847 +24
183 regnetx_320 13.307 86.693 40.720 59.280 107.81 224 0.875 bicubic -81.153 -58.020 -10 -12
184 pit_xs_distilled_224 13.240 86.760 44.573 55.427 11.00 224 0.900 bicubic -80.570 -54.097 +39 +40
185 efficientnet_b3_pruned 13.173 86.827 45.213 54.787 9.86 300 0.904 bicubic -81.457 -53.547 -27 -29
186 gluon_resnet101_v1d 13.160 86.840 41.493 58.507 44.57 224 0.875 bicubic -81.060 -57.057 +7 +6
187 mixnet_xl 13.120 86.880 43.253 56.747 11.90 224 0.875 bicubic -81.070 -55.087 +8 +7
188 nf_regnet_b1 13.027 86.973 44.413 55.587 10.22 288 0.900 bicubic -81.103 -54.217 +15
189 pit_xs_224 12.813 87.187 42.840 57.160 10.62 224 0.900 bicubic -80.297 -55.470 +79 +82
190 gluon_inception_v3 12.640 87.360 40.493 59.507 23.83 299 0.875 bicubic -80.820 -58.077 -58.037 +56 +59
191 coat_lite_tiny 12.520 87.480 41.160 58.840 5.72 224 0.900 bicubic -80.720 -57.100 +70 +73
192 regnety_120 12.427 87.573 42.200 57.800 51.82 224 0.875 bicubic -82.053 -56.610 -21 -23
193 efficientnet_em 12.360 87.640 43.880 56.120 6.90 240 0.882 bicubic -81.480 -54.930 +27 +28
194 nf_resnet50 12.320 87.680 46.760 53.240 25.56 288 0.940 bicubic -82.270 -52.050 -33 -35
195 vit_small_patch16_224 12.147 87.853 40.320 59.680 48.75 224 0.900 bicubic -80.613 -57.610 +89 +93
196 hrnet_w64 12.027 87.973 40.787 59.213 128.06 224 0.875 bilinear -81.983 -57.823 +14
197 cspdarknet53 12.013 87.987 43.253 56.747 27.64 256 0.887 bilinear -82.647 -55.547 -41 -43
198 gluon_resnet101_v1s 11.880 88.120 40.973 59.027 44.67 224 0.875 bicubic -82.840 -57.847 -45 -47
199 resnet50d 11.693 88.307 42.453 57.547 25.58 224 0.875 bicubic -82.567 -56.267 -13 -14
200 dpn92 11.627 88.373 40.267 59.733 37.67 224 0.875 bicubic -82.603 -58.463 -9 -10
201 xception41 11.600 88.400 39.133 60.867 26.97 299 0.903 bicubic -81.830 -59.297 +48 +50
202 dla102x2 11.573 88.427 41.293 58.707 41.28 224 0.875 bilinear -82.377 -57.197 +10
203 regnety_080 levit_128 11.413 11.427 88.587 88.573 40.613 40.267 59.387 59.733 39.18 9.21 224 0.875 0.900 bicubic -82.757 -81.913 -58.067 -58.113 -6 +52
204 efficientnet_b2_pruned regnety_080 11.360 11.413 88.640 88.587 42.027 40.613 57.973 59.387 8.31 39.18 260 224 0.890 0.875 bicubic -82.780 -82.757 -56.503 -58.067 -3 -7
205 tf_efficientnet_el efficientnet_b2_pruned 11.333 11.360 88.667 88.640 42.040 42.027 57.960 57.973 10.59 8.31 300 260 0.904 0.890 bicubic -83.077 -82.780 -56.670 -56.503 -30 -4
206 gluon_resnet152_v1c tf_efficientnet_el 11.093 11.333 88.907 88.667 37.120 42.040 62.880 57.960 60.21 10.59 224 300 0.875 0.904 bicubic -83.067 -83.077 -61.480 -56.670 -8 -32
207 gluon_resnet152_v1c 11.093 88.907 37.120 62.880 60.21 224 0.875 bicubic -83.067 -61.480 -9
208 dpn107 11.080 88.920 38.693 61.307 86.92 224 0.875 bicubic -83.230 -59.787 -27
209 hrnet_w48 11.080 88.920 40.320 59.680 77.47 224 0.875 bilinear -82.840 -58.290 +5
210 dpn107 ecaresnet50d_pruned 11.080 11.027 88.920 88.973 38.693 41.947 61.307 58.053 86.92 19.94 224 0.875 bicubic -83.230 -83.193 -59.787 -56.783 -25 -19
211 ecaresnet50d_pruned tf_efficientnetv2_b2 11.027 88.973 41.947 39.760 58.053 60.240 19.94 10.10 224 260 0.875 0.890 bicubic -83.193 -83.393 -56.783 -58.810 -17 -38
212 adv_inception_v3 11.013 88.987 36.720 63.280 23.83 299 0.875 bicubic -81.867 -61.420 +68 +70
213 tf_efficientnet_b0_ns 10.933 89.067 40.067 59.933 5.29 224 0.875 bicubic -82.697 -58.573 +24 +23
214 tf_inception_v3 10.840 89.160 36.853 63.147 23.83 299 0.875 bicubic -82.480 -61.177 +43 +44
215 resnext50_32x4d 10.800 89.200 40.307 59.693 25.03 224 0.875 bicubic -83.300 -58.043 -8 -10
216 dpn131 10.787 89.213 37.200 62.800 79.25 224 0.875 bicubic -83.223 -61.520 -5 -7
217 tf_efficientnet_b2_ap 10.533 89.467 40.107 59.893 9.11 260 0.890 bicubic -83.957 -58.513 -46 -50
218 resnext50d_32x4d 10.413 89.587 39.733 60.267 25.05 224 0.875 bicubic -83.767 -58.837 -20 -23
219 rexnet_130 10.400 89.600 41.547 58.453 7.56 224 0.875 bicubic -83.500 -56.853 -3 -4
220 hrnet_w44 10.320 89.680 39.507 60.493 67.06 224 0.875 bilinear -83.230 -59.193 +21
221 resnext101_32x8d 10.187 89.813 37.827 62.173 88.79 224 0.875 bilinear -83.643 -60.753 +2 +1
222 regnetx_160 10.147 89.853 38.000 62.000 54.28 224 0.875 bicubic -83.973 -60.750 -16 -18
223 dpn98 10.133 89.867 36.587 63.413 61.57 224 0.875 bicubic -83.997 -61.983 -19 -21
224 cspresnext50 10.120 89.880 40.373 59.627 20.57 224 0.875 bilinear -84.360 -58.307 -52 -56
225 legacy_seresnext50_32x4d 10.107 89.893 39.200 60.800 27.56 224 0.875 bilinear -83.623 -59.380 +7 +6
226 resnetrs50 10.093 89.907 37.507 62.493 35.69 224 0.910 bicubic -84.217 -61.133 -40 -43
227 inception_v3 10.027 89.973 35.227 64.773 23.83 299 0.875 bicubic -82.693 -62.743 +63 +65
228 efficientnet_b1 10.013 89.987 37.547 62.453 7.79 256 1.000 bicubic -83.237 -60.743 +34 +35
229 xception 9.987 90.013 38.027 61.973 22.86 299 0.897 bicubic -83.473 -60.503 -60.543 +20 +19
230 regnety_064 9.947 90.053 39.067 60.933 30.58 224 0.875 bicubic -84.203 -59.663 -28 -30
231 dpn68b 9.787 90.213 38.053 61.947 12.61 224 0.875 bicubic -83.903 -60.307 -60.457 +4 +2
232 gluon_resnet152_v1b 9.747 90.253 36.067 63.933 60.19 224 0.875 bicubic -84.333 -62.383 -23 -25
233 tf_efficientnet_lite3 9.667 90.333 39.000 61.000 8.20 300 0.904 bilinear -84.533 -59.640 -37 -40
234 tf_efficientnet_b2 9.653 90.347 38.880 61.120 9.11 260 0.890 bicubic -84.707 -59.730 -52 -55
235 tf_efficientnet_cc_b1_8e 9.573 90.427 36.773 63.227 39.72 240 0.882 bicubic -84.327 -61.487 -18 -19
236 res2net101_26w_4s 9.520 90.480 35.027 64.973 45.21 224 0.875 bilinear -84.230 -63.283 -6 -7
237 legacy_seresnet152 9.347 90.653 37.413 62.587 66.82 224 0.875 bilinear -84.053 -60.937 +16
238 cspresnet50 9.253 90.747 39.640 60.360 21.62 256 0.887 bilinear -84.487 -59.000 -7 -8
239 hrnet_w40 9.227 90.773 36.893 63.107 57.56 224 0.875 bilinear -84.263 -61.687 +8
240 regnetx_120 9.187 90.813 37.200 62.800 46.11 224 0.875 bicubic -85.053 -61.450 -48 -51
241 seresnext26d_32x4d 9.147 90.853 36.840 63.160 16.81 224 0.875 bicubic -83.553 -61.310 +50 +52
242 resnest26d 9.080 90.920 37.853 62.147 17.07 224 0.875 bilinear -84.250 -60.657 -60.777 +13 +15
243 regnety_040 9.000 91.000 37.053 62.947 20.65 224 0.875 bicubic -84.860 -61.597 -23 -24
244 gluon_resnext50_32x4d 8.947 91.053 36.333 63.667 25.03 224 0.875 bicubic -84.863 -62.057 -18 -19
245 rexnet_100 8.893 91.107 36.373 63.627 4.80 224 0.875 bicubic -84.137 -61.817 +29 +31
246 seresnext26t_32x4d 8.893 91.107 36.907 63.093 16.81 224 0.875 bicubic -83.927 -61.463 +37 +39
247 mixnet_l 8.853 91.147 36.187 63.813 7.33 224 0.875 bicubic -84.597 -62.033 +3
248 mobilenetv3_large_100_miil 8.840 91.160 32.973 67.027 5.48 224 0.875 bilinear -83.420 -64.667 +63 +64
249 dla169 convit_tiny 8.640 8.840 91.360 91.160 36.040 34.360 63.960 65.640 53.39 5.71 224 0.875 bilinear bicubic -84.700 -81.790 -62.560 -63.380 +5 +111
250 hrnet_w30 levit_128s 8.613 8.653 91.387 91.347 37.040 33.107 62.960 66.893 37.71 7.78 224 0.875 0.900 bilinear bicubic -84.587 -83.317 -61.370 -64.953 +14 +73
251 mixer_b16_224 dla169 8.600 8.640 91.400 91.360 29.413 36.040 70.587 63.960 59.88 53.39 224 0.875 bicubic bilinear -83.270 -84.700 -67.837 -62.560 +74 +3
252 legacy_seresnet101 hrnet_w30 8.533 8.613 91.467 91.387 36.013 37.040 63.987 62.960 49.33 37.71 224 0.875 bilinear -84.747 -84.587 -62.497 -61.370 +9 +13
253 tf_efficientnet_b1_ap mixer_b16_224 8.453 8.600 91.547 91.400 35.253 29.413 64.747 70.587 7.79 59.88 240 224 0.882 0.875 bicubic -85.237 -83.270 -63.257 -67.837 -19 +75
254 repvgg_b2 legacy_seresnet101 8.427 8.533 91.573 91.467 36.467 36.013 63.533 63.987 89.02 49.33 224 0.875 bilinear -85.073 -84.747 -61.893 -62.497 -8 +8
255 resnetblur50 tf_efficientnet_b1_ap 8.240 8.453 91.760 91.547 37.400 35.253 62.600 64.747 25.56 7.79 224 240 0.875 0.882 bicubic -85.720 -85.237 -61.190 -63.107 -42 -21
256 dla102x repvgg_b2 8.200 8.427 91.800 91.573 37.013 36.467 62.987 63.533 26.31 89.02 224 0.875 bilinear -85.320 -85.073 -61.497 -62.263 -12 -11
257 hrnet_w32 resnetblur50 8.040 8.240 91.960 91.760 37.507 37.400 62.493 62.600 41.23 25.56 224 0.875 bilinear bicubic -85.490 -85.720 -60.943 -61.190 -14 -46
258 res2net50_26w_8s dla102x 8.000 8.200 92.000 91.800 33.853 37.013 66.147 62.987 48.40 26.31 224 0.875 bilinear -85.540 -85.320 -64.407 -61.497 -16 -14
259 gluon_resnet101_v1c hrnet_w32 7.987 8.040 92.013 91.960 33.360 37.507 66.640 62.493 44.57 41.23 224 0.875 bicubic bilinear -85.683 -85.490 -65.060 -60.943 -23 -16
260 gluon_resnet50_v1d res2net50_26w_8s 7.920 8.000 92.080 92.000 35.000 33.853 65.000 66.147 25.58 48.40 224 0.875 bicubic bilinear -85.850 -85.540 -63.390 -64.407 -33 -18
261 dla60_res2next gluon_resnet101_v1c 7.787 7.987 92.213 92.013 34.987 33.360 65.013 66.640 17.03 44.57 224 0.875 bilinear bicubic -85.393 -85.683 -63.423 -65.060 +5 -26
262 densenetblur121d gluon_resnet50_v1d 7.720 7.920 92.280 92.080 34.733 35.000 65.267 65.000 8.00 25.58 224 0.875 bicubic -84.190 -85.850 -63.337 -63.390 +61 -36
263 vit_deit_tiny_distilled_patch16_224 dla60_res2next 7.707 7.787 92.293 92.213 33.560 34.987 66.440 65.013 5.91 17.03 224 0.900 0.875 bicubic bilinear -82.993 -85.393 -64.010 -63.423 +91 +4
264 dla60_res2net densenetblur121d 7.560 7.720 92.440 92.280 34.627 34.733 65.373 65.267 20.85 8.00 224 0.875 bilinear bicubic -85.620 -84.190 -63.793 -63.337 +1 +62
265 efficientnet_b1_pruned vit_deit_tiny_distilled_patch16_224 7.440 7.707 92.560 92.293 34.533 33.560 65.467 66.440 6.33 5.91 240 224 0.882 0.900 bicubic -85.330 -82.993 -63.507 -64.010 +20 +92
266 wide_resnet101_2 tf_efficientnetv2_b1 7.360 7.693 92.640 92.307 34.147 34.653 65.853 65.347 126.89 8.14 224 240 0.875 0.882 bilinear bicubic -86.360 -86.247 -64.393 -63.967 -33 -53
267 regnetx_064 dla60_res2net 7.333 7.560 92.667 92.440 34.373 34.627 65.627 65.373 26.21 20.85 224 0.875 bicubic bilinear -86.557 -85.620 -64.257 -63.793 -49 -1
268 vit_deit_tiny_patch16_224 efficientnet_b1_pruned 7.307 7.440 92.693 92.560 30.707 34.533 69.293 65.467 5.72 6.33 224 240 0.900 0.882 bicubic -82.363 -85.330 -66.743 -63.507 +99 +19
269 hardcorenas_e wide_resnet101_2 7.240 7.360 92.760 92.640 33.293 34.147 66.707 65.853 8.07 126.89 224 0.875 bilinear -85.330 -86.360 -64.817 -64.393 +29 -37
270 gluon_resnet101_v1b regnetx_064 7.227 7.333 92.773 92.667 32.773 34.373 67.227 65.627 44.55 26.21 224 0.875 bicubic -86.523 -86.557 -65.607 -64.257 -41 -53
271 efficientnet_b0 vit_deit_tiny_patch16_224 7.213 7.307 92.787 92.693 34.013 30.707 65.987 69.293 5.29 5.72 224 0.875 0.900 bicubic -85.477 -82.363 -64.057 -66.743 +21 +100
272 gluon_resnet50_v1s hardcorenas_e 7.213 7.240 92.787 92.760 33.507 33.293 66.493 66.707 25.68 8.07 224 0.875 bicubic bilinear -86.407 -85.330 -64.953 -64.817 -34 +28
273 tf_mixnet_l gluon_resnet101_v1b 7.147 7.227 92.853 92.773 31.613 32.773 68.387 67.227 7.33 44.55 224 0.875 bicubic -86.163 -86.523 -66.417 -65.607 -15 -45
274 tf_efficientnet_b1 efficientnet_b0 7.133 7.213 92.867 92.787 33.040 34.013 66.960 65.987 7.79 5.29 240 224 0.882 0.875 bicubic -86.367 -85.477 -65.690 -64.057 -29 +20
275 tf_efficientnet_cc_b0_8e gluon_resnet50_v1s 7.120 7.213 92.880 92.787 31.787 33.507 68.213 66.493 24.01 25.68 224 0.875 bicubic -85.710 -86.407 -66.393 -64.953 +7 -38
276 hardcorenas_f tf_mixnet_l 6.827 7.147 93.173 92.853 34.093 31.613 65.907 68.387 8.20 7.33 224 0.875 bilinear bicubic -86.123 -86.163 -64.067 -66.417 +2 -17
277 selecsls60b tf_efficientnet_b1 6.733 7.133 93.267 92.867 33.267 33.040 66.733 66.960 32.77 7.79 224 240 0.875 0.882 bicubic -86.567 -86.367 -65.013 -65.320 -19 -31
278 ese_vovnet19b_dw tf_efficientnet_cc_b0_8e 6.733 7.120 93.267 92.880 33.413 31.787 66.587 68.213 6.54 24.01 224 0.875 bicubic -85.557 -85.710 -64.677 -66.393 +32 +6
279 efficientnet_es hardcorenas_f 6.707 6.827 93.293 93.173 33.840 34.093 66.160 65.907 5.44 8.20 224 0.875 bicubic bilinear -86.433 -86.123 -64.580 -64.067 -12 +1
280 res2net50_26w_6s ese_vovnet19b_dw 6.693 6.733 93.307 93.267 31.653 33.413 68.347 66.587 37.05 6.54 224 0.875 bilinear bicubic -86.717 -85.557 -66.627 -64.677 -28 +31
281 legacy_seresnext26_32x4d selecsls60b 6.627 6.733 93.373 93.267 33.253 33.267 66.747 66.733 16.79 32.77 224 0.875 bicubic -86.013 -86.567 -64.877 -65.123 +14 -20
282 mixnet_m efficientnet_es 6.627 6.707 93.373 93.293 32.053 33.840 67.947 66.160 5.01 5.44 224 0.875 bicubic -85.803 -86.433 -65.817 -64.580 +21 -14
283 res2net50_26w_6s 6.693 93.307 31.653 68.347 37.05 224 0.875 bilinear -86.717 -66.627 -31
284 pit_ti_distilled_224 6.627 93.373 30.760 69.240 5.10 224 0.900 bicubic -84.273 -66.940 +66
285 skresnet34 mixnet_m 6.480 6.627 93.520 93.373 31.547 32.053 68.453 67.947 22.28 5.01 224 0.875 bicubic -85.910 -85.803 -66.603 -65.817 +22 +20
286 repvgg_b1 legacy_seresnext26_32x4d 6.467 6.627 93.533 93.373 33.827 33.253 66.173 66.747 57.42 16.79 224 0.875 bilinear bicubic -86.863 -86.013 -64.803 -64.877 -29 +13
287 hardcorenas_d skresnet34 6.440 6.480 93.560 93.520 32.213 31.547 67.787 68.453 7.50 22.28 224 0.875 bilinear bicubic -85.960 -85.910 -65.857 -66.603 +18 +21
288 dla60x repvgg_b1 6.427 6.467 93.573 93.533 34.080 33.827 65.920 66.173 17.35 57.42 224 0.875 bilinear -86.693 -86.863 -64.430 -64.683 -19 -32
289 resnet34d hardcorenas_d 6.400 6.440 93.600 93.560 31.493 32.213 68.507 67.787 21.82 7.50 224 0.875 bicubic bilinear -86.280 -85.960 -66.817 -65.857 +5 +17
290 regnetx_080 dla60x 6.307 6.427 93.693 93.573 32.320 34.080 67.680 65.920 39.57 17.35 224 0.875 bicubic bilinear -87.563 -86.693 -66.200 -64.430 -70 -21
291 resnet34d 6.400 93.600 31.493 68.507 21.82 224 0.875 bicubic -86.280 -66.817 +4
292 regnetx_080 6.307 93.693 32.320 67.680 39.57 224 0.875 bicubic -87.563 -66.200 -74
293 swsl_resnet18 6.240 93.760 31.600 68.400 11.69 224 0.875 bilinear -84.450 -66.100 +65
294 legacy_seresnet50 6.187 93.813 32.653 67.347 28.09 224 0.875 bilinear -86.773 -65.537 -14 -15
295 pit_ti_224 6.120 93.880 30.227 69.773 4.85 224 0.900 bicubic -83.820 -67.223 +72 +73
296 tv_resnet152 6.040 93.960 32.053 67.947 60.19 224 0.875 bilinear -87.260 -66.337 -66.227 -33 -36
297 regnetx_040 tf_efficientnet_cc_b0_4e 5.973 94.027 31.547 29.600 68.453 70.400 22.12 13.31 224 0.875 bicubic -87.587 -86.617 -66.993 -68.480 -54 +1
298 tf_efficientnet_cc_b0_4e regnetx_040 5.973 94.027 29.600 31.547 70.400 68.453 13.31 22.12 224 0.875 bicubic -86.617 -87.587 -68.480 -66.993 +2 -57
299 resnet50 5.933 94.067 29.093 70.907 25.56 224 0.875 bicubic -87.877 -69.317 -72 -76
300 dla102 tf_efficientnetv2_b0 5.880 5.893 94.120 94.107 32.707 30.773 67.293 69.227 33.27 7.14 224 0.875 bilinear bicubic -87.180 -87.217 -65.833 -67.617 -26 -28
301 dla102 5.880 94.120 32.707 67.293 33.27 224 0.875 bilinear -87.180 -65.833 -28
302 mixer_l16_224 5.867 94.133 18.533 81.467 208.20 224 0.875 bicubic -81.283 -74.987 +84
303 regnety_016 5.680 94.320 30.413 69.587 11.20 224 0.875 bicubic -87.350 -67.947 -26 -28
304 selecsls60 5.653 94.347 32.507 67.493 30.67 224 0.875 bicubic -87.377 -65.793 -25 -27
305 hardcorenas_c 5.640 94.360 30.400 69.600 5.52 224 0.875 bilinear -86.380 -67.440 +17 +15
306 res2next50 5.627 94.373 30.867 69.133 24.67 224 0.875 bilinear -87.213 -67.313 -21 -23
307 hrnet_w18 5.493 94.507 30.960 69.040 21.30 224 0.875 bilinear -86.827 -67.280 +5 +3
308 resnest14d 5.480 94.520 28.547 71.453 10.61 224 0.875 bilinear -86.240 -69.323 +26 +25
309 tf_efficientnet_lite2 5.360 94.640 30.907 69.093 6.09 260 0.890 bicubic -87.290 -67.323 -11 -13
310 tf_efficientnet_em 5.347 94.653 31.107 68.893 6.90 240 0.882 bicubic -87.583 -67.083 -27 -29
311 gernet_s 5.307 94.693 30.133 69.867 8.17 224 0.875 bilinear -86.833 -68.057 +9 +7
312 tf_efficientnet_b0_ap 5.307 94.693 28.813 71.187 5.29 224 0.875 bicubic -86.893 -69.207 +6 +4
313 densenet121 5.293 94.707 29.907 70.093 7.98 224 0.875 bicubic -86.277 -68.123 +22 +21
314 repvgg_b1g4 5.293 94.707 30.813 69.187 39.97 224 0.875 bilinear -87.687 -67.617 -34 -36
315 res2net50_26w_4s 5.160 94.840 29.360 70.640 25.70 224 0.875 bilinear -87.340 -68.930 -68.700 -10 -11
316 tf_mixnet_m 5.080 94.920 28.147 71.853 5.01 224 0.875 bicubic -87.250 -69.743 -5 -7
317 tf_efficientnet_b0 mobilenetv3_large_100 5.067 94.933 28.800 28.187 71.200 71.813 5.29 5.48 224 0.875 bicubic -87.183 -86.253 -69.200 -69.523 -2 +22
318 mobilenetv3_large_100 tf_efficientnet_b0 5.067 94.933 28.187 28.800 71.813 71.200 5.48 5.29 224 0.875 bicubic -86.253 -87.183 -69.523 -69.200 +23 -4
319 res2net50_14w_8s 5.040 94.960 28.773 71.227 25.06 224 0.875 bilinear -87.700 -69.407 -27 -29
320 hardcorenas_b 4.947 95.053 28.120 71.880 5.18 224 0.875 bilinear -86.823 -69.660 +12 +11
321 mixnet_s mobilenetv3_rw 4.907 95.093 28.573 29.853 71.427 70.147 4.13 5.48 224 0.875 bicubic -86.923 -86.303 -69.117 -67.807 +9 +20
322 mobilenetv3_rw mixnet_s 4.907 95.093 29.853 28.573 70.147 71.427 5.48 4.13 224 0.875 bicubic -86.303 -86.923 -67.807 -69.117 +21 +8
323 gluon_resnet50_v1c 4.893 95.107 28.147 71.853 25.58 224 0.875 bicubic -88.137 -70.243 -47 -49
324 hardcorenas_a 4.867 95.133 28.093 71.907 5.26 224 0.875 bilinear -86.483 -69.767 +15 +14
325 regnetx_032 4.853 95.147 30.280 69.720 15.30 224 0.875 bicubic -88.267 -68.110 -52 -55
326 tv_resnext50_32x4d 4.840 95.160 30.307 69.693 25.03 224 0.875 bilinear -87.900 -67.963 -33 -35
327 tv_resnet101 4.707 95.293 29.333 70.667 44.55 224 0.875 bilinear -88.103 -68.917 -39 -41
328 densenet161 4.693 95.307 29.547 70.453 28.68 224 0.875 bicubic -87.807 -68.513 -68.743 -22 -25
329 selecsls42b 4.667 95.333 28.587 71.413 32.46 224 0.875 bicubic -87.613 -69.563 -15 -17
330 tf_efficientnet_lite1 4.613 95.387 28.387 71.613 5.42 240 0.882 bicubic -88.007 -69.693 -30 -32
331 mobilenetv2_120d 4.533 95.467 29.280 70.720 5.83 224 0.875 bicubic -87.867 -68.770 -22 -24
332 efficientnet_es_pruned 4.187 95.813 26.520 73.480 5.44 224 0.875 bicubic -86.993 -71.230 +13 +12
333 fbnetc_100 4.133 95.867 25.933 74.067 5.57 224 0.875 bilinear -86.567 -71.277 +24 +23
334 densenet201 gluon_resnet50_v1b 4.120 95.880 27.547 26.933 72.453 73.067 20.01 25.56 224 0.875 bicubic -88.630 -88.420 -70.683 -71.237 -43 -33
335 gluon_resnet50_v1b densenet201 4.120 95.880 26.933 27.547 73.067 72.453 25.56 20.01 224 0.875 bicubic -88.420 -88.630 -71.237 -70.683 -31 -45
336 resnet26d 4.040 95.960 28.520 71.480 16.01 224 0.875 bicubic -88.030 -69.440 -15 -17
337 semnasnet_100 3.960 96.040 26.947 73.053 3.89 224 0.875 bicubic -87.320 -70.613 +4 +3
338 repvgg_a2 3.947 96.053 27.267 72.733 28.21 224 0.875 bilinear -87.993 -70.883 -13 -14
339 tf_mixnet_s 3.880 96.120 25.253 74.747 4.13 224 0.875 bicubic -87.630 -72.367 -3 -4
340 dpn68 3.867 96.133 26.080 73.920 12.61 224 0.875 bicubic -88.143 -71.970 -17 -19
341 tf_efficientnet_es 3.827 96.173 26.107 73.893 5.44 224 0.875 bicubic -88.153 -71.753 -17 -19
342 regnety_008 3.813 96.187 27.133 72.867 6.26 224 0.875 bicubic -87.937 -71.047 -9 -10
343 dla60 3.773 96.227 27.933 72.067 22.04 224 0.875 bilinear -88.457 -70.177 -26 -28
344 ssl_resnet18 3.747 96.253 25.427 74.573 11.69 224 0.875 bilinear -86.473 -72.123 +20
345 mobilenetv2_140 3.720 96.280 26.747 73.253 6.11 224 0.875 bicubic -88.110 -71.113 -14 -15
346 densenet169 3.707 96.293 25.613 74.387 14.15 224 0.875 bicubic -88.223 -72.487 -20 -21
347 regnetx_016 3.627 96.373 26.293 73.707 9.19 224 0.875 bicubic -88.543 -71.917 -28 -30
348 res2net50_48w_2s 3.587 96.413 26.613 73.387 25.29 224 0.875 bilinear -88.963 -71.467 -45 -47
349 spnasnet_100 3.547 96.453 24.293 75.707 4.42 224 0.875 bilinear -86.803 -73.137 -72.897 +13 +14
350 tf_mobilenetv3_large_100 3.547 96.453 25.053 74.947 5.48 224 0.875 bilinear -87.693 -72.607 -8 -9
351 regnety_006 3.467 96.533 24.893 75.107 6.06 224 0.875 bicubic -87.903 -72.817 -13 -14
352 legacy_seresnet34 3.333 96.667 23.800 76.200 21.96 224 0.875 bilinear -87.557 -73.780 +2 +1
353 efficientnet_lite0 3.253 96.747 25.867 74.133 4.65 224 0.875 bicubic -87.887 -71.763 -7 -8
354 dla34 3.227 96.773 23.573 76.427 15.74 224 0.875 bilinear -87.533 -74.087 +2 +1
355 ghostnet_100 3.227 96.773 24.853 75.147 5.18 224 0.875 bilinear -86.793 -72.517 +12
356 regnety_004 3.200 96.800 22.653 77.347 4.34 224 0.875 bicubic -87.300 -74.887 +5
357 mobilenetv2_110d 3.173 96.827 24.587 75.413 4.52 224 0.875 bicubic -87.777 -72.963 -5 -6
358 mnasnet_100 3.120 96.880 24.227 75.773 4.38 224 0.875 bicubic -87.390 -73.243 +2
359 tf_efficientnet_lite0 3.080 96.920 22.907 77.093 4.65 224 0.875 bicubic -87.960 -74.683 -9 -10
360 skresnet18 3.013 96.987 22.800 77.200 11.96 224 0.875 bicubic -86.647 -74.430 +12
361 vgg19_bn 2.947 97.053 23.480 76.520 143.68 224 0.875 bilinear -87.133 -74.100 +5
362 resnet34 2.920 97.080 23.680 76.320 21.80 224 0.875 bilinear -88.210 -73.940 -15 -16
363 tf_mobilenetv3_large_075 2.867 97.133 21.573 78.427 3.99 224 0.875 bilinear -86.813 -75.637 +7
364 hrnet_w18_small_v2 2.720 97.280 23.693 76.307 15.60 224 0.875 bilinear -88.470 -74.207 -20 -21
365 gluon_resnet34_v1b 2.667 97.333 21.680 78.320 21.80 224 0.875 bicubic -88.293 -75.950 -14 -15
366 regnetx_008 2.653 97.347 22.453 77.547 7.26 224 0.875 bicubic -88.397 -75.257 -17 -18
367 vgg16_bn 2.653 97.347 23.773 76.227 138.37 224 0.875 bilinear -87.437 -73.597 -2
368 vgg16 2.640 97.360 20.427 79.573 138.36 224 0.875 bilinear -85.910 -76.363 +13
369 resnet18d 2.600 97.400 21.613 78.387 11.71 224 0.875 bicubic -86.680 -75.537 +5
370 tv_densenet121 2.560 97.440 22.667 77.333 7.98 224 0.875 bicubic -88.330 -75.043 -15 -16
371 repvgg_b0 2.547 97.453 24.013 75.987 15.82 224 0.875 bilinear -88.883 -73.977 -34 -35
372 regnetx_006 2.507 97.493 20.653 79.347 6.20 224 0.875 bicubic -87.843 -76.537 -76.777 -9 -10
373 legacy_seresnet18 2.493 97.507 20.080 79.920 11.78 224 0.875 bicubic -86.387 -76.900 +6
374 resnet26 2.480 97.520 22.987 77.013 16.00 224 0.875 bicubic -88.630 -74.753 -26 -27
375 mobilenetv2_100 2.147 97.853 19.907 80.093 3.50 224 0.875 bicubic -87.453 -77.233 -2
376 regnety_002 2.147 97.853 18.880 81.120 3.16 224 0.875 bicubic -85.233 -77.710 +9
377 vgg19 2.107 97.893 20.733 79.267 143.67 224 0.875 bilinear -86.933 -76.137 -1
392 regnetx_002 1.373 98.627 15.027 84.973 2.68 224 0.875 bicubic -84.817 -80.953 -2
393 resnet18 1.160 98.840 16.213 83.787 11.69 224 0.875 bilinear -86.230 -80.077 -9
394 tf_mobilenetv3_small_minimal_100 1.013 98.987 11.493 88.507 2.04 224 0.875 bilinear -80.367 -82.177 +1
395 tv_resnet50 0.000 100.000 14.453 85.547 25.56 224 0.875 bilinear -91.880 -83.587 -67 -68

107
results/results-imagenet-r-clean.csv
Unescape View File

@ -4,12 +4,14 @@ tf_efficientnet_l2_ns_475,97.750,2.250,99.820,0.180,480.31,475,0.936,bicubic
tf_efficientnet_b7_ns,97.200,2.800,99.700,0.300,66.35,600,0.949,bicubic
swin_large_patch4_window12_384,97.170,2.830,99.680,0.320,196.74,384,1.000,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
tf_efficientnet_b6_ns,97.020,2.980,99.710,0.290,43.04,528,0.942,bicubic
dm_nfnet_f6,96.990,3.010,99.740,0.260,438.36,576,0.956,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
swin_large_patch4_window7_224,96.950,3.050,99.660,0.340,196.53,224,0.900,bicubic
cait_m48_448,96.880,3.120,99.620,0.380,356.46,448,1.000,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
cait_m36_384,96.830,3.170,99.660,0.340,271.22,384,1.000,bicubic
dm_nfnet_f4,96.820,3.180,99.600,0.400,316.07,512,0.951,bicubic
@ -18,25 +20,29 @@ dm_nfnet_f5,96.710,3.290,99.680,0.320,377.21,544,0.954,bicubic
tf_efficientnet_b4_ns,96.710,3.290,99.640,0.360,19.34,380,0.922,bicubic
tf_efficientnet_b8,96.700,3.300,99.530,0.470,87.41,672,0.954,bicubic
swin_base_patch4_window7_224,96.680,3.320,99.660,0.340,87.77,224,0.900,bicubic
tf_efficientnetv2_l,96.650,3.350,99.560,0.440,118.52,480,1.000,bicubic
cait_s36_384,96.630,3.370,99.600,0.400,68.37,384,1.000,bicubic
dm_nfnet_f3,96.630,3.370,99.640,0.360,254.92,416,0.940,bicubic
tf_efficientnet_b7,96.580,3.420,99.510,0.490,66.35,600,0.949,bicubic
cait_s24_384,96.570,3.430,99.550,0.450,47.06,384,1.000,bicubic
tf_efficientnet_b8_ap,96.550,3.450,99.540,0.460,87.41,672,0.954,bicubic
tf_efficientnetv2_m,96.550,3.450,99.570,0.430,54.14,480,1.000,bicubic
vit_deit_base_distilled_patch16_384,96.510,3.490,99.590,0.410,87.63,384,1.000,bicubic
dm_nfnet_f2,96.500,3.500,99.570,0.430,193.78,352,0.920,bicubic
resnetv2_152x2_bitm,96.500,3.500,99.620,0.380,236.34,480,1.000,bilinear
tf_efficientnetv2_s_in21ft1k,96.470,3.530,99.570,0.430,21.46,384,1.000,bicubic
ecaresnet269d,96.460,3.540,99.610,0.390,102.09,352,1.000,bicubic
vit_base_r50_s16_384,96.450,3.550,99.660,0.340,98.95,384,1.000,bicubic
ig_resnext101_32x16d,96.440,3.560,99.540,0.460,194.03,224,0.875,bilinear
resnetrs420,96.400,3.600,99.540,0.460,191.89,416,1.000,bicubic
dm_nfnet_f1,96.370,3.630,99.470,0.530,132.63,320,0.910,bicubic
tf_efficientnet_b6_ap,96.370,3.630,99.550,0.450,43.04,528,0.942,bicubic
dm_nfnet_f1,96.370,3.630,99.470,0.530,132.63,320,0.910,bicubic
resnetv2_101x3_bitm,96.360,3.640,99.600,0.400,387.93,480,1.000,bilinear
vit_large_patch16_384,96.360,3.640,99.630,0.370,304.72,384,1.000,bicubic
tf_efficientnet_b7_ap,96.350,3.650,99.590,0.410,66.35,600,0.949,bicubic
seresnet152d,96.310,3.690,99.510,0.490,66.84,320,1.000,bicubic
tf_efficientnet_b6,96.290,3.710,99.520,0.480,43.04,528,0.942,bicubic
efficientnetv2_rw_m,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
resnetrs350,96.240,3.760,99.470,0.530,163.96,384,1.000,bicubic
swsl_resnext101_32x8d,96.240,3.760,99.590,0.410,88.79,224,0.875,bilinear
@ -55,27 +61,32 @@ cait_xs24_384,96.010,3.990,99.430,0.570,26.67,384,1.000,bicubic
resnetrs200,95.990,4.010,99.440,0.560,93.21,320,1.000,bicubic
tf_efficientnet_b5,95.980,4.020,99.450,0.550,30.39,456,0.934,bicubic
resnetrs152,95.960,4.040,99.380,0.620,86.62,320,1.000,bicubic
ig_resnext101_32x8d,95.930,4.070,99.380,0.620,88.79,224,0.875,bilinear
eca_nfnet_l1,95.930,4.070,99.500,0.500,41.41,320,1.000,bicubic
ig_resnext101_32x8d,95.930,4.070,99.380,0.620,88.79,224,0.875,bilinear
regnety_160,95.880,4.120,99.560,0.440,83.59,288,1.000,bicubic
resnet152d,95.870,4.130,99.430,0.570,60.21,320,1.000,bicubic
resnet101d,95.750,4.250,99.440,0.560,44.57,320,1.000,bicubic
vit_deit_base_distilled_patch16_224,95.750,4.250,99.280,0.720,87.34,224,0.900,bicubic
swin_small_patch4_window7_224,95.720,4.280,99.290,0.710,49.61,224,0.900,bicubic
efficientnet_v2s,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic
cait_s24_224,95.650,4.350,99.390,0.610,46.92,224,1.000,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
efficientnetv2_rw_s,95.710,4.290,99.380,0.620,23.94,384,1.000,bicubic
tf_efficientnetv2_s,95.710,4.290,99.400,0.600,21.46,384,1.000,bicubic
vit_deit_base_patch16_384,95.650,4.350,99.240,0.760,86.86,384,1.000,bicubic
cait_s24_224,95.650,4.350,99.390,0.610,46.92,224,1.000,bicubic
dm_nfnet_f0,95.630,4.370,99.300,0.700,71.49,256,0.900,bicubic
swsl_resnext50_32x4d,95.620,4.380,99.440,0.560,25.03,224,0.875,bilinear
tf_efficientnet_b4,95.590,4.410,99.330,0.670,19.34,380,0.922,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
efficientnet_b4,95.520,4.480,99.390,0.610,19.34,384,1.000,bicubic
tf_efficientnet_b2_ns,95.520,4.480,99.340,0.660,9.11,260,0.890,bicubic
resnetv2_101x1_bitm,95.510,4.490,99.510,0.490,44.54,480,1.000,bilinear
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
eca_nfnet_l0,95.470,4.530,99.380,0.620,24.14,288,1.000,bicubic
regnety_032,95.470,4.530,99.320,0.680,19.44,288,1.000,bicubic
eca_nfnet_l0,95.470,4.530,99.380,0.620,24.14,288,1.000,bicubic
twins_pcpvt_base,95.460,4.540,99.390,0.610,43.83,224,0.900,bicubic
ssl_resnext101_32x16d,95.410,4.590,99.410,0.590,194.03,224,0.875,bilinear
tresnet_l_448,95.410,4.590,99.300,0.700,55.99,448,0.875,bilinear
nfnet_l0,95.390,4.610,99.420,0.580,35.07,288,1.000,bicubic
@ -87,50 +98,62 @@ vit_base_patch32_384,95.260,4.740,99.180,0.820,88.30,384,1.000,bicubic
resnetrs101,95.250,4.750,99.210,0.790,63.62,288,0.940,bicubic
vit_large_patch32_384,95.240,4.760,99.320,0.680,306.63,384,1.000,bicubic
cait_xxs36_384,95.220,4.780,99.320,0.680,17.37,384,1.000,bicubic
levit_384,95.210,4.790,99.160,0.840,39.13,224,0.900,bicubic
vit_base_patch16_224,95.210,4.790,99.230,0.770,86.57,224,0.900,bicubic
resnet51q,95.200,4.800,99.280,0.720,35.70,288,1.000,bilinear
swsl_resnet50,95.200,4.800,99.390,0.610,25.56,224,0.875,bilinear
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,95.160,4.840,99.300,0.700,44.18,224,0.875,bilinear
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
tf_efficientnetv2_b3,95.120,4.880,99.200,0.800,14.36,300,0.904,bicubic
convit_base,95.100,4.900,99.140,0.860,86.54,224,0.875,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
tresnet_xl,95.060,4.940,99.260,0.740,78.44,224,0.875,bilinear
vit_deit_base_patch16_224,95.010,4.990,98.980,1.020,86.57,224,0.900,bicubic
tf_efficientnet_b3_ap,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
gernet_l,94.930,5.070,99.200,0.800,31.08,256,0.875,bilinear
cait_xxs24_384,94.920,5.080,99.140,0.860,12.03,384,1.000,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
tresnet_l,94.900,5.100,99.030,0.970,55.99,224,0.875,bilinear
mixer_b16_224_miil,94.880,5.120,99.080,0.920,59.88,224,0.875,bilinear
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
seresnext50_32x4d,94.820,5.180,99.130,0.870,27.56,224,0.875,bicubic
pit_b_224,94.790,5.210,98.820,1.180,73.76,224,0.900,bicubic
coat_mini,94.770,5.230,98.950,1.050,10.34,224,0.900,bicubic
twins_svt_small,94.770,5.230,99.080,0.920,24.06,224,0.900,bicubic
legacy_senet154,94.730,5.270,99.100,0.900,115.09,224,0.875,bilinear
pit_s_distilled_224,94.730,5.270,99.190,0.810,24.04,224,0.900,bicubic
gluon_resnet152_v1s,94.720,5.280,99.060,0.940,60.32,224,0.875,bicubic
gluon_senet154,94.710,5.290,98.970,1.030,115.09,224,0.875,bicubic
resnest50d_4s2x40d,94.710,5.290,99.130,0.870,30.42,224,0.875,bicubic
ssl_resnext50_32x4d,94.700,5.300,99.240,0.760,25.03,224,0.875,bilinear
wide_resnet50_2,94.670,5.330,99.050,0.950,68.88,224,0.875,bicubic
efficientnet_el,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic
rexnet_200,94.660,5.340,99.090,0.910,16.37,224,0.875,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
gluon_seresnext101_64x4d,94.650,5.350,98.980,1.020,88.23,224,0.875,bicubic
resnest50d,94.620,5.380,99.030,0.970,27.48,224,0.875,bilinear
swin_tiny_patch4_window7_224,94.620,5.380,99.120,0.880,28.29,224,0.900,bicubic
twins_pcpvt_small,94.600,5.400,99.150,0.850,24.11,224,0.900,bicubic
pit_s_224,94.590,5.410,98.930,1.070,23.46,224,0.900,bicubic
vit_deit_small_distilled_patch16_224,94.590,5.410,99.100,0.900,22.44,224,0.900,bicubic
efficientnet_b3_pruned,94.580,5.420,99.070,0.930,9.86,300,0.904,bicubic
tnt_s_patch16_224,94.580,5.420,99.180,0.820,23.76,224,0.900,bicubic
resnetv2_50x1_bitm,94.550,5.450,99.250,0.750,25.55,480,1.000,bilinear
repvgg_b3,94.550,5.450,98.910,1.090,123.09,224,0.875,bilinear
gernet_m,94.550,5.450,98.930,1.070,21.14,224,0.875,bilinear
repvgg_b3,94.550,5.450,98.910,1.090,123.09,224,0.875,bilinear
resnetv2_50x1_bitm,94.550,5.450,99.250,0.750,25.55,480,1.000,bilinear
regnety_320,94.520,5.480,99.170,0.830,145.05,224,0.875,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
ecaresnet101d_pruned,94.450,5.550,99.100,0.900,24.88,224,0.875,bicubic
gluon_resnet152_v1d,94.440,5.560,99.010,0.990,60.21,224,0.875,bicubic
nf_resnet50,94.410,5.590,99.100,0.900,25.56,288,0.940,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
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
@ -139,18 +162,18 @@ gluon_resnext101_64x4d,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic
inception_resnet_v2,94.340,5.660,98.800,1.200,55.84,299,0.897,bicubic
ssl_resnet50,94.310,5.690,99.150,0.850,25.56,224,0.875,bilinear
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,94.270,5.730,98.950,1.050,9.11,260,0.890,bicubic
rexnet_150,94.270,5.730,99.080,0.920,9.73,224,0.875,bicubic
mixnet_xl,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic
regnetx_320,94.210,5.790,99.050,0.950,107.81,224,0.875,bicubic
tf_efficientnet_b2,94.210,5.790,99.030,0.970,9.11,260,0.890,bicubic
dpn92,94.190,5.810,98.930,1.070,37.67,224,0.875,bicubic
ecaresnet50d,94.190,5.810,99.020,0.980,25.58,224,0.875,bicubic
gluon_seresnext50_32x4d,94.170,5.830,98.910,1.090,27.56,224,0.875,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
ecaresnetlight,94.140,5.860,98.950,1.050,30.16,224,0.875,bicubic
gluon_resnet101_v1s,94.170,5.830,99.010,0.990,44.67,224,0.875,bicubic
gluon_seresnext50_32x4d,94.170,5.830,98.910,1.090,27.56,224,0.875,bicubic
regnety_064,94.140,5.860,99.030,0.970,30.58,224,0.875,bicubic
ecaresnetlight,94.140,5.860,98.950,1.050,30.16,224,0.875,bicubic
ens_adv_inception_resnet_v2,94.130,5.870,98.790,1.210,55.84,299,0.897,bicubic
legacy_seresnext101_32x4d,94.130,5.870,98.970,1.030,48.96,224,0.875,bilinear
tf_efficientnet_lite3,94.130,5.870,98.960,1.040,8.20,300,0.904,bilinear
@ -159,6 +182,7 @@ cspdarknet53,94.090,5.910,98.980,1.020,27.64,256,0.887,bilinear
efficientnet_el_pruned,94.090,5.910,99.010,0.990,10.59,300,0.904,bicubic
seresnet50,94.080,5.920,98.970,1.030,28.09,224,0.875,bicubic
resnet50d,94.070,5.930,98.920,1.080,25.58,224,0.875,bicubic
tf_efficientnetv2_b2,94.070,5.930,98.930,1.070,10.10,260,0.890,bicubic
gluon_resnet152_v1b,94.030,5.970,98.740,1.260,60.19,224,0.875,bicubic
hrnet_w48,94.030,5.970,99.040,0.960,77.47,224,0.875,bilinear
resnetrs50,94.020,5.980,98.850,1.150,35.69,224,0.910,bicubic
@ -168,35 +192,37 @@ dla102x2,94.000,6.000,99.030,0.970,41.28,224,0.875,bilinear
vit_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
cait_xxs36_224,93.940,6.060,98.890,1.110,17.30,224,1.000,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
nf_regnet_b1,93.890,6.110,98.750,1.250,10.22,288,0.900,bicubic
regnety_080,93.890,6.110,99.000,1.000,39.18,224,0.875,bicubic
xception71,93.890,6.110,98.950,1.050,42.34,299,0.903,bicubic
gluon_resnet152_v1c,93.880,6.120,98.800,1.200,60.21,224,0.875,bicubic
regnety_080,93.890,6.110,99.000,1.000,39.18,224,0.875,bicubic
nf_regnet_b1,93.890,6.110,98.750,1.250,10.22,288,0.900,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
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
resnext50_32x4d,93.840,6.160,98.830,1.170,25.03,224,0.875,bicubic
hrnet_w64,93.830,6.170,98.930,1.070,128.06,224,0.875,bilinear
repvgg_b2g4,93.820,6.180,98.930,1.070,61.76,224,0.875,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,93.810,6.190,98.740,1.260,25.05,224,0.875,bicubic
efficientnet_b2_pruned,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic
dla169,93.800,6.200,98.840,1.160,53.39,224,0.875,bilinear
efficientnet_b2_pruned,93.800,6.200,98.910,1.090,8.31,260,0.890,bicubic
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
cspresnext50,93.760,6.240,98.840,1.160,20.57,224,0.875,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
xception65,93.760,6.240,98.860,1.140,39.92,299,0.903,bicubic
cspresnext50,93.760,6.240,98.840,1.160,20.57,224,0.875,bilinear
dpn131,93.760,6.240,98.800,1.200,79.25,224,0.875,bicubic
efficientnet_em,93.740,6.260,98.930,1.070,6.90,240,0.882,bicubic
tf_efficientnet_b0_ns,93.740,6.260,98.980,1.020,5.29,224,0.875,bicubic
wide_resnet101_2,93.730,6.270,98.810,1.190,126.89,224,0.875,bilinear
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
levit_192,93.710,6.290,98.790,1.210,10.95,224,0.900,bicubic
resnetblur50,93.710,6.290,98.810,1.190,25.56,224,0.875,bicubic
tf_efficientnet_b1,93.710,6.290,98.800,1.200,7.79,240,0.882,bicubic
tf_efficientnetv2_b1,93.710,6.290,98.820,1.180,8.14,240,0.882,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
@ -204,18 +230,19 @@ gluon_resnext50_32x4d,93.650,6.350,98.690,1.310,25.03,224,0.875,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,93.630,6.370,98.800,1.200,7.79,240,0.882,bicubic
regnety_040,93.620,6.380,98.950,1.050,20.65,224,0.875,bicubic
dpn68b,93.620,6.380,98.700,1.300,12.61,224,0.875,bicubic
hrnet_w44,93.620,6.380,98.960,1.040,67.06,224,0.875,bilinear
gluon_resnet50_v1s,93.590,6.410,98.840,1.160,25.68,224,0.875,bicubic
regnety_040,93.620,6.380,98.950,1.050,20.65,224,0.875,bicubic
repvgg_b2,93.590,6.410,99.070,0.930,89.02,224,0.875,bilinear
res2net50_26w_6s,93.590,6.410,98.750,1.250,37.05,224,0.875,bilinear
gluon_resnet50_v1s,93.590,6.410,98.840,1.160,25.68,224,0.875,bicubic
dla60_res2next,93.570,6.430,98.800,1.200,17.03,224,0.875,bilinear
tf_efficientnet_cc_b1_8e,93.570,6.430,98.690,1.310,39.72,240,0.882,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,93.520,6.480,98.600,1.400,45.21,224,0.875,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
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
@ -231,31 +258,33 @@ 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,93.260,6.740,98.700,1.300,7.33,224,0.875,bicubic
regnetx_032,93.250,6.750,98.730,1.270,15.30,224,0.875,bicubic
tv_resnet152,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear
pit_xs_distilled_224,93.240,6.760,98.820,1.180,11.00,224,0.900,bicubic
resnest26d,93.240,6.760,98.850,1.150,17.07,224,0.875,bilinear
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
res2next50,93.150,6.850,98.660,1.340,24.67,224,0.875,bilinear
tf_efficientnetv2_b0,93.060,6.940,98.700,1.300,7.14,224,0.875,bicubic
levit_128,93.050,6.950,98.690,1.310,9.21,224,0.900,bicubic
tf_mixnet_l,93.040,6.960,98.540,1.460,7.33,224,0.875,bicubic
efficientnet_b1,93.030,6.970,98.710,1.290,7.79,256,1.000,bicubic
repvgg_b1g4,93.030,6.970,98.820,1.180,39.97,224,0.875,bilinear
res2net50_14w_8s,93.030,6.970,98.700,1.300,25.06,224,0.875,bilinear
efficientnet_b1,93.030,6.970,98.710,1.290,7.79,256,1.000,bicubic
adv_inception_v3,93.010,6.990,98.490,1.510,23.83,299,0.875,bicubic
selecsls60,93.010,6.990,98.830,1.170,30.67,224,0.875,bicubic
regnety_016,93.000,7.000,98.680,1.320,11.20,224,0.875,bicubic
efficientnet_b1_pruned,92.980,7.020,98.530,1.470,6.33,240,0.882,bicubic
hardcorenas_f,92.980,7.020,98.620,1.380,8.20,224,0.875,bilinear
hrnet_w32,92.950,7.050,98.840,1.160,41.23,224,0.875,bilinear
hardcorenas_e,92.950,7.050,98.570,1.430,8.07,224,0.875,bilinear
hrnet_w32,92.950,7.050,98.840,1.160,41.23,224,0.875,bilinear
efficientnet_es,92.910,7.090,98.690,1.310,5.44,224,0.875,bicubic
gluon_resnet50_v1c,92.910,7.090,98.710,1.290,25.58,224,0.875,bicubic
pit_xs_224,92.910,7.090,98.780,1.220,10.62,224,0.900,bicubic
densenet161,92.900,7.100,98.810,1.190,28.68,224,0.875,bicubic
inception_v3,92.900,7.100,98.330,1.670,23.83,299,0.875,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
densenet161,92.900,7.100,98.810,1.190,28.68,224,0.875,bicubic
tv_resnet101,92.880,7.120,98.660,1.340,44.55,224,0.875,bilinear
tf_efficientnet_cc_b0_8e,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
@ -266,13 +295,13 @@ 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
densenet201,92.690,7.310,98.650,1.350,20.01,224,0.875,bicubic
repvgg_a2,92.680,7.320,98.520,1.480,28.21,224,0.875,bilinear
dla60,92.670,7.330,98.630,1.370,22.04,224,0.875,bilinear
legacy_seresnet50,92.670,7.330,98.650,1.350,28.09,224,0.875,bilinear
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,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic
tf_efficientnet_b0_ap,92.610,7.390,98.370,1.630,5.29,224,0.875,bicubic
vit_small_patch16_224,92.600,7.400,98.390,1.610,48.75,224,0.900,bicubic
mobilenetv2_120d,92.610,7.390,98.510,1.490,5.83,224,0.875,bicubic
hardcorenas_d,92.600,7.400,98.430,1.570,7.50,224,0.875,bilinear
vit_small_patch16_224,92.600,7.400,98.390,1.610,48.75,224,0.900,bicubic
tf_efficientnet_lite2,92.590,7.410,98.550,1.450,6.09,260,0.890,bicubic
legacy_seresnext26_32x4d,92.570,7.430,98.420,1.580,16.79,224,0.875,bicubic
skresnet34,92.570,7.430,98.520,1.480,22.28,224,0.875,bicubic
@ -280,8 +309,8 @@ gluon_resnet50_v1b,92.560,7.440,98.550,1.450,25.56,224,0.875,bicubic
regnetx_016,92.540,7.460,98.550,1.450,9.19,224,0.875,bicubic
efficientnet_b0,92.480,7.520,98.680,1.320,5.29,224,0.875,bicubic
selecsls42b,92.480,7.520,98.440,1.560,32.46,224,0.875,bicubic
gernet_s,92.440,7.560,98.500,1.500,8.17,224,0.875,bilinear
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
densenetblur121d,92.400,7.600,98.410,1.590,8.00,224,0.875,bicubic
tf_efficientnet_b0,92.400,7.600,98.470,1.530,5.29,224,0.875,bicubic
hardcorenas_c,92.330,7.670,98.340,1.660,5.52,224,0.875,bilinear
@ -307,8 +336,9 @@ semnasnet_100,91.660,8.340,98.270,1.730,3.89,224,0.875,bicubic
hardcorenas_a,91.620,8.380,98.170,1.830,5.26,224,0.875,bilinear
regnety_006,91.570,8.430,98.430,1.570,6.06,224,0.875,bicubic
mobilenetv3_rw,91.550,8.450,98.270,1.730,5.48,224,0.875,bicubic
mobilenetv3_large_100,91.480,8.520,98.320,1.680,5.48,224,0.875,bicubic
levit_128s,91.500,8.500,98.400,1.600,7.78,224,0.900,bicubic
legacy_seresnet34,91.480,8.520,98.200,1.800,21.96,224,0.875,bilinear
mobilenetv3_large_100,91.480,8.520,98.320,1.680,5.48,224,0.875,bicubic
resnet26,91.440,8.560,98.280,1.720,16.00,224,0.875,bicubic
tf_mobilenetv3_large_100,91.420,8.580,98.260,1.740,5.48,224,0.875,bilinear
tv_densenet121,91.400,8.600,98.250,1.750,7.98,224,0.875,bicubic
@ -317,8 +347,8 @@ tf_efficientnet_lite0,91.300,8.700,98.090,1.910,4.65,224,0.875,bicubic
fbnetc_100,91.270,8.730,97.830,2.170,5.57,224,0.875,bilinear
efficientnet_lite0,91.260,8.740,98.250,1.750,4.65,224,0.875,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
mnasnet_100,91.200,8.800,98.050,1.950,4.38,224,0.875,bicubic
resnet34,91.200,8.800,98.240,1.760,21.80,224,0.875,bilinear
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
@ -333,6 +363,7 @@ 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_100,90.610,9.390,97.950,2.050,4.42,224,0.875,bilinear
vgg16_bn,90.540,9.460,97.990,2.010,138.37,224,0.875,bilinear
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
pit_ti_224,90.420,9.580,98.010,1.990,4.85,224,0.900,bicubic
tf_mobilenetv3_large_075,90.320,9.680,97.870,2.130,3.99,224,0.875,bilinear

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
4 tf_efficientnet_b7_ns 97.200 2.800 99.700 0.300 66.35 600 0.949 bicubic
5 swin_large_patch4_window12_384 97.170 2.830 99.680 0.320 196.74 384 1.000 bicubic
6 swin_base_patch4_window12_384 97.120 2.880 99.780 0.220 87.90 384 1.000 bicubic
7 tf_efficientnetv2_l_in21ft1k 97.110 2.890 99.710 0.290 118.52 480 1.000 bicubic
8 tf_efficientnet_b6_ns 97.020 2.980 99.710 0.290 43.04 528 0.942 bicubic
9 dm_nfnet_f6 96.990 3.010 99.740 0.260 438.36 576 0.956 bicubic
10 ig_resnext101_32x48d 96.970 3.030 99.670 0.330 828.41 224 0.875 bilinear
11 tf_efficientnetv2_m_in21ft1k 96.970 3.030 99.610 0.390 54.14 480 1.000 bicubic
12 swin_large_patch4_window7_224 96.950 3.050 99.660 0.340 196.53 224 0.900 bicubic
cait_m48_448 96.880 3.120 99.620 0.380 356.46 448 1.000 bicubic
13 resnetv2_152x4_bitm 96.880 3.120 99.660 0.340 936.53 480 1.000 bilinear
14 cait_m48_448 96.880 3.120 99.620 0.380 356.46 448 1.000 bicubic
15 tf_efficientnet_b5_ns 96.870 3.130 99.640 0.360 30.39 456 0.934 bicubic
16 cait_m36_384 96.830 3.170 99.660 0.340 271.22 384 1.000 bicubic
17 dm_nfnet_f4 96.820 3.180 99.600 0.400 316.07 512 0.951 bicubic
20 tf_efficientnet_b4_ns 96.710 3.290 99.640 0.360 19.34 380 0.922 bicubic
21 tf_efficientnet_b8 96.700 3.300 99.530 0.470 87.41 672 0.954 bicubic
22 swin_base_patch4_window7_224 96.680 3.320 99.660 0.340 87.77 224 0.900 bicubic
23 tf_efficientnetv2_l 96.650 3.350 99.560 0.440 118.52 480 1.000 bicubic
24 cait_s36_384 96.630 3.370 99.600 0.400 68.37 384 1.000 bicubic
25 dm_nfnet_f3 96.630 3.370 99.640 0.360 254.92 416 0.940 bicubic
26 tf_efficientnet_b7 96.580 3.420 99.510 0.490 66.35 600 0.949 bicubic
27 cait_s24_384 96.570 3.430 99.550 0.450 47.06 384 1.000 bicubic
28 tf_efficientnet_b8_ap 96.550 3.450 99.540 0.460 87.41 672 0.954 bicubic
29 tf_efficientnetv2_m 96.550 3.450 99.570 0.430 54.14 480 1.000 bicubic
30 vit_deit_base_distilled_patch16_384 96.510 3.490 99.590 0.410 87.63 384 1.000 bicubic
31 dm_nfnet_f2 96.500 3.500 99.570 0.430 193.78 352 0.920 bicubic
32 resnetv2_152x2_bitm 96.500 3.500 99.620 0.380 236.34 480 1.000 bilinear
33 tf_efficientnetv2_s_in21ft1k 96.470 3.530 99.570 0.430 21.46 384 1.000 bicubic
34 ecaresnet269d 96.460 3.540 99.610 0.390 102.09 352 1.000 bicubic
35 vit_base_r50_s16_384 96.450 3.550 99.660 0.340 98.95 384 1.000 bicubic
36 ig_resnext101_32x16d 96.440 3.560 99.540 0.460 194.03 224 0.875 bilinear
37 resnetrs420 96.400 3.600 99.540 0.460 191.89 416 1.000 bicubic
dm_nfnet_f1 96.370 3.630 99.470 0.530 132.63 320 0.910 bicubic
38 tf_efficientnet_b6_ap 96.370 3.630 99.550 0.450 43.04 528 0.942 bicubic
39 dm_nfnet_f1 96.370 3.630 99.470 0.530 132.63 320 0.910 bicubic
40 resnetv2_101x3_bitm 96.360 3.640 99.600 0.400 387.93 480 1.000 bilinear
41 vit_large_patch16_384 96.360 3.640 99.630 0.370 304.72 384 1.000 bicubic
42 tf_efficientnet_b7_ap 96.350 3.650 99.590 0.410 66.35 600 0.949 bicubic
43 seresnet152d 96.310 3.690 99.510 0.490 66.84 320 1.000 bicubic
44 tf_efficientnet_b6 96.290 3.710 99.520 0.480 43.04 528 0.942 bicubic
45 efficientnetv2_rw_m 96.270 3.730 99.560 0.440 53.24 416 1.000 bicubic
46 swsl_resnext101_32x16d 96.270 3.730 99.500 0.500 194.03 224 0.875 bilinear
47 resnetrs350 96.240 3.760 99.470 0.530 163.96 384 1.000 bicubic
48 swsl_resnext101_32x8d 96.240 3.760 99.590 0.410 88.79 224 0.875 bilinear
61 resnetrs200 95.990 4.010 99.440 0.560 93.21 320 1.000 bicubic
62 tf_efficientnet_b5 95.980 4.020 99.450 0.550 30.39 456 0.934 bicubic
63 resnetrs152 95.960 4.040 99.380 0.620 86.62 320 1.000 bicubic
ig_resnext101_32x8d 95.930 4.070 99.380 0.620 88.79 224 0.875 bilinear
64 eca_nfnet_l1 95.930 4.070 99.500 0.500 41.41 320 1.000 bicubic
65 ig_resnext101_32x8d 95.930 4.070 99.380 0.620 88.79 224 0.875 bilinear
66 regnety_160 95.880 4.120 99.560 0.440 83.59 288 1.000 bicubic
67 resnet152d 95.870 4.130 99.430 0.570 60.21 320 1.000 bicubic
68 resnet101d 95.750 4.250 99.440 0.560 44.57 320 1.000 bicubic
69 vit_deit_base_distilled_patch16_224 95.750 4.250 99.280 0.720 87.34 224 0.900 bicubic
70 swin_small_patch4_window7_224 95.720 4.280 99.290 0.710 49.61 224 0.900 bicubic
71 efficientnet_v2s twins_pcpvt_large 95.710 95.720 4.290 4.280 99.380 99.490 0.620 0.510 23.94 60.99 384 224 1.000 0.900 bicubic
72 cait_s24_224 twins_svt_large 95.650 95.720 4.350 4.280 99.390 99.370 0.610 0.630 46.92 99.27 224 1.000 0.900 bicubic
73 efficientnetv2_rw_s 95.710 4.290 99.380 0.620 23.94 384 1.000 bicubic
74 tf_efficientnetv2_s 95.710 4.290 99.400 0.600 21.46 384 1.000 bicubic
75 vit_deit_base_patch16_384 95.650 4.350 99.240 0.760 86.86 384 1.000 bicubic
76 cait_s24_224 95.650 4.350 99.390 0.610 46.92 224 1.000 bicubic
77 dm_nfnet_f0 95.630 4.370 99.300 0.700 71.49 256 0.900 bicubic
78 swsl_resnext50_32x4d 95.620 4.380 99.440 0.560 25.03 224 0.875 bilinear
79 tf_efficientnet_b4 95.590 4.410 99.330 0.670 19.34 380 0.922 bicubic
80 resnest101e 95.570 4.430 99.270 0.730 48.28 256 0.875 bilinear
81 twins_svt_base 95.570 4.430 99.230 0.770 56.07 224 0.900 bicubic
82 efficientnet_b4 95.520 4.480 99.390 0.610 19.34 384 1.000 bicubic
83 tf_efficientnet_b2_ns 95.520 4.480 99.340 0.660 9.11 260 0.890 bicubic
84 resnetv2_101x1_bitm 95.510 4.490 99.510 0.490 44.54 480 1.000 bilinear
85 tresnet_xl_448 95.510 4.490 99.340 0.660 78.44 448 0.875 bilinear
86 tf_efficientnet_b4_ap 95.490 4.510 99.390 0.610 19.34 380 0.922 bicubic
eca_nfnet_l0 95.470 4.530 99.380 0.620 24.14 288 1.000 bicubic
87 regnety_032 95.470 4.530 99.320 0.680 19.44 288 1.000 bicubic
88 eca_nfnet_l0 95.470 4.530 99.380 0.620 24.14 288 1.000 bicubic
89 twins_pcpvt_base 95.460 4.540 99.390 0.610 43.83 224 0.900 bicubic
90 ssl_resnext101_32x16d 95.410 4.590 99.410 0.590 194.03 224 0.875 bilinear
91 tresnet_l_448 95.410 4.590 99.300 0.700 55.99 448 0.875 bilinear
92 nfnet_l0 95.390 4.610 99.420 0.580 35.07 288 1.000 bicubic
98 resnetrs101 95.250 4.750 99.210 0.790 63.62 288 0.940 bicubic
99 vit_large_patch32_384 95.240 4.760 99.320 0.680 306.63 384 1.000 bicubic
100 cait_xxs36_384 95.220 4.780 99.320 0.680 17.37 384 1.000 bicubic
101 levit_384 95.210 4.790 99.160 0.840 39.13 224 0.900 bicubic
102 vit_base_patch16_224 95.210 4.790 99.230 0.770 86.57 224 0.900 bicubic
103 resnet51q 95.200 4.800 99.280 0.720 35.70 288 1.000 bilinear
104 swsl_resnet50 95.200 4.800 99.390 0.610 25.56 224 0.875 bilinear
ssl_resnext101_32x4d 95.160 4.840 99.300 0.700 44.18 224 0.875 bilinear
105 ecaresnet101d 95.160 4.840 99.230 0.770 44.57 224 0.875 bicubic
106 ssl_resnext101_32x4d 95.160 4.840 99.300 0.700 44.18 224 0.875 bilinear
107 nasnetalarge 95.150 4.850 99.130 0.870 88.75 331 0.911 bicubic
108 efficientnet_b3 95.140 4.860 99.210 0.790 12.23 320 1.000 bicubic
109 tf_efficientnetv2_b3 95.120 4.880 99.200 0.800 14.36 300 0.904 bicubic
110 convit_base 95.100 4.900 99.140 0.860 86.54 224 0.875 bicubic
111 coat_lite_small 95.080 4.920 99.020 0.980 19.84 224 0.900 bicubic
112 ecaresnet50t 95.070 4.930 99.290 0.710 25.57 320 0.950 bicubic
113 tresnet_xl 95.060 4.940 99.260 0.740 78.44 224 0.875 bilinear
114 vit_deit_base_patch16_224 95.010 4.990 98.980 1.020 86.57 224 0.900 bicubic
115 tf_efficientnet_b3_ap 94.970 5.030 99.110 0.890 12.23 300 0.904 bicubic
116 visformer_small 94.960 5.040 99.210 0.790 40.22 224 0.900 bicubic
117 gernet_l 94.930 5.070 99.200 0.800 31.08 256 0.875 bilinear
118 cait_xxs24_384 94.920 5.080 99.140 0.860 12.03 384 1.000 bicubic
119 convit_small 94.920 5.080 99.110 0.890 27.78 224 0.875 bicubic
120 tf_efficientnet_b3 94.910 5.090 99.110 0.890 12.23 300 0.904 bicubic
121 tresnet_l 94.900 5.100 99.030 0.970 55.99 224 0.875 bilinear
122 mixer_b16_224_miil 94.880 5.120 99.080 0.920 59.88 224 0.875 bilinear
123 tf_efficientnet_lite4 94.870 5.130 99.090 0.910 13.01 380 0.920 bilinear
124 tf_efficientnet_b1_ns 94.860 5.140 99.250 0.750 7.79 240 0.882 bicubic
125 seresnext50_32x4d 94.820 5.180 99.130 0.870 27.56 224 0.875 bicubic
126 pit_b_224 94.790 5.210 98.820 1.180 73.76 224 0.900 bicubic
127 coat_mini 94.770 5.230 98.950 1.050 10.34 224 0.900 bicubic
128 twins_svt_small 94.770 5.230 99.080 0.920 24.06 224 0.900 bicubic
129 legacy_senet154 94.730 5.270 99.100 0.900 115.09 224 0.875 bilinear
130 pit_s_distilled_224 94.730 5.270 99.190 0.810 24.04 224 0.900 bicubic
131 gluon_resnet152_v1s 94.720 5.280 99.060 0.940 60.32 224 0.875 bicubic
132 gluon_senet154 94.710 5.290 98.970 1.030 115.09 224 0.875 bicubic
133 resnest50d_4s2x40d 94.710 5.290 99.130 0.870 30.42 224 0.875 bicubic
134 ssl_resnext50_32x4d 94.700 5.300 99.240 0.760 25.03 224 0.875 bilinear
wide_resnet50_2 94.670 5.330 99.050 0.950 68.88 224 0.875 bicubic
135 efficientnet_el 94.670 5.330 99.130 0.870 10.59 300 0.904 bicubic
136 rexnet_200 wide_resnet50_2 94.660 94.670 5.340 5.330 99.090 99.050 0.910 0.950 16.37 68.88 224 0.875 bicubic
137 tresnet_m_448 94.660 5.340 99.150 0.850 31.39 448 0.875 bilinear
138 rexnet_200 94.660 5.340 99.090 0.910 16.37 224 0.875 bicubic
139 gluon_seresnext101_64x4d 94.650 5.350 98.980 1.020 88.23 224 0.875 bicubic
140 resnest50d 94.620 5.380 99.030 0.970 27.48 224 0.875 bilinear
141 swin_tiny_patch4_window7_224 94.620 5.380 99.120 0.880 28.29 224 0.900 bicubic
142 twins_pcpvt_small 94.600 5.400 99.150 0.850 24.11 224 0.900 bicubic
143 pit_s_224 94.590 5.410 98.930 1.070 23.46 224 0.900 bicubic
144 vit_deit_small_distilled_patch16_224 94.590 5.410 99.100 0.900 22.44 224 0.900 bicubic
145 efficientnet_b3_pruned 94.580 5.420 99.070 0.930 9.86 300 0.904 bicubic
146 tnt_s_patch16_224 94.580 5.420 99.180 0.820 23.76 224 0.900 bicubic
resnetv2_50x1_bitm 94.550 5.450 99.250 0.750 25.55 480 1.000 bilinear
repvgg_b3 94.550 5.450 98.910 1.090 123.09 224 0.875 bilinear
147 gernet_m 94.550 5.450 98.930 1.070 21.14 224 0.875 bilinear
148 repvgg_b3 94.550 5.450 98.910 1.090 123.09 224 0.875 bilinear
149 resnetv2_50x1_bitm 94.550 5.450 99.250 0.750 25.55 480 1.000 bilinear
150 regnety_320 94.520 5.480 99.170 0.830 145.05 224 0.875 bicubic
151 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
152 gluon_seresnext101_32x4d 94.450 5.550 99.090 0.910 48.96 224 0.875 bicubic
153 ecaresnet101d_pruned 94.450 5.550 99.100 0.900 24.88 224 0.875 bicubic
154 gluon_resnet152_v1d 94.440 5.560 99.010 0.990 60.21 224 0.875 bicubic
155 nf_resnet50 94.410 5.590 99.100 0.900 25.56 288 0.940 bicubic
156 levit_256 94.400 5.600 99.060 0.940 18.89 224 0.900 bicubic
157 resnest50d_1s4x24d 94.390 5.610 99.070 0.930 25.68 224 0.875 bicubic
158 inception_v4 94.380 5.620 98.820 1.180 42.68 299 0.875 bicubic
159 efficientnet_b2 94.370 5.630 99.050 0.950 9.11 288 1.000 bicubic
162 inception_resnet_v2 94.340 5.660 98.800 1.200 55.84 299 0.897 bicubic
163 ssl_resnet50 94.310 5.690 99.150 0.850 25.56 224 0.875 bilinear
164 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
165 tf_efficientnet_b2_ap 94.270 5.730 98.950 1.050 9.11 260 0.890 bicubic
166 rexnet_150 94.270 5.730 99.080 0.920 9.73 224 0.875 bicubic
167 mixnet_xl 94.230 5.770 98.820 1.180 11.90 224 0.875 bicubic
168 regnetx_320 94.210 5.790 99.050 0.950 107.81 224 0.875 bicubic
169 tf_efficientnet_b2 94.210 5.790 99.030 0.970 9.11 260 0.890 bicubic
170 dpn92 94.190 5.810 98.930 1.070 37.67 224 0.875 bicubic
171 ecaresnet50d 94.190 5.810 99.020 0.980 25.58 224 0.875 bicubic
gluon_seresnext50_32x4d 94.170 5.830 98.910 1.090 27.56 224 0.875 bicubic
gluon_resnet101_v1s 94.170 5.830 99.010 0.990 44.67 224 0.875 bicubic
172 gluon_resnet101_v1d 94.170 5.830 98.940 1.060 44.57 224 0.875 bicubic
173 ecaresnetlight gluon_resnet101_v1s 94.140 94.170 5.860 5.830 98.950 99.010 1.050 0.990 30.16 44.67 224 0.875 bicubic
174 gluon_seresnext50_32x4d 94.170 5.830 98.910 1.090 27.56 224 0.875 bicubic
175 regnety_064 94.140 5.860 99.030 0.970 30.58 224 0.875 bicubic
176 ecaresnetlight 94.140 5.860 98.950 1.050 30.16 224 0.875 bicubic
177 ens_adv_inception_resnet_v2 94.130 5.870 98.790 1.210 55.84 299 0.897 bicubic
178 legacy_seresnext101_32x4d 94.130 5.870 98.970 1.030 48.96 224 0.875 bilinear
179 tf_efficientnet_lite3 94.130 5.870 98.960 1.040 8.20 300 0.904 bilinear
182 efficientnet_el_pruned 94.090 5.910 99.010 0.990 10.59 300 0.904 bicubic
183 seresnet50 94.080 5.920 98.970 1.030 28.09 224 0.875 bicubic
184 resnet50d 94.070 5.930 98.920 1.080 25.58 224 0.875 bicubic
185 tf_efficientnetv2_b2 94.070 5.930 98.930 1.070 10.10 260 0.890 bicubic
186 gluon_resnet152_v1b 94.030 5.970 98.740 1.260 60.19 224 0.875 bicubic
187 hrnet_w48 94.030 5.970 99.040 0.960 77.47 224 0.875 bilinear
188 resnetrs50 94.020 5.980 98.850 1.150 35.69 224 0.910 bicubic
192 vit_deit_small_patch16_224 94.000 6.000 98.960 1.040 22.05 224 0.900 bicubic
193 dpn107 93.960 6.040 98.840 1.160 86.92 224 0.875 bicubic
194 skresnext50_32x4d 93.950 6.050 98.820 1.180 27.48 224 0.875 bicubic
195 cait_xxs36_224 93.940 6.060 98.890 1.110 17.30 224 1.000 bicubic
196 dpn98 93.940 6.060 98.920 1.080 61.57 224 0.875 bicubic
197 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
nf_regnet_b1 93.890 6.110 98.750 1.250 10.22 288 0.900 bicubic
regnety_080 93.890 6.110 99.000 1.000 39.18 224 0.875 bicubic
198 xception71 93.890 6.110 98.950 1.050 42.34 299 0.903 bicubic
199 gluon_resnet152_v1c regnety_080 93.880 93.890 6.120 6.110 98.800 99.000 1.200 1.000 60.21 39.18 224 0.875 bicubic
200 nf_regnet_b1 93.890 6.110 98.750 1.250 10.22 288 0.900 bicubic
201 regnetx_160 93.880 6.120 99.090 0.910 54.28 224 0.875 bicubic
202 gluon_resnet152_v1c 93.880 6.120 98.800 1.200 60.21 224 0.875 bicubic
203 cspresnet50 93.860 6.140 98.870 1.130 21.62 256 0.887 bilinear
204 ese_vovnet39b 93.850 6.150 98.900 1.100 24.57 224 0.875 bicubic
205 resnext50_32x4d 93.840 6.160 98.830 1.170 25.03 224 0.875 bicubic
206 hrnet_w64 93.830 6.170 98.930 1.070 128.06 224 0.875 bilinear
repvgg_b2g4 93.820 6.180 98.930 1.070 61.76 224 0.875 bilinear
207 ecaresnet50d_pruned 93.820 6.180 99.000 1.000 19.94 224 0.875 bicubic
208 repvgg_b2g4 93.820 6.180 98.930 1.070 61.76 224 0.875 bilinear
209 resnext50d_32x4d 93.810 6.190 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
210 dla169 93.800 6.200 98.840 1.160 53.39 224 0.875 bilinear
211 efficientnet_b2_pruned 93.800 6.200 98.910 1.090 8.31 260 0.890 bicubic
212 regnetx_080 93.790 6.210 98.910 1.090 39.57 224 0.875 bicubic
213 resnext101_32x8d 93.770 6.230 98.950 1.050 88.79 224 0.875 bilinear
cspresnext50 93.760 6.240 98.840 1.160 20.57 224 0.875 bilinear
dpn131 93.760 6.240 98.800 1.200 79.25 224 0.875 bicubic
214 gluon_resnet101_v1b 93.760 6.240 98.700 1.300 44.55 224 0.875 bicubic
215 xception65 93.760 6.240 98.860 1.140 39.92 299 0.903 bicubic
216 cspresnext50 93.760 6.240 98.840 1.160 20.57 224 0.875 bilinear
217 dpn131 93.760 6.240 98.800 1.200 79.25 224 0.875 bicubic
218 efficientnet_em 93.740 6.260 98.930 1.070 6.90 240 0.882 bicubic
219 tf_efficientnet_b0_ns 93.740 6.260 98.980 1.020 5.29 224 0.875 bicubic
220 wide_resnet101_2 93.730 6.270 98.810 1.190 126.89 224 0.875 bilinear
221 hrnet_w40 93.710 6.290 98.800 1.200 57.56 224 0.875 bilinear
222 tf_efficientnet_b1 levit_192 93.710 6.290 98.800 98.790 1.200 1.210 7.79 10.95 240 224 0.882 0.900 bicubic
223 resnetblur50 93.710 6.290 98.810 1.190 25.56 224 0.875 bicubic
224 tf_efficientnet_b1 93.710 6.290 98.800 1.200 7.79 240 0.882 bicubic
225 tf_efficientnetv2_b1 93.710 6.290 98.820 1.180 8.14 240 0.882 bicubic
226 gluon_resnet101_v1c 93.690 6.310 98.760 1.240 44.57 224 0.875 bicubic
227 regnetx_040 93.680 6.320 98.940 1.060 22.12 224 0.875 bicubic
228 rexnet_130 93.670 6.330 98.710 1.290 7.56 224 0.875 bicubic
230 xception 93.640 6.360 98.770 1.230 22.86 299 0.897 bicubic
231 regnetx_064 93.630 6.370 99.050 0.950 26.21 224 0.875 bicubic
232 tf_efficientnet_b1_ap 93.630 6.370 98.800 1.200 7.79 240 0.882 bicubic
regnety_040 93.620 6.380 98.950 1.050 20.65 224 0.875 bicubic
233 dpn68b 93.620 6.380 98.700 1.300 12.61 224 0.875 bicubic
234 hrnet_w44 93.620 6.380 98.960 1.040 67.06 224 0.875 bilinear
235 gluon_resnet50_v1s regnety_040 93.590 93.620 6.410 6.380 98.840 98.950 1.160 1.050 25.68 20.65 224 0.875 bicubic
236 repvgg_b2 93.590 6.410 99.070 0.930 89.02 224 0.875 bilinear
237 res2net50_26w_6s 93.590 6.410 98.750 1.250 37.05 224 0.875 bilinear
238 gluon_resnet50_v1s 93.590 6.410 98.840 1.160 25.68 224 0.875 bicubic
239 dla60_res2next 93.570 6.430 98.800 1.200 17.03 224 0.875 bilinear
240 tf_efficientnet_cc_b1_8e 93.570 6.430 98.690 1.310 39.72 240 0.882 bicubic
241 gluon_inception_v3 93.540 6.460 98.830 1.170 23.83 299 0.875 bicubic
242 dla102x 93.530 6.470 98.850 1.150 26.31 224 0.875 bilinear
243 gluon_resnet50_v1d 93.530 6.470 98.710 1.290 25.58 224 0.875 bicubic
244 res2net101_26w_4s 93.520 6.480 98.600 1.400 45.21 224 0.875 bilinear
245 coat_tiny 93.510 6.490 98.690 1.310 5.50 224 0.900 bicubic
246 selecsls60b 93.500 6.500 98.840 1.160 32.77 224 0.875 bicubic
247 cait_xxs24_224 93.490 6.510 98.770 1.230 11.96 224 1.000 bicubic
248 xception41 93.480 6.520 98.750 1.250 26.97 299 0.903 bicubic
258 legacy_seresnet101 93.260 6.740 98.740 1.260 49.33 224 0.875 bilinear
259 mixnet_l 93.260 6.740 98.700 1.300 7.33 224 0.875 bicubic
260 regnetx_032 93.250 6.750 98.730 1.270 15.30 224 0.875 bicubic
261 tv_resnet152 93.240 6.760 98.750 1.250 60.19 224 0.875 bilinear
262 pit_xs_distilled_224 93.240 6.760 98.820 1.180 11.00 224 0.900 bicubic
263 resnest26d 93.240 6.760 98.850 1.150 17.07 224 0.875 bilinear
tv_resnet152 93.240 6.760 98.750 1.250 60.19 224 0.875 bilinear
264 tf_inception_v3 93.200 6.800 98.480 1.520 23.83 299 0.875 bicubic
265 dla60x 93.190 6.810 98.710 1.290 17.35 224 0.875 bilinear
266 res2net50_26w_4s 93.180 6.820 98.670 1.330 25.70 224 0.875 bilinear
267 tf_efficientnet_em 93.170 6.830 98.670 1.330 6.90 240 0.882 bicubic
268 res2next50 93.150 6.850 98.660 1.340 24.67 224 0.875 bilinear
269 tf_efficientnetv2_b0 93.060 6.940 98.700 1.300 7.14 224 0.875 bicubic
270 levit_128 93.050 6.950 98.690 1.310 9.21 224 0.900 bicubic
271 tf_mixnet_l 93.040 6.960 98.540 1.460 7.33 224 0.875 bicubic
efficientnet_b1 93.030 6.970 98.710 1.290 7.79 256 1.000 bicubic
272 repvgg_b1g4 93.030 6.970 98.820 1.180 39.97 224 0.875 bilinear
273 res2net50_14w_8s 93.030 6.970 98.700 1.300 25.06 224 0.875 bilinear
274 efficientnet_b1 93.030 6.970 98.710 1.290 7.79 256 1.000 bicubic
275 adv_inception_v3 93.010 6.990 98.490 1.510 23.83 299 0.875 bicubic
276 selecsls60 93.010 6.990 98.830 1.170 30.67 224 0.875 bicubic
277 regnety_016 93.000 7.000 98.680 1.320 11.20 224 0.875 bicubic
278 efficientnet_b1_pruned 92.980 7.020 98.530 1.470 6.33 240 0.882 bicubic
279 hardcorenas_f 92.980 7.020 98.620 1.380 8.20 224 0.875 bilinear
hrnet_w32 92.950 7.050 98.840 1.160 41.23 224 0.875 bilinear
280 hardcorenas_e 92.950 7.050 98.570 1.430 8.07 224 0.875 bilinear
281 hrnet_w32 92.950 7.050 98.840 1.160 41.23 224 0.875 bilinear
282 efficientnet_es 92.910 7.090 98.690 1.310 5.44 224 0.875 bicubic
283 gluon_resnet50_v1c 92.910 7.090 98.710 1.290 25.58 224 0.875 bicubic
284 pit_xs_224 92.910 7.090 98.780 1.220 10.62 224 0.900 bicubic
densenet161 92.900 7.100 98.810 1.190 28.68 224 0.875 bicubic
inception_v3 92.900 7.100 98.330 1.670 23.83 299 0.875 bicubic
285 tv_resnext50_32x4d 92.900 7.100 98.720 1.280 25.03 224 0.875 bilinear
286 inception_v3 92.900 7.100 98.330 1.670 23.83 299 0.875 bicubic
287 densenet161 92.900 7.100 98.810 1.190 28.68 224 0.875 bicubic
288 tv_resnet101 92.880 7.120 98.660 1.340 44.55 224 0.875 bilinear
289 tf_efficientnet_cc_b0_8e 92.870 7.130 98.460 1.540 24.01 224 0.875 bicubic
290 coat_lite_tiny 92.850 7.150 98.640 1.360 5.72 224 0.900 bicubic
295 hrnet_w18 92.760 7.240 98.660 1.340 21.30 224 0.875 bilinear
296 densenet201 92.690 7.310 98.650 1.350 20.01 224 0.875 bicubic
297 repvgg_a2 92.680 7.320 98.520 1.480 28.21 224 0.875 bilinear
dla60 92.670 7.330 98.630 1.370 22.04 224 0.875 bilinear
298 legacy_seresnet50 92.670 7.330 98.650 1.350 28.09 224 0.875 bilinear
299 dla60 92.670 7.330 98.630 1.370 22.04 224 0.875 bilinear
300 resnet34d 92.640 7.360 98.420 1.580 21.82 224 0.875 bicubic
mobilenetv2_120d 92.610 7.390 98.510 1.490 5.83 224 0.875 bicubic
301 tf_efficientnet_b0_ap 92.610 7.390 98.370 1.630 5.29 224 0.875 bicubic
302 vit_small_patch16_224 mobilenetv2_120d 92.600 92.610 7.400 7.390 98.390 98.510 1.610 1.490 48.75 5.83 224 0.900 0.875 bicubic
303 hardcorenas_d 92.600 7.400 98.430 1.570 7.50 224 0.875 bilinear
304 vit_small_patch16_224 92.600 7.400 98.390 1.610 48.75 224 0.900 bicubic
305 tf_efficientnet_lite2 92.590 7.410 98.550 1.450 6.09 260 0.890 bicubic
306 legacy_seresnext26_32x4d 92.570 7.430 98.420 1.580 16.79 224 0.875 bicubic
307 skresnet34 92.570 7.430 98.520 1.480 22.28 224 0.875 bicubic
309 regnetx_016 92.540 7.460 98.550 1.450 9.19 224 0.875 bicubic
310 efficientnet_b0 92.480 7.520 98.680 1.320 5.29 224 0.875 bicubic
311 selecsls42b 92.480 7.520 98.440 1.560 32.46 224 0.875 bicubic
gernet_s 92.440 7.560 98.500 1.500 8.17 224 0.875 bilinear
312 seresnext26d_32x4d 92.440 7.560 98.540 1.460 16.81 224 0.875 bicubic
313 gernet_s 92.440 7.560 98.500 1.500 8.17 224 0.875 bilinear
314 densenetblur121d 92.400 7.600 98.410 1.590 8.00 224 0.875 bicubic
315 tf_efficientnet_b0 92.400 7.600 98.470 1.530 5.29 224 0.875 bicubic
316 hardcorenas_c 92.330 7.670 98.340 1.660 5.52 224 0.875 bilinear
336 hardcorenas_a 91.620 8.380 98.170 1.830 5.26 224 0.875 bilinear
337 regnety_006 91.570 8.430 98.430 1.570 6.06 224 0.875 bicubic
338 mobilenetv3_rw 91.550 8.450 98.270 1.730 5.48 224 0.875 bicubic
339 mobilenetv3_large_100 levit_128s 91.480 91.500 8.520 8.500 98.320 98.400 1.680 1.600 5.48 7.78 224 0.875 0.900 bicubic
340 legacy_seresnet34 91.480 8.520 98.200 1.800 21.96 224 0.875 bilinear
341 mobilenetv3_large_100 91.480 8.520 98.320 1.680 5.48 224 0.875 bicubic
342 resnet26 91.440 8.560 98.280 1.720 16.00 224 0.875 bicubic
343 tf_mobilenetv3_large_100 91.420 8.580 98.260 1.740 5.48 224 0.875 bilinear
344 tv_densenet121 91.400 8.600 98.250 1.750 7.98 224 0.875 bicubic
347 fbnetc_100 91.270 8.730 97.830 2.170 5.57 224 0.875 bilinear
348 efficientnet_lite0 91.260 8.740 98.250 1.750 4.65 224 0.875 bicubic
349 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
350 mnasnet_100 91.200 8.800 98.050 1.950 4.38 224 0.875 bicubic
351 resnet34 91.200 8.800 98.240 1.760 21.80 224 0.875 bilinear
352 regnetx_008 91.180 8.820 98.380 1.620 7.26 224 0.875 bicubic
353 hrnet_w18_small_v2 91.170 8.830 98.340 1.660 15.60 224 0.875 bilinear
354 mixer_b16_224 91.140 8.860 97.400 2.600 59.88 224 0.875 bicubic
363 ssl_resnet18 90.700 9.300 98.020 1.980 11.69 224 0.875 bilinear
364 spnasnet_100 90.610 9.390 97.950 2.050 4.42 224 0.875 bilinear
365 vgg16_bn 90.540 9.460 97.990 2.010 138.37 224 0.875 bilinear
366 convit_tiny 90.530 9.470 98.210 1.790 5.71 224 0.875 bicubic
367 ghostnet_100 90.440 9.560 97.830 2.170 5.18 224 0.875 bilinear
368 pit_ti_224 90.420 9.580 98.010 1.990 4.85 224 0.900 bicubic
369 tf_mobilenetv3_large_075 90.320 9.680 97.870 2.130 3.99 224 0.875 bilinear

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results/results-imagenet-r.csv
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model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_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,+7
ig_resnext101_32x32d,79.457,20.543,89.183,10.817,468.53,224,0.875,bilinear,-17.323,-10.347,+13
ig_resnext101_32x16d,78.837,21.163,88.480,11.520,194.03,224,0.875,bilinear,-17.603,-11.060,+27
ig_resnext101_32x48d,79.650,20.350,89.393,10.607,828.41,224,0.875,bilinear,-17.320,-10.277,+8
ig_resnext101_32x32d,79.457,20.543,89.183,10.817,468.53,224,0.875,bilinear,-17.323,-10.347,+15
ig_resnext101_32x16d,78.837,21.163,88.480,11.520,194.03,224,0.875,bilinear,-17.603,-11.060,+32
tf_efficientnet_l2_ns_475,76.480,23.520,88.653,11.347,480.31,475,0.936,bicubic,-21.270,-11.167,-2
swsl_resnext101_32x16d,76.303,23.697,87.733,12.267,194.03,224,0.875,bilinear,-19.967,-11.767,+34
ig_resnext101_32x8d,75.813,24.187,86.200,13.800,88.79,224,0.875,bilinear,-20.117,-13.300,+51
swsl_resnext101_32x8d,75.590,24.410,86.937,13.063,88.79,224,0.875,bilinear,-20.650,-12.653,+34
swsl_resnext101_32x16d,76.303,23.697,87.733,12.267,194.03,224,0.875,bilinear,-19.967,-11.767,+40
ig_resnext101_32x8d,75.813,24.187,86.200,13.800,88.79,224,0.875,bilinear,-20.117,-13.180,+58
swsl_resnext101_32x8d,75.590,24.410,86.937,13.063,88.79,224,0.875,bilinear,-20.650,-12.653,+40
tf_efficientnet_l2_ns,74.650,25.350,87.543,12.457,480.31,800,0.960,bicubic,-23.130,-12.347,-7
swsl_resnext101_32x4d,72.660,27.340,85.157,14.843,44.18,224,0.875,bilinear,-23.390,-14.373,+42
swsl_resnext50_32x4d,68.977,31.023,82.810,17.190,25.03,224,0.875,bilinear,-26.643,-16.630,+58
swsl_resnet50,68.297,31.703,83.313,16.687,25.56,224,0.875,bilinear,-26.903,-16.077,+79
swsl_resnext101_32x4d,72.660,27.340,85.157,14.843,44.18,224,0.875,bilinear,-23.390,-14.373,+48
swsl_resnext50_32x4d,68.977,31.023,82.810,17.190,25.03,224,0.875,bilinear,-26.643,-16.630,+67
swsl_resnet50,68.297,31.703,83.313,16.687,25.56,224,0.875,bilinear,-26.903,-16.077,+92
tf_efficientnet_b7_ns,67.510,32.490,81.383,18.617,66.35,600,0.949,bicubic,-29.690,-18.317,-9
swin_large_patch4_window12_384,66.283,33.717,79.783,20.217,196.74,384,1.000,bicubic,-30.887,-19.897,-9
tf_efficientnet_b6_ns,65.587,34.413,79.553,20.447,43.04,528,0.942,bicubic,-31.433,-20.157,-8
swin_large_patch4_window7_224,63.870,36.130,78.180,21.820,196.53,224,0.900,bicubic,-33.080,-21.480,-6
tf_efficientnet_b6_ns,65.587,34.413,79.553,20.447,43.04,528,0.942,bicubic,-31.433,-20.157,-7
swin_large_patch4_window7_224,63.870,36.130,78.180,21.820,196.53,224,0.900,bicubic,-33.080,-21.480,-4
swin_base_patch4_window12_384,63.470,36.530,78.063,21.937,87.90,384,1.000,bicubic,-33.650,-21.717,-11
tf_efficientnet_b5_ns,63.047,36.953,77.777,22.223,30.39,456,0.934,bicubic,-33.823,-21.863,-5
tf_efficientnet_b4_ns,61.230,38.770,76.173,23.827,19.34,380,0.922,bicubic,-35.480,-23.467,-1
swin_base_patch4_window7_224,59.537,40.463,74.247,25.753,87.77,224,0.900,bicubic,-37.143,-25.413,0
tf_efficientnet_b8_ap,57.830,42.170,72.957,27.043,87.41,672,0.954,bicubic,-38.720,-26.583,+4
cait_m48_448,57.470,42.530,71.860,28.140,356.46,448,1.000,bicubic,-39.410,-27.760,-11
tf_efficientnet_b5_ns,63.047,36.953,77.777,22.223,30.39,456,0.934,bicubic,-33.823,-21.863,-3
tf_efficientnet_b4_ns,61.230,38.770,76.173,23.827,19.34,380,0.922,bicubic,-35.480,-23.467,+1
tf_efficientnetv2_l_in21ft1k,60.953,39.047,75.847,24.153,118.52,480,1.000,bicubic,-36.157,-23.863,-13
swin_base_patch4_window7_224,59.537,40.463,74.247,25.753,87.77,224,0.900,bicubic,-37.143,-25.413,+1
tf_efficientnetv2_m_in21ft1k,58.647,41.353,73.983,26.017,54.14,480,1.000,bicubic,-38.323,-25.627,-11
tf_efficientnet_b8_ap,57.830,42.170,72.957,27.043,87.41,672,0.954,bicubic,-38.720,-26.583,+5
cait_m48_448,57.470,42.530,71.860,28.140,356.46,448,1.000,bicubic,-39.410,-27.800,-10
cait_m36_384,57.467,42.533,72.313,27.687,271.22,384,1.000,bicubic,-39.363,-27.347,-9
tf_efficientnet_b3_ns,57.417,42.583,72.387,27.613,12.23,300,0.904,bicubic,-38.683,-27.093,+23
vit_large_patch16_384,54.750,45.250,70.007,29.993,304.72,384,1.000,bicubic,-41.610,-29.623,+11
vit_base_r50_s16_384,54.400,45.600,69.560,30.440,98.95,384,1.000,bicubic,-42.050,-30.100,+4
resnetv2_152x4_bitm,54.263,45.737,70.137,29.863,936.53,480,1.000,bilinear,-42.617,-29.523,-15
dm_nfnet_f6,54.073,45.927,69.110,30.890,438.36,576,0.956,bicubic,-42.917,-30.630,-20
tf_efficientnet_b5_ap,53.870,46.130,69.160,30.840,30.39,456,0.934,bicubic,-42.210,-30.380,+19
tf_efficientnet_b3_ns,57.417,42.583,72.387,27.613,12.23,300,0.904,bicubic,-38.683,-27.093,+27
vit_large_patch16_384,54.750,45.250,70.007,29.993,304.72,384,1.000,bicubic,-41.610,-29.623,+14
vit_base_r50_s16_384,54.400,45.600,69.560,30.440,98.95,384,1.000,bicubic,-42.050,-30.100,+7
resnetv2_152x4_bitm,54.263,45.737,70.137,29.863,936.53,480,1.000,bilinear,-42.617,-29.483,-16
dm_nfnet_f6,54.073,45.927,69.110,30.890,438.36,576,0.956,bicubic,-42.917,-30.630,-21
tf_efficientnet_b5_ap,53.870,46.130,69.160,30.840,30.39,456,0.934,bicubic,-42.210,-30.380,+23
dm_nfnet_f5,53.773,46.227,68.500,31.500,377.21,544,0.954,bicubic,-42.937,-31.180,-13
tf_efficientnet_b2_ns,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+42
tf_efficientnet_b6_ap,53.560,46.440,68.550,31.450,43.04,528,0.942,bicubic,-42.810,-31.000,+2
cait_s36_384,53.550,46.450,68.000,32.000,68.37,384,1.000,bicubic,-43.080,-31.600,-12
tf_efficientnet_b2_ns,53.600,46.400,70.270,29.730,9.11,260,0.890,bicubic,-41.920,-29.070,+50
tf_efficientnet_b6_ap,53.560,46.440,68.550,31.450,43.04,528,0.942,bicubic,-42.810,-30.920,+4
cait_s36_384,53.550,46.450,68.000,32.000,68.37,384,1.000,bicubic,-43.080,-31.600,-11
tf_efficientnet_b8,53.410,46.590,69.090,30.910,87.41,672,0.954,bicubic,-43.290,-30.440,-15
tf_efficientnet_b7_ap,53.260,46.740,68.873,31.127,66.35,600,0.949,bicubic,-43.090,-30.717,+2
dm_nfnet_f3,53.190,46.810,68.083,31.917,254.92,416,0.940,bicubic,-43.440,-31.557,-14
tf_efficientnet_b4_ap,53.090,46.910,68.210,31.790,19.34,380,0.922,bicubic,-42.400,-31.180,+39
tf_efficientnet_b7_ap,53.260,46.740,68.873,31.127,66.35,600,0.949,bicubic,-43.090,-30.717,+5
dm_nfnet_f3,53.190,46.810,68.083,31.917,254.92,416,0.940,bicubic,-43.440,-31.557,-13
tf_efficientnetv2_s_in21ft1k,53.150,46.850,69.000,31.000,21.46,384,1.000,bicubic,-43.320,-30.570,-6
tf_efficientnet_b4_ap,53.090,46.910,68.210,31.790,19.34,380,0.922,bicubic,-42.400,-31.180,+46
tf_efficientnet_b7,52.393,47.607,68.233,31.767,66.35,600,0.949,bicubic,-44.187,-31.277,-15
swsl_resnet18,52.327,47.673,70.480,29.520,11.69,224,0.875,bilinear,-38.763,-27.730,+289
dm_nfnet_f4,52.260,47.740,67.120,32.880,316.07,512,0.951,bicubic,-44.560,-32.480,-25
vit_deit_base_distilled_patch16_384,52.257,47.743,67.733,32.267,87.63,384,1.000,bicubic,-44.253,-31.857,-15
cait_s24_384,51.783,48.217,66.313,33.687,47.06,384,1.000,bicubic,-44.787,-33.237,-18
tf_efficientnetv2_l,52.377,47.623,67.237,32.763,118.52,480,1.000,bicubic,-44.273,-32.323,-19
swsl_resnet18,52.327,47.673,70.480,29.520,11.69,224,0.875,bilinear,-38.763,-27.730,+315
efficientnetv2_rw_m,52.323,47.677,67.210,32.790,53.24,416,1.000,bicubic,-43.947,-32.350,+1
dm_nfnet_f4,52.260,47.740,67.120,32.880,316.07,512,0.951,bicubic,-44.560,-32.480,-28
vit_deit_base_distilled_patch16_384,52.257,47.743,67.733,32.267,87.63,384,1.000,bicubic,-44.253,-31.857,-16
cait_s24_384,51.783,48.217,66.313,33.687,47.06,384,1.000,bicubic,-44.787,-33.237,-20
ecaresnet269d,51.670,48.330,66.047,33.953,102.09,352,1.000,bicubic,-44.790,-33.563,-14
vit_base_patch16_224_miil,51.557,48.443,65.207,34.793,86.54,224,0.875,bilinear,-44.473,-34.143,+9
pit_b_distilled_224,51.153,48.847,66.770,33.230,74.79,224,0.900,bicubic,-44.917,-32.610,+4
resnetv2_152x2_bitm,51.040,48.960,68.527,31.473,236.34,480,1.000,bilinear,-45.460,-31.093,-18
vit_base_patch16_384,50.883,49.117,65.270,34.730,86.86,384,1.000,bicubic,-45.307,-34.260,-5
tf_efficientnet_b1_ns,50.883,49.117,67.910,32.090,7.79,240,0.882,bicubic,-43.977,-31.340,+58
vit_large_patch16_224,50.877,49.123,66.227,33.773,304.33,224,0.900,bicubic,-44.413,-33.083,+36
efficientnet_b4,50.510,49.490,65.703,34.297,19.34,384,1.000,bicubic,-45.010,-33.687,+22
ssl_resnext101_32x16d,50.257,49.743,66.033,33.967,194.03,224,0.875,bilinear,-45.153,-33.377,+28
cait_s24_224,50.243,49.757,65.027,34.973,46.92,224,1.000,bicubic,-45.407,-34.363,+14
vit_base_patch16_224_miil,51.557,48.443,65.207,34.793,86.54,224,0.875,bilinear,-44.473,-34.143,+10
pit_b_distilled_224,51.153,48.847,66.770,33.230,74.79,224,0.900,bicubic,-44.917,-32.610,+5
resnetv2_152x2_bitm,51.040,48.960,68.527,31.473,236.34,480,1.000,bilinear,-45.460,-31.093,-19
tf_efficientnet_b1_ns,50.883,49.117,67.910,32.090,7.79,240,0.882,bicubic,-43.977,-31.340,+72
vit_base_patch16_384,50.883,49.117,65.270,34.730,86.86,384,1.000,bicubic,-45.307,-34.260,-4
vit_large_patch16_224,50.877,49.123,66.227,33.773,304.33,224,0.900,bicubic,-44.413,-33.083,+42
tf_efficientnetv2_m,50.557,49.443,66.010,33.990,54.14,480,1.000,bicubic,-45.993,-33.560,-26
efficientnet_b4,50.510,49.490,65.703,34.297,19.34,384,1.000,bicubic,-45.010,-33.687,+26
ssl_resnext101_32x16d,50.257,49.743,66.033,33.967,194.03,224,0.875,bilinear,-45.153,-33.377,+33
cait_s24_224,50.243,49.757,65.027,34.973,46.92,224,1.000,bicubic,-45.407,-34.213,+18
resnest269e,50.153,49.847,64.670,35.330,110.93,416,0.928,bicubic,-45.967,-34.850,-8
vit_deit_base_distilled_patch16_224,50.063,49.937,66.227,33.773,87.34,224,0.900,bicubic,-45.687,-33.053,+9
tf_efficientnet_b3_ap,50.057,49.943,65.210,34.790,12.23,300,0.904,bicubic,-44.913,-33.900,+44
tf_efficientnet_b3_ap,50.057,49.943,65.210,34.790,12.23,300,0.904,bicubic,-44.913,-33.900,+54
resnest200e,49.873,50.127,64.743,35.257,70.20,320,0.909,bicubic,-46.197,-34.737,-6
resnetv2_101x3_bitm,49.823,50.177,66.917,33.083,387.93,480,1.000,bilinear,-46.537,-32.683,-22
resnetv2_101x3_bitm,49.823,50.177,66.917,33.083,387.93,480,1.000,bilinear,-46.537,-32.683,-23
cait_xs24_384,49.527,50.473,64.900,35.100,26.67,384,1.000,bicubic,-46.483,-34.530,-4
tf_efficientnet_b5,49.510,50.490,65.657,34.343,30.39,456,0.934,bicubic,-46.470,-33.793,-3
resnet200d,49.470,50.530,64.330,35.670,64.69,320,1.000,bicubic,-46.640,-35.130,-14
resnest101e,49.367,50.633,65.587,34.413,48.28,256,0.875,bilinear,-46.203,-33.683,+10
resnest101e,49.367,50.633,65.587,34.413,48.28,256,0.875,bilinear,-46.203,-33.683,+13
resnet152d,49.253,50.747,64.413,35.587,60.21,320,1.000,bicubic,-46.617,-35.017,-1
seresnet152d,49.247,50.753,64.170,35.830,66.84,320,1.000,bicubic,-47.063,-35.340,-25
ssl_resnext101_32x8d,49.067,50.933,65.480,34.520,88.79,224,0.875,bilinear,-46.273,-33.840,+20
repvgg_b3,48.917,51.083,64.887,35.113,123.09,224,0.875,bilinear,-45.633,-34.023,+61
resnetrs420,48.857,51.143,63.427,36.573,191.89,416,1.000,bicubic,-47.543,-36.113,-34
dm_nfnet_f2,48.623,51.377,63.537,36.463,193.78,352,0.920,bicubic,-47.877,-36.033,-40
efficientnet_v2s,48.603,51.397,63.840,36.160,23.94,384,1.000,bicubic,-47.107,-35.540,-3
efficientnet_b3,48.563,51.437,64.250,35.750,12.23,320,1.000,bicubic,-46.577,-34.960,+26
ecaresnet101d,48.527,51.473,64.100,35.900,44.57,224,0.875,bicubic,-46.633,-35.200,+23
repvgg_b3g4,48.310,51.690,64.800,35.200,83.83,224,0.875,bilinear,-46.180,-34.220,+58
vit_large_patch32_384,48.250,51.750,61.830,38.170,306.63,384,1.000,bicubic,-46.990,-37.490,+16
resnetrs350,48.050,51.950,62.653,37.347,163.96,384,1.000,bicubic,-48.190,-36.817,-32
repvgg_b2g4,47.787,52.213,64.390,35.610,61.76,224,0.875,bilinear,-46.033,-34.610,+109
eca_nfnet_l1,47.663,52.337,62.767,37.233,41.41,320,1.000,bicubic,-48.267,-36.613,-16
pit_s_distilled_224,47.543,52.457,63.493,36.507,24.04,224,0.900,bicubic,-47.187,-35.697,+33
resnest50d_4s2x40d,47.483,52.517,63.807,36.193,30.42,224,0.875,bicubic,-47.227,-35.323,+35
efficientnet_b3_pruned,47.447,52.553,62.793,37.207,9.86,300,0.904,bicubic,-47.133,-36.277,+45
vit_base_patch16_224,47.340,52.660,61.607,38.393,86.57,224,0.900,bicubic,-47.870,-37.623,+11
tresnet_m,47.230,52.770,61.993,38.007,31.39,224,0.875,bilinear,-48.150,-37.157,+2
tf_efficientnet_b6,47.213,52.787,63.110,36.890,43.04,528,0.942,bicubic,-49.077,-36.410,-42
ssl_resnext101_32x4d,47.177,52.823,63.367,36.633,44.18,224,0.875,bilinear,-47.983,-35.863,+10
resnetrs270,47.107,52.893,62.010,37.990,129.86,352,1.000,bicubic,-48.953,-37.480,-32
seresnet152d,49.247,50.753,64.170,35.830,66.84,320,1.000,bicubic,-47.063,-35.340,-26
ssl_resnext101_32x8d,49.067,50.933,65.480,34.520,88.79,224,0.875,bilinear,-46.273,-33.840,+25
repvgg_b3,48.917,51.083,64.887,35.113,123.09,224,0.875,bilinear,-45.633,-34.023,+77
resnetrs420,48.857,51.143,63.427,36.573,191.89,416,1.000,bicubic,-47.543,-36.113,-35
dm_nfnet_f2,48.623,51.377,63.537,36.463,193.78,352,0.920,bicubic,-47.877,-36.033,-42
efficientnetv2_rw_s,48.603,51.397,63.840,36.160,23.94,384,1.000,bicubic,-47.107,-35.540,-1
efficientnet_b3,48.563,51.437,64.250,35.750,12.23,320,1.000,bicubic,-46.577,-34.960,+33
ecaresnet101d,48.527,51.473,64.100,35.900,44.57,224,0.875,bicubic,-46.633,-35.130,+29
repvgg_b3g4,48.310,51.690,64.800,35.200,83.83,224,0.875,bilinear,-46.180,-34.220,+74
vit_large_patch32_384,48.250,51.750,61.830,38.170,306.63,384,1.000,bicubic,-46.990,-37.490,+21
convit_base,48.217,51.783,63.000,37.000,86.54,224,0.875,bicubic,-46.883,-36.140,+31
resnetrs350,48.050,51.950,62.653,37.347,163.96,384,1.000,bicubic,-48.190,-36.817,-33
twins_svt_large,47.947,52.053,62.907,37.093,99.27,224,0.900,bicubic,-47.773,-36.463,-9
mixer_b16_224_miil,47.790,52.210,63.400,36.600,59.88,224,0.875,bilinear,-47.090,-35.680,+40
repvgg_b2g4,47.787,52.213,64.390,35.610,61.76,224,0.875,bilinear,-46.033,-34.540,+125
eca_nfnet_l1,47.663,52.337,62.767,37.233,41.41,320,1.000,bicubic,-48.267,-36.733,-20
pit_s_distilled_224,47.543,52.457,63.493,36.507,24.04,224,0.900,bicubic,-47.187,-35.697,+45
resnest50d_4s2x40d,47.483,52.517,63.807,36.193,30.42,224,0.875,bicubic,-47.227,-35.323,+47
efficientnet_b3_pruned,47.447,52.553,62.793,37.207,9.86,300,0.904,bicubic,-47.133,-36.277,+58
vit_base_patch16_224,47.340,52.660,61.607,38.393,86.57,224,0.900,bicubic,-47.870,-37.623,+14
tresnet_m,47.230,52.770,61.993,38.007,31.39,224,0.875,bilinear,-48.150,-37.157,+4
tf_efficientnet_b6,47.213,52.787,63.110,36.890,43.04,528,0.942,bicubic,-49.077,-36.410,-46
ssl_resnext101_32x4d,47.177,52.823,63.367,36.633,44.18,224,0.875,bilinear,-47.983,-35.933,+15
resnetrs270,47.107,52.893,62.010,37.990,129.86,352,1.000,bicubic,-48.953,-37.480,-35
tf_efficientnet_b4,47.083,52.917,62.867,37.133,19.34,380,0.922,bicubic,-48.507,-36.463,-14
resnet101d,46.893,53.107,62.317,37.683,44.57,320,1.000,bicubic,-48.857,-37.123,-23
resnetrs200,46.837,53.163,62.487,37.513,93.21,320,1.000,bicubic,-49.153,-36.953,-31
resnetv2_50x3_bitm,46.827,53.173,64.873,35.127,217.32,480,1.000,bilinear,-49.313,-34.747,-43
dm_nfnet_f1,46.693,53.307,61.560,38.440,132.63,320,0.910,bicubic,-49.677,-37.910,-55
gluon_seresnext101_64x4d,46.677,53.323,61.303,38.697,88.23,224,0.875,bicubic,-47.973,-37.677,+29
tresnet_xl,46.283,53.717,61.943,38.057,78.44,224,0.875,bilinear,-48.777,-37.317,+7
vit_deit_small_distilled_patch16_224,46.160,53.840,62.417,37.583,22.44,224,0.900,bicubic,-48.430,-36.683,+31
regnety_160,46.153,53.847,61.837,38.163,83.59,288,1.000,bicubic,-49.727,-37.723,-32
gernet_m,46.150,53.850,62.700,37.300,21.14,224,0.875,bilinear,-48.400,-36.550,+34
resnest50d_1s4x24d,46.083,53.917,62.377,37.623,25.68,224,0.875,bicubic,-48.307,-36.693,+40
tf_efficientnet_b0_ns,46.047,53.953,63.253,36.747,5.29,224,0.875,bicubic,-47.693,-35.727,+100
resnest50d,45.937,54.063,62.623,37.377,27.48,224,0.875,bilinear,-48.683,-36.407,+23
regnety_032,45.893,54.107,61.537,38.463,19.44,288,1.000,bicubic,-49.577,-37.783,-19
gluon_seresnext101_32x4d,45.590,54.410,61.143,38.857,48.96,224,0.875,bicubic,-48.860,-37.947,+33
gluon_resnet152_v1d,45.430,54.570,60.077,39.923,60.21,224,0.875,bicubic,-49.010,-38.933,+33
dm_nfnet_f0,45.420,54.580,60.990,39.010,71.49,256,0.900,bicubic,-50.210,-38.310,-32
ssl_resnext50_32x4d,45.407,54.593,62.047,37.953,25.03,224,0.875,bilinear,-49.293,-37.193,+12
nfnet_l0,45.390,54.610,62.057,37.943,35.07,288,1.000,bicubic,-50.000,-37.363,-21
tresnet_xl_448,45.223,54.777,61.437,38.563,78.44,448,0.875,bilinear,-50.287,-37.903,-28
nasnetalarge,45.210,54.790,57.883,42.117,88.75,331,0.911,bicubic,-49.940,-41.247,-10
swin_small_patch4_window7_224,45.163,54.837,60.330,39.670,49.61,224,0.900,bicubic,-50.557,-38.960,-41
tf_efficientnet_b3,45.107,54.893,60.650,39.350,12.23,300,0.904,bicubic,-49.803,-38.460,-4
rexnet_200,45.047,54.953,62.317,37.683,16.37,224,0.875,bicubic,-49.613,-36.773,+9
resnetrs152,44.943,55.057,59.713,40.287,86.62,320,1.000,bicubic,-51.017,-39.667,-51
ecaresnetlight,44.890,55.110,60.770,39.230,30.16,224,0.875,bicubic,-49.250,-38.180,+43
vit_deit_base_patch16_224,44.870,55.130,59.177,40.823,86.57,224,0.900,bicubic,-50.140,-39.803,-12
vit_deit_base_patch16_384,44.777,55.223,59.617,40.383,86.86,384,1.000,bicubic,-50.873,-39.623,-44
cait_xxs36_384,44.773,55.227,59.380,40.620,17.37,384,1.000,bicubic,-50.447,-39.940,-23
gernet_l,44.740,55.260,58.943,41.057,31.08,256,0.875,bilinear,-50.190,-40.257,-13
tf_efficientnet_b2_ap,44.700,55.300,60.680,39.320,9.11,260,0.890,bicubic,-49.570,-38.270,+29
vit_base_patch32_384,44.693,55.307,58.530,41.470,88.30,384,1.000,bicubic,-50.567,-40.650,-29
ens_adv_inception_resnet_v2,44.393,55.607,58.117,41.883,55.84,299,0.897,bicubic,-49.737,-40.673,+38
tresnet_l,44.363,55.637,59.953,40.047,55.99,224,0.875,bilinear,-50.537,-39.077,-14
gluon_resnext101_32x4d,44.290,55.710,59.090,40.910,44.18,224,0.875,bicubic,-49.830,-39.840,+39
wide_resnet50_2,44.177,55.823,59.727,40.273,68.88,224,0.875,bicubic,-50.493,-39.403,-5
cspresnext50,44.147,55.853,60.533,39.467,20.57,224,0.875,bilinear,-49.613,-38.307,+70
seresnext50_32x4d,44.127,55.873,59.490,40.510,27.56,224,0.875,bicubic,-50.693,-39.640,-15
gluon_resnet152_v1s,44.073,55.927,58.703,41.297,60.32,224,0.875,bicubic,-50.647,-40.357,-12
pit_b_224,44.070,55.930,58.017,41.983,73.76,224,0.900,bicubic,-50.720,-40.803,-16
ssl_resnet50,44.010,55.990,61.887,38.113,25.56,224,0.875,bilinear,-50.300,-37.263,+16
inception_resnet_v2,44.003,55.997,57.907,42.093,55.84,299,0.897,bicubic,-50.337,-40.893,+14
pnasnet5large,43.950,56.050,56.730,43.270,86.06,331,0.911,bicubic,-51.410,-42.400,-43
pit_s_224,43.890,56.110,58.627,41.373,23.46,224,0.900,bicubic,-50.700,-40.303,-6
gluon_resnext101_64x4d,43.877,56.123,58.710,41.290,83.46,224,0.875,bicubic,-50.473,-40.170,+10
tnt_s_patch16_224,43.773,56.227,59.197,40.803,23.76,224,0.900,bicubic,-50.807,-39.983,-5
cait_xxs36_224,43.760,56.240,58.720,41.280,17.30,224,1.000,bicubic,-50.180,-40.200,+43
ecaresnet50d,43.750,56.250,60.387,39.613,25.58,224,0.875,bicubic,-50.440,-38.633,+17
ecaresnet101d_pruned,43.737,56.263,59.607,40.393,24.88,224,0.875,bicubic,-50.713,-39.493,-2
rexnet_150,43.690,56.310,60.897,39.103,9.73,224,0.875,bicubic,-50.580,-38.183,+9
pit_xs_distilled_224,43.663,56.337,60.703,39.297,11.00,224,0.900,bicubic,-49.577,-38.117,+100
gluon_resnet101_v1d,43.440,56.560,58.613,41.387,44.57,224,0.875,bicubic,-50.730,-40.297,+16
ecaresnet50t,43.407,56.593,59.300,40.700,25.57,320,0.950,bicubic,-51.663,-39.990,-40
gluon_resnet101_v1s,43.363,56.637,58.503,41.497,44.67,224,0.875,bicubic,-50.807,-40.507,+13
cspdarknet53,43.357,56.643,59.430,40.570,27.64,256,0.887,bilinear,-50.733,-39.550,+20
dpn68b,43.287,56.713,58.673,41.327,12.61,224,0.875,bicubic,-50.333,-40.287,+69
eca_nfnet_l0,43.230,56.770,59.913,40.087,24.14,288,1.000,bicubic,-52.240,-39.467,-63
resnest26d,43.140,56.860,60.623,39.377,17.07,224,0.875,bilinear,-50.100,-38.227,+94
resnetv2_101x1_bitm,43.113,56.887,60.950,39.050,44.54,480,1.000,bilinear,-52.397,-38.560,-68
dpn131,43.047,56.953,57.440,42.560,79.25,224,0.875,bicubic,-50.713,-41.360,+48
cspresnet50,43.030,56.970,59.153,40.847,21.62,256,0.887,bilinear,-50.830,-39.717,+35
resnet101d,46.893,53.107,62.317,37.683,44.57,320,1.000,bicubic,-48.857,-37.123,-26
resnetrs200,46.837,53.163,62.487,37.513,93.21,320,1.000,bicubic,-49.153,-36.953,-34
resnetv2_50x3_bitm,46.827,53.173,64.873,35.127,217.32,480,1.000,bilinear,-49.313,-34.747,-46
dm_nfnet_f1,46.693,53.307,61.560,38.440,132.63,320,0.910,bicubic,-49.677,-37.990,-58
gluon_seresnext101_64x4d,46.677,53.323,61.303,38.697,88.23,224,0.875,bicubic,-47.973,-37.677,+41
twins_pcpvt_large,46.637,53.363,62.240,37.760,60.99,224,0.900,bicubic,-49.083,-37.250,-28
tresnet_xl,46.283,53.717,61.943,38.057,78.44,224,0.875,bilinear,-48.777,-37.317,+13
vit_deit_small_distilled_patch16_224,46.160,53.840,62.417,37.583,22.44,224,0.900,bicubic,-48.430,-36.683,+43
regnety_160,46.153,53.847,61.837,38.163,83.59,288,1.000,bicubic,-49.727,-37.723,-36
gernet_m,46.150,53.850,62.700,37.300,21.14,224,0.875,bilinear,-48.400,-36.230,+44
resnest50d_1s4x24d,46.083,53.917,62.377,37.623,25.68,224,0.875,bicubic,-48.307,-36.693,+53
tf_efficientnet_b0_ns,46.047,53.953,63.253,36.747,5.29,224,0.875,bicubic,-47.693,-35.727,+114
resnet51q,46.027,53.973,60.910,39.090,35.70,288,1.000,bilinear,-49.173,-38.370,-3
resnest50d,45.937,54.063,62.623,37.377,27.48,224,0.875,bilinear,-48.683,-36.407,+33
regnety_032,45.893,54.107,61.537,38.463,19.44,288,1.000,bicubic,-49.577,-37.843,-21
twins_pcpvt_base,45.893,54.107,61.337,38.663,43.83,224,0.900,bicubic,-49.567,-38.053,-20
levit_384,45.877,54.123,61.693,38.307,39.13,224,0.900,bicubic,-49.333,-37.467,-9
twins_svt_base,45.877,54.123,60.967,39.033,56.07,224,0.900,bicubic,-49.693,-38.263,-30
gluon_seresnext101_32x4d,45.590,54.410,61.143,38.857,48.96,224,0.875,bicubic,-48.860,-37.957,+40
gluon_resnet152_v1d,45.430,54.570,60.077,39.923,60.21,224,0.875,bicubic,-49.010,-38.933,+41
dm_nfnet_f0,45.420,54.580,60.990,39.010,71.49,256,0.900,bicubic,-50.210,-38.310,-37
ssl_resnext50_32x4d,45.407,54.593,62.047,37.953,25.03,224,0.875,bilinear,-49.293,-37.193,+19
nfnet_l0,45.390,54.610,62.057,37.943,35.07,288,1.000,bicubic,-50.000,-37.363,-24
tresnet_xl_448,45.223,54.777,61.437,38.563,78.44,448,0.875,bilinear,-50.287,-37.903,-32
nasnetalarge,45.210,54.790,57.883,42.117,88.75,331,0.911,bicubic,-49.940,-41.247,-11
convit_small,45.203,54.797,60.510,39.490,27.78,224,0.875,bicubic,-49.717,-38.600,0
swin_small_patch4_window7_224,45.163,54.837,60.330,39.670,49.61,224,0.900,bicubic,-50.557,-38.960,-50
tf_efficientnet_b3,45.107,54.893,60.650,39.350,12.23,300,0.904,bicubic,-49.803,-38.460,-1
rexnet_200,45.047,54.953,62.317,37.683,16.37,224,0.875,bicubic,-49.613,-36.833,+16
resnetrs152,44.943,55.057,59.713,40.287,86.62,320,1.000,bicubic,-51.017,-39.667,-60
ecaresnetlight,44.890,55.110,60.770,39.230,30.16,224,0.875,bicubic,-49.250,-38.260,+52
vit_deit_base_patch16_224,44.870,55.130,59.177,40.823,86.57,224,0.900,bicubic,-50.140,-39.803,-11
vit_deit_base_patch16_384,44.777,55.223,59.617,40.383,86.86,384,1.000,bicubic,-50.873,-39.773,-51
cait_xxs36_384,44.773,55.227,59.380,40.620,17.37,384,1.000,bicubic,-50.447,-39.940,-27
gernet_l,44.740,55.260,58.943,41.057,31.08,256,0.875,bilinear,-50.190,-40.257,-11
tf_efficientnet_b2_ap,44.700,55.300,60.680,39.320,9.11,260,0.890,bicubic,-49.570,-38.400,+36
vit_base_patch32_384,44.693,55.307,58.530,41.470,88.30,384,1.000,bicubic,-50.567,-40.650,-33
ens_adv_inception_resnet_v2,44.393,55.607,58.117,41.883,55.84,299,0.897,bicubic,-49.737,-40.673,+46
tresnet_l,44.363,55.637,59.953,40.047,55.99,224,0.875,bilinear,-50.537,-39.077,-11
gluon_resnext101_32x4d,44.290,55.710,59.090,40.910,44.18,224,0.875,bicubic,-49.830,-39.840,+47
wide_resnet50_2,44.177,55.823,59.727,40.273,68.88,224,0.875,bicubic,-50.493,-39.323,+2
cspresnext50,44.147,55.853,60.533,39.467,20.57,224,0.875,bilinear,-49.613,-38.167,+81
seresnext50_32x4d,44.127,55.873,59.490,40.510,27.56,224,0.875,bicubic,-50.693,-39.640,-11
gluon_resnet152_v1s,44.073,55.927,58.703,41.297,60.32,224,0.875,bicubic,-50.647,-40.357,-6
pit_b_224,44.070,55.930,58.017,41.983,73.76,224,0.900,bicubic,-50.720,-40.803,-12
ssl_resnet50,44.010,55.990,61.887,38.113,25.56,224,0.875,bilinear,-50.300,-37.263,+24
inception_resnet_v2,44.003,55.997,57.907,42.093,55.84,299,0.897,bicubic,-50.337,-40.893,+22
pnasnet5large,43.950,56.050,56.730,43.270,86.06,331,0.911,bicubic,-51.410,-42.400,-47
pit_s_224,43.890,56.110,58.627,41.373,23.46,224,0.900,bicubic,-50.700,-40.303,+1
gluon_resnext101_64x4d,43.877,56.123,58.710,41.290,83.46,224,0.875,bicubic,-50.473,-40.170,+18
coat_lite_small,43.823,56.177,57.147,42.853,19.84,224,0.900,bicubic,-51.257,-41.873,-33
tnt_s_patch16_224,43.773,56.227,59.197,40.803,23.76,224,0.900,bicubic,-50.807,-39.983,+1
cait_xxs36_224,43.760,56.240,58.720,41.280,17.30,224,1.000,bicubic,-50.180,-40.170,+49
ecaresnet50d,43.750,56.250,60.387,39.613,25.58,224,0.875,bicubic,-50.440,-38.633,+24
ecaresnet101d_pruned,43.737,56.263,59.607,40.393,24.88,224,0.875,bicubic,-50.713,-39.483,+5
tf_efficientnetv2_s,43.710,56.290,58.597,41.403,21.46,384,1.000,bicubic,-52.000,-40.803,-75
rexnet_150,43.690,56.310,60.897,39.103,9.73,224,0.875,bicubic,-50.580,-38.053,+16
pit_xs_distilled_224,43.663,56.337,60.703,39.297,11.00,224,0.900,bicubic,-49.577,-38.147,+111
gluon_resnet101_v1d,43.440,56.560,58.613,41.387,44.57,224,0.875,bicubic,-50.730,-40.327,+20
ecaresnet50t,43.407,56.593,59.300,40.700,25.57,320,0.950,bicubic,-51.663,-39.990,-41
gluon_resnet101_v1s,43.363,56.637,58.503,41.497,44.67,224,0.875,bicubic,-50.807,-40.507,+19
cspdarknet53,43.357,56.643,59.430,40.570,27.64,256,0.887,bilinear,-50.733,-39.550,+26
dpn68b,43.287,56.713,58.673,41.327,12.61,224,0.875,bicubic,-50.333,-40.027,+77
visformer_small,43.253,56.747,57.993,42.007,40.22,224,0.900,bicubic,-51.707,-41.217,-41
eca_nfnet_l0,43.230,56.770,59.913,40.087,24.14,288,1.000,bicubic,-52.240,-39.407,-70
resnest26d,43.140,56.860,60.623,39.377,17.07,224,0.875,bilinear,-50.100,-38.127,+104
resnetv2_101x1_bitm,43.113,56.887,60.950,39.050,44.54,480,1.000,bilinear,-52.397,-38.560,-76
twins_pcpvt_small,43.090,56.910,58.873,41.127,24.11,224,0.900,bicubic,-51.510,-40.277,-19
dpn131,43.047,56.953,57.440,42.560,79.25,224,0.875,bicubic,-50.713,-41.420,+55
cspresnet50,43.030,56.970,59.153,40.847,21.62,256,0.887,bilinear,-50.830,-39.717,+40
tf_efficientnet_lite4,42.967,57.033,57.620,42.380,13.01,380,0.920,bilinear,-51.903,-41.470,-41
gluon_resnet152_v1b,42.903,57.097,57.750,42.250,60.19,224,0.875,bicubic,-51.127,-40.990,+16
dpn107,42.857,57.143,57.367,42.633,86.92,224,0.875,bicubic,-51.103,-41.473,+22
tf_efficientnet_b1_ap,42.803,57.197,58.813,41.187,7.79,240,0.882,bicubic,-50.827,-39.987,+58
gluon_resnet152_v1c,42.800,57.200,57.737,42.263,60.21,224,0.875,bicubic,-51.080,-41.063,+28
gluon_xception65,42.793,57.207,58.820,41.180,39.92,299,0.903,bicubic,-51.217,-40.200,+15
tresnet_l_448,42.753,57.247,58.947,41.053,55.99,448,0.875,bilinear,-52.657,-40.353,-71
resnet50d,42.703,57.297,58.697,41.303,25.58,224,0.875,bicubic,-51.367,-40.223,+9
gluon_seresnext50_32x4d,42.683,57.317,58.710,41.290,27.56,224,0.875,bicubic,-51.487,-40.230,-4
resnext101_32x8d,42.557,57.443,58.317,41.683,88.79,224,0.875,bilinear,-51.213,-40.633,+35
seresnet50,42.510,57.490,58.667,41.333,28.09,224,0.875,bicubic,-51.570,-40.303,+5
resnetrs101,42.437,57.563,57.300,42.700,63.62,288,0.940,bicubic,-52.813,-41.910,-69
nf_resnet50,42.400,57.600,59.540,40.460,25.56,288,0.940,bicubic,-52.010,-39.560,-24
dpn98,42.280,57.720,56.880,43.120,61.57,224,0.875,bicubic,-51.660,-42.010,+13
vit_deit_small_patch16_224,42.263,57.737,58.020,41.980,22.05,224,0.900,bicubic,-51.737,-40.940,+9
tf_efficientnet_cc_b1_8e,42.233,57.767,58.420,41.580,39.72,240,0.882,bicubic,-51.337,-40.270,+54
legacy_senet154,42.207,57.793,56.597,43.403,115.09,224,0.875,bilinear,-52.523,-42.503,-53
cait_xxs24_384,42.187,57.813,57.460,42.540,12.03,384,1.000,bicubic,-52.733,-41.680,-61
tf_efficientnet_b2,42.120,57.880,58.197,41.803,9.11,260,0.890,bicubic,-52.090,-40.833,-17
gluon_resnext50_32x4d,42.043,57.957,57.667,42.333,25.03,224,0.875,bicubic,-51.607,-41.023,+39
resnet50,42.013,57.987,56.000,44.000,25.56,224,0.875,bicubic,-51.447,-42.600,+57
ecaresnet50d_pruned,41.953,58.047,58.217,41.783,19.94,224,0.875,bicubic,-51.867,-40.713,+18
efficientnet_b2,41.933,58.067,58.300,41.700,9.11,288,1.000,bicubic,-52.437,-40.750,-31
dla102x2,41.647,58.353,57.967,42.033,41.28,224,0.875,bilinear,-52.353,-41.063,-1
hrnet_w64,41.637,58.363,57.130,42.870,128.06,224,0.875,bilinear,-52.193,-41.800,+13
gluon_senet154,41.627,58.373,56.373,43.627,115.09,224,0.875,bicubic,-53.083,-42.597,-59
inception_v4,41.577,58.423,55.383,44.617,42.68,299,0.875,bicubic,-52.803,-43.437,-36
efficientnet_el,41.497,58.503,58.303,41.697,10.59,300,0.904,bicubic,-53.173,-40.747,-57
efficientnet_em,41.493,58.507,58.877,41.123,6.90,240,0.882,bicubic,-52.247,-40.053,+21
tf_efficientnet_cc_b0_8e,41.487,58.513,57.377,42.623,24.01,224,0.875,bicubic,-51.383,-41.083,+86
swin_tiny_patch4_window7_224,41.457,58.543,57.303,42.697,28.29,224,0.900,bicubic,-53.163,-41.817,-55
resnext50_32x4d,41.443,58.557,56.997,43.003,25.03,224,0.875,bicubic,-52.397,-41.833,+5
cait_xxs24_224,41.383,58.617,57.527,42.473,11.96,224,1.000,bicubic,-52.107,-41.243,+43
tv_resnet152,41.330,58.670,57.520,42.480,60.19,224,0.875,bilinear,-51.910,-41.230,+58
xception71,41.270,58.730,55.873,44.127,42.34,299,0.903,bicubic,-52.620,-43.077,-3
dpn92,41.267,58.733,56.333,43.667,37.67,224,0.875,bicubic,-52.923,-42.597,-33
adv_inception_v3,41.263,58.737,56.317,43.683,23.83,299,0.875,bicubic,-51.747,-42.173,+65
gernet_s,41.247,58.753,58.830,41.170,8.17,224,0.875,bilinear,-51.193,-39.670,+101
resnetblur50,41.053,58.947,57.077,42.923,25.56,224,0.875,bicubic,-52.657,-41.723,+16
nf_regnet_b1,41.010,58.990,58.117,41.883,10.22,288,0.900,bicubic,-52.880,-40.633,-10
gluon_resnet50_v1d,40.970,59.030,57.137,42.863,25.58,224,0.875,bicubic,-52.560,-41.573,+32
gluon_inception_v3,40.907,59.093,55.617,44.383,23.83,299,0.875,bicubic,-52.633,-43.213,+29
ese_vovnet39b,40.867,59.133,56.950,43.050,24.57,224,0.875,bicubic,-52.983,-41.950,-7
regnety_320,40.813,59.187,56.117,43.883,145.05,224,0.875,bicubic,-53.707,-43.053,-60
resnet34d,40.810,59.190,56.530,43.470,21.82,224,0.875,bicubic,-51.830,-41.890,+82
xception,40.763,59.237,56.387,43.613,22.86,299,0.897,bicubic,-52.877,-42.383,+14
skresnext50_32x4d,40.700,59.300,56.023,43.977,27.48,224,0.875,bicubic,-53.250,-42.797,-21
gluon_resnet101_v1b,40.683,59.317,56.117,43.883,44.55,224,0.875,bicubic,-53.077,-42.583,0
hrnet_w40,40.660,59.340,56.757,43.243,57.56,224,0.875,bilinear,-53.050,-42.043,+4
repvgg_b1,40.593,59.407,57.837,42.163,57.42,224,0.875,bilinear,-52.817,-40.953,+33
twins_svt_small,42.923,57.077,58.453,41.547,24.06,224,0.900,bicubic,-51.847,-40.627,-37
gluon_resnet152_v1b,42.903,57.097,57.750,42.250,60.19,224,0.875,bicubic,-51.127,-40.990,+20
dpn107,42.857,57.143,57.367,42.633,86.92,224,0.875,bicubic,-51.103,-41.473,+26
levit_256,42.823,57.177,57.897,42.103,18.89,224,0.900,bicubic,-51.577,-41.163,-12
tf_efficientnet_b1_ap,42.803,57.197,58.813,41.187,7.79,240,0.882,bicubic,-50.827,-39.987,+63
gluon_resnet152_v1c,42.800,57.200,57.737,42.263,60.21,224,0.875,bicubic,-51.080,-41.353,+32
gluon_xception65,42.793,57.207,58.820,41.180,39.92,299,0.903,bicubic,-51.217,-40.200,+18
tresnet_l_448,42.753,57.247,58.947,41.053,55.99,448,0.875,bilinear,-52.657,-40.353,-81
resnet50d,42.703,57.297,58.697,41.303,25.58,224,0.875,bicubic,-51.367,-40.223,+11
gluon_seresnext50_32x4d,42.683,57.317,58.710,41.290,27.56,224,0.875,bicubic,-51.487,-40.200,0
resnext101_32x8d,42.557,57.443,58.317,41.683,88.79,224,0.875,bilinear,-51.213,-40.633,+38
seresnet50,42.510,57.490,58.667,41.333,28.09,224,0.875,bicubic,-51.570,-40.303,+7
resnetrs101,42.437,57.563,57.300,42.700,63.62,288,0.940,bicubic,-52.813,-41.910,-79
nf_resnet50,42.400,57.600,59.540,40.460,25.56,288,0.940,bicubic,-52.010,-39.560,-23
tf_efficientnetv2_b3,42.313,57.687,57.940,42.060,14.36,300,0.904,bicubic,-52.807,-41.260,-70
dpn98,42.280,57.720,56.880,43.120,61.57,224,0.875,bicubic,-51.660,-42.040,+16
vit_deit_small_patch16_224,42.263,57.737,58.020,41.980,22.05,224,0.900,bicubic,-51.737,-40.940,+11
tf_efficientnet_cc_b1_8e,42.233,57.767,58.420,41.580,39.72,240,0.882,bicubic,-51.337,-40.270,+58
legacy_senet154,42.207,57.793,56.597,43.403,115.09,224,0.875,bilinear,-52.523,-42.503,-54
cait_xxs24_384,42.187,57.813,57.460,42.540,12.03,384,1.000,bicubic,-52.733,-41.680,-66
tf_efficientnet_b2,42.120,57.880,58.197,41.803,9.11,260,0.890,bicubic,-52.090,-40.833,-16
gluon_resnext50_32x4d,42.043,57.957,57.667,42.333,25.03,224,0.875,bicubic,-51.607,-41.023,+43
resnet50,42.013,57.987,56.000,44.000,25.56,224,0.875,bicubic,-51.447,-42.600,+62
ecaresnet50d_pruned,41.953,58.047,58.217,41.783,19.94,224,0.875,bicubic,-51.867,-40.783,+19
efficientnet_b2,41.933,58.067,58.300,41.700,9.11,288,1.000,bicubic,-52.437,-40.750,-30
dla102x2,41.647,58.353,57.967,42.033,41.28,224,0.875,bilinear,-52.353,-41.063,+1
hrnet_w64,41.637,58.363,57.130,42.870,128.06,224,0.875,bilinear,-52.193,-41.800,+15
gluon_senet154,41.627,58.373,56.373,43.627,115.09,224,0.875,bicubic,-53.083,-42.597,-60
inception_v4,41.577,58.423,55.383,44.617,42.68,299,0.875,bicubic,-52.803,-43.437,-35
efficientnet_el,41.497,58.503,58.303,41.697,10.59,300,0.904,bicubic,-53.173,-40.827,-59
efficientnet_em,41.493,58.507,58.877,41.123,6.90,240,0.882,bicubic,-52.247,-40.053,+23
tf_efficientnet_cc_b0_8e,41.487,58.513,57.377,42.623,24.01,224,0.875,bicubic,-51.383,-41.083,+93
swin_tiny_patch4_window7_224,41.457,58.543,57.303,42.697,28.29,224,0.900,bicubic,-53.163,-41.817,-56
resnext50_32x4d,41.443,58.557,56.997,43.003,25.03,224,0.875,bicubic,-52.397,-41.833,+7
cait_xxs24_224,41.383,58.617,57.527,42.473,11.96,224,1.000,bicubic,-52.107,-41.243,+48
tv_resnet152,41.327,58.673,57.520,42.480,60.19,224,0.875,bilinear,-51.913,-41.300,+61
xception71,41.270,58.730,55.873,44.127,42.34,299,0.903,bicubic,-52.620,-42.877,-3
dpn92,41.267,58.733,56.333,43.667,37.67,224,0.875,bicubic,-52.923,-42.597,-32
adv_inception_v3,41.263,58.737,56.317,43.683,23.83,299,0.875,bicubic,-51.747,-42.173,+72
gernet_s,41.247,58.753,58.830,41.170,8.17,224,0.875,bilinear,-51.193,-39.710,+109
resnetblur50,41.053,58.947,57.077,42.923,25.56,224,0.875,bicubic,-52.657,-41.733,+18
nf_regnet_b1,41.010,58.990,58.117,41.883,10.22,288,0.900,bicubic,-52.880,-40.833,-6
gluon_resnet50_v1d,40.970,59.030,57.137,42.863,25.58,224,0.875,bicubic,-52.560,-41.573,+36
gluon_inception_v3,40.907,59.093,55.617,44.383,23.83,299,0.875,bicubic,-52.633,-43.213,+33
ese_vovnet39b,40.867,59.133,56.947,43.053,24.57,224,0.875,bicubic,-52.983,-41.953,-5
levit_192,40.847,59.153,56.687,43.313,10.95,224,0.900,bicubic,-52.863,-42.103,+12
regnety_320,40.813,59.187,56.117,43.883,145.05,224,0.875,bicubic,-53.707,-43.053,-61
resnet34d,40.810,59.190,56.530,43.470,21.82,224,0.875,bicubic,-51.830,-41.890,+88
xception,40.763,59.237,56.387,43.613,22.86,299,0.897,bicubic,-52.877,-42.383,+17
skresnext50_32x4d,40.700,59.300,56.023,43.977,27.48,224,0.875,bicubic,-53.250,-42.797,-20
gluon_resnet101_v1b,40.683,59.317,56.117,43.883,44.55,224,0.875,bicubic,-53.077,-42.723,-1
hrnet_w40,40.660,59.340,56.753,43.247,57.56,224,0.875,bilinear,-53.050,-42.047,+5
repvgg_b1,40.593,59.407,57.837,42.163,57.42,224,0.875,bilinear,-52.817,-40.953,+37
tf_efficientnet_lite3,40.563,59.437,56.477,43.523,8.20,300,0.904,bilinear,-53.567,-42.483,-39
tresnet_m_448,40.530,59.470,56.700,43.300,31.39,448,0.875,bilinear,-54.130,-42.450,-79
pit_xs_224,40.497,59.503,56.530,43.470,10.62,224,0.900,bicubic,-52.413,-42.250,+58
dla169,40.493,59.507,57.263,42.737,53.39,224,0.875,bilinear,-53.307,-41.647,-11
repvgg_b2,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.123,-41.290,+12
tresnet_m_448,40.530,59.470,56.700,43.300,31.39,448,0.875,bilinear,-54.130,-42.390,-82
pit_xs_224,40.497,59.503,56.530,43.470,10.62,224,0.900,bicubic,-52.413,-42.250,+64
dla169,40.493,59.507,57.263,42.737,53.39,224,0.875,bilinear,-53.307,-41.577,-11
repvgg_b2,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.123,-41.060,+14
regnetx_320,40.443,59.557,55.660,44.340,107.81,224,0.875,bicubic,-53.767,-43.390,-55
skresnet34,40.397,59.603,56.737,43.263,22.28,224,0.875,bicubic,-52.173,-41.783,+77
efficientnet_el_pruned,40.390,59.610,56.903,43.097,10.59,300,0.904,bicubic,-53.700,-42.107,-43
efficientnet_b2_pruned,40.383,59.617,56.537,43.463,8.31,260,0.890,bicubic,-53.417,-42.303,-17
legacy_seresnext101_32x4d,40.360,59.640,54.817,45.183,48.96,224,0.875,bilinear,-53.770,-44.153,-50
coat_mini,40.420,59.580,55.167,44.833,10.34,224,0.900,bicubic,-54.350,-43.783,-97
skresnet34,40.397,59.603,56.737,43.263,22.28,224,0.875,bicubic,-52.173,-41.783,+82
efficientnet_el_pruned,40.390,59.610,56.903,43.097,10.59,300,0.904,bicubic,-53.700,-42.107,-44
efficientnet_b2_pruned,40.383,59.617,56.537,43.463,8.31,260,0.890,bicubic,-53.417,-42.373,-16
coat_lite_mini,40.360,59.640,55.717,44.283,11.01,224,0.900,bicubic,-53.090,-43.063,+22
legacy_seresnext101_32x4d,40.360,59.640,54.817,45.183,48.96,224,0.875,bilinear,-53.770,-44.153,-51
wide_resnet101_2,40.360,59.640,55.780,44.220,126.89,224,0.875,bilinear,-53.370,-43.030,-10
coat_lite_mini,40.360,59.640,55.717,44.283,11.01,224,0.900,bicubic,-53.090,-43.063,+19
tf_efficientnet_b0_ap,40.337,59.663,56.787,43.213,5.29,224,0.875,bicubic,-52.273,-41.583,+66
xception65,40.273,59.727,55.283,44.717,39.92,299,0.903,bicubic,-53.487,-43.577,-15
regnetx_160,40.270,59.730,56.050,43.950,54.28,224,0.875,bicubic,-53.610,-43.040,-31
densenet201,40.267,59.733,56.710,43.290,20.01,224,0.875,bicubic,-52.423,-41.940,+57
tf_efficientnet_b0_ap,40.337,59.663,56.787,43.213,5.29,224,0.875,bicubic,-52.273,-41.723,+70
xception65,40.273,59.727,55.283,44.717,39.92,299,0.903,bicubic,-53.487,-43.517,-17
regnetx_160,40.270,59.730,56.050,43.950,54.28,224,0.875,bicubic,-53.610,-42.750,-32
densenet201,40.267,59.733,56.710,43.290,20.01,224,0.875,bicubic,-52.423,-41.940,+62
resnext50d_32x4d,40.170,59.830,55.487,44.513,25.05,224,0.875,bicubic,-53.640,-43.253,-26
vit_small_patch16_224,40.130,59.870,56.543,43.457,48.75,224,0.900,bicubic,-52.470,-41.887,+62
vit_small_patch16_224,40.130,59.870,56.543,43.457,48.75,224,0.900,bicubic,-52.470,-41.847,+68
hrnet_w48,40.093,59.907,56.640,43.360,77.47,224,0.875,bilinear,-53.937,-42.400,-50
legacy_seresnet152,40.043,59.957,55.820,44.180,66.82,224,0.875,bilinear,-53.397,-43.030,+11
hrnet_w30,40.030,59.970,57.093,42.907,37.71,224,0.875,bilinear,-53.340,-41.737,+14
legacy_seresnet152,40.043,59.957,55.820,44.180,66.82,224,0.875,bilinear,-53.397,-43.030,+14
hrnet_w30,40.030,59.970,57.093,42.907,37.71,224,0.875,bilinear,-53.340,-41.737,+17
regnetx_080,40.000,60.000,55.977,44.023,39.57,224,0.875,bicubic,-53.790,-42.933,-28
tf_efficientnet_b1,39.977,60.023,56.137,43.863,7.79,240,0.882,bicubic,-53.733,-42.673,-19
gluon_resnet101_v1c,39.953,60.047,55.300,44.700,44.57,224,0.875,bicubic,-53.737,-43.460,-18
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,-79
hrnet_w44,39.677,60.323,55.333,44.667,67.06,224,0.875,bilinear,-53.943,-43.617,-12
densenet161,39.620,60.380,56.133,43.867,28.68,224,0.875,bicubic,-53.280,-42.677,+34
mixnet_xl,39.617,60.383,55.887,44.113,11.90,224,0.875,bicubic,-54.613,-42.933,-79
xception41,39.610,60.390,55.037,44.963,26.97,299,0.903,bicubic,-53.870,-43.713,-3
res2net50_26w_8s,39.603,60.397,54.550,45.450,48.40,224,0.875,bilinear,-53.847,-44.150,-1
tf_efficientnet_b1,39.977,60.023,56.137,43.863,7.79,240,0.882,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
tf_efficientnetv2_b0,39.787,60.213,56.283,43.717,7.14,224,0.875,bicubic,-53.273,-42.417,+26
res2net101_26w_4s,39.717,60.283,54.550,45.450,45.21,224,0.875,bilinear,-53.803,-44.050,0
regnetx_120,39.687,60.313,55.633,44.367,46.11,224,0.875,bicubic,-54.583,-43.557,-81
hrnet_w44,39.677,60.323,55.333,44.667,67.06,224,0.875,bilinear,-53.943,-43.627,-12
densenet161,39.620,60.380,56.133,43.867,28.68,224,0.875,bicubic,-53.280,-42.587,+40
mixnet_xl,39.617,60.383,55.887,44.113,11.90,224,0.875,bicubic,-54.613,-42.933,-81
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,-44.150,+1
tf_efficientnetv2_b1,39.570,60.430,55.343,44.657,8.14,240,0.882,bicubic,-54.140,-43.477,-26
dla102x,39.553,60.447,56.323,43.677,26.31,224,0.875,bilinear,-53.977,-42.527,-10
rexnet_130,39.487,60.513,56.640,43.360,7.56,224,0.875,bicubic,-54.183,-42.070,-25
hrnet_w32,39.463,60.537,56.123,43.877,41.23,224,0.875,bilinear,-53.487,-42.447,+23
regnety_064,39.403,60.597,55.773,44.227,30.58,224,0.875,bicubic,-54.737,-43.257,-76
densenetblur121d,39.380,60.620,56.640,43.360,8.00,224,0.875,bicubic,-53.020,-41.770,+55
regnety_120,39.347,60.653,55.277,44.723,51.82,224,0.875,bicubic,-54.663,-43.753,-65
tv_resnet101,39.307,60.693,55.803,44.197,44.55,224,0.875,bilinear,-53.573,-42.857,+27
tf_efficientnet_el,39.303,60.697,55.387,44.613,10.59,300,0.904,bicubic,-55.057,-43.713,-96
hrnet_w32,39.463,60.537,56.123,43.877,41.23,224,0.875,bilinear,-53.487,-42.717,+27
levit_128,39.433,60.567,55.350,44.650,9.21,224,0.900,bicubic,-53.617,-43.340,+15
regnety_064,39.403,60.597,55.773,44.227,30.58,224,0.875,bicubic,-54.737,-43.177,-81
densenetblur121d,39.380,60.620,56.640,43.360,8.00,224,0.875,bicubic,-53.020,-41.770,+57
regnety_120,39.347,60.653,55.277,44.723,51.82,224,0.875,bicubic,-54.663,-43.753,-68
tv_resnet101,39.307,60.693,55.803,44.197,44.55,224,0.875,bilinear,-53.573,-42.857,+29
tf_efficientnet_el,39.303,60.697,55.387,44.613,10.59,300,0.904,bicubic,-55.057,-43.713,-100
tf_inception_v3,39.237,60.763,54.300,45.700,23.83,299,0.875,bicubic,-53.963,-44.180,+3
gluon_resnet50_v1s,39.233,60.767,55.010,44.990,25.68,224,0.875,bicubic,-54.357,-43.830,-25
densenet169,39.167,60.833,55.843,44.157,14.15,224,0.875,bicubic,-53.133,-42.747,+53
legacy_seresnet101,39.037,60.963,55.003,44.997,49.33,224,0.875,bilinear,-54.223,-43.737,-6
efficientnet_b1_pruned,39.010,60.990,55.647,44.353,6.33,240,0.882,bicubic,-53.970,-42.883,+11
repvgg_b1g4,38.990,61.010,56.350,43.650,39.97,224,0.875,bilinear,-54.040,-42.470,+5
inception_v3,38.960,61.040,53.853,46.147,23.83,299,0.875,bicubic,-53.940,-44.477,+17
dpn68,38.933,61.067,54.933,45.067,12.61,224,0.875,bicubic,-53.307,-43.677,+51
regnety_080,38.917,61.083,55.213,44.787,39.18,224,0.875,bicubic,-54.973,-43.787,-67
legacy_seresnext50_32x4d,38.877,61.123,54.593,45.407,27.56,224,0.875,bilinear,-54.553,-44.207,-17
dla102,38.833,61.167,55.323,44.677,33.27,224,0.875,bilinear,-54.427,-43.457,-14
regnety_040,38.820,61.180,55.557,44.443,20.65,224,0.875,bicubic,-54.800,-43.143,-38
densenet121,38.783,61.217,56.273,43.727,7.98,224,0.875,bicubic,-53.157,-42.007,+53
res2net50_14w_8s,38.710,61.290,54.077,45.923,25.06,224,0.875,bilinear,-54.320,-44.623,-2
regnetx_040,38.703,61.297,55.340,44.660,22.12,224,0.875,bicubic,-54.977,-43.600,-47
res2net50_26w_6s,38.687,61.313,53.743,46.257,37.05,224,0.875,bilinear,-54.903,-45.007,-37
regnetx_032,38.680,61.320,55.157,44.843,15.30,224,0.875,bicubic,-54.570,-43.573,-17
selecsls60,38.623,61.377,55.630,44.370,30.67,224,0.875,bicubic,-54.387,-43.200,-4
dla60x,38.617,61.383,55.383,44.617,17.35,224,0.875,bilinear,-54.573,-43.327,-14
tf_efficientnet_b0,38.600,61.400,55.957,44.043,5.29,224,0.875,bicubic,-53.800,-42.513,+33
dla60_res2net,38.590,61.410,54.560,45.440,20.85,224,0.875,bilinear,-54.790,-44.300,-26
selecsls60b,38.573,61.427,55.307,44.693,32.77,224,0.875,bicubic,-54.927,-43.533,-36
repvgg_a2,38.563,61.437,55.770,44.230,28.21,224,0.875,bilinear,-54.117,-42.750,+12
hardcorenas_f,38.500,61.500,55.657,44.343,8.20,224,0.875,bilinear,-54.480,-42.963,-7
dla60_res2next,38.450,61.550,54.950,45.050,17.03,224,0.875,bilinear,-55.120,-43.850,-45
regnetx_064,38.430,61.570,54.990,45.010,26.21,224,0.875,bicubic,-55.200,-44.060,-54
tf_efficientnet_cc_b0_4e,38.413,61.587,55.150,44.850,13.31,224,0.875,bicubic,-54.427,-43.290,+3
gluon_resnet50_v1b,38.407,61.593,54.833,45.167,25.56,224,0.875,bicubic,-54.153,-43.717,+18
resnetv2_50x1_bitm,38.287,61.713,56.967,43.033,25.55,480,1.000,bilinear,-56.263,-41.963,-137
hrnet_w18,38.277,61.723,55.643,44.357,21.30,224,0.875,bilinear,-54.483,-43.017,+3
mixnet_l,38.160,61.840,54.757,45.243,7.33,224,0.875,bicubic,-55.100,-43.943,-32
hardcorenas_e,38.137,61.863,55.173,44.827,8.07,224,0.875,bilinear,-54.813,-43.667,-13
efficientnet_b1,38.087,61.913,54.010,45.990,7.79,256,1.000,bicubic,-54.943,-44.700,-23
coat_lite_tiny,38.070,61.930,53.453,46.547,5.72,224,0.900,bicubic,-54.780,-45.187,-6
resnetrs50,37.957,62.043,53.310,46.690,35.69,224,0.910,bicubic,-56.063,-45.540,-104
hardcorenas_c,37.883,62.117,55.717,44.283,5.52,224,0.875,bilinear,-54.447,-42.623,+18
gluon_resnet50_v1c,37.843,62.157,54.123,45.877,25.58,224,0.875,bicubic,-55.067,-44.587,-16
res2net50_26w_4s,37.827,62.173,53.073,46.927,25.70,224,0.875,bilinear,-55.353,-45.597,-32
efficientnet_es,37.770,62.230,54.967,45.033,5.44,224,0.875,bicubic,-55.140,-43.723,-19
resnest14d,37.767,62.233,56.470,43.530,10.61,224,0.875,bilinear,-53.363,-41.860,+52
tv_resnext50_32x4d,37.750,62.250,54.113,45.887,25.03,224,0.875,bilinear,-55.150,-44.607,-16
ecaresnet26t,37.650,62.350,54.350,45.650,16.01,320,0.950,bicubic,-56.290,-44.570,-103
hardcorenas_d,37.550,62.450,54.723,45.277,7.50,224,0.875,bilinear,-55.050,-43.667,-1
res2next50,37.477,62.523,52.853,47.147,24.67,224,0.875,bilinear,-55.673,-45.807,-36
resnet34,37.443,62.557,54.297,45.703,21.80,224,0.875,bilinear,-53.757,-43.753,+42
pit_ti_distilled_224,37.337,62.663,55.137,44.863,5.10,224,0.900,bicubic,-53.563,-43.083,+51
hardcorenas_b,37.243,62.757,55.073,44.927,5.18,224,0.875,bilinear,-54.697,-43.327,+20
mobilenetv3_large_100_miil,37.210,62.790,53.513,46.487,5.48,224,0.875,bilinear,-55.040,-44.737,+10
res2net50_48w_2s,37.117,62.883,53.333,46.667,25.29,224,0.875,bilinear,-55.673,-45.137,-17
dla60,37.073,62.927,54.200,45.800,22.04,224,0.875,bilinear,-55.597,-44.430,-14
rexnet_100,37.063,62.937,54.020,45.980,4.80,224,0.875,bicubic,-55.787,-44.600,-22
regnety_016,37.017,62.983,54.093,45.907,11.20,224,0.875,bicubic,-55.983,-44.587,-37
tf_mixnet_l,36.987,63.013,52.583,47.417,7.33,224,0.875,bicubic,-56.053,-45.957,-44
legacy_seresnet50,36.873,63.127,53.487,46.513,28.09,224,0.875,bilinear,-55.797,-45.163,-17
tv_densenet121,36.810,63.190,54.033,45.967,7.98,224,0.875,bicubic,-54.590,-44.217,+26
tf_efficientnet_lite2,36.807,63.193,53.320,46.680,6.09,260,0.890,bicubic,-55.783,-45.230,-13
mobilenetv2_120d,36.780,63.220,54.047,45.953,5.83,224,0.875,bicubic,-55.830,-44.463,-18
tf_efficientnet_lite1,36.737,63.263,53.590,46.410,5.42,240,0.882,bicubic,-55.573,-44.900,-3
regnetx_016,36.683,63.317,53.297,46.703,9.19,224,0.875,bicubic,-55.857,-45.253,-12
hardcorenas_a,36.640,63.360,54.910,45.090,5.26,224,0.875,bilinear,-54.980,-43.260,+14
gluon_resnet50_v1s,39.233,60.767,55.010,44.990,25.68,224,0.875,bicubic,-54.357,-43.740,-24
tf_efficientnetv2_b2,39.180,60.820,54.570,45.430,10.10,260,0.890,bicubic,-54.890,-44.360,-78
densenet169,39.167,60.833,55.843,44.157,14.15,224,0.875,bicubic,-53.133,-42.747,+54
legacy_seresnet101,39.037,60.963,55.003,44.997,49.33,224,0.875,bilinear,-54.223,-43.737,-7
efficientnet_b1_pruned,39.010,60.990,55.647,44.353,6.33,240,0.882,bicubic,-53.970,-42.883,+12
repvgg_b1g4,38.990,61.010,56.350,43.650,39.97,224,0.875,bilinear,-54.040,-42.360,+5
inception_v3,38.960,61.040,53.853,46.147,23.83,299,0.875,bicubic,-53.940,-44.477,+18
dpn68,38.933,61.067,54.933,45.067,12.61,224,0.875,bicubic,-53.307,-43.677,+52
regnety_080,38.917,61.083,55.213,44.787,39.18,224,0.875,bicubic,-54.973,-43.787,-71
legacy_seresnext50_32x4d,38.877,61.123,54.593,45.407,27.56,224,0.875,bilinear,-54.553,-44.207,-18
dla102,38.833,61.167,55.323,44.677,33.27,224,0.875,bilinear,-54.427,-43.457,-15
regnety_040,38.820,61.180,55.557,44.443,20.65,224,0.875,bicubic,-54.800,-43.393,-38
densenet121,38.783,61.217,56.273,43.727,7.98,224,0.875,bicubic,-53.157,-42.007,+54
res2net50_14w_8s,38.710,61.290,54.077,45.923,25.06,224,0.875,bilinear,-54.320,-44.743,-2
regnetx_040,38.703,61.297,55.340,44.660,22.12,224,0.875,bicubic,-54.977,-43.600,-49
res2net50_26w_6s,38.687,61.313,53.743,46.257,37.05,224,0.875,bilinear,-54.903,-45.327,-40
regnetx_032,38.680,61.320,55.157,44.843,15.30,224,0.875,bicubic,-54.570,-43.573,-18
selecsls60,38.623,61.377,55.630,44.370,30.67,224,0.875,bicubic,-54.387,-43.200,-3
dla60x,38.617,61.383,55.383,44.617,17.35,224,0.875,bilinear,-54.573,-43.327,-15
tf_efficientnet_b0,38.600,61.400,55.957,44.043,5.29,224,0.875,bicubic,-53.800,-42.513,+34
dla60_res2net,38.590,61.410,54.560,45.440,20.85,224,0.875,bilinear,-54.790,-44.300,-27
selecsls60b,38.573,61.427,55.307,44.693,32.77,224,0.875,bicubic,-54.927,-43.533,-37
repvgg_a2,38.563,61.437,55.770,44.230,28.21,224,0.875,bilinear,-54.117,-42.750,+13
hardcorenas_f,38.500,61.500,55.657,44.343,8.20,224,0.875,bilinear,-54.480,-42.963,-6
dla60_res2next,38.450,61.550,54.950,45.050,17.03,224,0.875,bilinear,-55.120,-43.850,-47
regnetx_064,38.430,61.570,54.990,45.010,26.21,224,0.875,bicubic,-55.200,-44.060,-56
tf_efficientnet_cc_b0_4e,38.413,61.587,55.150,44.850,13.31,224,0.875,bicubic,-54.427,-43.290,+4
gluon_resnet50_v1b,38.407,61.593,54.833,45.167,25.56,224,0.875,bicubic,-54.153,-43.717,+19
resnetv2_50x1_bitm,38.287,61.713,56.967,43.033,25.55,480,1.000,bilinear,-56.263,-42.283,-141
hrnet_w18,38.277,61.723,55.643,44.357,21.30,224,0.875,bilinear,-54.483,-43.017,+4
mixnet_l,38.160,61.840,54.757,45.243,7.33,224,0.875,bicubic,-55.100,-43.943,-33
hardcorenas_e,38.137,61.863,55.173,44.827,8.07,224,0.875,bilinear,-54.813,-43.397,-13
efficientnet_b1,38.087,61.913,54.010,45.990,7.79,256,1.000,bicubic,-54.943,-44.690,-20
coat_lite_tiny,38.070,61.930,53.453,46.547,5.72,224,0.900,bicubic,-54.780,-45.187,-5
resnetrs50,37.957,62.043,53.310,46.690,35.69,224,0.910,bicubic,-56.063,-45.540,-108
hardcorenas_c,37.883,62.117,55.717,44.283,5.52,224,0.875,bilinear,-54.447,-42.623,+19
gluon_resnet50_v1c,37.843,62.157,54.123,45.877,25.58,224,0.875,bicubic,-55.067,-44.587,-15
res2net50_26w_4s,37.827,62.173,53.073,46.927,25.70,224,0.875,bilinear,-55.353,-45.597,-33
efficientnet_es,37.770,62.230,54.967,45.033,5.44,224,0.875,bicubic,-55.140,-43.723,-18
resnest14d,37.767,62.233,56.470,43.530,10.61,224,0.875,bilinear,-53.363,-41.860,+54
tv_resnext50_32x4d,37.750,62.250,54.113,45.887,25.03,224,0.875,bilinear,-55.150,-44.697,-17
ecaresnet26t,37.650,62.350,54.350,45.650,16.01,320,0.950,bicubic,-56.290,-44.570,-106
hardcorenas_d,37.550,62.450,54.723,45.277,7.50,224,0.875,bilinear,-55.050,-43.707,-1
res2next50,37.477,62.523,52.853,47.147,24.67,224,0.875,bilinear,-55.673,-45.807,-37
resnet34,37.443,62.557,54.297,45.703,21.80,224,0.875,bilinear,-53.757,-43.943,+45
pit_ti_distilled_224,37.337,62.663,55.137,44.863,5.10,224,0.900,bicubic,-53.563,-43.083,+53
hardcorenas_b,37.243,62.757,55.073,44.927,5.18,224,0.875,bilinear,-54.697,-43.327,+21
mobilenetv3_large_100_miil,37.210,62.790,53.513,46.487,5.48,224,0.875,bilinear,-55.040,-44.737,+11
res2net50_48w_2s,37.117,62.883,53.333,46.667,25.29,224,0.875,bilinear,-55.673,-45.137,-16
dla60,37.073,62.927,54.200,45.800,22.04,224,0.875,bilinear,-55.597,-44.450,-12
rexnet_100,37.063,62.937,54.020,45.980,4.80,224,0.875,bicubic,-55.787,-44.600,-21
regnety_016,37.017,62.983,54.093,45.907,11.20,224,0.875,bicubic,-55.983,-44.587,-36
tf_mixnet_l,36.987,63.013,52.583,47.417,7.33,224,0.875,bicubic,-56.053,-45.957,-43
legacy_seresnet50,36.873,63.127,53.487,46.513,28.09,224,0.875,bilinear,-55.797,-45.143,-17
tv_densenet121,36.810,63.190,54.033,45.967,7.98,224,0.875,bicubic,-54.590,-44.217,+28
tf_efficientnet_lite2,36.807,63.193,53.320,46.680,6.09,260,0.890,bicubic,-55.783,-45.230,-12
mobilenetv2_120d,36.780,63.220,54.047,45.953,5.83,224,0.875,bicubic,-55.830,-44.323,-16
tf_efficientnet_lite1,36.737,63.263,53.590,46.410,5.42,240,0.882,bicubic,-55.573,-44.900,-2
regnetx_016,36.683,63.317,53.297,46.703,9.19,224,0.875,bicubic,-55.857,-45.253,-11
hardcorenas_a,36.640,63.360,54.910,45.090,5.26,224,0.875,bilinear,-54.980,-43.260,+15
levit_128s,36.620,63.380,53.117,46.883,7.78,224,0.900,bicubic,-54.880,-45.283,+17
efficientnet_b0,36.600,63.400,53.497,46.503,5.29,224,0.875,bicubic,-55.880,-45.183,-13
tf_efficientnet_em,36.380,63.620,52.840,47.160,6.90,240,0.882,bicubic,-56.790,-45.830,-55
skresnet18,36.320,63.680,54.197,45.803,11.96,224,0.875,bicubic,-53.840,-43.583,+44
tf_efficientnet_em,36.380,63.620,52.840,47.160,6.90,240,0.882,bicubic,-56.790,-45.830,-57
skresnet18,36.320,63.680,54.197,45.803,11.96,224,0.875,bicubic,-53.840,-43.583,+46
repvgg_b0,36.287,63.713,54.057,45.943,15.82,224,0.875,bilinear,-55.393,-44.393,+7
tv_resnet50,36.177,63.823,52.803,47.197,25.56,224,0.875,bilinear,-55.963,-45.617,-3
legacy_seresnet34,36.143,63.857,52.553,47.447,21.96,224,0.875,bilinear,-55.337,-45.767,+12
tv_resnet34,36.087,63.913,53.533,46.467,21.80,224,0.875,bilinear,-54.203,-44.447,+39
legacy_seresnet34,36.143,63.857,52.553,47.447,21.96,224,0.875,bilinear,-55.337,-45.647,+12
coat_tiny,36.123,63.877,51.063,48.937,5.50,224,0.900,bicubic,-57.387,-47.627,-84
tv_resnet34,36.087,63.913,53.533,46.467,21.80,224,0.875,bilinear,-54.203,-44.447,+40
vit_deit_tiny_distilled_patch16_224,36.023,63.977,54.240,45.760,5.91,224,0.900,bicubic,-55.077,-44.030,+26
mobilenetv2_140,36.000,64.000,53.943,46.057,6.11,224,0.875,bicubic,-56.030,-44.307,-5
mobilenetv2_140,36.000,64.000,53.943,46.057,6.11,224,0.875,bicubic,-56.030,-44.307,-6
tf_efficientnet_lite0,35.930,64.070,53.480,46.520,4.65,224,0.875,bicubic,-55.370,-44.610,+13
selecsls42b,35.813,64.187,52.487,47.513,32.46,224,0.875,bicubic,-56.667,-45.953,-22
gluon_resnet34_v1b,35.760,64.240,52.187,47.813,21.80,224,0.875,bicubic,-55.340,-45.993,+21
selecsls42b,35.813,64.187,52.487,47.513,32.46,224,0.875,bicubic,-56.667,-45.953,-23
gluon_resnet34_v1b,35.763,64.237,52.187,47.813,21.80,224,0.875,bicubic,-55.337,-45.993,+21
dla34,35.643,64.357,52.783,47.217,15.74,224,0.875,bilinear,-55.597,-45.397,+13
mixnet_m,35.640,64.360,52.430,47.570,5.01,224,0.875,bicubic,-56.630,-45.920,-17
mixnet_m,35.640,64.360,52.430,47.570,5.01,224,0.875,bicubic,-56.630,-45.920,-18
efficientnet_lite0,35.620,64.380,53.657,46.343,4.65,224,0.875,bicubic,-55.640,-44.593,+10
ssl_resnet18,35.597,64.403,53.740,46.260,11.69,224,0.875,bilinear,-55.103,-44.280,+24
mobilenetv3_rw,35.547,64.453,53.713,46.287,5.48,224,0.875,bicubic,-56.003,-44.557,-1
efficientnet_es_pruned,35.390,64.610,52.850,47.150,5.44,224,0.875,bicubic,-56.310,-45.570,-8
mobilenetv3_rw,35.547,64.453,53.713,46.287,5.48,224,0.875,bicubic,-56.003,-44.557,-2
efficientnet_es_pruned,35.390,64.610,52.850,47.150,5.44,224,0.875,bicubic,-56.310,-45.570,-9
mobilenetv2_110d,35.293,64.707,52.830,47.170,4.52,224,0.875,bicubic,-56.057,-45.360,+3
tf_mixnet_m,35.180,64.820,50.987,49.013,5.01,224,0.875,bicubic,-57.020,-47.433,-19
tf_mixnet_m,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,+9
resnet18d,35.127,64.873,52.890,47.110,11.71,224,0.875,bicubic,-54.863,-44.940,+26
ese_vovnet19b_dw,34.840,65.160,52.030,47.970,6.54,224,0.875,bicubic,-57.170,-46.480,-18
regnety_008,34.807,65.193,51.743,48.257,6.26,224,0.875,bicubic,-57.093,-46.677,-16
resnet18d,35.127,64.873,52.890,47.110,11.71,224,0.875,bicubic,-54.863,-44.940,+27
convit_tiny,35.047,64.953,51.787,48.213,5.71,224,0.875,bicubic,-55.483,-46.423,+20
ese_vovnet19b_dw,34.840,65.160,52.030,47.970,6.54,224,0.875,bicubic,-57.170,-46.480,-20
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,+19
mobilenetv3_large_100,34.603,65.397,52.860,47.140,5.48,224,0.875,bicubic,-56.877,-45.340,-9
seresnext26d_32x4d,34.543,65.457,51.543,48.457,16.81,224,0.875,bicubic,-57.897,-46.997,-36
seresnext26t_32x4d,34.540,65.460,51.377,48.623,16.81,224,0.875,bicubic,-58.280,-47.183,-57
mixer_b16_224,34.423,65.577,48.093,51.907,59.88,224,0.875,bicubic,-56.717,-49.307,+2
resnet26d,34.273,65.727,51.687,48.313,16.01,224,0.875,bicubic,-57.957,-46.763,-30
tf_efficientnet_es,34.263,65.737,51.350,48.650,5.44,224,0.875,bicubic,-57.837,-47.090,-28
fbnetc_100,34.253,65.747,51.180,48.820,5.57,224,0.875,bilinear,-57.017,-46.650,-8
regnety_006,34.150,65.850,51.277,48.723,6.06,224,0.875,bicubic,-57.420,-47.153,-18
tf_mobilenetv3_large_100,33.950,66.050,51.490,48.510,5.48,224,0.875,bilinear,-57.470,-46.770,-14
regnetx_008,33.770,66.230,50.547,49.453,7.26,224,0.875,bicubic,-57.410,-47.833,-6
mnasnet_100,33.763,66.237,51.170,48.830,4.38,224,0.875,bicubic,-57.437,-47.070,-8
semnasnet_100,33.520,66.480,50.787,49.213,3.89,224,0.875,bicubic,-58.140,-47.483,-24
resnet26,33.500,66.500,50.927,49.073,16.00,224,0.875,bicubic,-57.940,-47.353,-19
mixnet_s,33.480,66.520,50.997,49.003,4.13,224,0.875,bicubic,-58.300,-47.303,-30
spnasnet_100,33.477,66.523,51.267,48.733,4.42,224,0.875,bilinear,-57.133,-46.683,+1
vgg19_bn,33.230,66.770,50.803,49.197,143.68,224,0.875,bilinear,-57.760,-47.307,-5
mobilenetv3_large_100,34.603,65.397,52.860,47.140,5.48,224,0.875,bicubic,-56.877,-45.460,-9
seresnext26d_32x4d,34.543,65.457,51.543,48.457,16.81,224,0.875,bicubic,-57.897,-46.957,-39
seresnext26t_32x4d,34.540,65.460,51.377,48.623,16.81,224,0.875,bicubic,-58.280,-47.183,-59
mixer_b16_224,34.423,65.577,48.093,51.907,59.88,224,0.875,bicubic,-56.717,-49.307,+1
resnet26d,34.273,65.727,51.687,48.313,16.01,224,0.875,bicubic,-57.957,-46.763,-32
tf_efficientnet_es,34.263,65.737,51.350,48.650,5.44,224,0.875,bicubic,-57.837,-47.090,-30
fbnetc_100,34.253,65.747,51.180,48.820,5.57,224,0.875,bilinear,-57.017,-46.650,-9
regnety_006,34.150,65.850,51.277,48.723,6.06,224,0.875,bicubic,-57.420,-47.153,-20
tf_mobilenetv3_large_100,33.950,66.050,51.490,48.510,5.48,224,0.875,bilinear,-57.470,-46.770,-15
regnetx_008,33.770,66.230,50.547,49.453,7.26,224,0.875,bicubic,-57.410,-47.833,-7
mnasnet_100,33.763,66.237,51.170,48.830,4.38,224,0.875,bicubic,-57.437,-46.880,-10
semnasnet_100,33.520,66.480,50.787,49.213,3.89,224,0.875,bicubic,-58.140,-47.483,-26
resnet26,33.500,66.500,50.927,49.073,16.00,224,0.875,bicubic,-57.940,-47.353,-20
mixnet_s,33.480,66.520,50.997,49.003,4.13,224,0.875,bicubic,-58.300,-47.303,-32
spnasnet_100,33.477,66.523,51.267,48.733,4.42,224,0.875,bilinear,-57.133,-46.683,0
vgg19_bn,33.230,66.770,50.803,49.197,143.68,224,0.875,bilinear,-57.760,-47.307,-6
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,-4
regnetx_006,33.157,66.843,50.250,49.750,6.20,224,0.875,bicubic,-57.603,-47.850,-5
resnet18,33.067,66.933,51.170,48.830,11.69,224,0.875,bilinear,-55.083,-45.950,+17
legacy_seresnext26_32x4d,32.757,67.243,49.237,50.763,16.79,224,0.875,bicubic,-59.813,-49.183,-61
legacy_seresnext26_32x4d,32.757,67.243,49.237,50.763,16.79,224,0.875,bicubic,-59.813,-49.183,-63
hrnet_w18_small,32.667,67.333,50.587,49.413,13.19,224,0.875,bilinear,-57.213,-47.313,+3
vit_deit_tiny_patch16_224,32.667,67.333,50.273,49.727,5.72,224,0.900,bicubic,-56.953,-47.687,+5
legacy_seresnet18,32.600,67.400,50.340,49.660,11.78,224,0.875,bicubic,-56.670,-47.340,+7
mobilenetv2_100,32.523,67.477,50.800,49.200,3.50,224,0.875,bicubic,-57.307,-47.030,+1
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,+7
regnety_004,32.333,67.667,49.453,50.547,4.34,224,0.875,bicubic,-58.447,-48.627,-14
tf_mixnet_s,32.183,67.817,48.493,51.507,4.13,224,0.875,bicubic,-59.497,-49.747,-41
regnety_004,32.333,67.667,49.453,50.547,4.34,224,0.875,bicubic,-58.447,-48.627,-15
tf_mixnet_s,32.183,67.817,48.493,51.507,4.13,224,0.875,bicubic,-59.497,-49.747,-43
tf_mobilenetv3_large_075,31.867,68.133,49.110,50.890,3.99,224,0.875,bilinear,-58.453,-48.760,-9
tf_mobilenetv3_large_minimal_100,31.597,68.403,49.337,50.663,3.92,224,0.875,bilinear,-57.583,-47.983,+2
vgg16_bn,30.357,69.643,47.260,52.740,138.37,224,0.875,bilinear,-60.183,-50.730,-14
vgg16_bn,30.357,69.643,47.260,52.740,138.37,224,0.875,bilinear,-60.183,-50.730,-15
regnety_002,29.687,70.313,46.787,53.213,3.16,224,0.875,bicubic,-58.513,-50.643,+3
vgg13_bn,28.883,71.117,46.737,53.263,133.05,224,0.875,bilinear,-60.317,-50.793,-2
regnetx_002,28.860,71.140,45.420,54.580,2.68,224,0.875,bicubic,-58.520,-51.570,+4

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
2 ig_resnext101_32x48d 79.650 20.350 89.393 10.607 828.41 224 0.875 bilinear -17.320 -10.277 +7 +8
3 ig_resnext101_32x32d 79.457 20.543 89.183 10.817 468.53 224 0.875 bilinear -17.323 -10.347 +13 +15
4 ig_resnext101_32x16d 78.837 21.163 88.480 11.520 194.03 224 0.875 bilinear -17.603 -11.060 +27 +32
5 tf_efficientnet_l2_ns_475 76.480 23.520 88.653 11.347 480.31 475 0.936 bicubic -21.270 -11.167 -2
6 swsl_resnext101_32x16d 76.303 23.697 87.733 12.267 194.03 224 0.875 bilinear -19.967 -11.767 +34 +40
7 ig_resnext101_32x8d 75.813 24.187 86.200 13.800 88.79 224 0.875 bilinear -20.117 -13.300 -13.180 +51 +58
8 swsl_resnext101_32x8d 75.590 24.410 86.937 13.063 88.79 224 0.875 bilinear -20.650 -12.653 +34 +40
9 tf_efficientnet_l2_ns 74.650 25.350 87.543 12.457 480.31 800 0.960 bicubic -23.130 -12.347 -7
10 swsl_resnext101_32x4d 72.660 27.340 85.157 14.843 44.18 224 0.875 bilinear -23.390 -14.373 +42 +48
11 swsl_resnext50_32x4d 68.977 31.023 82.810 17.190 25.03 224 0.875 bilinear -26.643 -16.630 +58 +67
12 swsl_resnet50 68.297 31.703 83.313 16.687 25.56 224 0.875 bilinear -26.903 -16.077 +79 +92
13 tf_efficientnet_b7_ns 67.510 32.490 81.383 18.617 66.35 600 0.949 bicubic -29.690 -18.317 -9
14 swin_large_patch4_window12_384 66.283 33.717 79.783 20.217 196.74 384 1.000 bicubic -30.887 -19.897 -9
15 tf_efficientnet_b6_ns 65.587 34.413 79.553 20.447 43.04 528 0.942 bicubic -31.433 -20.157 -8 -7
16 swin_large_patch4_window7_224 63.870 36.130 78.180 21.820 196.53 224 0.900 bicubic -33.080 -21.480 -6 -4
17 swin_base_patch4_window12_384 63.470 36.530 78.063 21.937 87.90 384 1.000 bicubic -33.650 -21.717 -11
18 tf_efficientnet_b5_ns 63.047 36.953 77.777 22.223 30.39 456 0.934 bicubic -33.823 -21.863 -5 -3
19 tf_efficientnet_b4_ns 61.230 38.770 76.173 23.827 19.34 380 0.922 bicubic -35.480 -23.467 -1 +1
20 swin_base_patch4_window7_224 tf_efficientnetv2_l_in21ft1k 59.537 60.953 40.463 39.047 74.247 75.847 25.753 24.153 87.77 118.52 224 480 0.900 1.000 bicubic -37.143 -36.157 -25.413 -23.863 0 -13
21 tf_efficientnet_b8_ap swin_base_patch4_window7_224 57.830 59.537 42.170 40.463 72.957 74.247 27.043 25.753 87.41 87.77 672 224 0.954 0.900 bicubic -38.720 -37.143 -26.583 -25.413 +4 +1
22 cait_m48_448 tf_efficientnetv2_m_in21ft1k 57.470 58.647 42.530 41.353 71.860 73.983 28.140 26.017 356.46 54.14 448 480 1.000 bicubic -39.410 -38.323 -27.760 -25.627 -11
23 tf_efficientnet_b8_ap 57.830 42.170 72.957 27.043 87.41 672 0.954 bicubic -38.720 -26.583 +5
24 cait_m48_448 57.470 42.530 71.860 28.140 356.46 448 1.000 bicubic -39.410 -27.800 -10
25 cait_m36_384 57.467 42.533 72.313 27.687 271.22 384 1.000 bicubic -39.363 -27.347 -9
26 tf_efficientnet_b3_ns 57.417 42.583 72.387 27.613 12.23 300 0.904 bicubic -38.683 -27.093 +23 +27
27 vit_large_patch16_384 54.750 45.250 70.007 29.993 304.72 384 1.000 bicubic -41.610 -29.623 +11 +14
28 vit_base_r50_s16_384 54.400 45.600 69.560 30.440 98.95 384 1.000 bicubic -42.050 -30.100 +4 +7
29 resnetv2_152x4_bitm 54.263 45.737 70.137 29.863 936.53 480 1.000 bilinear -42.617 -29.523 -29.483 -15 -16
30 dm_nfnet_f6 54.073 45.927 69.110 30.890 438.36 576 0.956 bicubic -42.917 -30.630 -20 -21
31 tf_efficientnet_b5_ap 53.870 46.130 69.160 30.840 30.39 456 0.934 bicubic -42.210 -30.380 +19 +23
32 dm_nfnet_f5 53.773 46.227 68.500 31.500 377.21 544 0.954 bicubic -42.937 -31.180 -13
33 tf_efficientnet_b2_ns 53.600 46.400 70.270 29.730 9.11 260 0.890 bicubic -41.920 -29.070 +42 +50
34 tf_efficientnet_b6_ap 53.560 46.440 68.550 31.450 43.04 528 0.942 bicubic -42.810 -31.000 -30.920 +2 +4
35 cait_s36_384 53.550 46.450 68.000 32.000 68.37 384 1.000 bicubic -43.080 -31.600 -12 -11
36 tf_efficientnet_b8 53.410 46.590 69.090 30.910 87.41 672 0.954 bicubic -43.290 -30.440 -15
37 tf_efficientnet_b7_ap 53.260 46.740 68.873 31.127 66.35 600 0.949 bicubic -43.090 -30.717 +2 +5
38 dm_nfnet_f3 53.190 46.810 68.083 31.917 254.92 416 0.940 bicubic -43.440 -31.557 -14 -13
39 tf_efficientnet_b4_ap tf_efficientnetv2_s_in21ft1k 53.090 53.150 46.910 46.850 68.210 69.000 31.790 31.000 19.34 21.46 380 384 0.922 1.000 bicubic -42.400 -43.320 -31.180 -30.570 +39 -6
40 tf_efficientnet_b4_ap 53.090 46.910 68.210 31.790 19.34 380 0.922 bicubic -42.400 -31.180 +46
41 tf_efficientnet_b7 52.393 47.607 68.233 31.767 66.35 600 0.949 bicubic -44.187 -31.277 -15
42 swsl_resnet18 tf_efficientnetv2_l 52.327 52.377 47.673 47.623 70.480 67.237 29.520 32.763 11.69 118.52 224 480 0.875 1.000 bilinear bicubic -38.763 -44.273 -27.730 -32.323 +289 -19
43 dm_nfnet_f4 swsl_resnet18 52.260 52.327 47.740 47.673 67.120 70.480 32.880 29.520 316.07 11.69 512 224 0.951 0.875 bicubic bilinear -44.560 -38.763 -32.480 -27.730 -25 +315
44 vit_deit_base_distilled_patch16_384 efficientnetv2_rw_m 52.257 52.323 47.743 47.677 67.733 67.210 32.267 32.790 87.63 53.24 384 416 1.000 bicubic -44.253 -43.947 -31.857 -32.350 -15 +1
45 cait_s24_384 dm_nfnet_f4 51.783 52.260 48.217 47.740 66.313 67.120 33.687 32.880 47.06 316.07 384 512 1.000 0.951 bicubic -44.787 -44.560 -33.237 -32.480 -18 -28
46 vit_deit_base_distilled_patch16_384 52.257 47.743 67.733 32.267 87.63 384 1.000 bicubic -44.253 -31.857 -16
47 cait_s24_384 51.783 48.217 66.313 33.687 47.06 384 1.000 bicubic -44.787 -33.237 -20
48 ecaresnet269d 51.670 48.330 66.047 33.953 102.09 352 1.000 bicubic -44.790 -33.563 -14
49 vit_base_patch16_224_miil 51.557 48.443 65.207 34.793 86.54 224 0.875 bilinear -44.473 -34.143 +9 +10
50 pit_b_distilled_224 51.153 48.847 66.770 33.230 74.79 224 0.900 bicubic -44.917 -32.610 +4 +5
51 resnetv2_152x2_bitm 51.040 48.960 68.527 31.473 236.34 480 1.000 bilinear -45.460 -31.093 -18 -19
52 vit_base_patch16_384 tf_efficientnet_b1_ns 50.883 49.117 65.270 67.910 34.730 32.090 86.86 7.79 384 240 1.000 0.882 bicubic -45.307 -43.977 -34.260 -31.340 -5 +72
53 tf_efficientnet_b1_ns vit_base_patch16_384 50.883 49.117 67.910 65.270 32.090 34.730 7.79 86.86 240 384 0.882 1.000 bicubic -43.977 -45.307 -31.340 -34.260 +58 -4
54 vit_large_patch16_224 50.877 49.123 66.227 33.773 304.33 224 0.900 bicubic -44.413 -33.083 +36 +42
55 efficientnet_b4 tf_efficientnetv2_m 50.510 50.557 49.490 49.443 65.703 66.010 34.297 33.990 19.34 54.14 384 480 1.000 bicubic -45.010 -45.993 -33.687 -33.560 +22 -26
56 ssl_resnext101_32x16d efficientnet_b4 50.257 50.510 49.743 49.490 66.033 65.703 33.967 34.297 194.03 19.34 224 384 0.875 1.000 bilinear bicubic -45.153 -45.010 -33.377 -33.687 +28 +26
57 cait_s24_224 ssl_resnext101_32x16d 50.243 50.257 49.757 49.743 65.027 66.033 34.973 33.967 46.92 194.03 224 1.000 0.875 bicubic bilinear -45.407 -45.153 -34.363 -33.377 +14 +33
58 cait_s24_224 50.243 49.757 65.027 34.973 46.92 224 1.000 bicubic -45.407 -34.213 +18
59 resnest269e 50.153 49.847 64.670 35.330 110.93 416 0.928 bicubic -45.967 -34.850 -8
60 vit_deit_base_distilled_patch16_224 50.063 49.937 66.227 33.773 87.34 224 0.900 bicubic -45.687 -33.053 +9
61 tf_efficientnet_b3_ap 50.057 49.943 65.210 34.790 12.23 300 0.904 bicubic -44.913 -33.900 +44 +54
62 resnest200e 49.873 50.127 64.743 35.257 70.20 320 0.909 bicubic -46.197 -34.737 -6
63 resnetv2_101x3_bitm 49.823 50.177 66.917 33.083 387.93 480 1.000 bilinear -46.537 -32.683 -22 -23
64 cait_xs24_384 49.527 50.473 64.900 35.100 26.67 384 1.000 bicubic -46.483 -34.530 -4
65 tf_efficientnet_b5 49.510 50.490 65.657 34.343 30.39 456 0.934 bicubic -46.470 -33.793 -3
66 resnet200d 49.470 50.530 64.330 35.670 64.69 320 1.000 bicubic -46.640 -35.130 -14
67 resnest101e 49.367 50.633 65.587 34.413 48.28 256 0.875 bilinear -46.203 -33.683 +10 +13
68 resnet152d 49.253 50.747 64.413 35.587 60.21 320 1.000 bicubic -46.617 -35.017 -1
69 seresnet152d 49.247 50.753 64.170 35.830 66.84 320 1.000 bicubic -47.063 -35.340 -25 -26
70 ssl_resnext101_32x8d 49.067 50.933 65.480 34.520 88.79 224 0.875 bilinear -46.273 -33.840 +20 +25
71 repvgg_b3 48.917 51.083 64.887 35.113 123.09 224 0.875 bilinear -45.633 -34.023 +61 +77
72 resnetrs420 48.857 51.143 63.427 36.573 191.89 416 1.000 bicubic -47.543 -36.113 -34 -35
73 dm_nfnet_f2 48.623 51.377 63.537 36.463 193.78 352 0.920 bicubic -47.877 -36.033 -40 -42
74 efficientnet_v2s efficientnetv2_rw_s 48.603 51.397 63.840 36.160 23.94 384 1.000 bicubic -47.107 -35.540 -3 -1
75 efficientnet_b3 48.563 51.437 64.250 35.750 12.23 320 1.000 bicubic -46.577 -34.960 +26 +33
76 ecaresnet101d 48.527 51.473 64.100 35.900 44.57 224 0.875 bicubic -46.633 -35.200 -35.130 +23 +29
77 repvgg_b3g4 48.310 51.690 64.800 35.200 83.83 224 0.875 bilinear -46.180 -34.220 +58 +74
78 vit_large_patch32_384 48.250 51.750 61.830 38.170 306.63 384 1.000 bicubic -46.990 -37.490 +16 +21
79 resnetrs350 convit_base 48.050 48.217 51.950 51.783 62.653 63.000 37.347 37.000 163.96 86.54 384 224 1.000 0.875 bicubic -48.190 -46.883 -36.817 -36.140 -32 +31
80 repvgg_b2g4 resnetrs350 47.787 48.050 52.213 51.950 64.390 62.653 35.610 37.347 61.76 163.96 224 384 0.875 1.000 bilinear bicubic -46.033 -48.190 -34.610 -36.817 +109 -33
81 eca_nfnet_l1 twins_svt_large 47.663 47.947 52.337 52.053 62.767 62.907 37.233 37.093 41.41 99.27 320 224 1.000 0.900 bicubic -48.267 -47.773 -36.613 -36.463 -16 -9
82 pit_s_distilled_224 mixer_b16_224_miil 47.543 47.790 52.457 52.210 63.493 63.400 36.507 36.600 24.04 59.88 224 0.900 0.875 bicubic bilinear -47.187 -47.090 -35.697 -35.680 +33 +40
83 resnest50d_4s2x40d repvgg_b2g4 47.483 47.787 52.517 52.213 63.807 64.390 36.193 35.610 30.42 61.76 224 0.875 bicubic bilinear -47.227 -46.033 -35.323 -34.540 +35 +125
84 efficientnet_b3_pruned eca_nfnet_l1 47.447 47.663 52.553 52.337 62.793 62.767 37.207 37.233 9.86 41.41 300 320 0.904 1.000 bicubic -47.133 -48.267 -36.277 -36.733 +45 -20
85 vit_base_patch16_224 pit_s_distilled_224 47.340 47.543 52.660 52.457 61.607 63.493 38.393 36.507 86.57 24.04 224 0.900 bicubic -47.870 -47.187 -37.623 -35.697 +11 +45
86 tresnet_m resnest50d_4s2x40d 47.230 47.483 52.770 52.517 61.993 63.807 38.007 36.193 31.39 30.42 224 0.875 bilinear bicubic -48.150 -47.227 -37.157 -35.323 +2 +47
87 tf_efficientnet_b6 efficientnet_b3_pruned 47.213 47.447 52.787 52.553 63.110 62.793 36.890 37.207 43.04 9.86 528 300 0.942 0.904 bicubic -49.077 -47.133 -36.410 -36.277 -42 +58
88 ssl_resnext101_32x4d vit_base_patch16_224 47.177 47.340 52.823 52.660 63.367 61.607 36.633 38.393 44.18 86.57 224 0.875 0.900 bilinear bicubic -47.983 -47.870 -35.863 -37.623 +10 +14
89 resnetrs270 tresnet_m 47.107 47.230 52.893 52.770 62.010 61.993 37.990 38.007 129.86 31.39 352 224 1.000 0.875 bicubic bilinear -48.953 -48.150 -37.480 -37.157 -32 +4
90 tf_efficientnet_b6 47.213 52.787 63.110 36.890 43.04 528 0.942 bicubic -49.077 -36.410 -46
91 ssl_resnext101_32x4d 47.177 52.823 63.367 36.633 44.18 224 0.875 bilinear -47.983 -35.933 +15
92 resnetrs270 47.107 52.893 62.010 37.990 129.86 352 1.000 bicubic -48.953 -37.480 -35
93 tf_efficientnet_b4 47.083 52.917 62.867 37.133 19.34 380 0.922 bicubic -48.507 -36.463 -14
94 resnet101d 46.893 53.107 62.317 37.683 44.57 320 1.000 bicubic -48.857 -37.123 -23 -26
95 resnetrs200 46.837 53.163 62.487 37.513 93.21 320 1.000 bicubic -49.153 -36.953 -31 -34
96 resnetv2_50x3_bitm 46.827 53.173 64.873 35.127 217.32 480 1.000 bilinear -49.313 -34.747 -43 -46
97 dm_nfnet_f1 46.693 53.307 61.560 38.440 132.63 320 0.910 bicubic -49.677 -37.910 -37.990 -55 -58
98 gluon_seresnext101_64x4d 46.677 53.323 61.303 38.697 88.23 224 0.875 bicubic -47.973 -37.677 +29 +41
99 tresnet_xl twins_pcpvt_large 46.283 46.637 53.717 53.363 61.943 62.240 38.057 37.760 78.44 60.99 224 0.875 0.900 bilinear bicubic -48.777 -49.083 -37.317 -37.250 +7 -28
100 vit_deit_small_distilled_patch16_224 tresnet_xl 46.160 46.283 53.840 53.717 62.417 61.943 37.583 38.057 22.44 78.44 224 0.900 0.875 bicubic bilinear -48.430 -48.777 -36.683 -37.317 +31 +13
101 regnety_160 vit_deit_small_distilled_patch16_224 46.153 46.160 53.847 53.840 61.837 62.417 38.163 37.583 83.59 22.44 288 224 1.000 0.900 bicubic -49.727 -48.430 -37.723 -36.683 -32 +43
102 gernet_m regnety_160 46.150 46.153 53.850 53.847 62.700 61.837 37.300 38.163 21.14 83.59 224 288 0.875 1.000 bilinear bicubic -48.400 -49.727 -36.550 -37.723 +34 -36
103 resnest50d_1s4x24d gernet_m 46.083 46.150 53.917 53.850 62.377 62.700 37.623 37.300 25.68 21.14 224 0.875 bicubic bilinear -48.307 -48.400 -36.693 -36.230 +40 +44
104 tf_efficientnet_b0_ns resnest50d_1s4x24d 46.047 46.083 53.953 53.917 63.253 62.377 36.747 37.623 5.29 25.68 224 0.875 bicubic -47.693 -48.307 -35.727 -36.693 +100 +53
105 resnest50d tf_efficientnet_b0_ns 45.937 46.047 54.063 53.953 62.623 63.253 37.377 36.747 27.48 5.29 224 0.875 bilinear bicubic -48.683 -47.693 -36.407 -35.727 +23 +114
106 regnety_032 resnet51q 45.893 46.027 54.107 53.973 61.537 60.910 38.463 39.090 19.44 35.70 288 1.000 bicubic bilinear -49.577 -49.173 -37.783 -38.370 -19 -3
107 gluon_seresnext101_32x4d resnest50d 45.590 45.937 54.410 54.063 61.143 62.623 38.857 37.377 48.96 27.48 224 0.875 bicubic bilinear -48.860 -48.683 -37.947 -36.407 +33
108 gluon_resnet152_v1d regnety_032 45.430 45.893 54.570 54.107 60.077 61.537 39.923 38.463 60.21 19.44 224 288 0.875 1.000 bicubic -49.010 -49.577 -38.933 -37.843 +33 -21
109 dm_nfnet_f0 twins_pcpvt_base 45.420 45.893 54.580 54.107 60.990 61.337 39.010 38.663 71.49 43.83 256 224 0.900 bicubic -50.210 -49.567 -38.310 -38.053 -32 -20
110 ssl_resnext50_32x4d levit_384 45.407 45.877 54.593 54.123 62.047 61.693 37.953 38.307 25.03 39.13 224 0.875 0.900 bilinear bicubic -49.293 -49.333 -37.193 -37.467 +12 -9
111 nfnet_l0 twins_svt_base 45.390 45.877 54.610 54.123 62.057 60.967 37.943 39.033 35.07 56.07 288 224 1.000 0.900 bicubic -50.000 -49.693 -37.363 -38.263 -21 -30
112 tresnet_xl_448 gluon_seresnext101_32x4d 45.223 45.590 54.777 54.410 61.437 61.143 38.563 38.857 78.44 48.96 448 224 0.875 bilinear bicubic -50.287 -48.860 -37.903 -37.957 -28 +40
113 nasnetalarge gluon_resnet152_v1d 45.210 45.430 54.790 54.570 57.883 60.077 42.117 39.923 88.75 60.21 331 224 0.911 0.875 bicubic -49.940 -49.010 -41.247 -38.933 -10 +41
114 swin_small_patch4_window7_224 dm_nfnet_f0 45.163 45.420 54.837 54.580 60.330 60.990 39.670 39.010 49.61 71.49 224 256 0.900 bicubic -50.557 -50.210 -38.960 -38.310 -41 -37
115 tf_efficientnet_b3 ssl_resnext50_32x4d 45.107 45.407 54.893 54.593 60.650 62.047 39.350 37.953 12.23 25.03 300 224 0.904 0.875 bicubic bilinear -49.803 -49.293 -38.460 -37.193 -4 +19
116 rexnet_200 nfnet_l0 45.047 45.390 54.953 54.610 62.317 62.057 37.683 37.943 16.37 35.07 224 288 0.875 1.000 bicubic -49.613 -50.000 -36.773 -37.363 +9 -24
117 resnetrs152 tresnet_xl_448 44.943 45.223 55.057 54.777 59.713 61.437 40.287 38.563 86.62 78.44 320 448 1.000 0.875 bicubic bilinear -51.017 -50.287 -39.667 -37.903 -51 -32
118 ecaresnetlight nasnetalarge 44.890 45.210 55.110 54.790 60.770 57.883 39.230 42.117 30.16 88.75 224 331 0.875 0.911 bicubic -49.250 -49.940 -38.180 -41.247 +43 -11
119 vit_deit_base_patch16_224 convit_small 44.870 45.203 55.130 54.797 59.177 60.510 40.823 39.490 86.57 27.78 224 0.900 0.875 bicubic -50.140 -49.717 -39.803 -38.600 -12 0
120 vit_deit_base_patch16_384 swin_small_patch4_window7_224 44.777 45.163 55.223 54.837 59.617 60.330 40.383 39.670 86.86 49.61 384 224 1.000 0.900 bicubic -50.873 -50.557 -39.623 -38.960 -44 -50
121 cait_xxs36_384 tf_efficientnet_b3 44.773 45.107 55.227 54.893 59.380 60.650 40.620 39.350 17.37 12.23 384 300 1.000 0.904 bicubic -50.447 -49.803 -39.940 -38.460 -23 -1
122 gernet_l rexnet_200 44.740 45.047 55.260 54.953 58.943 62.317 41.057 37.683 31.08 16.37 256 224 0.875 bilinear bicubic -50.190 -49.613 -40.257 -36.833 -13 +16
123 tf_efficientnet_b2_ap resnetrs152 44.700 44.943 55.300 55.057 60.680 59.713 39.320 40.287 9.11 86.62 260 320 0.890 1.000 bicubic -49.570 -51.017 -38.270 -39.667 +29 -60
124 vit_base_patch32_384 ecaresnetlight 44.693 44.890 55.307 55.110 58.530 60.770 41.470 39.230 88.30 30.16 384 224 1.000 0.875 bicubic -50.567 -49.250 -40.650 -38.260 -29 +52
125 ens_adv_inception_resnet_v2 vit_deit_base_patch16_224 44.393 44.870 55.607 55.130 58.117 59.177 41.883 40.823 55.84 86.57 299 224 0.897 0.900 bicubic -49.737 -50.140 -40.673 -39.803 +38 -11
126 tresnet_l vit_deit_base_patch16_384 44.363 44.777 55.637 55.223 59.953 59.617 40.047 40.383 55.99 86.86 224 384 0.875 1.000 bilinear bicubic -50.537 -50.873 -39.077 -39.773 -14 -51
127 gluon_resnext101_32x4d cait_xxs36_384 44.290 44.773 55.710 55.227 59.090 59.380 40.910 40.620 44.18 17.37 224 384 0.875 1.000 bicubic -49.830 -50.447 -39.840 -39.940 +39 -27
128 wide_resnet50_2 gernet_l 44.177 44.740 55.823 55.260 59.727 58.943 40.273 41.057 68.88 31.08 224 256 0.875 bicubic bilinear -50.493 -50.190 -39.403 -40.257 -5 -11
129 cspresnext50 tf_efficientnet_b2_ap 44.147 44.700 55.853 55.300 60.533 60.680 39.467 39.320 20.57 9.11 224 260 0.875 0.890 bilinear bicubic -49.613 -49.570 -38.307 -38.400 +70 +36
130 seresnext50_32x4d vit_base_patch32_384 44.127 44.693 55.873 55.307 59.490 58.530 40.510 41.470 27.56 88.30 224 384 0.875 1.000 bicubic -50.693 -50.567 -39.640 -40.650 -15 -33
131 gluon_resnet152_v1s ens_adv_inception_resnet_v2 44.073 44.393 55.927 55.607 58.703 58.117 41.297 41.883 60.32 55.84 224 299 0.875 0.897 bicubic -50.647 -49.737 -40.357 -40.673 -12 +46
132 pit_b_224 tresnet_l 44.070 44.363 55.930 55.637 58.017 59.953 41.983 40.047 73.76 55.99 224 0.900 0.875 bicubic bilinear -50.720 -50.537 -40.803 -39.077 -16 -11
133 ssl_resnet50 gluon_resnext101_32x4d 44.010 44.290 55.990 55.710 61.887 59.090 38.113 40.910 25.56 44.18 224 0.875 bilinear bicubic -50.300 -49.830 -37.263 -39.840 +16 +47
134 inception_resnet_v2 wide_resnet50_2 44.003 44.177 55.997 55.823 57.907 59.727 42.093 40.273 55.84 68.88 299 224 0.897 0.875 bicubic -50.337 -50.493 -40.893 -39.323 +14 +2
135 pnasnet5large cspresnext50 43.950 44.147 56.050 55.853 56.730 60.533 43.270 39.467 86.06 20.57 331 224 0.911 0.875 bicubic bilinear -51.410 -49.613 -42.400 -38.167 -43 +81
136 pit_s_224 seresnext50_32x4d 43.890 44.127 56.110 55.873 58.627 59.490 41.373 40.510 23.46 27.56 224 0.900 0.875 bicubic -50.700 -50.693 -40.303 -39.640 -6 -11
137 gluon_resnext101_64x4d gluon_resnet152_v1s 43.877 44.073 56.123 55.927 58.710 58.703 41.290 41.297 83.46 60.32 224 0.875 bicubic -50.473 -50.647 -40.170 -40.357 +10 -6
138 tnt_s_patch16_224 pit_b_224 43.773 44.070 56.227 55.930 59.197 58.017 40.803 41.983 23.76 73.76 224 0.900 bicubic -50.807 -50.720 -39.983 -40.803 -5 -12
139 cait_xxs36_224 ssl_resnet50 43.760 44.010 56.240 55.990 58.720 61.887 41.280 38.113 17.30 25.56 224 1.000 0.875 bicubic bilinear -50.180 -50.300 -40.200 -37.263 +43 +24
140 ecaresnet50d inception_resnet_v2 43.750 44.003 56.250 55.997 60.387 57.907 39.613 42.093 25.58 55.84 224 299 0.875 0.897 bicubic -50.440 -50.337 -38.633 -40.893 +17 +22
141 ecaresnet101d_pruned pnasnet5large 43.737 43.950 56.263 56.050 59.607 56.730 40.393 43.270 24.88 86.06 224 331 0.875 0.911 bicubic -50.713 -51.410 -39.493 -42.400 -2 -47
142 rexnet_150 pit_s_224 43.690 43.890 56.310 56.110 60.897 58.627 39.103 41.373 9.73 23.46 224 0.875 0.900 bicubic -50.580 -50.700 -38.183 -40.303 +9 +1
143 pit_xs_distilled_224 gluon_resnext101_64x4d 43.663 43.877 56.337 56.123 60.703 58.710 39.297 41.290 11.00 83.46 224 0.900 0.875 bicubic -49.577 -50.473 -38.117 -40.170 +100 +18
144 gluon_resnet101_v1d coat_lite_small 43.440 43.823 56.560 56.177 58.613 57.147 41.387 42.853 44.57 19.84 224 0.875 0.900 bicubic -50.730 -51.257 -40.297 -41.873 +16 -33
145 ecaresnet50t tnt_s_patch16_224 43.407 43.773 56.593 56.227 59.300 59.197 40.700 40.803 25.57 23.76 320 224 0.950 0.900 bicubic -51.663 -50.807 -39.990 -39.983 -40 +1
146 gluon_resnet101_v1s cait_xxs36_224 43.363 43.760 56.637 56.240 58.503 58.720 41.497 41.280 44.67 17.30 224 0.875 1.000 bicubic -50.807 -50.180 -40.507 -40.170 +13 +49
147 cspdarknet53 ecaresnet50d 43.357 43.750 56.643 56.250 59.430 60.387 40.570 39.613 27.64 25.58 256 224 0.887 0.875 bilinear bicubic -50.733 -50.440 -39.550 -38.633 +20 +24
148 dpn68b ecaresnet101d_pruned 43.287 43.737 56.713 56.263 58.673 59.607 41.327 40.393 12.61 24.88 224 0.875 bicubic -50.333 -50.713 -40.287 -39.483 +69 +5
149 eca_nfnet_l0 tf_efficientnetv2_s 43.230 43.710 56.770 56.290 59.913 58.597 40.087 41.403 24.14 21.46 288 384 1.000 bicubic -52.240 -52.000 -39.467 -40.803 -63 -75
150 resnest26d rexnet_150 43.140 43.690 56.860 56.310 60.623 60.897 39.377 39.103 17.07 9.73 224 0.875 bilinear bicubic -50.100 -50.580 -38.227 -38.053 +94 +16
151 resnetv2_101x1_bitm pit_xs_distilled_224 43.113 43.663 56.887 56.337 60.950 60.703 39.050 39.297 44.54 11.00 480 224 1.000 0.900 bilinear bicubic -52.397 -49.577 -38.560 -38.147 -68 +111
152 dpn131 gluon_resnet101_v1d 43.047 43.440 56.953 56.560 57.440 58.613 42.560 41.387 79.25 44.57 224 0.875 bicubic -50.713 -50.730 -41.360 -40.327 +48 +20
153 cspresnet50 ecaresnet50t 43.030 43.407 56.970 56.593 59.153 59.300 40.847 40.700 21.62 25.57 256 320 0.887 0.950 bilinear bicubic -50.830 -51.663 -39.717 -39.990 +35 -41
154 gluon_resnet101_v1s 43.363 56.637 58.503 41.497 44.67 224 0.875 bicubic -50.807 -40.507 +19
155 cspdarknet53 43.357 56.643 59.430 40.570 27.64 256 0.887 bilinear -50.733 -39.550 +26
156 dpn68b 43.287 56.713 58.673 41.327 12.61 224 0.875 bicubic -50.333 -40.027 +77
157 visformer_small 43.253 56.747 57.993 42.007 40.22 224 0.900 bicubic -51.707 -41.217 -41
158 eca_nfnet_l0 43.230 56.770 59.913 40.087 24.14 288 1.000 bicubic -52.240 -39.407 -70
159 resnest26d 43.140 56.860 60.623 39.377 17.07 224 0.875 bilinear -50.100 -38.127 +104
160 resnetv2_101x1_bitm 43.113 56.887 60.950 39.050 44.54 480 1.000 bilinear -52.397 -38.560 -76
161 twins_pcpvt_small 43.090 56.910 58.873 41.127 24.11 224 0.900 bicubic -51.510 -40.277 -19
162 dpn131 43.047 56.953 57.440 42.560 79.25 224 0.875 bicubic -50.713 -41.420 +55
163 cspresnet50 43.030 56.970 59.153 40.847 21.62 256 0.887 bilinear -50.830 -39.717 +40
164 tf_efficientnet_lite4 42.967 57.033 57.620 42.380 13.01 380 0.920 bilinear -51.903 -41.470 -41
165 gluon_resnet152_v1b twins_svt_small 42.903 42.923 57.097 57.077 57.750 58.453 42.250 41.547 60.19 24.06 224 0.875 0.900 bicubic -51.127 -51.847 -40.990 -40.627 +16 -37
166 dpn107 gluon_resnet152_v1b 42.857 42.903 57.143 57.097 57.367 57.750 42.633 42.250 86.92 60.19 224 0.875 bicubic -51.103 -51.127 -41.473 -40.990 +22 +20
167 tf_efficientnet_b1_ap dpn107 42.803 42.857 57.197 57.143 58.813 57.367 41.187 42.633 7.79 86.92 240 224 0.882 0.875 bicubic -50.827 -51.103 -39.987 -41.473 +58 +26
168 gluon_resnet152_v1c levit_256 42.800 42.823 57.200 57.177 57.737 57.897 42.263 42.103 60.21 18.89 224 0.875 0.900 bicubic -51.080 -51.577 -41.063 -41.163 +28 -12
169 gluon_xception65 tf_efficientnet_b1_ap 42.793 42.803 57.207 57.197 58.820 58.813 41.180 41.187 39.92 7.79 299 240 0.903 0.882 bicubic -51.217 -50.827 -40.200 -39.987 +15 +63
170 tresnet_l_448 gluon_resnet152_v1c 42.753 42.800 57.247 57.200 58.947 57.737 41.053 42.263 55.99 60.21 448 224 0.875 bilinear bicubic -52.657 -51.080 -40.353 -41.353 -71 +32
171 resnet50d gluon_xception65 42.703 42.793 57.297 57.207 58.697 58.820 41.303 41.180 25.58 39.92 224 299 0.875 0.903 bicubic -51.367 -51.217 -40.223 -40.200 +9 +18
172 gluon_seresnext50_32x4d tresnet_l_448 42.683 42.753 57.317 57.247 58.710 58.947 41.290 41.053 27.56 55.99 224 448 0.875 bicubic bilinear -51.487 -52.657 -40.230 -40.353 -4 -81
173 resnext101_32x8d resnet50d 42.557 42.703 57.443 57.297 58.317 58.697 41.683 41.303 88.79 25.58 224 0.875 bilinear bicubic -51.213 -51.367 -40.633 -40.223 +35 +11
174 seresnet50 gluon_seresnext50_32x4d 42.510 42.683 57.490 57.317 58.667 58.710 41.333 41.290 28.09 27.56 224 0.875 bicubic -51.570 -51.487 -40.303 -40.200 +5 0
175 resnetrs101 resnext101_32x8d 42.437 42.557 57.563 57.443 57.300 58.317 42.700 41.683 63.62 88.79 288 224 0.940 0.875 bicubic bilinear -52.813 -51.213 -41.910 -40.633 -69 +38
176 nf_resnet50 seresnet50 42.400 42.510 57.600 57.490 59.540 58.667 40.460 41.333 25.56 28.09 288 224 0.940 0.875 bicubic -52.010 -51.570 -39.560 -40.303 -24 +7
177 dpn98 resnetrs101 42.280 42.437 57.720 57.563 56.880 57.300 43.120 42.700 61.57 63.62 224 288 0.875 0.940 bicubic -51.660 -52.813 -42.010 -41.910 +13 -79
178 vit_deit_small_patch16_224 nf_resnet50 42.263 42.400 57.737 57.600 58.020 59.540 41.980 40.460 22.05 25.56 224 288 0.900 0.940 bicubic -51.737 -52.010 -40.940 -39.560 +9 -23
179 tf_efficientnet_cc_b1_8e tf_efficientnetv2_b3 42.233 42.313 57.767 57.687 58.420 57.940 41.580 42.060 39.72 14.36 240 300 0.882 0.904 bicubic -51.337 -52.807 -40.270 -41.260 +54 -70
180 legacy_senet154 dpn98 42.207 42.280 57.793 57.720 56.597 56.880 43.403 43.120 115.09 61.57 224 0.875 bilinear bicubic -52.523 -51.660 -42.503 -42.040 -53 +16
181 cait_xxs24_384 vit_deit_small_patch16_224 42.187 42.263 57.813 57.737 57.460 58.020 42.540 41.980 12.03 22.05 384 224 1.000 0.900 bicubic -52.733 -51.737 -41.680 -40.940 -61 +11
182 tf_efficientnet_b2 tf_efficientnet_cc_b1_8e 42.120 42.233 57.880 57.767 58.197 58.420 41.803 41.580 9.11 39.72 260 240 0.890 0.882 bicubic -52.090 -51.337 -40.833 -40.270 -17 +58
183 gluon_resnext50_32x4d legacy_senet154 42.043 42.207 57.957 57.793 57.667 56.597 42.333 43.403 25.03 115.09 224 0.875 bicubic bilinear -51.607 -52.523 -41.023 -42.503 +39 -54
184 resnet50 cait_xxs24_384 42.013 42.187 57.987 57.813 56.000 57.460 44.000 42.540 25.56 12.03 224 384 0.875 1.000 bicubic -51.447 -52.733 -42.600 -41.680 +57 -66
185 ecaresnet50d_pruned tf_efficientnet_b2 41.953 42.120 58.047 57.880 58.217 58.197 41.783 41.803 19.94 9.11 224 260 0.875 0.890 bicubic -51.867 -52.090 -40.713 -40.833 +18 -16
186 efficientnet_b2 gluon_resnext50_32x4d 41.933 42.043 58.067 57.957 58.300 57.667 41.700 42.333 9.11 25.03 288 224 1.000 0.875 bicubic -52.437 -51.607 -40.750 -41.023 -31 +43
187 dla102x2 resnet50 41.647 42.013 58.353 57.987 57.967 56.000 42.033 44.000 41.28 25.56 224 0.875 bilinear bicubic -52.353 -51.447 -41.063 -42.600 -1 +62
188 hrnet_w64 ecaresnet50d_pruned 41.637 41.953 58.363 58.047 57.130 58.217 42.870 41.783 128.06 19.94 224 0.875 bilinear bicubic -52.193 -51.867 -41.800 -40.783 +13 +19
189 gluon_senet154 efficientnet_b2 41.627 41.933 58.373 58.067 56.373 58.300 43.627 41.700 115.09 9.11 224 288 0.875 1.000 bicubic -53.083 -52.437 -42.597 -40.750 -59 -30
190 inception_v4 dla102x2 41.577 41.647 58.423 58.353 55.383 57.967 44.617 42.033 42.68 41.28 299 224 0.875 bicubic bilinear -52.803 -52.353 -43.437 -41.063 -36 +1
191 efficientnet_el hrnet_w64 41.497 41.637 58.503 58.363 58.303 57.130 41.697 42.870 10.59 128.06 300 224 0.904 0.875 bicubic bilinear -53.173 -52.193 -40.747 -41.800 -57 +15
192 efficientnet_em gluon_senet154 41.493 41.627 58.507 58.373 58.877 56.373 41.123 43.627 6.90 115.09 240 224 0.882 0.875 bicubic -52.247 -53.083 -40.053 -42.597 +21 -60
193 tf_efficientnet_cc_b0_8e inception_v4 41.487 41.577 58.513 58.423 57.377 55.383 42.623 44.617 24.01 42.68 224 299 0.875 bicubic -51.383 -52.803 -41.083 -43.437 +86 -35
194 swin_tiny_patch4_window7_224 efficientnet_el 41.457 41.497 58.543 58.503 57.303 58.303 42.697 41.697 28.29 10.59 224 300 0.900 0.904 bicubic -53.163 -53.173 -41.817 -40.827 -55 -59
195 resnext50_32x4d efficientnet_em 41.443 41.493 58.557 58.507 56.997 58.877 43.003 41.123 25.03 6.90 224 240 0.875 0.882 bicubic -52.397 -52.247 -41.833 -40.053 +5 +23
196 cait_xxs24_224 tf_efficientnet_cc_b0_8e 41.383 41.487 58.617 58.513 57.527 57.377 42.473 42.623 11.96 24.01 224 1.000 0.875 bicubic -52.107 -51.383 -41.243 -41.083 +43 +93
197 tv_resnet152 swin_tiny_patch4_window7_224 41.330 41.457 58.670 58.543 57.520 57.303 42.480 42.697 60.19 28.29 224 0.875 0.900 bilinear bicubic -51.910 -53.163 -41.230 -41.817 +58 -56
198 xception71 resnext50_32x4d 41.270 41.443 58.730 58.557 55.873 56.997 44.127 43.003 42.34 25.03 299 224 0.903 0.875 bicubic -52.620 -52.397 -43.077 -41.833 -3 +7
199 dpn92 cait_xxs24_224 41.267 41.383 58.733 58.617 56.333 57.527 43.667 42.473 37.67 11.96 224 0.875 1.000 bicubic -52.923 -52.107 -42.597 -41.243 -33 +48
200 adv_inception_v3 tv_resnet152 41.263 41.327 58.737 58.673 56.317 57.520 43.683 42.480 23.83 60.19 299 224 0.875 bicubic bilinear -51.747 -51.913 -42.173 -41.300 +65 +61
201 gernet_s xception71 41.247 41.270 58.753 58.730 58.830 55.873 41.170 44.127 8.17 42.34 224 299 0.875 0.903 bilinear bicubic -51.193 -52.620 -39.670 -42.877 +101 -3
202 resnetblur50 dpn92 41.053 41.267 58.947 58.733 57.077 56.333 42.923 43.667 25.56 37.67 224 0.875 bicubic -52.657 -52.923 -41.723 -42.597 +16 -32
203 nf_regnet_b1 adv_inception_v3 41.010 41.263 58.990 58.737 58.117 56.317 41.883 43.683 10.22 23.83 288 299 0.900 0.875 bicubic -52.880 -51.747 -40.633 -42.173 -10 +72
204 gluon_resnet50_v1d gernet_s 40.970 41.247 59.030 58.753 57.137 58.830 42.863 41.170 25.58 8.17 224 0.875 bicubic bilinear -52.560 -51.193 -41.573 -39.710 +32 +109
205 gluon_inception_v3 resnetblur50 40.907 41.053 59.093 58.947 55.617 57.077 44.383 42.923 23.83 25.56 299 224 0.875 bicubic -52.633 -52.657 -43.213 -41.733 +29 +18
206 ese_vovnet39b nf_regnet_b1 40.867 41.010 59.133 58.990 56.950 58.117 43.050 41.883 24.57 10.22 224 288 0.875 0.900 bicubic -52.983 -52.880 -41.950 -40.833 -7 -6
207 regnety_320 gluon_resnet50_v1d 40.813 40.970 59.187 59.030 56.117 57.137 43.883 42.863 145.05 25.58 224 0.875 bicubic -53.707 -52.560 -43.053 -41.573 -60 +36
208 resnet34d gluon_inception_v3 40.810 40.907 59.190 59.093 56.530 55.617 43.470 44.383 21.82 23.83 224 299 0.875 bicubic -51.830 -52.633 -41.890 -43.213 +82 +33
209 xception ese_vovnet39b 40.763 40.867 59.237 59.133 56.387 56.947 43.613 43.053 22.86 24.57 299 224 0.897 0.875 bicubic -52.877 -52.983 -42.383 -41.953 +14 -5
210 skresnext50_32x4d levit_192 40.700 40.847 59.300 59.153 56.023 56.687 43.977 43.313 27.48 10.95 224 0.875 0.900 bicubic -53.250 -52.863 -42.797 -42.103 -21 +12
211 gluon_resnet101_v1b regnety_320 40.683 40.813 59.317 59.187 56.117 43.883 44.55 145.05 224 0.875 bicubic -53.077 -53.707 -42.583 -43.053 0 -61
212 hrnet_w40 resnet34d 40.660 40.810 59.340 59.190 56.757 56.530 43.243 43.470 57.56 21.82 224 0.875 bilinear bicubic -53.050 -51.830 -42.043 -41.890 +4 +88
213 repvgg_b1 xception 40.593 40.763 59.407 59.237 57.837 56.387 42.163 43.613 57.42 22.86 224 299 0.875 0.897 bilinear bicubic -52.817 -52.877 -40.953 -42.383 +33 +17
214 skresnext50_32x4d 40.700 59.300 56.023 43.977 27.48 224 0.875 bicubic -53.250 -42.797 -20
215 gluon_resnet101_v1b 40.683 59.317 56.117 43.883 44.55 224 0.875 bicubic -53.077 -42.723 -1
216 hrnet_w40 40.660 59.340 56.753 43.247 57.56 224 0.875 bilinear -53.050 -42.047 +5
217 repvgg_b1 40.593 59.407 57.837 42.163 57.42 224 0.875 bilinear -52.817 -40.953 +37
218 tf_efficientnet_lite3 40.563 59.437 56.477 43.523 8.20 300 0.904 bilinear -53.567 -42.483 -39
219 tresnet_m_448 40.530 59.470 56.700 43.300 31.39 448 0.875 bilinear -54.130 -42.450 -42.390 -79 -82
220 pit_xs_224 40.497 59.503 56.530 43.470 10.62 224 0.900 bicubic -52.413 -42.250 +58 +64
221 dla169 40.493 59.507 57.263 42.737 53.39 224 0.875 bilinear -53.307 -41.647 -41.577 -11
222 repvgg_b2 40.467 59.533 57.780 42.220 89.02 224 0.875 bilinear -53.123 -41.290 -41.060 +12 +14
223 regnetx_320 40.443 59.557 55.660 44.340 107.81 224 0.875 bicubic -53.767 -43.390 -55
224 skresnet34 coat_mini 40.397 40.420 59.603 59.580 56.737 55.167 43.263 44.833 22.28 10.34 224 0.875 0.900 bicubic -52.173 -54.350 -41.783 -43.783 +77 -97
225 efficientnet_el_pruned skresnet34 40.390 40.397 59.610 59.603 56.903 56.737 43.097 43.263 10.59 22.28 300 224 0.904 0.875 bicubic -53.700 -52.173 -42.107 -41.783 -43 +82
226 efficientnet_b2_pruned efficientnet_el_pruned 40.383 40.390 59.617 59.610 56.537 56.903 43.463 43.097 8.31 10.59 260 300 0.890 0.904 bicubic -53.417 -53.700 -42.303 -42.107 -17 -44
227 legacy_seresnext101_32x4d efficientnet_b2_pruned 40.360 40.383 59.640 59.617 54.817 56.537 45.183 43.463 48.96 8.31 224 260 0.875 0.890 bilinear bicubic -53.770 -53.417 -44.153 -42.373 -50 -16
228 coat_lite_mini 40.360 59.640 55.717 44.283 11.01 224 0.900 bicubic -53.090 -43.063 +22
229 legacy_seresnext101_32x4d 40.360 59.640 54.817 45.183 48.96 224 0.875 bilinear -53.770 -44.153 -51
230 wide_resnet101_2 40.360 59.640 55.780 44.220 126.89 224 0.875 bilinear -53.370 -43.030 -10
231 coat_lite_mini tf_efficientnet_b0_ap 40.360 40.337 59.640 59.663 55.717 56.787 44.283 43.213 11.01 5.29 224 0.900 0.875 bicubic -53.090 -52.273 -43.063 -41.723 +19 +70
232 tf_efficientnet_b0_ap xception65 40.337 40.273 59.663 59.727 56.787 55.283 43.213 44.717 5.29 39.92 224 299 0.875 0.903 bicubic -52.273 -53.487 -41.583 -43.517 +66 -17
233 xception65 regnetx_160 40.273 40.270 59.727 59.730 55.283 56.050 44.717 43.950 39.92 54.28 299 224 0.903 0.875 bicubic -53.487 -53.610 -43.577 -42.750 -15 -32
234 regnetx_160 densenet201 40.270 40.267 59.730 59.733 56.050 56.710 43.950 43.290 54.28 20.01 224 0.875 bicubic -53.610 -52.423 -43.040 -41.940 -31 +62
densenet201 40.267 59.733 56.710 43.290 20.01 224 0.875 bicubic -52.423 -41.940 +57
235 resnext50d_32x4d 40.170 59.830 55.487 44.513 25.05 224 0.875 bicubic -53.640 -43.253 -26
236 vit_small_patch16_224 40.130 59.870 56.543 43.457 48.75 224 0.900 bicubic -52.470 -41.887 -41.847 +62 +68
237 hrnet_w48 40.093 59.907 56.640 43.360 77.47 224 0.875 bilinear -53.937 -42.400 -50
238 legacy_seresnet152 40.043 59.957 55.820 44.180 66.82 224 0.875 bilinear -53.397 -43.030 +11 +14
239 hrnet_w30 40.030 59.970 57.093 42.907 37.71 224 0.875 bilinear -53.340 -41.737 +14 +17
240 regnetx_080 40.000 60.000 55.977 44.023 39.57 224 0.875 bicubic -53.790 -42.933 -28
241 tf_efficientnet_b1 39.977 60.023 56.137 43.863 7.79 240 0.882 bicubic -53.733 -42.673 -42.663 -19 -17
242 gluon_resnet101_v1c 39.953 60.047 55.300 44.700 44.57 224 0.875 bicubic -53.737 -43.460 -18 -16
243 res2net101_26w_4s tf_efficientnetv2_b0 39.717 39.787 60.283 60.213 54.550 56.283 45.450 43.717 45.21 7.14 224 0.875 bilinear bicubic -53.803 -53.273 -44.050 -42.417 -1 +26
244 regnetx_120 res2net101_26w_4s 39.687 39.717 60.313 60.283 55.633 54.550 44.367 45.450 46.11 45.21 224 0.875 bicubic bilinear -54.583 -53.803 -43.557 -44.050 -79 0
245 hrnet_w44 regnetx_120 39.677 39.687 60.323 60.313 55.333 55.633 44.667 44.367 67.06 46.11 224 0.875 bilinear bicubic -53.943 -54.583 -43.617 -43.557 -12 -81
246 densenet161 hrnet_w44 39.620 39.677 60.380 60.323 56.133 55.333 43.867 44.667 28.68 67.06 224 0.875 bicubic bilinear -53.280 -53.943 -42.677 -43.627 +34 -12
247 mixnet_xl densenet161 39.617 39.620 60.383 60.380 55.887 56.133 44.113 43.867 11.90 28.68 224 0.875 bicubic -54.613 -53.280 -42.933 -42.587 -79 +40
248 xception41 mixnet_xl 39.610 39.617 60.390 60.383 55.037 55.887 44.963 44.113 26.97 11.90 299 224 0.903 0.875 bicubic -53.870 -54.613 -43.713 -42.933 -3 -81
249 res2net50_26w_8s xception41 39.603 39.610 60.397 60.390 54.550 55.037 45.450 44.963 48.40 26.97 224 299 0.875 0.903 bilinear bicubic -53.847 -53.870 -44.150 -43.713 -1
250 res2net50_26w_8s 39.603 60.397 54.550 45.450 48.40 224 0.875 bilinear -53.847 -44.150 +1
251 tf_efficientnetv2_b1 39.570 60.430 55.343 44.657 8.14 240 0.882 bicubic -54.140 -43.477 -26
252 dla102x 39.553 60.447 56.323 43.677 26.31 224 0.875 bilinear -53.977 -42.527 -10
253 rexnet_130 39.487 60.513 56.640 43.360 7.56 224 0.875 bicubic -54.183 -42.070 -25
254 hrnet_w32 39.463 60.537 56.123 43.877 41.23 224 0.875 bilinear -53.487 -42.447 -42.717 +23 +27
255 regnety_064 levit_128 39.403 39.433 60.597 60.567 55.773 55.350 44.227 44.650 30.58 9.21 224 0.875 0.900 bicubic -54.737 -53.617 -43.257 -43.340 -76 +15
256 densenetblur121d regnety_064 39.380 39.403 60.620 60.597 56.640 55.773 43.360 44.227 8.00 30.58 224 0.875 bicubic -53.020 -54.737 -41.770 -43.177 +55 -81
257 regnety_120 densenetblur121d 39.347 39.380 60.653 60.620 55.277 56.640 44.723 43.360 51.82 8.00 224 0.875 bicubic -54.663 -53.020 -43.753 -41.770 -65 +57
258 tv_resnet101 regnety_120 39.307 39.347 60.693 60.653 55.803 55.277 44.197 44.723 44.55 51.82 224 0.875 bilinear bicubic -53.573 -54.663 -42.857 -43.753 +27 -68
259 tf_efficientnet_el tv_resnet101 39.303 39.307 60.697 60.693 55.387 55.803 44.613 44.197 10.59 44.55 300 224 0.904 0.875 bicubic bilinear -55.057 -53.573 -43.713 -42.857 -96 +29
260 tf_efficientnet_el 39.303 60.697 55.387 44.613 10.59 300 0.904 bicubic -55.057 -43.713 -100
261 tf_inception_v3 39.237 60.763 54.300 45.700 23.83 299 0.875 bicubic -53.963 -44.180 +3
262 gluon_resnet50_v1s 39.233 60.767 55.010 44.990 25.68 224 0.875 bicubic -54.357 -43.830 -43.740 -25 -24
263 densenet169 tf_efficientnetv2_b2 39.167 39.180 60.833 60.820 55.843 54.570 44.157 45.430 14.15 10.10 224 260 0.875 0.890 bicubic -53.133 -54.890 -42.747 -44.360 +53 -78
264 legacy_seresnet101 densenet169 39.037 39.167 60.963 60.833 55.003 55.843 44.997 44.157 49.33 14.15 224 0.875 bilinear bicubic -54.223 -53.133 -43.737 -42.747 -6 +54
265 efficientnet_b1_pruned legacy_seresnet101 39.010 39.037 60.990 60.963 55.647 55.003 44.353 44.997 6.33 49.33 240 224 0.882 0.875 bicubic bilinear -53.970 -54.223 -42.883 -43.737 +11 -7
266 repvgg_b1g4 efficientnet_b1_pruned 38.990 39.010 61.010 60.990 56.350 55.647 43.650 44.353 39.97 6.33 224 240 0.875 0.882 bilinear bicubic -54.040 -53.970 -42.470 -42.883 +5 +12
267 inception_v3 repvgg_b1g4 38.960 38.990 61.040 61.010 53.853 56.350 46.147 43.650 23.83 39.97 299 224 0.875 bicubic bilinear -53.940 -54.040 -44.477 -42.360 +17 +5
268 dpn68 inception_v3 38.933 38.960 61.067 61.040 54.933 53.853 45.067 46.147 12.61 23.83 224 299 0.875 bicubic -53.307 -53.940 -43.677 -44.477 +51 +18
269 regnety_080 dpn68 38.917 38.933 61.083 61.067 55.213 54.933 44.787 45.067 39.18 12.61 224 0.875 bicubic -54.973 -53.307 -43.787 -43.677 -67 +52
270 legacy_seresnext50_32x4d regnety_080 38.877 38.917 61.123 61.083 54.593 55.213 45.407 44.787 27.56 39.18 224 0.875 bilinear bicubic -54.553 -54.973 -44.207 -43.787 -17 -71
271 dla102 legacy_seresnext50_32x4d 38.833 38.877 61.167 61.123 55.323 54.593 44.677 45.407 33.27 27.56 224 0.875 bilinear -54.427 -54.553 -43.457 -44.207 -14 -18
272 regnety_040 dla102 38.820 38.833 61.180 61.167 55.557 55.323 44.443 44.677 20.65 33.27 224 0.875 bicubic bilinear -54.800 -54.427 -43.143 -43.457 -38 -15
273 densenet121 regnety_040 38.783 38.820 61.217 61.180 56.273 55.557 43.727 44.443 7.98 20.65 224 0.875 bicubic -53.157 -54.800 -42.007 -43.393 +53 -38
274 res2net50_14w_8s densenet121 38.710 38.783 61.290 61.217 54.077 56.273 45.923 43.727 25.06 7.98 224 0.875 bilinear bicubic -54.320 -53.157 -44.623 -42.007 -2 +54
275 regnetx_040 res2net50_14w_8s 38.703 38.710 61.297 61.290 55.340 54.077 44.660 45.923 22.12 25.06 224 0.875 bicubic bilinear -54.977 -54.320 -43.600 -44.743 -47 -2
276 res2net50_26w_6s regnetx_040 38.687 38.703 61.313 61.297 53.743 55.340 46.257 44.660 37.05 22.12 224 0.875 bilinear bicubic -54.903 -54.977 -45.007 -43.600 -37 -49
277 regnetx_032 res2net50_26w_6s 38.680 38.687 61.320 61.313 55.157 53.743 44.843 46.257 15.30 37.05 224 0.875 bicubic bilinear -54.570 -54.903 -43.573 -45.327 -17 -40
278 selecsls60 regnetx_032 38.623 38.680 61.377 61.320 55.630 55.157 44.370 44.843 30.67 15.30 224 0.875 bicubic -54.387 -54.570 -43.200 -43.573 -4 -18
279 dla60x selecsls60 38.617 38.623 61.383 61.377 55.383 55.630 44.617 44.370 17.35 30.67 224 0.875 bilinear bicubic -54.573 -54.387 -43.327 -43.200 -14 -3
280 tf_efficientnet_b0 dla60x 38.600 38.617 61.400 61.383 55.957 55.383 44.043 44.617 5.29 17.35 224 0.875 bicubic bilinear -53.800 -54.573 -42.513 -43.327 +33 -15
281 dla60_res2net tf_efficientnet_b0 38.590 38.600 61.410 61.400 54.560 55.957 45.440 44.043 20.85 5.29 224 0.875 bilinear bicubic -54.790 -53.800 -44.300 -42.513 -26 +34
282 selecsls60b dla60_res2net 38.573 38.590 61.427 61.410 55.307 54.560 44.693 45.440 32.77 20.85 224 0.875 bicubic bilinear -54.927 -54.790 -43.533 -44.300 -36 -27
283 repvgg_a2 selecsls60b 38.563 38.573 61.437 61.427 55.770 55.307 44.230 44.693 28.21 32.77 224 0.875 bilinear bicubic -54.117 -54.927 -42.750 -43.533 +12 -37
284 hardcorenas_f repvgg_a2 38.500 38.563 61.500 61.437 55.657 55.770 44.343 44.230 8.20 28.21 224 0.875 bilinear -54.480 -54.117 -42.963 -42.750 -7 +13
285 dla60_res2next hardcorenas_f 38.450 38.500 61.550 61.500 54.950 55.657 45.050 44.343 17.03 8.20 224 0.875 bilinear -55.120 -54.480 -43.850 -42.963 -45 -6
286 regnetx_064 dla60_res2next 38.430 38.450 61.570 61.550 54.990 54.950 45.010 45.050 26.21 17.03 224 0.875 bicubic bilinear -55.200 -55.120 -44.060 -43.850 -54 -47
287 tf_efficientnet_cc_b0_4e regnetx_064 38.413 38.430 61.587 61.570 55.150 54.990 44.850 45.010 13.31 26.21 224 0.875 bicubic -54.427 -55.200 -43.290 -44.060 +3 -56
288 gluon_resnet50_v1b tf_efficientnet_cc_b0_4e 38.407 38.413 61.593 61.587 54.833 55.150 45.167 44.850 25.56 13.31 224 0.875 bicubic -54.153 -54.427 -43.717 -43.290 +18 +4
289 resnetv2_50x1_bitm gluon_resnet50_v1b 38.287 38.407 61.713 61.593 56.967 54.833 43.033 45.167 25.55 25.56 480 224 1.000 0.875 bilinear bicubic -56.263 -54.153 -41.963 -43.717 -137 +19
290 hrnet_w18 resnetv2_50x1_bitm 38.277 38.287 61.723 61.713 55.643 56.967 44.357 43.033 21.30 25.55 224 480 0.875 1.000 bilinear -54.483 -56.263 -43.017 -42.283 +3 -141
291 mixnet_l hrnet_w18 38.160 38.277 61.840 61.723 54.757 55.643 45.243 44.357 7.33 21.30 224 0.875 bicubic bilinear -55.100 -54.483 -43.943 -43.017 -32 +4
292 hardcorenas_e mixnet_l 38.137 38.160 61.863 61.840 55.173 54.757 44.827 45.243 8.07 7.33 224 0.875 bilinear bicubic -54.813 -55.100 -43.667 -43.943 -13 -33
293 efficientnet_b1 hardcorenas_e 38.087 38.137 61.913 61.863 54.010 55.173 45.990 44.827 7.79 8.07 256 224 1.000 0.875 bicubic bilinear -54.943 -54.813 -44.700 -43.397 -23 -13
294 coat_lite_tiny efficientnet_b1 38.070 38.087 61.930 61.913 53.453 54.010 46.547 45.990 5.72 7.79 224 256 0.900 1.000 bicubic -54.780 -54.943 -45.187 -44.690 -6 -20
295 resnetrs50 coat_lite_tiny 37.957 38.070 62.043 61.930 53.310 53.453 46.690 46.547 35.69 5.72 224 0.910 0.900 bicubic -56.063 -54.780 -45.540 -45.187 -104 -5
296 hardcorenas_c resnetrs50 37.883 37.957 62.117 62.043 55.717 53.310 44.283 46.690 5.52 35.69 224 0.875 0.910 bilinear bicubic -54.447 -56.063 -42.623 -45.540 +18 -108
297 gluon_resnet50_v1c hardcorenas_c 37.843 37.883 62.157 62.117 54.123 55.717 45.877 44.283 25.58 5.52 224 0.875 bicubic bilinear -55.067 -54.447 -44.587 -42.623 -16 +19
298 res2net50_26w_4s gluon_resnet50_v1c 37.827 37.843 62.173 62.157 53.073 54.123 46.927 45.877 25.70 25.58 224 0.875 bilinear bicubic -55.353 -55.067 -45.597 -44.587 -32 -15
299 efficientnet_es res2net50_26w_4s 37.770 37.827 62.230 62.173 54.967 53.073 45.033 46.927 5.44 25.70 224 0.875 bicubic bilinear -55.140 -55.353 -43.723 -45.597 -19 -33
300 resnest14d efficientnet_es 37.767 37.770 62.233 62.230 56.470 54.967 43.530 45.033 10.61 5.44 224 0.875 bilinear bicubic -53.363 -55.140 -41.860 -43.723 +52 -18
301 tv_resnext50_32x4d resnest14d 37.750 37.767 62.250 62.233 54.113 56.470 45.887 43.530 25.03 10.61 224 0.875 bilinear -55.150 -53.363 -44.607 -41.860 -16 +54
302 ecaresnet26t tv_resnext50_32x4d 37.650 37.750 62.350 62.250 54.350 54.113 45.650 45.887 16.01 25.03 320 224 0.950 0.875 bicubic bilinear -56.290 -55.150 -44.570 -44.697 -103 -17
303 hardcorenas_d ecaresnet26t 37.550 37.650 62.450 62.350 54.723 54.350 45.277 45.650 7.50 16.01 224 320 0.875 0.950 bilinear bicubic -55.050 -56.290 -43.667 -44.570 -1 -106
304 res2next50 hardcorenas_d 37.477 37.550 62.523 62.450 52.853 54.723 47.147 45.277 24.67 7.50 224 0.875 bilinear -55.673 -55.050 -45.807 -43.707 -36 -1
305 resnet34 res2next50 37.443 37.477 62.557 62.523 54.297 52.853 45.703 47.147 21.80 24.67 224 0.875 bilinear -53.757 -55.673 -43.753 -45.807 +42 -37
306 pit_ti_distilled_224 resnet34 37.337 37.443 62.663 62.557 55.137 54.297 44.863 45.703 5.10 21.80 224 0.900 0.875 bicubic bilinear -53.563 -53.757 -43.083 -43.943 +51 +45
307 hardcorenas_b pit_ti_distilled_224 37.243 37.337 62.757 62.663 55.073 55.137 44.927 44.863 5.18 5.10 224 0.875 0.900 bilinear bicubic -54.697 -53.563 -43.327 -43.083 +20 +53
308 mobilenetv3_large_100_miil hardcorenas_b 37.210 37.243 62.790 62.757 53.513 55.073 46.487 44.927 5.48 5.18 224 0.875 bilinear -55.040 -54.697 -44.737 -43.327 +10 +21
309 res2net50_48w_2s mobilenetv3_large_100_miil 37.117 37.210 62.883 62.790 53.333 53.513 46.667 46.487 25.29 5.48 224 0.875 bilinear -55.673 -55.040 -45.137 -44.737 -17 +11
310 dla60 res2net50_48w_2s 37.073 37.117 62.927 62.883 54.200 53.333 45.800 46.667 22.04 25.29 224 0.875 bilinear -55.597 -55.673 -44.430 -45.137 -14 -16
311 rexnet_100 dla60 37.063 37.073 62.937 62.927 54.020 54.200 45.980 45.800 4.80 22.04 224 0.875 bicubic bilinear -55.787 -55.597 -44.600 -44.450 -22 -12
312 regnety_016 rexnet_100 37.017 37.063 62.983 62.937 54.093 54.020 45.907 45.980 11.20 4.80 224 0.875 bicubic -55.983 -55.787 -44.587 -44.600 -37 -21
313 tf_mixnet_l regnety_016 36.987 37.017 63.013 62.983 52.583 54.093 47.417 45.907 7.33 11.20 224 0.875 bicubic -56.053 -55.983 -45.957 -44.587 -44 -36
314 legacy_seresnet50 tf_mixnet_l 36.873 36.987 63.127 63.013 53.487 52.583 46.513 47.417 28.09 7.33 224 0.875 bilinear bicubic -55.797 -56.053 -45.163 -45.957 -17 -43
315 tv_densenet121 legacy_seresnet50 36.810 36.873 63.190 63.127 54.033 53.487 45.967 46.513 7.98 28.09 224 0.875 bicubic bilinear -54.590 -55.797 -44.217 -45.143 +26 -17
316 tf_efficientnet_lite2 tv_densenet121 36.807 36.810 63.193 63.190 53.320 54.033 46.680 45.967 6.09 7.98 260 224 0.890 0.875 bicubic -55.783 -54.590 -45.230 -44.217 -13 +28
317 mobilenetv2_120d tf_efficientnet_lite2 36.780 36.807 63.220 63.193 54.047 53.320 45.953 46.680 5.83 6.09 224 260 0.875 0.890 bicubic -55.830 -55.783 -44.463 -45.230 -18 -12
318 tf_efficientnet_lite1 mobilenetv2_120d 36.737 36.780 63.263 63.220 53.590 54.047 46.410 45.953 5.42 5.83 240 224 0.882 0.875 bicubic -55.573 -55.830 -44.900 -44.323 -3 -16
319 regnetx_016 tf_efficientnet_lite1 36.683 36.737 63.317 63.263 53.297 53.590 46.703 46.410 9.19 5.42 224 240 0.875 0.882 bicubic -55.857 -55.573 -45.253 -44.900 -12 -2
320 hardcorenas_a regnetx_016 36.640 36.683 63.360 63.317 54.910 53.297 45.090 46.703 5.26 9.19 224 0.875 bilinear bicubic -54.980 -55.857 -43.260 -45.253 +14 -11
321 hardcorenas_a 36.640 63.360 54.910 45.090 5.26 224 0.875 bilinear -54.980 -43.260 +15
322 levit_128s 36.620 63.380 53.117 46.883 7.78 224 0.900 bicubic -54.880 -45.283 +17
323 efficientnet_b0 36.600 63.400 53.497 46.503 5.29 224 0.875 bicubic -55.880 -45.183 -13
324 tf_efficientnet_em 36.380 63.620 52.840 47.160 6.90 240 0.882 bicubic -56.790 -45.830 -55 -57
325 skresnet18 36.320 63.680 54.197 45.803 11.96 224 0.875 bicubic -53.840 -43.583 +44 +46
326 repvgg_b0 36.287 63.713 54.057 45.943 15.82 224 0.875 bilinear -55.393 -44.393 +7
327 tv_resnet50 36.177 63.823 52.803 47.197 25.56 224 0.875 bilinear -55.963 -45.617 -3
328 legacy_seresnet34 36.143 63.857 52.553 47.447 21.96 224 0.875 bilinear -55.337 -45.767 -45.647 +12
329 tv_resnet34 coat_tiny 36.087 36.123 63.913 63.877 53.533 51.063 46.467 48.937 21.80 5.50 224 0.875 0.900 bilinear bicubic -54.203 -57.387 -44.447 -47.627 +39 -84
330 tv_resnet34 36.087 63.913 53.533 46.467 21.80 224 0.875 bilinear -54.203 -44.447 +40
331 vit_deit_tiny_distilled_patch16_224 36.023 63.977 54.240 45.760 5.91 224 0.900 bicubic -55.077 -44.030 +26
332 mobilenetv2_140 36.000 64.000 53.943 46.057 6.11 224 0.875 bicubic -56.030 -44.307 -5 -6
333 tf_efficientnet_lite0 35.930 64.070 53.480 46.520 4.65 224 0.875 bicubic -55.370 -44.610 +13
334 selecsls42b 35.813 64.187 52.487 47.513 32.46 224 0.875 bicubic -56.667 -45.953 -22 -23
335 gluon_resnet34_v1b 35.760 35.763 64.240 64.237 52.187 47.813 21.80 224 0.875 bicubic -55.340 -55.337 -45.993 +21
336 dla34 35.643 64.357 52.783 47.217 15.74 224 0.875 bilinear -55.597 -45.397 +13
337 mixnet_m 35.640 64.360 52.430 47.570 5.01 224 0.875 bicubic -56.630 -45.920 -17 -18
338 efficientnet_lite0 35.620 64.380 53.657 46.343 4.65 224 0.875 bicubic -55.640 -44.593 +10
339 ssl_resnet18 35.597 64.403 53.740 46.260 11.69 224 0.875 bilinear -55.103 -44.280 +24
340 mobilenetv3_rw 35.547 64.453 53.713 46.287 5.48 224 0.875 bicubic -56.003 -44.557 -1 -2
341 efficientnet_es_pruned 35.390 64.610 52.850 47.150 5.44 224 0.875 bicubic -56.310 -45.570 -8 -9
342 mobilenetv2_110d 35.293 64.707 52.830 47.170 4.52 224 0.875 bicubic -56.057 -45.360 +3
343 tf_mixnet_m 35.180 64.820 50.987 49.013 5.01 224 0.875 bicubic -57.020 -47.433 -19 -20
344 hrnet_w18_small_v2 35.173 64.827 52.440 47.560 15.60 224 0.875 bilinear -55.997 -45.900 +9
345 resnet18d 35.127 64.873 52.890 47.110 11.71 224 0.875 bicubic -54.863 -44.940 +26 +27
346 ese_vovnet19b_dw convit_tiny 34.840 35.047 65.160 64.953 52.030 51.787 47.970 48.213 6.54 5.71 224 0.875 bicubic -57.170 -55.483 -46.480 -46.423 -18 +20
347 regnety_008 ese_vovnet19b_dw 34.807 34.840 65.193 65.160 51.743 52.030 48.257 47.970 6.26 6.54 224 0.875 bicubic -57.093 -57.170 -46.677 -46.480 -16 -20
348 regnety_008 34.807 65.193 51.743 48.257 6.26 224 0.875 bicubic -57.093 -46.677 -18
349 pit_ti_224 34.670 65.330 52.170 47.830 4.85 224 0.900 bicubic -55.750 -45.840 +19
350 mobilenetv3_large_100 34.603 65.397 52.860 47.140 5.48 224 0.875 bicubic -56.877 -45.340 -45.460 -9
351 seresnext26d_32x4d 34.543 65.457 51.543 48.457 16.81 224 0.875 bicubic -57.897 -46.997 -46.957 -36 -39
352 seresnext26t_32x4d 34.540 65.460 51.377 48.623 16.81 224 0.875 bicubic -58.280 -47.183 -57 -59
353 mixer_b16_224 34.423 65.577 48.093 51.907 59.88 224 0.875 bicubic -56.717 -49.307 +2 +1
354 resnet26d 34.273 65.727 51.687 48.313 16.01 224 0.875 bicubic -57.957 -46.763 -30 -32
355 tf_efficientnet_es 34.263 65.737 51.350 48.650 5.44 224 0.875 bicubic -57.837 -47.090 -28 -30
356 fbnetc_100 34.253 65.747 51.180 48.820 5.57 224 0.875 bilinear -57.017 -46.650 -8 -9
357 regnety_006 34.150 65.850 51.277 48.723 6.06 224 0.875 bicubic -57.420 -47.153 -18 -20
358 tf_mobilenetv3_large_100 33.950 66.050 51.490 48.510 5.48 224 0.875 bilinear -57.470 -46.770 -14 -15
359 regnetx_008 33.770 66.230 50.547 49.453 7.26 224 0.875 bicubic -57.410 -47.833 -6 -7
360 mnasnet_100 33.763 66.237 51.170 48.830 4.38 224 0.875 bicubic -57.437 -47.070 -46.880 -8 -10
361 semnasnet_100 33.520 66.480 50.787 49.213 3.89 224 0.875 bicubic -58.140 -47.483 -24 -26
362 resnet26 33.500 66.500 50.927 49.073 16.00 224 0.875 bicubic -57.940 -47.353 -19 -20
363 mixnet_s 33.480 66.520 50.997 49.003 4.13 224 0.875 bicubic -58.300 -47.303 -30 -32
364 spnasnet_100 33.477 66.523 51.267 48.733 4.42 224 0.875 bilinear -57.133 -46.683 +1 0
365 vgg19_bn 33.230 66.770 50.803 49.197 143.68 224 0.875 bilinear -57.760 -47.307 -5 -6
366 ghostnet_100 33.207 66.793 51.163 48.837 5.18 224 0.875 bilinear -57.233 -46.667 +1
367 regnetx_006 33.157 66.843 50.250 49.750 6.20 224 0.875 bicubic -57.603 -47.850 -4 -5
368 resnet18 33.067 66.933 51.170 48.830 11.69 224 0.875 bilinear -55.083 -45.950 +17
369 legacy_seresnext26_32x4d 32.757 67.243 49.237 50.763 16.79 224 0.875 bicubic -59.813 -49.183 -61 -63
370 hrnet_w18_small 32.667 67.333 50.587 49.413 13.19 224 0.875 bilinear -57.213 -47.313 +3
371 vit_deit_tiny_patch16_224 32.667 67.333 50.273 49.727 5.72 224 0.900 bicubic -56.953 -47.687 +5
372 legacy_seresnet18 32.600 67.400 50.340 49.660 11.78 224 0.875 bicubic -56.670 -47.340 +7
373 mobilenetv2_100 32.523 67.477 50.800 49.200 3.50 224 0.875 bicubic -57.307 -47.030 +1
374 regnetx_004 32.517 67.483 49.343 50.657 5.16 224 0.875 bicubic -56.943 -48.427 +3
375 gluon_resnet18_v1b 32.407 67.593 49.727 50.273 11.69 224 0.875 bicubic -56.253 -47.373 +7
376 regnety_004 32.333 67.667 49.453 50.547 4.34 224 0.875 bicubic -58.447 -48.627 -14 -15
377 tf_mixnet_s 32.183 67.817 48.493 51.507 4.13 224 0.875 bicubic -59.497 -49.747 -41 -43
378 tf_mobilenetv3_large_075 31.867 68.133 49.110 50.890 3.99 224 0.875 bilinear -58.453 -48.760 -9
379 tf_mobilenetv3_large_minimal_100 31.597 68.403 49.337 50.663 3.92 224 0.875 bilinear -57.583 -47.983 +2
380 vgg16_bn 30.357 69.643 47.260 52.740 138.37 224 0.875 bilinear -60.183 -50.730 -14 -15
381 regnety_002 29.687 70.313 46.787 53.213 3.16 224 0.875 bicubic -58.513 -50.643 +3
382 vgg13_bn 28.883 71.117 46.737 53.263 133.05 224 0.875 bilinear -60.317 -50.793 -2
383 regnetx_002 28.860 71.140 45.420 54.580 2.68 224 0.875 bicubic -58.520 -51.570 +4

383
results/results-imagenet-real.csv
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@ -3,135 +3,158 @@ tf_efficientnet_l2_ns,90.563,9.437,98.779,1.221,480.31,800,0.960,bicubic,+2.211,
tf_efficientnet_l2_ns_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,0
cait_m48_448,90.196,9.804,98.484,1.516,356.46,448,1.000,bicubic,+3.712,+0.730,+2
tf_efficientnet_b7_ns,90.100,9.900,98.614,1.386,66.35,600,0.949,bicubic,+3.260,+0.520,0
cait_m36_384,90.046,9.954,98.493,1.507,271.22,384,1.000,bicubic,+3.992,+0.763,+6
cait_m36_384,90.046,9.954,98.493,1.507,271.22,384,1.000,bicubic,+3.992,+0.763,+7
swin_large_patch4_window12_384,90.027,9.973,98.657,1.343,196.74,384,1.000,bicubic,+2.879,+0.423,-3
swin_base_patch4_window12_384,89.995,10.005,98.695,1.304,87.90,384,1.000,bicubic,+3.563,+0.637,0
tf_efficientnetv2_l_in21ft1k,90.008,9.992,98.619,1.381,118.52,480,1.000,bicubic,+3.704,+0.641,+2
swin_base_patch4_window12_384,89.995,10.005,98.695,1.304,87.90,384,1.000,bicubic,+3.563,+0.637,-1
dm_nfnet_f6,89.901,10.099,98.529,1.471,438.36,576,0.956,bicubic,+3.605,+0.785,+1
cait_s36_384,89.844,10.156,98.427,1.573,68.37,384,1.000,bicubic,+4.384,+0.947,+6
swin_large_patch4_window7_224,89.796,10.204,98.640,1.360,196.53,224,0.900,bicubic,+3.477,+0.744,-2
tf_efficientnet_b6_ns,89.782,10.218,98.510,1.490,43.04,528,0.942,bicubic,+3.330,+0.628,-5
tf_efficientnet_b5_ns,89.651,10.349,98.482,1.518,30.39,456,0.934,bicubic,+3.563,+0.730,-2
tf_efficientnet_b8_ap,89.581,10.419,98.305,1.695,87.41,672,0.954,bicubic,+4.211,+1.011,+6
cait_s24_384,89.502,10.498,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+11
tf_efficientnet_b7_ap,89.429,10.571,98.347,1.653,66.35,600,0.949,bicubic,+4.309,+1.096,+8
vit_deit_base_distilled_patch16_384,89.429,10.571,98.441,1.559,87.63,384,1.000,bicubic,+4.007,+1.109,+1
cait_s36_384,89.844,10.156,98.427,1.573,68.37,384,1.000,bicubic,+4.384,+0.947,+8
swin_large_patch4_window7_224,89.796,10.204,98.640,1.360,196.53,224,0.900,bicubic,+3.477,+0.744,-3
tf_efficientnet_b6_ns,89.782,10.218,98.510,1.490,43.04,528,0.942,bicubic,+3.330,+0.628,-6
tf_efficientnetv2_m_in21ft1k,89.775,10.225,98.503,1.497,54.14,480,1.000,bicubic,+4.187,+0.751,+2
tf_efficientnet_b5_ns,89.651,10.349,98.482,1.518,30.39,456,0.934,bicubic,+3.563,+0.730,-3
tf_efficientnet_b8_ap,89.581,10.419,98.305,1.695,87.41,672,0.954,bicubic,+4.211,+1.011,+7
cait_s24_384,89.502,10.498,98.362,1.638,47.06,384,1.000,bicubic,+4.456,+1.016,+12
tf_efficientnet_b7_ap,89.429,10.571,98.347,1.653,66.35,600,0.949,bicubic,+4.309,+1.096,+9
vit_deit_base_distilled_patch16_384,89.429,10.571,98.441,1.559,87.63,384,1.000,bicubic,+4.007,+1.109,+2
dm_nfnet_f3,89.393,10.607,98.315,1.685,254.92,416,0.940,bicubic,+3.833,+0.909,-3
tf_efficientnetv2_l,89.367,10.633,98.275,1.725,118.52,480,1.000,bicubic,+3.877,+0.903,-3
tf_efficientnet_b8,89.355,10.645,98.303,1.697,87.41,672,0.954,bicubic,+3.985,+0.913,0
tf_efficientnet_b6_ap,89.342,10.658,98.281,1.719,43.04,528,0.942,bicubic,+4.554,+1.143,+13
tf_efficientnet_b6_ap,89.342,10.658,98.281,1.719,43.04,528,0.942,bicubic,+4.554,+1.143,+15
tf_efficientnet_b4_ns,89.305,10.694,98.347,1.653,19.34,380,0.922,bicubic,+4.143,+0.877,+1
dm_nfnet_f4,89.299,10.701,98.224,1.776,316.07,512,0.951,bicubic,+3.641,+0.714,-8
dm_nfnet_f5,89.184,10.816,98.232,1.768,377.21,544,0.954,bicubic,+3.470,+0.790,-10
swin_base_patch4_window7_224,89.145,10.855,98.429,1.571,87.77,224,0.900,bicubic,+3.893,+0.867,-3
cait_xs24_384,89.139,10.861,98.290,1.710,26.67,384,1.000,bicubic,+5.077,+1.402,+24
ig_resnext101_32x48d,89.120,10.880,98.130,1.870,828.41,224,0.875,bilinear,+3.692,+0.558,-9
ig_resnext101_32x32d,89.111,10.889,98.181,1.819,468.53,224,0.875,bilinear,+4.017,+0.743,-2
dm_nfnet_f4,89.299,10.701,98.224,1.776,316.07,512,0.951,bicubic,+3.641,+0.714,-10
tf_efficientnetv2_m,89.284,10.716,98.236,1.764,54.14,480,1.000,bicubic,+4.240,+0.958,+4
dm_nfnet_f5,89.184,10.816,98.232,1.768,377.21,544,0.954,bicubic,+3.470,+0.790,-13
swin_base_patch4_window7_224,89.145,10.855,98.429,1.571,87.77,224,0.900,bicubic,+3.893,+0.867,-4
cait_xs24_384,89.139,10.861,98.290,1.710,26.67,384,1.000,bicubic,+5.077,+1.402,+26
ig_resnext101_32x48d,89.120,10.880,98.130,1.870,828.41,224,0.875,bilinear,+3.692,+0.558,-10
ig_resnext101_32x32d,89.111,10.889,98.181,1.819,468.53,224,0.875,bilinear,+4.017,+0.743,-3
tf_efficientnet_b7,89.086,10.914,98.183,1.817,66.35,600,0.949,bicubic,+4.150,+0.979,+3
ecaresnet269d,89.069,10.931,98.234,1.766,102.09,352,1.000,bicubic,+4.093,+1.008,0
tf_efficientnet_b5_ap,88.938,11.062,98.164,1.836,30.39,456,0.934,bicubic,+4.686,+1.190,+13
dm_nfnet_f2,88.889,11.111,98.117,1.883,193.78,352,0.920,bicubic,+3.899,+0.973,-3
dm_nfnet_f1,88.853,11.147,98.093,1.907,132.63,320,0.910,bicubic,+4.249,+1.025,+3
resnetrs420,88.840,11.160,98.034,1.966,191.89,416,1.000,bicubic,+3.832,+0.910,-6
efficientnetv2_rw_m,88.987,11.013,98.213,1.787,53.24,416,1.000,bicubic,+4.179,+1.065,+3
tf_efficientnet_b5_ap,88.938,11.062,98.164,1.836,30.39,456,0.934,bicubic,+4.686,+1.190,+14
tf_efficientnetv2_s_in21ft1k,88.904,11.096,98.277,1.723,21.46,384,1.000,bicubic,+4.602,+1.025,+10
dm_nfnet_f2,88.889,11.111,98.117,1.883,193.78,352,0.920,bicubic,+3.899,+0.973,-5
dm_nfnet_f1,88.853,11.147,98.093,1.907,132.63,320,0.910,bicubic,+4.249,+1.025,+2
resnetrs420,88.840,11.160,98.034,1.966,191.89,416,1.000,bicubic,+3.832,+0.910,-8
ig_resnext101_32x16d,88.834,11.166,98.049,1.951,194.03,224,0.875,bilinear,+4.664,+0.853,+11
resnetrs270,88.834,11.166,98.136,1.864,129.86,352,1.000,bicubic,+4.400,+1.166,+3
vit_base_r50_s16_384,88.808,11.192,98.232,1.768,98.95,384,1.000,bicubic,+3.836,+0.944,-6
seresnet152d,88.795,11.205,98.172,1.828,66.84,320,1.000,bicubic,+4.433,+1.132,+3
resnetrs270,88.834,11.166,98.136,1.864,129.86,352,1.000,bicubic,+4.400,+1.166,+2
vit_base_r50_s16_384,88.808,11.192,98.232,1.768,98.95,384,1.000,bicubic,+3.836,+0.944,-8
seresnet152d,88.795,11.205,98.172,1.828,66.84,320,1.000,bicubic,+4.433,+1.132,+2
swsl_resnext101_32x8d,88.770,11.230,98.147,1.853,88.79,224,0.875,bilinear,+4.486,+0.971,+3
tf_efficientnet_b6,88.761,11.239,98.064,1.937,43.04,528,0.942,bicubic,+4.651,+1.178,+8
resnetrs350,88.759,11.241,98.029,1.971,163.96,384,1.000,bicubic,+4.039,+1.041,-6
resnetrs350,88.759,11.241,98.029,1.971,163.96,384,1.000,bicubic,+4.039,+1.041,-7
vit_base_patch16_224_miil,88.737,11.262,98.027,1.973,86.54,224,0.875,bilinear,+4.469,+1.225,+1
resnetv2_152x2_bitm,88.699,11.301,98.337,1.663,236.34,480,1.000,bilinear,+4.259,+0.891,-5
regnety_160,88.697,11.303,98.068,1.932,83.59,288,1.000,bicubic,+5.011,+1.292,+15
resnetv2_152x2_bitm,88.699,11.301,98.337,1.663,236.34,480,1.000,bilinear,+4.259,+0.891,-6
regnety_160,88.697,11.303,98.068,1.932,83.59,288,1.000,bicubic,+5.011,+1.292,+16
pit_b_distilled_224,88.676,11.324,98.093,1.907,74.79,224,0.900,bicubic,+4.532,+1.237,+2
resnetrs200,88.605,11.395,98.034,1.966,93.21,320,1.000,bicubic,+4.539,+1.160,+3
eca_nfnet_l1,88.575,11.425,98.130,1.870,41.41,320,1.000,bicubic,+4.567,+1.102,+5
resnetv2_152x4_bitm,88.565,11.435,98.185,1.815,936.53,480,1.000,bilinear,+3.633,+0.749,-15
resnetv2_152x4_bitm,88.565,11.435,98.185,1.815,936.53,480,1.000,bilinear,+3.633,+0.749,-17
resnet200d,88.543,11.457,97.959,2.041,64.69,320,1.000,bicubic,+4.581,+1.135,+4
resnest269e,88.522,11.478,98.027,1.973,110.93,416,0.928,bicubic,+4.004,+1.041,-13
resnetv2_101x3_bitm,88.492,11.508,98.162,1.838,387.93,480,1.000,bilinear,+4.098,+0.800,-11
efficientnet_v2s,88.473,11.527,97.974,2.026,23.94,384,1.000,bicubic,+4.665,+1.250,+4
cait_s24_224,88.447,11.553,97.957,2.043,46.92,224,1.000,bicubic,+4.995,+1.393,+8
resnest200e,88.432,11.568,98.042,1.958,70.20,320,0.909,bicubic,+4.600,+1.148,0
resnest269e,88.522,11.478,98.027,1.973,110.93,416,0.928,bicubic,+4.004,+1.041,-14
resnetv2_101x3_bitm,88.492,11.508,98.162,1.838,387.93,480,1.000,bilinear,+4.098,+0.800,-12
efficientnetv2_rw_s,88.473,11.527,97.974,2.026,23.94,384,1.000,bicubic,+4.665,+1.250,+5
cait_s24_224,88.447,11.553,97.957,2.043,46.92,224,1.000,bicubic,+4.995,+1.393,+10
resnest200e,88.432,11.568,98.042,1.958,70.20,320,0.909,bicubic,+4.600,+1.148,+1
tf_efficientnet_b3_ns,88.426,11.574,98.029,1.971,12.23,300,0.904,bicubic,+4.378,+1.119,-4
vit_large_patch16_384,88.407,11.593,98.187,1.813,304.72,384,1.000,bicubic,+3.249,+0.831,-32
vit_base_patch16_384,88.389,11.611,98.155,1.845,86.86,384,1.000,bicubic,+4.180,+0.937,-12
efficientnet_b4,88.372,11.628,97.961,2.039,19.34,384,1.000,bicubic,+4.944,+1.365,+4
vit_large_patch16_384,88.407,11.593,98.187,1.813,304.72,384,1.000,bicubic,+3.249,+0.831,-35
tf_efficientnetv2_s,88.402,11.598,97.927,2.073,21.46,384,1.000,bicubic,+4.508,+1.229,-3
vit_base_patch16_384,88.389,11.611,98.155,1.845,86.86,384,1.000,bicubic,+4.180,+0.937,-13
efficientnet_b4,88.372,11.628,97.961,2.039,19.34,384,1.000,bicubic,+4.944,+1.365,+5
resnet152d,88.355,11.645,97.935,2.065,60.21,320,1.000,bicubic,+4.675,+1.197,+1
tf_efficientnet_b4_ap,88.349,11.651,97.893,2.107,19.34,380,0.922,bicubic,+5.101,+1.501,+5
resnetv2_50x3_bitm,88.349,11.651,98.108,1.892,217.32,480,1.000,bilinear,+4.565,+1.002,-3
tf_efficientnet_b4_ap,88.349,11.651,97.893,2.107,19.34,380,0.922,bicubic,+5.101,+1.501,+6
tf_efficientnet_b5,88.321,11.679,97.912,2.088,30.39,456,0.934,bicubic,+4.509,+1.164,-7
resnetrs152,88.251,11.749,97.737,2.263,86.62,320,1.000,bicubic,+4.539,+1.123,-5
vit_deit_base_distilled_patch16_224,88.214,11.786,97.914,2.086,87.34,224,0.900,bicubic,+4.826,+1.426,-1
ig_resnext101_32x8d,88.146,11.854,97.856,2.144,88.79,224,0.875,bilinear,+5.458,+1.220,+14
cait_xxs36_384,88.140,11.860,97.908,2.092,17.37,384,1.000,bicubic,+5.946,+1.760,+23
dm_nfnet_f0,88.112,11.888,97.837,2.163,71.49,256,0.900,bicubic,+4.770,+1.277,-2
swsl_resnext101_32x4d,88.099,11.901,97.967,2.033,44.18,224,0.875,bilinear,+4.869,+1.207,-1
tf_efficientnet_b4,87.963,12.037,97.739,2.261,19.34,380,0.922,bicubic,+4.941,+1.439,+5
nfnet_l0,87.948,12.052,97.850,2.150,35.07,288,1.000,bicubic,+5.188,+1.352,+7
eca_nfnet_l0,87.943,12.057,97.861,2.139,24.14,288,1.000,bicubic,+5.355,+1.387,+10
resnet101d,87.941,12.059,97.908,2.092,44.57,320,1.000,bicubic,+4.919,+1.462,+1
regnety_032,87.937,12.063,97.891,2.109,19.44,288,1.000,bicubic,+5.213,+1.467,+5
vit_deit_base_patch16_384,87.845,12.155,97.510,2.490,86.86,384,1.000,bicubic,+4.739,+1.138,-5
tresnet_xl_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.746,+1.285,-3
tresnet_m,87.736,12.264,97.523,2.477,31.39,224,0.875,bilinear,+4.656,+1.405,-6
swin_small_patch4_window7_224,87.664,12.336,97.566,2.434,49.61,224,0.900,bicubic,+4.452,+1.244,-9
resnetv2_101x1_bitm,87.638,12.362,97.955,2.045,44.54,480,1.000,bilinear,+5.426,+1.483,+10
pnasnet5large,87.636,12.364,97.485,2.515,86.06,331,0.911,bicubic,+4.854,+1.445,-3
swsl_resnext101_32x16d,87.615,12.386,97.820,2.180,194.03,224,0.875,bilinear,+4.269,+0.974,-16
swsl_resnext50_32x4d,87.600,12.400,97.651,2.349,25.03,224,0.875,bilinear,+5.418,+1.421,+9
tf_efficientnet_b2_ns,87.557,12.443,97.628,2.372,9.11,260,0.890,bicubic,+5.177,+1.380,+1
ecaresnet50t,87.538,12.462,97.643,2.357,25.57,320,0.950,bicubic,+5.192,+1.505,+1
efficientnet_b3,87.435,12.565,97.681,2.319,12.23,320,1.000,bicubic,+5.193,+1.567,+3
cait_xxs24_384,87.416,12.584,97.619,2.381,12.03,384,1.000,bicubic,+6.450,+1.973,+37
tresnet_l_448,87.377,12.623,97.485,2.515,55.99,448,0.875,bilinear,+5.109,+1.509,0
nasnetalarge,87.350,12.650,97.417,2.583,88.75,331,0.911,bicubic,+4.730,+1.371,-7
ecaresnet101d,87.288,12.712,97.562,2.438,44.57,224,0.875,bicubic,+5.116,+1.516,+3
resnest101e,87.284,12.716,97.560,2.440,48.28,256,0.875,bilinear,+4.394,+1.240,-14
pit_s_distilled_224,87.277,12.723,97.500,2.500,24.04,224,0.900,bicubic,+5.281,+1.702,+4
resnetrs101,87.247,12.753,97.457,2.543,63.62,288,0.940,bicubic,+4.959,+1.449,-6
tresnet_xl,87.224,12.776,97.400,2.600,78.44,224,0.875,bilinear,+5.170,+1.463,0
tf_efficientnet_b3_ap,87.192,12.808,97.380,2.620,12.23,300,0.904,bicubic,+5.370,+1.756,+3
vit_deit_base_distilled_patch16_224,88.214,11.786,97.914,2.086,87.34,224,0.900,bicubic,+4.826,+1.426,0
ig_resnext101_32x8d,88.146,11.854,97.856,2.144,88.79,224,0.875,bilinear,+5.458,+1.220,+18
cait_xxs36_384,88.140,11.860,97.908,2.092,17.37,384,1.000,bicubic,+5.946,+1.760,+32
dm_nfnet_f0,88.112,11.888,97.837,2.163,71.49,256,0.900,bicubic,+4.770,+1.277,-1
swsl_resnext101_32x4d,88.099,11.901,97.967,2.033,44.18,224,0.875,bilinear,+4.869,+1.207,0
tf_efficientnet_b4,87.963,12.037,97.739,2.261,19.34,380,0.922,bicubic,+4.941,+1.439,+8
nfnet_l0,87.948,12.052,97.850,2.150,35.07,288,1.000,bicubic,+5.188,+1.352,+10
eca_nfnet_l0,87.943,12.057,97.861,2.139,24.14,288,1.000,bicubic,+5.355,+1.387,+14
resnet101d,87.941,12.059,97.908,2.092,44.57,320,1.000,bicubic,+4.919,+1.462,+4
regnety_032,87.937,12.063,97.891,2.109,19.44,288,1.000,bicubic,+5.213,+1.467,+8
twins_svt_large,87.901,12.099,97.581,2.419,99.27,224,0.900,bicubic,+4.223,+0.987,-13
twins_pcpvt_large,87.877,12.123,97.856,2.144,60.99,224,0.900,bicubic,+4.737,+1.258,-5
vit_deit_base_patch16_384,87.845,12.155,97.510,2.490,86.86,384,1.000,bicubic,+4.739,+1.138,-4
tresnet_xl_448,87.796,12.204,97.459,2.541,78.44,448,0.875,bilinear,+4.746,+1.285,-2
tresnet_m,87.736,12.264,97.523,2.477,31.39,224,0.875,bilinear,+4.656,+1.405,-5
twins_pcpvt_base,87.736,12.264,97.726,2.274,43.83,224,0.900,bicubic,+5.028,+1.380,+3
swin_small_patch4_window7_224,87.664,12.336,97.566,2.434,49.61,224,0.900,bicubic,+4.452,+1.244,-11
twins_svt_base,87.638,12.362,97.523,2.477,56.07,224,0.900,bicubic,+4.502,+1.105,-11
resnetv2_101x1_bitm,87.638,12.362,97.955,2.045,44.54,480,1.000,bilinear,+5.426,+1.483,+16
pnasnet5large,87.636,12.364,97.485,2.515,86.06,331,0.911,bicubic,+4.854,+1.445,-4
swsl_resnext101_32x16d,87.615,12.386,97.820,2.180,194.03,224,0.875,bilinear,+4.269,+0.974,-19
swsl_resnext50_32x4d,87.600,12.400,97.651,2.349,25.03,224,0.875,bilinear,+5.418,+1.421,+14
tf_efficientnet_b2_ns,87.557,12.443,97.628,2.372,9.11,260,0.890,bicubic,+5.177,+1.380,+2
levit_384,87.553,12.447,97.545,2.455,39.13,224,0.900,bicubic,+4.967,+1.529,-1
ecaresnet50t,87.538,12.462,97.643,2.357,25.57,320,0.950,bicubic,+5.192,+1.505,+2
efficientnet_b3,87.435,12.565,97.681,2.319,12.23,320,1.000,bicubic,+5.193,+1.567,+7
cait_xxs24_384,87.416,12.584,97.619,2.381,12.03,384,1.000,bicubic,+6.450,+1.973,+48
resnet51q,87.395,12.605,97.587,2.413,35.70,288,1.000,bilinear,+5.035,+1.407,-2
coat_lite_small,87.380,12.620,97.365,2.635,19.84,224,0.900,bicubic,+5.072,+1.515,-1
tresnet_l_448,87.377,12.623,97.485,2.515,55.99,448,0.875,bilinear,+5.109,+1.509,+2
nasnetalarge,87.350,12.650,97.417,2.583,88.75,331,0.911,bicubic,+4.730,+1.371,-10
ecaresnet101d,87.288,12.712,97.562,2.438,44.57,224,0.875,bicubic,+5.116,+1.516,+5
resnest101e,87.284,12.716,97.560,2.440,48.28,256,0.875,bilinear,+4.394,+1.240,-18
pit_s_distilled_224,87.277,12.723,97.500,2.500,24.04,224,0.900,bicubic,+5.281,+1.702,+7
resnetrs101,87.247,12.753,97.457,2.543,63.62,288,0.940,bicubic,+4.959,+1.449,-4
mixer_b16_224_miil,87.226,12.774,97.410,2.590,59.88,224,0.875,bilinear,+4.918,+1.694,-7
tresnet_xl,87.224,12.776,97.400,2.600,78.44,224,0.875,bilinear,+5.170,+1.463,+2
convit_base,87.200,12.800,97.286,2.714,86.54,224,0.875,bicubic,+4.910,+1.348,-8
tf_efficientnet_b3_ap,87.192,12.808,97.380,2.620,12.23,300,0.904,bicubic,+5.370,+1.756,+5
visformer_small,87.181,12.819,97.323,2.677,40.22,224,0.900,bicubic,+5.075,+1.451,-2
convit_small,87.053,12.947,97.350,2.650,27.78,224,0.875,bicubic,+5.627,+1.606,+17
tf_efficientnetv2_b3,87.032,12.968,97.303,2.697,14.36,300,0.904,bicubic,+5.062,+1.521,0
vit_base_patch32_384,87.019,12.981,97.654,2.346,88.30,384,1.000,bicubic,+5.367,+1.526,+5
vit_large_patch16_224,87.006,12.994,97.690,2.310,304.33,224,0.900,bicubic,+3.944,+1.252,-24
vit_deit_small_distilled_patch16_224,86.993,13.007,97.316,2.684,22.44,224,0.900,bicubic,+5.793,+1.938,+19
vit_large_patch16_224,87.006,12.994,97.690,2.310,304.33,224,0.900,bicubic,+3.944,+1.252,-33
vit_deit_small_distilled_patch16_224,86.993,13.007,97.316,2.684,22.44,224,0.900,bicubic,+5.793,+1.938,+22
tnt_s_patch16_224,86.903,13.097,97.368,2.632,23.76,224,0.900,bicubic,+5.385,+1.620,+7
ssl_resnext101_32x16d,86.856,13.143,97.517,2.483,194.03,224,0.875,bilinear,+5.013,+1.421,-3
ssl_resnext101_32x16d,86.856,13.143,97.517,2.483,194.03,224,0.875,bilinear,+5.013,+1.421,-4
rexnet_200,86.846,13.154,97.276,2.724,16.37,224,0.875,bicubic,+5.214,+1.608,+2
tf_efficientnet_b3,86.835,13.165,97.297,2.703,12.23,300,0.904,bicubic,+5.199,+1.579,0
vit_deit_base_patch16_224,86.829,13.171,97.049,2.951,86.57,224,0.900,bicubic,+4.831,+1.315,-8
tresnet_m_448,86.820,13.180,97.212,2.788,31.39,448,0.875,bilinear,+5.106,+1.640,-4
vit_deit_base_patch16_224,86.829,13.171,97.049,2.951,86.57,224,0.900,bicubic,+4.831,+1.315,-10
tresnet_m_448,86.820,13.180,97.212,2.788,31.39,448,0.875,bilinear,+5.106,+1.640,-5
ssl_resnext101_32x8d,86.807,13.193,97.466,2.534,88.79,224,0.875,bilinear,+5.191,+1.428,-1
swsl_resnet50,86.807,13.193,97.498,2.502,25.56,224,0.875,bilinear,+5.641,+1.526,+12
swsl_resnet50,86.807,13.193,97.498,2.502,25.56,224,0.875,bilinear,+5.641,+1.526,+15
tf_efficientnet_lite4,86.803,13.197,97.263,2.737,13.01,380,0.920,bilinear,+5.267,+1.595,-2
vit_base_patch16_224,86.778,13.223,97.438,2.562,86.57,224,0.900,bicubic,+4.992,+1.316,-9
coat_mini,86.793,13.207,97.162,2.837,10.34,224,0.900,bicubic,+5.525,+1.770,+9
vit_base_patch16_224,86.778,13.223,97.438,2.562,86.57,224,0.900,bicubic,+4.992,+1.316,-11
tresnet_l,86.767,13.233,97.271,2.729,55.99,224,0.875,bilinear,+5.277,+1.647,-1
twins_svt_small,86.756,13.244,97.175,2.825,24.06,224,0.900,bicubic,+5.074,+1.505,-11
levit_256,86.728,13.272,97.259,2.741,18.89,224,0.900,bicubic,+5.218,+1.769,-5
seresnext50_32x4d,86.699,13.301,97.214,2.786,27.56,224,0.875,bicubic,+5.433,+1.594,+5
pit_b_224,86.686,13.314,96.898,3.102,73.76,224,0.900,bicubic,+4.240,+1.188,-27
tf_efficientnet_b1_ns,86.669,13.331,97.378,2.622,7.79,240,0.882,bicubic,+5.281,+1.640,-2
swin_tiny_patch4_window7_224,86.664,13.336,97.197,2.803,28.29,224,0.900,bicubic,+5.286,+1.657,-2
gernet_l,86.654,13.346,97.186,2.814,31.08,256,0.875,bilinear,+5.300,+1.650,-2
wide_resnet50_2,86.647,13.353,97.214,2.786,68.88,224,0.875,bicubic,+5.191,+1.682,-6
efficientnet_el,86.635,13.366,97.175,2.825,10.59,300,0.904,bicubic,+5.319,+1.649,-3
pit_b_224,86.686,13.314,96.898,3.102,73.76,224,0.900,bicubic,+4.240,+1.188,-37
tf_efficientnet_b1_ns,86.669,13.331,97.378,2.622,7.79,240,0.882,bicubic,+5.281,+1.640,-3
swin_tiny_patch4_window7_224,86.664,13.336,97.197,2.803,28.29,224,0.900,bicubic,+5.286,+1.657,-3
gernet_l,86.654,13.346,97.186,2.814,31.08,256,0.875,bilinear,+5.300,+1.650,-3
wide_resnet50_2,86.647,13.353,97.214,2.786,68.88,224,0.875,bicubic,+5.191,+1.682,-8
efficientnet_el,86.635,13.366,97.175,2.825,10.59,300,0.904,bicubic,+5.319,+1.649,-4
twins_pcpvt_small,86.620,13.380,97.340,2.660,24.11,224,0.900,bicubic,+5.532,+1.698,+4
nf_resnet50,86.617,13.383,97.282,2.718,25.56,288,0.940,bicubic,+5.923,+1.926,+15
resnest50d_4s2x40d,86.592,13.408,97.269,2.731,30.42,224,0.875,bicubic,+5.484,+1.711,+1
resnest50d_4s2x40d,86.592,13.408,97.269,2.731,30.42,224,0.875,bicubic,+5.484,+1.711,0
efficientnet_b3_pruned,86.581,13.419,97.190,2.810,9.86,300,0.904,bicubic,+5.723,+1.948,+9
repvgg_b3,86.566,13.434,97.139,2.861,123.09,224,0.875,bilinear,+6.074,+1.879,+17
ssl_resnext101_32x4d,86.479,13.521,97.468,2.532,44.18,224,0.875,bilinear,+5.555,+1.740,+4
ecaresnet50d,86.470,13.530,97.186,2.814,25.58,224,0.875,bicubic,+5.878,+1.866,+13
gluon_resnet152_v1s,86.468,13.532,97.109,2.891,60.32,224,0.875,bicubic,+5.452,+1.697,-2
resnest50d_1s4x24d,86.447,13.553,97.148,2.852,25.68,224,0.875,bicubic,+5.459,+1.826,-2
repvgg_b3g4,86.361,13.639,97.054,2.946,83.83,224,0.875,bilinear,+6.149,+1.944,+29
legacy_senet154,86.342,13.658,96.928,3.072,115.09,224,0.875,bilinear,+5.032,+1.432,-12
cait_xxs36_224,86.340,13.660,97.111,2.889,17.30,224,1.000,bicubic,+6.590,+2.245,+49
repvgg_b3g4,86.361,13.639,97.054,2.946,83.83,224,0.875,bilinear,+6.147,+1.944,+29
legacy_senet154,86.342,13.658,96.928,3.072,115.09,224,0.875,bilinear,+5.032,+1.432,-14
cait_xxs36_224,86.340,13.660,97.111,2.889,17.30,224,1.000,bicubic,+6.590,+2.245,+51
gernet_m,86.319,13.681,97.096,2.904,21.14,224,0.875,bilinear,+5.587,+1.912,+3
pit_s_224,86.316,13.684,97.045,2.955,23.46,224,0.900,bicubic,+5.222,+1.713,-9
pit_s_224,86.316,13.684,97.045,2.955,23.46,224,0.900,bicubic,+5.222,+1.713,-10
efficientnet_b2,86.304,13.696,96.990,3.010,9.11,288,1.000,bicubic,+5.692,+1.672,+3
gluon_senet154,86.278,13.722,96.949,3.051,115.09,224,0.875,bicubic,+5.044,+1.601,-15
gluon_senet154,86.278,13.722,96.949,3.051,115.09,224,0.875,bicubic,+5.044,+1.601,-16
resnest50d,86.240,13.761,97.073,2.927,27.48,224,0.875,bilinear,+5.266,+1.695,-9
ecaresnet101d_pruned,86.210,13.790,97.335,2.665,24.88,224,0.875,bicubic,+5.392,+1.707,-4
ecaresnet101d_pruned,86.207,13.793,97.335,2.665,24.88,224,0.875,bicubic,+5.389,+1.707,-4
efficientnet_el_pruned,86.192,13.807,97.026,2.974,10.59,300,0.904,bicubic,+5.892,+1.808,+14
cspdarknet53,86.182,13.818,97.013,2.987,27.64,256,0.887,bilinear,+6.124,+1.929,+25
inception_v4,86.169,13.831,96.919,3.081,42.68,299,0.875,bicubic,+6.001,+1.951,+20
resnetv2_50x1_bitm,86.154,13.846,97.560,2.440,25.55,480,1.000,bilinear,+5.982,+1.934,+18
cspdarknet53,86.182,13.818,97.013,2.987,27.64,256,0.887,bilinear,+6.124,+1.929,+26
inception_v4,86.169,13.831,96.919,3.081,42.68,299,0.875,bicubic,+6.001,+1.951,+21
resnetv2_50x1_bitm,86.154,13.846,97.560,2.440,25.55,480,1.000,bilinear,+5.982,+1.934,+19
rexnet_150,86.154,13.846,97.058,2.942,9.73,224,0.875,bicubic,+5.844,+1.892,+8
inception_resnet_v2,86.133,13.867,97.043,2.957,55.84,299,0.897,bicubic,+5.675,+1.737,+2
ssl_resnext50_32x4d,86.086,13.914,97.212,2.788,25.03,224,0.875,bilinear,+5.768,+1.806,+5
@ -140,106 +163,112 @@ gluon_resnet101_v1s,86.054,13.946,97.022,2.978,44.67,224,0.875,bicubic,+5.752,+1
ecaresnetlight,86.052,13.948,97.069,2.931,30.16,224,0.875,bicubic,+5.590,+1.819,-3
gluon_seresnext101_32x4d,86.032,13.968,96.977,3.023,48.96,224,0.875,bicubic,+5.128,+1.683,-18
resnet50d,86.009,13.991,96.979,3.021,25.58,224,0.875,bicubic,+5.479,+1.819,-9
ecaresnet26t,85.983,14.017,97.041,2.959,16.01,320,0.950,bicubic,+6.129,+1.957,+26
ecaresnet26t,85.983,14.017,97.041,2.959,16.01,320,0.950,bicubic,+6.129,+1.957,+27
tf_efficientnet_b2_ap,85.975,14.025,96.810,3.190,9.11,260,0.890,bicubic,+5.675,+1.782,+2
gluon_seresnext101_64x4d,85.960,14.040,96.979,3.021,88.23,224,0.875,bicubic,+5.066,+1.671,-21
gluon_resnet152_v1d,85.917,14.083,96.812,3.188,60.21,224,0.875,bicubic,+5.443,+1.606,-10
vit_large_patch32_384,85.909,14.091,97.368,2.632,306.63,384,1.000,bicubic,+4.403,+1.276,-43
tf_efficientnet_b2,85.902,14.098,96.862,3.139,9.11,260,0.890,bicubic,+5.816,+1.954,+8
seresnet50,85.857,14.143,97.004,2.995,28.09,224,0.875,bicubic,+5.583,+1.934,-2
repvgg_b2g4,85.855,14.145,96.812,3.188,61.76,224,0.875,bilinear,+6.489,+2.124,+37
gluon_resnet101_v1d,85.849,14.151,96.663,3.337,44.57,224,0.875,bicubic,+5.435,+1.649,-12
resnet50,85.804,14.196,96.712,3.288,25.56,224,0.875,bicubic,+6.766,+2.322,+58
mixnet_xl,85.798,14.202,96.712,3.288,11.90,224,0.875,bicubic,+5.322,+1.776,-18
vit_large_patch32_384,85.909,14.091,97.368,2.632,306.63,384,1.000,bicubic,+4.403,+1.276,-46
tf_efficientnet_b2,85.902,14.098,96.862,3.139,9.11,260,0.890,bicubic,+5.816,+1.954,+9
tf_efficientnetv2_b2,85.900,14.100,96.889,3.111,10.10,260,0.890,bicubic,+5.692,+1.847,+3
seresnet50,85.857,14.143,97.004,2.995,28.09,224,0.875,bicubic,+5.583,+1.934,-3
repvgg_b2g4,85.855,14.145,96.812,3.188,61.76,224,0.875,bilinear,+6.489,+2.124,+39
gluon_resnet101_v1d,85.849,14.151,96.663,3.337,44.57,224,0.875,bicubic,+5.435,+1.649,-13
resnet50,85.804,14.196,96.712,3.288,25.56,224,0.875,bicubic,+6.766,+2.322,+60
mixnet_xl,85.798,14.202,96.712,3.288,11.90,224,0.875,bicubic,+5.322,+1.776,-19
ens_adv_inception_resnet_v2,85.781,14.220,96.759,3.241,55.84,299,0.897,bicubic,+5.799,+1.823,+6
tf_efficientnet_lite3,85.755,14.245,96.887,3.113,8.20,300,0.904,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,+33
gluon_resnext101_32x4d,85.746,14.254,96.635,3.365,44.18,224,0.875,bicubic,+5.412,+1.709,-16
legacy_seresnext101_32x4d,85.746,14.254,96.757,3.243,48.96,224,0.875,bilinear,+5.518,+1.739,-8
tf_efficientnet_lite3,85.755,14.245,96.887,3.113,8.20,300,0.904,bilinear,+5.935,+1.973,+17
ese_vovnet39b,85.751,14.249,96.891,3.109,24.57,224,0.875,bicubic,+6.431,+2.179,+35
gluon_resnext101_32x4d,85.746,14.254,96.635,3.365,44.18,224,0.875,bicubic,+5.412,+1.709,-17
legacy_seresnext101_32x4d,85.746,14.254,96.757,3.243,48.96,224,0.875,bilinear,+5.518,+1.739,-9
cspresnext50,85.740,14.260,96.840,3.160,20.57,224,0.875,bilinear,+5.700,+1.896,-1
regnety_320,85.727,14.273,96.725,3.275,145.05,224,0.875,bicubic,+4.915,+1.481,-33
cspresnet50,85.721,14.279,96.795,3.205,21.62,256,0.887,bilinear,+6.147,+2.083,+20
regnety_320,85.727,14.273,96.725,3.275,145.05,224,0.875,bicubic,+4.915,+1.481,-34
cspresnet50,85.721,14.279,96.795,3.205,21.62,256,0.887,bilinear,+6.147,+2.083,+21
xception71,85.697,14.303,96.776,3.224,42.34,299,0.903,bicubic,+5.823,+1.854,+4
gluon_resnext101_64x4d,85.693,14.307,96.644,3.356,83.46,224,0.875,bicubic,+5.089,+1.656,-32
efficientnet_em,85.684,14.316,96.938,3.062,6.90,240,0.882,bicubic,+6.432,+2.144,+34
gluon_resnext101_64x4d,85.693,14.307,96.644,3.356,83.46,224,0.875,bicubic,+5.089,+1.656,-33
efficientnet_em,85.684,14.316,96.938,3.062,6.90,240,0.882,bicubic,+6.432,+2.144,+36
vit_deit_small_patch16_224,85.678,14.322,96.906,3.094,22.05,224,0.900,bicubic,+5.822,+1.854,+3
pit_xs_distilled_224,85.657,14.343,96.667,3.333,11.00,224,0.900,bicubic,+6.351,+2.303,+28
pit_xs_distilled_224,85.657,14.343,96.667,3.333,11.00,224,0.900,bicubic,+6.351,+2.303,+30
efficientnet_b2_pruned,85.642,14.358,96.746,3.254,8.31,260,0.890,bicubic,+5.726,+1.890,-5
dpn107,85.640,14.360,96.729,3.271,86.92,224,0.875,bicubic,+5.484,+1.819,-14
ecaresnet50d_pruned,85.580,14.420,96.936,3.064,19.94,224,0.875,bicubic,+5.864,+2.056,+6
ecaresnet50d_pruned,85.580,14.420,96.936,3.064,19.94,224,0.875,bicubic,+5.864,+2.056,+7
gluon_resnet152_v1c,85.580,14.420,96.646,3.354,60.21,224,0.875,bicubic,+5.670,+1.806,-7
levit_192,85.580,14.420,96.740,3.260,10.95,224,0.900,bicubic,+5.738,+1.954,-1
resnext50d_32x4d,85.569,14.431,96.748,3.252,25.05,224,0.875,bicubic,+5.893,+1.882,+7
regnety_120,85.543,14.457,96.785,3.215,51.82,224,0.875,bicubic,+5.177,+1.659,-32
regnetx_320,85.524,14.476,96.669,3.331,107.81,224,0.875,bicubic,+5.278,+1.643,-24
tf_efficientnetv2_b1,85.561,14.439,96.727,3.273,8.14,240,0.882,bicubic,+6.099,+2.005,+14
regnety_120,85.543,14.457,96.785,3.215,51.82,224,0.875,bicubic,+5.177,+1.659,-35
regnetx_320,85.524,14.476,96.669,3.331,107.81,224,0.875,bicubic,+5.278,+1.643,-27
nf_regnet_b1,85.499,14.501,96.799,3.200,10.22,288,0.900,bicubic,+6.193,+2.051,+19
dpn92,85.494,14.506,96.635,3.365,37.67,224,0.875,bicubic,+5.486,+1.799,-16
gluon_resnet152_v1b,85.475,14.525,96.550,3.450,60.19,224,0.875,bicubic,+5.789,+1.814,+1
rexnet_130,85.473,14.527,96.684,3.316,7.56,224,0.875,bicubic,+5.973,+2.002,+7
resnetrs50,85.462,14.538,96.736,3.264,35.69,224,0.910,bicubic,+5.570,+1.767,-14
dpn131,85.398,14.602,96.639,3.361,79.25,224,0.875,bicubic,+5.576,+1.929,-9
regnetx_160,85.390,14.610,96.637,3.363,54.28,224,0.875,bicubic,+5.534,+1.807,-13
dpn92,85.494,14.506,96.635,3.365,37.67,224,0.875,bicubic,+5.486,+1.799,-18
gluon_resnet152_v1b,85.475,14.525,96.550,3.450,60.19,224,0.875,bicubic,+5.789,+1.814,0
rexnet_130,85.473,14.527,96.684,3.316,7.56,224,0.875,bicubic,+5.973,+2.002,+6
resnetrs50,85.462,14.538,96.736,3.264,35.69,224,0.910,bicubic,+5.570,+1.767,-16
dpn131,85.398,14.602,96.639,3.361,79.25,224,0.875,bicubic,+5.576,+1.929,-10
regnetx_160,85.390,14.610,96.637,3.363,54.28,224,0.875,bicubic,+5.534,+1.807,-15
dla102x2,85.366,14.634,96.629,3.371,41.28,224,0.875,bilinear,+5.918,+1.989,+5
gluon_seresnext50_32x4d,85.336,14.664,96.667,3.333,27.56,224,0.875,bicubic,+5.418,+1.845,-21
xception65,85.315,14.685,96.637,3.363,39.92,299,0.903,bicubic,+5.763,+1.983,-1
skresnext50_32x4d,85.313,14.687,96.390,3.610,27.48,224,0.875,bicubic,+5.157,+1.748,-29
dpn98,85.311,14.689,96.469,3.531,61.57,224,0.875,bicubic,+5.669,+1.871,-6
gluon_resnet101_v1c,85.304,14.696,96.405,3.595,44.57,224,0.875,bicubic,+5.770,+1.827,-3
gluon_seresnext50_32x4d,85.336,14.664,96.667,3.333,27.56,224,0.875,bicubic,+5.418,+1.845,-23
xception65,85.315,14.685,96.637,3.363,39.92,299,0.903,bicubic,+5.763,+1.983,-2
skresnext50_32x4d,85.313,14.687,96.390,3.610,27.48,224,0.875,bicubic,+5.157,+1.748,-31
dpn98,85.311,14.689,96.469,3.531,61.57,224,0.875,bicubic,+5.669,+1.871,-7
gluon_resnet101_v1c,85.304,14.696,96.405,3.595,44.57,224,0.875,bicubic,+5.770,+1.827,-4
dpn68b,85.291,14.709,96.464,3.536,12.61,224,0.875,bicubic,+6.076,+2.050,+14
regnety_064,85.283,14.717,96.639,3.361,30.58,224,0.875,bicubic,+5.561,+1.871,-14
regnety_064,85.283,14.717,96.639,3.361,30.58,224,0.875,bicubic,+5.561,+1.871,-15
resnetblur50,85.283,14.717,96.531,3.470,25.56,224,0.875,bicubic,+5.997,+1.892,+7
coat_lite_mini,85.251,14.749,96.680,3.320,11.01,224,0.900,bicubic,+6.163,+2.076,+15
regnety_080,85.245,14.755,96.633,3.367,39.18,224,0.875,bicubic,+5.369,+1.803,-26
cait_xxs24_224,85.228,14.773,96.712,3.288,11.96,224,1.000,bicubic,+6.842,+2.402,+41
resnext50_32x4d,85.221,14.779,96.526,3.474,25.03,224,0.875,bicubic,+5.453,+1.928,-21
regnety_080,85.245,14.755,96.633,3.367,39.18,224,0.875,bicubic,+5.369,+1.803,-28
cait_xxs24_224,85.228,14.773,96.712,3.288,11.96,224,1.000,bicubic,+6.842,+2.402,+43
resnext50_32x4d,85.221,14.779,96.526,3.474,25.03,224,0.875,bicubic,+5.453,+1.928,-22
resnext101_32x8d,85.187,14.813,96.445,3.555,88.79,224,0.875,bilinear,+5.879,+1.927,-4
gluon_inception_v3,85.183,14.817,96.526,3.474,23.83,299,0.875,bicubic,+6.377,+2.156,+21
hrnet_w48,85.151,14.849,96.492,3.508,77.47,224,0.875,bilinear,+5.851,+1.980,-1
gluon_xception65,85.148,14.851,96.597,3.403,39.92,299,0.903,bicubic,+5.433,+1.737,-21
gluon_xception65,85.148,14.851,96.597,3.403,39.92,299,0.903,bicubic,+5.433,+1.737,-22
gluon_resnet101_v1b,85.142,14.858,96.366,3.634,44.55,224,0.875,bicubic,+5.836,+1.842,-6
regnetx_120,85.131,14.869,96.477,3.523,46.11,224,0.875,bicubic,+5.535,+1.739,-19
regnetx_120,85.131,14.869,96.477,3.523,46.11,224,0.875,bicubic,+5.535,+1.739,-20
xception,85.129,14.871,96.471,3.529,22.86,299,0.897,bicubic,+6.077,+2.079,+9
tf_efficientnet_b1_ap,85.127,14.873,96.405,3.595,7.79,240,0.882,bicubic,+5.847,+2.099,-4
hrnet_w64,85.119,14.881,96.744,3.256,128.06,224,0.875,bilinear,+5.645,+2.092,-17
hrnet_w64,85.119,14.881,96.744,3.256,128.06,224,0.875,bilinear,+5.645,+2.092,-18
ssl_resnet50,85.097,14.903,96.866,3.134,25.56,224,0.875,bilinear,+5.875,+2.034,-4
res2net101_26w_4s,85.093,14.907,96.381,3.619,45.21,224,0.875,bilinear,+5.895,+1.949,-1
tf_efficientnet_cc_b1_8e,85.063,14.937,96.422,3.578,39.72,240,0.882,bicubic,+5.755,+2.052,-14
res2net50_26w_8s,85.029,14.971,96.419,3.580,48.40,224,0.875,bilinear,+5.831,+2.052,-4
resnest26d,85.008,14.992,96.637,3.363,17.07,224,0.875,bilinear,+6.530,+2.339,+21
resnest26d,85.008,14.992,96.637,3.363,17.07,224,0.875,bilinear,+6.530,+2.339,+22
gluon_resnext50_32x4d,84.995,15.005,96.426,3.574,25.03,224,0.875,bicubic,+5.641,+2.000,-20
tf_efficientnet_b0_ns,84.984,15.016,96.503,3.497,5.29,224,0.875,bicubic,+6.326,+2.127,+14
regnety_040,84.948,15.052,96.612,3.388,20.65,224,0.875,bicubic,+5.728,+1.956,-10
dla169,84.920,15.080,96.535,3.465,53.39,224,0.875,bilinear,+6.232,+2.199,+10
tf_efficientnet_b1,84.918,15.082,96.364,3.636,7.79,240,0.882,bicubic,+6.092,+2.166,+3
legacy_seresnext50_32x4d,84.901,15.099,96.434,3.566,27.56,224,0.875,bilinear,+5.823,+1.998,-7
hrnet_w44,84.884,15.116,96.434,3.566,67.06,224,0.875,bilinear,+5.988,+2.066,-1
regnetx_080,84.862,15.138,96.434,3.566,39.57,224,0.875,bicubic,+5.668,+1.874,-11
gluon_resnet50_v1s,84.860,15.140,96.443,3.557,25.68,224,0.875,bicubic,+6.148,+2.205,+4
gluon_resnet50_v1d,84.832,15.168,96.398,3.602,25.58,224,0.875,bicubic,+5.758,+1.928,-10
dla60_res2next,84.830,15.170,96.411,3.589,17.03,224,0.875,bilinear,+6.390,+2.259,+13
mixnet_l,84.822,15.178,96.328,3.672,7.33,224,0.875,bicubic,+5.846,+2.146,-8
tv_resnet152,84.815,15.185,96.225,3.775,60.19,224,0.875,bilinear,+6.503,+2.187,+16
dla60_res2net,84.813,15.187,96.481,3.519,20.85,224,0.875,bilinear,+6.349,+2.275,+8
dla102x,84.813,15.187,96.552,3.448,26.31,224,0.875,bilinear,+6.303,+2.324,+4
pit_xs_224,84.792,15.208,96.492,3.508,10.62,224,0.900,bicubic,+6.610,+2.324,+18
xception41,84.792,15.208,96.413,3.587,26.97,299,0.903,bicubic,+6.276,+2.135,+1
regnetx_064,84.781,15.219,96.490,3.510,26.21,224,0.875,bicubic,+5.709,+2.032,-17
hrnet_w40,84.743,15.257,96.554,3.446,57.56,224,0.875,bilinear,+5.823,+2.084,-14
res2net50_26w_6s,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.156,+2.157,-3
repvgg_b2,84.724,15.276,96.469,3.531,89.02,224,0.875,bilinear,+5.932,+2.055,-10
legacy_seresnet152,84.704,15.296,96.417,3.583,66.82,224,0.875,bilinear,+6.044,+2.047,-7
coat_tiny,84.976,15.024,96.409,3.591,5.50,224,0.900,bicubic,+6.542,+2.371,+23
regnety_040,84.948,15.052,96.612,3.388,20.65,224,0.875,bicubic,+5.728,+1.956,-11
dla169,84.920,15.080,96.535,3.465,53.39,224,0.875,bilinear,+6.232,+2.199,+9
tf_efficientnet_b1,84.918,15.082,96.364,3.636,7.79,240,0.882,bicubic,+6.092,+2.166,+2
legacy_seresnext50_32x4d,84.901,15.099,96.434,3.566,27.56,224,0.875,bilinear,+5.823,+1.998,-8
hrnet_w44,84.884,15.116,96.434,3.566,67.06,224,0.875,bilinear,+5.988,+2.066,-2
regnetx_080,84.862,15.138,96.434,3.566,39.57,224,0.875,bicubic,+5.668,+1.874,-12
gluon_resnet50_v1s,84.860,15.140,96.443,3.557,25.68,224,0.875,bicubic,+6.150,+2.205,+3
levit_128,84.843,15.157,96.360,3.640,9.21,224,0.900,bicubic,+6.357,+2.350,+9
gluon_resnet50_v1d,84.832,15.168,96.398,3.602,25.58,224,0.875,bicubic,+5.758,+1.928,-12
dla60_res2next,84.830,15.170,96.411,3.589,17.03,224,0.875,bilinear,+6.390,+2.259,+12
mixnet_l,84.822,15.178,96.328,3.672,7.33,224,0.875,bicubic,+5.846,+2.146,-10
tv_resnet152,84.815,15.185,96.225,3.775,60.19,224,0.875,bilinear,+6.503,+2.187,+17
dla102x,84.813,15.187,96.552,3.448,26.31,224,0.875,bilinear,+6.303,+2.324,+2
dla60_res2net,84.813,15.187,96.481,3.519,20.85,224,0.875,bilinear,+6.349,+2.275,+7
pit_xs_224,84.792,15.208,96.492,3.508,10.62,224,0.900,bicubic,+6.610,+2.324,+19
xception41,84.792,15.208,96.413,3.587,26.97,299,0.903,bicubic,+6.276,+2.135,-1
regnetx_064,84.781,15.219,96.490,3.510,26.21,224,0.875,bicubic,+5.709,+2.032,-19
hrnet_w40,84.743,15.257,96.554,3.446,57.56,224,0.875,bilinear,+5.823,+2.084,-16
res2net50_26w_6s,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.156,+2.157,-5
repvgg_b2,84.724,15.276,96.469,3.531,89.02,224,0.875,bilinear,+5.932,+2.055,-12
legacy_seresnet152,84.704,15.296,96.417,3.583,66.82,224,0.875,bilinear,+6.044,+2.047,-9
selecsls60b,84.657,15.343,96.300,3.700,32.77,224,0.875,bicubic,+6.245,+2.126,+2
hrnet_w32,84.651,15.349,96.407,3.593,41.23,224,0.875,bilinear,+6.201,+2.221,-1
efficientnet_b1,84.608,15.392,96.332,3.668,7.79,256,1.000,bicubic,+5.814,+1.990,-15
regnetx_040,84.600,15.400,96.383,3.617,22.12,224,0.875,bicubic,+6.118,+2.139,-6
hrnet_w32,84.651,15.349,96.407,3.593,41.23,224,0.875,bilinear,+6.201,+2.221,-2
tf_efficientnetv2_b0,84.625,15.375,96.274,3.726,7.14,224,0.875,bicubic,+6.269,+2.250,+4
efficientnet_b1,84.608,15.392,96.332,3.668,7.79,256,1.000,bicubic,+5.814,+1.990,-18
regnetx_040,84.600,15.400,96.383,3.617,22.12,224,0.875,bicubic,+6.118,+2.139,-8
efficientnet_es,84.591,15.409,96.311,3.689,5.44,224,0.875,bicubic,+6.525,+2.385,+12
hrnet_w30,84.572,15.428,96.388,3.612,37.71,224,0.875,bilinear,+6.366,+2.166,+5
tf_mixnet_l,84.564,15.437,96.244,3.756,7.33,224,0.875,bicubic,+5.790,+2.246,-17
wide_resnet101_2,84.557,15.443,96.349,3.651,126.89,224,0.875,bilinear,+5.701,+2.067,-23
tf_mixnet_l,84.564,15.437,96.244,3.756,7.33,224,0.875,bicubic,+5.790,+2.246,-20
wide_resnet101_2,84.557,15.443,96.349,3.651,126.89,224,0.875,bilinear,+5.701,+2.067,-26
dla60x,84.523,15.477,96.285,3.715,17.35,224,0.875,bilinear,+6.277,+2.267,-1
legacy_seresnet101,84.504,15.496,96.330,3.670,49.33,224,0.875,bilinear,+6.122,+2.066,-5
tf_efficientnet_em,84.450,15.550,96.180,3.820,6.90,240,0.882,bicubic,+6.320,+2.136,+3
legacy_seresnet101,84.504,15.496,96.330,3.670,49.33,224,0.875,bilinear,+6.122,+2.066,-6
coat_lite_tiny,84.450,15.550,96.368,3.632,5.72,224,0.900,bicubic,+6.938,+2.452,+27
repvgg_b1,84.416,15.584,96.221,3.779,57.42,224,0.875,bilinear,+6.050,+2.123,-7
tf_efficientnet_em,84.450,15.550,96.180,3.820,6.90,240,0.882,bicubic,+6.320,+2.136,+3
repvgg_b1,84.416,15.584,96.221,3.779,57.42,224,0.875,bilinear,+6.050,+2.123,-8
efficientnet_b1_pruned,84.393,15.607,96.140,3.860,6.33,240,0.882,bicubic,+6.157,+2.306,-4
res2net50_26w_4s,84.365,15.635,96.082,3.918,25.70,224,0.875,bilinear,+6.401,+2.228,+7
hardcorenas_f,84.326,15.674,96.025,3.975,8.20,224,0.875,bilinear,+6.222,+2.222,0
@ -249,8 +278,8 @@ regnetx_032,84.237,15.763,96.247,3.753,15.30,224,0.875,bicubic,+6.065,+2.159,-6
res2next50,84.226,15.774,95.997,4.003,24.67,224,0.875,bilinear,+5.980,+2.105,-11
gluon_resnet50_v1c,84.207,15.793,96.161,3.839,25.58,224,0.875,bicubic,+6.195,+2.173,-2
dla102,84.190,15.810,96.206,3.794,33.27,224,0.875,bilinear,+6.158,+2.260,-4
rexnet_100,84.162,15.838,96.255,3.745,4.80,224,0.875,bicubic,+6.304,+2.385,+3
tf_inception_v3,84.132,15.868,95.920,4.080,23.83,299,0.875,bicubic,+6.276,+2.280,+4
rexnet_100,84.162,15.838,96.255,3.745,4.80,224,0.875,bicubic,+6.304,+2.839,+5
tf_inception_v3,84.134,15.866,95.920,4.080,23.83,299,0.875,bicubic,+6.276,+2.050,+2
res2net50_48w_2s,84.126,15.874,95.965,4.035,25.29,224,0.875,bilinear,+6.604,+2.411,+12
resnet34d,84.098,15.902,95.978,4.022,21.82,224,0.875,bicubic,+6.982,+2.596,+23
tf_efficientnet_lite2,84.094,15.906,96.069,3.931,6.09,260,0.890,bicubic,+6.626,+2.315,+12
@ -268,7 +297,7 @@ tv_resnet101,83.848,16.152,95.892,4.108,44.55,224,0.875,bilinear,+6.474,+2.352,+
inception_v3,83.761,16.239,95.879,4.121,23.83,299,0.875,bicubic,+6.323,+2.403,+1
hardcorenas_d,83.759,16.241,95.734,4.266,7.50,224,0.875,bilinear,+6.327,+2.250,+1
seresnext26d_32x4d,83.754,16.246,95.849,4.151,16.81,224,0.875,bicubic,+6.152,+2.241,-8
vit_small_patch16_224,83.735,16.265,95.758,4.242,48.75,224,0.900,bicubic,+5.877,+2.342,-15
vit_small_patch16_224,83.735,16.265,95.758,4.242,48.75,224,0.900,bicubic,+5.877,+2.118,-15
dla60,83.729,16.271,95.933,4.067,22.04,224,0.875,bilinear,+6.697,+2.615,+10
repvgg_b1g4,83.699,16.301,96.020,3.980,39.97,224,0.875,bilinear,+6.105,+2.194,-10
legacy_seresnet50,83.662,16.337,95.973,4.027,28.09,224,0.875,bilinear,+6.032,+2.225,-14
@ -279,23 +308,24 @@ densenet201,83.556,16.444,95.811,4.189,20.01,224,0.875,bicubic,+6.270,+2.333,-4
mobilenetv3_large_100_miil,83.556,16.444,95.452,4.548,5.48,224,0.875,bilinear,+5.640,+2.542,-27
gernet_s,83.522,16.478,95.794,4.206,8.17,224,0.875,bilinear,+6.606,+2.662,+5
legacy_seresnext26_32x4d,83.517,16.483,95.719,4.281,16.79,224,0.875,bicubic,+6.413,+2.403,-2
mixnet_m,83.515,16.485,95.689,4.311,5.01,224,0.875,bicubic,+6.255,+2.265,-6
tf_efficientnet_b0,83.515,16.485,95.719,4.281,5.29,224,0.875,bicubic,+6.667,+2.491,+4
mixnet_m,83.515,16.485,95.689,4.311,5.01,224,0.875,bicubic,+6.255,+2.265,-6
hrnet_w18,83.500,16.500,95.907,4.093,21.30,224,0.875,bilinear,+6.742,+2.463,+5
densenetblur121d,83.472,16.527,95.822,4.178,8.00,224,0.875,bicubic,+6.885,+2.630,+9
selecsls42b,83.457,16.543,95.745,4.255,32.46,224,0.875,bicubic,+6.283,+2.355,-9
tf_efficientnet_lite1,83.344,16.656,95.642,4.358,5.42,240,0.882,bicubic,+6.702,+2.416,+4
hardcorenas_c,83.342,16.658,95.706,4.294,5.52,224,0.875,bilinear,+6.288,+2.548,-7
regnetx_016,83.195,16.805,95.740,4.260,9.19,224,0.875,bicubic,+6.245,+2.320,-6
mobilenetv2_140,83.182,16.818,95.689,4.311,6.11,224,0.875,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,+7
mobilenetv2_140,83.182,16.818,95.689,4.311,6.11,224,0.875,bicubic,+6.666,+2.693,+7
dpn68,83.178,16.822,95.597,4.402,12.61,224,0.875,bicubic,+6.860,+2.620,+8
tf_efficientnet_es,83.178,16.822,95.585,4.415,5.44,224,0.875,bicubic,+6.584,+2.383,+1
tf_mixnet_m,83.176,16.824,95.461,4.539,5.01,224,0.875,bicubic,+6.234,+2.309,-9
ese_vovnet19b_dw,83.109,16.890,95.779,4.221,6.54,224,0.875,bicubic,+6.311,+2.511,-6
resnet26d,83.050,16.950,95.604,4.396,16.01,224,0.875,bicubic,+6.354,+2.454,-5
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,-6
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,+3
hardcorenas_b,82.873,17.128,95.392,4.607,5.18,224,0.875,bilinear,+6.335,+2.638,-3
hardcorenas_b,82.873,17.128,95.392,4.607,5.18,224,0.875,bilinear,+6.335,+2.638,-4
densenet121,82.823,17.177,95.585,4.415,7.98,224,0.875,bicubic,+7.245,+2.933,+8
densenet169,82.683,17.317,95.600,4.400,14.15,224,0.875,bicubic,+6.776,+2.574,+3
mixnet_s,82.525,17.476,95.356,4.644,4.13,224,0.875,bicubic,+6.532,+2.560,0
@ -312,7 +342,7 @@ mobilenetv2_110d,82.070,17.930,95.076,4.923,4.52,224,0.875,bicubic,+7.034,+2.890
tf_mixnet_s,82.038,17.962,95.121,4.879,4.13,224,0.875,bicubic,+6.388,+2.493,-7
repvgg_b0,82.001,17.999,95.100,4.900,15.82,224,0.875,bilinear,+6.849,+2.682,+1
vit_deit_tiny_distilled_patch16_224,81.997,18.003,95.141,4.859,5.91,224,0.900,bicubic,+7.487,+3.251,+14
mixer_b16_224,81.978,18.022,94.449,5.551,59.88,224,0.875,bicubic,+5.376,+2.221,-23
mixer_b16_224,81.978,18.022,94.449,5.551,59.88,224,0.875,bicubic,+5.376,+2.221,-24
pit_ti_distilled_224,81.967,18.033,95.145,4.855,5.10,224,0.900,bicubic,+7.437,+3.049,+11
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_lite0,81.952,18.048,95.168,4.832,4.65,224,0.875,bicubic,+7.122,+2.992,+3
@ -327,21 +357,22 @@ gluon_resnet34_v1b,81.500,18.500,94.810,5.190,21.80,224,0.875,bicubic,+6.912,+2.
regnetx_008,81.485,18.515,95.059,4.941,7.26,224,0.875,bicubic,+6.447,+2.724,-9
mnasnet_100,81.459,18.541,94.899,5.101,4.38,224,0.875,bicubic,+6.801,+2.785,-4
vgg19_bn,81.444,18.556,94.763,5.237,143.68,224,0.875,bilinear,+7.230,+2.921,0
spnasnet_100,80.878,19.122,94.526,5.474,4.42,224,0.875,bilinear,+6.794,+2.708,0
ghostnet_100,80.699,19.301,94.291,5.709,5.18,224,0.875,bilinear,+6.721,+2.835,+1
regnety_004,80.659,19.341,94.686,5.314,4.34,224,0.875,bicubic,+6.624,+2.934,-1
convit_tiny,81.126,18.874,95.044,4.955,5.71,224,0.875,bicubic,+8.010,+3.331,+8
spnasnet_100,80.878,19.122,94.526,5.474,4.42,224,0.875,bilinear,+6.794,+2.708,-1
ghostnet_100,80.699,19.301,94.291,5.709,5.18,224,0.875,bilinear,+6.721,+2.835,0
regnety_004,80.659,19.341,94.686,5.314,4.34,224,0.875,bicubic,+6.624,+2.934,-2
skresnet18,80.637,19.363,94.378,5.622,11.96,224,0.875,bicubic,+7.599,+3.210,+5
regnetx_006,80.629,19.371,94.524,5.476,6.20,224,0.875,bicubic,+6.777,+2.852,-1
regnetx_006,80.629,19.371,94.524,5.476,6.20,224,0.875,bicubic,+6.777,+2.852,-2
pit_ti_224,80.605,19.395,94.618,5.383,4.85,224,0.900,bicubic,+7.693,+3.216,+5
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,-2
tv_resnet34,80.389,19.611,94.436,5.564,21.80,224,0.875,bilinear,+7.077,+3.010,-2
swsl_resnet18,80.575,19.425,94.743,5.256,11.69,224,0.875,bilinear,+7.299,+3.010,0
vgg16_bn,80.556,19.444,94.592,5.408,138.37,224,0.875,bilinear,+7.206,+3.086,-3
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,+6
mobilenetv2_100,80.257,19.743,94.195,5.805,3.50,224,0.875,bicubic,+7.287,+3.179,-1
ssl_resnet18,80.101,19.899,94.590,5.410,11.69,224,0.875,bilinear,+7.491,+3.174,0
tf_mobilenetv3_large_075,80.093,19.907,94.184,5.816,3.99,224,0.875,bilinear,+6.655,+2.834,-8
tf_mobilenetv3_large_075,80.093,19.907,94.184,5.816,3.99,224,0.875,bilinear,+6.655,+2.834,-9
vit_deit_tiny_patch16_224,80.018,19.982,94.449,5.551,5.72,224,0.900,bicubic,+7.850,+3.331,+4
hrnet_w18_small,79.557,20.443,93.898,6.102,13.19,224,0.875,bilinear,+7.215,+3.220,0
hrnet_w18_small,79.557,20.443,93.898,6.102,13.19,224,0.875,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,-2
regnetx_004,79.435,20.565,93.853,6.147,5.16,224,0.875,bicubic,+7.039,+3.023,-4
tf_mobilenetv3_large_minimal_100,79.222,20.778,93.706,6.294,3.92,224,0.875,bilinear,+6.974,+3.076,-1
@ -360,5 +391,5 @@ dla60x_c,75.637,24.363,92.177,7.823,1.32,224,0.875,bilinear,+7.745,+3.751,+1
tf_mobilenetv3_small_100,74.717,25.283,91.257,8.743,2.54,224,0.875,bilinear,+6.795,+3.593,-1
dla46x_c,73.647,26.353,91.095,8.905,1.07,224,0.875,bilinear,+7.677,+4.115,0
tf_mobilenetv3_small_075,72.812,27.188,90.036,9.964,2.04,224,0.875,bilinear,+7.096,+3.906,0
dla46_c,72.603,27.397,90.499,9.501,1.30,224,0.875,bilinear,+7.737,+4.207,0
dla46_c,72.601,27.399,90.499,9.501,1.30,224,0.875,bilinear,+7.737,+4.207,0
tf_mobilenetv3_small_minimal_100,70.111,29.889,88.505,11.495,2.04,224,0.875,bilinear,+7.205,+4.275,0

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
3 tf_efficientnet_l2_ns_475 90.537 9.463 98.710 1.290 480.31 475 0.936 bicubic +2.303 +0.164 0
4 cait_m48_448 90.196 9.804 98.484 1.516 356.46 448 1.000 bicubic +3.712 +0.730 +2
5 tf_efficientnet_b7_ns 90.100 9.900 98.614 1.386 66.35 600 0.949 bicubic +3.260 +0.520 0
6 cait_m36_384 90.046 9.954 98.493 1.507 271.22 384 1.000 bicubic +3.992 +0.763 +6 +7
7 swin_large_patch4_window12_384 90.027 9.973 98.657 1.343 196.74 384 1.000 bicubic +2.879 +0.423 -3
8 swin_base_patch4_window12_384 tf_efficientnetv2_l_in21ft1k 89.995 90.008 10.005 9.992 98.695 98.619 1.304 1.381 87.90 118.52 384 480 1.000 bicubic +3.563 +3.704 +0.637 +0.641 0 +2
9 swin_base_patch4_window12_384 89.995 10.005 98.695 1.304 87.90 384 1.000 bicubic +3.563 +0.637 -1
10 dm_nfnet_f6 89.901 10.099 98.529 1.471 438.36 576 0.956 bicubic +3.605 +0.785 +1
11 cait_s36_384 89.844 10.156 98.427 1.573 68.37 384 1.000 bicubic +4.384 +0.947 +6 +8
12 swin_large_patch4_window7_224 89.796 10.204 98.640 1.360 196.53 224 0.900 bicubic +3.477 +0.744 -2 -3
13 tf_efficientnet_b6_ns 89.782 10.218 98.510 1.490 43.04 528 0.942 bicubic +3.330 +0.628 -5 -6
14 tf_efficientnet_b5_ns tf_efficientnetv2_m_in21ft1k 89.651 89.775 10.349 10.225 98.482 98.503 1.518 1.497 30.39 54.14 456 480 0.934 1.000 bicubic +3.563 +4.187 +0.730 +0.751 -2 +2
15 tf_efficientnet_b8_ap tf_efficientnet_b5_ns 89.581 89.651 10.419 10.349 98.305 98.482 1.695 1.518 87.41 30.39 672 456 0.954 0.934 bicubic +4.211 +3.563 +1.011 +0.730 +6 -3
16 cait_s24_384 tf_efficientnet_b8_ap 89.502 89.581 10.498 10.419 98.362 98.305 1.638 1.695 47.06 87.41 384 672 1.000 0.954 bicubic +4.456 +4.211 +1.016 +1.011 +11 +7
17 tf_efficientnet_b7_ap cait_s24_384 89.429 89.502 10.571 10.498 98.347 98.362 1.653 1.638 66.35 47.06 600 384 0.949 1.000 bicubic +4.309 +4.456 +1.096 +1.016 +8 +12
18 vit_deit_base_distilled_patch16_384 tf_efficientnet_b7_ap 89.429 10.571 98.441 98.347 1.559 1.653 87.63 66.35 384 600 1.000 0.949 bicubic +4.007 +4.309 +1.109 +1.096 +1 +9
19 vit_deit_base_distilled_patch16_384 89.429 10.571 98.441 1.559 87.63 384 1.000 bicubic +4.007 +1.109 +2
20 dm_nfnet_f3 89.393 10.607 98.315 1.685 254.92 416 0.940 bicubic +3.833 +0.909 -3
21 tf_efficientnetv2_l 89.367 10.633 98.275 1.725 118.52 480 1.000 bicubic +3.877 +0.903 -3
22 tf_efficientnet_b8 89.355 10.645 98.303 1.697 87.41 672 0.954 bicubic +3.985 +0.913 0
23 tf_efficientnet_b6_ap 89.342 10.658 98.281 1.719 43.04 528 0.942 bicubic +4.554 +1.143 +13 +15
24 tf_efficientnet_b4_ns 89.305 10.694 98.347 1.653 19.34 380 0.922 bicubic +4.143 +0.877 +1
25 dm_nfnet_f4 89.299 10.701 98.224 1.776 316.07 512 0.951 bicubic +3.641 +0.714 -8 -10
26 dm_nfnet_f5 tf_efficientnetv2_m 89.184 89.284 10.816 10.716 98.232 98.236 1.768 1.764 377.21 54.14 544 480 0.954 1.000 bicubic +3.470 +4.240 +0.790 +0.958 -10 +4
27 swin_base_patch4_window7_224 dm_nfnet_f5 89.145 89.184 10.855 10.816 98.429 98.232 1.571 1.768 87.77 377.21 224 544 0.900 0.954 bicubic +3.893 +3.470 +0.867 +0.790 -3 -13
28 cait_xs24_384 swin_base_patch4_window7_224 89.139 89.145 10.861 10.855 98.290 98.429 1.710 1.571 26.67 87.77 384 224 1.000 0.900 bicubic +5.077 +3.893 +1.402 +0.867 +24 -4
29 ig_resnext101_32x48d cait_xs24_384 89.120 89.139 10.880 10.861 98.130 98.290 1.870 1.710 828.41 26.67 224 384 0.875 1.000 bilinear bicubic +3.692 +5.077 +0.558 +1.402 -9 +26
30 ig_resnext101_32x32d ig_resnext101_32x48d 89.111 89.120 10.889 10.880 98.181 98.130 1.819 1.870 468.53 828.41 224 0.875 bilinear +4.017 +3.692 +0.743 +0.558 -2 -10
31 ig_resnext101_32x32d 89.111 10.889 98.181 1.819 468.53 224 0.875 bilinear +4.017 +0.743 -3
32 tf_efficientnet_b7 89.086 10.914 98.183 1.817 66.35 600 0.949 bicubic +4.150 +0.979 +3
33 ecaresnet269d 89.069 10.931 98.234 1.766 102.09 352 1.000 bicubic +4.093 +1.008 0
34 tf_efficientnet_b5_ap efficientnetv2_rw_m 88.938 88.987 11.062 11.013 98.164 98.213 1.836 1.787 30.39 53.24 456 416 0.934 1.000 bicubic +4.686 +4.179 +1.190 +1.065 +13 +3
35 dm_nfnet_f2 tf_efficientnet_b5_ap 88.889 88.938 11.111 11.062 98.117 98.164 1.883 1.836 193.78 30.39 352 456 0.920 0.934 bicubic +3.899 +4.686 +0.973 +1.190 -3 +14
36 dm_nfnet_f1 tf_efficientnetv2_s_in21ft1k 88.853 88.904 11.147 11.096 98.093 98.277 1.907 1.723 132.63 21.46 320 384 0.910 1.000 bicubic +4.249 +4.602 +1.025 +3 +10
37 resnetrs420 dm_nfnet_f2 88.840 88.889 11.160 11.111 98.034 98.117 1.966 1.883 191.89 193.78 416 352 1.000 0.920 bicubic +3.832 +3.899 +0.910 +0.973 -6 -5
38 dm_nfnet_f1 88.853 11.147 98.093 1.907 132.63 320 0.910 bicubic +4.249 +1.025 +2
39 resnetrs420 88.840 11.160 98.034 1.966 191.89 416 1.000 bicubic +3.832 +0.910 -8
40 ig_resnext101_32x16d 88.834 11.166 98.049 1.951 194.03 224 0.875 bilinear +4.664 +0.853 +11
41 resnetrs270 88.834 11.166 98.136 1.864 129.86 352 1.000 bicubic +4.400 +1.166 +3 +2
42 vit_base_r50_s16_384 88.808 11.192 98.232 1.768 98.95 384 1.000 bicubic +3.836 +0.944 -6 -8
43 seresnet152d 88.795 11.205 98.172 1.828 66.84 320 1.000 bicubic +4.433 +1.132 +3 +2
44 swsl_resnext101_32x8d 88.770 11.230 98.147 1.853 88.79 224 0.875 bilinear +4.486 +0.971 +3
45 tf_efficientnet_b6 88.761 11.239 98.064 1.937 43.04 528 0.942 bicubic +4.651 +1.178 +8
46 resnetrs350 88.759 11.241 98.029 1.971 163.96 384 1.000 bicubic +4.039 +1.041 -6 -7
47 vit_base_patch16_224_miil 88.737 11.262 98.027 1.973 86.54 224 0.875 bilinear +4.469 +1.225 +1
48 resnetv2_152x2_bitm 88.699 11.301 98.337 1.663 236.34 480 1.000 bilinear +4.259 +0.891 -5 -6
49 regnety_160 88.697 11.303 98.068 1.932 83.59 288 1.000 bicubic +5.011 +1.292 +15 +16
50 pit_b_distilled_224 88.676 11.324 98.093 1.907 74.79 224 0.900 bicubic +4.532 +1.237 +2
51 resnetrs200 88.605 11.395 98.034 1.966 93.21 320 1.000 bicubic +4.539 +1.160 +3
52 eca_nfnet_l1 88.575 11.425 98.130 1.870 41.41 320 1.000 bicubic +4.567 +1.102 +5
53 resnetv2_152x4_bitm 88.565 11.435 98.185 1.815 936.53 480 1.000 bilinear +3.633 +0.749 -15 -17
54 resnet200d 88.543 11.457 97.959 2.041 64.69 320 1.000 bicubic +4.581 +1.135 +4
55 resnest269e 88.522 11.478 98.027 1.973 110.93 416 0.928 bicubic +4.004 +1.041 -13 -14
56 resnetv2_101x3_bitm 88.492 11.508 98.162 1.838 387.93 480 1.000 bilinear +4.098 +0.800 -11 -12
57 efficientnet_v2s efficientnetv2_rw_s 88.473 11.527 97.974 2.026 23.94 384 1.000 bicubic +4.665 +1.250 +4 +5
58 cait_s24_224 88.447 11.553 97.957 2.043 46.92 224 1.000 bicubic +4.995 +1.393 +8 +10
59 resnest200e 88.432 11.568 98.042 1.958 70.20 320 0.909 bicubic +4.600 +1.148 0 +1
60 tf_efficientnet_b3_ns 88.426 11.574 98.029 1.971 12.23 300 0.904 bicubic +4.378 +1.119 -4
61 vit_large_patch16_384 88.407 11.593 98.187 1.813 304.72 384 1.000 bicubic +3.249 +0.831 -32 -35
62 vit_base_patch16_384 tf_efficientnetv2_s 88.389 88.402 11.611 11.598 98.155 97.927 1.845 2.073 86.86 21.46 384 1.000 bicubic +4.180 +4.508 +0.937 +1.229 -12 -3
63 efficientnet_b4 vit_base_patch16_384 88.372 88.389 11.628 11.611 97.961 98.155 2.039 1.845 19.34 86.86 384 1.000 bicubic +4.944 +4.180 +1.365 +0.937 +4 -13
64 efficientnet_b4 88.372 11.628 97.961 2.039 19.34 384 1.000 bicubic +4.944 +1.365 +5
65 resnet152d 88.355 11.645 97.935 2.065 60.21 320 1.000 bicubic +4.675 +1.197 +1
tf_efficientnet_b4_ap 88.349 11.651 97.893 2.107 19.34 380 0.922 bicubic +5.101 +1.501 +5
66 resnetv2_50x3_bitm 88.349 11.651 98.108 1.892 217.32 480 1.000 bilinear +4.565 +1.002 -3
67 tf_efficientnet_b4_ap 88.349 11.651 97.893 2.107 19.34 380 0.922 bicubic +5.101 +1.501 +6
68 tf_efficientnet_b5 88.321 11.679 97.912 2.088 30.39 456 0.934 bicubic +4.509 +1.164 -7
69 resnetrs152 88.251 11.749 97.737 2.263 86.62 320 1.000 bicubic +4.539 +1.123 -5
70 vit_deit_base_distilled_patch16_224 88.214 11.786 97.914 2.086 87.34 224 0.900 bicubic +4.826 +1.426 -1 0
71 ig_resnext101_32x8d 88.146 11.854 97.856 2.144 88.79 224 0.875 bilinear +5.458 +1.220 +14 +18
72 cait_xxs36_384 88.140 11.860 97.908 2.092 17.37 384 1.000 bicubic +5.946 +1.760 +23 +32
73 dm_nfnet_f0 88.112 11.888 97.837 2.163 71.49 256 0.900 bicubic +4.770 +1.277 -2 -1
74 swsl_resnext101_32x4d 88.099 11.901 97.967 2.033 44.18 224 0.875 bilinear +4.869 +1.207 -1 0
75 tf_efficientnet_b4 87.963 12.037 97.739 2.261 19.34 380 0.922 bicubic +4.941 +1.439 +5 +8
76 nfnet_l0 87.948 12.052 97.850 2.150 35.07 288 1.000 bicubic +5.188 +1.352 +7 +10
77 eca_nfnet_l0 87.943 12.057 97.861 2.139 24.14 288 1.000 bicubic +5.355 +1.387 +10 +14
78 resnet101d 87.941 12.059 97.908 2.092 44.57 320 1.000 bicubic +4.919 +1.462 +1 +4
79 regnety_032 87.937 12.063 97.891 2.109 19.44 288 1.000 bicubic +5.213 +1.467 +5 +8
80 vit_deit_base_patch16_384 twins_svt_large 87.845 87.901 12.155 12.099 97.510 97.581 2.490 2.419 86.86 99.27 384 224 1.000 0.900 bicubic +4.739 +4.223 +1.138 +0.987 -5 -13
81 tresnet_xl_448 twins_pcpvt_large 87.796 87.877 12.204 12.123 97.459 97.856 2.541 2.144 78.44 60.99 448 224 0.875 0.900 bilinear bicubic +4.746 +4.737 +1.285 +1.258 -3 -5
82 tresnet_m vit_deit_base_patch16_384 87.736 87.845 12.264 12.155 97.523 97.510 2.477 2.490 31.39 86.86 224 384 0.875 1.000 bilinear bicubic +4.656 +4.739 +1.405 +1.138 -6 -4
83 swin_small_patch4_window7_224 tresnet_xl_448 87.664 87.796 12.336 12.204 97.566 97.459 2.434 2.541 49.61 78.44 224 448 0.900 0.875 bicubic bilinear +4.452 +4.746 +1.244 +1.285 -9 -2
84 resnetv2_101x1_bitm tresnet_m 87.638 87.736 12.362 12.264 97.955 97.523 2.045 2.477 44.54 31.39 480 224 1.000 0.875 bilinear +5.426 +4.656 +1.483 +1.405 +10 -5
85 pnasnet5large twins_pcpvt_base 87.636 87.736 12.364 12.264 97.485 97.726 2.515 2.274 86.06 43.83 331 224 0.911 0.900 bicubic +4.854 +5.028 +1.445 +1.380 -3 +3
86 swsl_resnext101_32x16d swin_small_patch4_window7_224 87.615 87.664 12.386 12.336 97.820 97.566 2.180 2.434 194.03 49.61 224 0.875 0.900 bilinear bicubic +4.269 +4.452 +0.974 +1.244 -16 -11
87 swsl_resnext50_32x4d twins_svt_base 87.600 87.638 12.400 12.362 97.651 97.523 2.349 2.477 25.03 56.07 224 0.875 0.900 bilinear bicubic +5.418 +4.502 +1.421 +1.105 +9 -11
88 tf_efficientnet_b2_ns resnetv2_101x1_bitm 87.557 87.638 12.443 12.362 97.628 97.955 2.372 2.045 9.11 44.54 260 480 0.890 1.000 bicubic bilinear +5.177 +5.426 +1.380 +1.483 +1 +16
89 ecaresnet50t pnasnet5large 87.538 87.636 12.462 12.364 97.643 97.485 2.357 2.515 25.57 86.06 320 331 0.950 0.911 bicubic +5.192 +4.854 +1.505 +1.445 +1 -4
90 efficientnet_b3 swsl_resnext101_32x16d 87.435 87.615 12.565 12.386 97.681 97.820 2.319 2.180 12.23 194.03 320 224 1.000 0.875 bicubic bilinear +5.193 +4.269 +1.567 +0.974 +3 -19
91 cait_xxs24_384 swsl_resnext50_32x4d 87.416 87.600 12.584 12.400 97.619 97.651 2.381 2.349 12.03 25.03 384 224 1.000 0.875 bicubic bilinear +6.450 +5.418 +1.973 +1.421 +37 +14
92 tresnet_l_448 tf_efficientnet_b2_ns 87.377 87.557 12.623 12.443 97.485 97.628 2.515 2.372 55.99 9.11 448 260 0.875 0.890 bilinear bicubic +5.109 +5.177 +1.509 +1.380 0 +2
93 nasnetalarge levit_384 87.350 87.553 12.650 12.447 97.417 97.545 2.583 2.455 88.75 39.13 331 224 0.911 0.900 bicubic +4.730 +4.967 +1.371 +1.529 -7 -1
94 ecaresnet101d ecaresnet50t 87.288 87.538 12.712 12.462 97.562 97.643 2.438 2.357 44.57 25.57 224 320 0.875 0.950 bicubic +5.116 +5.192 +1.516 +1.505 +3 +2
95 resnest101e efficientnet_b3 87.284 87.435 12.716 12.565 97.560 97.681 2.440 2.319 48.28 12.23 256 320 0.875 1.000 bilinear bicubic +4.394 +5.193 +1.240 +1.567 -14 +7
96 pit_s_distilled_224 cait_xxs24_384 87.277 87.416 12.723 12.584 97.500 97.619 2.500 2.381 24.04 12.03 224 384 0.900 1.000 bicubic +5.281 +6.450 +1.702 +1.973 +4 +48
97 resnetrs101 resnet51q 87.247 87.395 12.753 12.605 97.457 97.587 2.543 2.413 63.62 35.70 288 0.940 1.000 bicubic bilinear +4.959 +5.035 +1.449 +1.407 -6 -2
98 tresnet_xl coat_lite_small 87.224 87.380 12.776 12.620 97.400 97.365 2.600 2.635 78.44 19.84 224 0.875 0.900 bilinear bicubic +5.170 +5.072 +1.463 +1.515 0 -1
99 tf_efficientnet_b3_ap tresnet_l_448 87.192 87.377 12.808 12.623 97.380 97.485 2.620 2.515 12.23 55.99 300 448 0.904 0.875 bicubic bilinear +5.370 +5.109 +1.756 +1.509 +3 +2
100 nasnetalarge 87.350 12.650 97.417 2.583 88.75 331 0.911 bicubic +4.730 +1.371 -10
101 ecaresnet101d 87.288 12.712 97.562 2.438 44.57 224 0.875 bicubic +5.116 +1.516 +5
102 resnest101e 87.284 12.716 97.560 2.440 48.28 256 0.875 bilinear +4.394 +1.240 -18
103 pit_s_distilled_224 87.277 12.723 97.500 2.500 24.04 224 0.900 bicubic +5.281 +1.702 +7
104 resnetrs101 87.247 12.753 97.457 2.543 63.62 288 0.940 bicubic +4.959 +1.449 -4
105 mixer_b16_224_miil 87.226 12.774 97.410 2.590 59.88 224 0.875 bilinear +4.918 +1.694 -7
106 tresnet_xl 87.224 12.776 97.400 2.600 78.44 224 0.875 bilinear +5.170 +1.463 +2
107 convit_base 87.200 12.800 97.286 2.714 86.54 224 0.875 bicubic +4.910 +1.348 -8
108 tf_efficientnet_b3_ap 87.192 12.808 97.380 2.620 12.23 300 0.904 bicubic +5.370 +1.756 +5
109 visformer_small 87.181 12.819 97.323 2.677 40.22 224 0.900 bicubic +5.075 +1.451 -2
110 convit_small 87.053 12.947 97.350 2.650 27.78 224 0.875 bicubic +5.627 +1.606 +17
111 tf_efficientnetv2_b3 87.032 12.968 97.303 2.697 14.36 300 0.904 bicubic +5.062 +1.521 0
112 vit_base_patch32_384 87.019 12.981 97.654 2.346 88.30 384 1.000 bicubic +5.367 +1.526 +5
113 vit_large_patch16_224 87.006 12.994 97.690 2.310 304.33 224 0.900 bicubic +3.944 +1.252 -24 -33
114 vit_deit_small_distilled_patch16_224 86.993 13.007 97.316 2.684 22.44 224 0.900 bicubic +5.793 +1.938 +19 +22
115 tnt_s_patch16_224 86.903 13.097 97.368 2.632 23.76 224 0.900 bicubic +5.385 +1.620 +7
116 ssl_resnext101_32x16d 86.856 13.143 97.517 2.483 194.03 224 0.875 bilinear +5.013 +1.421 -3 -4
117 rexnet_200 86.846 13.154 97.276 2.724 16.37 224 0.875 bicubic +5.214 +1.608 +2
118 tf_efficientnet_b3 86.835 13.165 97.297 2.703 12.23 300 0.904 bicubic +5.199 +1.579 0
119 vit_deit_base_patch16_224 86.829 13.171 97.049 2.951 86.57 224 0.900 bicubic +4.831 +1.315 -8 -10
120 tresnet_m_448 86.820 13.180 97.212 2.788 31.39 448 0.875 bilinear +5.106 +1.640 -4 -5
121 ssl_resnext101_32x8d 86.807 13.193 97.466 2.534 88.79 224 0.875 bilinear +5.191 +1.428 -1
122 swsl_resnet50 86.807 13.193 97.498 2.502 25.56 224 0.875 bilinear +5.641 +1.526 +12 +15
123 tf_efficientnet_lite4 86.803 13.197 97.263 2.737 13.01 380 0.920 bilinear +5.267 +1.595 -2
124 vit_base_patch16_224 coat_mini 86.778 86.793 13.223 13.207 97.438 97.162 2.562 2.837 86.57 10.34 224 0.900 bicubic +4.992 +5.525 +1.316 +1.770 -9 +9
125 vit_base_patch16_224 86.778 13.223 97.438 2.562 86.57 224 0.900 bicubic +4.992 +1.316 -11
126 tresnet_l 86.767 13.233 97.271 2.729 55.99 224 0.875 bilinear +5.277 +1.647 -1
127 twins_svt_small 86.756 13.244 97.175 2.825 24.06 224 0.900 bicubic +5.074 +1.505 -11
128 levit_256 86.728 13.272 97.259 2.741 18.89 224 0.900 bicubic +5.218 +1.769 -5
129 seresnext50_32x4d 86.699 13.301 97.214 2.786 27.56 224 0.875 bicubic +5.433 +1.594 +5
130 pit_b_224 86.686 13.314 96.898 3.102 73.76 224 0.900 bicubic +4.240 +1.188 -27 -37
131 tf_efficientnet_b1_ns 86.669 13.331 97.378 2.622 7.79 240 0.882 bicubic +5.281 +1.640 -2 -3
132 swin_tiny_patch4_window7_224 86.664 13.336 97.197 2.803 28.29 224 0.900 bicubic +5.286 +1.657 -2 -3
133 gernet_l 86.654 13.346 97.186 2.814 31.08 256 0.875 bilinear +5.300 +1.650 -2 -3
134 wide_resnet50_2 86.647 13.353 97.214 2.786 68.88 224 0.875 bicubic +5.191 +1.682 -6 -8
135 efficientnet_el 86.635 13.366 97.175 2.825 10.59 300 0.904 bicubic +5.319 +1.649 -3 -4
136 twins_pcpvt_small 86.620 13.380 97.340 2.660 24.11 224 0.900 bicubic +5.532 +1.698 +4
137 nf_resnet50 86.617 13.383 97.282 2.718 25.56 288 0.940 bicubic +5.923 +1.926 +15
138 resnest50d_4s2x40d 86.592 13.408 97.269 2.731 30.42 224 0.875 bicubic +5.484 +1.711 +1 0
139 efficientnet_b3_pruned 86.581 13.419 97.190 2.810 9.86 300 0.904 bicubic +5.723 +1.948 +9
140 repvgg_b3 86.566 13.434 97.139 2.861 123.09 224 0.875 bilinear +6.074 +1.879 +17
141 ssl_resnext101_32x4d 86.479 13.521 97.468 2.532 44.18 224 0.875 bilinear +5.555 +1.740 +4
142 ecaresnet50d 86.470 13.530 97.186 2.814 25.58 224 0.875 bicubic +5.878 +1.866 +13
143 gluon_resnet152_v1s 86.468 13.532 97.109 2.891 60.32 224 0.875 bicubic +5.452 +1.697 -2
144 resnest50d_1s4x24d 86.447 13.553 97.148 2.852 25.68 224 0.875 bicubic +5.459 +1.826 -2
145 repvgg_b3g4 86.361 13.639 97.054 2.946 83.83 224 0.875 bilinear +6.149 +6.147 +1.944 +29
146 legacy_senet154 86.342 13.658 96.928 3.072 115.09 224 0.875 bilinear +5.032 +1.432 -12 -14
147 cait_xxs36_224 86.340 13.660 97.111 2.889 17.30 224 1.000 bicubic +6.590 +2.245 +49 +51
148 gernet_m 86.319 13.681 97.096 2.904 21.14 224 0.875 bilinear +5.587 +1.912 +3
149 pit_s_224 86.316 13.684 97.045 2.955 23.46 224 0.900 bicubic +5.222 +1.713 -9 -10
150 efficientnet_b2 86.304 13.696 96.990 3.010 9.11 288 1.000 bicubic +5.692 +1.672 +3
151 gluon_senet154 86.278 13.722 96.949 3.051 115.09 224 0.875 bicubic +5.044 +1.601 -15 -16
152 resnest50d 86.240 13.761 97.073 2.927 27.48 224 0.875 bilinear +5.266 +1.695 -9
153 ecaresnet101d_pruned 86.210 86.207 13.790 13.793 97.335 2.665 24.88 224 0.875 bicubic +5.392 +5.389 +1.707 -4
154 efficientnet_el_pruned 86.192 13.807 97.026 2.974 10.59 300 0.904 bicubic +5.892 +1.808 +14
155 cspdarknet53 86.182 13.818 97.013 2.987 27.64 256 0.887 bilinear +6.124 +1.929 +25 +26
156 inception_v4 86.169 13.831 96.919 3.081 42.68 299 0.875 bicubic +6.001 +1.951 +20 +21
157 resnetv2_50x1_bitm 86.154 13.846 97.560 2.440 25.55 480 1.000 bilinear +5.982 +1.934 +18 +19
158 rexnet_150 86.154 13.846 97.058 2.942 9.73 224 0.875 bicubic +5.844 +1.892 +8
159 inception_resnet_v2 86.133 13.867 97.043 2.957 55.84 299 0.897 bicubic +5.675 +1.737 +2
160 ssl_resnext50_32x4d 86.086 13.914 97.212 2.788 25.03 224 0.875 bilinear +5.768 +1.806 +5
163 ecaresnetlight 86.052 13.948 97.069 2.931 30.16 224 0.875 bicubic +5.590 +1.819 -3
164 gluon_seresnext101_32x4d 86.032 13.968 96.977 3.023 48.96 224 0.875 bicubic +5.128 +1.683 -18
165 resnet50d 86.009 13.991 96.979 3.021 25.58 224 0.875 bicubic +5.479 +1.819 -9
166 ecaresnet26t 85.983 14.017 97.041 2.959 16.01 320 0.950 bicubic +6.129 +1.957 +26 +27
167 tf_efficientnet_b2_ap 85.975 14.025 96.810 3.190 9.11 260 0.890 bicubic +5.675 +1.782 +2
168 gluon_seresnext101_64x4d 85.960 14.040 96.979 3.021 88.23 224 0.875 bicubic +5.066 +1.671 -21
169 gluon_resnet152_v1d 85.917 14.083 96.812 3.188 60.21 224 0.875 bicubic +5.443 +1.606 -10
170 vit_large_patch32_384 85.909 14.091 97.368 2.632 306.63 384 1.000 bicubic +4.403 +1.276 -43 -46
171 tf_efficientnet_b2 85.902 14.098 96.862 3.139 9.11 260 0.890 bicubic +5.816 +1.954 +8 +9
172 seresnet50 tf_efficientnetv2_b2 85.857 85.900 14.143 14.100 97.004 96.889 2.995 3.111 28.09 10.10 224 260 0.875 0.890 bicubic +5.583 +5.692 +1.934 +1.847 -2 +3
173 repvgg_b2g4 seresnet50 85.855 85.857 14.145 14.143 96.812 97.004 3.188 2.995 61.76 28.09 224 0.875 bilinear bicubic +6.489 +5.583 +2.124 +1.934 +37 -3
174 gluon_resnet101_v1d repvgg_b2g4 85.849 85.855 14.151 14.145 96.663 96.812 3.337 3.188 44.57 61.76 224 0.875 bicubic bilinear +5.435 +6.489 +1.649 +2.124 -12 +39
175 resnet50 gluon_resnet101_v1d 85.804 85.849 14.196 14.151 96.712 96.663 3.288 3.337 25.56 44.57 224 0.875 bicubic +6.766 +5.435 +2.322 +1.649 +58 -13
176 mixnet_xl resnet50 85.798 85.804 14.202 14.196 96.712 3.288 11.90 25.56 224 0.875 bicubic +5.322 +6.766 +1.776 +2.322 -18 +60
177 mixnet_xl 85.798 14.202 96.712 3.288 11.90 224 0.875 bicubic +5.322 +1.776 -19
178 ens_adv_inception_resnet_v2 85.781 14.220 96.759 3.241 55.84 299 0.897 bicubic +5.799 +1.823 +6
179 tf_efficientnet_lite3 85.755 14.245 96.887 3.113 8.20 300 0.904 bilinear +5.935 +1.973 +16 +17
180 ese_vovnet39b 85.751 14.249 96.891 3.109 24.57 224 0.875 bicubic +6.431 +2.179 +33 +35
181 gluon_resnext101_32x4d 85.746 14.254 96.635 3.365 44.18 224 0.875 bicubic +5.412 +1.709 -16 -17
182 legacy_seresnext101_32x4d 85.746 14.254 96.757 3.243 48.96 224 0.875 bilinear +5.518 +1.739 -8 -9
183 cspresnext50 85.740 14.260 96.840 3.160 20.57 224 0.875 bilinear +5.700 +1.896 -1
184 regnety_320 85.727 14.273 96.725 3.275 145.05 224 0.875 bicubic +4.915 +1.481 -33 -34
185 cspresnet50 85.721 14.279 96.795 3.205 21.62 256 0.887 bilinear +6.147 +2.083 +20 +21
186 xception71 85.697 14.303 96.776 3.224 42.34 299 0.903 bicubic +5.823 +1.854 +4
187 gluon_resnext101_64x4d 85.693 14.307 96.644 3.356 83.46 224 0.875 bicubic +5.089 +1.656 -32 -33
188 efficientnet_em 85.684 14.316 96.938 3.062 6.90 240 0.882 bicubic +6.432 +2.144 +34 +36
189 vit_deit_small_patch16_224 85.678 14.322 96.906 3.094 22.05 224 0.900 bicubic +5.822 +1.854 +3
190 pit_xs_distilled_224 85.657 14.343 96.667 3.333 11.00 224 0.900 bicubic +6.351 +2.303 +28 +30
191 efficientnet_b2_pruned 85.642 14.358 96.746 3.254 8.31 260 0.890 bicubic +5.726 +1.890 -5
192 dpn107 85.640 14.360 96.729 3.271 86.92 224 0.875 bicubic +5.484 +1.819 -14
193 ecaresnet50d_pruned 85.580 14.420 96.936 3.064 19.94 224 0.875 bicubic +5.864 +2.056 +6 +7
194 gluon_resnet152_v1c 85.580 14.420 96.646 3.354 60.21 224 0.875 bicubic +5.670 +1.806 -7
195 levit_192 85.580 14.420 96.740 3.260 10.95 224 0.900 bicubic +5.738 +1.954 -1
196 resnext50d_32x4d 85.569 14.431 96.748 3.252 25.05 224 0.875 bicubic +5.893 +1.882 +7
197 regnety_120 tf_efficientnetv2_b1 85.543 85.561 14.457 14.439 96.785 96.727 3.215 3.273 51.82 8.14 224 240 0.875 0.882 bicubic +5.177 +6.099 +1.659 +2.005 -32 +14
198 regnetx_320 regnety_120 85.524 85.543 14.476 14.457 96.669 96.785 3.331 3.215 107.81 51.82 224 0.875 bicubic +5.278 +5.177 +1.643 +1.659 -24 -35
199 regnetx_320 85.524 14.476 96.669 3.331 107.81 224 0.875 bicubic +5.278 +1.643 -27
200 nf_regnet_b1 85.499 14.501 96.799 3.200 10.22 288 0.900 bicubic +6.193 +2.051 +19
201 dpn92 85.494 14.506 96.635 3.365 37.67 224 0.875 bicubic +5.486 +1.799 -16 -18
202 gluon_resnet152_v1b 85.475 14.525 96.550 3.450 60.19 224 0.875 bicubic +5.789 +1.814 +1 0
203 rexnet_130 85.473 14.527 96.684 3.316 7.56 224 0.875 bicubic +5.973 +2.002 +7 +6
204 resnetrs50 85.462 14.538 96.736 3.264 35.69 224 0.910 bicubic +5.570 +1.767 -14 -16
205 dpn131 85.398 14.602 96.639 3.361 79.25 224 0.875 bicubic +5.576 +1.929 -9 -10
206 regnetx_160 85.390 14.610 96.637 3.363 54.28 224 0.875 bicubic +5.534 +1.807 -13 -15
207 dla102x2 85.366 14.634 96.629 3.371 41.28 224 0.875 bilinear +5.918 +1.989 +5
208 gluon_seresnext50_32x4d 85.336 14.664 96.667 3.333 27.56 224 0.875 bicubic +5.418 +1.845 -21 -23
209 xception65 85.315 14.685 96.637 3.363 39.92 299 0.903 bicubic +5.763 +1.983 -1 -2
210 skresnext50_32x4d 85.313 14.687 96.390 3.610 27.48 224 0.875 bicubic +5.157 +1.748 -29 -31
211 dpn98 85.311 14.689 96.469 3.531 61.57 224 0.875 bicubic +5.669 +1.871 -6 -7
212 gluon_resnet101_v1c 85.304 14.696 96.405 3.595 44.57 224 0.875 bicubic +5.770 +1.827 -3 -4
213 dpn68b 85.291 14.709 96.464 3.536 12.61 224 0.875 bicubic +6.076 +2.050 +14
214 regnety_064 85.283 14.717 96.639 3.361 30.58 224 0.875 bicubic +5.561 +1.871 -14 -15
215 resnetblur50 85.283 14.717 96.531 3.470 25.56 224 0.875 bicubic +5.997 +1.892 +7
216 coat_lite_mini 85.251 14.749 96.680 3.320 11.01 224 0.900 bicubic +6.163 +2.076 +15
217 regnety_080 85.245 14.755 96.633 3.367 39.18 224 0.875 bicubic +5.369 +1.803 -26 -28
218 cait_xxs24_224 85.228 14.773 96.712 3.288 11.96 224 1.000 bicubic +6.842 +2.402 +41 +43
219 resnext50_32x4d 85.221 14.779 96.526 3.474 25.03 224 0.875 bicubic +5.453 +1.928 -21 -22
220 resnext101_32x8d 85.187 14.813 96.445 3.555 88.79 224 0.875 bilinear +5.879 +1.927 -4
221 gluon_inception_v3 85.183 14.817 96.526 3.474 23.83 299 0.875 bicubic +6.377 +2.156 +21
222 hrnet_w48 85.151 14.849 96.492 3.508 77.47 224 0.875 bilinear +5.851 +1.980 -1
223 gluon_xception65 85.148 14.851 96.597 3.403 39.92 299 0.903 bicubic +5.433 +1.737 -21 -22
224 gluon_resnet101_v1b 85.142 14.858 96.366 3.634 44.55 224 0.875 bicubic +5.836 +1.842 -6
225 regnetx_120 85.131 14.869 96.477 3.523 46.11 224 0.875 bicubic +5.535 +1.739 -19 -20
226 xception 85.129 14.871 96.471 3.529 22.86 299 0.897 bicubic +6.077 +2.079 +9
227 tf_efficientnet_b1_ap 85.127 14.873 96.405 3.595 7.79 240 0.882 bicubic +5.847 +2.099 -4
228 hrnet_w64 85.119 14.881 96.744 3.256 128.06 224 0.875 bilinear +5.645 +2.092 -17 -18
229 ssl_resnet50 85.097 14.903 96.866 3.134 25.56 224 0.875 bilinear +5.875 +2.034 -4
230 res2net101_26w_4s 85.093 14.907 96.381 3.619 45.21 224 0.875 bilinear +5.895 +1.949 -1
231 tf_efficientnet_cc_b1_8e 85.063 14.937 96.422 3.578 39.72 240 0.882 bicubic +5.755 +2.052 -14
232 res2net50_26w_8s 85.029 14.971 96.419 3.580 48.40 224 0.875 bilinear +5.831 +2.052 -4
233 resnest26d 85.008 14.992 96.637 3.363 17.07 224 0.875 bilinear +6.530 +2.339 +21 +22
234 gluon_resnext50_32x4d 84.995 15.005 96.426 3.574 25.03 224 0.875 bicubic +5.641 +2.000 -20
235 tf_efficientnet_b0_ns 84.984 15.016 96.503 3.497 5.29 224 0.875 bicubic +6.326 +2.127 +14
236 regnety_040 coat_tiny 84.948 84.976 15.052 15.024 96.612 96.409 3.388 3.591 20.65 5.50 224 0.875 0.900 bicubic +5.728 +6.542 +1.956 +2.371 -10 +23
237 dla169 regnety_040 84.920 84.948 15.080 15.052 96.535 96.612 3.465 3.388 53.39 20.65 224 0.875 bilinear bicubic +6.232 +5.728 +2.199 +1.956 +10 -11
238 tf_efficientnet_b1 dla169 84.918 84.920 15.082 15.080 96.364 96.535 3.636 3.465 7.79 53.39 240 224 0.882 0.875 bicubic bilinear +6.092 +6.232 +2.166 +2.199 +3 +9
239 legacy_seresnext50_32x4d tf_efficientnet_b1 84.901 84.918 15.099 15.082 96.434 96.364 3.566 3.636 27.56 7.79 224 240 0.875 0.882 bilinear bicubic +5.823 +6.092 +1.998 +2.166 -7 +2
240 hrnet_w44 legacy_seresnext50_32x4d 84.884 84.901 15.116 15.099 96.434 3.566 67.06 27.56 224 0.875 bilinear +5.988 +5.823 +2.066 +1.998 -1 -8
241 regnetx_080 hrnet_w44 84.862 84.884 15.138 15.116 96.434 3.566 39.57 67.06 224 0.875 bicubic bilinear +5.668 +5.988 +1.874 +2.066 -11 -2
242 gluon_resnet50_v1s regnetx_080 84.860 84.862 15.140 15.138 96.443 96.434 3.557 3.566 25.68 39.57 224 0.875 bicubic +6.148 +5.668 +2.205 +1.874 +4 -12
243 gluon_resnet50_v1d gluon_resnet50_v1s 84.832 84.860 15.168 15.140 96.398 96.443 3.602 3.557 25.58 25.68 224 0.875 bicubic +5.758 +6.150 +1.928 +2.205 -10 +3
244 dla60_res2next levit_128 84.830 84.843 15.170 15.157 96.411 96.360 3.589 3.640 17.03 9.21 224 0.875 0.900 bilinear bicubic +6.390 +6.357 +2.259 +2.350 +13 +9
245 mixnet_l gluon_resnet50_v1d 84.822 84.832 15.178 15.168 96.328 96.398 3.672 3.602 7.33 25.58 224 0.875 bicubic +5.846 +5.758 +2.146 +1.928 -8 -12
246 tv_resnet152 dla60_res2next 84.815 84.830 15.185 15.170 96.225 96.411 3.775 3.589 60.19 17.03 224 0.875 bilinear +6.503 +6.390 +2.187 +2.259 +16 +12
247 dla60_res2net mixnet_l 84.813 84.822 15.187 15.178 96.481 96.328 3.519 3.672 20.85 7.33 224 0.875 bilinear bicubic +6.349 +5.846 +2.275 +2.146 +8 -10
248 dla102x tv_resnet152 84.813 84.815 15.187 15.185 96.552 96.225 3.448 3.775 26.31 60.19 224 0.875 bilinear +6.303 +6.503 +2.324 +2.187 +4 +17
249 pit_xs_224 dla102x 84.792 84.813 15.208 15.187 96.492 96.552 3.508 3.448 10.62 26.31 224 0.900 0.875 bicubic bilinear +6.610 +6.303 +2.324 +18 +2
250 xception41 dla60_res2net 84.792 84.813 15.208 15.187 96.413 96.481 3.587 3.519 26.97 20.85 299 224 0.903 0.875 bicubic bilinear +6.276 +6.349 +2.135 +2.275 +1 +7
251 regnetx_064 pit_xs_224 84.781 84.792 15.219 15.208 96.490 96.492 3.510 3.508 26.21 10.62 224 0.875 0.900 bicubic +5.709 +6.610 +2.032 +2.324 -17 +19
252 hrnet_w40 xception41 84.743 84.792 15.257 15.208 96.554 96.413 3.446 3.587 57.56 26.97 224 299 0.875 0.903 bilinear bicubic +5.823 +6.276 +2.084 +2.135 -14 -1
253 res2net50_26w_6s regnetx_064 84.726 84.781 15.274 15.219 96.281 96.490 3.719 3.510 37.05 26.21 224 0.875 bilinear bicubic +6.156 +5.709 +2.157 +2.032 -3 -19
254 repvgg_b2 hrnet_w40 84.724 84.743 15.276 15.257 96.469 96.554 3.531 3.446 89.02 57.56 224 0.875 bilinear +5.932 +5.823 +2.055 +2.084 -10 -16
255 legacy_seresnet152 res2net50_26w_6s 84.704 84.726 15.296 15.274 96.417 96.281 3.583 3.719 66.82 37.05 224 0.875 bilinear +6.044 +6.156 +2.047 +2.157 -7 -5
256 repvgg_b2 84.724 15.276 96.469 3.531 89.02 224 0.875 bilinear +5.932 +2.055 -12
257 legacy_seresnet152 84.704 15.296 96.417 3.583 66.82 224 0.875 bilinear +6.044 +2.047 -9
258 selecsls60b 84.657 15.343 96.300 3.700 32.77 224 0.875 bicubic +6.245 +2.126 +2
259 hrnet_w32 84.651 15.349 96.407 3.593 41.23 224 0.875 bilinear +6.201 +2.221 -1 -2
260 efficientnet_b1 tf_efficientnetv2_b0 84.608 84.625 15.392 15.375 96.332 96.274 3.668 3.726 7.79 7.14 256 224 1.000 0.875 bicubic +5.814 +6.269 +1.990 +2.250 -15 +4
261 regnetx_040 efficientnet_b1 84.600 84.608 15.400 15.392 96.383 96.332 3.617 3.668 22.12 7.79 224 256 0.875 1.000 bicubic +6.118 +5.814 +2.139 +1.990 -6 -18
262 regnetx_040 84.600 15.400 96.383 3.617 22.12 224 0.875 bicubic +6.118 +2.139 -8
263 efficientnet_es 84.591 15.409 96.311 3.689 5.44 224 0.875 bicubic +6.525 +2.385 +12
264 hrnet_w30 84.572 15.428 96.388 3.612 37.71 224 0.875 bilinear +6.366 +2.166 +5
265 tf_mixnet_l 84.564 15.437 96.244 3.756 7.33 224 0.875 bicubic +5.790 +2.246 -17 -20
266 wide_resnet101_2 84.557 15.443 96.349 3.651 126.89 224 0.875 bilinear +5.701 +2.067 -23 -26
267 dla60x 84.523 15.477 96.285 3.715 17.35 224 0.875 bilinear +6.277 +2.267 -1
268 legacy_seresnet101 84.504 15.496 96.330 3.670 49.33 224 0.875 bilinear +6.122 +2.066 -5 -6
tf_efficientnet_em 84.450 15.550 96.180 3.820 6.90 240 0.882 bicubic +6.320 +2.136 +3
269 coat_lite_tiny 84.450 15.550 96.368 3.632 5.72 224 0.900 bicubic +6.938 +2.452 +27
270 repvgg_b1 tf_efficientnet_em 84.416 84.450 15.584 15.550 96.221 96.180 3.779 3.820 57.42 6.90 224 240 0.875 0.882 bilinear bicubic +6.050 +6.320 +2.123 +2.136 -7 +3
271 repvgg_b1 84.416 15.584 96.221 3.779 57.42 224 0.875 bilinear +6.050 +2.123 -8
272 efficientnet_b1_pruned 84.393 15.607 96.140 3.860 6.33 240 0.882 bicubic +6.157 +2.306 -4
273 res2net50_26w_4s 84.365 15.635 96.082 3.918 25.70 224 0.875 bilinear +6.401 +2.228 +7
274 hardcorenas_f 84.326 15.674 96.025 3.975 8.20 224 0.875 bilinear +6.222 +2.222 0
278 res2next50 84.226 15.774 95.997 4.003 24.67 224 0.875 bilinear +5.980 +2.105 -11
279 gluon_resnet50_v1c 84.207 15.793 96.161 3.839 25.58 224 0.875 bicubic +6.195 +2.173 -2
280 dla102 84.190 15.810 96.206 3.794 33.27 224 0.875 bilinear +6.158 +2.260 -4
281 rexnet_100 84.162 15.838 96.255 3.745 4.80 224 0.875 bicubic +6.304 +2.385 +2.839 +3 +5
282 tf_inception_v3 84.132 84.134 15.868 15.866 95.920 4.080 23.83 299 0.875 bicubic +6.276 +2.280 +2.050 +4 +2
283 res2net50_48w_2s 84.126 15.874 95.965 4.035 25.29 224 0.875 bilinear +6.604 +2.411 +12
284 resnet34d 84.098 15.902 95.978 4.022 21.82 224 0.875 bicubic +6.982 +2.596 +23
285 tf_efficientnet_lite2 84.094 15.906 96.069 3.931 6.09 260 0.890 bicubic +6.626 +2.315 +12
297 inception_v3 83.761 16.239 95.879 4.121 23.83 299 0.875 bicubic +6.323 +2.403 +1
298 hardcorenas_d 83.759 16.241 95.734 4.266 7.50 224 0.875 bilinear +6.327 +2.250 +1
299 seresnext26d_32x4d 83.754 16.246 95.849 4.151 16.81 224 0.875 bicubic +6.152 +2.241 -8
300 vit_small_patch16_224 83.735 16.265 95.758 4.242 48.75 224 0.900 bicubic +5.877 +2.342 +2.118 -15
301 dla60 83.729 16.271 95.933 4.067 22.04 224 0.875 bilinear +6.697 +2.615 +10
302 repvgg_b1g4 83.699 16.301 96.020 3.980 39.97 224 0.875 bilinear +6.105 +2.194 -10
303 legacy_seresnet50 83.662 16.337 95.973 4.027 28.09 224 0.875 bilinear +6.032 +2.225 -14
308 mobilenetv3_large_100_miil 83.556 16.444 95.452 4.548 5.48 224 0.875 bilinear +5.640 +2.542 -27
309 gernet_s 83.522 16.478 95.794 4.206 8.17 224 0.875 bilinear +6.606 +2.662 +5
310 legacy_seresnext26_32x4d 83.517 16.483 95.719 4.281 16.79 224 0.875 bicubic +6.413 +2.403 -2
mixnet_m 83.515 16.485 95.689 4.311 5.01 224 0.875 bicubic +6.255 +2.265 -6
311 tf_efficientnet_b0 83.515 16.485 95.719 4.281 5.29 224 0.875 bicubic +6.667 +2.491 +4
312 mixnet_m 83.515 16.485 95.689 4.311 5.01 224 0.875 bicubic +6.255 +2.265 -6
313 hrnet_w18 83.500 16.500 95.907 4.093 21.30 224 0.875 bilinear +6.742 +2.463 +5
314 densenetblur121d 83.472 16.527 95.822 4.178 8.00 224 0.875 bicubic +6.885 +2.630 +9
315 selecsls42b 83.457 16.543 95.745 4.255 32.46 224 0.875 bicubic +6.283 +2.355 -9
316 tf_efficientnet_lite1 83.344 16.656 95.642 4.358 5.42 240 0.882 bicubic +6.702 +2.416 +4
317 hardcorenas_c 83.342 16.658 95.706 4.294 5.52 224 0.875 bilinear +6.288 +2.548 -7
318 regnetx_016 83.195 16.805 95.740 4.260 9.19 224 0.875 bicubic +6.245 +2.320 -6
319 mobilenetv2_140 83.182 16.818 95.689 4.311 6.11 224 0.875 bicubic +6.666 +2.693 +6 +7
320 dpn68 83.178 16.822 95.597 4.402 12.61 224 0.875 bicubic +6.860 +2.620 +7 +8
321 tf_efficientnet_es 83.178 16.822 95.585 4.415 5.44 224 0.875 bicubic +6.584 +2.383 +1
322 tf_mixnet_m 83.176 16.824 95.461 4.539 5.01 224 0.875 bicubic +6.234 +2.309 -9
323 ese_vovnet19b_dw 83.109 16.890 95.779 4.221 6.54 224 0.875 bicubic +6.311 +2.511 -6
324 resnet26d levit_128s 83.050 83.069 16.950 16.931 95.604 95.531 4.396 4.469 16.01 7.78 224 0.875 0.900 bicubic +6.354 +6.539 +2.454 +2.665 -5 +1
325 resnet26d 83.050 16.950 95.604 4.396 16.01 224 0.875 bicubic +6.354 +2.454 -6
326 repvgg_a2 83.001 16.999 95.589 4.411 28.21 224 0.875 bilinear +6.541 +2.585 +1
327 tv_resnet50 82.958 17.042 95.467 4.533 25.56 224 0.875 bilinear +6.820 +2.603 +3
328 hardcorenas_b 82.873 17.128 95.392 4.607 5.18 224 0.875 bilinear +6.335 +2.638 -3 -4
329 densenet121 82.823 17.177 95.585 4.415 7.98 224 0.875 bicubic +7.245 +2.933 +8
330 densenet169 82.683 17.317 95.600 4.400 14.15 224 0.875 bicubic +6.776 +2.574 +3
331 mixnet_s 82.525 17.476 95.356 4.644 4.13 224 0.875 bicubic +6.532 +2.560 0
342 tf_mixnet_s 82.038 17.962 95.121 4.879 4.13 224 0.875 bicubic +6.388 +2.493 -7
343 repvgg_b0 82.001 17.999 95.100 4.900 15.82 224 0.875 bilinear +6.849 +2.682 +1
344 vit_deit_tiny_distilled_patch16_224 81.997 18.003 95.141 4.859 5.91 224 0.900 bicubic +7.487 +3.251 +14
345 mixer_b16_224 81.978 18.022 94.449 5.551 59.88 224 0.875 bicubic +5.376 +2.221 -23 -24
346 pit_ti_distilled_224 81.967 18.033 95.145 4.855 5.10 224 0.900 bicubic +7.437 +3.049 +11
347 hrnet_w18_small_v2 81.961 18.039 95.164 4.836 15.60 224 0.875 bilinear +6.847 +2.748 -1
348 tf_efficientnet_lite0 81.952 18.048 95.168 4.832 4.65 224 0.875 bicubic +7.122 +2.992 +3
357 regnetx_008 81.485 18.515 95.059 4.941 7.26 224 0.875 bicubic +6.447 +2.724 -9
358 mnasnet_100 81.459 18.541 94.899 5.101 4.38 224 0.875 bicubic +6.801 +2.785 -4
359 vgg19_bn 81.444 18.556 94.763 5.237 143.68 224 0.875 bilinear +7.230 +2.921 0
360 spnasnet_100 convit_tiny 80.878 81.126 19.122 18.874 94.526 95.044 5.474 4.955 4.42 5.71 224 0.875 bilinear bicubic +6.794 +8.010 +2.708 +3.331 0 +8
361 ghostnet_100 spnasnet_100 80.699 80.878 19.301 19.122 94.291 94.526 5.709 5.474 5.18 4.42 224 0.875 bilinear +6.721 +6.794 +2.835 +2.708 +1 -1
362 regnety_004 ghostnet_100 80.659 80.699 19.341 19.301 94.686 94.291 5.314 5.709 4.34 5.18 224 0.875 bicubic bilinear +6.624 +6.721 +2.934 +2.835 -1 0
363 regnety_004 80.659 19.341 94.686 5.314 4.34 224 0.875 bicubic +6.624 +2.934 -2
364 skresnet18 80.637 19.363 94.378 5.622 11.96 224 0.875 bicubic +7.599 +3.210 +5
365 regnetx_006 80.629 19.371 94.524 5.476 6.20 224 0.875 bicubic +6.777 +2.852 -1 -2
366 pit_ti_224 80.605 19.395 94.618 5.383 4.85 224 0.900 bicubic +7.693 +3.216 +5
367 swsl_resnet18 80.575 19.425 94.743 5.256 11.69 224 0.875 bilinear +7.299 +3.010 +1 0
368 vgg16_bn 80.556 19.444 94.592 5.408 138.37 224 0.875 bilinear +7.206 +3.086 -2 -3
369 tv_resnet34 80.389 19.611 94.436 5.564 21.80 224 0.875 bilinear +7.077 +3.010 -2 -3
370 resnet18d 80.387 19.613 94.252 5.748 11.71 224 0.875 bicubic +8.127 +3.556 +6
371 mobilenetv2_100 80.257 19.743 94.195 5.805 3.50 224 0.875 bicubic +7.287 +3.179 -1
372 ssl_resnet18 80.101 19.899 94.590 5.410 11.69 224 0.875 bilinear +7.491 +3.174 0
373 tf_mobilenetv3_large_075 80.093 19.907 94.184 5.816 3.99 224 0.875 bilinear +6.655 +2.834 -8 -9
374 vit_deit_tiny_patch16_224 80.018 19.982 94.449 5.551 5.72 224 0.900 bicubic +7.850 +3.331 +4
375 hrnet_w18_small 79.557 20.443 93.898 6.102 13.19 224 0.875 bilinear +7.215 +7.213 +3.220 0
376 vgg19 79.480 20.520 93.870 6.130 143.67 224 0.875 bilinear +7.112 +2.998 -2
377 regnetx_004 79.435 20.565 93.853 6.147 5.16 224 0.875 bicubic +7.039 +3.023 -4
378 tf_mobilenetv3_large_minimal_100 79.222 20.778 93.706 6.294 3.92 224 0.875 bilinear +6.974 +3.076 -1
391 tf_mobilenetv3_small_100 74.717 25.283 91.257 8.743 2.54 224 0.875 bilinear +6.795 +3.593 -1
392 dla46x_c 73.647 26.353 91.095 8.905 1.07 224 0.875 bilinear +7.677 +4.115 0
393 tf_mobilenetv3_small_075 72.812 27.188 90.036 9.964 2.04 224 0.875 bilinear +7.096 +3.906 0
394 dla46_c 72.603 72.601 27.397 27.399 90.499 9.501 1.30 224 0.875 bilinear +7.737 +4.207 0
395 tf_mobilenetv3_small_minimal_100 70.111 29.889 88.505 11.495 2.04 224 0.875 bilinear +7.205 +4.275 0

45
results/results-imagenet.csv
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@ -7,12 +7,15 @@ cait_m48_448,86.484,13.516,97.754,2.246,356.46,448,1.000,bicubic
tf_efficientnet_b6_ns,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
tf_efficientnetv2_l_in21ft1k,86.304,13.696,97.978,2.022,118.52,480,1.000,bicubic
dm_nfnet_f6,86.296,13.704,97.744,2.256,438.36,576,0.956,bicubic
tf_efficientnet_b5_ns,86.088,13.912,97.752,2.248,30.39,456,0.934,bicubic
cait_m36_384,86.054,13.946,97.730,2.270,271.22,384,1.000,bicubic
dm_nfnet_f5,85.714,14.286,97.442,2.558,377.21,544,0.954,bicubic
dm_nfnet_f4,85.658,14.342,97.510,2.490,316.07,512,0.951,bicubic
tf_efficientnetv2_m_in21ft1k,85.588,14.412,97.752,2.248,54.14,480,1.000,bicubic
dm_nfnet_f3,85.560,14.440,97.406,2.594,254.92,416,0.940,bicubic
tf_efficientnetv2_l,85.490,14.510,97.372,2.628,118.52,480,1.000,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
vit_deit_base_distilled_patch16_384,85.422,14.578,97.332,2.668,87.63,384,1.000,bicubic
@ -24,12 +27,14 @@ vit_large_patch16_384,85.158,14.842,97.356,2.644,304.72,384,1.000,bicubic
tf_efficientnet_b7_ap,85.120,14.880,97.252,2.748,66.35,600,0.949,bicubic
ig_resnext101_32x32d,85.094,14.906,97.438,2.562,468.53,224,0.875,bilinear
cait_s24_384,85.046,14.954,97.346,2.654,47.06,384,1.000,bicubic
tf_efficientnetv2_m,85.044,14.956,97.278,2.722,54.14,480,1.000,bicubic
resnetrs420,85.008,14.992,97.124,2.876,191.89,416,1.000,bicubic
dm_nfnet_f2,84.990,15.010,97.144,2.856,193.78,352,0.920,bicubic
ecaresnet269d,84.976,15.024,97.226,2.774,102.09,352,1.000,bicubic
vit_base_r50_s16_384,84.972,15.028,97.288,2.712,98.95,384,1.000,bicubic
tf_efficientnet_b7,84.936,15.064,97.204,2.796,66.35,600,0.949,bicubic
resnetv2_152x4_bitm,84.932,15.068,97.436,2.564,936.53,480,1.000,bilinear
efficientnetv2_rw_m,84.808,15.192,97.148,2.852,53.24,416,1.000,bicubic
tf_efficientnet_b6_ap,84.788,15.212,97.138,2.862,43.04,528,0.942,bicubic
resnetrs350,84.720,15.280,96.988,3.012,163.96,384,1.000,bicubic
dm_nfnet_f1,84.604,15.396,97.068,2.932,132.63,320,0.910,bicubic
@ -38,6 +43,7 @@ resnetv2_152x2_bitm,84.440,15.560,97.446,2.554,236.34,480,1.000,bilinear
resnetrs270,84.434,15.566,96.970,3.030,129.86,352,1.000,bicubic
resnetv2_101x3_bitm,84.394,15.606,97.362,2.638,387.93,480,1.000,bilinear
seresnet152d,84.362,15.638,97.040,2.960,66.84,320,1.000,bicubic
tf_efficientnetv2_s_in21ft1k,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
vit_base_patch16_224_miil,84.268,15.732,96.802,3.198,86.54,224,0.875,bilinear
tf_efficientnet_b5_ap,84.252,15.748,96.974,3.026,30.39,456,0.934,bicubic
@ -50,13 +56,15 @@ cait_xs24_384,84.062,15.938,96.888,3.112,26.67,384,1.000,bicubic
tf_efficientnet_b3_ns,84.048,15.952,96.910,3.090,12.23,300,0.904,bicubic
eca_nfnet_l1,84.008,15.992,97.028,2.972,41.41,320,1.000,bicubic
resnet200d,83.962,16.038,96.824,3.176,64.69,320,1.000,bicubic
tf_efficientnetv2_s,83.894,16.106,96.698,3.302,21.46,384,1.000,bicubic
resnest200e,83.832,16.168,96.894,3.106,70.20,320,0.909,bicubic
tf_efficientnet_b5,83.812,16.188,96.748,3.252,30.39,456,0.934,bicubic
efficientnet_v2s,83.808,16.192,96.724,3.276,23.94,384,1.000,bicubic
efficientnetv2_rw_s,83.808,16.192,96.724,3.276,23.94,384,1.000,bicubic
resnetv2_50x3_bitm,83.784,16.216,97.106,2.894,217.32,480,1.000,bilinear
resnetrs152,83.712,16.288,96.614,3.386,86.62,320,1.000,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
cait_s24_224,83.452,16.548,96.564,3.436,46.92,224,1.000,bicubic
efficientnet_b4,83.428,16.572,96.596,3.404,19.34,384,1.000,bicubic
vit_deit_base_distilled_patch16_224,83.388,16.612,96.488,3.512,87.34,224,0.900,bicubic
@ -65,6 +73,8 @@ dm_nfnet_f0,83.342,16.658,96.560,3.440,71.49,256,0.900,bicubic
tf_efficientnet_b4_ap,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
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
vit_deit_base_patch16_384,83.106,16.894,96.372,3.628,86.86,384,1.000,bicubic
tresnet_m,83.080,16.920,96.118,3.882,31.39,224,0.875,bilinear
vit_large_patch16_224,83.062,16.938,96.438,3.562,304.33,224,0.900,bicubic
@ -75,12 +85,18 @@ resnest101e,82.890,17.110,96.320,3.680,48.28,256,0.875,bilinear
pnasnet5large,82.782,17.218,96.040,3.960,86.06,331,0.911,bicubic
nfnet_l0,82.760,17.240,96.498,3.502,35.07,288,1.000,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
ig_resnext101_32x8d,82.688,17.312,96.636,3.364,88.79,224,0.875,bilinear
nasnetalarge,82.620,17.380,96.046,3.954,88.75,331,0.911,bicubic
eca_nfnet_l0,82.588,17.412,96.474,3.526,24.14,288,1.000,bicubic
levit_384,82.586,17.414,96.016,3.984,39.13,224,0.900,bicubic
pit_b_224,82.446,17.554,95.710,4.290,73.76,224,0.900,bicubic
tf_efficientnet_b2_ns,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
ecaresnet50t,82.346,17.654,96.138,3.862,25.57,320,0.950,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
convit_base,82.290,17.710,95.938,4.062,86.54,224,0.875,bicubic
resnetrs101,82.288,17.712,96.008,3.992,63.62,288,0.940,bicubic
tresnet_l_448,82.268,17.732,95.976,4.024,55.99,448,0.875,bilinear
efficientnet_b3,82.242,17.758,96.114,3.886,12.23,320,1.000,bicubic
@ -88,33 +104,40 @@ resnetv2_101x1_bitm,82.212,17.788,96.472,3.528,44.54,480,1.000,bilinear
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
visformer_small,82.106,17.894,95.872,4.128,40.22,224,0.900,bicubic
tresnet_xl,82.054,17.946,95.936,4.064,78.44,224,0.875,bilinear
vit_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
tf_efficientnetv2_b3,81.970,18.030,95.782,4.218,14.36,300,0.904,bicubic
ssl_resnext101_32x16d,81.844,18.156,96.096,3.904,194.03,224,0.875,bilinear
tf_efficientnet_b3_ap,81.822,18.178,95.624,4.376,12.23,300,0.904,bicubic
vit_base_patch16_224,81.786,18.214,96.122,3.878,86.57,224,0.900,bicubic
tresnet_m_448,81.714,18.286,95.572,4.428,31.39,448,0.875,bilinear
twins_svt_small,81.682,18.318,95.670,4.330,24.06,224,0.900,bicubic
vit_base_patch32_384,81.652,18.348,96.128,3.872,88.30,384,1.000,bicubic
tf_efficientnet_b3,81.636,18.364,95.718,4.282,12.23,300,0.904,bicubic
rexnet_200,81.632,18.368,95.668,4.332,16.37,224,0.875,bicubic
ssl_resnext101_32x8d,81.616,18.384,96.038,3.962,88.79,224,0.875,bilinear
tf_efficientnet_lite4,81.536,18.464,95.668,4.332,13.01,380,0.920,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_384,81.506,18.494,96.092,3.908,306.63,384,1.000,bicubic
tresnet_l,81.490,18.510,95.624,4.376,55.99,224,0.875,bilinear
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
tf_efficientnet_b1_ns,81.388,18.612,95.738,4.262,7.79,240,0.882,bicubic
swin_tiny_patch4_window7_224,81.378,18.622,95.540,4.460,28.29,224,0.900,bicubic
gernet_l,81.354,18.646,95.536,4.464,31.08,256,0.875,bilinear
efficientnet_el,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
vit_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
resnest50d_4s2x40d,81.108,18.892,95.558,4.442,30.42,224,0.875,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
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
@ -148,7 +171,8 @@ seresnet50,80.274,19.726,95.070,4.930,28.09,224,0.875,bicubic
tf_efficientnet_el,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
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
repvgg_b3g4,80.214,19.786,95.110,4.890,83.83,224,0.875,bilinear
tf_efficientnetv2_b2,80.208,19.792,95.042,4.958,10.10,260,0.890,bicubic
resnetv2_50x1_bitm,80.172,19.828,95.626,4.374,25.55,480,1.000,bilinear
inception_v4,80.168,19.832,94.968,5.032,42.68,299,0.875,bicubic
dpn107,80.156,19.844,94.910,5.090,86.92,224,0.875,bicubic
@ -167,6 +191,7 @@ xception71,79.874,20.126,94.922,5.078,42.34,299,0.903,bicubic
regnetx_160,79.856,20.144,94.830,5.170,54.28,224,0.875,bicubic
vit_deit_small_patch16_224,79.856,20.144,95.052,4.948,22.05,224,0.900,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_lite3,79.820,20.180,94.914,5.086,8.20,300,0.904,bilinear
resnext50_32x4d,79.768,20.232,94.598,5.402,25.03,224,0.875,bicubic
@ -183,6 +208,7 @@ xception65,79.552,20.448,94.654,5.346,39.92,299,0.903,bicubic
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
hrnet_w64,79.474,20.526,94.652,5.348,128.06,224,0.875,bilinear
tf_efficientnetv2_b1,79.462,20.538,94.722,5.278,8.14,240,0.882,bicubic
dla102x2,79.448,20.552,94.640,5.360,41.28,224,0.875,bilinear
repvgg_b2g4,79.366,20.634,94.688,5.312,61.76,224,0.875,bilinear
gluon_resnext50_32x4d,79.354,20.646,94.426,5.574,25.03,224,0.875,bicubic
@ -217,22 +243,25 @@ gluon_inception_v3,78.806,21.194,94.370,5.630,23.83,299,0.875,bicubic
efficientnet_b1,78.794,21.206,94.342,5.658,7.79,256,1.000,bicubic
repvgg_b2,78.792,21.208,94.414,5.586,89.02,224,0.875,bilinear
tf_mixnet_l,78.774,21.226,93.998,6.002,7.33,224,0.875,bicubic
gluon_resnet50_v1s,78.712,21.288,94.238,5.762,25.68,224,0.875,bicubic
gluon_resnet50_v1s,78.710,21.290,94.238,5.762,25.68,224,0.875,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,78.658,21.342,94.376,5.624,5.29,224,0.875,bicubic
res2net50_26w_6s,78.570,21.430,94.124,5.876,37.05,224,0.875,bilinear
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
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_b0,78.356,21.644,94.024,5.976,7.14,224,0.875,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
@ -252,9 +281,9 @@ res2net50_26w_4s,77.964,22.036,93.854,6.146,25.70,224,0.875,bilinear
mobilenetv3_large_100_miil,77.916,22.084,92.910,7.090,5.48,224,0.875,bilinear
tf_efficientnet_cc_b0_8e,77.908,22.092,93.654,6.346,24.01,224,0.875,bicubic
regnety_016,77.862,22.138,93.720,6.280,11.20,224,0.875,bicubic
rexnet_100,77.858,22.142,93.870,6.130,4.80,224,0.875,bicubic
tf_inception_v3,77.858,22.142,93.640,6.360,23.83,299,0.875,bicubic
vit_small_patch16_224,77.858,22.142,93.416,6.584,48.75,224,0.900,bicubic
tf_inception_v3,77.856,22.144,93.640,6.360,23.83,299,0.875,bicubic
rexnet_100,77.858,22.142,93.870,6.130,4.80,224,0.875,bicubic
hardcorenas_e,77.794,22.206,93.694,6.306,8.07,224,0.875,bilinear
efficientnet_b0,77.698,22.302,93.532,6.468,5.29,224,0.875,bicubic
legacy_seresnet50,77.630,22.370,93.748,6.252,28.09,224,0.875,bilinear
@ -293,6 +322,7 @@ mixer_b16_224,76.602,23.398,92.228,7.772,59.88,224,0.875,bicubic
tf_efficientnet_es,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_140,76.516,23.484,92.996,7.004,6.11,224,0.875,bicubic
repvgg_a2,76.460,23.540,93.004,6.996,28.21,224,0.875,bilinear
dpn68,76.318,23.682,92.978,7.022,12.61,224,0.875,bicubic
@ -335,13 +365,14 @@ tf_mobilenetv3_large_075,73.438,26.562,91.350,8.650,3.99,224,0.875,bilinear
vgg16_bn,73.350,26.650,91.506,8.494,138.37,224,0.875,bilinear
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
mobilenetv2_100,72.970,27.030,91.016,8.984,3.50,224,0.875,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
hrnet_w18_small,72.342,27.658,90.678,9.322,13.19,224,0.875,bilinear
hrnet_w18_small,72.344,27.656,90.678,9.322,13.19,224,0.875,bilinear
resnet18d,72.260,27.740,90.696,9.304,11.71,224,0.875,bicubic
tf_mobilenetv3_large_minimal_100,72.248,27.752,90.630,9.370,3.92,224,0.875,bilinear
vit_deit_tiny_patch16_224,72.168,27.832,91.118,8.882,5.72,224,0.900,bicubic
@ -360,5 +391,5 @@ tf_mobilenetv3_small_100,67.922,32.078,87.664,12.336,2.54,224,0.875,bilinear
dla60x_c,67.892,32.108,88.426,11.574,1.32,224,0.875,bilinear
dla46x_c,65.970,34.030,86.980,13.020,1.07,224,0.875,bilinear
tf_mobilenetv3_small_075,65.716,34.284,86.130,13.870,2.04,224,0.875,bilinear
dla46_c,64.866,35.134,86.292,13.708,1.30,224,0.875,bilinear
dla46_c,64.864,35.136,86.292,13.708,1.30,224,0.875,bilinear
tf_mobilenetv3_small_minimal_100,62.906,37.094,84.230,15.770,2.04,224,0.875,bilinear

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
7 tf_efficientnet_b6_ns 86.452 13.548 97.882 2.118 43.04 528 0.942 bicubic
8 swin_base_patch4_window12_384 86.432 13.568 98.058 1.942 87.90 384 1.000 bicubic
9 swin_large_patch4_window7_224 86.320 13.680 97.896 2.104 196.53 224 0.900 bicubic
10 tf_efficientnetv2_l_in21ft1k 86.304 13.696 97.978 2.022 118.52 480 1.000 bicubic
11 dm_nfnet_f6 86.296 13.704 97.744 2.256 438.36 576 0.956 bicubic
12 tf_efficientnet_b5_ns 86.088 13.912 97.752 2.248 30.39 456 0.934 bicubic
13 cait_m36_384 86.054 13.946 97.730 2.270 271.22 384 1.000 bicubic
14 dm_nfnet_f5 85.714 14.286 97.442 2.558 377.21 544 0.954 bicubic
15 dm_nfnet_f4 85.658 14.342 97.510 2.490 316.07 512 0.951 bicubic
16 tf_efficientnetv2_m_in21ft1k 85.588 14.412 97.752 2.248 54.14 480 1.000 bicubic
17 dm_nfnet_f3 85.560 14.440 97.406 2.594 254.92 416 0.940 bicubic
18 tf_efficientnetv2_l 85.490 14.510 97.372 2.628 118.52 480 1.000 bicubic
19 cait_s36_384 85.460 14.540 97.480 2.520 68.37 384 1.000 bicubic
20 ig_resnext101_32x48d 85.428 14.572 97.572 2.428 828.41 224 0.875 bilinear
21 vit_deit_base_distilled_patch16_384 85.422 14.578 97.332 2.668 87.63 384 1.000 bicubic
27 tf_efficientnet_b7_ap 85.120 14.880 97.252 2.748 66.35 600 0.949 bicubic
28 ig_resnext101_32x32d 85.094 14.906 97.438 2.562 468.53 224 0.875 bilinear
29 cait_s24_384 85.046 14.954 97.346 2.654 47.06 384 1.000 bicubic
30 tf_efficientnetv2_m 85.044 14.956 97.278 2.722 54.14 480 1.000 bicubic
31 resnetrs420 85.008 14.992 97.124 2.876 191.89 416 1.000 bicubic
32 dm_nfnet_f2 84.990 15.010 97.144 2.856 193.78 352 0.920 bicubic
33 ecaresnet269d 84.976 15.024 97.226 2.774 102.09 352 1.000 bicubic
34 vit_base_r50_s16_384 84.972 15.028 97.288 2.712 98.95 384 1.000 bicubic
35 tf_efficientnet_b7 84.936 15.064 97.204 2.796 66.35 600 0.949 bicubic
36 resnetv2_152x4_bitm 84.932 15.068 97.436 2.564 936.53 480 1.000 bilinear
37 efficientnetv2_rw_m 84.808 15.192 97.148 2.852 53.24 416 1.000 bicubic
38 tf_efficientnet_b6_ap 84.788 15.212 97.138 2.862 43.04 528 0.942 bicubic
39 resnetrs350 84.720 15.280 96.988 3.012 163.96 384 1.000 bicubic
40 dm_nfnet_f1 84.604 15.396 97.068 2.932 132.63 320 0.910 bicubic
43 resnetrs270 84.434 15.566 96.970 3.030 129.86 352 1.000 bicubic
44 resnetv2_101x3_bitm 84.394 15.606 97.362 2.638 387.93 480 1.000 bilinear
45 seresnet152d 84.362 15.638 97.040 2.960 66.84 320 1.000 bicubic
46 tf_efficientnetv2_s_in21ft1k 84.302 15.698 97.252 2.748 21.46 384 1.000 bicubic
47 swsl_resnext101_32x8d 84.284 15.716 97.176 2.824 88.79 224 0.875 bilinear
48 vit_base_patch16_224_miil 84.268 15.732 96.802 3.198 86.54 224 0.875 bilinear
49 tf_efficientnet_b5_ap 84.252 15.748 96.974 3.026 30.39 456 0.934 bicubic
56 tf_efficientnet_b3_ns 84.048 15.952 96.910 3.090 12.23 300 0.904 bicubic
57 eca_nfnet_l1 84.008 15.992 97.028 2.972 41.41 320 1.000 bicubic
58 resnet200d 83.962 16.038 96.824 3.176 64.69 320 1.000 bicubic
59 tf_efficientnetv2_s 83.894 16.106 96.698 3.302 21.46 384 1.000 bicubic
60 resnest200e 83.832 16.168 96.894 3.106 70.20 320 0.909 bicubic
61 tf_efficientnet_b5 83.812 16.188 96.748 3.252 30.39 456 0.934 bicubic
62 efficientnet_v2s efficientnetv2_rw_s 83.808 16.192 96.724 3.276 23.94 384 1.000 bicubic
63 resnetv2_50x3_bitm 83.784 16.216 97.106 2.894 217.32 480 1.000 bilinear
64 resnetrs152 83.712 16.288 96.614 3.386 86.62 320 1.000 bicubic
65 regnety_160 83.686 16.314 96.776 3.224 83.59 288 1.000 bicubic
66 resnet152d 83.680 16.320 96.738 3.262 60.21 320 1.000 bicubic
67 twins_svt_large 83.678 16.322 96.594 3.406 99.27 224 0.900 bicubic
68 cait_s24_224 83.452 16.548 96.564 3.436 46.92 224 1.000 bicubic
69 efficientnet_b4 83.428 16.572 96.596 3.404 19.34 384 1.000 bicubic
70 vit_deit_base_distilled_patch16_224 83.388 16.612 96.488 3.512 87.34 224 0.900 bicubic
73 tf_efficientnet_b4_ap 83.248 16.752 96.392 3.608 19.34 380 0.922 bicubic
74 swsl_resnext101_32x4d 83.230 16.770 96.760 3.240 44.18 224 0.875 bilinear
75 swin_small_patch4_window7_224 83.212 16.788 96.322 3.678 49.61 224 0.900 bicubic
76 twins_pcpvt_large 83.140 16.860 96.598 3.402 60.99 224 0.900 bicubic
77 twins_svt_base 83.136 16.864 96.418 3.582 56.07 224 0.900 bicubic
78 vit_deit_base_patch16_384 83.106 16.894 96.372 3.628 86.86 384 1.000 bicubic
79 tresnet_m 83.080 16.920 96.118 3.882 31.39 224 0.875 bilinear
80 vit_large_patch16_224 83.062 16.938 96.438 3.562 304.33 224 0.900 bicubic
85 pnasnet5large 82.782 17.218 96.040 3.960 86.06 331 0.911 bicubic
86 nfnet_l0 82.760 17.240 96.498 3.502 35.07 288 1.000 bicubic
87 regnety_032 82.724 17.276 96.424 3.576 19.44 288 1.000 bicubic
88 twins_pcpvt_base 82.708 17.292 96.346 3.654 43.83 224 0.900 bicubic
89 ig_resnext101_32x8d 82.688 17.312 96.636 3.364 88.79 224 0.875 bilinear
90 nasnetalarge 82.620 17.380 96.046 3.954 88.75 331 0.911 bicubic
91 eca_nfnet_l0 82.588 17.412 96.474 3.526 24.14 288 1.000 bicubic
92 levit_384 82.586 17.414 96.016 3.984 39.13 224 0.900 bicubic
93 pit_b_224 82.446 17.554 95.710 4.290 73.76 224 0.900 bicubic
94 tf_efficientnet_b2_ns 82.380 17.620 96.248 3.752 9.11 260 0.890 bicubic
95 resnet51q 82.360 17.640 96.180 3.820 35.70 288 1.000 bilinear
96 ecaresnet50t 82.346 17.654 96.138 3.862 25.57 320 0.950 bicubic
97 coat_lite_small 82.308 17.692 95.850 4.150 19.84 224 0.900 bicubic
98 mixer_b16_224_miil 82.308 17.692 95.716 4.284 59.88 224 0.875 bilinear
99 convit_base 82.290 17.710 95.938 4.062 86.54 224 0.875 bicubic
100 resnetrs101 82.288 17.712 96.008 3.992 63.62 288 0.940 bicubic
101 tresnet_l_448 82.268 17.732 95.976 4.024 55.99 448 0.875 bilinear
102 efficientnet_b3 82.242 17.758 96.114 3.886 12.23 320 1.000 bicubic
104 cait_xxs36_384 82.194 17.806 96.148 3.852 17.37 384 1.000 bicubic
105 swsl_resnext50_32x4d 82.182 17.818 96.230 3.770 25.03 224 0.875 bilinear
106 ecaresnet101d 82.172 17.828 96.046 3.954 44.57 224 0.875 bicubic
107 visformer_small 82.106 17.894 95.872 4.128 40.22 224 0.900 bicubic
108 tresnet_xl 82.054 17.946 95.936 4.064 78.44 224 0.875 bilinear
109 vit_deit_base_patch16_224 81.998 18.002 95.734 4.266 86.57 224 0.900 bicubic
110 pit_s_distilled_224 81.996 18.004 95.798 4.202 24.04 224 0.900 bicubic
111 tf_efficientnetv2_b3 81.970 18.030 95.782 4.218 14.36 300 0.904 bicubic
112 ssl_resnext101_32x16d 81.844 18.156 96.096 3.904 194.03 224 0.875 bilinear
113 tf_efficientnet_b3_ap 81.822 18.178 95.624 4.376 12.23 300 0.904 bicubic
114 vit_base_patch16_224 81.786 18.214 96.122 3.878 86.57 224 0.900 bicubic
115 tresnet_m_448 81.714 18.286 95.572 4.428 31.39 448 0.875 bilinear
116 twins_svt_small 81.682 18.318 95.670 4.330 24.06 224 0.900 bicubic
117 vit_base_patch32_384 81.652 18.348 96.128 3.872 88.30 384 1.000 bicubic
118 tf_efficientnet_b3 81.636 18.364 95.718 4.282 12.23 300 0.904 bicubic
119 rexnet_200 81.632 18.368 95.668 4.332 16.37 224 0.875 bicubic
120 ssl_resnext101_32x8d 81.616 18.384 96.038 3.962 88.79 224 0.875 bilinear
121 tf_efficientnet_lite4 81.536 18.464 95.668 4.332 13.01 380 0.920 bilinear
122 tnt_s_patch16_224 81.518 18.482 95.748 4.252 23.76 224 0.900 bicubic
123 levit_256 81.510 18.490 95.490 4.510 18.89 224 0.900 bicubic
124 vit_large_patch32_384 81.506 18.494 96.092 3.908 306.63 384 1.000 bicubic
125 tresnet_l 81.490 18.510 95.624 4.376 55.99 224 0.875 bilinear
126 wide_resnet50_2 81.456 18.544 95.532 4.468 68.88 224 0.875 bicubic
127 convit_small 81.426 18.574 95.744 4.256 27.78 224 0.875 bicubic
128 tf_efficientnet_b1_ns 81.388 18.612 95.738 4.262 7.79 240 0.882 bicubic
129 swin_tiny_patch4_window7_224 81.378 18.622 95.540 4.460 28.29 224 0.900 bicubic
130 gernet_l 81.354 18.646 95.536 4.464 31.08 256 0.875 bilinear
131 efficientnet_el 81.316 18.684 95.526 4.474 10.59 300 0.904 bicubic
132 legacy_senet154 81.310 18.690 95.496 4.504 115.09 224 0.875 bilinear
133 coat_mini 81.268 18.732 95.392 4.608 10.34 224 0.900 bicubic
134 seresnext50_32x4d 81.266 18.734 95.620 4.380 27.56 224 0.875 bicubic
135 gluon_senet154 81.234 18.766 95.348 4.652 115.09 224 0.875 bicubic
136 vit_deit_small_distilled_patch16_224 81.200 18.800 95.378 4.622 22.44 224 0.900 bicubic
137 swsl_resnet50 81.166 18.834 95.972 4.028 25.56 224 0.875 bilinear
138 resnest50d_4s2x40d 81.108 18.892 95.558 4.442 30.42 224 0.875 bicubic
139 pit_s_224 81.094 18.906 95.332 4.668 23.46 224 0.900 bicubic
140 twins_pcpvt_small 81.088 18.912 95.642 4.358 24.11 224 0.900 bicubic
141 gluon_resnet152_v1s 81.016 18.984 95.412 4.588 60.32 224 0.875 bicubic
142 resnest50d_1s4x24d 80.988 19.012 95.322 4.678 25.68 224 0.875 bicubic
143 resnest50d 80.974 19.026 95.378 4.622 27.48 224 0.875 bilinear
171 tf_efficientnet_el 80.250 19.750 95.128 4.872 10.59 300 0.904 bicubic
172 regnetx_320 80.246 19.754 95.026 4.974 107.81 224 0.875 bicubic
173 legacy_seresnext101_32x4d 80.228 19.772 95.018 4.982 48.96 224 0.875 bilinear
174 repvgg_b3g4 80.212 80.214 19.788 19.786 95.110 4.890 83.83 224 0.875 bilinear
175 tf_efficientnetv2_b2 80.208 19.792 95.042 4.958 10.10 260 0.890 bicubic
176 resnetv2_50x1_bitm 80.172 19.828 95.626 4.374 25.55 480 1.000 bilinear
177 inception_v4 80.168 19.832 94.968 5.032 42.68 299 0.875 bicubic
178 dpn107 80.156 19.844 94.910 5.090 86.92 224 0.875 bicubic
191 regnetx_160 79.856 20.144 94.830 5.170 54.28 224 0.875 bicubic
192 vit_deit_small_patch16_224 79.856 20.144 95.052 4.948 22.05 224 0.900 bicubic
193 ecaresnet26t 79.854 20.146 95.084 4.916 16.01 320 0.950 bicubic
194 levit_192 79.842 20.158 94.786 5.214 10.95 224 0.900 bicubic
195 dpn131 79.822 20.178 94.710 5.290 79.25 224 0.875 bicubic
196 tf_efficientnet_lite3 79.820 20.180 94.914 5.086 8.20 300 0.904 bilinear
197 resnext50_32x4d 79.768 20.232 94.598 5.402 25.03 224 0.875 bicubic
208 gluon_resnet101_v1c 79.534 20.466 94.578 5.422 44.57 224 0.875 bicubic
209 rexnet_130 79.500 20.500 94.682 5.318 7.56 224 0.875 bicubic
210 hrnet_w64 79.474 20.526 94.652 5.348 128.06 224 0.875 bilinear
211 tf_efficientnetv2_b1 79.462 20.538 94.722 5.278 8.14 240 0.882 bicubic
212 dla102x2 79.448 20.552 94.640 5.360 41.28 224 0.875 bilinear
213 repvgg_b2g4 79.366 20.634 94.688 5.312 61.76 224 0.875 bilinear
214 gluon_resnext50_32x4d 79.354 20.646 94.426 5.574 25.03 224 0.875 bicubic
243 efficientnet_b1 78.794 21.206 94.342 5.658 7.79 256 1.000 bicubic
244 repvgg_b2 78.792 21.208 94.414 5.586 89.02 224 0.875 bilinear
245 tf_mixnet_l 78.774 21.226 93.998 6.002 7.33 224 0.875 bicubic
246 gluon_resnet50_v1s 78.712 78.710 21.288 21.290 94.238 5.762 25.68 224 0.875 bicubic
247 dla169 78.688 21.312 94.336 5.664 53.39 224 0.875 bilinear
248 legacy_seresnet152 78.660 21.340 94.370 5.630 66.82 224 0.875 bilinear
249 tf_efficientnet_b0_ns 78.658 21.342 94.376 5.624 5.29 224 0.875 bicubic
250 res2net50_26w_6s 78.570 21.430 94.124 5.876 37.05 224 0.875 bilinear
251 xception41 78.516 21.484 94.278 5.722 26.97 299 0.903 bicubic
252 dla102x 78.510 21.490 94.228 5.772 26.31 224 0.875 bilinear
253 levit_128 78.486 21.514 94.010 5.990 9.21 224 0.900 bicubic
254 regnetx_040 78.482 21.518 94.244 5.756 22.12 224 0.875 bicubic
255 resnest26d 78.478 21.522 94.298 5.702 17.07 224 0.875 bilinear
256 dla60_res2net 78.464 21.536 94.206 5.794 20.85 224 0.875 bilinear
257 hrnet_w32 78.450 21.550 94.186 5.814 41.23 224 0.875 bilinear
258 dla60_res2next 78.440 21.560 94.152 5.848 17.03 224 0.875 bilinear
259 coat_tiny 78.434 21.566 94.038 5.962 5.50 224 0.900 bicubic
260 selecsls60b 78.412 21.588 94.174 5.826 32.77 224 0.875 bicubic
261 cait_xxs24_224 78.386 21.614 94.310 5.690 11.96 224 1.000 bicubic
262 legacy_seresnet101 78.382 21.618 94.264 5.736 49.33 224 0.875 bilinear
263 repvgg_b1 78.366 21.634 94.098 5.902 57.42 224 0.875 bilinear
264 tf_efficientnetv2_b0 78.356 21.644 94.024 5.976 7.14 224 0.875 bicubic
265 tv_resnet152 78.312 21.688 94.038 5.962 60.19 224 0.875 bilinear
266 dla60x 78.246 21.754 94.018 5.982 17.35 224 0.875 bilinear
267 res2next50 78.246 21.754 93.892 6.108 24.67 224 0.875 bilinear
281 mobilenetv3_large_100_miil 77.916 22.084 92.910 7.090 5.48 224 0.875 bilinear
282 tf_efficientnet_cc_b0_8e 77.908 22.092 93.654 6.346 24.01 224 0.875 bicubic
283 regnety_016 77.862 22.138 93.720 6.280 11.20 224 0.875 bicubic
284 rexnet_100 tf_inception_v3 77.858 22.142 93.870 93.640 6.130 6.360 4.80 23.83 224 299 0.875 bicubic
285 vit_small_patch16_224 77.858 22.142 93.416 6.584 48.75 224 0.900 bicubic
286 tf_inception_v3 rexnet_100 77.856 77.858 22.144 22.142 93.640 93.870 6.360 6.130 23.83 4.80 299 224 0.875 bicubic
287 hardcorenas_e 77.794 22.206 93.694 6.306 8.07 224 0.875 bilinear
288 efficientnet_b0 77.698 22.302 93.532 6.468 5.29 224 0.875 bicubic
289 legacy_seresnet50 77.630 22.370 93.748 6.252 28.09 224 0.875 bilinear
322 tf_efficientnet_es 76.594 23.406 93.202 6.798 5.44 224 0.875 bicubic
323 densenetblur121d 76.588 23.412 93.192 6.808 8.00 224 0.875 bicubic
324 hardcorenas_b 76.538 23.462 92.754 7.246 5.18 224 0.875 bilinear
325 levit_128s 76.530 23.470 92.866 7.134 7.78 224 0.900 bicubic
326 mobilenetv2_140 76.516 23.484 92.996 7.004 6.11 224 0.875 bicubic
327 repvgg_a2 76.460 23.540 93.004 6.996 28.21 224 0.875 bilinear
328 dpn68 76.318 23.682 92.978 7.022 12.61 224 0.875 bicubic
365 vgg16_bn 73.350 26.650 91.506 8.494 138.37 224 0.875 bilinear
366 tv_resnet34 73.312 26.688 91.426 8.574 21.80 224 0.875 bilinear
367 swsl_resnet18 73.276 26.724 91.734 8.266 11.69 224 0.875 bilinear
368 convit_tiny 73.116 26.884 91.714 8.286 5.71 224 0.875 bicubic
369 skresnet18 73.038 26.962 91.168 8.832 11.96 224 0.875 bicubic
370 mobilenetv2_100 72.970 27.030 91.016 8.984 3.50 224 0.875 bicubic
371 pit_ti_224 72.912 27.088 91.402 8.598 4.85 224 0.900 bicubic
372 ssl_resnet18 72.610 27.390 91.416 8.584 11.69 224 0.875 bilinear
373 regnetx_004 72.396 27.604 90.830 9.170 5.16 224 0.875 bicubic
374 vgg19 72.368 27.632 90.872 9.128 143.67 224 0.875 bilinear
375 hrnet_w18_small 72.342 72.344 27.658 27.656 90.678 9.322 13.19 224 0.875 bilinear
376 resnet18d 72.260 27.740 90.696 9.304 11.71 224 0.875 bicubic
377 tf_mobilenetv3_large_minimal_100 72.248 27.752 90.630 9.370 3.92 224 0.875 bilinear
378 vit_deit_tiny_patch16_224 72.168 27.832 91.118 8.882 5.72 224 0.900 bicubic
391 dla60x_c 67.892 32.108 88.426 11.574 1.32 224 0.875 bilinear
392 dla46x_c 65.970 34.030 86.980 13.020 1.07 224 0.875 bilinear
393 tf_mobilenetv3_small_075 65.716 34.284 86.130 13.870 2.04 224 0.875 bilinear
394 dla46_c 64.866 64.864 35.134 35.136 86.292 13.708 1.30 224 0.875 bilinear
395 tf_mobilenetv3_small_minimal_100 62.906 37.094 84.230 15.770 2.04 224 0.875 bilinear

387
results/results-imagenetv2-matched-frequency.csv
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@ -4,128 +4,151 @@ tf_efficientnet_l2_ns,80.250,19.750,95.840,4.160,480.31,800,0.960,bicubic,-8.102
tf_efficientnet_b7_ns,78.510,21.490,94.380,5.620,66.35,600,0.949,bicubic,-8.330,-3.714,+1
tf_efficientnet_b6_ns,77.280,22.720,93.890,6.110,43.04,528,0.942,bicubic,-9.172,-3.992,+2
swin_large_patch4_window12_384,77.040,22.960,93.750,6.250,196.74,384,1.000,bicubic,-10.108,-4.484,-2
cait_m48_448,76.870,23.130,93.370,6.630,356.46,448,1.000,bicubic,-9.614,-4.384,-1
ig_resnext101_32x48d,76.870,23.130,93.310,6.690,828.41,224,0.875,bilinear,-8.558,-4.262,+9
ig_resnext101_32x32d,76.840,23.160,93.200,6.800,468.53,224,0.875,bilinear,-8.254,-4.238,+16
tf_efficientnetv2_l_in21ft1k,76.940,23.060,93.950,6.050,118.52,480,1.000,bicubic,-9.364,-4.028,+3
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,+11
ig_resnext101_32x32d,76.840,23.160,93.200,6.800,468.53,224,0.875,bilinear,-8.254,-4.238,+18
tf_efficientnet_b5_ns,76.810,23.190,93.580,6.420,30.39,456,0.934,bicubic,-9.278,-4.172,+1
cait_m36_384,76.320,23.680,93.050,6.950,271.22,384,1.000,bicubic,-9.734,-4.680,+1
swin_base_patch4_window12_384,76.280,23.720,93.320,6.680,87.90,384,1.000,bicubic,-10.152,-4.738,-4
swin_large_patch4_window7_224,76.270,23.730,93.420,6.580,196.53,224,0.900,bicubic,-10.050,-4.476,-4
cait_s36_384,76.210,23.790,92.970,7.030,68.37,384,1.000,bicubic,-9.250,-4.510,+2
dm_nfnet_f6,76.180,23.820,93.220,6.780,438.36,576,0.956,bicubic,-10.116,-4.524,-5
tf_efficientnet_b7_ap,76.090,23.910,92.970,7.030,66.35,600,0.949,bicubic,-9.030,-4.282,+8
tf_efficientnet_b8_ap,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.280,-4.564,+3
dm_nfnet_f4,75.750,24.250,92.790,7.210,316.07,512,0.951,bicubic,-9.908,-4.720,-4
ig_resnext101_32x16d,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.450,-4.286,+26
tf_efficientnetv2_l,76.280,23.720,92.970,7.030,118.52,480,1.000,bicubic,-9.210,-4.402,+4
swin_base_patch4_window12_384,76.280,23.720,93.320,6.680,87.90,384,1.000,bicubic,-10.152,-4.738,-5
swin_large_patch4_window7_224,76.270,23.730,93.420,6.580,196.53,224,0.900,bicubic,-10.050,-4.476,-6
cait_s36_384,76.210,23.790,92.970,7.030,68.37,384,1.000,bicubic,-9.250,-4.510,+3
dm_nfnet_f6,76.180,23.820,93.220,6.780,438.36,576,0.956,bicubic,-10.116,-4.524,-6
tf_efficientnet_b7_ap,76.090,23.910,92.970,7.030,66.35,600,0.949,bicubic,-9.030,-4.282,+9
tf_efficientnet_b8_ap,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.280,-4.564,+4
tf_efficientnetv2_m_in21ft1k,75.920,24.080,93.280,6.720,54.14,480,1.000,bicubic,-9.668,-4.472,-4
dm_nfnet_f4,75.750,24.250,92.790,7.210,316.07,512,0.951,bicubic,-9.908,-4.720,-6
ig_resnext101_32x16d,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.450,-4.286,+29
tf_efficientnet_b4_ns,75.670,24.330,93.050,6.950,19.34,380,0.922,bicubic,-9.492,-4.420,+2
vit_base_r50_s16_384,75.590,24.410,92.790,7.210,98.95,384,1.000,bicubic,-9.382,-4.498,+9
vit_base_r50_s16_384,75.590,24.410,92.790,7.210,98.95,384,1.000,bicubic,-9.382,-4.498,+10
vit_deit_base_distilled_patch16_384,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.872,-4.832,-4
cait_s24_384,75.480,24.520,92.600,7.400,47.06,384,1.000,bicubic,-9.566,-4.746,+3
swsl_resnext101_32x8d,75.430,24.570,92.760,7.240,88.79,224,0.875,bilinear,-8.854,-4.416,+17
dm_nfnet_f3,75.410,24.590,92.830,7.170,254.92,416,0.940,bicubic,-10.150,-4.576,-10
tf_efficientnet_b6_ap,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+7
vit_large_patch16_384,75.150,24.850,92.660,7.340,304.72,384,1.000,bicubic,-10.008,-4.696,-4
ecaresnet269d,75.120,24.880,92.840,7.160,102.09,352,1.000,bicubic,-9.856,-4.386,+1
tf_efficientnet_b8,74.940,25.060,92.310,7.690,87.41,672,0.954,bicubic,-10.430,-5.080,-10
dm_nfnet_f5,74.790,25.210,92.460,7.540,377.21,544,0.954,bicubic,-10.924,-4.982,-17
tf_efficientnet_b7,74.720,25.280,92.220,7.780,66.35,600,0.949,bicubic,-10.216,-4.984,0
tf_efficientnet_b5_ap,74.600,25.400,91.990,8.010,30.39,456,0.934,bicubic,-9.652,-4.984,+11
swin_base_patch4_window7_224,74.570,25.430,92.560,7.440,87.77,224,0.900,bicubic,-10.682,-5.002,-12
tf_efficientnetv2_m,75.520,24.480,92.620,7.380,54.14,480,1.000,bicubic,-9.524,-4.658,+4
cait_s24_384,75.480,24.520,92.600,7.400,47.06,384,1.000,bicubic,-9.566,-4.746,+2
swsl_resnext101_32x8d,75.430,24.570,92.760,7.240,88.79,224,0.875,bilinear,-8.854,-4.416,+19
dm_nfnet_f3,75.410,24.590,92.830,7.170,254.92,416,0.940,bicubic,-10.150,-4.576,-12
tf_efficientnet_b6_ap,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+8
efficientnetv2_rw_m,75.170,24.830,92.570,7.430,53.24,416,1.000,bicubic,-9.638,-4.578,+6
vit_large_patch16_384,75.150,24.850,92.660,7.340,304.72,384,1.000,bicubic,-10.008,-4.696,-6
ecaresnet269d,75.120,24.880,92.840,7.160,102.09,352,1.000,bicubic,-9.856,-4.386,0
tf_efficientnet_b8,74.940,25.060,92.310,7.690,87.41,672,0.954,bicubic,-10.430,-5.080,-12
dm_nfnet_f5,74.790,25.210,92.460,7.540,377.21,544,0.954,bicubic,-10.924,-4.982,-21
tf_efficientnet_b7,74.720,25.280,92.220,7.780,66.35,600,0.949,bicubic,-10.216,-4.984,-1
tf_efficientnet_b5_ap,74.600,25.400,91.990,8.010,30.39,456,0.934,bicubic,-9.652,-4.984,+12
swin_base_patch4_window7_224,74.570,25.430,92.560,7.440,87.77,224,0.900,bicubic,-10.682,-5.002,-14
seresnet152d,74.510,25.490,92.080,7.920,66.84,320,1.000,bicubic,-9.852,-4.960,+6
resnest200e,74.480,25.520,91.860,8.140,70.20,320,0.909,bicubic,-9.352,-5.034,+18
dm_nfnet_f2,74.450,25.550,92.230,7.770,193.78,352,0.920,bicubic,-10.540,-4.914,-8
dm_nfnet_f1,74.400,25.600,92.350,7.650,132.63,320,0.910,bicubic,-10.204,-4.718,-2
efficientnet_v2s,74.170,25.830,91.710,8.290,23.94,384,1.000,bicubic,-9.638,-5.014,+17
resnest269e,74.170,25.830,91.950,8.050,110.93,416,0.928,bicubic,-10.348,-5.036,-3
resnest200e,74.480,25.520,91.860,8.140,70.20,320,0.909,bicubic,-9.352,-5.034,+20
dm_nfnet_f2,74.450,25.550,92.230,7.770,193.78,352,0.920,bicubic,-10.540,-4.914,-9
tf_efficientnetv2_s_in21ft1k,74.450,25.550,92.510,7.490,21.46,384,1.000,bicubic,-9.852,-4.742,+4
dm_nfnet_f1,74.400,25.600,92.350,7.650,132.63,320,0.910,bicubic,-10.204,-4.718,-3
efficientnetv2_rw_s,74.170,25.830,91.710,8.290,23.94,384,1.000,bicubic,-9.638,-5.014,+18
resnest269e,74.170,25.830,91.950,8.050,110.93,416,0.928,bicubic,-10.348,-5.036,-4
cait_xs24_384,74.160,25.840,91.910,8.090,26.67,384,1.000,bicubic,-9.902,-4.978,+9
pit_b_distilled_224,74.160,25.840,91.680,8.320,74.79,224,0.900,bicubic,-9.984,-5.176,+5
swsl_resnext101_32x4d,74.140,25.860,91.990,8.010,44.18,224,0.875,bilinear,-9.090,-4.770,+24
swsl_resnext101_32x4d,74.140,25.860,91.990,8.010,44.18,224,0.875,bilinear,-9.090,-4.770,+26
vit_base_patch16_384,74.130,25.870,92.360,7.640,86.86,384,1.000,bicubic,-10.080,-4.858,+1
eca_nfnet_l1,74.060,25.940,92.120,7.880,41.41,320,1.000,bicubic,-9.948,-4.908,+7
vit_base_patch16_224_miil,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.228,-5.102,-3
swsl_resnext101_32x16d,74.020,25.980,92.160,7.840,194.03,224,0.875,bilinear,-9.326,-4.686,+17
resnetv2_152x4_bitm,74.000,26.000,92.340,7.660,936.53,480,1.000,bilinear,-10.932,-5.096,-15
resnetrs420,73.920,26.080,91.760,8.240,191.89,416,1.000,bicubic,-11.088,-5.364,-21
tf_efficientnet_b6,73.900,26.100,91.750,8.250,43.04,528,0.942,bicubic,-10.210,-5.136,-2
tf_efficientnet_b3_ns,73.890,26.110,91.870,8.130,12.23,300,0.904,bicubic,-10.158,-5.040,0
resnetrs270,73.710,26.290,91.580,8.420,129.86,352,1.000,bicubic,-10.724,-5.390,-13
resnet200d,73.680,26.320,91.570,8.430,64.69,320,1.000,bicubic,-10.282,-5.254,0
ig_resnext101_32x8d,73.650,26.350,92.190,7.810,88.79,224,0.875,bilinear,-9.038,-4.446,+25
resnetv2_152x2_bitm,73.630,26.370,92.590,7.410,236.34,480,1.000,bilinear,-10.810,-4.856,-17
swsl_resnext101_32x16d,74.020,25.980,92.160,7.840,194.03,224,0.875,bilinear,-9.326,-4.686,+19
tf_efficientnetv2_s,74.000,26.000,91.530,8.470,21.46,384,1.000,bicubic,-9.894,-5.168,+5
resnetv2_152x4_bitm,74.000,26.000,92.340,7.660,936.53,480,1.000,bilinear,-10.932,-5.096,-17
resnetrs420,73.920,26.080,91.760,8.240,191.89,416,1.000,bicubic,-11.088,-5.364,-24
tf_efficientnet_b6,73.900,26.100,91.750,8.250,43.04,528,0.942,bicubic,-10.210,-5.136,-3
tf_efficientnet_b3_ns,73.890,26.110,91.870,8.130,12.23,300,0.904,bicubic,-10.158,-5.040,-1
resnetrs270,73.710,26.290,91.580,8.420,129.86,352,1.000,bicubic,-10.724,-5.390,-15
resnet200d,73.680,26.320,91.570,8.430,64.69,320,1.000,bicubic,-10.282,-5.254,-1
ig_resnext101_32x8d,73.650,26.350,92.190,7.810,88.79,224,0.875,bilinear,-9.038,-4.446,+29
resnetv2_152x2_bitm,73.630,26.370,92.590,7.410,236.34,480,1.000,bilinear,-10.810,-4.856,-19
tf_efficientnet_b5,73.550,26.450,91.460,8.540,30.39,456,0.934,bicubic,-10.262,-5.288,-1
resnetv2_101x3_bitm,73.530,26.470,92.570,7.430,387.93,480,1.000,bilinear,-10.864,-4.792,-17
resnetv2_101x3_bitm,73.530,26.470,92.570,7.430,387.93,480,1.000,bilinear,-10.864,-4.792,-19
resnet152d,73.520,26.480,91.230,8.770,60.21,320,1.000,bicubic,-10.160,-5.508,+2
resnetrs200,73.500,26.500,91.250,8.750,93.21,320,1.000,bicubic,-10.566,-5.624,-10
resnetrs350,73.400,26.600,91.310,8.690,163.96,384,1.000,bicubic,-11.320,-5.678,-25
regnety_160,73.360,26.640,91.690,8.310,83.59,288,1.000,bicubic,-10.326,-5.086,-2
resnetrs200,73.500,26.500,91.250,8.750,93.21,320,1.000,bicubic,-10.566,-5.624,-11
resnetrs350,73.400,26.600,91.310,8.690,163.96,384,1.000,bicubic,-11.320,-5.678,-27
twins_svt_large,73.390,26.610,90.910,9.090,99.27,224,0.900,bicubic,-10.288,-5.684,0
regnety_160,73.360,26.640,91.690,8.310,83.59,288,1.000,bicubic,-10.326,-5.086,-3
efficientnet_b4,73.320,26.680,91.280,8.720,19.34,384,1.000,bicubic,-10.108,-5.316,0
vit_deit_base_distilled_patch16_224,73.240,26.760,91.000,9.000,87.34,224,0.900,bicubic,-10.148,-5.488,0
resnetrs152,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.512,-5.354,-6
resnetrs152,73.200,26.800,91.260,8.740,86.62,320,1.000,bicubic,-10.512,-5.354,-7
cait_s24_224,73.070,26.930,91.130,8.870,46.92,224,1.000,bicubic,-10.382,-5.434,-4
tf_efficientnet_b4_ap,72.890,27.110,90.980,9.020,19.34,380,0.922,bicubic,-10.358,-5.412,0
dm_nfnet_f0,72.790,27.210,91.040,8.960,71.49,256,0.900,bicubic,-10.552,-5.520,-2
regnety_032,72.770,27.230,90.950,9.050,19.44,288,1.000,bicubic,-9.954,-5.474,+10
nfnet_l0,72.610,27.390,91.010,8.990,35.07,288,1.000,bicubic,-10.150,-5.488,+8
pnasnet5large,72.610,27.390,90.510,9.490,86.06,331,0.911,bicubic,-10.172,-5.530,+6
resnest101e,72.570,27.430,90.820,9.180,48.28,256,0.875,bilinear,-10.320,-5.500,+4
swsl_resnext50_32x4d,72.560,27.440,90.870,9.130,25.03,224,0.875,bilinear,-9.622,-5.360,+18
tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.500,-5.864,-1
regnety_032,72.770,27.230,90.950,9.050,19.44,288,1.000,bicubic,-9.954,-5.474,+12
pnasnet5large,72.610,27.390,90.510,9.490,86.06,331,0.911,bicubic,-10.172,-5.530,+8
nfnet_l0,72.610,27.390,91.010,8.990,35.07,288,1.000,bicubic,-10.150,-5.488,+10
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,+5
swsl_resnext50_32x4d,72.560,27.440,90.870,9.130,25.03,224,0.875,bilinear,-9.622,-5.360,+25
tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.500,-5.864,0
twins_svt_base,72.550,27.450,90.460,9.540,56.07,224,0.900,bicubic,-10.586,-5.958,-5
vit_deit_base_patch16_384,72.530,27.470,90.250,9.750,86.86,384,1.000,bicubic,-10.576,-6.122,-5
resnet101d,72.410,27.590,90.650,9.350,44.57,320,1.000,bicubic,-10.612,-5.796,-2
tf_efficientnet_b4,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.732,-5.710,-2
tf_efficientnet_b2_ns,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.100,-5.158,+6
tf_efficientnet_b2_ns,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.100,-5.158,+8
tresnet_m,72.270,27.730,90.240,9.760,31.39,224,0.875,bilinear,-10.810,-5.878,-8
vit_large_patch16_224,72.250,27.750,90.990,9.010,304.33,224,0.900,bicubic,-10.812,-5.448,-8
nasnetalarge,72.230,27.770,90.470,9.530,88.75,331,0.911,bicubic,-10.390,-5.576,0
cait_xxs36_384,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.004,-5.308,+8
resnetv2_50x3_bitm,72.180,27.820,91.790,8.210,217.32,480,1.000,bilinear,-11.604,-5.316,-25
nasnetalarge,72.230,27.770,90.470,9.530,88.75,331,0.911,bicubic,-10.390,-5.576,+1
cait_xxs36_384,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.004,-5.308,+14
resnetv2_50x3_bitm,72.180,27.820,91.790,8.210,217.32,480,1.000,bilinear,-11.604,-5.316,-28
twins_pcpvt_base,72.180,27.820,90.510,9.490,43.83,224,0.900,bicubic,-10.528,-5.836,-4
eca_nfnet_l0,71.850,28.150,91.130,8.870,24.14,288,1.000,bicubic,-10.738,-5.344,-2
swin_small_patch4_window7_224,71.740,28.260,90.240,9.760,49.61,224,0.900,bicubic,-11.472,-6.082,-16
pit_b_224,71.700,28.300,89.250,10.750,73.76,224,0.900,bicubic,-10.746,-6.460,-3
swsl_resnet50,71.700,28.300,90.500,9.500,25.56,224,0.875,bilinear,-9.466,-5.472,+30
tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.394,-6.306,+5
tresnet_l_448,71.600,28.400,90.050,9.950,55.99,448,0.875,bilinear,-10.668,-5.926,-2
ssl_resnext101_32x8d,71.500,28.500,90.460,9.540,88.79,224,0.875,bilinear,-10.116,-5.578,+13
ecaresnet101d,71.490,28.510,90.330,9.670,44.57,224,0.875,bicubic,-10.682,-5.716,+1
efficientnet_b3,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.762,-6.054,-4
ssl_resnext101_32x16d,71.410,28.590,90.560,9.440,194.03,224,0.875,bilinear,-10.434,-5.536,+3
pit_s_distilled_224,71.380,28.620,89.780,10.220,24.04,224,0.900,bicubic,-10.616,-6.018,+1
vit_base_patch16_224,71.330,28.670,90.460,9.540,86.57,224,0.900,bicubic,-10.456,-5.662,+3
ecaresnet50t,71.280,28.720,90.420,9.580,25.57,320,0.950,bicubic,-11.066,-5.718,-11
vit_base_patch32_384,71.180,28.820,90.630,9.370,88.30,384,1.000,bicubic,-10.472,-5.498,+3
vit_deit_base_patch16_224,71.170,28.830,89.200,10.800,86.57,224,0.900,bicubic,-10.828,-6.534,-4
tresnet_m_448,70.990,29.010,88.680,11.320,31.39,448,0.875,bilinear,-10.724,-6.892,0
resnest50d_4s2x40d,70.950,29.050,89.710,10.290,30.42,224,0.875,bicubic,-10.158,-5.848,+18
wide_resnet50_2,70.950,29.050,89.230,10.770,68.88,224,0.875,bicubic,-10.506,-6.302,+7
tnt_s_patch16_224,70.930,29.070,89.600,10.400,23.76,224,0.900,bicubic,-10.588,-6.148,+3
tf_efficientnet_b3_ap,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.902,-6.194,-6
tf_efficientnet_b1_ns,70.870,29.130,90.120,9.880,7.79,240,0.882,bicubic,-10.518,-5.618,+5
vit_large_patch32_384,70.860,29.140,90.570,9.430,306.63,384,1.000,bicubic,-10.646,-5.522,+1
tresnet_l,70.840,29.160,89.630,10.370,55.99,224,0.875,bilinear,-10.650,-5.994,-1
rexnet_200,70.840,29.160,89.700,10.300,16.37,224,0.875,bicubic,-10.792,-5.968,-5
resnetrs101,70.840,29.160,89.830,10.170,63.62,288,0.940,bicubic,-11.448,-6.178,-20
resnetv2_101x1_bitm,70.710,29.290,90.800,9.200,44.54,480,1.000,bilinear,-11.502,-5.672,-20
tf_efficientnet_b3,70.640,29.360,89.440,10.560,12.23,300,0.904,bicubic,-10.996,-6.278,-9
cait_xxs24_384,70.600,29.400,89.720,10.280,12.03,384,1.000,bicubic,-10.366,-5.926,+12
gluon_senet154,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.634,-6.428,+3
ssl_resnext101_32x4d,70.530,29.470,89.760,10.240,44.18,224,0.875,bilinear,-10.394,-5.968,+11
vit_deit_small_distilled_patch16_224,70.520,29.480,89.470,10.530,22.44,224,0.900,bicubic,-10.680,-5.908,+2
legacy_senet154,70.500,29.500,89.010,10.990,115.09,224,0.875,bilinear,-10.810,-6.486,-2
gluon_seresnext101_64x4d,70.430,29.570,89.350,10.650,88.23,224,0.875,bicubic,-10.464,-5.958,+10
tf_efficientnet_lite4,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.106,-6.558,-13
resnest50d,70.410,29.590,88.760,11.240,27.48,224,0.875,bilinear,-10.564,-6.618,+4
resnest50d_1s4x24d,70.400,29.600,89.220,10.780,25.68,224,0.875,bicubic,-10.588,-6.102,+2
seresnext50_32x4d,70.400,29.600,89.110,10.890,27.56,224,0.875,bicubic,-10.866,-6.510,-6
swin_small_patch4_window7_224,71.740,28.260,90.240,9.760,49.61,224,0.900,bicubic,-11.472,-6.082,-19
pit_b_224,71.700,28.300,89.250,10.750,73.76,224,0.900,bicubic,-10.746,-6.460,-2
swsl_resnet50,71.700,28.300,90.500,9.500,25.56,224,0.875,bilinear,-9.466,-5.472,+41
tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.394,-6.306,+11
convit_base,71.600,28.400,90.150,9.850,86.54,224,0.875,bicubic,-10.690,-5.788,+1
tresnet_l_448,71.600,28.400,90.050,9.950,55.99,448,0.875,bilinear,-10.668,-5.926,+2
ssl_resnext101_32x8d,71.500,28.500,90.460,9.540,88.79,224,0.875,bilinear,-10.116,-5.578,+20
ecaresnet101d,71.490,28.510,90.330,9.670,44.57,224,0.875,bicubic,-10.682,-5.716,+5
efficientnet_b3,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.762,-6.054,0
resnet51q,71.430,28.570,90.180,9.820,35.70,288,1.000,bilinear,-10.930,-6.000,-8
ssl_resnext101_32x16d,71.410,28.590,90.560,9.440,194.03,224,0.875,bilinear,-10.434,-5.536,+8
pit_s_distilled_224,71.380,28.620,89.780,10.220,24.04,224,0.900,bicubic,-10.616,-6.018,+5
vit_base_patch16_224,71.330,28.670,90.460,9.540,86.57,224,0.900,bicubic,-10.456,-5.662,+8
mixer_b16_224_miil,71.300,28.700,89.650,10.350,59.88,224,0.875,bilinear,-11.008,-6.066,-9
ecaresnet50t,71.280,28.720,90.420,9.580,25.57,320,0.950,bicubic,-11.066,-5.718,-12
vit_base_patch32_384,71.180,28.820,90.630,9.370,88.30,384,1.000,bicubic,-10.472,-5.498,+8
vit_deit_base_patch16_224,71.170,28.830,89.200,10.800,86.57,224,0.900,bicubic,-10.828,-6.534,-1
visformer_small,71.010,28.990,89.460,10.540,40.22,224,0.900,bicubic,-11.096,-6.412,-4
tresnet_m_448,70.990,29.010,88.680,11.320,31.39,448,0.875,bilinear,-10.724,-6.892,+3
wide_resnet50_2,70.950,29.050,89.230,10.770,68.88,224,0.875,bicubic,-10.506,-6.302,+12
resnest50d_4s2x40d,70.950,29.050,89.710,10.290,30.42,224,0.875,bicubic,-10.158,-5.848,+25
tnt_s_patch16_224,70.930,29.070,89.600,10.400,23.76,224,0.900,bicubic,-10.588,-6.148,+7
tf_efficientnet_b3_ap,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.902,-6.194,-3
tf_efficientnet_b1_ns,70.870,29.130,90.120,9.880,7.79,240,0.882,bicubic,-10.518,-5.618,+11
vit_large_patch32_384,70.860,29.140,90.570,9.430,306.63,384,1.000,bicubic,-10.646,-5.522,+6
resnetrs101,70.840,29.160,89.830,10.170,63.62,288,0.940,bicubic,-11.448,-6.178,-19
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,-10.650,-5.994,+4
tf_efficientnetv2_b3,70.830,29.170,89.500,10.500,14.36,300,0.904,bicubic,-11.140,-6.282,-11
coat_lite_small,70.800,29.200,89.570,10.430,19.84,224,0.900,bicubic,-11.508,-6.280,-26
levit_384,70.750,29.250,89.300,10.700,39.13,224,0.900,bicubic,-11.836,-6.716,-32
resnetv2_101x1_bitm,70.710,29.290,90.800,9.200,44.54,480,1.000,bilinear,-11.502,-5.672,-22
tf_efficientnet_b3,70.640,29.360,89.440,10.560,12.23,300,0.904,bicubic,-10.996,-6.278,-8
gluon_senet154,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.634,-6.428,+7
cait_xxs24_384,70.600,29.400,89.720,10.280,12.03,384,1.000,bicubic,-10.366,-5.926,+17
convit_small,70.580,29.420,89.580,10.420,27.78,224,0.875,bicubic,-10.846,-6.164,-2
twins_pcpvt_small,70.550,29.450,89.070,10.930,24.11,224,0.900,bicubic,-10.538,-6.572,+10
ssl_resnext101_32x4d,70.530,29.470,89.760,10.240,44.18,224,0.875,bilinear,-10.394,-5.968,+14
vit_deit_small_distilled_patch16_224,70.520,29.480,89.470,10.530,22.44,224,0.900,bicubic,-10.680,-5.908,+4
legacy_senet154,70.500,29.500,89.010,10.990,115.09,224,0.875,bilinear,-10.810,-6.486,-1
twins_svt_small,70.440,29.560,89.360,10.640,24.06,224,0.900,bicubic,-11.242,-6.310,-18
gluon_seresnext101_64x4d,70.430,29.570,89.350,10.650,88.23,224,0.875,bicubic,-10.464,-5.958,+12
tf_efficientnet_lite4,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.106,-6.558,-15
resnest50d,70.410,29.590,88.760,11.240,27.48,224,0.875,bilinear,-10.564,-6.618,+6
seresnext50_32x4d,70.400,29.600,89.110,10.890,27.56,224,0.875,bicubic,-10.866,-6.510,-5
resnest50d_1s4x24d,70.400,29.600,89.220,10.780,25.68,224,0.875,bicubic,-10.588,-6.102,+4
gernet_l,70.350,29.650,88.980,11.020,31.08,256,0.875,bilinear,-11.004,-6.556,-10
gluon_resnet152_v1s,70.290,29.710,88.850,11.150,60.32,224,0.875,bicubic,-10.726,-6.562,-2
repvgg_b3,70.250,29.750,88.730,11.270,123.09,224,0.875,bilinear,-10.242,-6.530,+13
ecaresnet101d_pruned,70.130,29.870,89.590,10.410,24.88,224,0.875,bicubic,-10.688,-6.038,+4
efficientnet_el,70.120,29.880,89.290,10.710,10.59,300,0.904,bicubic,-11.196,-6.236,-13
inception_resnet_v2,70.120,29.880,88.700,11.300,55.84,299,0.897,bicubic,-10.338,-6.606,+14
gluon_seresnext101_32x4d,70.010,29.990,88.900,11.100,48.96,224,0.875,bicubic,-10.894,-6.394,-2
regnety_320,70.000,30.000,88.890,11.110,145.05,224,0.875,bicubic,-10.812,-6.354,+1
gluon_resnet152_v1s,70.290,29.710,88.850,11.150,60.32,224,0.875,bicubic,-10.726,-6.562,0
repvgg_b3,70.250,29.750,88.730,11.270,123.09,224,0.875,bilinear,-10.242,-6.530,+15
coat_mini,70.220,29.780,89.440,10.560,10.34,224,0.900,bicubic,-11.048,-5.952,-10
ecaresnet101d_pruned,70.130,29.870,89.590,10.410,24.88,224,0.875,bicubic,-10.688,-6.038,+5
efficientnet_el,70.120,29.880,89.290,10.710,10.59,300,0.904,bicubic,-11.196,-6.236,-14
inception_resnet_v2,70.120,29.880,88.700,11.300,55.84,299,0.897,bicubic,-10.338,-6.606,+15
gluon_seresnext101_32x4d,70.010,29.990,88.900,11.100,48.96,224,0.875,bicubic,-10.894,-6.394,-1
regnety_320,70.000,30.000,88.890,11.110,145.05,224,0.875,bicubic,-10.812,-6.354,+2
levit_256,69.970,30.030,89.250,10.750,18.89,224,0.900,bicubic,-11.540,-6.240,-26
gluon_resnet152_v1d,69.960,30.040,88.490,11.510,60.21,224,0.875,bicubic,-10.514,-6.716,+9
pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.204,-6.402,-11
pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.204,-6.402,-12
ecaresnet50d,69.840,30.160,89.400,10.600,25.58,224,0.875,bicubic,-10.752,-5.920,+3
ssl_resnext50_32x4d,69.710,30.290,89.440,10.560,25.03,224,0.875,bilinear,-10.608,-5.966,+12
gluon_resnext101_64x4d,69.680,30.320,88.270,11.730,83.46,224,0.875,bicubic,-10.924,-6.718,0
@ -133,62 +156,65 @@ efficientnet_b3_pruned,69.580,30.420,88.980,11.020,9.86,300,0.904,bicubic,-11.27
nf_resnet50,69.580,30.420,88.730,11.270,25.56,288,0.940,bicubic,-11.114,-6.626,-4
gernet_m,69.530,30.470,88.690,11.310,21.14,224,0.875,bilinear,-11.202,-6.494,-6
efficientnet_el_pruned,69.520,30.480,88.930,11.070,10.59,300,0.904,bicubic,-10.780,-6.288,+10
ens_adv_inception_resnet_v2,69.520,30.480,88.510,11.490,55.84,299,0.897,bicubic,-10.462,-6.426,+24
repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.692,-6.660,+14
ens_adv_inception_resnet_v2,69.520,30.480,88.510,11.490,55.84,299,0.897,bicubic,-10.462,-6.426,+25
repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.694,-6.660,+14
efficientnet_b2,69.500,30.500,88.680,11.320,9.11,288,1.000,bicubic,-11.112,-6.638,-8
rexnet_150,69.470,30.530,88.980,11.020,9.73,224,0.875,bicubic,-10.840,-6.186,+4
swin_tiny_patch4_window7_224,69.450,30.550,89.020,10.980,28.29,224,0.900,bicubic,-11.928,-6.520,-32
swin_tiny_patch4_window7_224,69.450,30.550,89.020,10.980,28.29,224,0.900,bicubic,-11.928,-6.520,-34
regnetx_320,69.440,30.560,88.270,11.730,107.81,224,0.875,bicubic,-10.806,-6.756,+8
inception_v4,69.360,30.640,88.780,11.220,42.68,299,0.875,bicubic,-10.808,-6.188,+11
inception_v4,69.360,30.640,88.780,11.220,42.68,299,0.875,bicubic,-10.808,-6.188,+12
legacy_seresnext101_32x4d,69.360,30.640,88.070,11.930,48.96,224,0.875,bilinear,-10.868,-6.948,+7
ecaresnetlight,69.340,30.660,89.220,10.780,30.16,224,0.875,bicubic,-11.122,-6.030,-7
resnet50d,69.330,30.670,88.220,11.780,25.58,224,0.875,bicubic,-11.200,-6.940,-12
xception71,69.320,30.680,88.260,11.740,42.34,299,0.903,bicubic,-10.554,-6.662,+20
gluon_xception65,69.160,30.840,88.090,11.910,39.92,299,0.903,bicubic,-10.556,-6.770,+29
gluon_resnet152_v1c,69.140,30.860,87.870,12.130,60.21,224,0.875,bicubic,-10.770,-6.970,+15
xception71,69.320,30.680,88.260,11.740,42.34,299,0.903,bicubic,-10.554,-6.662,+21
gluon_xception65,69.160,30.840,88.090,11.910,39.92,299,0.903,bicubic,-10.556,-6.770,+31
gluon_resnet152_v1c,69.140,30.860,87.870,12.130,60.21,224,0.875,bicubic,-10.770,-6.970,+16
mixnet_xl,69.100,30.900,88.310,11.690,11.90,224,0.875,bicubic,-11.376,-6.626,-14
gluon_resnet101_v1d,69.010,30.990,88.100,11.900,44.57,224,0.875,bicubic,-11.404,-6.914,-11
repvgg_b2g4,69.000,31.000,88.360,11.640,61.76,224,0.875,bilinear,-10.366,-6.328,+36
seresnet50,68.980,31.020,88.710,11.290,28.09,224,0.875,bicubic,-11.294,-6.360,-5
xception65,68.980,31.020,88.480,11.520,39.92,299,0.903,bicubic,-10.572,-6.174,+29
gluon_resnext101_32x4d,68.960,31.040,88.360,11.640,44.18,224,0.875,bicubic,-11.374,-6.566,-13
tf_efficientnet_b2_ap,68.920,31.080,88.350,11.650,9.11,260,0.890,bicubic,-11.380,-6.678,-9
tf_efficientnetv2_b2,69.090,30.910,88.220,11.780,10.10,260,0.890,bicubic,-11.118,-6.822,+2
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,+38
seresnet50,68.980,31.020,88.710,11.290,28.09,224,0.875,bicubic,-11.294,-6.360,-6
xception65,68.980,31.020,88.480,11.520,39.92,299,0.903,bicubic,-10.572,-6.174,+30
gluon_resnext101_32x4d,68.960,31.040,88.360,11.640,44.18,224,0.875,bicubic,-11.374,-6.566,-14
tf_efficientnet_b2_ap,68.920,31.080,88.350,11.650,9.11,260,0.890,bicubic,-11.380,-6.678,-10
cspdarknet53,68.890,31.110,88.600,11.400,27.64,256,0.887,bilinear,-11.168,-6.484,+1
regnety_120,68.850,31.150,88.330,11.670,51.82,224,0.875,bicubic,-11.516,-6.796,-17
gluon_resnet152_v1b,68.820,31.180,87.710,12.290,60.19,224,0.875,bicubic,-10.866,-7.026,+19
dpn131,68.770,31.230,87.470,12.530,79.25,224,0.875,bicubic,-11.052,-7.240,+11
regnety_120,68.850,31.150,88.330,11.670,51.82,224,0.875,bicubic,-11.516,-6.796,-18
gluon_resnet152_v1b,68.820,31.180,87.710,12.290,60.19,224,0.875,bicubic,-10.866,-7.026,+20
dpn131,68.770,31.230,87.470,12.530,79.25,224,0.875,bicubic,-11.052,-7.240,+12
cspresnext50,68.760,31.240,87.950,12.050,20.57,224,0.875,bilinear,-11.280,-6.994,-2
tf_efficientnet_b2,68.750,31.250,87.990,12.010,9.11,260,0.890,bicubic,-11.336,-6.918,-5
resnext50d_32x4d,68.740,31.260,88.300,11.700,25.05,224,0.875,bicubic,-10.936,-6.566,+16
resnext50d_32x4d,68.740,31.260,88.300,11.700,25.05,224,0.875,bicubic,-10.936,-6.566,+17
vit_deit_small_patch16_224,68.720,31.280,88.200,11.800,22.05,224,0.900,bicubic,-11.136,-6.852,+5
gluon_resnet101_v1s,68.710,31.290,87.910,12.090,44.67,224,0.875,bicubic,-11.592,-7.250,-20
gluon_resnet101_v1s,68.710,31.290,87.910,12.090,44.67,224,0.875,bicubic,-11.592,-7.250,-21
regnety_080,68.700,31.300,87.970,12.030,39.18,224,0.875,bicubic,-11.176,-6.860,0
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,-6
hrnet_w64,68.640,31.360,88.050,11.950,128.06,224,0.875,bilinear,-10.834,-6.602,+17
resnext50_32x4d,68.640,31.360,87.570,12.430,25.03,224,0.875,bicubic,-11.128,-7.028,+3
dpn98,68.590,31.410,87.680,12.320,61.57,224,0.875,bicubic,-11.052,-6.918,+9
hrnet_w64,68.640,31.360,88.050,11.950,128.06,224,0.875,bilinear,-10.834,-6.602,+18
resnext50_32x4d,68.640,31.360,87.570,12.430,25.03,224,0.875,bicubic,-11.128,-7.028,+4
dpn98,68.590,31.410,87.680,12.320,61.57,224,0.875,bicubic,-11.052,-6.918,+10
regnetx_160,68.530,31.470,88.450,11.550,54.28,224,0.875,bicubic,-11.326,-6.380,-4
cspresnet50,68.460,31.540,88.010,11.990,21.62,256,0.887,bilinear,-11.114,-6.702,+9
rexnet_130,68.450,31.550,88.040,11.960,7.56,224,0.875,bicubic,-11.050,-6.642,+11
ecaresnet50d_pruned,68.420,31.580,88.370,11.630,19.94,224,0.875,bicubic,-11.296,-6.510,+1
regnety_064,68.420,31.580,88.080,11.920,30.58,224,0.875,bicubic,-11.302,-6.688,-1
tf_efficientnet_el,68.420,31.580,88.210,11.790,10.59,300,0.904,bicubic,-11.830,-6.918,-28
cait_xxs36_224,68.410,31.590,88.630,11.370,17.30,224,1.000,bicubic,-11.340,-6.236,-4
ssl_resnet50,68.410,31.590,88.560,11.440,25.56,224,0.875,bilinear,-10.812,-6.272,+21
cspresnet50,68.460,31.540,88.010,11.990,21.62,256,0.887,bilinear,-11.114,-6.702,+10
rexnet_130,68.450,31.550,88.040,11.960,7.56,224,0.875,bicubic,-11.050,-6.642,+12
tf_efficientnet_el,68.420,31.580,88.210,11.790,10.59,300,0.904,bicubic,-11.830,-6.918,-29
regnety_064,68.420,31.580,88.080,11.920,30.58,224,0.875,bicubic,-11.302,-6.688,0
ecaresnet50d_pruned,68.420,31.580,88.370,11.630,19.94,224,0.875,bicubic,-11.296,-6.510,+2
ssl_resnet50,68.410,31.590,88.560,11.440,25.56,224,0.875,bilinear,-10.812,-6.272,+23
cait_xxs36_224,68.410,31.590,88.630,11.370,17.30,224,1.000,bicubic,-11.340,-6.236,-3
skresnext50_32x4d,68.350,31.650,87.570,12.430,27.48,224,0.875,bicubic,-11.806,-7.072,-24
dla102x2,68.330,31.670,87.890,12.110,41.28,224,0.875,bilinear,-11.118,-6.750,+6
dla102x2,68.330,31.670,87.890,12.110,41.28,224,0.875,bilinear,-11.118,-6.750,+8
efficientnet_b2_pruned,68.320,31.680,88.100,11.900,8.31,260,0.890,bicubic,-11.596,-6.756,-19
gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.044,-7.126,+6
tf_efficientnet_lite3,68.230,31.770,87.740,12.260,8.20,300,0.904,bilinear,-11.590,-7.174,-13
gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.044,-7.126,+8
ecaresnet26t,68.230,31.770,88.790,11.210,16.01,320,0.950,bicubic,-11.624,-6.294,-14
ese_vovnet39b,68.210,31.790,88.250,11.750,24.57,224,0.875,bicubic,-11.110,-6.462,+4
regnetx_120,68.150,31.850,87.660,12.340,46.11,224,0.875,bicubic,-11.446,-7.078,-6
tf_efficientnet_lite3,68.230,31.770,87.740,12.260,8.20,300,0.904,bilinear,-11.590,-7.174,-12
ese_vovnet39b,68.210,31.790,88.250,11.750,24.57,224,0.875,bicubic,-11.110,-6.462,+6
regnetx_120,68.150,31.850,87.660,12.340,46.11,224,0.875,bicubic,-11.446,-7.078,-5
resnetrs50,68.030,31.970,87.710,12.290,35.69,224,0.910,bicubic,-11.862,-7.258,-23
pit_xs_distilled_224,68.020,31.980,87.720,12.280,11.00,224,0.900,bicubic,-11.286,-6.644,+6
pit_xs_distilled_224,68.020,31.980,87.720,12.280,11.00,224,0.900,bicubic,-11.286,-6.644,+8
dpn92,67.990,32.010,87.580,12.420,37.67,224,0.875,bicubic,-12.018,-7.256,-30
nf_regnet_b1,67.980,32.020,88.180,11.820,10.22,288,0.900,bicubic,-11.326,-6.568,+3
gluon_resnet50_v1d,67.940,32.060,87.130,12.870,25.58,224,0.875,bicubic,-11.134,-7.340,+16
nf_regnet_b1,67.980,32.020,88.180,11.820,10.22,288,0.900,bicubic,-11.326,-6.568,+5
gluon_resnet50_v1d,67.940,32.060,87.130,12.870,25.58,224,0.875,bicubic,-11.134,-7.340,+18
levit_192,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.942,-6.896,-22
tf_efficientnetv2_b1,67.890,32.110,87.800,12.200,8.14,240,0.882,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,+12
resnext101_32x8d,67.860,32.140,87.490,12.510,88.79,224,0.875,bilinear,-11.448,-7.028,-3
efficientnet_em,67.840,32.160,88.120,11.880,6.90,240,0.882,bicubic,-11.412,-6.674,+4
@ -202,12 +228,12 @@ xception,67.650,32.350,87.570,12.430,22.86,299,0.897,bicubic,-11.402,-6.822,+8
dpn68b,67.630,32.370,87.660,12.340,12.61,224,0.875,bicubic,-11.586,-6.754,-1
dla169,67.610,32.390,87.590,12.410,53.39,224,0.875,bilinear,-11.078,-6.746,+18
gluon_inception_v3,67.590,32.410,87.470,12.530,23.83,299,0.875,bicubic,-11.216,-6.900,+12
gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.954,-7.398,-22
gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.954,-7.398,-23
regnety_040,67.580,32.420,87.510,12.490,20.65,224,0.875,bicubic,-11.640,-7.146,-6
res2net50_26w_8s,67.570,32.430,87.280,12.720,48.40,224,0.875,bilinear,-11.628,-7.088,-5
hrnet_w40,67.560,32.440,87.140,12.860,57.56,224,0.875,bilinear,-11.360,-7.330,+4
legacy_seresnet152,67.520,32.480,87.390,12.610,66.82,224,0.875,bilinear,-11.140,-6.980,+13
resnetv2_50x1_bitm,67.520,32.480,89.250,10.750,25.55,480,1.000,bilinear,-12.652,-6.376,-58
resnetv2_50x1_bitm,67.520,32.480,89.250,10.750,25.55,480,1.000,bilinear,-12.652,-6.376,-60
tf_efficientnet_b1_ap,67.520,32.480,87.760,12.240,7.79,240,0.882,bicubic,-11.760,-6.546,-14
efficientnet_b1,67.470,32.530,87.510,12.490,7.79,256,1.000,bicubic,-11.324,-6.832,+5
gluon_resnet101_v1b,67.460,32.540,87.240,12.760,44.55,224,0.875,bicubic,-11.846,-7.284,-21
@ -215,37 +241,40 @@ tf_efficientnet_cc_b1_8e,67.450,32.550,87.310,12.690,39.72,240,0.882,bicubic,-11
res2net101_26w_4s,67.440,32.560,87.010,12.990,45.21,224,0.875,bilinear,-11.758,-7.422,-12
resnet50,67.440,32.560,87.420,12.580,25.56,224,0.875,bicubic,-11.598,-6.970,-6
resnetblur50,67.430,32.570,87.440,12.560,25.56,224,0.875,bicubic,-11.856,-7.198,-21
cait_xxs24_224,67.330,32.670,87.510,12.490,11.96,224,1.000,bicubic,-11.056,-6.800,+15
regnetx_032,67.290,32.710,87.000,13.000,15.30,224,0.875,bicubic,-10.882,-7.088,+23
xception41,67.250,32.750,87.200,12.800,26.97,299,0.903,bicubic,-11.266,-7.078,+5
cait_xxs24_224,67.330,32.670,87.510,12.490,11.96,224,1.000,bicubic,-11.056,-6.800,+17
regnetx_032,67.290,32.710,87.000,13.000,15.30,224,0.875,bicubic,-10.882,-7.088,+26
coat_tiny,67.250,32.750,87.340,12.660,5.50,224,0.900,bicubic,-11.184,-6.698,+13
xception41,67.250,32.750,87.200,12.800,26.97,299,0.903,bicubic,-11.266,-7.078,+4
resnest26d,67.200,32.800,87.170,12.830,17.07,224,0.875,bilinear,-11.278,-7.128,+7
legacy_seresnet101,67.160,32.840,87.060,12.940,49.33,224,0.875,bilinear,-11.222,-7.204,+12
repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.632,-7.084,-5
dla60x,67.100,32.900,87.190,12.810,17.35,224,0.875,bilinear,-11.146,-6.828,+13
gluon_resnet50_v1s,67.060,32.940,86.860,13.140,25.68,224,0.875,bicubic,-11.652,-7.378,-5
tv_resnet152,67.050,32.950,87.550,12.450,60.19,224,0.875,bilinear,-11.262,-6.488,+10
repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.632,-7.084,-6
legacy_seresnet101,67.160,32.840,87.060,12.940,49.33,224,0.875,bilinear,-11.222,-7.204,+13
dla60x,67.100,32.900,87.190,12.810,17.35,224,0.875,bilinear,-11.146,-6.828,+15
gluon_resnet50_v1s,67.060,32.940,86.860,13.140,25.68,224,0.875,bicubic,-11.650,-7.378,-6
tv_resnet152,67.050,32.950,87.550,12.450,60.19,224,0.875,bilinear,-11.262,-6.488,+12
dla60_res2net,67.020,32.980,87.160,12.840,20.85,224,0.875,bilinear,-11.444,-7.046,+2
dla102x,67.010,32.990,86.770,13.230,26.31,224,0.875,bilinear,-11.500,-7.458,-2
mixnet_l,66.940,33.060,86.910,13.090,7.33,224,0.875,bicubic,-12.036,-7.272,-18
pit_xs_224,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.262,-6.888,+11
res2net50_26w_6s,66.910,33.090,86.860,13.140,37.05,224,0.875,bilinear,-11.660,-7.264,-7
repvgg_b1,66.900,33.100,86.780,13.220,57.42,224,0.875,bilinear,-11.466,-7.318,+3
efficientnet_es,66.880,33.120,86.730,13.270,5.44,224,0.875,bicubic,-11.186,-7.196,+13
tf_efficientnet_b1,66.880,33.120,87.010,12.990,7.79,240,0.882,bicubic,-11.946,-7.188,-19
dla102x,67.010,32.990,86.770,13.230,26.31,224,0.875,bilinear,-11.500,-7.458,-3
mixnet_l,66.940,33.060,86.910,13.090,7.33,224,0.875,bicubic,-12.036,-7.272,-19
pit_xs_224,66.920,33.080,87.280,12.720,10.62,224,0.900,bicubic,-11.262,-6.888,+13
res2net50_26w_6s,66.910,33.090,86.860,13.140,37.05,224,0.875,bilinear,-11.660,-7.264,-8
repvgg_b1,66.900,33.100,86.780,13.220,57.42,224,0.875,bilinear,-11.466,-7.318,+4
efficientnet_es,66.880,33.120,86.730,13.270,5.44,224,0.875,bicubic,-11.186,-7.196,+15
tf_efficientnet_b1,66.880,33.120,87.010,12.990,7.79,240,0.882,bicubic,-11.946,-7.188,-20
regnetx_040,66.840,33.160,86.730,13.270,22.12,224,0.875,bicubic,-11.642,-7.514,-8
hrnet_w30,66.780,33.220,86.800,13.200,37.71,224,0.875,bilinear,-11.426,-7.422,+4
tf_mixnet_l,66.780,33.220,86.470,13.530,7.33,224,0.875,bicubic,-11.994,-7.528,-18
selecsls60b,66.760,33.240,86.530,13.470,32.77,224,0.875,bicubic,-11.652,-7.644,-6
hrnet_w30,66.780,33.220,86.800,13.200,37.71,224,0.875,bilinear,-11.426,-7.422,+6
tf_mixnet_l,66.780,33.220,86.470,13.530,7.33,224,0.875,bicubic,-11.994,-7.528,-19
selecsls60b,66.760,33.240,86.530,13.470,32.77,224,0.875,bicubic,-11.652,-7.644,-5
hrnet_w32,66.750,33.250,87.300,12.700,41.23,224,0.875,bilinear,-11.700,-6.886,-9
wide_resnet101_2,66.730,33.270,87.030,12.970,126.89,224,0.875,bilinear,-12.126,-7.252,-26
adv_inception_v3,66.650,33.350,86.540,13.460,23.83,299,0.875,bicubic,-10.932,-7.196,+23
dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.800,-7.122,-11
gluon_resnet50_v1c,66.560,33.440,86.180,13.820,25.58,224,0.875,bicubic,-11.452,-7.808,+5
wide_resnet101_2,66.730,33.270,87.030,12.970,126.89,224,0.875,bilinear,-12.126,-7.252,-27
tf_efficientnetv2_b0,66.700,33.300,86.710,13.290,7.14,224,0.875,bicubic,-11.656,-7.314,-4
adv_inception_v3,66.650,33.350,86.540,13.460,23.83,299,0.875,bicubic,-10.932,-7.196,+24
dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.800,-7.122,-12
gluon_resnet50_v1c,66.560,33.440,86.180,13.820,25.58,224,0.875,bicubic,-11.452,-7.808,+6
levit_128,66.550,33.450,86.750,13.250,9.21,224,0.900,bicubic,-11.936,-7.260,-19
dla102,66.540,33.460,86.910,13.090,33.27,224,0.875,bilinear,-11.492,-7.036,+3
tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.446,-6.980,+12
tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.448,-7.210,+10
hardcorenas_f,66.370,33.630,86.200,13.800,8.20,224,0.875,bilinear,-11.734,-7.602,-1
coat_lite_tiny,66.290,33.710,86.980,13.020,5.72,224,0.900,bicubic,-11.222,-6.936,+20
efficientnet_b0,66.290,33.710,85.960,14.040,5.29,224,0.875,bicubic,-11.408,-7.572,+11
coat_lite_tiny,66.290,33.710,86.980,13.020,5.72,224,0.900,bicubic,-11.222,-6.936,+20
legacy_seresnet50,66.250,33.750,86.330,13.670,28.09,224,0.875,bilinear,-11.380,-7.418,+11
selecsls60,66.210,33.790,86.340,13.660,30.67,224,0.875,bicubic,-11.772,-7.488,0
tf_efficientnet_em,66.180,33.820,86.360,13.640,6.90,240,0.882,bicubic,-11.950,-7.684,-7
@ -254,19 +283,19 @@ tf_efficientnet_cc_b0_8e,66.170,33.830,86.240,13.760,24.01,224,0.875,bicubic,-11
inception_v3,66.160,33.840,86.320,13.680,23.83,299,0.875,bicubic,-11.278,-7.156,+15
res2net50_26w_4s,66.140,33.860,86.600,13.400,25.70,224,0.875,bilinear,-11.824,-7.254,-4
efficientnet_b1_pruned,66.090,33.910,86.570,13.430,6.33,240,0.882,bicubic,-12.146,-7.264,-17
rexnet_100,66.070,33.930,86.490,13.510,4.80,224,0.875,bicubic,-11.788,-6.926,-1
gluon_resnet50_v1b,66.070,33.930,86.260,13.740,25.56,224,0.875,bicubic,-11.510,-7.456,+8
rexnet_100,66.070,33.930,86.490,13.510,4.80,224,0.875,bicubic,-11.788,-7.380,-3
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,-17
seresnext26t_32x4d,65.880,34.120,85.680,14.320,16.81,224,0.875,bicubic,-12.106,-8.066,-12
repvgg_b1g4,65.850,34.150,86.120,13.880,39.97,224,0.875,bilinear,-11.744,-7.706,+1
res2next50,65.850,34.150,85.840,14.160,24.67,224,0.875,bilinear,-12.396,-8.052,-25
hardcorenas_e,65.840,34.160,85.980,14.020,8.07,224,0.875,bilinear,-11.954,-7.714,-7
densenet161,65.840,34.160,86.450,13.550,28.68,224,0.875,bicubic,-11.518,-7.188,+8
hardcorenas_e,65.840,34.160,85.980,14.020,8.07,224,0.875,bilinear,-11.954,-7.714,-7
resnet34d,65.780,34.220,86.720,13.280,21.82,224,0.875,bicubic,-11.336,-6.662,+12
mobilenetv3_large_100_miil,65.760,34.240,85.200,14.800,5.48,224,0.875,bilinear,-12.156,-7.710,-15
skresnet34,65.750,34.250,85.960,14.040,22.28,224,0.875,bicubic,-11.162,-7.362,+18
vit_small_patch16_224,65.740,34.260,86.120,13.880,48.75,224,0.900,bicubic,-12.118,-7.296,-13
vit_small_patch16_224,65.740,34.260,86.120,13.880,48.75,224,0.900,bicubic,-12.118,-7.520,-13
tv_resnet101,65.690,34.310,85.980,14.020,44.55,224,0.875,bilinear,-11.684,-7.560,+1
hardcorenas_d,65.630,34.370,85.460,14.540,7.50,224,0.875,bilinear,-11.802,-8.024,-1
selecsls42b,65.610,34.390,85.810,14.190,32.46,224,0.875,bicubic,-11.564,-7.580,+5
@ -284,17 +313,18 @@ legacy_seresnext26_32x4d,65.050,34.950,85.660,14.340,16.79,224,0.875,bicubic,-12
mobilenetv2_120d,65.030,34.970,85.960,14.040,5.83,224,0.875,bicubic,-12.254,-7.532,-9
hrnet_w18,64.920,35.080,85.740,14.260,21.30,224,0.875,bilinear,-11.838,-7.704,+4
hardcorenas_c,64.860,35.140,85.250,14.750,5.52,224,0.875,bilinear,-12.194,-7.908,-5
densenet169,64.760,35.240,85.240,14.760,14.15,224,0.875,bicubic,-11.146,-7.786,+16
densenet169,64.760,35.240,85.240,14.760,14.15,224,0.875,bicubic,-11.146,-7.786,+17
mixnet_m,64.700,35.300,85.450,14.550,5.01,224,0.875,bicubic,-12.560,-7.974,-12
resnet26d,64.680,35.320,85.120,14.880,16.01,224,0.875,bicubic,-12.016,-8.030,+1
levit_128s,64.610,35.390,84.730,15.270,7.78,224,0.900,bicubic,-11.920,-8.136,+6
repvgg_a2,64.450,35.550,85.130,14.870,28.21,224,0.875,bilinear,-12.010,-7.874,+7
hardcorenas_b,64.420,35.580,84.870,15.130,5.18,224,0.875,bilinear,-12.118,-7.884,+4
regnetx_016,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.570,-7.950,-9
tf_efficientnet_lite1,64.380,35.620,85.470,14.530,5.42,240,0.882,bicubic,-12.262,-7.756,-2
tf_efficientnet_b0,64.310,35.690,85.280,14.720,5.29,224,0.875,bicubic,-12.538,-7.948,-7
tf_mixnet_m,64.270,35.730,85.090,14.910,5.01,224,0.875,bicubic,-12.672,-8.062,-11
hardcorenas_b,64.420,35.580,84.870,15.130,5.18,224,0.875,bilinear,-12.118,-7.884,+3
tf_efficientnet_lite1,64.380,35.620,85.470,14.530,5.42,240,0.882,bicubic,-12.262,-7.756,-3
regnetx_016,64.380,35.620,85.470,14.530,9.19,224,0.875,bicubic,-12.570,-7.950,-10
tf_efficientnet_b0,64.310,35.690,85.280,14.720,5.29,224,0.875,bicubic,-12.538,-7.948,-8
tf_mixnet_m,64.270,35.730,85.090,14.910,5.01,224,0.875,bicubic,-12.672,-8.062,-12
dpn68,64.230,35.770,85.180,14.820,12.61,224,0.875,bicubic,-12.088,-7.798,+2
tf_efficientnet_es,64.230,35.770,84.740,15.260,5.44,224,0.875,bicubic,-12.364,-8.462,-4
tf_efficientnet_es,64.230,35.770,84.740,15.260,5.44,224,0.875,bicubic,-12.364,-8.462,-5
regnety_008,64.160,35.840,85.270,14.730,6.26,224,0.875,bicubic,-12.156,-7.796,+1
mobilenetv2_140,64.060,35.940,85.040,14.960,6.11,224,0.875,bicubic,-12.456,-7.956,-3
densenet121,63.750,36.250,84.590,15.410,7.98,224,0.875,bicubic,-11.828,-8.062,+7
@ -306,7 +336,7 @@ mixnet_s,63.390,36.610,84.740,15.260,4.13,224,0.875,bicubic,-12.602,-8.056,-4
mobilenetv3_large_100,63.360,36.640,84.090,15.910,5.48,224,0.875,bicubic,-12.406,-8.452,-2
efficientnet_es_pruned,63.330,36.670,84.950,15.050,5.44,224,0.875,bicubic,-11.670,-7.498,+13
tv_resnet50,63.330,36.670,84.640,15.360,25.56,224,0.875,bilinear,-12.808,-8.224,-8
mixer_b16_224,63.280,36.720,83.310,16.690,59.88,224,0.875,bicubic,-13.322,-8.918,-17
mixer_b16_224,63.280,36.720,83.310,16.690,59.88,224,0.875,bicubic,-13.322,-8.918,-18
efficientnet_lite0,63.240,36.760,84.440,15.560,4.65,224,0.875,bicubic,-12.244,-8.070,0
mobilenetv3_rw,63.220,36.780,84.510,15.490,5.48,224,0.875,bicubic,-12.414,-8.198,-5
pit_ti_distilled_224,63.150,36.850,83.960,16.040,5.10,224,0.900,bicubic,-11.380,-8.136,+15
@ -329,15 +359,16 @@ fbnetc_100,62.440,37.560,83.380,16.620,5.57,224,0.875,bilinear,-12.684,-9.006,-1
mnasnet_100,61.900,38.100,83.710,16.290,4.38,224,0.875,bicubic,-12.758,-8.404,-5
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,-2
convit_tiny,61.590,38.410,84.120,15.880,5.71,224,0.875,bicubic,-11.526,-7.594,+6
ssl_resnet18,61.480,38.520,83.300,16.700,11.69,224,0.875,bilinear,-11.130,-8.116,+9
regnetx_006,61.350,38.650,83.450,16.550,6.20,224,0.875,bicubic,-12.502,-8.222,0
spnasnet_100,61.220,38.780,82.790,17.210,4.42,224,0.875,bilinear,-12.864,-9.028,-4
tv_resnet34,61.190,38.810,82.710,17.290,21.80,224,0.875,bilinear,-12.122,-8.716,+1
regnetx_006,61.350,38.650,83.450,16.550,6.20,224,0.875,bicubic,-12.502,-8.222,-1
spnasnet_100,61.220,38.780,82.790,17.210,4.42,224,0.875,bilinear,-12.864,-9.028,-5
tv_resnet34,61.190,38.810,82.710,17.290,21.80,224,0.875,bilinear,-12.122,-8.716,0
pit_ti_224,60.980,39.020,83.860,16.140,4.85,224,0.900,bicubic,-11.932,-7.542,+4
skresnet18,60.860,39.140,82.880,17.120,11.96,224,0.875,bicubic,-12.178,-8.288,+1
ghostnet_100,60.830,39.170,82.360,17.640,5.18,224,0.875,bilinear,-13.148,-9.096,-6
vgg16_bn,60.760,39.240,82.950,17.050,138.37,224,0.875,bilinear,-12.590,-8.556,-4
tf_mobilenetv3_large_075,60.400,39.600,81.950,18.050,3.99,224,0.875,bilinear,-13.038,-9.400,-6
ghostnet_100,60.830,39.170,82.360,17.640,5.18,224,0.875,bilinear,-13.148,-9.096,-7
vgg16_bn,60.760,39.240,82.950,17.050,138.37,224,0.875,bilinear,-12.590,-8.556,-5
tf_mobilenetv3_large_075,60.400,39.600,81.950,18.050,3.99,224,0.875,bilinear,-13.038,-9.400,-7
mobilenetv2_100,60.190,39.810,82.240,17.760,3.50,224,0.875,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,+3
vit_deit_tiny_patch16_224,59.830,40.170,82.670,17.330,5.72,224,0.900,bicubic,-12.338,-8.448,+4
@ -346,7 +377,7 @@ vgg19,59.710,40.290,81.450,18.550,143.67,224,0.875,bilinear,-12.658,-9.422,-2
regnetx_004,59.410,40.590,81.690,18.310,5.16,224,0.875,bicubic,-12.986,-9.140,-4
tf_mobilenetv3_large_minimal_100,59.070,40.930,81.150,18.850,3.92,224,0.875,bilinear,-13.178,-9.480,-1
vgg13_bn,59.000,41.000,81.070,18.930,133.05,224,0.875,bilinear,-12.594,-9.306,+2
hrnet_w18_small,58.950,41.050,81.340,18.660,13.19,224,0.875,bilinear,-13.392,-9.338,-5
hrnet_w18_small,58.950,41.050,81.340,18.660,13.19,224,0.875,bilinear,-13.394,-9.338,-5
vgg16,58.830,41.170,81.660,18.340,138.36,224,0.875,bilinear,-12.764,-8.722,+1
gluon_resnet18_v1b,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.496,-8.792,+1
vgg11_bn,57.410,42.590,80.020,19.980,132.87,224,0.875,bilinear,-12.950,-9.782,+1
@ -360,5 +391,5 @@ vgg11,55.800,44.200,78.830,21.170,132.86,224,0.875,bilinear,-13.224,-9.798,-2
tf_mobilenetv3_small_100,54.530,45.470,77.060,22.940,2.54,224,0.875,bilinear,-13.392,-10.604,-1
dla46x_c,53.050,46.950,76.870,23.130,1.07,224,0.875,bilinear,-12.920,-10.110,0
tf_mobilenetv3_small_075,52.160,47.840,75.470,24.530,2.04,224,0.875,bilinear,-13.556,-10.660,0
dla46_c,52.130,47.870,75.690,24.310,1.30,224,0.875,bilinear,-12.736,-10.602,0
dla46_c,52.130,47.870,75.690,24.310,1.30,224,0.875,bilinear,-12.734,-10.602,0
tf_mobilenetv3_small_minimal_100,49.500,50.500,73.050,26.950,2.04,224,0.875,bilinear,-13.406,-11.180,0

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
4 tf_efficientnet_b7_ns 78.510 21.490 94.380 5.620 66.35 600 0.949 bicubic -8.330 -3.714 +1
5 tf_efficientnet_b6_ns 77.280 22.720 93.890 6.110 43.04 528 0.942 bicubic -9.172 -3.992 +2
6 swin_large_patch4_window12_384 77.040 22.960 93.750 6.250 196.74 384 1.000 bicubic -10.108 -4.484 -2
7 cait_m48_448 tf_efficientnetv2_l_in21ft1k 76.870 76.940 23.130 23.060 93.370 93.950 6.630 6.050 356.46 118.52 448 480 1.000 bicubic -9.614 -9.364 -4.384 -4.028 -1 +3
8 ig_resnext101_32x48d cait_m48_448 76.870 23.130 93.310 93.370 6.690 6.630 828.41 356.46 224 448 0.875 1.000 bilinear bicubic -8.558 -9.614 -4.262 -4.384 +9 -2
9 ig_resnext101_32x32d ig_resnext101_32x48d 76.840 76.870 23.160 23.130 93.200 93.310 6.800 6.690 468.53 828.41 224 0.875 bilinear -8.254 -8.558 -4.238 -4.262 +16 +11
10 ig_resnext101_32x32d 76.840 23.160 93.200 6.800 468.53 224 0.875 bilinear -8.254 -4.238 +18
11 tf_efficientnet_b5_ns 76.810 23.190 93.580 6.420 30.39 456 0.934 bicubic -9.278 -4.172 +1
12 cait_m36_384 76.320 23.680 93.050 6.950 271.22 384 1.000 bicubic -9.734 -4.680 +1
13 swin_base_patch4_window12_384 tf_efficientnetv2_l 76.280 23.720 93.320 92.970 6.680 7.030 87.90 118.52 384 480 1.000 bicubic -10.152 -9.210 -4.738 -4.402 -4 +4
14 swin_large_patch4_window7_224 swin_base_patch4_window12_384 76.270 76.280 23.730 23.720 93.420 93.320 6.580 6.680 196.53 87.90 224 384 0.900 1.000 bicubic -10.050 -10.152 -4.476 -4.738 -4 -5
15 cait_s36_384 swin_large_patch4_window7_224 76.210 76.270 23.790 23.730 92.970 93.420 7.030 6.580 68.37 196.53 384 224 1.000 0.900 bicubic -9.250 -10.050 -4.510 -4.476 +2 -6
16 dm_nfnet_f6 cait_s36_384 76.180 76.210 23.820 23.790 93.220 92.970 6.780 7.030 438.36 68.37 576 384 0.956 1.000 bicubic -10.116 -9.250 -4.524 -4.510 -5 +3
17 tf_efficientnet_b7_ap dm_nfnet_f6 76.090 76.180 23.910 23.820 92.970 93.220 7.030 6.780 66.35 438.36 600 576 0.949 0.956 bicubic -9.030 -10.116 -4.282 -4.524 +8 -6
18 tf_efficientnet_b8_ap tf_efficientnet_b7_ap 76.090 23.910 92.730 92.970 7.270 7.030 87.41 66.35 672 600 0.954 0.949 bicubic -9.280 -9.030 -4.564 -4.282 +3 +9
19 dm_nfnet_f4 tf_efficientnet_b8_ap 75.750 76.090 24.250 23.910 92.790 92.730 7.210 7.270 316.07 87.41 512 672 0.951 0.954 bicubic -9.908 -9.280 -4.720 -4.564 -4 +4
20 ig_resnext101_32x16d tf_efficientnetv2_m_in21ft1k 75.720 75.920 24.280 24.080 92.910 93.280 7.090 6.720 194.03 54.14 224 480 0.875 1.000 bilinear bicubic -8.450 -9.668 -4.286 -4.472 +26 -4
21 dm_nfnet_f4 75.750 24.250 92.790 7.210 316.07 512 0.951 bicubic -9.908 -4.720 -6
22 ig_resnext101_32x16d 75.720 24.280 92.910 7.090 194.03 224 0.875 bilinear -8.450 -4.286 +29
23 tf_efficientnet_b4_ns 75.670 24.330 93.050 6.950 19.34 380 0.922 bicubic -9.492 -4.420 +2
24 vit_base_r50_s16_384 75.590 24.410 92.790 7.210 98.95 384 1.000 bicubic -9.382 -4.498 +9 +10
25 vit_deit_base_distilled_patch16_384 75.550 24.450 92.500 7.500 87.63 384 1.000 bicubic -9.872 -4.832 -4
26 cait_s24_384 tf_efficientnetv2_m 75.480 75.520 24.520 24.480 92.600 92.620 7.400 7.380 47.06 54.14 384 480 1.000 bicubic -9.566 -9.524 -4.746 -4.658 +3 +4
27 swsl_resnext101_32x8d cait_s24_384 75.430 75.480 24.570 24.520 92.760 92.600 7.240 7.400 88.79 47.06 224 384 0.875 1.000 bilinear bicubic -8.854 -9.566 -4.416 -4.746 +17 +2
28 dm_nfnet_f3 swsl_resnext101_32x8d 75.410 75.430 24.590 24.570 92.830 92.760 7.170 7.240 254.92 88.79 416 224 0.940 0.875 bicubic bilinear -10.150 -8.854 -4.576 -4.416 -10 +19
29 tf_efficientnet_b6_ap dm_nfnet_f3 75.380 75.410 24.620 24.590 92.440 92.830 7.560 7.170 43.04 254.92 528 416 0.942 0.940 bicubic -9.408 -10.150 -4.698 -4.576 +7 -12
30 vit_large_patch16_384 tf_efficientnet_b6_ap 75.150 75.380 24.850 24.620 92.660 92.440 7.340 7.560 304.72 43.04 384 528 1.000 0.942 bicubic -10.008 -9.408 -4.696 -4.698 -4 +8
31 ecaresnet269d efficientnetv2_rw_m 75.120 75.170 24.880 24.830 92.840 92.570 7.160 7.430 102.09 53.24 352 416 1.000 bicubic -9.856 -9.638 -4.386 -4.578 +1 +6
32 tf_efficientnet_b8 vit_large_patch16_384 74.940 75.150 25.060 24.850 92.310 92.660 7.690 7.340 87.41 304.72 672 384 0.954 1.000 bicubic -10.430 -10.008 -5.080 -4.696 -10 -6
33 dm_nfnet_f5 ecaresnet269d 74.790 75.120 25.210 24.880 92.460 92.840 7.540 7.160 377.21 102.09 544 352 0.954 1.000 bicubic -10.924 -9.856 -4.982 -4.386 -17 0
34 tf_efficientnet_b7 tf_efficientnet_b8 74.720 74.940 25.280 25.060 92.220 92.310 7.780 7.690 66.35 87.41 600 672 0.949 0.954 bicubic -10.216 -10.430 -4.984 -5.080 0 -12
35 tf_efficientnet_b5_ap dm_nfnet_f5 74.600 74.790 25.400 25.210 91.990 92.460 8.010 7.540 30.39 377.21 456 544 0.934 0.954 bicubic -9.652 -10.924 -4.984 -4.982 +11 -21
36 swin_base_patch4_window7_224 tf_efficientnet_b7 74.570 74.720 25.430 25.280 92.560 92.220 7.440 7.780 87.77 66.35 224 600 0.900 0.949 bicubic -10.682 -10.216 -5.002 -4.984 -12 -1
37 tf_efficientnet_b5_ap 74.600 25.400 91.990 8.010 30.39 456 0.934 bicubic -9.652 -4.984 +12
38 swin_base_patch4_window7_224 74.570 25.430 92.560 7.440 87.77 224 0.900 bicubic -10.682 -5.002 -14
39 seresnet152d 74.510 25.490 92.080 7.920 66.84 320 1.000 bicubic -9.852 -4.960 +6
40 resnest200e 74.480 25.520 91.860 8.140 70.20 320 0.909 bicubic -9.352 -5.034 +18 +20
41 dm_nfnet_f2 74.450 25.550 92.230 7.770 193.78 352 0.920 bicubic -10.540 -4.914 -8 -9
42 dm_nfnet_f1 tf_efficientnetv2_s_in21ft1k 74.400 74.450 25.600 25.550 92.350 92.510 7.650 7.490 132.63 21.46 320 384 0.910 1.000 bicubic -10.204 -9.852 -4.718 -4.742 -2 +4
43 efficientnet_v2s dm_nfnet_f1 74.170 74.400 25.830 25.600 91.710 92.350 8.290 7.650 23.94 132.63 384 320 1.000 0.910 bicubic -9.638 -10.204 -5.014 -4.718 +17 -3
44 resnest269e efficientnetv2_rw_s 74.170 25.830 91.950 91.710 8.050 8.290 110.93 23.94 416 384 0.928 1.000 bicubic -10.348 -9.638 -5.036 -5.014 -3 +18
45 resnest269e 74.170 25.830 91.950 8.050 110.93 416 0.928 bicubic -10.348 -5.036 -4
46 cait_xs24_384 74.160 25.840 91.910 8.090 26.67 384 1.000 bicubic -9.902 -4.978 +9
47 pit_b_distilled_224 74.160 25.840 91.680 8.320 74.79 224 0.900 bicubic -9.984 -5.176 +5
48 swsl_resnext101_32x4d 74.140 25.860 91.990 8.010 44.18 224 0.875 bilinear -9.090 -4.770 +24 +26
49 vit_base_patch16_384 74.130 25.870 92.360 7.640 86.86 384 1.000 bicubic -10.080 -4.858 +1
50 eca_nfnet_l1 74.060 25.940 92.120 7.880 41.41 320 1.000 bicubic -9.948 -4.908 +7
51 vit_base_patch16_224_miil 74.040 25.960 91.700 8.300 86.54 224 0.875 bilinear -10.228 -5.102 -3
52 swsl_resnext101_32x16d 74.020 25.980 92.160 7.840 194.03 224 0.875 bilinear -9.326 -4.686 +17 +19
53 resnetv2_152x4_bitm tf_efficientnetv2_s 74.000 26.000 92.340 91.530 7.660 8.470 936.53 21.46 480 384 1.000 bilinear bicubic -10.932 -9.894 -5.096 -5.168 -15 +5
54 resnetrs420 resnetv2_152x4_bitm 73.920 74.000 26.080 26.000 91.760 92.340 8.240 7.660 191.89 936.53 416 480 1.000 bicubic bilinear -11.088 -10.932 -5.364 -5.096 -21 -17
55 tf_efficientnet_b6 resnetrs420 73.900 73.920 26.100 26.080 91.750 91.760 8.250 8.240 43.04 191.89 528 416 0.942 1.000 bicubic -10.210 -11.088 -5.136 -5.364 -2 -24
56 tf_efficientnet_b3_ns tf_efficientnet_b6 73.890 73.900 26.110 26.100 91.870 91.750 8.130 8.250 12.23 43.04 300 528 0.904 0.942 bicubic -10.158 -10.210 -5.040 -5.136 0 -3
57 resnetrs270 tf_efficientnet_b3_ns 73.710 73.890 26.290 26.110 91.580 91.870 8.420 8.130 129.86 12.23 352 300 1.000 0.904 bicubic -10.724 -10.158 -5.390 -5.040 -13 -1
58 resnet200d resnetrs270 73.680 73.710 26.320 26.290 91.570 91.580 8.430 8.420 64.69 129.86 320 352 1.000 bicubic -10.282 -10.724 -5.254 -5.390 0 -15
59 ig_resnext101_32x8d resnet200d 73.650 73.680 26.350 26.320 92.190 91.570 7.810 8.430 88.79 64.69 224 320 0.875 1.000 bilinear bicubic -9.038 -10.282 -4.446 -5.254 +25 -1
60 resnetv2_152x2_bitm ig_resnext101_32x8d 73.630 73.650 26.370 26.350 92.590 92.190 7.410 7.810 236.34 88.79 480 224 1.000 0.875 bilinear -10.810 -9.038 -4.856 -4.446 -17 +29
61 resnetv2_152x2_bitm 73.630 26.370 92.590 7.410 236.34 480 1.000 bilinear -10.810 -4.856 -19
62 tf_efficientnet_b5 73.550 26.450 91.460 8.540 30.39 456 0.934 bicubic -10.262 -5.288 -1
63 resnetv2_101x3_bitm 73.530 26.470 92.570 7.430 387.93 480 1.000 bilinear -10.864 -4.792 -17 -19
64 resnet152d 73.520 26.480 91.230 8.770 60.21 320 1.000 bicubic -10.160 -5.508 +2
65 resnetrs200 73.500 26.500 91.250 8.750 93.21 320 1.000 bicubic -10.566 -5.624 -10 -11
66 resnetrs350 73.400 26.600 91.310 8.690 163.96 384 1.000 bicubic -11.320 -5.678 -25 -27
67 regnety_160 twins_svt_large 73.360 73.390 26.640 26.610 91.690 90.910 8.310 9.090 83.59 99.27 288 224 1.000 0.900 bicubic -10.326 -10.288 -5.086 -5.684 -2 0
68 regnety_160 73.360 26.640 91.690 8.310 83.59 288 1.000 bicubic -10.326 -5.086 -3
69 efficientnet_b4 73.320 26.680 91.280 8.720 19.34 384 1.000 bicubic -10.108 -5.316 0
70 vit_deit_base_distilled_patch16_224 73.240 26.760 91.000 9.000 87.34 224 0.900 bicubic -10.148 -5.488 0
71 resnetrs152 73.200 26.800 91.260 8.740 86.62 320 1.000 bicubic -10.512 -5.354 -6 -7
72 cait_s24_224 73.070 26.930 91.130 8.870 46.92 224 1.000 bicubic -10.382 -5.434 -4
73 tf_efficientnet_b4_ap 72.890 27.110 90.980 9.020 19.34 380 0.922 bicubic -10.358 -5.412 0
74 dm_nfnet_f0 72.790 27.210 91.040 8.960 71.49 256 0.900 bicubic -10.552 -5.520 -2
75 regnety_032 72.770 27.230 90.950 9.050 19.44 288 1.000 bicubic -9.954 -5.474 +10 +12
76 nfnet_l0 pnasnet5large 72.610 27.390 91.010 90.510 8.990 9.490 35.07 86.06 288 331 1.000 0.911 bicubic -10.150 -10.172 -5.488 -5.530 +8
77 pnasnet5large nfnet_l0 72.610 27.390 90.510 91.010 9.490 8.990 86.06 35.07 331 288 0.911 1.000 bicubic -10.172 -10.150 -5.530 -5.488 +6 +10
78 resnest101e twins_pcpvt_large 72.570 72.580 27.430 27.420 90.820 90.700 9.180 9.300 48.28 60.99 256 224 0.875 0.900 bilinear bicubic -10.320 -10.560 -5.500 -5.898 +4 -2
79 swsl_resnext50_32x4d resnest101e 72.560 72.570 27.440 27.430 90.870 90.820 9.130 9.180 25.03 48.28 224 256 0.875 bilinear -9.622 -10.320 -5.360 -5.500 +18 +5
80 tresnet_xl_448 swsl_resnext50_32x4d 72.550 72.560 27.450 27.440 90.310 90.870 9.690 9.130 78.44 25.03 448 224 0.875 bilinear -10.500 -9.622 -5.864 -5.360 -1 +25
81 tresnet_xl_448 72.550 27.450 90.310 9.690 78.44 448 0.875 bilinear -10.500 -5.864 0
82 twins_svt_base 72.550 27.450 90.460 9.540 56.07 224 0.900 bicubic -10.586 -5.958 -5
83 vit_deit_base_patch16_384 72.530 27.470 90.250 9.750 86.86 384 1.000 bicubic -10.576 -6.122 -5
84 resnet101d 72.410 27.590 90.650 9.350 44.57 320 1.000 bicubic -10.612 -5.796 -2
85 tf_efficientnet_b4 72.290 27.710 90.590 9.410 19.34 380 0.922 bicubic -10.732 -5.710 -2
86 tf_efficientnet_b2_ns 72.280 27.720 91.090 8.910 9.11 260 0.890 bicubic -10.100 -5.158 +6 +8
87 tresnet_m 72.270 27.730 90.240 9.760 31.39 224 0.875 bilinear -10.810 -5.878 -8
88 vit_large_patch16_224 72.250 27.750 90.990 9.010 304.33 224 0.900 bicubic -10.812 -5.448 -8
89 nasnetalarge 72.230 27.770 90.470 9.530 88.75 331 0.911 bicubic -10.390 -5.576 0 +1
90 cait_xxs36_384 72.190 27.810 90.840 9.160 17.37 384 1.000 bicubic -10.004 -5.308 +8 +14
91 resnetv2_50x3_bitm 72.180 27.820 91.790 8.210 217.32 480 1.000 bilinear -11.604 -5.316 -25 -28
92 twins_pcpvt_base 72.180 27.820 90.510 9.490 43.83 224 0.900 bicubic -10.528 -5.836 -4
93 eca_nfnet_l0 71.850 28.150 91.130 8.870 24.14 288 1.000 bicubic -10.738 -5.344 -2
94 swin_small_patch4_window7_224 71.740 28.260 90.240 9.760 49.61 224 0.900 bicubic -11.472 -6.082 -16 -19
95 pit_b_224 71.700 28.300 89.250 10.750 73.76 224 0.900 bicubic -10.746 -6.460 -3 -2
96 swsl_resnet50 71.700 28.300 90.500 9.500 25.56 224 0.875 bilinear -9.466 -5.472 +30 +41
97 tresnet_xl 71.660 28.340 89.630 10.370 78.44 224 0.875 bilinear -10.394 -6.306 +5 +11
98 tresnet_l_448 convit_base 71.600 28.400 90.050 90.150 9.950 9.850 55.99 86.54 448 224 0.875 bilinear bicubic -10.668 -10.690 -5.926 -5.788 -2 +1
99 ssl_resnext101_32x8d tresnet_l_448 71.500 71.600 28.500 28.400 90.460 90.050 9.540 9.950 88.79 55.99 224 448 0.875 bilinear -10.116 -10.668 -5.578 -5.926 +13 +2
100 ecaresnet101d ssl_resnext101_32x8d 71.490 71.500 28.510 28.500 90.330 90.460 9.670 9.540 44.57 88.79 224 0.875 bicubic bilinear -10.682 -10.116 -5.716 -5.578 +1 +20
101 efficientnet_b3 ecaresnet101d 71.480 71.490 28.520 28.510 90.060 90.330 9.940 9.670 12.23 44.57 320 224 1.000 0.875 bicubic -10.762 -10.682 -6.054 -5.716 -4 +5
102 ssl_resnext101_32x16d efficientnet_b3 71.410 71.480 28.590 28.520 90.560 90.060 9.440 9.940 194.03 12.23 224 320 0.875 1.000 bilinear bicubic -10.434 -10.762 -5.536 -6.054 +3 0
103 pit_s_distilled_224 resnet51q 71.380 71.430 28.620 28.570 89.780 90.180 10.220 9.820 24.04 35.70 224 288 0.900 1.000 bicubic bilinear -10.616 -10.930 -6.018 -6.000 +1 -8
104 vit_base_patch16_224 ssl_resnext101_32x16d 71.330 71.410 28.670 28.590 90.460 90.560 9.540 9.440 86.57 194.03 224 0.900 0.875 bicubic bilinear -10.456 -10.434 -5.662 -5.536 +3 +8
105 ecaresnet50t pit_s_distilled_224 71.280 71.380 28.720 28.620 90.420 89.780 9.580 10.220 25.57 24.04 320 224 0.950 0.900 bicubic -11.066 -10.616 -5.718 -6.018 -11 +5
106 vit_base_patch32_384 vit_base_patch16_224 71.180 71.330 28.820 28.670 90.630 90.460 9.370 9.540 88.30 86.57 384 224 1.000 0.900 bicubic -10.472 -10.456 -5.498 -5.662 +3 +8
107 vit_deit_base_patch16_224 mixer_b16_224_miil 71.170 71.300 28.830 28.700 89.200 89.650 10.800 10.350 86.57 59.88 224 0.900 0.875 bicubic bilinear -10.828 -11.008 -6.534 -6.066 -4 -9
108 tresnet_m_448 ecaresnet50t 70.990 71.280 29.010 28.720 88.680 90.420 11.320 9.580 31.39 25.57 448 320 0.875 0.950 bilinear bicubic -10.724 -11.066 -6.892 -5.718 0 -12
109 resnest50d_4s2x40d vit_base_patch32_384 70.950 71.180 29.050 28.820 89.710 90.630 10.290 9.370 30.42 88.30 224 384 0.875 1.000 bicubic -10.158 -10.472 -5.848 -5.498 +18 +8
110 wide_resnet50_2 vit_deit_base_patch16_224 70.950 71.170 29.050 28.830 89.230 89.200 10.770 10.800 68.88 86.57 224 0.875 0.900 bicubic -10.506 -10.828 -6.302 -6.534 +7 -1
111 tnt_s_patch16_224 visformer_small 70.930 71.010 29.070 28.990 89.600 89.460 10.400 10.540 23.76 40.22 224 0.900 bicubic -10.588 -11.096 -6.148 -6.412 +3 -4
112 tf_efficientnet_b3_ap tresnet_m_448 70.920 70.990 29.080 29.010 89.430 88.680 10.570 11.320 12.23 31.39 300 448 0.904 0.875 bicubic bilinear -10.902 -10.724 -6.194 -6.892 -6 +3
113 tf_efficientnet_b1_ns wide_resnet50_2 70.870 70.950 29.130 29.050 90.120 89.230 9.880 10.770 7.79 68.88 240 224 0.882 0.875 bicubic -10.518 -10.506 -5.618 -6.302 +5 +12
114 vit_large_patch32_384 resnest50d_4s2x40d 70.860 70.950 29.140 29.050 90.570 89.710 9.430 10.290 306.63 30.42 384 224 1.000 0.875 bicubic -10.646 -10.158 -5.522 -5.848 +1 +25
115 tresnet_l tnt_s_patch16_224 70.840 70.930 29.160 29.070 89.630 89.600 10.370 10.400 55.99 23.76 224 0.875 0.900 bilinear bicubic -10.650 -10.588 -5.994 -6.148 -1 +7
116 rexnet_200 tf_efficientnet_b3_ap 70.840 70.920 29.160 29.080 89.700 89.430 10.300 10.570 16.37 12.23 224 300 0.875 0.904 bicubic -10.792 -10.902 -5.968 -6.194 -5 -3
117 resnetrs101 tf_efficientnet_b1_ns 70.840 70.870 29.160 29.130 89.830 90.120 10.170 9.880 63.62 7.79 288 240 0.940 0.882 bicubic -11.448 -10.518 -6.178 -5.618 -20 +11
118 resnetv2_101x1_bitm vit_large_patch32_384 70.710 70.860 29.290 29.140 90.800 90.570 9.200 9.430 44.54 306.63 480 384 1.000 bilinear bicubic -11.502 -10.646 -5.672 -5.522 -20 +6
119 tf_efficientnet_b3 resnetrs101 70.640 70.840 29.360 29.160 89.440 89.830 10.560 10.170 12.23 63.62 300 288 0.904 0.940 bicubic -10.996 -11.448 -6.278 -6.178 -9 -19
120 cait_xxs24_384 rexnet_200 70.600 70.840 29.400 29.160 89.720 89.700 10.280 10.300 12.03 16.37 384 224 1.000 0.875 bicubic -10.366 -10.792 -5.926 -5.968 +12 -1
121 gluon_senet154 tresnet_l 70.600 70.840 29.400 29.160 88.920 89.630 11.080 10.370 115.09 55.99 224 0.875 bicubic bilinear -10.634 -10.650 -6.428 -5.994 +3 +4
122 ssl_resnext101_32x4d tf_efficientnetv2_b3 70.530 70.830 29.470 29.170 89.760 89.500 10.240 10.500 44.18 14.36 224 300 0.875 0.904 bilinear bicubic -10.394 -11.140 -5.968 -6.282 +11 -11
123 vit_deit_small_distilled_patch16_224 coat_lite_small 70.520 70.800 29.480 29.200 89.470 89.570 10.530 10.430 22.44 19.84 224 0.900 bicubic -10.680 -11.508 -5.908 -6.280 +2 -26
124 legacy_senet154 levit_384 70.500 70.750 29.500 29.250 89.010 89.300 10.990 10.700 115.09 39.13 224 0.875 0.900 bilinear bicubic -10.810 -11.836 -6.486 -6.716 -2 -32
125 gluon_seresnext101_64x4d resnetv2_101x1_bitm 70.430 70.710 29.570 29.290 89.350 90.800 10.650 9.200 88.23 44.54 224 480 0.875 1.000 bicubic bilinear -10.464 -11.502 -5.958 -5.672 +10 -22
126 tf_efficientnet_lite4 tf_efficientnet_b3 70.430 70.640 29.570 29.360 89.110 89.440 10.890 10.560 13.01 12.23 380 300 0.920 0.904 bilinear bicubic -11.106 -10.996 -6.558 -6.278 -13 -8
127 resnest50d gluon_senet154 70.410 70.600 29.590 29.400 88.760 88.920 11.240 11.080 27.48 115.09 224 0.875 bilinear bicubic -10.564 -10.634 -6.618 -6.428 +4 +7
128 resnest50d_1s4x24d cait_xxs24_384 70.400 70.600 29.600 29.400 89.220 89.720 10.780 10.280 25.68 12.03 224 384 0.875 1.000 bicubic -10.588 -10.366 -6.102 -5.926 +2 +17
129 seresnext50_32x4d convit_small 70.400 70.580 29.600 29.420 89.110 89.580 10.890 10.420 27.56 27.78 224 0.875 bicubic -10.866 -10.846 -6.510 -6.164 -6 -2
130 twins_pcpvt_small 70.550 29.450 89.070 10.930 24.11 224 0.900 bicubic -10.538 -6.572 +10
131 ssl_resnext101_32x4d 70.530 29.470 89.760 10.240 44.18 224 0.875 bilinear -10.394 -5.968 +14
132 vit_deit_small_distilled_patch16_224 70.520 29.480 89.470 10.530 22.44 224 0.900 bicubic -10.680 -5.908 +4
133 legacy_senet154 70.500 29.500 89.010 10.990 115.09 224 0.875 bilinear -10.810 -6.486 -1
134 twins_svt_small 70.440 29.560 89.360 10.640 24.06 224 0.900 bicubic -11.242 -6.310 -18
135 gluon_seresnext101_64x4d 70.430 29.570 89.350 10.650 88.23 224 0.875 bicubic -10.464 -5.958 +12
136 tf_efficientnet_lite4 70.430 29.570 89.110 10.890 13.01 380 0.920 bilinear -11.106 -6.558 -15
137 resnest50d 70.410 29.590 88.760 11.240 27.48 224 0.875 bilinear -10.564 -6.618 +6
138 seresnext50_32x4d 70.400 29.600 89.110 10.890 27.56 224 0.875 bicubic -10.866 -6.510 -5
139 resnest50d_1s4x24d 70.400 29.600 89.220 10.780 25.68 224 0.875 bicubic -10.588 -6.102 +4
140 gernet_l 70.350 29.650 88.980 11.020 31.08 256 0.875 bilinear -11.004 -6.556 -10
141 gluon_resnet152_v1s 70.290 29.710 88.850 11.150 60.32 224 0.875 bicubic -10.726 -6.562 -2 0
142 repvgg_b3 70.250 29.750 88.730 11.270 123.09 224 0.875 bilinear -10.242 -6.530 +13 +15
143 ecaresnet101d_pruned coat_mini 70.130 70.220 29.870 29.780 89.590 89.440 10.410 10.560 24.88 10.34 224 0.875 0.900 bicubic -10.688 -11.048 -6.038 -5.952 +4 -10
144 efficientnet_el ecaresnet101d_pruned 70.120 70.130 29.880 29.870 89.290 89.590 10.710 10.410 10.59 24.88 300 224 0.904 0.875 bicubic -11.196 -10.688 -6.236 -6.038 -13 +5
145 inception_resnet_v2 efficientnet_el 70.120 29.880 88.700 89.290 11.300 10.710 55.84 10.59 299 300 0.897 0.904 bicubic -10.338 -11.196 -6.606 -6.236 +14 -14
146 gluon_seresnext101_32x4d inception_resnet_v2 70.010 70.120 29.990 29.880 88.900 88.700 11.100 11.300 48.96 55.84 224 299 0.875 0.897 bicubic -10.894 -10.338 -6.394 -6.606 -2 +15
147 regnety_320 gluon_seresnext101_32x4d 70.000 70.010 30.000 29.990 88.890 88.900 11.110 11.100 145.05 48.96 224 0.875 bicubic -10.812 -10.894 -6.354 -6.394 +1 -1
148 regnety_320 70.000 30.000 88.890 11.110 145.05 224 0.875 bicubic -10.812 -6.354 +2
149 levit_256 69.970 30.030 89.250 10.750 18.89 224 0.900 bicubic -11.540 -6.240 -26
150 gluon_resnet152_v1d 69.960 30.040 88.490 11.510 60.21 224 0.875 bicubic -10.514 -6.716 +9
151 pit_s_224 69.890 30.110 88.930 11.070 23.46 224 0.900 bicubic -11.204 -6.402 -11 -12
152 ecaresnet50d 69.840 30.160 89.400 10.600 25.58 224 0.875 bicubic -10.752 -5.920 +3
153 ssl_resnext50_32x4d 69.710 30.290 89.440 10.560 25.03 224 0.875 bilinear -10.608 -5.966 +12
154 gluon_resnext101_64x4d 69.680 30.320 88.270 11.730 83.46 224 0.875 bicubic -10.924 -6.718 0
156 nf_resnet50 69.580 30.420 88.730 11.270 25.56 288 0.940 bicubic -11.114 -6.626 -4
157 gernet_m 69.530 30.470 88.690 11.310 21.14 224 0.875 bilinear -11.202 -6.494 -6
158 efficientnet_el_pruned 69.520 30.480 88.930 11.070 10.59 300 0.904 bicubic -10.780 -6.288 +10
159 ens_adv_inception_resnet_v2 69.520 30.480 88.510 11.490 55.84 299 0.897 bicubic -10.462 -6.426 +24 +25
160 repvgg_b3g4 69.520 30.480 88.450 11.550 83.83 224 0.875 bilinear -10.692 -10.694 -6.660 +14
161 efficientnet_b2 69.500 30.500 88.680 11.320 9.11 288 1.000 bicubic -11.112 -6.638 -8
162 rexnet_150 69.470 30.530 88.980 11.020 9.73 224 0.875 bicubic -10.840 -6.186 +4
163 swin_tiny_patch4_window7_224 69.450 30.550 89.020 10.980 28.29 224 0.900 bicubic -11.928 -6.520 -32 -34
164 regnetx_320 69.440 30.560 88.270 11.730 107.81 224 0.875 bicubic -10.806 -6.756 +8
165 inception_v4 69.360 30.640 88.780 11.220 42.68 299 0.875 bicubic -10.808 -6.188 +11 +12
166 legacy_seresnext101_32x4d 69.360 30.640 88.070 11.930 48.96 224 0.875 bilinear -10.868 -6.948 +7
167 ecaresnetlight 69.340 30.660 89.220 10.780 30.16 224 0.875 bicubic -11.122 -6.030 -7
168 resnet50d 69.330 30.670 88.220 11.780 25.58 224 0.875 bicubic -11.200 -6.940 -12
169 xception71 69.320 30.680 88.260 11.740 42.34 299 0.903 bicubic -10.554 -6.662 +20 +21
170 gluon_xception65 69.160 30.840 88.090 11.910 39.92 299 0.903 bicubic -10.556 -6.770 +29 +31
171 gluon_resnet152_v1c 69.140 30.860 87.870 12.130 60.21 224 0.875 bicubic -10.770 -6.970 +15 +16
172 mixnet_xl 69.100 30.900 88.310 11.690 11.90 224 0.875 bicubic -11.376 -6.626 -14
173 gluon_resnet101_v1d tf_efficientnetv2_b2 69.010 69.090 30.990 30.910 88.100 88.220 11.900 11.780 44.57 10.10 224 260 0.875 0.890 bicubic -11.404 -11.118 -6.914 -6.822 -11 +2
174 repvgg_b2g4 gluon_resnet101_v1d 69.000 69.010 31.000 30.990 88.360 88.100 11.640 11.900 61.76 44.57 224 0.875 bilinear bicubic -10.366 -11.404 -6.328 -6.914 +36 -12
175 seresnet50 repvgg_b2g4 68.980 69.000 31.020 31.000 88.710 88.360 11.290 11.640 28.09 61.76 224 0.875 bicubic bilinear -11.294 -10.366 -6.360 -6.328 -5 +38
176 xception65 seresnet50 68.980 31.020 88.480 88.710 11.520 11.290 39.92 28.09 299 224 0.903 0.875 bicubic -10.572 -11.294 -6.174 -6.360 +29 -6
177 gluon_resnext101_32x4d xception65 68.960 68.980 31.040 31.020 88.360 88.480 11.640 11.520 44.18 39.92 224 299 0.875 0.903 bicubic -11.374 -10.572 -6.566 -6.174 -13 +30
178 tf_efficientnet_b2_ap gluon_resnext101_32x4d 68.920 68.960 31.080 31.040 88.350 88.360 11.650 11.640 9.11 44.18 260 224 0.890 0.875 bicubic -11.380 -11.374 -6.678 -6.566 -9 -14
179 tf_efficientnet_b2_ap 68.920 31.080 88.350 11.650 9.11 260 0.890 bicubic -11.380 -6.678 -10
180 cspdarknet53 68.890 31.110 88.600 11.400 27.64 256 0.887 bilinear -11.168 -6.484 +1
181 regnety_120 68.850 31.150 88.330 11.670 51.82 224 0.875 bicubic -11.516 -6.796 -17 -18
182 gluon_resnet152_v1b 68.820 31.180 87.710 12.290 60.19 224 0.875 bicubic -10.866 -7.026 +19 +20
183 dpn131 68.770 31.230 87.470 12.530 79.25 224 0.875 bicubic -11.052 -7.240 +11 +12
184 cspresnext50 68.760 31.240 87.950 12.050 20.57 224 0.875 bilinear -11.280 -6.994 -2
185 tf_efficientnet_b2 68.750 31.250 87.990 12.010 9.11 260 0.890 bicubic -11.336 -6.918 -5
186 resnext50d_32x4d 68.740 31.260 88.300 11.700 25.05 224 0.875 bicubic -10.936 -6.566 +16 +17
187 vit_deit_small_patch16_224 68.720 31.280 88.200 11.800 22.05 224 0.900 bicubic -11.136 -6.852 +5
188 gluon_resnet101_v1s 68.710 31.290 87.910 12.090 44.67 224 0.875 bicubic -11.592 -7.250 -20 -21
189 regnety_080 68.700 31.300 87.970 12.030 39.18 224 0.875 bicubic -11.176 -6.860 0
190 dpn107 68.690 31.310 88.130 11.870 86.92 224 0.875 bicubic -11.466 -6.780 -12
191 gluon_seresnext50_32x4d 68.670 31.330 88.310 11.690 27.56 224 0.875 bicubic -11.248 -6.512 -6
192 hrnet_w64 68.640 31.360 88.050 11.950 128.06 224 0.875 bilinear -10.834 -6.602 +17 +18
193 resnext50_32x4d 68.640 31.360 87.570 12.430 25.03 224 0.875 bicubic -11.128 -7.028 +3 +4
194 dpn98 68.590 31.410 87.680 12.320 61.57 224 0.875 bicubic -11.052 -6.918 +9 +10
195 regnetx_160 68.530 31.470 88.450 11.550 54.28 224 0.875 bicubic -11.326 -6.380 -4
196 cspresnet50 68.460 31.540 88.010 11.990 21.62 256 0.887 bilinear -11.114 -6.702 +9 +10
197 rexnet_130 68.450 31.550 88.040 11.960 7.56 224 0.875 bicubic -11.050 -6.642 +11 +12
198 ecaresnet50d_pruned tf_efficientnet_el 68.420 31.580 88.370 88.210 11.630 11.790 19.94 10.59 224 300 0.875 0.904 bicubic -11.296 -11.830 -6.510 -6.918 +1 -29
199 regnety_064 68.420 31.580 88.080 11.920 30.58 224 0.875 bicubic -11.302 -6.688 -1 0
200 tf_efficientnet_el ecaresnet50d_pruned 68.420 31.580 88.210 88.370 11.790 11.630 10.59 19.94 300 224 0.904 0.875 bicubic -11.830 -11.296 -6.918 -6.510 -28 +2
201 cait_xxs36_224 ssl_resnet50 68.410 31.590 88.630 88.560 11.370 11.440 17.30 25.56 224 1.000 0.875 bicubic bilinear -11.340 -10.812 -6.236 -6.272 -4 +23
202 ssl_resnet50 cait_xxs36_224 68.410 31.590 88.560 88.630 11.440 11.370 25.56 17.30 224 0.875 1.000 bilinear bicubic -10.812 -11.340 -6.272 -6.236 +21 -3
203 skresnext50_32x4d 68.350 31.650 87.570 12.430 27.48 224 0.875 bicubic -11.806 -7.072 -24
204 dla102x2 68.330 31.670 87.890 12.110 41.28 224 0.875 bilinear -11.118 -6.750 +6 +8
205 efficientnet_b2_pruned 68.320 31.680 88.100 11.900 8.31 260 0.890 bicubic -11.596 -6.756 -19
206 gluon_resnext50_32x4d 68.310 31.690 87.300 12.700 25.03 224 0.875 bicubic -11.044 -7.126 +6 +8
tf_efficientnet_lite3 68.230 31.770 87.740 12.260 8.20 300 0.904 bilinear -11.590 -7.174 -13
207 ecaresnet26t 68.230 31.770 88.790 11.210 16.01 320 0.950 bicubic -11.624 -6.294 -14
208 ese_vovnet39b tf_efficientnet_lite3 68.210 68.230 31.790 31.770 88.250 87.740 11.750 12.260 24.57 8.20 224 300 0.875 0.904 bicubic bilinear -11.110 -11.590 -6.462 -7.174 +4 -12
209 regnetx_120 ese_vovnet39b 68.150 68.210 31.850 31.790 87.660 88.250 12.340 11.750 46.11 24.57 224 0.875 bicubic -11.446 -11.110 -7.078 -6.462 -6 +6
210 regnetx_120 68.150 31.850 87.660 12.340 46.11 224 0.875 bicubic -11.446 -7.078 -5
211 resnetrs50 68.030 31.970 87.710 12.290 35.69 224 0.910 bicubic -11.862 -7.258 -23
212 pit_xs_distilled_224 68.020 31.980 87.720 12.280 11.00 224 0.900 bicubic -11.286 -6.644 +6 +8
213 dpn92 67.990 32.010 87.580 12.420 37.67 224 0.875 bicubic -12.018 -7.256 -30
214 nf_regnet_b1 67.980 32.020 88.180 11.820 10.22 288 0.900 bicubic -11.326 -6.568 +3 +5
215 gluon_resnet50_v1d 67.940 32.060 87.130 12.870 25.58 224 0.875 bicubic -11.134 -7.340 +16 +18
216 levit_192 67.900 32.100 87.890 12.110 10.95 224 0.900 bicubic -11.942 -6.896 -22
217 tf_efficientnetv2_b1 67.890 32.110 87.800 12.200 8.14 240 0.882 bicubic -11.572 -6.922 -6
218 regnetx_080 67.880 32.120 86.990 13.010 39.57 224 0.875 bicubic -11.314 -7.570 +12
219 resnext101_32x8d 67.860 32.140 87.490 12.510 88.79 224 0.875 bilinear -11.448 -7.028 -3
220 efficientnet_em 67.840 32.160 88.120 11.880 6.90 240 0.882 bicubic -11.412 -6.674 +4
228 dpn68b 67.630 32.370 87.660 12.340 12.61 224 0.875 bicubic -11.586 -6.754 -1
229 dla169 67.610 32.390 87.590 12.410 53.39 224 0.875 bilinear -11.078 -6.746 +18
230 gluon_inception_v3 67.590 32.410 87.470 12.530 23.83 299 0.875 bicubic -11.216 -6.900 +12
231 gluon_resnet101_v1c 67.580 32.420 87.180 12.820 44.57 224 0.875 bicubic -11.954 -7.398 -22 -23
232 regnety_040 67.580 32.420 87.510 12.490 20.65 224 0.875 bicubic -11.640 -7.146 -6
233 res2net50_26w_8s 67.570 32.430 87.280 12.720 48.40 224 0.875 bilinear -11.628 -7.088 -5
234 hrnet_w40 67.560 32.440 87.140 12.860 57.56 224 0.875 bilinear -11.360 -7.330 +4
235 legacy_seresnet152 67.520 32.480 87.390 12.610 66.82 224 0.875 bilinear -11.140 -6.980 +13
236 resnetv2_50x1_bitm 67.520 32.480 89.250 10.750 25.55 480 1.000 bilinear -12.652 -6.376 -58 -60
237 tf_efficientnet_b1_ap 67.520 32.480 87.760 12.240 7.79 240 0.882 bicubic -11.760 -6.546 -14
238 efficientnet_b1 67.470 32.530 87.510 12.490 7.79 256 1.000 bicubic -11.324 -6.832 +5
239 gluon_resnet101_v1b 67.460 32.540 87.240 12.760 44.55 224 0.875 bicubic -11.846 -7.284 -21
241 res2net101_26w_4s 67.440 32.560 87.010 12.990 45.21 224 0.875 bilinear -11.758 -7.422 -12
242 resnet50 67.440 32.560 87.420 12.580 25.56 224 0.875 bicubic -11.598 -6.970 -6
243 resnetblur50 67.430 32.570 87.440 12.560 25.56 224 0.875 bicubic -11.856 -7.198 -21
244 cait_xxs24_224 67.330 32.670 87.510 12.490 11.96 224 1.000 bicubic -11.056 -6.800 +15 +17
245 regnetx_032 67.290 32.710 87.000 13.000 15.30 224 0.875 bicubic -10.882 -7.088 +23 +26
246 xception41 coat_tiny 67.250 32.750 87.200 87.340 12.800 12.660 26.97 5.50 299 224 0.903 0.900 bicubic -11.266 -11.184 -7.078 -6.698 +5 +13
247 xception41 67.250 32.750 87.200 12.800 26.97 299 0.903 bicubic -11.266 -7.078 +4
248 resnest26d 67.200 32.800 87.170 12.830 17.07 224 0.875 bilinear -11.278 -7.128 +7
249 legacy_seresnet101 repvgg_b2 67.160 32.840 87.060 87.330 12.940 12.670 49.33 89.02 224 0.875 bilinear -11.222 -11.632 -7.204 -7.084 +12 -6
250 repvgg_b2 legacy_seresnet101 67.160 32.840 87.330 87.060 12.670 12.940 89.02 49.33 224 0.875 bilinear -11.632 -11.222 -7.084 -7.204 -5 +13
251 dla60x 67.100 32.900 87.190 12.810 17.35 224 0.875 bilinear -11.146 -6.828 +13 +15
252 gluon_resnet50_v1s 67.060 32.940 86.860 13.140 25.68 224 0.875 bicubic -11.652 -11.650 -7.378 -5 -6
253 tv_resnet152 67.050 32.950 87.550 12.450 60.19 224 0.875 bilinear -11.262 -6.488 +10 +12
254 dla60_res2net 67.020 32.980 87.160 12.840 20.85 224 0.875 bilinear -11.444 -7.046 +2
255 dla102x 67.010 32.990 86.770 13.230 26.31 224 0.875 bilinear -11.500 -7.458 -2 -3
256 mixnet_l 66.940 33.060 86.910 13.090 7.33 224 0.875 bicubic -12.036 -7.272 -18 -19
257 pit_xs_224 66.920 33.080 87.280 12.720 10.62 224 0.900 bicubic -11.262 -6.888 +11 +13
258 res2net50_26w_6s 66.910 33.090 86.860 13.140 37.05 224 0.875 bilinear -11.660 -7.264 -7 -8
259 repvgg_b1 66.900 33.100 86.780 13.220 57.42 224 0.875 bilinear -11.466 -7.318 +3 +4
260 efficientnet_es 66.880 33.120 86.730 13.270 5.44 224 0.875 bicubic -11.186 -7.196 +13 +15
261 tf_efficientnet_b1 66.880 33.120 87.010 12.990 7.79 240 0.882 bicubic -11.946 -7.188 -19 -20
262 regnetx_040 66.840 33.160 86.730 13.270 22.12 224 0.875 bicubic -11.642 -7.514 -8
263 hrnet_w30 66.780 33.220 86.800 13.200 37.71 224 0.875 bilinear -11.426 -7.422 +4 +6
264 tf_mixnet_l 66.780 33.220 86.470 13.530 7.33 224 0.875 bicubic -11.994 -7.528 -18 -19
265 selecsls60b 66.760 33.240 86.530 13.470 32.77 224 0.875 bicubic -11.652 -7.644 -6 -5
266 hrnet_w32 66.750 33.250 87.300 12.700 41.23 224 0.875 bilinear -11.700 -6.886 -9
267 wide_resnet101_2 66.730 33.270 87.030 12.970 126.89 224 0.875 bilinear -12.126 -7.252 -26 -27
268 adv_inception_v3 tf_efficientnetv2_b0 66.650 66.700 33.350 33.300 86.540 86.710 13.460 13.290 23.83 7.14 299 224 0.875 bicubic -10.932 -11.656 -7.196 -7.314 +23 -4
269 dla60_res2next adv_inception_v3 66.640 66.650 33.360 33.350 87.030 86.540 12.970 13.460 17.03 23.83 224 299 0.875 bilinear bicubic -11.800 -10.932 -7.122 -7.196 -11 +24
270 gluon_resnet50_v1c dla60_res2next 66.560 66.640 33.440 33.360 86.180 87.030 13.820 12.970 25.58 17.03 224 0.875 bicubic bilinear -11.452 -11.800 -7.808 -7.122 +5 -12
271 gluon_resnet50_v1c 66.560 33.440 86.180 13.820 25.58 224 0.875 bicubic -11.452 -7.808 +6
272 levit_128 66.550 33.450 86.750 13.250 9.21 224 0.900 bicubic -11.936 -7.260 -19
273 dla102 66.540 33.460 86.910 13.090 33.27 224 0.875 bilinear -11.492 -7.036 +3
274 tf_inception_v3 66.410 33.590 86.660 13.340 23.83 299 0.875 bicubic -11.446 -11.448 -6.980 -7.210 +12 +10
275 hardcorenas_f 66.370 33.630 86.200 13.800 8.20 224 0.875 bilinear -11.734 -7.602 -1
coat_lite_tiny 66.290 33.710 86.980 13.020 5.72 224 0.900 bicubic -11.222 -6.936 +20
276 efficientnet_b0 66.290 33.710 85.960 14.040 5.29 224 0.875 bicubic -11.408 -7.572 +11
277 coat_lite_tiny 66.290 33.710 86.980 13.020 5.72 224 0.900 bicubic -11.222 -6.936 +20
278 legacy_seresnet50 66.250 33.750 86.330 13.670 28.09 224 0.875 bilinear -11.380 -7.418 +11
279 selecsls60 66.210 33.790 86.340 13.660 30.67 224 0.875 bicubic -11.772 -7.488 0
280 tf_efficientnet_em 66.180 33.820 86.360 13.640 6.90 240 0.882 bicubic -11.950 -7.684 -7
283 inception_v3 66.160 33.840 86.320 13.680 23.83 299 0.875 bicubic -11.278 -7.156 +15
284 res2net50_26w_4s 66.140 33.860 86.600 13.400 25.70 224 0.875 bilinear -11.824 -7.254 -4
285 efficientnet_b1_pruned 66.090 33.910 86.570 13.430 6.33 240 0.882 bicubic -12.146 -7.264 -17
286 rexnet_100 66.070 33.930 86.490 13.510 4.80 224 0.875 bicubic -11.788 -6.926 -1
287 gluon_resnet50_v1b 66.070 33.930 86.260 13.740 25.56 224 0.875 bicubic -11.510 -7.456 +8
rexnet_100 66.070 33.930 86.490 13.510 4.80 224 0.875 bicubic -11.788 -7.380 -3
288 regnety_016 66.060 33.940 86.380 13.620 11.20 224 0.875 bicubic -11.802 -7.340 -5
289 res2net50_14w_8s 66.020 33.980 86.250 13.750 25.06 224 0.875 bilinear -12.130 -7.598 -17
290 seresnext26t_32x4d 65.880 34.120 85.680 14.320 16.81 224 0.875 bicubic -12.106 -8.066 -12
291 repvgg_b1g4 65.850 34.150 86.120 13.880 39.97 224 0.875 bilinear -11.744 -7.706 +1
292 res2next50 65.850 34.150 85.840 14.160 24.67 224 0.875 bilinear -12.396 -8.052 -25
hardcorenas_e 65.840 34.160 85.980 14.020 8.07 224 0.875 bilinear -11.954 -7.714 -7
293 densenet161 65.840 34.160 86.450 13.550 28.68 224 0.875 bicubic -11.518 -7.188 +8
294 hardcorenas_e 65.840 34.160 85.980 14.020 8.07 224 0.875 bilinear -11.954 -7.714 -7
295 resnet34d 65.780 34.220 86.720 13.280 21.82 224 0.875 bicubic -11.336 -6.662 +12
296 mobilenetv3_large_100_miil 65.760 34.240 85.200 14.800 5.48 224 0.875 bilinear -12.156 -7.710 -15
297 skresnet34 65.750 34.250 85.960 14.040 22.28 224 0.875 bicubic -11.162 -7.362 +18
298 vit_small_patch16_224 65.740 34.260 86.120 13.880 48.75 224 0.900 bicubic -12.118 -7.296 -7.520 -13
299 tv_resnet101 65.690 34.310 85.980 14.020 44.55 224 0.875 bilinear -11.684 -7.560 +1
300 hardcorenas_d 65.630 34.370 85.460 14.540 7.50 224 0.875 bilinear -11.802 -8.024 -1
301 selecsls42b 65.610 34.390 85.810 14.190 32.46 224 0.875 bicubic -11.564 -7.580 +5
313 mobilenetv2_120d 65.030 34.970 85.960 14.040 5.83 224 0.875 bicubic -12.254 -7.532 -9
314 hrnet_w18 64.920 35.080 85.740 14.260 21.30 224 0.875 bilinear -11.838 -7.704 +4
315 hardcorenas_c 64.860 35.140 85.250 14.750 5.52 224 0.875 bilinear -12.194 -7.908 -5
316 densenet169 64.760 35.240 85.240 14.760 14.15 224 0.875 bicubic -11.146 -7.786 +16 +17
317 mixnet_m 64.700 35.300 85.450 14.550 5.01 224 0.875 bicubic -12.560 -7.974 -12
318 resnet26d 64.680 35.320 85.120 14.880 16.01 224 0.875 bicubic -12.016 -8.030 +1
319 levit_128s 64.610 35.390 84.730 15.270 7.78 224 0.900 bicubic -11.920 -8.136 +6
320 repvgg_a2 64.450 35.550 85.130 14.870 28.21 224 0.875 bilinear -12.010 -7.874 +7
321 hardcorenas_b 64.420 35.580 84.870 15.130 5.18 224 0.875 bilinear -12.118 -7.884 +4 +3
322 regnetx_016 tf_efficientnet_lite1 64.380 35.620 85.470 14.530 9.19 5.42 224 240 0.875 0.882 bicubic -12.570 -12.262 -7.950 -7.756 -9 -3
323 tf_efficientnet_lite1 regnetx_016 64.380 35.620 85.470 14.530 5.42 9.19 240 224 0.882 0.875 bicubic -12.262 -12.570 -7.756 -7.950 -2 -10
324 tf_efficientnet_b0 64.310 35.690 85.280 14.720 5.29 224 0.875 bicubic -12.538 -7.948 -7 -8
325 tf_mixnet_m 64.270 35.730 85.090 14.910 5.01 224 0.875 bicubic -12.672 -8.062 -11 -12
326 dpn68 64.230 35.770 85.180 14.820 12.61 224 0.875 bicubic -12.088 -7.798 +2
327 tf_efficientnet_es 64.230 35.770 84.740 15.260 5.44 224 0.875 bicubic -12.364 -8.462 -4 -5
328 regnety_008 64.160 35.840 85.270 14.730 6.26 224 0.875 bicubic -12.156 -7.796 +1
329 mobilenetv2_140 64.060 35.940 85.040 14.960 6.11 224 0.875 bicubic -12.456 -7.956 -3
330 densenet121 63.750 36.250 84.590 15.410 7.98 224 0.875 bicubic -11.828 -8.062 +7
336 mobilenetv3_large_100 63.360 36.640 84.090 15.910 5.48 224 0.875 bicubic -12.406 -8.452 -2
337 efficientnet_es_pruned 63.330 36.670 84.950 15.050 5.44 224 0.875 bicubic -11.670 -7.498 +13
338 tv_resnet50 63.330 36.670 84.640 15.360 25.56 224 0.875 bilinear -12.808 -8.224 -8
339 mixer_b16_224 63.280 36.720 83.310 16.690 59.88 224 0.875 bicubic -13.322 -8.918 -17 -18
340 efficientnet_lite0 63.240 36.760 84.440 15.560 4.65 224 0.875 bicubic -12.244 -8.070 0
341 mobilenetv3_rw 63.220 36.780 84.510 15.490 5.48 224 0.875 bicubic -12.414 -8.198 -5
342 pit_ti_distilled_224 63.150 36.850 83.960 16.040 5.10 224 0.900 bicubic -11.380 -8.136 +15
359 mnasnet_100 61.900 38.100 83.710 16.290 4.38 224 0.875 bicubic -12.758 -8.404 -5
360 regnety_004 61.870 38.130 83.430 16.570 4.34 224 0.875 bicubic -12.164 -8.322 +1
361 vgg19_bn 61.860 38.140 83.450 16.550 143.68 224 0.875 bilinear -12.354 -8.392 -2
362 convit_tiny 61.590 38.410 84.120 15.880 5.71 224 0.875 bicubic -11.526 -7.594 +6
363 ssl_resnet18 61.480 38.520 83.300 16.700 11.69 224 0.875 bilinear -11.130 -8.116 +9
364 regnetx_006 61.350 38.650 83.450 16.550 6.20 224 0.875 bicubic -12.502 -8.222 0 -1
365 spnasnet_100 61.220 38.780 82.790 17.210 4.42 224 0.875 bilinear -12.864 -9.028 -4 -5
366 tv_resnet34 61.190 38.810 82.710 17.290 21.80 224 0.875 bilinear -12.122 -8.716 +1 0
367 pit_ti_224 60.980 39.020 83.860 16.140 4.85 224 0.900 bicubic -11.932 -7.542 +4
368 skresnet18 60.860 39.140 82.880 17.120 11.96 224 0.875 bicubic -12.178 -8.288 +1
369 ghostnet_100 60.830 39.170 82.360 17.640 5.18 224 0.875 bilinear -13.148 -9.096 -6 -7
370 vgg16_bn 60.760 39.240 82.950 17.050 138.37 224 0.875 bilinear -12.590 -8.556 -4 -5
371 tf_mobilenetv3_large_075 60.400 39.600 81.950 18.050 3.99 224 0.875 bilinear -13.038 -9.400 -6 -7
372 mobilenetv2_100 60.190 39.810 82.240 17.760 3.50 224 0.875 bicubic -12.780 -8.776 -2
373 resnet18d 60.160 39.840 82.300 17.700 11.71 224 0.875 bicubic -12.100 -8.396 +3
374 vit_deit_tiny_patch16_224 59.830 40.170 82.670 17.330 5.72 224 0.900 bicubic -12.338 -8.448 +4
377 regnetx_004 59.410 40.590 81.690 18.310 5.16 224 0.875 bicubic -12.986 -9.140 -4
378 tf_mobilenetv3_large_minimal_100 59.070 40.930 81.150 18.850 3.92 224 0.875 bilinear -13.178 -9.480 -1
379 vgg13_bn 59.000 41.000 81.070 18.930 133.05 224 0.875 bilinear -12.594 -9.306 +2
380 hrnet_w18_small 58.950 41.050 81.340 18.660 13.19 224 0.875 bilinear -13.392 -13.394 -9.338 -5
381 vgg16 58.830 41.170 81.660 18.340 138.36 224 0.875 bilinear -12.764 -8.722 +1
382 gluon_resnet18_v1b 58.340 41.660 80.970 19.030 11.69 224 0.875 bicubic -12.496 -8.792 +1
383 vgg11_bn 57.410 42.590 80.020 19.980 132.87 224 0.875 bilinear -12.950 -9.782 +1
391 tf_mobilenetv3_small_100 54.530 45.470 77.060 22.940 2.54 224 0.875 bilinear -13.392 -10.604 -1
392 dla46x_c 53.050 46.950 76.870 23.130 1.07 224 0.875 bilinear -12.920 -10.110 0
393 tf_mobilenetv3_small_075 52.160 47.840 75.470 24.530 2.04 224 0.875 bilinear -13.556 -10.660 0
394 dla46_c 52.130 47.870 75.690 24.310 1.30 224 0.875 bilinear -12.736 -12.734 -10.602 0
395 tf_mobilenetv3_small_minimal_100 49.500 50.500 73.050 26.950 2.04 224 0.875 bilinear -13.406 -11.180 0

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results/results-sketch.csv
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model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation,top1_diff,top5_diff,rank_diff
ig_resnext101_32x48d,58.810,41.190,81.076,18.924,828.41,224,0.875,bilinear,-26.618,-16.496,+15
ig_resnext101_32x32d,58.386,41.614,80.381,19.619,468.53,224,0.875,bilinear,-26.708,-17.057,+22
ig_resnext101_32x16d,57.690,42.310,79.905,20.095,194.03,224,0.875,bilinear,-26.480,-17.291,+41
swsl_resnext101_32x16d,57.458,42.542,80.385,19.615,194.03,224,0.875,bilinear,-25.888,-16.461,+58
swsl_resnext101_32x8d,56.438,43.562,78.944,21.056,88.79,224,0.875,bilinear,-27.846,-18.232,+35
ig_resnext101_32x8d,54.918,45.082,77.534,22.466,88.79,224,0.875,bilinear,-27.770,-19.102,+71
swsl_resnext101_32x4d,53.603,46.397,76.347,23.653,44.18,224,0.875,bilinear,-29.627,-20.413,+58
ig_resnext101_32x48d,58.810,41.190,81.076,18.924,828.41,224,0.875,bilinear,-26.618,-16.496,+18
ig_resnext101_32x32d,58.386,41.614,80.381,19.619,468.53,224,0.875,bilinear,-26.708,-17.057,+25
ig_resnext101_32x16d,57.690,42.310,79.905,20.095,194.03,224,0.875,bilinear,-26.480,-17.291,+47
swsl_resnext101_32x16d,57.458,42.542,80.385,19.615,194.03,224,0.875,bilinear,-25.888,-16.461,+66
swsl_resnext101_32x8d,56.438,43.562,78.944,21.056,88.79,224,0.875,bilinear,-27.846,-18.232,+41
ig_resnext101_32x8d,54.918,45.082,77.534,22.466,88.79,224,0.875,bilinear,-27.770,-19.102,+82
swsl_resnext101_32x4d,53.603,46.397,76.347,23.653,44.18,224,0.875,bilinear,-29.627,-20.413,+66
tf_efficientnet_l2_ns_475,51.494,48.506,73.928,26.072,480.31,475,0.936,bicubic,-36.740,-24.618,-6
swsl_resnext50_32x4d,50.437,49.563,73.368,26.633,25.03,224,0.875,bilinear,-31.745,-22.862,+79
swsl_resnext50_32x4d,50.437,49.563,73.368,26.633,25.03,224,0.875,bilinear,-31.745,-22.862,+95
swin_large_patch4_window12_384,50.404,49.596,72.564,27.436,196.74,384,1.000,bicubic,-36.744,-25.670,-7
swsl_resnet50,49.541,50.459,72.334,27.666,25.56,224,0.875,bilinear,-31.625,-23.638,+103
swsl_resnet50,49.541,50.459,72.334,27.666,25.56,224,0.875,bilinear,-31.625,-23.638,+125
swin_large_patch4_window7_224,48.991,51.009,71.391,28.609,196.53,224,0.900,bicubic,-37.329,-26.505,-4
swin_base_patch4_window12_384,48.553,51.447,71.813,28.187,87.90,384,1.000,bicubic,-37.879,-26.245,-6
tf_efficientnet_b7_ns,47.800,52.200,69.640,30.360,66.35,600,0.949,bicubic,-39.040,-28.454,-10
tf_efficientnet_b6_ns,47.761,52.239,69.968,30.032,43.04,528,0.942,bicubic,-38.691,-27.914,-9
tf_efficientnet_l2_ns,47.570,52.430,70.019,29.981,480.31,800,0.960,bicubic,-40.782,-28.631,-15
tf_efficientnet_b8_ap,45.774,54.226,67.911,32.089,87.41,672,0.954,bicubic,-39.596,-29.383,+2
tf_efficientnetv2_l_in21ft1k,46.939,53.061,70.310,29.690,118.52,480,1.000,bicubic,-39.365,-27.668,-8
tf_efficientnet_b8_ap,45.774,54.226,67.911,32.089,87.41,672,0.954,bicubic,-39.596,-29.383,+4
tf_efficientnet_b5_ns,45.615,54.385,67.842,32.158,30.39,456,0.934,bicubic,-40.473,-29.910,-8
swin_base_patch4_window7_224,45.560,54.440,68.512,31.488,87.77,224,0.900,bicubic,-39.692,-29.050,+1
cait_m48_448,44.245,55.755,64.653,35.347,356.46,448,1.000,bicubic,-42.239,-33.102,-15
vit_base_r50_s16_384,43.512,56.488,66.781,33.219,98.95,384,1.000,bicubic,-41.460,-30.507,+8
tf_efficientnet_b4_ns,43.450,56.550,65.519,34.481,19.34,380,0.922,bicubic,-41.713,-31.951,-1
vit_large_patch16_384,43.300,56.700,66.454,33.546,304.72,384,1.000,bicubic,-41.858,-30.902,-1
tf_efficientnet_b8,42.508,57.492,64.857,35.143,87.41,672,0.954,bicubic,-42.862,-32.533,-6
cait_m36_384,42.398,57.602,63.324,36.676,271.22,384,1.000,bicubic,-43.656,-34.406,-14
dm_nfnet_f6,41.593,58.407,63.192,36.808,438.36,576,0.956,bicubic,-44.704,-34.552,-17
tf_efficientnet_b7,41.431,58.569,63.017,36.983,66.35,600,0.949,bicubic,-43.505,-34.186,+3
tf_efficientnet_b7_ap,41.429,58.571,62.874,37.126,66.35,600,0.949,bicubic,-43.691,-34.378,-5
tf_efficientnet_b5_ap,41.418,58.582,62.084,37.916,30.39,456,0.934,bicubic,-42.834,-34.890,+13
resnetv2_152x4_bitm,41.241,58.759,64.238,35.762,936.53,480,1.000,bilinear,-43.691,-33.198,+1
tf_efficientnet_b6_ap,41.099,58.901,62.355,37.645,43.04,528,0.942,bicubic,-43.689,-34.783,+1
dm_nfnet_f5,41.003,58.997,61.911,38.089,377.21,544,0.954,bicubic,-44.711,-35.531,-20
dm_nfnet_f3,40.920,59.080,61.949,38.051,254.92,416,0.940,bicubic,-44.640,-35.457,-19
vit_large_patch16_224,40.732,59.268,63.593,36.407,304.33,224,0.900,bicubic,-42.330,-32.845,+35
tf_efficientnet_b4_ap,40.484,59.516,61.723,38.277,19.34,380,0.922,bicubic,-42.764,-34.669,+29
vit_base_patch16_224_miil,40.168,59.832,60.887,39.113,86.54,224,0.875,bilinear,-44.100,-35.915,+5
cait_s36_384,39.765,60.235,60.475,39.525,68.37,384,1.000,bicubic,-45.695,-37.005,-22
ecaresnet269d,39.594,60.406,60.343,39.657,102.09,352,1.000,bicubic,-45.382,-36.883,-10
tf_efficientnet_b3_ns,39.584,60.416,61.453,38.547,12.23,300,0.904,bicubic,-44.464,-35.457,+10
dm_nfnet_f4,39.474,60.526,60.420,39.580,316.07,512,0.951,bicubic,-46.184,-37.090,-27
efficientnet_b4,39.079,60.921,59.608,40.392,19.34,384,1.000,bicubic,-44.349,-36.988,+19
tf_efficientnet_b5,38.356,61.644,59.913,40.087,30.39,456,0.934,bicubic,-45.456,-36.835,+11
vit_deit_base_distilled_patch16_384,38.260,61.740,57.783,42.217,87.63,384,1.000,bicubic,-47.162,-39.549,-26
vit_base_patch16_384,38.099,61.901,60.428,39.572,86.86,384,1.000,bicubic,-46.111,-36.790,-1
cait_s24_384,37.873,62.127,58.079,41.921,47.06,384,1.000,bicubic,-47.173,-39.267,-20
resnet152d,37.857,62.143,58.356,41.644,60.21,320,1.000,bicubic,-45.823,-38.382,+12
resnetrs420,37.747,62.253,58.215,41.785,191.89,416,1.000,bicubic,-47.261,-38.909,-21
resnetrs350,37.676,62.324,58.083,41.917,163.96,384,1.000,bicubic,-47.044,-38.905,-15
tf_efficientnetv2_m_in21ft1k,45.582,54.418,69.150,30.849,54.14,480,1.000,bicubic,-40.006,-28.602,-5
swin_base_patch4_window7_224,45.560,54.440,68.512,31.488,87.77,224,0.900,bicubic,-39.692,-29.050,+2
cait_m48_448,44.245,55.755,64.653,35.347,356.46,448,1.000,bicubic,-42.239,-33.102,-17
vit_base_r50_s16_384,43.512,56.488,66.781,33.219,98.95,384,1.000,bicubic,-41.460,-30.507,+10
tf_efficientnet_b4_ns,43.450,56.550,65.519,34.481,19.34,380,0.922,bicubic,-41.713,-31.951,0
vit_large_patch16_384,43.300,56.700,66.454,33.546,304.72,384,1.000,bicubic,-41.858,-30.902,0
tf_efficientnet_b8,42.508,57.492,64.857,35.143,87.41,672,0.954,bicubic,-42.862,-32.533,-5
cait_m36_384,42.398,57.602,63.324,36.676,271.22,384,1.000,bicubic,-43.656,-34.406,-15
dm_nfnet_f6,41.593,58.407,63.192,36.808,438.36,576,0.956,bicubic,-44.704,-34.552,-18
tf_efficientnet_b7,41.431,58.569,63.017,36.983,66.35,600,0.949,bicubic,-43.505,-34.186,+5
tf_efficientnet_b7_ap,41.429,58.571,62.874,37.126,66.35,600,0.949,bicubic,-43.691,-34.378,-4
tf_efficientnet_b5_ap,41.418,58.582,62.084,37.916,30.39,456,0.934,bicubic,-42.834,-34.890,+17
resnetv2_152x4_bitm,41.241,58.759,64.238,35.762,936.53,480,1.000,bilinear,-43.691,-33.198,+3
tf_efficientnet_b6_ap,41.099,58.901,62.355,37.645,43.04,528,0.942,bicubic,-43.689,-34.783,+4
dm_nfnet_f5,41.003,58.997,61.911,38.089,377.21,544,0.954,bicubic,-44.711,-35.531,-21
tf_efficientnetv2_s_in21ft1k,40.950,59.050,63.849,36.151,21.46,384,1.000,bicubic,-43.352,-33.403,+10
dm_nfnet_f3,40.920,59.080,61.949,38.051,254.92,416,0.940,bicubic,-44.640,-35.457,-20
vit_large_patch16_224,40.732,59.268,63.593,36.407,304.33,224,0.900,bicubic,-42.330,-32.845,+42
tf_efficientnet_b4_ap,40.484,59.516,61.723,38.277,19.34,380,0.922,bicubic,-42.764,-34.669,+34
vit_base_patch16_224_miil,40.168,59.832,60.887,39.113,86.54,224,0.875,bilinear,-44.100,-35.915,+8
tf_efficientnetv2_l,39.830,60.170,60.801,39.199,118.52,480,1.000,bicubic,-45.660,-36.571,-23
cait_s36_384,39.765,60.235,60.475,39.525,68.37,384,1.000,bicubic,-45.695,-37.005,-23
efficientnetv2_rw_m,39.667,60.333,59.687,40.313,53.24,416,1.000,bicubic,-45.141,-37.461,-6
ecaresnet269d,39.594,60.406,60.343,39.657,102.09,352,1.000,bicubic,-45.382,-36.883,-11
tf_efficientnet_b3_ns,39.584,60.416,61.453,38.547,12.23,300,0.904,bicubic,-44.464,-35.457,+11
dm_nfnet_f4,39.474,60.526,60.420,39.580,316.07,512,0.951,bicubic,-46.184,-37.090,-31
efficientnet_b4,39.079,60.921,59.608,40.392,19.34,384,1.000,bicubic,-44.349,-36.988,+22
tf_efficientnet_b5,38.356,61.644,59.913,40.087,30.39,456,0.934,bicubic,-45.456,-36.835,+13
vit_deit_base_distilled_patch16_384,38.260,61.740,57.783,42.217,87.63,384,1.000,bicubic,-47.162,-39.549,-28
vit_base_patch16_384,38.099,61.901,60.428,39.572,86.86,384,1.000,bicubic,-46.111,-36.790,0
cait_s24_384,37.873,62.127,58.079,41.921,47.06,384,1.000,bicubic,-47.173,-39.267,-22
resnet152d,37.857,62.143,58.356,41.644,60.21,320,1.000,bicubic,-45.823,-38.382,+14
tf_efficientnetv2_m,37.824,62.176,58.710,41.290,54.14,480,1.000,bicubic,-47.220,-38.568,-23
resnetrs420,37.747,62.253,58.215,41.785,191.89,416,1.000,bicubic,-47.261,-38.909,-23
resnetrs350,37.676,62.324,58.083,41.917,163.96,384,1.000,bicubic,-47.044,-38.905,-16
pit_b_distilled_224,37.590,62.410,57.238,42.762,74.79,224,0.900,bicubic,-46.554,-39.618,-4
resnet200d,37.505,62.495,58.297,41.703,64.69,320,1.000,bicubic,-46.457,-38.526,+1
resnest269e,37.315,62.685,57.468,42.532,110.93,416,0.928,bicubic,-47.203,-39.518,-16
cait_s24_224,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.299,-39.840,+7
tf_efficientnet_b3_ap,37.055,62.945,57.240,42.760,12.23,300,0.904,bicubic,-44.767,-38.384,+41
efficientnet_v2s,37.049,62.951,56.814,43.186,23.94,384,1.000,bicubic,-46.759,-39.910,0
resnetv2_152x2_bitm,36.847,63.153,59.899,40.101,236.34,480,1.000,bilinear,-47.593,-37.547,-19
seresnet152d,36.790,63.210,56.718,43.282,66.84,320,1.000,bicubic,-47.572,-40.322,-17
resnest269e,37.315,62.685,57.468,42.532,110.93,416,0.928,bicubic,-47.203,-39.518,-17
cait_s24_224,37.153,62.847,56.724,43.276,46.92,224,1.000,bicubic,-46.299,-39.840,+9
tf_efficientnet_b3_ap,37.055,62.945,57.240,42.760,12.23,300,0.904,bicubic,-44.767,-38.384,+53
efficientnetv2_rw_s,37.049,62.951,56.814,43.186,23.94,384,1.000,bicubic,-46.759,-39.910,+1
resnetv2_152x2_bitm,36.847,63.153,59.899,40.101,236.34,480,1.000,bilinear,-47.593,-37.547,-20
seresnet152d,36.790,63.210,56.718,43.282,66.84,320,1.000,bicubic,-47.572,-40.322,-18
resnetrs200,36.639,63.361,56.828,43.172,93.21,320,1.000,bicubic,-47.427,-40.046,-10
efficientnet_b3,36.420,63.580,56.845,43.155,12.23,320,1.000,bicubic,-45.822,-39.269,+27
efficientnet_b3,36.420,63.580,56.845,43.155,12.23,320,1.000,bicubic,-45.822,-39.269,+37
cait_xs24_384,36.416,63.584,56.944,43.056,26.67,384,1.000,bicubic,-47.645,-39.945,-11
vit_deit_base_distilled_patch16_224,36.397,63.603,56.617,43.383,87.34,224,0.900,bicubic,-46.991,-39.871,+1
resnetrs270,36.320,63.680,56.562,43.438,129.86,352,1.000,bicubic,-48.114,-40.408,-24
tresnet_m,36.285,63.715,55.796,44.204,31.39,224,0.875,bilinear,-46.795,-40.322,+6
dm_nfnet_f2,36.257,63.743,55.847,44.153,193.78,352,0.920,bicubic,-48.733,-41.297,-36
tf_efficientnet_b2_ns,36.183,63.817,57.551,42.449,9.11,260,0.890,bicubic,-46.197,-38.697,+17
ecaresnet101d,36.004,63.996,56.165,43.835,44.57,224,0.875,bicubic,-46.168,-39.881,+24
vit_deit_base_distilled_patch16_224,36.397,63.603,56.617,43.383,87.34,224,0.900,bicubic,-46.991,-39.871,+3
resnetrs270,36.320,63.680,56.562,43.438,129.86,352,1.000,bicubic,-48.114,-40.408,-25
tresnet_m,36.285,63.715,55.796,44.204,31.39,224,0.875,bilinear,-46.795,-40.322,+10
mixer_b16_224_miil,36.269,63.731,55.965,44.035,59.88,224,0.875,bilinear,-46.039,-39.751,+28
dm_nfnet_f2,36.257,63.743,55.847,44.153,193.78,352,0.920,bicubic,-48.733,-41.297,-39
tf_efficientnet_b2_ns,36.183,63.817,57.551,42.449,9.11,260,0.890,bicubic,-46.197,-38.697,+22
ecaresnet101d,36.004,63.996,56.165,43.835,44.57,224,0.875,bicubic,-46.168,-39.881,+33
resnest200e,35.931,64.069,55.849,44.151,70.20,320,0.909,bicubic,-47.901,-41.045,-14
swsl_resnet18,35.858,64.142,58.455,41.545,11.69,224,0.875,bilinear,-37.418,-33.279,+269
eca_nfnet_l1,35.856,64.144,55.955,44.045,41.41,320,1.000,bicubic,-48.151,-41.073,-18
vit_base_patch16_224,35.768,64.232,57.390,42.610,86.57,224,0.900,bicubic,-46.018,-38.732,+26
resnest101e,35.373,64.627,55.780,44.220,48.28,256,0.875,bilinear,-47.517,-40.540,+3
resnetv2_101x3_bitm,35.261,64.739,57.851,42.149,387.93,480,1.000,bilinear,-49.133,-39.511,-33
dm_nfnet_f1,35.192,64.808,54.413,45.587,132.63,320,0.910,bicubic,-49.412,-42.655,-38
repvgg_b3,35.043,64.957,54.542,45.458,123.09,224,0.875,bilinear,-45.449,-40.718,+60
repvgg_b3g4,35.043,64.957,54.772,45.228,83.83,224,0.875,bilinear,-45.169,-40.338,+76
resnet101d,34.872,65.128,54.202,45.798,44.57,320,1.000,bicubic,-48.150,-42.244,-4
vit_large_patch32_384,34.673,65.326,55.729,44.271,306.63,384,1.000,bicubic,-46.833,-40.363,+27
dm_nfnet_f0,34.642,65.358,54.762,45.238,71.49,256,0.900,bicubic,-48.700,-41.798,-14
ssl_resnext101_32x16d,34.603,65.397,55.931,44.069,194.03,224,0.875,bilinear,-47.241,-40.165,+15
repvgg_b2g4,34.587,65.413,54.782,45.218,61.76,224,0.875,bilinear,-44.779,-39.906,+107
resnetrs152,34.355,65.645,53.562,46.438,86.62,320,1.000,bicubic,-49.357,-43.052,-25
resnest50d_4s2x40d,34.355,65.645,54.725,45.275,30.42,224,0.875,bicubic,-46.753,-40.833,+35
tf_efficientnet_b1_ns,34.157,65.843,55.489,44.511,7.79,240,0.882,bicubic,-47.231,-40.249,+24
swsl_resnet18,35.858,64.142,58.455,41.545,11.69,224,0.875,bilinear,-37.418,-33.279,+292
eca_nfnet_l1,35.856,64.144,55.955,44.045,41.41,320,1.000,bicubic,-48.151,-41.073,-19
vit_base_patch16_224,35.768,64.232,57.390,42.610,86.57,224,0.900,bicubic,-46.018,-38.732,+37
resnest101e,35.373,64.627,55.780,44.220,48.28,256,0.875,bilinear,-47.517,-40.540,+6
convit_base,35.314,64.686,54.927,45.073,86.54,224,0.875,bicubic,-46.976,-41.011,+20
resnetv2_101x3_bitm,35.261,64.739,57.851,42.149,387.93,480,1.000,bilinear,-49.133,-39.511,-36
dm_nfnet_f1,35.192,64.808,54.413,45.587,132.63,320,0.910,bicubic,-49.412,-42.655,-41
twins_svt_large,35.086,64.914,54.721,45.279,99.27,224,0.900,bicubic,-48.592,-41.873,-15
repvgg_b3,35.043,64.957,54.542,45.458,123.09,224,0.875,bilinear,-45.449,-40.718,+74
repvgg_b3g4,35.043,64.957,54.772,45.228,83.83,224,0.875,bilinear,-45.171,-40.338,+90
resnet101d,34.872,65.128,54.202,45.798,44.57,320,1.000,bicubic,-48.150,-42.244,-3
vit_large_patch32_384,34.673,65.326,55.729,44.271,306.63,384,1.000,bicubic,-46.833,-40.363,+38
dm_nfnet_f0,34.642,65.358,54.762,45.238,71.49,256,0.900,bicubic,-48.700,-41.798,-15
ssl_resnext101_32x16d,34.605,65.395,55.931,44.069,194.03,224,0.875,bilinear,-47.239,-40.165,+24
repvgg_b2g4,34.587,65.413,54.782,45.218,61.76,224,0.875,bilinear,-44.779,-39.906,+124
resnest50d_4s2x40d,34.355,65.645,54.725,45.275,30.42,224,0.875,bicubic,-46.753,-40.833,+48
resnetrs152,34.355,65.645,53.562,46.438,86.62,320,1.000,bicubic,-49.357,-43.052,-27
tf_efficientnet_b1_ns,34.157,65.843,55.489,44.511,7.79,240,0.882,bicubic,-47.231,-40.249,+36
twins_pcpvt_large,34.111,65.888,54.128,45.872,60.99,224,0.900,bicubic,-49.029,-42.470,-17
tf_efficientnet_b4,34.064,65.936,54.198,45.802,19.34,380,0.922,bicubic,-48.958,-42.102,-11
nfnet_l0,34.029,65.971,54.418,45.582,35.07,288,1.000,bicubic,-48.731,-42.080,-9
ssl_resnext101_32x8d,34.017,65.983,55.601,44.399,88.79,224,0.875,bilinear,-47.599,-40.437,+15
tf_efficientnet_b6,33.998,66.002,54.544,45.456,43.04,528,0.942,bicubic,-50.112,-42.342,-40
efficientnet_b3_pruned,33.996,66.004,54.108,45.892,9.86,300,0.904,bicubic,-46.862,-41.134,+37
regnety_160,33.976,66.024,53.546,46.454,83.59,288,1.000,bicubic,-49.710,-43.230,-31
pit_s_distilled_224,33.939,66.061,53.265,46.735,24.04,224,0.900,bicubic,-48.057,-42.533,+3
regnety_032,33.412,66.588,52.754,47.246,19.44,288,1.000,bicubic,-49.312,-43.670,-14
gernet_l,33.357,66.643,51.901,48.099,31.08,256,0.875,bilinear,-47.997,-43.635,+17
tresnet_xl,33.257,66.743,52.294,47.706,78.44,224,0.875,bilinear,-48.797,-43.642,-2
resnest50d_1s4x24d,33.147,66.853,52.839,47.161,25.68,224,0.875,bicubic,-47.841,-42.483,+25
rexnet_200,32.987,67.013,52.939,47.061,16.37,224,0.875,bicubic,-48.645,-42.729,+5
resnest50d,32.972,67.028,52.713,47.287,27.48,224,0.875,bilinear,-48.002,-42.665,+24
tf_efficientnet_b3,32.860,67.140,52.950,47.050,12.23,300,0.904,bicubic,-48.776,-42.768,+2
pnasnet5large,32.848,67.152,50.500,49.500,86.06,331,0.911,bicubic,-49.934,-45.540,-23
nasnetalarge,32.775,67.225,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.906,-20
gernet_m,32.740,67.260,51.913,48.087,21.14,224,0.875,bilinear,-47.992,-43.271,+28
inception_resnet_v2,32.738,67.262,50.648,49.352,55.84,299,0.897,bicubic,-47.720,-44.658,+37
gluon_resnet152_v1d,32.734,67.266,51.088,48.912,60.21,224,0.875,bicubic,-47.740,-44.118,+34
pit_b_224,32.718,67.282,49.852,50.148,73.76,224,0.900,bicubic,-49.728,-45.858,-22
tf_efficientnet_b2_ap,32.681,67.319,52.239,47.761,9.11,260,0.890,bicubic,-47.619,-42.789,+42
tresnet_l,32.559,67.441,51.139,48.861,55.99,224,0.875,bilinear,-48.931,-44.485,0
cait_xxs36_384,32.549,67.451,52.233,47.767,17.37,384,1.000,bicubic,-49.645,-43.915,-18
vit_base_patch32_384,32.461,67.539,52.444,47.556,88.30,384,1.000,bicubic,-49.191,-43.684,-9
wide_resnet50_2,32.439,67.561,51.459,48.541,68.88,224,0.875,bicubic,-49.017,-44.073,-2
resnetv2_50x3_bitm,32.410,67.590,54.314,45.686,217.32,480,1.000,bilinear,-51.374,-42.792,-53
ens_adv_inception_resnet_v2,32.370,67.629,50.427,49.573,55.84,299,0.897,bicubic,-47.611,-44.509,+50
vit_deit_base_patch16_224,32.363,67.637,51.011,48.989,86.57,224,0.900,bicubic,-49.635,-44.723,-19
swin_small_patch4_window7_224,32.341,67.659,50.905,49.095,49.61,224,0.900,bicubic,-50.871,-45.417,-45
gluon_resnet152_v1s,32.331,67.669,50.526,49.474,60.32,224,0.875,bicubic,-48.685,-44.886,+5
vit_deit_small_distilled_patch16_224,32.284,67.716,52.102,47.898,22.44,224,0.900,bicubic,-48.916,-43.276,0
gluon_seresnext101_64x4d,32.205,67.795,50.319,49.681,88.23,224,0.875,bicubic,-48.689,-44.989,+9
gluon_seresnext101_32x4d,32.107,67.893,51.237,48.763,48.96,224,0.875,bicubic,-48.797,-44.057,+7
vit_deit_base_patch16_384,31.989,68.011,50.547,49.453,86.86,384,1.000,bicubic,-51.117,-45.825,-49
seresnext50_32x4d,31.985,68.015,51.231,48.769,27.56,224,0.875,bicubic,-49.281,-44.389,-6
resnetrs101,31.858,68.142,51.017,48.983,63.62,288,0.940,bicubic,-50.430,-44.991,-35
cspresnext50,31.822,68.178,51.602,48.398,20.57,224,0.875,bilinear,-48.218,-43.342,+38
eca_nfnet_l0,31.657,68.343,51.654,48.346,24.14,288,1.000,bicubic,-50.931,-44.820,-41
tnt_s_patch16_224,31.643,68.357,51.143,48.857,23.76,224,0.900,bicubic,-49.875,-44.605,-19
resnet50,31.547,68.453,50.170,49.830,25.56,224,0.875,bicubic,-47.491,-44.220,+87
ssl_resnext101_32x4d,31.423,68.577,52.121,47.879,44.18,224,0.875,bilinear,-49.501,-43.607,-2
inception_v4,31.378,68.622,49.244,50.756,42.68,299,0.875,bicubic,-48.790,-45.724,+28
rexnet_150,31.366,68.634,51.288,48.712,9.73,224,0.875,bicubic,-48.944,-43.878,+17
pit_s_224,31.333,68.667,49.661,50.339,23.46,224,0.900,bicubic,-49.761,-45.671,-10
cait_xxs36_224,31.278,68.722,50.616,49.384,17.30,224,1.000,bicubic,-48.472,-44.250,+45
cspresnet50,31.270,68.730,51.223,48.777,21.62,256,0.887,bilinear,-48.304,-43.489,+52
ecaresnetlight,31.121,68.879,50.243,49.757,30.16,224,0.875,bicubic,-49.341,-45.007,+7
gluon_resnet101_v1s,31.115,68.885,49.793,50.207,44.67,224,0.875,bicubic,-49.187,-45.367,+13
tf_efficientnet_cc_b0_8e,31.087,68.913,50.761,49.239,24.01,224,0.875,bicubic,-46.821,-42.892,+121
ecaresnet50d,31.058,68.942,50.848,49.152,25.58,224,0.875,bicubic,-49.534,-44.472,-1
ecaresnet50t,31.058,68.942,50.577,49.423,25.57,320,0.950,bicubic,-51.288,-45.561,-51
resnet50d,31.020,68.980,49.808,50.192,25.58,224,0.875,bicubic,-49.510,-45.352,-2
cspdarknet53,31.018,68.981,50.390,49.610,27.64,256,0.887,bilinear,-49.040,-44.694,+21
gluon_resnet152_v1c,30.991,69.009,48.924,51.076,60.21,224,0.875,bicubic,-48.919,-45.916,+26
gluon_resnext101_64x4d,30.987,69.013,48.549,51.451,83.46,224,0.875,bicubic,-49.617,-46.439,-7
tf_efficientnet_cc_b1_8e,30.899,69.101,50.080,49.920,39.72,240,0.882,bicubic,-48.409,-44.290,+52
ecaresnet101d_pruned,30.897,69.103,50.013,49.987,24.88,224,0.875,bicubic,-49.921,-45.615,-14
gluon_resnext101_32x4d,30.877,69.123,48.537,51.463,44.18,224,0.875,bicubic,-49.457,-46.389,0
tf_efficientnet_lite4,30.830,69.170,50.386,49.614,13.01,380,0.920,bilinear,-50.706,-45.282,-40
nf_resnet50,30.775,69.225,50.074,49.926,25.56,288,0.940,bicubic,-49.919,-45.282,-14
dpn107,30.678,69.322,48.810,51.190,86.92,224,0.875,bicubic,-49.478,-46.100,+10
ese_vovnet39b,30.657,69.343,49.875,50.125,24.57,224,0.875,bicubic,-48.663,-44.837,+44
gluon_resnet152_v1b,30.623,69.376,48.521,51.479,60.19,224,0.875,bicubic,-49.063,-46.215,+31
tresnet_xl_448,30.614,69.386,49.069,50.931,78.44,448,0.875,bilinear,-52.436,-47.105,-76
ssl_resnext50_32x4d,30.594,69.406,50.657,49.343,25.03,224,0.875,bilinear,-49.724,-44.749,-6
gluon_resnet101_v1d,30.523,69.477,47.950,52.050,44.57,224,0.875,bicubic,-49.891,-47.064,-10
dpn68b,30.517,69.483,49.158,50.842,12.61,224,0.875,bicubic,-48.699,-45.256,+51
resnest26d,30.490,69.510,50.677,49.323,17.07,224,0.875,bilinear,-47.988,-43.621,+77
efficientnet_b2,30.435,69.565,49.698,50.302,9.11,288,1.000,bicubic,-50.177,-45.620,-22
tf_efficientnet_b1_ap,30.421,69.579,49.553,50.447,7.79,240,0.882,bicubic,-48.859,-44.753,+44
pit_xs_distilled_224,30.278,69.722,49.836,50.164,11.00,224,0.900,bicubic,-49.028,-44.528,+40
seresnet50,30.077,69.923,49.292,50.708,28.09,224,0.875,bicubic,-50.197,-45.778,-8
dpn98,30.067,69.933,48.244,51.756,61.57,224,0.875,bicubic,-49.575,-46.354,+23
tf_efficientnet_b2,30.026,69.974,49.581,50.419,9.11,260,0.890,bicubic,-50.060,-45.328,-1
dpn131,30.024,69.976,48.146,51.854,79.25,224,0.875,bicubic,-49.798,-46.564,+12
efficientnet_el,30.018,69.982,48.834,51.166,10.59,300,0.904,bicubic,-51.298,-46.692,-49
legacy_senet154,30.001,69.999,48.034,51.966,115.09,224,0.875,bilinear,-51.309,-47.462,-49
dpn92,29.953,70.047,49.162,50.838,37.67,224,0.875,bicubic,-50.055,-45.674,-2
ssl_resnext101_32x8d,34.017,65.983,55.601,44.399,88.79,224,0.875,bilinear,-47.599,-40.437,+24
tf_efficientnet_b6,33.998,66.002,54.544,45.456,43.04,528,0.942,bicubic,-50.112,-42.342,-44
efficientnet_b3_pruned,33.996,66.004,54.108,45.892,9.86,300,0.904,bicubic,-46.862,-41.134,+50
regnety_160,33.976,66.024,53.546,46.454,83.59,288,1.000,bicubic,-49.710,-43.230,-34
pit_s_distilled_224,33.939,66.061,53.265,46.735,24.04,224,0.900,bicubic,-48.057,-42.533,+10
resnet51q,33.563,66.437,53.021,46.979,35.70,288,1.000,bilinear,-48.797,-43.159,-6
regnety_032,33.412,66.588,52.754,47.246,19.44,288,1.000,bicubic,-49.312,-43.670,-15
gernet_l,33.357,66.643,51.901,48.099,31.08,256,0.875,bilinear,-47.997,-43.635,+27
tresnet_xl,33.257,66.743,52.294,47.706,78.44,224,0.875,bilinear,-48.797,-43.642,+4
resnest50d_1s4x24d,33.147,66.853,52.839,47.161,25.68,224,0.875,bicubic,-47.841,-42.483,+37
twins_pcpvt_base,33.021,66.979,52.485,47.515,43.83,224,0.900,bicubic,-49.687,-43.861,-18
rexnet_200,32.987,67.013,52.939,47.061,16.37,224,0.875,bicubic,-48.645,-42.729,+12
resnest50d,32.972,67.028,52.713,47.287,27.48,224,0.875,bilinear,-48.002,-42.665,+35
tf_efficientnetv2_s,32.915,67.085,51.726,48.274,21.46,384,1.000,bicubic,-50.979,-44.972,-50
convit_small,32.913,67.087,52.123,47.877,27.78,224,0.875,bicubic,-48.513,-43.621,+17
tf_efficientnet_b3,32.860,67.140,52.950,47.050,12.23,300,0.904,bicubic,-48.776,-42.768,+7
pnasnet5large,32.848,67.152,50.500,49.500,86.06,331,0.911,bicubic,-49.934,-45.540,-27
twins_svt_base,32.836,67.164,51.559,48.441,56.07,224,0.900,bicubic,-50.300,-44.859,-36
nasnetalarge,32.775,67.225,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.906,-24
gernet_m,32.740,67.260,51.913,48.087,21.14,224,0.875,bilinear,-47.992,-43.271,+36
inception_resnet_v2,32.738,67.262,50.648,49.352,55.84,299,0.897,bicubic,-47.720,-44.658,+45
gluon_resnet152_v1d,32.734,67.266,51.088,48.912,60.21,224,0.875,bicubic,-47.740,-44.118,+42
pit_b_224,32.718,67.282,49.852,50.148,73.76,224,0.900,bicubic,-49.728,-45.858,-25
tf_efficientnet_b2_ap,32.681,67.319,52.239,47.761,9.11,260,0.890,bicubic,-47.619,-42.789,+50
tresnet_l,32.559,67.441,51.139,48.861,55.99,224,0.875,bilinear,-48.931,-44.485,+5
cait_xxs36_384,32.549,67.451,52.233,47.767,17.37,384,1.000,bicubic,-49.645,-43.915,-17
vit_base_patch32_384,32.461,67.539,52.444,47.556,88.30,384,1.000,bicubic,-49.191,-43.684,-5
wide_resnet50_2,32.439,67.561,51.459,48.541,68.88,224,0.875,bicubic,-49.017,-44.073,+3
resnetv2_50x3_bitm,32.410,67.590,54.314,45.686,217.32,480,1.000,bilinear,-51.374,-42.792,-61
ens_adv_inception_resnet_v2,32.370,67.629,50.427,49.573,55.84,299,0.897,bicubic,-47.611,-44.509,+59
vit_deit_base_patch16_224,32.363,67.637,51.011,48.989,86.57,224,0.900,bicubic,-49.635,-44.723,-17
swin_small_patch4_window7_224,32.341,67.659,50.905,49.095,49.61,224,0.900,bicubic,-50.871,-45.417,-52
gluon_resnet152_v1s,32.331,67.669,50.526,49.474,60.32,224,0.875,bicubic,-48.685,-44.886,+13
vit_deit_small_distilled_patch16_224,32.284,67.716,52.102,47.898,22.44,224,0.900,bicubic,-48.916,-43.276,+7
gluon_seresnext101_64x4d,32.205,67.795,50.319,49.681,88.23,224,0.875,bicubic,-48.689,-44.989,+17
coat_lite_small,32.127,67.873,49.934,50.066,19.84,224,0.900,bicubic,-50.181,-45.916,-34
gluon_seresnext101_32x4d,32.107,67.893,51.237,48.763,48.96,224,0.875,bicubic,-48.797,-44.057,+14
vit_deit_base_patch16_384,31.989,68.011,50.547,49.453,86.86,384,1.000,bicubic,-51.117,-45.825,-55
seresnext50_32x4d,31.985,68.015,51.231,48.769,27.56,224,0.875,bicubic,-49.281,-44.389,0
levit_384,31.877,68.123,50.598,49.402,39.13,224,0.900,bicubic,-50.709,-45.418,-43
resnetrs101,31.858,68.142,51.017,48.983,63.62,288,0.940,bicubic,-50.430,-44.991,-36
cspresnext50,31.822,68.178,51.602,48.398,20.57,224,0.875,bilinear,-48.218,-43.342,+45
eca_nfnet_l0,31.657,68.343,51.654,48.346,24.14,288,1.000,bicubic,-50.931,-44.820,-47
tnt_s_patch16_224,31.643,68.357,51.143,48.857,23.76,224,0.900,bicubic,-49.875,-44.605,-17
resnet50,31.547,68.453,50.170,49.830,25.56,224,0.875,bicubic,-47.491,-44.220,+96
ssl_resnext101_32x4d,31.423,68.577,52.121,47.879,44.18,224,0.875,bilinear,-49.501,-43.607,+4
inception_v4,31.378,68.622,49.244,50.756,42.68,299,0.875,bicubic,-48.790,-45.724,+35
rexnet_150,31.366,68.634,51.288,48.712,9.73,224,0.875,bicubic,-48.944,-43.878,+23
pit_s_224,31.333,68.667,49.661,50.339,23.46,224,0.900,bicubic,-49.761,-45.671,-5
cait_xxs36_224,31.278,68.722,50.616,49.384,17.30,224,1.000,bicubic,-48.472,-44.250,+53
cspresnet50,31.270,68.730,51.223,48.777,21.62,256,0.887,bilinear,-48.304,-43.489,+60
coat_mini,31.203,68.797,49.773,50.227,10.34,224,0.900,bicubic,-50.065,-45.619,-14
ecaresnetlight,31.121,68.879,50.243,49.757,30.16,224,0.875,bicubic,-49.341,-45.007,+12
gluon_resnet101_v1s,31.115,68.885,49.793,50.207,44.67,224,0.875,bicubic,-49.187,-45.367,+18
tf_efficientnet_cc_b0_8e,31.087,68.913,50.761,49.239,24.01,224,0.875,bicubic,-46.821,-42.892,+132
ecaresnet50d,31.058,68.942,50.848,49.152,25.58,224,0.875,bicubic,-49.534,-44.472,+4
ecaresnet50t,31.058,68.942,50.577,49.423,25.57,320,0.950,bicubic,-51.288,-45.561,-56
resnet50d,31.020,68.980,49.808,50.192,25.58,224,0.875,bicubic,-49.510,-45.352,+3
cspdarknet53,31.018,68.981,50.390,49.610,27.64,256,0.887,bilinear,-49.040,-44.694,+27
gluon_resnet152_v1c,30.991,69.009,48.924,51.076,60.21,224,0.875,bicubic,-48.919,-45.916,+32
gluon_resnext101_64x4d,30.987,69.013,48.549,51.451,83.46,224,0.875,bicubic,-49.617,-46.439,-2
twins_svt_small,30.985,69.015,49.223,50.777,24.06,224,0.900,bicubic,-50.697,-46.447,-41
tf_efficientnet_cc_b1_8e,30.899,69.101,50.080,49.920,39.72,240,0.882,bicubic,-48.409,-44.290,+59
ecaresnet101d_pruned,30.897,69.103,50.013,49.987,24.88,224,0.875,bicubic,-49.921,-45.615,-10
gluon_resnext101_32x4d,30.877,69.123,48.537,51.463,44.18,224,0.875,bicubic,-49.457,-46.389,+4
tf_efficientnetv2_b3,30.861,69.139,49.814,50.186,14.36,300,0.904,bicubic,-51.109,-45.968,-50
tf_efficientnet_lite4,30.830,69.170,50.386,49.614,13.01,380,0.920,bilinear,-50.706,-45.282,-41
nf_resnet50,30.775,69.225,50.074,49.926,25.56,288,0.940,bicubic,-49.919,-45.282,-11
dpn107,30.678,69.322,48.810,51.190,86.92,224,0.875,bicubic,-49.478,-46.100,+14
ese_vovnet39b,30.657,69.343,49.875,50.125,24.57,224,0.875,bicubic,-48.663,-44.837,+50
gluon_resnet152_v1b,30.623,69.376,48.521,51.479,60.19,224,0.875,bicubic,-49.063,-46.215,+36
tresnet_xl_448,30.614,69.386,49.069,50.931,78.44,448,0.875,bilinear,-52.436,-47.105,-86
ssl_resnext50_32x4d,30.594,69.406,50.657,49.343,25.03,224,0.875,bilinear,-49.724,-44.749,-3
gluon_resnet101_v1d,30.523,69.477,47.950,52.050,44.57,224,0.875,bicubic,-49.891,-47.064,-7
dpn68b,30.517,69.483,49.158,50.842,12.61,224,0.875,bicubic,-48.699,-45.256,+57
resnest26d,30.490,69.510,50.677,49.323,17.07,224,0.875,bilinear,-47.988,-43.621,+84
efficientnet_b2,30.435,69.565,49.698,50.302,9.11,288,1.000,bicubic,-50.177,-45.620,-19
tf_efficientnet_b1_ap,30.421,69.579,49.553,50.447,7.79,240,0.882,bicubic,-48.859,-44.753,+50
twins_pcpvt_small,30.382,69.618,49.386,50.614,24.11,224,0.900,bicubic,-50.706,-46.256,-34
visformer_small,30.329,69.671,48.285,51.715,40.22,224,0.900,bicubic,-51.777,-47.587,-68
pit_xs_distilled_224,30.278,69.722,49.836,50.164,11.00,224,0.900,bicubic,-49.028,-44.528,+44
seresnet50,30.077,69.923,49.292,50.708,28.09,224,0.875,bicubic,-50.197,-45.778,-7
dpn98,30.067,69.933,48.244,51.756,61.57,224,0.875,bicubic,-49.575,-46.354,+26
tf_efficientnet_b2,30.026,69.974,49.581,50.419,9.11,260,0.890,bicubic,-50.060,-45.328,+1
dpn131,30.024,69.976,48.146,51.854,79.25,224,0.875,bicubic,-49.798,-46.564,+15
efficientnet_el,30.018,69.982,48.834,51.166,10.59,300,0.904,bicubic,-51.298,-46.692,-50
legacy_senet154,30.001,69.999,48.034,51.966,115.09,224,0.875,bilinear,-51.309,-47.462,-50
dpn92,29.953,70.047,49.162,50.838,37.67,224,0.875,bicubic,-50.055,-45.674,0
gluon_senet154,29.877,70.123,47.894,52.106,115.09,224,0.875,bicubic,-51.357,-47.454,-49
xception,29.865,70.135,48.686,51.314,22.86,299,0.897,bicubic,-49.187,-45.706,+46
adv_inception_v3,29.816,70.184,47.847,52.153,23.83,299,0.875,bicubic,-47.766,-45.889,+100
gluon_xception65,29.784,70.216,47.755,52.245,39.92,299,0.903,bicubic,-49.932,-47.105,+11
resnetblur50,29.625,70.375,48.248,51.752,25.56,224,0.875,bicubic,-49.661,-46.390,+30
efficientnet_em,29.486,70.514,48.946,51.054,6.90,240,0.882,bicubic,-49.766,-45.848,+31
resnext101_32x8d,29.439,70.561,48.486,51.514,88.79,224,0.875,bilinear,-49.869,-46.032,+22
coat_lite_mini,29.433,70.567,47.724,52.276,11.01,224,0.900,bicubic,-49.655,-46.880,+36
ssl_resnet50,29.423,70.577,49.781,50.219,25.56,224,0.875,bilinear,-49.799,-45.051,+29
vit_deit_small_patch16_224,29.421,70.579,48.256,51.744,22.05,224,0.900,bicubic,-50.435,-46.796,-3
nf_regnet_b1,29.397,70.603,49.445,50.555,10.22,288,0.900,bicubic,-49.909,-45.303,+21
cait_xxs24_384,29.387,70.612,48.753,51.247,12.03,384,1.000,bicubic,-51.578,-46.893,-52
swin_tiny_patch4_window7_224,29.334,70.666,47.602,52.398,28.29,224,0.900,bicubic,-52.044,-47.938,-66
resnext50_32x4d,29.331,70.669,47.397,52.603,25.03,224,0.875,bicubic,-50.438,-47.201,-3
resnet34d,29.328,70.671,48.409,51.591,21.82,224,0.875,bicubic,-47.788,-44.973,+102
cait_xxs24_224,29.303,70.697,48.535,51.465,11.96,224,1.000,bicubic,-49.083,-45.775,+56
ecaresnet50d_pruned,29.215,70.785,48.453,51.547,19.94,224,0.875,bicubic,-50.501,-46.427,-3
tresnet_l_448,29.165,70.835,47.232,52.768,55.99,448,0.875,bilinear,-53.103,-48.744,-94
gluon_inception_v3,29.124,70.876,46.957,53.043,23.83,299,0.875,bicubic,-49.682,-47.413,+36
xception71,29.047,70.953,47.405,52.595,42.34,299,0.903,bicubic,-50.826,-47.517,-15
hrnet_w64,28.989,71.011,47.142,52.858,128.06,224,0.875,bilinear,-50.485,-47.510,+3
resnetv2_101x1_bitm,28.910,71.090,49.502,50.498,44.54,480,1.000,bilinear,-53.302,-46.970,-96
tf_efficientnet_b0_ns,28.902,71.098,49.011,50.989,5.29,224,0.875,bicubic,-49.756,-45.365,+39
xception65,28.896,71.104,47.167,52.833,39.92,299,0.903,bicubic,-50.656,-47.487,-3
tf_efficientnet_b1,28.886,71.114,47.503,52.497,7.79,240,0.882,bicubic,-49.940,-46.695,+29
gluon_resnet101_v1b,28.878,71.121,46.389,53.611,44.55,224,0.875,bicubic,-50.427,-48.135,+5
skresnext50_32x4d,28.818,71.182,46.497,53.503,27.48,224,0.875,bicubic,-51.338,-48.145,-33
tf_efficientnet_lite3,28.660,71.340,47.354,52.646,8.20,300,0.904,bilinear,-51.160,-47.560,-18
gluon_seresnext50_32x4d,28.651,71.349,46.436,53.564,27.56,224,0.875,bicubic,-51.267,-48.386,-29
skresnet34,28.645,71.355,47.953,52.047,22.28,224,0.875,bicubic,-48.267,-45.369,+95
hrnet_w40,28.641,71.359,47.454,52.546,57.56,224,0.875,bilinear,-50.279,-47.016,+20
tv_resnet152,28.533,71.467,47.118,52.882,60.19,224,0.875,bilinear,-49.779,-46.920,+43
repvgg_b2,28.427,71.573,47.038,52.962,89.02,224,0.875,bilinear,-50.365,-47.376,+24
hrnet_w48,28.413,71.587,47.586,52.414,77.47,224,0.875,bilinear,-50.887,-46.926,0
gluon_resnext50_32x4d,28.375,71.624,45.328,54.672,25.03,224,0.875,bicubic,-50.978,-49.098,-8
xception,29.865,70.135,48.686,51.314,22.86,299,0.897,bicubic,-49.187,-45.706,+50
adv_inception_v3,29.816,70.184,47.847,52.153,23.83,299,0.875,bicubic,-47.766,-45.889,+107
gluon_xception65,29.784,70.216,47.755,52.245,39.92,299,0.903,bicubic,-49.932,-47.105,+14
resnetblur50,29.625,70.375,48.248,51.752,25.56,224,0.875,bicubic,-49.661,-46.390,+34
efficientnet_em,29.486,70.514,48.946,51.054,6.90,240,0.882,bicubic,-49.766,-45.848,+35
resnext101_32x8d,29.439,70.561,48.486,51.514,88.79,224,0.875,bilinear,-49.869,-46.032,+26
coat_lite_mini,29.433,70.567,47.724,52.276,11.01,224,0.900,bicubic,-49.655,-46.880,+40
ssl_resnet50,29.423,70.577,49.781,50.219,25.56,224,0.875,bilinear,-49.799,-45.051,+33
vit_deit_small_patch16_224,29.421,70.579,48.256,51.744,22.05,224,0.900,bicubic,-50.435,-46.796,-1
nf_regnet_b1,29.397,70.603,49.445,50.555,10.22,288,0.900,bicubic,-49.909,-45.303,+25
cait_xxs24_384,29.387,70.612,48.753,51.247,12.03,384,1.000,bicubic,-51.578,-46.893,-51
swin_tiny_patch4_window7_224,29.334,70.666,47.602,52.398,28.29,224,0.900,bicubic,-52.044,-47.938,-67
resnext50_32x4d,29.331,70.669,47.397,52.603,25.03,224,0.875,bicubic,-50.438,-47.201,0
resnet34d,29.328,70.671,48.409,51.591,21.82,224,0.875,bicubic,-47.788,-44.973,+109
cait_xxs24_224,29.303,70.697,48.535,51.465,11.96,224,1.000,bicubic,-49.083,-45.775,+62
ecaresnet50d_pruned,29.215,70.785,48.453,51.547,19.94,224,0.875,bicubic,-50.501,-46.427,0
tresnet_l_448,29.165,70.835,47.232,52.768,55.99,448,0.875,bilinear,-53.103,-48.744,-100
gluon_inception_v3,29.122,70.878,46.957,53.043,23.83,299,0.875,bicubic,-49.684,-47.413,+40
xception71,29.047,70.953,47.405,52.595,42.34,299,0.903,bicubic,-50.826,-47.517,-13
hrnet_w64,28.989,71.011,47.142,52.858,128.06,224,0.875,bilinear,-50.485,-47.510,+6
resnetv2_101x1_bitm,28.910,71.090,49.502,50.498,44.54,480,1.000,bilinear,-53.302,-46.970,-102
tf_efficientnet_b0_ns,28.902,71.098,49.011,50.989,5.29,224,0.875,bicubic,-49.756,-45.365,+43
xception65,28.896,71.104,47.167,52.833,39.92,299,0.903,bicubic,-50.656,-47.487,0
tf_efficientnet_b1,28.886,71.114,47.503,52.497,7.79,240,0.882,bicubic,-49.940,-46.695,+33
gluon_resnet101_v1b,28.878,71.121,46.389,53.611,44.55,224,0.875,bicubic,-50.427,-48.135,+9
skresnext50_32x4d,28.818,71.182,46.497,53.503,27.48,224,0.875,bicubic,-51.338,-48.145,-31
levit_256,28.745,71.255,46.723,53.277,18.89,224,0.900,bicubic,-52.765,-48.767,-88
tf_efficientnet_lite3,28.660,71.340,47.354,52.646,8.20,300,0.904,bilinear,-51.160,-47.560,-16
gluon_seresnext50_32x4d,28.651,71.349,46.436,53.564,27.56,224,0.875,bicubic,-51.267,-48.386,-28
skresnet34,28.645,71.355,47.953,52.047,22.28,224,0.875,bicubic,-48.267,-45.369,+101
hrnet_w40,28.641,71.359,47.454,52.546,57.56,224,0.875,bilinear,-50.279,-47.016,+23
tf_efficientnetv2_b0,28.566,71.434,47.079,52.921,7.14,224,0.875,bicubic,-49.790,-46.945,+48
tv_resnet152,28.533,71.467,47.118,52.882,60.19,224,0.875,bilinear,-49.779,-46.920,+48
repvgg_b2,28.427,71.573,47.038,52.962,89.02,224,0.875,bilinear,-50.365,-47.376,+26
hrnet_w48,28.413,71.587,47.586,52.414,77.47,224,0.875,bilinear,-50.887,-46.926,+2
gluon_resnext50_32x4d,28.375,71.624,45.328,54.672,25.03,224,0.875,bicubic,-50.978,-49.098,-6
efficientnet_b2_pruned,28.362,71.638,47.051,52.949,8.31,260,0.890,bicubic,-51.554,-47.805,-35
tf_efficientnet_b0_ap,28.346,71.654,47.531,52.469,5.29,224,0.875,bicubic,-48.740,-45.725,+82
tf_efficientnet_cc_b0_4e,28.315,71.685,47.364,52.636,13.31,224,0.875,bicubic,-48.991,-45.970,+74
dla102x2,28.313,71.687,46.761,53.239,41.28,224,0.875,bilinear,-51.135,-47.879,-14
dla169,28.313,71.687,47.391,52.609,53.39,224,0.875,bilinear,-50.375,-46.945,+20
mixnet_xl,28.287,71.713,46.702,53.298,11.90,224,0.875,bicubic,-52.189,-48.234,-67
gluon_resnet50_v1d,28.246,71.754,45.878,54.122,25.58,224,0.875,bicubic,-50.828,-48.592,+4
wide_resnet101_2,28.108,71.892,46.401,53.599,126.89,224,0.875,bilinear,-50.748,-47.881,+10
gluon_resnet101_v1c,28.104,71.896,45.961,54.039,44.57,224,0.875,bicubic,-51.430,-48.617,-22
regnetx_320,28.093,71.907,45.126,54.874,107.81,224,0.875,bicubic,-52.153,-49.900,-57
densenet161,28.081,71.919,46.641,53.359,28.68,224,0.875,bicubic,-49.277,-46.997,+65
regnety_320,28.059,71.941,45.444,54.556,145.05,224,0.875,bicubic,-52.753,-49.800,-81
gernet_s,28.022,71.978,46.723,53.277,8.17,224,0.875,bilinear,-48.894,-46.409,+76
efficientnet_el_pruned,28.016,71.984,46.790,53.210,10.59,300,0.904,bicubic,-52.284,-48.428,-65
xception41,27.888,72.112,45.890,54.110,26.97,299,0.903,bicubic,-50.628,-48.388,+14
regnetx_160,27.817,72.183,45.617,54.383,54.28,224,0.875,bicubic,-52.039,-49.213,-45
tf_inception_v3,27.782,72.218,45.719,54.281,23.83,299,0.875,bicubic,-50.074,-47.921,+44
res2net101_26w_4s,27.768,72.232,45.179,54.821,45.21,224,0.875,bilinear,-51.430,-49.253,-11
repvgg_b1,27.656,72.344,46.531,53.469,57.42,224,0.875,bilinear,-50.710,-47.567,+20
tf_efficientnet_b0_ap,28.346,71.654,47.531,52.469,5.29,224,0.875,bicubic,-48.740,-45.725,+87
tf_efficientnet_cc_b0_4e,28.315,71.685,47.364,52.636,13.31,224,0.875,bicubic,-48.991,-45.970,+79
dla169,28.313,71.687,47.391,52.609,53.39,224,0.875,bilinear,-50.375,-46.945,+22
dla102x2,28.313,71.687,46.761,53.239,41.28,224,0.875,bilinear,-51.135,-47.879,-12
mixnet_xl,28.287,71.713,46.702,53.298,11.90,224,0.875,bicubic,-52.189,-48.234,-68
gluon_resnet50_v1d,28.246,71.754,45.878,54.122,25.58,224,0.875,bicubic,-50.828,-48.592,+6
wide_resnet101_2,28.108,71.892,46.401,53.599,126.89,224,0.875,bilinear,-50.748,-47.881,+12
gluon_resnet101_v1c,28.104,71.896,45.961,54.039,44.57,224,0.875,bicubic,-51.430,-48.617,-21
regnetx_320,28.093,71.907,45.126,54.874,107.81,224,0.875,bicubic,-52.153,-49.900,-58
densenet161,28.081,71.919,46.641,53.359,28.68,224,0.875,bicubic,-49.277,-46.997,+70
regnety_320,28.059,71.941,45.444,54.556,145.05,224,0.875,bicubic,-52.753,-49.800,-82
gernet_s,28.022,71.978,46.723,53.277,8.17,224,0.875,bilinear,-48.894,-46.409,+81
efficientnet_el_pruned,28.016,71.984,46.790,53.210,10.59,300,0.904,bicubic,-52.284,-48.428,-66
levit_192,28.016,71.984,45.880,54.120,10.95,224,0.900,bicubic,-51.826,-48.906,-41
xception41,27.888,72.112,45.890,54.110,26.97,299,0.903,bicubic,-50.628,-48.388,+15
regnetx_160,27.817,72.183,45.617,54.383,54.28,224,0.875,bicubic,-52.039,-49.213,-46
tf_inception_v3,27.780,72.220,45.721,54.279,23.83,299,0.875,bicubic,-50.078,-48.149,+46
res2net101_26w_4s,27.768,72.232,45.179,54.821,45.21,224,0.875,bilinear,-51.430,-49.253,-10
tf_efficientnetv2_b1,27.760,72.240,46.578,53.422,8.14,240,0.882,bicubic,-51.702,-48.144,-29
repvgg_b1,27.656,72.344,46.531,53.469,57.42,224,0.875,bilinear,-50.710,-47.567,+22
hrnet_w44,27.621,72.379,45.837,54.163,67.06,224,0.875,bilinear,-51.275,-48.531,-3
inception_v3,27.556,72.444,45.263,54.737,23.83,299,0.875,bicubic,-49.882,-48.213,+52
pit_xs_224,27.491,72.509,45.900,54.100,10.62,224,0.900,bicubic,-50.691,-48.268,+23
inception_v3,27.556,72.444,45.263,54.737,23.83,299,0.875,bicubic,-49.882,-48.213,+55
pit_xs_224,27.491,72.509,45.900,54.100,10.62,224,0.900,bicubic,-50.691,-48.268,+26
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,+20
hrnet_w32,27.369,72.631,45.994,54.006,41.23,224,0.875,bilinear,-51.081,-48.192,+9
gluon_resnet50_v1s,27.326,72.674,45.222,54.778,25.68,224,0.875,bicubic,-51.386,-49.016,-2
densenet201,27.265,72.735,46.222,53.778,20.01,224,0.875,bicubic,-50.021,-47.256,+51
densenetblur121d,27.228,72.772,46.299,53.701,8.00,224,0.875,bicubic,-49.360,-46.893,+70
regnety_064,27.220,72.780,44.847,55.153,30.58,224,0.875,bicubic,-52.502,-49.921,-51
efficientnet_b1_pruned,27.181,72.819,45.872,54.128,6.33,240,0.882,bicubic,-51.055,-47.962,+13
resnetrs50,27.110,72.890,45.029,54.971,35.69,224,0.910,bicubic,-52.782,-49.939,-63
rexnet_130,27.094,72.906,45.933,54.067,7.56,224,0.875,bicubic,-52.406,-48.749,-44
vit_small_patch16_224,27.086,72.914,45.701,54.299,48.75,224,0.900,bicubic,-50.772,-47.715,+27
res2net50_26w_8s,27.078,72.921,44.428,55.572,48.40,224,0.875,bilinear,-52.119,-49.940,-28
dla102x,27.061,72.939,45.475,54.525,26.31,224,0.875,bilinear,-51.449,-48.753,-5
tv_resnet101,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.411,-48.306,+39
resnext50d_32x4d,26.876,73.124,44.436,55.564,25.05,224,0.875,bicubic,-52.800,-50.430,-55
regnetx_120,26.868,73.132,44.682,55.318,46.11,224,0.875,bicubic,-52.728,-50.056,-54
rexnet_100,26.831,73.169,45.369,54.631,4.80,224,0.875,bicubic,-51.027,-48.501,+20
densenet169,26.829,73.171,45.373,54.627,14.15,224,0.875,bicubic,-49.077,-47.653,+67
legacy_seresnext101_32x4d,26.811,73.189,43.497,56.503,48.96,224,0.875,bilinear,-53.417,-51.521,-87
regnety_120,26.788,73.212,44.454,55.546,51.82,224,0.875,bicubic,-53.578,-50.672,-98
regnetx_064,26.784,73.216,44.927,55.073,26.21,224,0.875,bicubic,-52.288,-49.531,-31
regnetx_032,26.703,73.297,45.236,54.764,15.30,224,0.875,bicubic,-51.469,-48.852,+2
legacy_seresnet152,26.676,73.324,43.947,56.053,66.82,224,0.875,bilinear,-51.984,-50.423,-19
densenet121,26.664,73.336,45.900,54.100,7.98,224,0.875,bicubic,-48.914,-46.752,+65
efficientnet_es,26.621,73.379,45.112,54.888,5.44,224,0.875,bicubic,-51.445,-48.814,+3
res2net50_26w_6s,26.595,73.405,43.990,56.010,37.05,224,0.875,bilinear,-51.975,-50.134,-20
repvgg_b1g4,26.579,73.421,45.084,54.916,39.97,224,0.875,bilinear,-51.015,-48.742,+18
dla60x,26.552,73.448,45.023,54.977,17.35,224,0.875,bilinear,-51.694,-48.995,-9
regnety_080,26.524,73.476,44.359,55.641,39.18,224,0.875,bicubic,-53.352,-50.471,-82
coat_lite_tiny,26.507,73.493,44.644,55.356,5.72,224,0.900,bicubic,-51.005,-49.272,+19
tf_efficientnet_b0,26.485,73.515,45.646,54.354,5.29,224,0.875,bicubic,-50.363,-47.582,+38
res2net50_14w_8s,26.483,73.517,44.371,55.629,25.06,224,0.875,bilinear,-51.667,-49.477,-7
mobilenetv3_large_100_miil,26.481,73.519,44.473,55.527,5.48,224,0.875,bilinear,-51.435,-48.437,+1
gluon_resnet50_v1b,26.436,73.564,44.035,55.965,25.56,224,0.875,bicubic,-51.144,-49.681,+13
tf_efficientnet_el,26.357,73.643,44.175,55.825,10.59,300,0.904,bicubic,-53.893,-50.953,-105
regnetx_040,26.243,73.757,44.438,55.562,22.12,224,0.875,bicubic,-52.239,-49.806,-27
dpn68,26.129,73.871,44.228,55.772,12.61,224,0.875,bicubic,-50.189,-48.750,+43
efficientnet_b1,26.061,73.939,44.080,55.920,7.79,256,1.000,bicubic,-52.733,-50.262,-39
hrnet_w18,25.986,74.014,44.813,55.187,21.30,224,0.875,bilinear,-50.772,-48.631,+32
hardcorenas_f,25.951,74.049,44.220,55.780,8.20,224,0.875,bilinear,-52.153,-49.582,-13
regnety_040,25.923,74.077,43.848,56.152,20.65,224,0.875,bicubic,-53.297,-50.808,-59
resnet34,25.888,74.112,43.982,56.018,21.80,224,0.875,bilinear,-49.222,-48.302,+57
res2net50_26w_4s,25.866,74.134,43.155,56.845,25.70,224,0.875,bilinear,-52.098,-50.699,-10
tresnet_m_448,25.852,74.148,42.874,57.126,31.39,448,0.875,bilinear,-55.862,-52.698,-165
hrnet_w30,27.381,72.619,46.554,53.446,37.71,224,0.875,bilinear,-50.825,-47.668,+23
hrnet_w32,27.369,72.631,45.994,54.006,41.23,224,0.875,bilinear,-51.081,-48.192,+10
gluon_resnet50_v1s,27.326,72.674,45.222,54.778,25.68,224,0.875,bicubic,-51.384,-49.016,-2
densenet201,27.265,72.735,46.222,53.778,20.01,224,0.875,bicubic,-50.021,-47.256,+54
densenetblur121d,27.228,72.772,46.299,53.701,8.00,224,0.875,bicubic,-49.360,-46.893,+73
regnety_064,27.220,72.780,44.847,55.153,30.58,224,0.875,bicubic,-52.502,-49.921,-52
efficientnet_b1_pruned,27.181,72.819,45.872,54.128,6.33,240,0.882,bicubic,-51.055,-47.962,+16
tf_efficientnetv2_b2,27.163,72.837,44.570,55.430,10.10,260,0.890,bicubic,-53.045,-50.472,-78
resnetrs50,27.110,72.890,45.029,54.971,35.69,224,0.910,bicubic,-52.782,-49.939,-66
rexnet_130,27.094,72.906,45.933,54.067,7.56,224,0.875,bicubic,-52.406,-48.749,-46
vit_small_patch16_224,27.086,72.914,45.701,54.299,48.75,224,0.900,bicubic,-50.772,-47.939,+29
res2net50_26w_8s,27.078,72.921,44.428,55.572,48.40,224,0.875,bilinear,-52.119,-49.940,-29
dla102x,27.061,72.939,45.475,54.525,26.31,224,0.875,bilinear,-51.449,-48.753,-6
tv_resnet101,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.411,-48.306,+41
resnext50d_32x4d,26.876,73.124,44.436,55.564,25.05,224,0.875,bicubic,-52.800,-50.430,-57
regnetx_120,26.868,73.132,44.682,55.318,46.11,224,0.875,bicubic,-52.728,-50.056,-56
rexnet_100,26.831,73.169,45.369,54.631,4.80,224,0.875,bicubic,-51.027,-48.047,+24
densenet169,26.829,73.171,45.373,54.627,14.15,224,0.875,bicubic,-49.077,-47.653,+70
legacy_seresnext101_32x4d,26.811,73.189,43.497,56.503,48.96,224,0.875,bilinear,-53.417,-51.521,-91
regnety_120,26.788,73.212,44.454,55.546,51.82,224,0.875,bicubic,-53.578,-50.672,-102
regnetx_064,26.784,73.216,44.927,55.073,26.21,224,0.875,bicubic,-52.288,-49.531,-32
regnetx_032,26.703,73.297,45.236,54.764,15.30,224,0.875,bicubic,-51.469,-48.852,+4
legacy_seresnet152,26.676,73.324,43.947,56.053,66.82,224,0.875,bilinear,-51.984,-50.423,-20
densenet121,26.664,73.336,45.900,54.100,7.98,224,0.875,bicubic,-48.914,-46.752,+68
efficientnet_es,26.621,73.379,45.112,54.888,5.44,224,0.875,bicubic,-51.445,-48.814,+5
res2net50_26w_6s,26.595,73.405,43.990,56.010,37.05,224,0.875,bilinear,-51.975,-50.134,-21
repvgg_b1g4,26.579,73.421,45.084,54.916,39.97,224,0.875,bilinear,-51.015,-48.742,+20
dla60x,26.552,73.448,45.023,54.977,17.35,224,0.875,bilinear,-51.694,-48.995,-7
regnety_080,26.524,73.476,44.359,55.641,39.18,224,0.875,bicubic,-53.352,-50.471,-85
coat_lite_tiny,26.507,73.493,44.644,55.356,5.72,224,0.900,bicubic,-51.005,-49.272,+21
tf_efficientnet_b0,26.485,73.515,45.646,54.354,5.29,224,0.875,bicubic,-50.363,-47.582,+40
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,26.481,73.519,44.473,55.527,5.48,224,0.875,bilinear,-51.435,-48.437,+3
gluon_resnet50_v1b,26.436,73.564,44.035,55.965,25.56,224,0.875,bicubic,-51.144,-49.681,+15
tf_efficientnet_el,26.357,73.643,44.175,55.825,10.59,300,0.904,bicubic,-53.893,-50.953,-109
levit_128,26.332,73.668,44.096,55.904,9.21,224,0.900,bicubic,-52.154,-49.914,-28
regnetx_040,26.243,73.757,44.438,55.562,22.12,224,0.875,bicubic,-52.239,-49.806,-28
dpn68,26.129,73.871,44.228,55.772,12.61,224,0.875,bicubic,-50.189,-48.750,+45
efficientnet_b1,26.061,73.939,44.080,55.920,7.79,256,1.000,bicubic,-52.733,-50.262,-41
hrnet_w18,25.986,74.014,44.813,55.187,21.30,224,0.875,bilinear,-50.772,-48.631,+33
hardcorenas_f,25.951,74.049,44.220,55.780,8.20,224,0.875,bilinear,-52.153,-49.582,-12
regnety_040,25.923,74.077,43.848,56.152,20.65,224,0.875,bicubic,-53.297,-50.808,-61
resnet34,25.888,74.112,43.982,56.018,21.80,224,0.875,bilinear,-49.222,-48.302,+59
res2net50_26w_4s,25.866,74.134,43.155,56.845,25.70,224,0.875,bilinear,-52.098,-50.699,-9
tresnet_m_448,25.852,74.148,42.874,57.126,31.39,448,0.875,bilinear,-55.862,-52.698,-175
coat_tiny,25.843,74.157,43.276,56.724,5.50,224,0.900,bicubic,-52.591,-50.761,-32
hardcorenas_c,25.815,74.185,44.772,55.228,5.52,224,0.875,bilinear,-51.239,-48.386,+18
gluon_resnet50_v1c,25.784,74.216,43.031,56.969,25.58,224,0.875,bicubic,-52.228,-50.957,-16
selecsls60,25.729,74.272,44.065,55.935,30.67,224,0.875,bicubic,-52.254,-49.764,-15
hardcorenas_e,25.662,74.338,43.412,56.588,8.07,224,0.875,bilinear,-52.132,-50.282,-8
dla60_res2net,25.652,74.348,43.599,56.401,20.85,224,0.875,bilinear,-52.812,-50.607,-38
dla60_res2next,25.640,74.360,43.670,56.330,17.03,224,0.875,bilinear,-52.800,-50.482,-37
ecaresnet26t,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.316,-51.424,-100
mixnet_l,25.512,74.488,43.455,56.545,7.33,224,0.875,bicubic,-53.464,-50.727,-59
dla60_res2net,25.652,74.348,43.599,56.401,20.85,224,0.875,bilinear,-52.812,-50.607,-40
dla60_res2next,25.640,74.360,43.670,56.330,17.03,224,0.875,bilinear,-52.800,-50.482,-39
ecaresnet26t,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.316,-51.424,-105
mixnet_l,25.512,74.488,43.455,56.545,7.33,224,0.875,bicubic,-53.464,-50.727,-62
tf_efficientnet_lite1,25.499,74.501,43.585,56.415,5.42,240,0.882,bicubic,-51.143,-49.641,+20
tv_resnext50_32x4d,25.455,74.545,42.787,57.213,25.03,224,0.875,bilinear,-52.165,-50.909,-11
repvgg_a2,25.436,74.564,43.939,56.061,28.21,224,0.875,bilinear,-51.024,-49.065,+24
tf_mixnet_l,25.422,74.578,42.534,57.466,7.33,224,0.875,bicubic,-53.352,-51.464,-55
repvgg_a2,25.436,74.564,43.939,56.061,28.21,224,0.875,bilinear,-51.024,-49.065,+25
tf_mixnet_l,25.422,74.578,42.534,57.466,7.33,224,0.875,bicubic,-53.352,-51.464,-58
hardcorenas_b,25.402,74.598,44.190,55.810,5.18,224,0.875,bilinear,-51.136,-48.564,+20
res2next50,25.389,74.611,42.508,57.492,24.67,224,0.875,bilinear,-52.857,-51.384,-38
legacy_seresnet101,25.334,74.666,42.825,57.175,49.33,224,0.875,bilinear,-53.048,-51.439,-43
selecsls60b,25.332,74.668,43.559,56.441,32.77,224,0.875,bicubic,-53.080,-50.615,-46
legacy_seresnet101,25.334,74.666,42.825,57.175,49.33,224,0.875,bilinear,-53.048,-51.439,-44
selecsls60b,25.332,74.668,43.559,56.441,32.77,224,0.875,bicubic,-53.080,-50.615,-47
dla102,25.316,74.684,43.827,56.173,33.27,224,0.875,bilinear,-52.716,-50.119,-32
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,+28
legacy_seresnext50_32x4d,25.210,74.790,41.936,58.064,27.56,224,0.875,bilinear,-53.868,-52.500,-76
resnest14d,25.284,74.716,44.114,55.886,10.61,224,0.875,bilinear,-50.222,-48.404,+29
legacy_seresnext50_32x4d,25.210,74.790,41.936,58.064,27.56,224,0.875,bilinear,-53.868,-52.500,-79
mixer_b16_224,25.121,74.879,41.227,58.773,59.88,224,0.875,bicubic,-51.481,-51.001,+9
res2net50_48w_2s,25.027,74.973,42.208,57.792,25.29,224,0.875,bilinear,-52.495,-51.346,-18
efficientnet_b0,25.015,74.985,42.787,57.213,5.29,224,0.875,bicubic,-52.683,-50.745,-26
gluon_resnet34_v1b,24.939,75.061,42.243,57.757,21.80,224,0.875,bicubic,-49.649,-49.747,+40
gluon_resnet34_v1b,24.939,75.061,42.243,57.757,21.80,224,0.875,bicubic,-49.649,-49.747,+41
mobilenetv2_120d,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,-6
regnety_016,24.811,75.189,42.616,57.384,11.20,224,0.875,bicubic,-53.051,-51.104,-35
tf_efficientnet_lite2,24.530,75.470,42.280,57.720,6.09,260,0.890,bicubic,-52.938,-51.474,-22
skresnet18,24.483,75.517,42.536,57.464,11.96,224,0.875,bicubic,-48.555,-48.632,+47
skresnet18,24.483,75.517,42.536,57.464,11.96,224,0.875,bicubic,-48.555,-48.632,+49
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,+34
tf_efficientnet_lite0,24.373,75.627,42.487,57.513,4.65,224,0.875,bicubic,-50.457,-49.689,+27
hardcorenas_a,24.369,75.631,43.284,56.716,5.26,224,0.875,bilinear,-51.547,-49.230,+7
resnetv2_50x1_bitm,24.231,75.769,43.477,56.523,25.55,480,1.000,bilinear,-55.941,-52.149,-144
tv_resnet50,24.070,75.930,41.313,58.687,25.56,224,0.875,bilinear,-52.068,-51.551,+3
pit_ti_distilled_224,24.406,75.594,42.730,57.270,5.10,224,0.900,bicubic,-50.124,-49.366,+35
tf_efficientnet_lite0,24.373,75.627,42.487,57.513,4.65,224,0.875,bicubic,-50.457,-49.689,+28
hardcorenas_a,24.369,75.631,43.284,56.716,5.26,224,0.875,bilinear,-51.547,-49.230,+8
resnetv2_50x1_bitm,24.233,75.767,43.477,56.523,25.55,480,1.000,bilinear,-55.939,-52.149,-149
tv_resnet50,24.070,75.930,41.313,58.687,25.56,224,0.875,bilinear,-52.068,-51.551,+4
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,+24
resnet18d,23.929,76.071,42.300,57.700,11.71,224,0.875,bicubic,-48.331,-48.396,+46
resnet18d,23.929,76.071,42.300,57.700,11.71,224,0.875,bicubic,-48.331,-48.396,+47
efficientnet_lite0,23.909,76.091,42.088,57.912,4.65,224,0.875,bicubic,-51.575,-50.422,+10
tv_densenet121,23.844,76.156,41.925,58.075,7.98,224,0.875,bicubic,-50.894,-50.225,+22
efficientnet_es_pruned,23.828,76.172,41.995,58.005,5.44,224,0.875,bicubic,-51.172,-50.453,+18
mobilenetv2_140,23.712,76.288,41.477,58.523,6.11,224,0.875,bicubic,-52.804,-51.519,-7
mixnet_m,23.710,76.290,41.141,58.859,5.01,224,0.875,bicubic,-53.550,-52.284,-28
mixnet_m,23.710,76.290,41.141,58.859,5.01,224,0.875,bicubic,-53.550,-52.284,-29
dla34,23.669,76.331,41.551,58.449,15.74,224,0.875,bilinear,-50.961,-50.527,+20
legacy_seresnet50,23.651,76.349,40.091,59.909,28.09,224,0.875,bilinear,-53.978,-53.657,-46
ese_vovnet19b_dw,23.535,76.465,41.288,58.712,6.54,224,0.875,bicubic,-53.263,-51.980,-19
tf_mixnet_m,23.484,76.516,40.989,59.011,5.01,224,0.875,bicubic,-53.458,-52.163,-24
legacy_seresnet50,23.651,76.349,40.091,59.909,28.09,224,0.875,bilinear,-53.978,-53.657,-47
ese_vovnet19b_dw,23.535,76.465,41.288,58.712,6.54,224,0.875,bicubic,-53.263,-51.980,-20
tf_mixnet_m,23.484,76.516,40.989,59.011,5.01,224,0.875,bicubic,-53.458,-52.163,-25
tv_resnet34,23.473,76.527,41.367,58.633,21.80,224,0.875,bilinear,-49.839,-50.059,+27
tf_efficientnet_em,23.359,76.641,40.404,59.596,6.90,240,0.882,bicubic,-54.771,-53.640,-66
selecsls42b,23.357,76.643,40.677,59.323,32.46,224,0.875,bicubic,-53.817,-52.713,-34
tf_efficientnet_em,23.359,76.641,40.404,59.596,6.90,240,0.882,bicubic,-54.771,-53.640,-67
selecsls42b,23.357,76.643,40.677,59.323,32.46,224,0.875,bicubic,-53.817,-52.713,-35
repvgg_b0,23.316,76.684,41.182,58.818,15.82,224,0.875,bilinear,-51.837,-51.236,+2
mobilenetv2_110d,23.066,76.934,40.716,59.284,4.52,224,0.875,bicubic,-51.970,-51.470,+6
vit_deit_tiny_distilled_patch16_224,22.718,77.282,40.771,59.229,5.91,224,0.900,bicubic,-51.792,-51.119,+14
mobilenetv3_large_100,22.655,77.345,40.781,59.219,5.48,224,0.875,bicubic,-53.111,-51.761,-11
mobilenetv3_rw,22.630,77.370,40.374,59.626,5.48,224,0.875,bicubic,-53.004,-52.334,-10
tf_mobilenetv3_large_100,22.569,77.431,39.767,60.233,5.48,224,0.875,bilinear,-52.949,-52.839,-9
tf_efficientnet_es,22.413,77.587,39.095,60.905,5.44,224,0.875,bicubic,-54.180,-54.107,-25
tf_efficientnet_es,22.413,77.587,39.095,60.905,5.44,224,0.875,bicubic,-54.180,-54.107,-26
hrnet_w18_small_v2,22.337,77.663,39.861,60.139,15.60,224,0.875,bilinear,-52.777,-52.555,-3
regnety_008,22.119,77.881,38.900,61.100,6.26,224,0.875,bicubic,-54.197,-54.166,-21
seresnext26t_32x4d,21.991,78.009,38.482,61.518,16.81,224,0.875,bicubic,-55.995,-55.264,-72
regnety_006,21.971,78.029,38.955,61.045,6.06,224,0.875,bicubic,-53.275,-53.577,-9
regnetx_008,21.940,78.060,38.928,61.072,7.26,224,0.875,bicubic,-53.098,-53.408,-5
resnet26d,21.907,78.094,38.619,61.381,16.01,224,0.875,bicubic,-54.789,-54.531,-34
semnasnet_100,21.903,78.097,38.600,61.400,3.89,224,0.875,bicubic,-53.545,-54.004,-14
convit_tiny,22.282,77.718,39.669,60.331,5.71,224,0.875,bicubic,-50.834,-52.045,+18
regnety_008,22.119,77.881,38.900,61.100,6.26,224,0.875,bicubic,-54.197,-54.166,-22
seresnext26t_32x4d,21.991,78.009,38.482,61.518,16.81,224,0.875,bicubic,-55.995,-55.264,-74
regnety_006,21.971,78.029,38.955,61.045,6.06,224,0.875,bicubic,-53.275,-53.577,-10
regnetx_008,21.940,78.060,38.928,61.072,7.26,224,0.875,bicubic,-53.098,-53.408,-6
resnet26d,21.907,78.094,38.619,61.381,16.01,224,0.875,bicubic,-54.789,-54.531,-36
semnasnet_100,21.903,78.097,38.600,61.400,3.89,224,0.875,bicubic,-53.545,-54.004,-15
pit_ti_224,21.875,78.125,39.541,60.459,4.85,224,0.900,bicubic,-51.037,-51.861,+14
regnetx_006,21.738,78.263,38.904,61.096,6.20,224,0.875,bicubic,-52.115,-52.768,+6
vgg19_bn,21.628,78.373,39.283,60.717,143.68,224,0.875,bilinear,-52.587,-52.559,+1
ghostnet_100,21.620,78.380,38.692,61.308,5.18,224,0.875,bilinear,-52.358,-52.764,+3
regnetx_006,21.738,78.263,38.904,61.096,6.20,224,0.875,bicubic,-52.115,-52.768,+5
vgg19_bn,21.628,78.373,39.283,60.717,143.68,224,0.875,bilinear,-52.587,-52.559,0
ghostnet_100,21.620,78.380,38.692,61.308,5.18,224,0.875,bilinear,-52.358,-52.764,+2
gluon_resnet18_v1b,21.549,78.451,38.869,61.131,11.69,224,0.875,bicubic,-49.287,-50.893,+22
fbnetc_100,21.484,78.516,38.161,61.839,5.57,224,0.875,bilinear,-53.640,-54.224,-16
mnasnet_100,21.350,78.650,37.719,62.281,4.38,224,0.875,bicubic,-53.308,-54.395,-8
resnet26,21.295,78.705,38.018,61.982,16.00,224,0.875,bicubic,-53.997,-54.552,-21
fbnetc_100,21.484,78.516,38.161,61.839,5.57,224,0.875,bilinear,-53.640,-54.224,-17
mnasnet_100,21.350,78.650,37.719,62.281,4.38,224,0.875,bicubic,-53.308,-54.395,-9
resnet26,21.295,78.705,38.018,61.982,16.00,224,0.875,bicubic,-53.997,-54.552,-22
ssl_resnet18,21.278,78.722,39.113,60.887,11.69,224,0.875,bilinear,-51.332,-52.303,+7
mixnet_s,21.254,78.746,38.187,61.813,4.13,224,0.875,bicubic,-54.738,-54.609,-34
seresnext26d_32x4d,21.252,78.748,37.311,62.689,16.81,224,0.875,bicubic,-56.350,-56.297,-74
legacy_seresnext26_32x4d,21.093,78.907,37.633,62.367,16.79,224,0.875,bicubic,-56.011,-55.683,-58
mixnet_s,21.254,78.746,38.187,61.813,4.13,224,0.875,bicubic,-54.738,-54.609,-35
seresnext26d_32x4d,21.252,78.748,37.311,62.689,16.81,224,0.875,bicubic,-56.350,-56.297,-76
legacy_seresnext26_32x4d,21.093,78.907,37.633,62.367,16.79,224,0.875,bicubic,-56.011,-55.683,-60
regnetx_004,20.898,79.102,37.566,62.434,5.16,224,0.875,bicubic,-51.498,-53.264,+4
spnasnet_100,20.863,79.137,37.896,62.104,4.42,224,0.875,bilinear,-53.221,-53.922,-9
spnasnet_100,20.863,79.137,37.896,62.104,4.42,224,0.875,bilinear,-53.221,-53.922,-10
legacy_seresnet18,20.837,79.162,37.619,62.381,11.78,224,0.875,bicubic,-50.905,-52.715,+9
mobilenetv2_100,20.773,79.227,37.759,62.241,3.50,224,0.875,bicubic,-52.197,-53.257,-2
tf_mixnet_s,20.470,79.530,36.607,63.393,4.13,224,0.875,bicubic,-55.180,-56.021,-37
regnety_004,20.415,79.585,37.002,62.998,4.34,224,0.875,bicubic,-53.619,-54.750,-12
hrnet_w18_small,20.368,79.632,37.093,62.907,13.19,224,0.875,bilinear,-51.974,-53.585,0
tf_mobilenetv3_large_075,20.366,79.634,36.764,63.236,3.99,224,0.875,bilinear,-53.072,-54.586,-11
tf_mixnet_s,20.470,79.530,36.607,63.393,4.13,224,0.875,bicubic,-55.180,-56.021,-38
regnety_004,20.415,79.585,37.002,62.998,4.34,224,0.875,bicubic,-53.619,-54.750,-13
hrnet_w18_small,20.368,79.632,37.093,62.907,13.19,224,0.875,bilinear,-51.976,-53.585,0
tf_mobilenetv3_large_075,20.366,79.634,36.764,63.236,3.99,224,0.875,bilinear,-53.072,-54.586,-12
resnet18,20.228,79.772,37.261,62.739,11.69,224,0.875,bilinear,-49.520,-51.817,+10
mixer_l16_224,20.171,79.829,32.956,67.044,208.20,224,0.875,bicubic,-51.887,-54.712,+1
vit_deit_tiny_patch16_224,20.162,79.838,37.546,62.454,5.72,224,0.900,bicubic,-52.007,-53.572,-1
tf_mobilenetv3_large_minimal_100,20.122,79.878,36.908,63.092,3.92,224,0.875,bilinear,-52.126,-53.722,-3
vgg16_bn,19.959,80.041,36.301,63.699,138.37,224,0.875,bilinear,-53.391,-55.205,-15
vgg16_bn,19.959,80.041,36.301,63.699,138.37,224,0.875,bilinear,-53.391,-55.205,-16
vgg19,17.929,82.071,33.054,66.946,143.67,224,0.875,bilinear,-54.439,-57.818,-8
vgg13_bn,17.802,82.198,34.039,65.961,133.05,224,0.875,bilinear,-53.792,-56.337,-2
vgg16,17.540,82.460,32.773,67.227,138.36,224,0.875,bilinear,-54.054,-57.609,-2
@ -359,6 +390,6 @@ tf_mobilenetv3_small_100,16.227,83.772,31.223,68.777,2.54,224,0.875,bilinear,-51
vgg13,16.100,83.900,30.985,69.015,133.05,224,0.875,bilinear,-53.826,-58.261,-4
vgg11,15.728,84.272,30.453,69.547,132.86,224,0.875,bilinear,-53.296,-58.175,-3
tf_mobilenetv3_small_075,14.944,85.056,29.572,70.428,2.04,224,0.875,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,+1
dla46_c,14.657,85.343,29.380,70.620,1.30,224,0.875,bilinear,-50.207,-56.912,+1
dla46x_c,14.382,85.618,29.191,70.809,1.07,224,0.875,bilinear,-51.588,-57.789,-2
tf_mobilenetv3_small_minimal_100,13.964,86.036,27.988,72.012,2.04,224,0.875,bilinear,-48.942,-56.242,0

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
2 ig_resnext101_32x48d 58.810 41.190 81.076 18.924 828.41 224 0.875 bilinear -26.618 -16.496 +15 +18
3 ig_resnext101_32x32d 58.386 41.614 80.381 19.619 468.53 224 0.875 bilinear -26.708 -17.057 +22 +25
4 ig_resnext101_32x16d 57.690 42.310 79.905 20.095 194.03 224 0.875 bilinear -26.480 -17.291 +41 +47
5 swsl_resnext101_32x16d 57.458 42.542 80.385 19.615 194.03 224 0.875 bilinear -25.888 -16.461 +58 +66
6 swsl_resnext101_32x8d 56.438 43.562 78.944 21.056 88.79 224 0.875 bilinear -27.846 -18.232 +35 +41
7 ig_resnext101_32x8d 54.918 45.082 77.534 22.466 88.79 224 0.875 bilinear -27.770 -19.102 +71 +82
8 swsl_resnext101_32x4d 53.603 46.397 76.347 23.653 44.18 224 0.875 bilinear -29.627 -20.413 +58 +66
9 tf_efficientnet_l2_ns_475 51.494 48.506 73.928 26.072 480.31 475 0.936 bicubic -36.740 -24.618 -6
10 swsl_resnext50_32x4d 50.437 49.563 73.368 26.633 25.03 224 0.875 bilinear -31.745 -22.862 +79 +95
11 swin_large_patch4_window12_384 50.404 49.596 72.564 27.436 196.74 384 1.000 bicubic -36.744 -25.670 -7
12 swsl_resnet50 49.541 50.459 72.334 27.666 25.56 224 0.875 bilinear -31.625 -23.638 +103 +125
13 swin_large_patch4_window7_224 48.991 51.009 71.391 28.609 196.53 224 0.900 bicubic -37.329 -26.505 -4
14 swin_base_patch4_window12_384 48.553 51.447 71.813 28.187 87.90 384 1.000 bicubic -37.879 -26.245 -6
15 tf_efficientnet_b7_ns 47.800 52.200 69.640 30.360 66.35 600 0.949 bicubic -39.040 -28.454 -10
16 tf_efficientnet_b6_ns 47.761 52.239 69.968 30.032 43.04 528 0.942 bicubic -38.691 -27.914 -9
17 tf_efficientnet_l2_ns 47.570 52.430 70.019 29.981 480.31 800 0.960 bicubic -40.782 -28.631 -15
18 tf_efficientnet_b8_ap tf_efficientnetv2_l_in21ft1k 45.774 46.939 54.226 53.061 67.911 70.310 32.089 29.690 87.41 118.52 672 480 0.954 1.000 bicubic -39.596 -39.365 -29.383 -27.668 +2 -8
19 tf_efficientnet_b8_ap 45.774 54.226 67.911 32.089 87.41 672 0.954 bicubic -39.596 -29.383 +4
20 tf_efficientnet_b5_ns 45.615 54.385 67.842 32.158 30.39 456 0.934 bicubic -40.473 -29.910 -8
21 swin_base_patch4_window7_224 tf_efficientnetv2_m_in21ft1k 45.560 45.582 54.440 54.418 68.512 69.150 31.488 30.849 87.77 54.14 224 480 0.900 1.000 bicubic -39.692 -40.006 -29.050 -28.602 +1 -5
22 cait_m48_448 swin_base_patch4_window7_224 44.245 45.560 55.755 54.440 64.653 68.512 35.347 31.488 356.46 87.77 448 224 1.000 0.900 bicubic -42.239 -39.692 -33.102 -29.050 -15 +2
23 vit_base_r50_s16_384 cait_m48_448 43.512 44.245 56.488 55.755 66.781 64.653 33.219 35.347 98.95 356.46 384 448 1.000 bicubic -41.460 -42.239 -30.507 -33.102 +8 -17
24 tf_efficientnet_b4_ns vit_base_r50_s16_384 43.450 43.512 56.550 56.488 65.519 66.781 34.481 33.219 19.34 98.95 380 384 0.922 1.000 bicubic -41.713 -41.460 -31.951 -30.507 -1 +10
25 vit_large_patch16_384 tf_efficientnet_b4_ns 43.300 43.450 56.700 56.550 66.454 65.519 33.546 34.481 304.72 19.34 384 380 1.000 0.922 bicubic -41.858 -41.713 -30.902 -31.951 -1 0
26 tf_efficientnet_b8 vit_large_patch16_384 42.508 43.300 57.492 56.700 64.857 66.454 35.143 33.546 87.41 304.72 672 384 0.954 1.000 bicubic -42.862 -41.858 -32.533 -30.902 -6 0
27 cait_m36_384 tf_efficientnet_b8 42.398 42.508 57.602 57.492 63.324 64.857 36.676 35.143 271.22 87.41 384 672 1.000 0.954 bicubic -43.656 -42.862 -34.406 -32.533 -14 -5
28 dm_nfnet_f6 cait_m36_384 41.593 42.398 58.407 57.602 63.192 63.324 36.808 36.676 438.36 271.22 576 384 0.956 1.000 bicubic -44.704 -43.656 -34.552 -34.406 -17 -15
29 tf_efficientnet_b7 dm_nfnet_f6 41.431 41.593 58.569 58.407 63.017 63.192 36.983 36.808 66.35 438.36 600 576 0.949 0.956 bicubic -43.505 -44.704 -34.186 -34.552 +3 -18
30 tf_efficientnet_b7_ap tf_efficientnet_b7 41.429 41.431 58.571 58.569 62.874 63.017 37.126 36.983 66.35 600 0.949 bicubic -43.691 -43.505 -34.378 -34.186 -5 +5
31 tf_efficientnet_b5_ap tf_efficientnet_b7_ap 41.418 41.429 58.582 58.571 62.084 62.874 37.916 37.126 30.39 66.35 456 600 0.934 0.949 bicubic -42.834 -43.691 -34.890 -34.378 +13 -4
32 resnetv2_152x4_bitm tf_efficientnet_b5_ap 41.241 41.418 58.759 58.582 64.238 62.084 35.762 37.916 936.53 30.39 480 456 1.000 0.934 bilinear bicubic -43.691 -42.834 -33.198 -34.890 +1 +17
33 tf_efficientnet_b6_ap resnetv2_152x4_bitm 41.099 41.241 58.901 58.759 62.355 64.238 37.645 35.762 43.04 936.53 528 480 0.942 1.000 bicubic bilinear -43.689 -43.691 -34.783 -33.198 +1 +3
34 dm_nfnet_f5 tf_efficientnet_b6_ap 41.003 41.099 58.997 58.901 61.911 62.355 38.089 37.645 377.21 43.04 544 528 0.954 0.942 bicubic -44.711 -43.689 -35.531 -34.783 -20 +4
35 dm_nfnet_f3 dm_nfnet_f5 40.920 41.003 59.080 58.997 61.949 61.911 38.051 38.089 254.92 377.21 416 544 0.940 0.954 bicubic -44.640 -44.711 -35.457 -35.531 -19 -21
36 vit_large_patch16_224 tf_efficientnetv2_s_in21ft1k 40.732 40.950 59.268 59.050 63.593 63.849 36.407 36.151 304.33 21.46 224 384 0.900 1.000 bicubic -42.330 -43.352 -32.845 -33.403 +35 +10
37 tf_efficientnet_b4_ap dm_nfnet_f3 40.484 40.920 59.516 59.080 61.723 61.949 38.277 38.051 19.34 254.92 380 416 0.922 0.940 bicubic -42.764 -44.640 -34.669 -35.457 +29 -20
38 vit_base_patch16_224_miil vit_large_patch16_224 40.168 40.732 59.832 59.268 60.887 63.593 39.113 36.407 86.54 304.33 224 0.875 0.900 bilinear bicubic -44.100 -42.330 -35.915 -32.845 +5 +42
39 cait_s36_384 tf_efficientnet_b4_ap 39.765 40.484 60.235 59.516 60.475 61.723 39.525 38.277 68.37 19.34 384 380 1.000 0.922 bicubic -45.695 -42.764 -37.005 -34.669 -22 +34
40 ecaresnet269d vit_base_patch16_224_miil 39.594 40.168 60.406 59.832 60.343 60.887 39.657 39.113 102.09 86.54 352 224 1.000 0.875 bicubic bilinear -45.382 -44.100 -36.883 -35.915 -10 +8
41 tf_efficientnet_b3_ns tf_efficientnetv2_l 39.584 39.830 60.416 60.170 61.453 60.801 38.547 39.199 12.23 118.52 300 480 0.904 1.000 bicubic -44.464 -45.660 -35.457 -36.571 +10 -23
42 dm_nfnet_f4 cait_s36_384 39.474 39.765 60.526 60.235 60.420 60.475 39.580 39.525 316.07 68.37 512 384 0.951 1.000 bicubic -46.184 -45.695 -37.090 -37.005 -27 -23
43 efficientnet_b4 efficientnetv2_rw_m 39.079 39.667 60.921 60.333 59.608 59.687 40.392 40.313 19.34 53.24 384 416 1.000 bicubic -44.349 -45.141 -36.988 -37.461 +19 -6
44 tf_efficientnet_b5 ecaresnet269d 38.356 39.594 61.644 60.406 59.913 60.343 40.087 39.657 30.39 102.09 456 352 0.934 1.000 bicubic -45.456 -45.382 -36.835 -36.883 +11 -11
45 vit_deit_base_distilled_patch16_384 tf_efficientnet_b3_ns 38.260 39.584 61.740 60.416 57.783 61.453 42.217 38.547 87.63 12.23 384 300 1.000 0.904 bicubic -47.162 -44.464 -39.549 -35.457 -26 +11
46 vit_base_patch16_384 dm_nfnet_f4 38.099 39.474 61.901 60.526 60.428 60.420 39.572 39.580 86.86 316.07 384 512 1.000 0.951 bicubic -46.111 -46.184 -36.790 -37.090 -1 -31
47 cait_s24_384 efficientnet_b4 37.873 39.079 62.127 60.921 58.079 59.608 41.921 40.392 47.06 19.34 384 1.000 bicubic -47.173 -44.349 -39.267 -36.988 -20 +22
48 resnet152d tf_efficientnet_b5 37.857 38.356 62.143 61.644 58.356 59.913 41.644 40.087 60.21 30.39 320 456 1.000 0.934 bicubic -45.823 -45.456 -38.382 -36.835 +12 +13
49 resnetrs420 vit_deit_base_distilled_patch16_384 37.747 38.260 62.253 61.740 58.215 57.783 41.785 42.217 191.89 87.63 416 384 1.000 bicubic -47.261 -47.162 -38.909 -39.549 -21 -28
50 resnetrs350 vit_base_patch16_384 37.676 38.099 62.324 61.901 58.083 60.428 41.917 39.572 163.96 86.86 384 1.000 bicubic -47.044 -46.111 -38.905 -36.790 -15 0
51 cait_s24_384 37.873 62.127 58.079 41.921 47.06 384 1.000 bicubic -47.173 -39.267 -22
52 resnet152d 37.857 62.143 58.356 41.644 60.21 320 1.000 bicubic -45.823 -38.382 +14
53 tf_efficientnetv2_m 37.824 62.176 58.710 41.290 54.14 480 1.000 bicubic -47.220 -38.568 -23
54 resnetrs420 37.747 62.253 58.215 41.785 191.89 416 1.000 bicubic -47.261 -38.909 -23
55 resnetrs350 37.676 62.324 58.083 41.917 163.96 384 1.000 bicubic -47.044 -38.905 -16
56 pit_b_distilled_224 37.590 62.410 57.238 42.762 74.79 224 0.900 bicubic -46.554 -39.618 -4
57 resnet200d 37.505 62.495 58.297 41.703 64.69 320 1.000 bicubic -46.457 -38.526 +1
58 resnest269e 37.315 62.685 57.468 42.532 110.93 416 0.928 bicubic -47.203 -39.518 -16 -17
59 cait_s24_224 37.153 62.847 56.724 43.276 46.92 224 1.000 bicubic -46.299 -39.840 +7 +9
60 tf_efficientnet_b3_ap 37.055 62.945 57.240 42.760 12.23 300 0.904 bicubic -44.767 -38.384 +41 +53
61 efficientnet_v2s efficientnetv2_rw_s 37.049 62.951 56.814 43.186 23.94 384 1.000 bicubic -46.759 -39.910 0 +1
62 resnetv2_152x2_bitm 36.847 63.153 59.899 40.101 236.34 480 1.000 bilinear -47.593 -37.547 -19 -20
63 seresnet152d 36.790 63.210 56.718 43.282 66.84 320 1.000 bicubic -47.572 -40.322 -17 -18
64 resnetrs200 36.639 63.361 56.828 43.172 93.21 320 1.000 bicubic -47.427 -40.046 -10
65 efficientnet_b3 36.420 63.580 56.845 43.155 12.23 320 1.000 bicubic -45.822 -39.269 +27 +37
66 cait_xs24_384 36.416 63.584 56.944 43.056 26.67 384 1.000 bicubic -47.645 -39.945 -11
67 vit_deit_base_distilled_patch16_224 36.397 63.603 56.617 43.383 87.34 224 0.900 bicubic -46.991 -39.871 +1 +3
68 resnetrs270 36.320 63.680 56.562 43.438 129.86 352 1.000 bicubic -48.114 -40.408 -24 -25
69 tresnet_m 36.285 63.715 55.796 44.204 31.39 224 0.875 bilinear -46.795 -40.322 +6 +10
70 dm_nfnet_f2 mixer_b16_224_miil 36.257 36.269 63.743 63.731 55.847 55.965 44.153 44.035 193.78 59.88 352 224 0.920 0.875 bicubic bilinear -48.733 -46.039 -41.297 -39.751 -36 +28
71 tf_efficientnet_b2_ns dm_nfnet_f2 36.183 36.257 63.817 63.743 57.551 55.847 42.449 44.153 9.11 193.78 260 352 0.890 0.920 bicubic -46.197 -48.733 -38.697 -41.297 +17 -39
72 ecaresnet101d tf_efficientnet_b2_ns 36.004 36.183 63.996 63.817 56.165 57.551 43.835 42.449 44.57 9.11 224 260 0.875 0.890 bicubic -46.168 -46.197 -39.881 -38.697 +24 +22
73 ecaresnet101d 36.004 63.996 56.165 43.835 44.57 224 0.875 bicubic -46.168 -39.881 +33
74 resnest200e 35.931 64.069 55.849 44.151 70.20 320 0.909 bicubic -47.901 -41.045 -14
75 swsl_resnet18 35.858 64.142 58.455 41.545 11.69 224 0.875 bilinear -37.418 -33.279 +269 +292
76 eca_nfnet_l1 35.856 64.144 55.955 44.045 41.41 320 1.000 bicubic -48.151 -41.073 -18 -19
77 vit_base_patch16_224 35.768 64.232 57.390 42.610 86.57 224 0.900 bicubic -46.018 -38.732 +26 +37
78 resnest101e 35.373 64.627 55.780 44.220 48.28 256 0.875 bilinear -47.517 -40.540 +3 +6
79 resnetv2_101x3_bitm convit_base 35.261 35.314 64.739 64.686 57.851 54.927 42.149 45.073 387.93 86.54 480 224 1.000 0.875 bilinear bicubic -49.133 -46.976 -39.511 -41.011 -33 +20
80 dm_nfnet_f1 resnetv2_101x3_bitm 35.192 35.261 64.808 64.739 54.413 57.851 45.587 42.149 132.63 387.93 320 480 0.910 1.000 bicubic bilinear -49.412 -49.133 -42.655 -39.511 -38 -36
81 repvgg_b3 dm_nfnet_f1 35.043 35.192 64.957 64.808 54.542 54.413 45.458 45.587 123.09 132.63 224 320 0.875 0.910 bilinear bicubic -45.449 -49.412 -40.718 -42.655 +60 -41
82 repvgg_b3g4 twins_svt_large 35.043 35.086 64.957 64.914 54.772 54.721 45.228 45.279 83.83 99.27 224 0.875 0.900 bilinear bicubic -45.169 -48.592 -40.338 -41.873 +76 -15
83 resnet101d repvgg_b3 34.872 35.043 65.128 64.957 54.202 54.542 45.798 45.458 44.57 123.09 320 224 1.000 0.875 bicubic bilinear -48.150 -45.449 -42.244 -40.718 -4 +74
84 vit_large_patch32_384 repvgg_b3g4 34.673 35.043 65.326 64.957 55.729 54.772 44.271 45.228 306.63 83.83 384 224 1.000 0.875 bicubic bilinear -46.833 -45.171 -40.363 -40.338 +27 +90
85 dm_nfnet_f0 resnet101d 34.642 34.872 65.358 65.128 54.762 54.202 45.238 45.798 71.49 44.57 256 320 0.900 1.000 bicubic -48.700 -48.150 -41.798 -42.244 -14 -3
86 ssl_resnext101_32x16d vit_large_patch32_384 34.603 34.673 65.397 65.326 55.931 55.729 44.069 44.271 194.03 306.63 224 384 0.875 1.000 bilinear bicubic -47.241 -46.833 -40.165 -40.363 +15 +38
87 repvgg_b2g4 dm_nfnet_f0 34.587 34.642 65.413 65.358 54.782 54.762 45.218 45.238 61.76 71.49 224 256 0.875 0.900 bilinear bicubic -44.779 -48.700 -39.906 -41.798 +107 -15
88 resnetrs152 ssl_resnext101_32x16d 34.355 34.605 65.645 65.395 53.562 55.931 46.438 44.069 86.62 194.03 320 224 1.000 0.875 bicubic bilinear -49.357 -47.239 -43.052 -40.165 -25 +24
89 resnest50d_4s2x40d repvgg_b2g4 34.355 34.587 65.645 65.413 54.725 54.782 45.275 45.218 30.42 61.76 224 0.875 bicubic bilinear -46.753 -44.779 -40.833 -39.906 +35 +124
90 tf_efficientnet_b1_ns resnest50d_4s2x40d 34.157 34.355 65.843 65.645 55.489 54.725 44.511 45.275 7.79 30.42 240 224 0.882 0.875 bicubic -47.231 -46.753 -40.249 -40.833 +24 +48
91 resnetrs152 34.355 65.645 53.562 46.438 86.62 320 1.000 bicubic -49.357 -43.052 -27
92 tf_efficientnet_b1_ns 34.157 65.843 55.489 44.511 7.79 240 0.882 bicubic -47.231 -40.249 +36
93 twins_pcpvt_large 34.111 65.888 54.128 45.872 60.99 224 0.900 bicubic -49.029 -42.470 -17
94 tf_efficientnet_b4 34.064 65.936 54.198 45.802 19.34 380 0.922 bicubic -48.958 -42.102 -11
95 nfnet_l0 34.029 65.971 54.418 45.582 35.07 288 1.000 bicubic -48.731 -42.080 -9
96 ssl_resnext101_32x8d 34.017 65.983 55.601 44.399 88.79 224 0.875 bilinear -47.599 -40.437 +15 +24
97 tf_efficientnet_b6 33.998 66.002 54.544 45.456 43.04 528 0.942 bicubic -50.112 -42.342 -40 -44
98 efficientnet_b3_pruned 33.996 66.004 54.108 45.892 9.86 300 0.904 bicubic -46.862 -41.134 +37 +50
99 regnety_160 33.976 66.024 53.546 46.454 83.59 288 1.000 bicubic -49.710 -43.230 -31 -34
100 pit_s_distilled_224 33.939 66.061 53.265 46.735 24.04 224 0.900 bicubic -48.057 -42.533 +3 +10
101 regnety_032 resnet51q 33.412 33.563 66.588 66.437 52.754 53.021 47.246 46.979 19.44 35.70 288 1.000 bicubic bilinear -49.312 -48.797 -43.670 -43.159 -14 -6
102 gernet_l regnety_032 33.357 33.412 66.643 66.588 51.901 52.754 48.099 47.246 31.08 19.44 256 288 0.875 1.000 bilinear bicubic -47.997 -49.312 -43.635 -43.670 +17 -15
103 tresnet_xl gernet_l 33.257 33.357 66.743 66.643 52.294 51.901 47.706 48.099 78.44 31.08 224 256 0.875 bilinear -48.797 -47.997 -43.642 -43.635 -2 +27
104 resnest50d_1s4x24d tresnet_xl 33.147 33.257 66.853 66.743 52.839 52.294 47.161 47.706 25.68 78.44 224 0.875 bicubic bilinear -47.841 -48.797 -42.483 -43.642 +25 +4
105 rexnet_200 resnest50d_1s4x24d 32.987 33.147 67.013 66.853 52.939 52.839 47.061 47.161 16.37 25.68 224 0.875 bicubic -48.645 -47.841 -42.729 -42.483 +5 +37
106 resnest50d twins_pcpvt_base 32.972 33.021 67.028 66.979 52.713 52.485 47.287 47.515 27.48 43.83 224 0.875 0.900 bilinear bicubic -48.002 -49.687 -42.665 -43.861 +24 -18
107 tf_efficientnet_b3 rexnet_200 32.860 32.987 67.140 67.013 52.950 52.939 47.050 47.061 12.23 16.37 300 224 0.904 0.875 bicubic -48.776 -48.645 -42.768 -42.729 +2 +12
108 pnasnet5large resnest50d 32.848 32.972 67.152 67.028 50.500 52.713 49.500 47.287 86.06 27.48 331 224 0.911 0.875 bicubic bilinear -49.934 -48.002 -45.540 -42.665 -23 +35
109 nasnetalarge tf_efficientnetv2_s 32.775 32.915 67.225 67.085 50.141 51.726 49.859 48.274 88.75 21.46 331 384 0.911 1.000 bicubic -49.845 -50.979 -45.906 -44.972 -20 -50
110 gernet_m convit_small 32.740 32.913 67.260 67.087 51.913 52.123 48.087 47.877 21.14 27.78 224 0.875 bilinear bicubic -47.992 -48.513 -43.271 -43.621 +28 +17
111 inception_resnet_v2 tf_efficientnet_b3 32.738 32.860 67.262 67.140 50.648 52.950 49.352 47.050 55.84 12.23 299 300 0.897 0.904 bicubic -47.720 -48.776 -44.658 -42.768 +37 +7
112 gluon_resnet152_v1d pnasnet5large 32.734 32.848 67.266 67.152 51.088 50.500 48.912 49.500 60.21 86.06 224 331 0.875 0.911 bicubic -47.740 -49.934 -44.118 -45.540 +34 -27
113 pit_b_224 twins_svt_base 32.718 32.836 67.282 67.164 49.852 51.559 50.148 48.441 73.76 56.07 224 0.900 bicubic -49.728 -50.300 -45.858 -44.859 -22 -36
114 tf_efficientnet_b2_ap nasnetalarge 32.681 32.775 67.319 67.225 52.239 50.141 47.761 49.859 9.11 88.75 260 331 0.890 0.911 bicubic -47.619 -49.845 -42.789 -45.906 +42 -24
115 tresnet_l gernet_m 32.559 32.740 67.441 67.260 51.139 51.913 48.861 48.087 55.99 21.14 224 0.875 bilinear -48.931 -47.992 -44.485 -43.271 0 +36
116 cait_xxs36_384 inception_resnet_v2 32.549 32.738 67.451 67.262 52.233 50.648 47.767 49.352 17.37 55.84 384 299 1.000 0.897 bicubic -49.645 -47.720 -43.915 -44.658 -18 +45
117 vit_base_patch32_384 gluon_resnet152_v1d 32.461 32.734 67.539 67.266 52.444 51.088 47.556 48.912 88.30 60.21 384 224 1.000 0.875 bicubic -49.191 -47.740 -43.684 -44.118 -9 +42
118 wide_resnet50_2 pit_b_224 32.439 32.718 67.561 67.282 51.459 49.852 48.541 50.148 68.88 73.76 224 0.875 0.900 bicubic -49.017 -49.728 -44.073 -45.858 -2 -25
119 resnetv2_50x3_bitm tf_efficientnet_b2_ap 32.410 32.681 67.590 67.319 54.314 52.239 45.686 47.761 217.32 9.11 480 260 1.000 0.890 bilinear bicubic -51.374 -47.619 -42.792 -42.789 -53 +50
120 ens_adv_inception_resnet_v2 tresnet_l 32.370 32.559 67.629 67.441 50.427 51.139 49.573 48.861 55.84 55.99 299 224 0.897 0.875 bicubic bilinear -47.611 -48.931 -44.509 -44.485 +50 +5
121 vit_deit_base_patch16_224 cait_xxs36_384 32.363 32.549 67.637 67.451 51.011 52.233 48.989 47.767 86.57 17.37 224 384 0.900 1.000 bicubic -49.635 -49.645 -44.723 -43.915 -19 -17
122 swin_small_patch4_window7_224 vit_base_patch32_384 32.341 32.461 67.659 67.539 50.905 52.444 49.095 47.556 49.61 88.30 224 384 0.900 1.000 bicubic -50.871 -49.191 -45.417 -43.684 -45 -5
123 gluon_resnet152_v1s wide_resnet50_2 32.331 32.439 67.669 67.561 50.526 51.459 49.474 48.541 60.32 68.88 224 0.875 bicubic -48.685 -49.017 -44.886 -44.073 +5 +3
124 vit_deit_small_distilled_patch16_224 resnetv2_50x3_bitm 32.284 32.410 67.716 67.590 52.102 54.314 47.898 45.686 22.44 217.32 224 480 0.900 1.000 bicubic bilinear -48.916 -51.374 -43.276 -42.792 0 -61
125 gluon_seresnext101_64x4d ens_adv_inception_resnet_v2 32.205 32.370 67.795 67.629 50.319 50.427 49.681 49.573 88.23 55.84 224 299 0.875 0.897 bicubic -48.689 -47.611 -44.989 -44.509 +9 +59
126 gluon_seresnext101_32x4d vit_deit_base_patch16_224 32.107 32.363 67.893 67.637 51.237 51.011 48.763 48.989 48.96 86.57 224 0.875 0.900 bicubic -48.797 -49.635 -44.057 -44.723 +7 -17
127 vit_deit_base_patch16_384 swin_small_patch4_window7_224 31.989 32.341 68.011 67.659 50.547 50.905 49.453 49.095 86.86 49.61 384 224 1.000 0.900 bicubic -51.117 -50.871 -45.825 -45.417 -49 -52
128 seresnext50_32x4d gluon_resnet152_v1s 31.985 32.331 68.015 67.669 51.231 50.526 48.769 49.474 27.56 60.32 224 0.875 bicubic -49.281 -48.685 -44.389 -44.886 -6 +13
129 resnetrs101 vit_deit_small_distilled_patch16_224 31.858 32.284 68.142 67.716 51.017 52.102 48.983 47.898 63.62 22.44 288 224 0.940 0.900 bicubic -50.430 -48.916 -44.991 -43.276 -35 +7
130 cspresnext50 gluon_seresnext101_64x4d 31.822 32.205 68.178 67.795 51.602 50.319 48.398 49.681 20.57 88.23 224 0.875 bilinear bicubic -48.218 -48.689 -43.342 -44.989 +38 +17
131 eca_nfnet_l0 coat_lite_small 31.657 32.127 68.343 67.873 51.654 49.934 48.346 50.066 24.14 19.84 288 224 1.000 0.900 bicubic -50.931 -50.181 -44.820 -45.916 -41 -34
132 tnt_s_patch16_224 gluon_seresnext101_32x4d 31.643 32.107 68.357 67.893 51.143 51.237 48.857 48.763 23.76 48.96 224 0.900 0.875 bicubic -49.875 -48.797 -44.605 -44.057 -19 +14
133 resnet50 vit_deit_base_patch16_384 31.547 31.989 68.453 68.011 50.170 50.547 49.830 49.453 25.56 86.86 224 384 0.875 1.000 bicubic -47.491 -51.117 -44.220 -45.825 +87 -55
134 ssl_resnext101_32x4d seresnext50_32x4d 31.423 31.985 68.577 68.015 52.121 51.231 47.879 48.769 44.18 27.56 224 0.875 bilinear bicubic -49.501 -49.281 -43.607 -44.389 -2 0
135 inception_v4 levit_384 31.378 31.877 68.622 68.123 49.244 50.598 50.756 49.402 42.68 39.13 299 224 0.875 0.900 bicubic -48.790 -50.709 -45.724 -45.418 +28 -43
136 rexnet_150 resnetrs101 31.366 31.858 68.634 68.142 51.288 51.017 48.712 48.983 9.73 63.62 224 288 0.875 0.940 bicubic -48.944 -50.430 -43.878 -44.991 +17 -36
137 pit_s_224 cspresnext50 31.333 31.822 68.667 68.178 49.661 51.602 50.339 48.398 23.46 20.57 224 0.900 0.875 bicubic bilinear -49.761 -48.218 -45.671 -43.342 -10 +45
138 cait_xxs36_224 eca_nfnet_l0 31.278 31.657 68.722 68.343 50.616 51.654 49.384 48.346 17.30 24.14 224 288 1.000 bicubic -48.472 -50.931 -44.250 -44.820 +45 -47
139 cspresnet50 tnt_s_patch16_224 31.270 31.643 68.730 68.357 51.223 51.143 48.777 48.857 21.62 23.76 256 224 0.887 0.900 bilinear bicubic -48.304 -49.875 -43.489 -44.605 +52 -17
140 ecaresnetlight resnet50 31.121 31.547 68.879 68.453 50.243 50.170 49.757 49.830 30.16 25.56 224 0.875 bicubic -49.341 -47.491 -45.007 -44.220 +7 +96
141 gluon_resnet101_v1s ssl_resnext101_32x4d 31.115 31.423 68.885 68.577 49.793 52.121 50.207 47.879 44.67 44.18 224 0.875 bicubic bilinear -49.187 -49.501 -45.367 -43.607 +13 +4
142 tf_efficientnet_cc_b0_8e inception_v4 31.087 31.378 68.913 68.622 50.761 49.244 49.239 50.756 24.01 42.68 224 299 0.875 bicubic -46.821 -48.790 -42.892 -45.724 +121 +35
143 ecaresnet50d rexnet_150 31.058 31.366 68.942 68.634 50.848 51.288 49.152 48.712 25.58 9.73 224 0.875 bicubic -49.534 -48.944 -44.472 -43.878 -1 +23
144 ecaresnet50t pit_s_224 31.058 31.333 68.942 68.667 50.577 49.661 49.423 50.339 25.57 23.46 320 224 0.950 0.900 bicubic -51.288 -49.761 -45.561 -45.671 -51 -5
145 resnet50d cait_xxs36_224 31.020 31.278 68.980 68.722 49.808 50.616 50.192 49.384 25.58 17.30 224 0.875 1.000 bicubic -49.510 -48.472 -45.352 -44.250 -2 +53
146 cspdarknet53 cspresnet50 31.018 31.270 68.981 68.730 50.390 51.223 49.610 48.777 27.64 21.62 256 0.887 bilinear -49.040 -48.304 -44.694 -43.489 +21 +60
147 gluon_resnet152_v1c coat_mini 30.991 31.203 69.009 68.797 48.924 49.773 51.076 50.227 60.21 10.34 224 0.875 0.900 bicubic -48.919 -50.065 -45.916 -45.619 +26 -14
148 gluon_resnext101_64x4d ecaresnetlight 30.987 31.121 69.013 68.879 48.549 50.243 51.451 49.757 83.46 30.16 224 0.875 bicubic -49.617 -49.341 -46.439 -45.007 -7 +12
149 tf_efficientnet_cc_b1_8e gluon_resnet101_v1s 30.899 31.115 69.101 68.885 50.080 49.793 49.920 50.207 39.72 44.67 240 224 0.882 0.875 bicubic -48.409 -49.187 -44.290 -45.367 +52 +18
150 ecaresnet101d_pruned tf_efficientnet_cc_b0_8e 30.897 31.087 69.103 68.913 50.013 50.761 49.987 49.239 24.88 24.01 224 0.875 bicubic -49.921 -46.821 -45.615 -42.892 -14 +132
151 gluon_resnext101_32x4d ecaresnet50d 30.877 31.058 69.123 68.942 48.537 50.848 51.463 49.152 44.18 25.58 224 0.875 bicubic -49.457 -49.534 -46.389 -44.472 0 +4
152 tf_efficientnet_lite4 ecaresnet50t 30.830 31.058 69.170 68.942 50.386 50.577 49.614 49.423 13.01 25.57 380 320 0.920 0.950 bilinear bicubic -50.706 -51.288 -45.282 -45.561 -40 -56
153 nf_resnet50 resnet50d 30.775 31.020 69.225 68.980 50.074 49.808 49.926 50.192 25.56 25.58 288 224 0.940 0.875 bicubic -49.919 -49.510 -45.282 -45.352 -14 +3
154 dpn107 cspdarknet53 30.678 31.018 69.322 68.981 48.810 50.390 51.190 49.610 86.92 27.64 224 256 0.875 0.887 bicubic bilinear -49.478 -49.040 -46.100 -44.694 +10 +27
155 ese_vovnet39b gluon_resnet152_v1c 30.657 30.991 69.343 69.009 49.875 48.924 50.125 51.076 24.57 60.21 224 0.875 bicubic -48.663 -48.919 -44.837 -45.916 +44 +32
156 gluon_resnet152_v1b gluon_resnext101_64x4d 30.623 30.987 69.376 69.013 48.521 48.549 51.479 51.451 60.19 83.46 224 0.875 bicubic -49.063 -49.617 -46.215 -46.439 +31 -2
157 tresnet_xl_448 twins_svt_small 30.614 30.985 69.386 69.015 49.069 49.223 50.931 50.777 78.44 24.06 448 224 0.875 0.900 bilinear bicubic -52.436 -50.697 -47.105 -46.447 -76 -41
158 ssl_resnext50_32x4d tf_efficientnet_cc_b1_8e 30.594 30.899 69.406 69.101 50.657 50.080 49.343 49.920 25.03 39.72 224 240 0.875 0.882 bilinear bicubic -49.724 -48.409 -44.749 -44.290 -6 +59
159 gluon_resnet101_v1d ecaresnet101d_pruned 30.523 30.897 69.477 69.103 47.950 50.013 52.050 49.987 44.57 24.88 224 0.875 bicubic -49.891 -49.921 -47.064 -45.615 -10
160 dpn68b gluon_resnext101_32x4d 30.517 30.877 69.483 69.123 49.158 48.537 50.842 51.463 12.61 44.18 224 0.875 bicubic -48.699 -49.457 -45.256 -46.389 +51 +4
161 resnest26d tf_efficientnetv2_b3 30.490 30.861 69.510 69.139 50.677 49.814 49.323 50.186 17.07 14.36 224 300 0.875 0.904 bilinear bicubic -47.988 -51.109 -43.621 -45.968 +77 -50
162 efficientnet_b2 tf_efficientnet_lite4 30.435 30.830 69.565 69.170 49.698 50.386 50.302 49.614 9.11 13.01 288 380 1.000 0.920 bicubic bilinear -50.177 -50.706 -45.620 -45.282 -22 -41
163 tf_efficientnet_b1_ap nf_resnet50 30.421 30.775 69.579 69.225 49.553 50.074 50.447 49.926 7.79 25.56 240 288 0.882 0.940 bicubic -48.859 -49.919 -44.753 -45.282 +44 -11
164 pit_xs_distilled_224 dpn107 30.278 30.678 69.722 69.322 49.836 48.810 50.164 51.190 11.00 86.92 224 0.900 0.875 bicubic -49.028 -49.478 -44.528 -46.100 +40 +14
165 seresnet50 ese_vovnet39b 30.077 30.657 69.923 69.343 49.292 49.875 50.708 50.125 28.09 24.57 224 0.875 bicubic -50.197 -48.663 -45.778 -44.837 -8 +50
166 dpn98 gluon_resnet152_v1b 30.067 30.623 69.933 69.376 48.244 48.521 51.756 51.479 61.57 60.19 224 0.875 bicubic -49.575 -49.063 -46.354 -46.215 +23 +36
167 tf_efficientnet_b2 tresnet_xl_448 30.026 30.614 69.974 69.386 49.581 49.069 50.419 50.931 9.11 78.44 260 448 0.890 0.875 bicubic bilinear -50.060 -52.436 -45.328 -47.105 -1 -86
168 dpn131 ssl_resnext50_32x4d 30.024 30.594 69.976 69.406 48.146 50.657 51.854 49.343 79.25 25.03 224 0.875 bicubic bilinear -49.798 -49.724 -46.564 -44.749 +12 -3
169 efficientnet_el gluon_resnet101_v1d 30.018 30.523 69.982 69.477 48.834 47.950 51.166 52.050 10.59 44.57 300 224 0.904 0.875 bicubic -51.298 -49.891 -46.692 -47.064 -49 -7
170 legacy_senet154 dpn68b 30.001 30.517 69.999 69.483 48.034 49.158 51.966 50.842 115.09 12.61 224 0.875 bilinear bicubic -51.309 -48.699 -47.462 -45.256 -49 +57
171 dpn92 resnest26d 29.953 30.490 70.047 69.510 49.162 50.677 50.838 49.323 37.67 17.07 224 0.875 bicubic bilinear -50.055 -47.988 -45.674 -43.621 -2 +84
172 efficientnet_b2 30.435 69.565 49.698 50.302 9.11 288 1.000 bicubic -50.177 -45.620 -19
173 tf_efficientnet_b1_ap 30.421 69.579 49.553 50.447 7.79 240 0.882 bicubic -48.859 -44.753 +50
174 twins_pcpvt_small 30.382 69.618 49.386 50.614 24.11 224 0.900 bicubic -50.706 -46.256 -34
175 visformer_small 30.329 69.671 48.285 51.715 40.22 224 0.900 bicubic -51.777 -47.587 -68
176 pit_xs_distilled_224 30.278 69.722 49.836 50.164 11.00 224 0.900 bicubic -49.028 -44.528 +44
177 seresnet50 30.077 69.923 49.292 50.708 28.09 224 0.875 bicubic -50.197 -45.778 -7
178 dpn98 30.067 69.933 48.244 51.756 61.57 224 0.875 bicubic -49.575 -46.354 +26
179 tf_efficientnet_b2 30.026 69.974 49.581 50.419 9.11 260 0.890 bicubic -50.060 -45.328 +1
180 dpn131 30.024 69.976 48.146 51.854 79.25 224 0.875 bicubic -49.798 -46.564 +15
181 efficientnet_el 30.018 69.982 48.834 51.166 10.59 300 0.904 bicubic -51.298 -46.692 -50
182 legacy_senet154 30.001 69.999 48.034 51.966 115.09 224 0.875 bilinear -51.309 -47.462 -50
183 dpn92 29.953 70.047 49.162 50.838 37.67 224 0.875 bicubic -50.055 -45.674 0
184 gluon_senet154 29.877 70.123 47.894 52.106 115.09 224 0.875 bicubic -51.357 -47.454 -49
185 xception 29.865 70.135 48.686 51.314 22.86 299 0.897 bicubic -49.187 -45.706 +46 +50
186 adv_inception_v3 29.816 70.184 47.847 52.153 23.83 299 0.875 bicubic -47.766 -45.889 +100 +107
187 gluon_xception65 29.784 70.216 47.755 52.245 39.92 299 0.903 bicubic -49.932 -47.105 +11 +14
188 resnetblur50 29.625 70.375 48.248 51.752 25.56 224 0.875 bicubic -49.661 -46.390 +30 +34
189 efficientnet_em 29.486 70.514 48.946 51.054 6.90 240 0.882 bicubic -49.766 -45.848 +31 +35
190 resnext101_32x8d 29.439 70.561 48.486 51.514 88.79 224 0.875 bilinear -49.869 -46.032 +22 +26
191 coat_lite_mini 29.433 70.567 47.724 52.276 11.01 224 0.900 bicubic -49.655 -46.880 +36 +40
192 ssl_resnet50 29.423 70.577 49.781 50.219 25.56 224 0.875 bilinear -49.799 -45.051 +29 +33
193 vit_deit_small_patch16_224 29.421 70.579 48.256 51.744 22.05 224 0.900 bicubic -50.435 -46.796 -3 -1
194 nf_regnet_b1 29.397 70.603 49.445 50.555 10.22 288 0.900 bicubic -49.909 -45.303 +21 +25
195 cait_xxs24_384 29.387 70.612 48.753 51.247 12.03 384 1.000 bicubic -51.578 -46.893 -52 -51
196 swin_tiny_patch4_window7_224 29.334 70.666 47.602 52.398 28.29 224 0.900 bicubic -52.044 -47.938 -66 -67
197 resnext50_32x4d 29.331 70.669 47.397 52.603 25.03 224 0.875 bicubic -50.438 -47.201 -3 0
198 resnet34d 29.328 70.671 48.409 51.591 21.82 224 0.875 bicubic -47.788 -44.973 +102 +109
199 cait_xxs24_224 29.303 70.697 48.535 51.465 11.96 224 1.000 bicubic -49.083 -45.775 +56 +62
200 ecaresnet50d_pruned 29.215 70.785 48.453 51.547 19.94 224 0.875 bicubic -50.501 -46.427 -3 0
201 tresnet_l_448 29.165 70.835 47.232 52.768 55.99 448 0.875 bilinear -53.103 -48.744 -94 -100
202 gluon_inception_v3 29.124 29.122 70.876 70.878 46.957 53.043 23.83 299 0.875 bicubic -49.682 -49.684 -47.413 +36 +40
203 xception71 29.047 70.953 47.405 52.595 42.34 299 0.903 bicubic -50.826 -47.517 -15 -13
204 hrnet_w64 28.989 71.011 47.142 52.858 128.06 224 0.875 bilinear -50.485 -47.510 +3 +6
205 resnetv2_101x1_bitm 28.910 71.090 49.502 50.498 44.54 480 1.000 bilinear -53.302 -46.970 -96 -102
206 tf_efficientnet_b0_ns 28.902 71.098 49.011 50.989 5.29 224 0.875 bicubic -49.756 -45.365 +39 +43
207 xception65 28.896 71.104 47.167 52.833 39.92 299 0.903 bicubic -50.656 -47.487 -3 0
208 tf_efficientnet_b1 28.886 71.114 47.503 52.497 7.79 240 0.882 bicubic -49.940 -46.695 +29 +33
209 gluon_resnet101_v1b 28.878 71.121 46.389 53.611 44.55 224 0.875 bicubic -50.427 -48.135 +5 +9
210 skresnext50_32x4d 28.818 71.182 46.497 53.503 27.48 224 0.875 bicubic -51.338 -48.145 -33 -31
211 tf_efficientnet_lite3 levit_256 28.660 28.745 71.340 71.255 47.354 46.723 52.646 53.277 8.20 18.89 300 224 0.904 0.900 bilinear bicubic -51.160 -52.765 -47.560 -48.767 -18 -88
212 gluon_seresnext50_32x4d tf_efficientnet_lite3 28.651 28.660 71.349 71.340 46.436 47.354 53.564 52.646 27.56 8.20 224 300 0.875 0.904 bicubic bilinear -51.267 -51.160 -48.386 -47.560 -29 -16
213 skresnet34 gluon_seresnext50_32x4d 28.645 28.651 71.355 71.349 47.953 46.436 52.047 53.564 22.28 27.56 224 0.875 bicubic -48.267 -51.267 -45.369 -48.386 +95 -28
214 hrnet_w40 skresnet34 28.641 28.645 71.359 71.355 47.454 47.953 52.546 52.047 57.56 22.28 224 0.875 bilinear bicubic -50.279 -48.267 -47.016 -45.369 +20 +101
215 tv_resnet152 hrnet_w40 28.533 28.641 71.467 71.359 47.118 47.454 52.882 52.546 60.19 57.56 224 0.875 bilinear -49.779 -50.279 -46.920 -47.016 +43 +23
216 repvgg_b2 tf_efficientnetv2_b0 28.427 28.566 71.573 71.434 47.038 47.079 52.962 52.921 89.02 7.14 224 0.875 bilinear bicubic -50.365 -49.790 -47.376 -46.945 +24 +48
217 hrnet_w48 tv_resnet152 28.413 28.533 71.587 71.467 47.586 47.118 52.414 52.882 77.47 60.19 224 0.875 bilinear -50.887 -49.779 -46.926 -46.920 0 +48
218 gluon_resnext50_32x4d repvgg_b2 28.375 28.427 71.624 71.573 45.328 47.038 54.672 52.962 25.03 89.02 224 0.875 bicubic bilinear -50.978 -50.365 -49.098 -47.376 -8 +26
219 hrnet_w48 28.413 71.587 47.586 52.414 77.47 224 0.875 bilinear -50.887 -46.926 +2
220 gluon_resnext50_32x4d 28.375 71.624 45.328 54.672 25.03 224 0.875 bicubic -50.978 -49.098 -6
221 efficientnet_b2_pruned 28.362 71.638 47.051 52.949 8.31 260 0.890 bicubic -51.554 -47.805 -35
222 tf_efficientnet_b0_ap 28.346 71.654 47.531 52.469 5.29 224 0.875 bicubic -48.740 -45.725 +82 +87
223 tf_efficientnet_cc_b0_4e 28.315 71.685 47.364 52.636 13.31 224 0.875 bicubic -48.991 -45.970 +74 +79
224 dla102x2 dla169 28.313 71.687 46.761 47.391 53.239 52.609 41.28 53.39 224 0.875 bilinear -51.135 -50.375 -47.879 -46.945 -14 +22
225 dla169 dla102x2 28.313 71.687 47.391 46.761 52.609 53.239 53.39 41.28 224 0.875 bilinear -50.375 -51.135 -46.945 -47.879 +20 -12
226 mixnet_xl 28.287 71.713 46.702 53.298 11.90 224 0.875 bicubic -52.189 -48.234 -67 -68
227 gluon_resnet50_v1d 28.246 71.754 45.878 54.122 25.58 224 0.875 bicubic -50.828 -48.592 +4 +6
228 wide_resnet101_2 28.108 71.892 46.401 53.599 126.89 224 0.875 bilinear -50.748 -47.881 +10 +12
229 gluon_resnet101_v1c 28.104 71.896 45.961 54.039 44.57 224 0.875 bicubic -51.430 -48.617 -22 -21
230 regnetx_320 28.093 71.907 45.126 54.874 107.81 224 0.875 bicubic -52.153 -49.900 -57 -58
231 densenet161 28.081 71.919 46.641 53.359 28.68 224 0.875 bicubic -49.277 -46.997 +65 +70
232 regnety_320 28.059 71.941 45.444 54.556 145.05 224 0.875 bicubic -52.753 -49.800 -81 -82
233 gernet_s 28.022 71.978 46.723 53.277 8.17 224 0.875 bilinear -48.894 -46.409 +76 +81
234 efficientnet_el_pruned 28.016 71.984 46.790 53.210 10.59 300 0.904 bicubic -52.284 -48.428 -65 -66
235 xception41 levit_192 27.888 28.016 72.112 71.984 45.890 45.880 54.110 54.120 26.97 10.95 299 224 0.903 0.900 bicubic -50.628 -51.826 -48.388 -48.906 +14 -41
236 regnetx_160 xception41 27.817 27.888 72.183 72.112 45.617 45.890 54.383 54.110 54.28 26.97 224 299 0.875 0.903 bicubic -52.039 -50.628 -49.213 -48.388 -45 +15
237 tf_inception_v3 regnetx_160 27.782 27.817 72.218 72.183 45.719 45.617 54.281 54.383 23.83 54.28 299 224 0.875 bicubic -50.074 -52.039 -47.921 -49.213 +44 -46
238 res2net101_26w_4s tf_inception_v3 27.768 27.780 72.232 72.220 45.179 45.721 54.821 54.279 45.21 23.83 224 299 0.875 bilinear bicubic -51.430 -50.078 -49.253 -48.149 -11 +46
239 repvgg_b1 res2net101_26w_4s 27.656 27.768 72.344 72.232 46.531 45.179 53.469 54.821 57.42 45.21 224 0.875 bilinear -50.710 -51.430 -47.567 -49.253 +20 -10
240 tf_efficientnetv2_b1 27.760 72.240 46.578 53.422 8.14 240 0.882 bicubic -51.702 -48.144 -29
241 repvgg_b1 27.656 72.344 46.531 53.469 57.42 224 0.875 bilinear -50.710 -47.567 +22
242 hrnet_w44 27.621 72.379 45.837 54.163 67.06 224 0.875 bilinear -51.275 -48.531 -3
243 inception_v3 27.556 72.444 45.263 54.737 23.83 299 0.875 bicubic -49.882 -48.213 +52 +55
244 pit_xs_224 27.491 72.509 45.900 54.100 10.62 224 0.900 bicubic -50.691 -48.268 +23 +26
245 regnetx_080 27.405 72.595 45.002 54.998 39.57 224 0.875 bicubic -51.789 -49.558 -15
246 hrnet_w30 27.381 72.619 46.554 53.446 37.71 224 0.875 bilinear -50.825 -47.668 +20 +23
247 hrnet_w32 27.369 72.631 45.994 54.006 41.23 224 0.875 bilinear -51.081 -48.192 +9 +10
248 gluon_resnet50_v1s 27.326 72.674 45.222 54.778 25.68 224 0.875 bicubic -51.386 -51.384 -49.016 -2
249 densenet201 27.265 72.735 46.222 53.778 20.01 224 0.875 bicubic -50.021 -47.256 +51 +54
250 densenetblur121d 27.228 72.772 46.299 53.701 8.00 224 0.875 bicubic -49.360 -46.893 +70 +73
251 regnety_064 27.220 72.780 44.847 55.153 30.58 224 0.875 bicubic -52.502 -49.921 -51 -52
252 efficientnet_b1_pruned 27.181 72.819 45.872 54.128 6.33 240 0.882 bicubic -51.055 -47.962 +13 +16
253 resnetrs50 tf_efficientnetv2_b2 27.110 27.163 72.890 72.837 45.029 44.570 54.971 55.430 35.69 10.10 224 260 0.910 0.890 bicubic -52.782 -53.045 -49.939 -50.472 -63 -78
254 rexnet_130 resnetrs50 27.094 27.110 72.906 72.890 45.933 45.029 54.067 54.971 7.56 35.69 224 0.875 0.910 bicubic -52.406 -52.782 -48.749 -49.939 -44 -66
255 vit_small_patch16_224 rexnet_130 27.086 27.094 72.914 72.906 45.701 45.933 54.299 54.067 48.75 7.56 224 0.900 0.875 bicubic -50.772 -52.406 -47.715 -48.749 +27 -46
256 res2net50_26w_8s vit_small_patch16_224 27.078 27.086 72.921 72.914 44.428 45.701 55.572 54.299 48.40 48.75 224 0.875 0.900 bilinear bicubic -52.119 -50.772 -49.940 -47.939 -28 +29
257 dla102x res2net50_26w_8s 27.061 27.078 72.939 72.921 45.475 44.428 54.525 55.572 26.31 48.40 224 0.875 bilinear -51.449 -52.119 -48.753 -49.940 -5 -29
258 tv_resnet101 dla102x 26.963 27.061 73.037 72.939 45.234 45.475 54.766 54.525 44.55 26.31 224 0.875 bilinear -50.411 -51.449 -48.306 -48.753 +39 -6
259 resnext50d_32x4d tv_resnet101 26.876 26.963 73.124 73.037 44.436 45.234 55.564 54.766 25.05 44.55 224 0.875 bicubic bilinear -52.800 -50.411 -50.430 -48.306 -55 +41
260 regnetx_120 resnext50d_32x4d 26.868 26.876 73.132 73.124 44.682 44.436 55.318 55.564 46.11 25.05 224 0.875 bicubic -52.728 -52.800 -50.056 -50.430 -54 -57
261 rexnet_100 regnetx_120 26.831 26.868 73.169 73.132 45.369 44.682 54.631 55.318 4.80 46.11 224 0.875 bicubic -51.027 -52.728 -48.501 -50.056 +20 -56
262 densenet169 rexnet_100 26.829 26.831 73.171 73.169 45.373 45.369 54.627 54.631 14.15 4.80 224 0.875 bicubic -49.077 -51.027 -47.653 -48.047 +67 +24
263 legacy_seresnext101_32x4d densenet169 26.811 26.829 73.189 73.171 43.497 45.373 56.503 54.627 48.96 14.15 224 0.875 bilinear bicubic -53.417 -49.077 -51.521 -47.653 -87 +70
264 regnety_120 legacy_seresnext101_32x4d 26.788 26.811 73.212 73.189 44.454 43.497 55.546 56.503 51.82 48.96 224 0.875 bicubic bilinear -53.578 -53.417 -50.672 -51.521 -98 -91
265 regnetx_064 regnety_120 26.784 26.788 73.216 73.212 44.927 44.454 55.073 55.546 26.21 51.82 224 0.875 bicubic -52.288 -53.578 -49.531 -50.672 -31 -102
266 regnetx_032 regnetx_064 26.703 26.784 73.297 73.216 45.236 44.927 54.764 55.073 15.30 26.21 224 0.875 bicubic -51.469 -52.288 -48.852 -49.531 +2 -32
267 legacy_seresnet152 regnetx_032 26.676 26.703 73.324 73.297 43.947 45.236 56.053 54.764 66.82 15.30 224 0.875 bilinear bicubic -51.984 -51.469 -50.423 -48.852 -19 +4
268 densenet121 legacy_seresnet152 26.664 26.676 73.336 73.324 45.900 43.947 54.100 56.053 7.98 66.82 224 0.875 bicubic bilinear -48.914 -51.984 -46.752 -50.423 +65 -20
269 efficientnet_es densenet121 26.621 26.664 73.379 73.336 45.112 45.900 54.888 54.100 5.44 7.98 224 0.875 bicubic -51.445 -48.914 -48.814 -46.752 +3 +68
270 res2net50_26w_6s efficientnet_es 26.595 26.621 73.405 73.379 43.990 45.112 56.010 54.888 37.05 5.44 224 0.875 bilinear bicubic -51.975 -51.445 -50.134 -48.814 -20 +5
271 repvgg_b1g4 res2net50_26w_6s 26.579 26.595 73.421 73.405 45.084 43.990 54.916 56.010 39.97 37.05 224 0.875 bilinear -51.015 -51.975 -48.742 -50.134 +18 -21
272 dla60x repvgg_b1g4 26.552 26.579 73.448 73.421 45.023 45.084 54.977 54.916 17.35 39.97 224 0.875 bilinear -51.694 -51.015 -48.995 -48.742 -9 +20
273 regnety_080 dla60x 26.524 26.552 73.476 73.448 44.359 45.023 55.641 54.977 39.18 17.35 224 0.875 bicubic bilinear -53.352 -51.694 -50.471 -48.995 -82 -7
274 coat_lite_tiny regnety_080 26.507 26.524 73.493 73.476 44.644 44.359 55.356 55.641 5.72 39.18 224 0.900 0.875 bicubic -51.005 -53.352 -49.272 -50.471 +19 -85
275 tf_efficientnet_b0 coat_lite_tiny 26.485 26.507 73.515 73.493 45.646 44.644 54.354 55.356 5.29 5.72 224 0.875 0.900 bicubic -50.363 -51.005 -47.582 -49.272 +38 +21
276 res2net50_14w_8s tf_efficientnet_b0 26.483 26.485 73.517 73.515 44.371 45.646 55.629 54.354 25.06 5.29 224 0.875 bilinear bicubic -51.667 -50.363 -49.477 -47.582 -7 +40
277 mobilenetv3_large_100_miil res2net50_14w_8s 26.481 26.483 73.519 73.517 44.473 44.371 55.527 55.629 5.48 25.06 224 0.875 bilinear -51.435 -51.667 -48.437 -49.477 +1 -5
278 gluon_resnet50_v1b mobilenetv3_large_100_miil 26.436 26.481 73.564 73.519 44.035 44.473 55.965 55.527 25.56 5.48 224 0.875 bicubic bilinear -51.144 -51.435 -49.681 -48.437 +13 +3
279 tf_efficientnet_el gluon_resnet50_v1b 26.357 26.436 73.643 73.564 44.175 44.035 55.825 55.965 10.59 25.56 300 224 0.904 0.875 bicubic -53.893 -51.144 -50.953 -49.681 -105 +15
280 regnetx_040 tf_efficientnet_el 26.243 26.357 73.757 73.643 44.438 44.175 55.562 55.825 22.12 10.59 224 300 0.875 0.904 bicubic -52.239 -53.893 -49.806 -50.953 -27 -109
281 dpn68 levit_128 26.129 26.332 73.871 73.668 44.228 44.096 55.772 55.904 12.61 9.21 224 0.875 0.900 bicubic -50.189 -52.154 -48.750 -49.914 +43 -28
282 efficientnet_b1 regnetx_040 26.061 26.243 73.939 73.757 44.080 44.438 55.920 55.562 7.79 22.12 256 224 1.000 0.875 bicubic -52.733 -52.239 -50.262 -49.806 -39 -28
283 hrnet_w18 dpn68 25.986 26.129 74.014 73.871 44.813 44.228 55.187 55.772 21.30 12.61 224 0.875 bilinear bicubic -50.772 -50.189 -48.631 -48.750 +32 +45
284 hardcorenas_f efficientnet_b1 25.951 26.061 74.049 73.939 44.220 44.080 55.780 55.920 8.20 7.79 224 256 0.875 1.000 bilinear bicubic -52.153 -52.733 -49.582 -50.262 -13 -41
285 regnety_040 hrnet_w18 25.923 25.986 74.077 74.014 43.848 44.813 56.152 55.187 20.65 21.30 224 0.875 bicubic bilinear -53.297 -50.772 -50.808 -48.631 -59 +33
286 resnet34 hardcorenas_f 25.888 25.951 74.112 74.049 43.982 44.220 56.018 55.780 21.80 8.20 224 0.875 bilinear -49.222 -52.153 -48.302 -49.582 +57 -12
287 res2net50_26w_4s regnety_040 25.866 25.923 74.134 74.077 43.155 43.848 56.845 56.152 25.70 20.65 224 0.875 bilinear bicubic -52.098 -53.297 -50.699 -50.808 -10 -61
288 tresnet_m_448 resnet34 25.852 25.888 74.148 74.112 42.874 43.982 57.126 56.018 31.39 21.80 448 224 0.875 bilinear -55.862 -49.222 -52.698 -48.302 -165 +59
289 res2net50_26w_4s 25.866 74.134 43.155 56.845 25.70 224 0.875 bilinear -52.098 -50.699 -9
290 tresnet_m_448 25.852 74.148 42.874 57.126 31.39 448 0.875 bilinear -55.862 -52.698 -175
291 coat_tiny 25.843 74.157 43.276 56.724 5.50 224 0.900 bicubic -52.591 -50.761 -32
292 hardcorenas_c 25.815 74.185 44.772 55.228 5.52 224 0.875 bilinear -51.239 -48.386 +18
293 gluon_resnet50_v1c 25.784 74.216 43.031 56.969 25.58 224 0.875 bicubic -52.228 -50.957 -16
294 selecsls60 25.729 74.272 44.065 55.935 30.67 224 0.875 bicubic -52.254 -49.764 -15
295 hardcorenas_e 25.662 74.338 43.412 56.588 8.07 224 0.875 bilinear -52.132 -50.282 -8
296 dla60_res2net 25.652 74.348 43.599 56.401 20.85 224 0.875 bilinear -52.812 -50.607 -38 -40
297 dla60_res2next 25.640 74.360 43.670 56.330 17.03 224 0.875 bilinear -52.800 -50.482 -37 -39
298 ecaresnet26t 25.538 74.462 43.660 56.340 16.01 320 0.950 bicubic -54.316 -51.424 -100 -105
299 mixnet_l 25.512 74.488 43.455 56.545 7.33 224 0.875 bicubic -53.464 -50.727 -59 -62
300 tf_efficientnet_lite1 25.499 74.501 43.585 56.415 5.42 240 0.882 bicubic -51.143 -49.641 +20
301 tv_resnext50_32x4d 25.455 74.545 42.787 57.213 25.03 224 0.875 bilinear -52.165 -50.909 -11
302 repvgg_a2 25.436 74.564 43.939 56.061 28.21 224 0.875 bilinear -51.024 -49.065 +24 +25
303 tf_mixnet_l 25.422 74.578 42.534 57.466 7.33 224 0.875 bicubic -53.352 -51.464 -55 -58
304 hardcorenas_b 25.402 74.598 44.190 55.810 5.18 224 0.875 bilinear -51.136 -48.564 +20
305 res2next50 25.389 74.611 42.508 57.492 24.67 224 0.875 bilinear -52.857 -51.384 -38
306 legacy_seresnet101 25.334 74.666 42.825 57.175 49.33 224 0.875 bilinear -53.048 -51.439 -43 -44
307 selecsls60b 25.332 74.668 43.559 56.441 32.77 224 0.875 bicubic -53.080 -50.615 -46 -47
308 dla102 25.316 74.684 43.827 56.173 33.27 224 0.875 bilinear -52.716 -50.119 -32
309 hardcorenas_d 25.300 74.700 43.121 56.879 7.50 224 0.875 bilinear -52.132 -50.363 -10
310 resnest14d 25.284 74.716 44.114 55.886 10.61 224 0.875 bilinear -50.222 -48.404 +28 +29
311 legacy_seresnext50_32x4d 25.210 74.790 41.936 58.064 27.56 224 0.875 bilinear -53.868 -52.500 -76 -79
312 mixer_b16_224 25.121 74.879 41.227 58.773 59.88 224 0.875 bicubic -51.481 -51.001 +9
313 res2net50_48w_2s 25.027 74.973 42.208 57.792 25.29 224 0.875 bilinear -52.495 -51.346 -18
314 efficientnet_b0 25.015 74.985 42.787 57.213 5.29 224 0.875 bicubic -52.683 -50.745 -26
315 gluon_resnet34_v1b 24.939 75.061 42.243 57.757 21.80 224 0.875 bicubic -49.649 -49.747 +40 +41
316 mobilenetv2_120d 24.937 75.063 43.058 56.942 5.83 224 0.875 bicubic -52.347 -50.434 -12
317 dla60 24.933 75.067 43.296 56.704 22.04 224 0.875 bilinear -52.099 -50.022 -6
318 regnety_016 24.811 75.189 42.616 57.384 11.20 224 0.875 bicubic -53.051 -51.104 -35
319 tf_efficientnet_lite2 24.530 75.470 42.280 57.720 6.09 260 0.890 bicubic -52.938 -51.474 -22
320 skresnet18 24.483 75.517 42.536 57.464 11.96 224 0.875 bicubic -48.555 -48.632 +47 +49
321 regnetx_016 24.473 75.527 42.514 57.486 9.19 224 0.875 bicubic -52.477 -50.906 -9
322 pit_ti_distilled_224 24.406 75.594 42.730 57.270 5.10 224 0.900 bicubic -50.124 -49.366 +34 +35
323 tf_efficientnet_lite0 24.373 75.627 42.487 57.513 4.65 224 0.875 bicubic -50.457 -49.689 +27 +28
324 hardcorenas_a 24.369 75.631 43.284 56.716 5.26 224 0.875 bilinear -51.547 -49.230 +7 +8
325 resnetv2_50x1_bitm 24.231 24.233 75.769 75.767 43.477 56.523 25.55 480 1.000 bilinear -55.941 -55.939 -52.149 -144 -149
326 tv_resnet50 24.070 75.930 41.313 58.687 25.56 224 0.875 bilinear -52.068 -51.551 +3 +4
327 levit_128s 24.058 75.942 41.007 58.993 7.78 224 0.900 bicubic -52.472 -51.859 -2
328 legacy_seresnet34 24.027 75.973 41.909 58.091 21.96 224 0.875 bilinear -50.781 -50.215 +24
329 resnet18d 23.929 76.071 42.300 57.700 11.71 224 0.875 bicubic -48.331 -48.396 +46 +47
330 efficientnet_lite0 23.909 76.091 42.088 57.912 4.65 224 0.875 bicubic -51.575 -50.422 +10
331 tv_densenet121 23.844 76.156 41.925 58.075 7.98 224 0.875 bicubic -50.894 -50.225 +22
332 efficientnet_es_pruned 23.828 76.172 41.995 58.005 5.44 224 0.875 bicubic -51.172 -50.453 +18
333 mobilenetv2_140 23.712 76.288 41.477 58.523 6.11 224 0.875 bicubic -52.804 -51.519 -7
334 mixnet_m 23.710 76.290 41.141 58.859 5.01 224 0.875 bicubic -53.550 -52.284 -28 -29
335 dla34 23.669 76.331 41.551 58.449 15.74 224 0.875 bilinear -50.961 -50.527 +20
336 legacy_seresnet50 23.651 76.349 40.091 59.909 28.09 224 0.875 bilinear -53.978 -53.657 -46 -47
337 ese_vovnet19b_dw 23.535 76.465 41.288 58.712 6.54 224 0.875 bicubic -53.263 -51.980 -19 -20
338 tf_mixnet_m 23.484 76.516 40.989 59.011 5.01 224 0.875 bicubic -53.458 -52.163 -24 -25
339 tv_resnet34 23.473 76.527 41.367 58.633 21.80 224 0.875 bilinear -49.839 -50.059 +27
340 tf_efficientnet_em 23.359 76.641 40.404 59.596 6.90 240 0.882 bicubic -54.771 -53.640 -66 -67
341 selecsls42b 23.357 76.643 40.677 59.323 32.46 224 0.875 bicubic -53.817 -52.713 -34 -35
342 repvgg_b0 23.316 76.684 41.182 58.818 15.82 224 0.875 bilinear -51.837 -51.236 +2
343 mobilenetv2_110d 23.066 76.934 40.716 59.284 4.52 224 0.875 bicubic -51.970 -51.470 +6
344 vit_deit_tiny_distilled_patch16_224 22.718 77.282 40.771 59.229 5.91 224 0.900 bicubic -51.792 -51.119 +14
345 mobilenetv3_large_100 22.655 77.345 40.781 59.219 5.48 224 0.875 bicubic -53.111 -51.761 -11
346 mobilenetv3_rw 22.630 77.370 40.374 59.626 5.48 224 0.875 bicubic -53.004 -52.334 -10
347 tf_mobilenetv3_large_100 22.569 77.431 39.767 60.233 5.48 224 0.875 bilinear -52.949 -52.839 -9
348 tf_efficientnet_es 22.413 77.587 39.095 60.905 5.44 224 0.875 bicubic -54.180 -54.107 -25 -26
349 hrnet_w18_small_v2 22.337 77.663 39.861 60.139 15.60 224 0.875 bilinear -52.777 -52.555 -3
350 regnety_008 convit_tiny 22.119 22.282 77.881 77.718 38.900 39.669 61.100 60.331 6.26 5.71 224 0.875 bicubic -54.197 -50.834 -54.166 -52.045 -21 +18
351 seresnext26t_32x4d regnety_008 21.991 22.119 78.009 77.881 38.482 38.900 61.518 61.100 16.81 6.26 224 0.875 bicubic -55.995 -54.197 -55.264 -54.166 -72 -22
352 regnety_006 seresnext26t_32x4d 21.971 21.991 78.029 78.009 38.955 38.482 61.045 61.518 6.06 16.81 224 0.875 bicubic -53.275 -55.995 -53.577 -55.264 -9 -74
353 regnetx_008 regnety_006 21.940 21.971 78.060 78.029 38.928 38.955 61.072 61.045 7.26 6.06 224 0.875 bicubic -53.098 -53.275 -53.408 -53.577 -5 -10
354 resnet26d regnetx_008 21.907 21.940 78.094 78.060 38.619 38.928 61.381 61.072 16.01 7.26 224 0.875 bicubic -54.789 -53.098 -54.531 -53.408 -34 -6
355 semnasnet_100 resnet26d 21.903 21.907 78.097 78.094 38.600 38.619 61.400 61.381 3.89 16.01 224 0.875 bicubic -53.545 -54.789 -54.004 -54.531 -14 -36
356 semnasnet_100 21.903 78.097 38.600 61.400 3.89 224 0.875 bicubic -53.545 -54.004 -15
357 pit_ti_224 21.875 78.125 39.541 60.459 4.85 224 0.900 bicubic -51.037 -51.861 +14
358 regnetx_006 21.738 78.263 38.904 61.096 6.20 224 0.875 bicubic -52.115 -52.768 +6 +5
359 vgg19_bn 21.628 78.373 39.283 60.717 143.68 224 0.875 bilinear -52.587 -52.559 +1 0
360 ghostnet_100 21.620 78.380 38.692 61.308 5.18 224 0.875 bilinear -52.358 -52.764 +3 +2
361 gluon_resnet18_v1b 21.549 78.451 38.869 61.131 11.69 224 0.875 bicubic -49.287 -50.893 +22
362 fbnetc_100 21.484 78.516 38.161 61.839 5.57 224 0.875 bilinear -53.640 -54.224 -16 -17
363 mnasnet_100 21.350 78.650 37.719 62.281 4.38 224 0.875 bicubic -53.308 -54.395 -8 -9
364 resnet26 21.295 78.705 38.018 61.982 16.00 224 0.875 bicubic -53.997 -54.552 -21 -22
365 ssl_resnet18 21.278 78.722 39.113 60.887 11.69 224 0.875 bilinear -51.332 -52.303 +7
366 mixnet_s 21.254 78.746 38.187 61.813 4.13 224 0.875 bicubic -54.738 -54.609 -34 -35
367 seresnext26d_32x4d 21.252 78.748 37.311 62.689 16.81 224 0.875 bicubic -56.350 -56.297 -74 -76
368 legacy_seresnext26_32x4d 21.093 78.907 37.633 62.367 16.79 224 0.875 bicubic -56.011 -55.683 -58 -60
369 regnetx_004 20.898 79.102 37.566 62.434 5.16 224 0.875 bicubic -51.498 -53.264 +4
370 spnasnet_100 20.863 79.137 37.896 62.104 4.42 224 0.875 bilinear -53.221 -53.922 -9 -10
371 legacy_seresnet18 20.837 79.162 37.619 62.381 11.78 224 0.875 bicubic -50.905 -52.715 +9
372 mobilenetv2_100 20.773 79.227 37.759 62.241 3.50 224 0.875 bicubic -52.197 -53.257 -2
373 tf_mixnet_s 20.470 79.530 36.607 63.393 4.13 224 0.875 bicubic -55.180 -56.021 -37 -38
374 regnety_004 20.415 79.585 37.002 62.998 4.34 224 0.875 bicubic -53.619 -54.750 -12 -13
375 hrnet_w18_small 20.368 79.632 37.093 62.907 13.19 224 0.875 bilinear -51.974 -51.976 -53.585 0
376 tf_mobilenetv3_large_075 20.366 79.634 36.764 63.236 3.99 224 0.875 bilinear -53.072 -54.586 -11 -12
377 resnet18 20.228 79.772 37.261 62.739 11.69 224 0.875 bilinear -49.520 -51.817 +10
378 mixer_l16_224 20.171 79.829 32.956 67.044 208.20 224 0.875 bicubic -51.887 -54.712 +1
379 vit_deit_tiny_patch16_224 20.162 79.838 37.546 62.454 5.72 224 0.900 bicubic -52.007 -53.572 -1
380 tf_mobilenetv3_large_minimal_100 20.122 79.878 36.908 63.092 3.92 224 0.875 bilinear -52.126 -53.722 -3
381 vgg16_bn 19.959 80.041 36.301 63.699 138.37 224 0.875 bilinear -53.391 -55.205 -15 -16
382 vgg19 17.929 82.071 33.054 66.946 143.67 224 0.875 bilinear -54.439 -57.818 -8
383 vgg13_bn 17.802 82.198 34.039 65.961 133.05 224 0.875 bilinear -53.792 -56.337 -2
384 vgg16 17.540 82.460 32.773 67.227 138.36 224 0.875 bilinear -54.054 -57.609 -2
390 vgg13 16.100 83.900 30.985 69.015 133.05 224 0.875 bilinear -53.826 -58.261 -4
391 vgg11 15.728 84.272 30.453 69.547 132.86 224 0.875 bilinear -53.296 -58.175 -3
392 tf_mobilenetv3_small_075 14.944 85.056 29.572 70.428 2.04 224 0.875 bilinear -50.772 -56.558 +1
393 dla46_c 14.657 85.343 29.380 70.620 1.30 224 0.875 bilinear -50.209 -50.207 -56.912 +1
394 dla46x_c 14.382 85.618 29.191 70.809 1.07 224 0.875 bilinear -51.588 -57.789 -2
395 tf_mobilenetv3_small_minimal_100 13.964 86.036 27.988 72.012 2.04 224 0.875 bilinear -48.942 -56.242 0

72
tests/test_models.py
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@ -17,7 +17,7 @@ if hasattr(torch._C, '_jit_set_profiling_executor'):
# transformer models don't support many of the spatial / feature based model functionalities
NON_STD_FILTERS = [
'vit_*', 'tnt_*', 'pit_*', 'swin_*', 'coat_*', 'cait_*', '*mixer_*', 'gmlp_*', 'resmlp_*', 'twins_*',
'convit_*', 'levit*', 'visformer*']
'convit_*', 'levit*', 'visformer*', 'deit*']
NUM_NON_STD = len(NON_STD_FILTERS)
# exclude models that cause specific test failures
@ -120,7 +120,6 @@ def test_model_default_cfgs(model_name, batch_size):
state_dict = model.state_dict()
cfg = model.default_cfg
classifier = cfg['classifier']
pool_size = cfg['pool_size']
input_size = model.default_cfg['input_size']
@ -148,8 +147,74 @@ def test_model_default_cfgs(model_name, batch_size):
# FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
if 'pruned' not in model_name: # FIXME better pruned model handling
# test classifier + global pool deletion via __init__
model = create_model(model_name, pretrained=False, num_classes=0, global_pool='').eval()
outputs = model.forward(input_tensor)
assert len(outputs.shape) == 4
if not isinstance(model, timm.models.MobileNetV3) and not isinstance(model, timm.models.GhostNet):
# FIXME mobilenetv3/ghostnet forward_features vs removed pooling differ
assert outputs.shape[-1] == pool_size[-1] and outputs.shape[-2] == pool_size[-2]
# check classifier name matches default_cfg
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
if isinstance(first_conv, str):
first_conv = (first_conv,)
assert isinstance(first_conv, (tuple, list))
for fc in first_conv:
assert fc + ".weight" in state_dict.keys(), f'{fc} not in model params'
@pytest.mark.timeout(300)
@pytest.mark.parametrize('model_name', list_models(filter=NON_STD_FILTERS))
@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"""
model = create_model(model_name, pretrained=False)
model.eval()
state_dict = model.state_dict()
cfg = model.default_cfg
input_size = _get_input_size(model=model)
if max(input_size) > 320: # FIXME const
pytest.skip("Fixed input size model > limit.")
input_tensor = torch.randn((batch_size, *input_size))
# test forward_features (always unpooled)
outputs = model.forward_features(input_tensor)
if isinstance(outputs, tuple):
outputs = outputs[0]
assert outputs.shape[1] == model.num_features
# test forward after deleting the classifier, output should be poooled, size(-1) == model.num_features
model.reset_classifier(0)
outputs = model.forward(input_tensor)
if isinstance(outputs, tuple):
outputs = outputs[0]
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.num_features
model = create_model(model_name, pretrained=False, num_classes=0).eval()
outputs = model.forward(input_tensor)
if isinstance(outputs, tuple):
outputs = outputs[0]
assert len(outputs.shape) == 2
assert outputs.shape[1] == model.num_features
# check classifier name matches default_cfg
assert classifier + ".weight" in state_dict.keys(), f'{classifier} not in model params'
classifier = cfg['classifier']
if not isinstance(classifier, (tuple, list)):
classifier = classifier,
for c in classifier:
assert c + ".weight" in state_dict.keys(), f'{c} not in model params'
# check first conv(s) names match default_cfg
first_conv = cfg['first_conv']
@ -168,6 +233,7 @@ if 'GITHUB_ACTIONS' not in os.environ:
"""Create that pretrained weights load, verify support for in_chans != 3 while doing so."""
in_chans = 3 if 'pruned' in model_name else 1 # pruning not currently supported with in_chans change
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=5)
create_model(model_name, pretrained=True, in_chans=in_chans, num_classes=0)
@pytest.mark.timeout(120)
@pytest.mark.parametrize('model_name', list_models(pretrained=True, exclude_filters=NON_STD_FILTERS))

51
timm/models/byobnet.py
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@ -98,6 +98,12 @@ default_cfgs = {
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnet50t': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'gcresnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic'),
'bat_resnext26ts': _cfg(
first_conv='stem.conv1.conv', input_size=(3, 256, 256), pool_size=(8, 8), interpolation='bicubic',
min_input_size=(3, 256, 256)),
}
@ -307,6 +313,37 @@ model_cfgs = dict(
stem_pool=None,
attn_layer='gc'
),
gcresnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=3, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=4, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=6, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=3, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='gc',
),
bat_resnext26ts=ByoModelCfg(
blocks=(
ByoBlockCfg(type='bottle', d=2, c=256, s=1, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=512, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=1024, s=2, gs=32, br=0.25),
ByoBlockCfg(type='bottle', d=2, c=2048, s=2, gs=32, br=0.25),
),
stem_chs=64,
stem_type='tiered',
stem_pool='maxpool',
num_features=0,
act_layer='silu',
attn_layer='bat',
attn_kwargs=dict(block_size=8)
),
)
@ -426,6 +463,20 @@ def gcresnet50t(pretrained=False, **kwargs):
return _create_byobnet('gcresnet50t', pretrained=pretrained, **kwargs)
@register_model
def gcresnext26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('gcresnext26ts', pretrained=pretrained, **kwargs)
@register_model
def bat_resnext26ts(pretrained=False, **kwargs):
"""
"""
return _create_byobnet('bat_resnext26ts', pretrained=pretrained, **kwargs)
def expand_blocks_cfg(stage_blocks_cfg: Union[ByoBlockCfg, Sequence[ByoBlockCfg]]) -> List[ByoBlockCfg]:
if not isinstance(stage_blocks_cfg, Sequence):
stage_blocks_cfg = (stage_blocks_cfg,)

30
timm/models/cait.py
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@ -74,11 +74,11 @@ default_cfgs = dict(
class ClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to do CA
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
self.q = nn.Linear(dim, dim, bias=qkv_bias)
self.k = nn.Linear(dim, dim, bias=qkv_bias)
@ -110,13 +110,13 @@ class LayerScaleBlockClassAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add CA and LayerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=ClassAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
@ -134,14 +134,14 @@ class LayerScaleBlockClassAttn(nn.Module):
class TalkingHeadAttn(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add Talking Heads Attention (https://arxiv.org/pdf/2003.02436v1.pdf)
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
@ -177,13 +177,13 @@ class LayerScaleBlock(nn.Module):
# taken from https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
# with slight modifications to add layerScale
def __init__(
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, attn_block=TalkingHeadAttn,
mlp_block=Mlp, init_values=1e-4):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = attn_block(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
@ -202,7 +202,7 @@ class Cait(nn.Module):
# with slight modifications to adapt to our cait models
def __init__(
self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
num_heads=12, mlp_ratio=4., qkv_bias=True, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0.,
norm_layer=partial(nn.LayerNorm, eps=1e-6),
global_pool=None,
@ -235,14 +235,14 @@ class Cait(nn.Module):
dpr = [drop_path_rate for i in range(depth)]
self.blocks = nn.ModuleList([
block_layers(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
act_layer=act_layer, attn_block=attn_block, mlp_block=mlp_block, init_values=init_scale)
for i in range(depth)])
self.blocks_token_only = nn.ModuleList([
block_layers_token(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio_clstk, qkv_bias=qkv_bias, qk_scale=qk_scale,
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio_clstk, qkv_bias=qkv_bias,
drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=norm_layer,
act_layer=act_layer, attn_block=attn_block_token_only,
mlp_block=mlp_block_token_only, init_values=init_scale)
@ -270,6 +270,13 @@ class Cait(nn.Module):
def no_weight_decay(self):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
B = x.shape[0]
x = self.patch_embed(x)
@ -293,7 +300,6 @@ class Cait(nn.Module):
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x

8
timm/models/coat.py
Unescape View File

@ -335,6 +335,8 @@ class CoaT(nn.Module):
crpe_window = crpe_window or {3: 2, 5: 3, 7: 3}
self.return_interm_layers = return_interm_layers
self.out_features = out_features
self.embed_dims = embed_dims
self.num_features = embed_dims[-1]
self.num_classes = num_classes
# Patch embeddings.
@ -441,10 +443,10 @@ class CoaT(nn.Module):
# CoaT series: Aggregate features of last three scales for classification.
assert embed_dims[1] == embed_dims[2] == embed_dims[3]
self.aggregate = torch.nn.Conv1d(in_channels=3, out_channels=1, kernel_size=1)
self.head = nn.Linear(embed_dims[3], num_classes)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
else:
# CoaT-Lite series: Use feature of last scale for classification.
self.head = nn.Linear(embed_dims[3], num_classes)
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
# Initialize weights.
trunc_normal_(self.cls_token1, std=.02)
@ -471,7 +473,7 @@ class CoaT(nn.Module):
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def insert_cls(self, x, cls_token):
""" Insert CLS token. """

24
timm/models/convit.py
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@ -57,13 +57,13 @@ default_cfgs = {
class GPSA(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.,
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.,
locality_strength=1.):
super().__init__()
self.num_heads = num_heads
self.dim = dim
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
self.locality_strength = locality_strength
self.qk = nn.Linear(dim, dim * 2, bias=qkv_bias)
@ -73,7 +73,6 @@ class GPSA(nn.Module):
self.proj = nn.Linear(dim, dim)
self.pos_proj = nn.Linear(3, num_heads)
self.proj_drop = nn.Dropout(proj_drop)
self.locality_strength = locality_strength
self.gating_param = nn.Parameter(torch.ones(self.num_heads))
self.rel_indices: torch.Tensor = torch.zeros(1, 1, 1, 3) # silly torchscript hack, won't work with None
@ -142,11 +141,11 @@ class GPSA(nn.Module):
class MHSA(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
@ -191,19 +190,16 @@ class MHSA(nn.Module):
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm, use_gpsa=True, **kwargs):
super().__init__()
self.norm1 = norm_layer(dim)
self.use_gpsa = use_gpsa
if self.use_gpsa:
self.attn = GPSA(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, **kwargs)
dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop, **kwargs)
else:
self.attn = MHSA(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
proj_drop=drop, **kwargs)
self.attn = MHSA(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
@ -220,7 +216,7 @@ class ConViT(nn.Module):
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0.,
num_heads=12, mlp_ratio=4., qkv_bias=False, drop_rate=0., attn_drop_rate=0.,
drop_path_rate=0., hybrid_backbone=None, norm_layer=nn.LayerNorm, global_pool=None,
local_up_to_layer=3, locality_strength=1., use_pos_embed=True):
super().__init__()
@ -250,13 +246,13 @@ class ConViT(nn.Module):
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
use_gpsa=True,
locality_strength=locality_strength)
if i < local_up_to_layer else
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer,
use_gpsa=False)
for i in range(depth)])

6
timm/models/dla.py
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@ -288,6 +288,8 @@ class DLA(nn.Module):
self.num_features = channels[-1]
self.global_pool, self.fc = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
@ -314,6 +316,7 @@ class DLA(nn.Module):
self.num_classes = num_classes
self.global_pool, self.fc = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
def forward_features(self, x):
x = self.base_layer(x)
@ -331,8 +334,7 @@ class DLA(nn.Module):
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.fc(x)
if not self.global_pool.is_identity():
x = x.flatten(1) # conv classifier, flatten if pooling isn't pass-through (disabled)
x = self.flatten(x)
return x

5
timm/models/dpn.py
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@ -237,6 +237,7 @@ class DPN(nn.Module):
# Using 1x1 conv for the FC layer to allow the extra pooling scheme
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
def get_classifier(self):
return self.classifier
@ -245,6 +246,7 @@ class DPN(nn.Module):
self.num_classes = num_classes
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool, use_conv=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity()
def forward_features(self, x):
return self.features(x)
@ -255,8 +257,7 @@ class DPN(nn.Module):
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
x = self.classifier(x)
if not self.global_pool.is_identity():
x = x.flatten(1) # conv classifier, flatten if pooling isn't pass-through (disabled)
x = self.flatten(x)
return x

11
timm/models/ghostnet.py
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@ -133,7 +133,7 @@ class GhostBottleneck(nn.Module):
class GhostNet(nn.Module):
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3, output_stride=32):
def __init__(self, cfgs, num_classes=1000, width=1.0, dropout=0.2, in_chans=3, output_stride=32, global_pool='avg'):
super(GhostNet, self).__init__()
# setting of inverted residual blocks
assert output_stride == 32, 'only output_stride==32 is valid, dilation not supported'
@ -178,10 +178,11 @@ class GhostNet(nn.Module):
# building last several layers
self.num_features = out_chs = 1280
self.global_pool = SelectAdaptivePool2d(pool_type='avg')
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.conv_head = nn.Conv2d(prev_chs, out_chs, 1, 1, 0, bias=True)
self.act2 = nn.ReLU(inplace=True)
self.classifier = Linear(out_chs, num_classes)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(out_chs, num_classes) if num_classes > 0 else nn.Identity()
def get_classifier(self):
return self.classifier
@ -190,6 +191,7 @@ class GhostNet(nn.Module):
self.num_classes = num_classes
# cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.pool_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
@ -204,8 +206,7 @@ class GhostNet(nn.Module):
def forward(self, x):
x = self.forward_features(x)
if not self.global_pool.is_identity():
x = x.view(x.size(0), -1)
x = self.flatten(x)
if self.dropout > 0.:
x = F.dropout(x, p=self.dropout, training=self.training)
x = self.classifier(x)

29
timm/models/helpers.py
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@ -45,6 +45,13 @@ def load_state_dict(checkpoint_path, use_ema=False):
def load_checkpoint(model, checkpoint_path, use_ema=False, strict=True):
if os.path.splitext(checkpoint_path)[-1].lower() in ('.npz', '.npy'):
# numpy checkpoint, try to load via model specific load_pretrained fn
if hasattr(model, 'load_pretrained'):
model.load_pretrained(checkpoint_path)
else:
raise NotImplementedError('Model cannot load numpy checkpoint')
return
state_dict = load_state_dict(checkpoint_path, use_ema)
model.load_state_dict(state_dict, strict=strict)
@ -477,3 +484,25 @@ def model_parameters(model, exclude_head=False):
return [p for p in model.parameters()][:-2]
else:
return model.parameters()
def named_apply(fn: Callable, module: nn.Module, name='', depth_first=True, include_root=False) -> nn.Module:
if not depth_first and include_root:
fn(module=module, name=name)
for child_name, child_module in module.named_children():
child_name = '.'.join((name, child_name)) if name else child_name
named_apply(fn=fn, module=child_module, name=child_name, depth_first=depth_first, include_root=True)
if depth_first and include_root:
fn(module=module, name=name)
return module
def named_modules(module: nn.Module, name='', depth_first=True, include_root=False):
if not depth_first and include_root:
yield name, module
for child_name, child_module in module.named_children():
child_name = '.'.join((name, child_name)) if name else child_name
yield from named_modules(
module=child_module, name=child_name, depth_first=depth_first, include_root=True)
if depth_first and include_root:
yield name, module

13
timm/models/layers/adaptive_avgmax_pool.py
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@ -55,7 +55,7 @@ class FastAdaptiveAvgPool2d(nn.Module):
self.flatten = flatten
def forward(self, x):
return x.mean((2, 3)) if self.flatten else x.mean((2, 3), keepdim=True)
return x.mean((2, 3), keepdim=not self.flatten)
class AdaptiveAvgMaxPool2d(nn.Module):
@ -82,13 +82,13 @@ class SelectAdaptivePool2d(nn.Module):
def __init__(self, output_size=1, pool_type='fast', flatten=False):
super(SelectAdaptivePool2d, self).__init__()
self.pool_type = pool_type or '' # convert other falsy values to empty string for consistent TS typing
self.flatten = flatten
self.flatten = nn.Flatten(1) if flatten else nn.Identity()
if pool_type == '':
self.pool = nn.Identity() # pass through
elif pool_type == 'fast':
assert output_size == 1
self.pool = FastAdaptiveAvgPool2d(self.flatten)
self.flatten = False
self.pool = FastAdaptiveAvgPool2d(flatten)
self.flatten = nn.Identity()
elif pool_type == 'avg':
self.pool = nn.AdaptiveAvgPool2d(output_size)
elif pool_type == 'avgmax':
@ -101,12 +101,11 @@ class SelectAdaptivePool2d(nn.Module):
assert False, 'Invalid pool type: %s' % pool_type
def is_identity(self):
return self.pool_type == ''
return not self.pool_type
def forward(self, x):
x = self.pool(x)
if self.flatten:
x = x.flatten(1)
x = self.flatten(x)
return x
def feat_mult(self):

5
timm/models/layers/classifier.py
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@ -20,7 +20,7 @@ def _create_pool(num_features, num_classes, pool_type='avg', use_conv=False):
return global_pool, num_pooled_features
def _create_fc(num_features, num_classes, pool_type='avg', use_conv=False):
def _create_fc(num_features, num_classes, use_conv=False):
if num_classes <= 0:
fc = nn.Identity() # pass-through (no classifier)
elif use_conv:
@ -45,11 +45,12 @@ class ClassifierHead(nn.Module):
self.drop_rate = drop_rate
self.global_pool, num_pooled_features = _create_pool(in_chs, num_classes, pool_type, use_conv=use_conv)
self.fc = _create_fc(num_pooled_features, num_classes, use_conv=use_conv)
self.flatten_after_fc = use_conv and pool_type
self.flatten = nn.Flatten(1) if use_conv and pool_type else nn.Identity()
def forward(self, x):
x = self.global_pool(x)
if self.drop_rate:
x = F.dropout(x, p=float(self.drop_rate), training=self.training)
x = self.fc(x)
x = self.flatten(x)
return x

6
timm/models/layers/mlp.py
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@ -40,6 +40,12 @@ class GluMlp(nn.Module):
self.fc2 = nn.Linear(hidden_features // 2, out_features)
self.drop = nn.Dropout(drop)
def init_weights(self):
# override init of fc1 w/ gate portion set to weight near zero, bias=1
fc1_mid = self.fc1.bias.shape[0] // 2
nn.init.ones_(self.fc1.bias[fc1_mid:])
nn.init.normal_(self.fc1.weight[fc1_mid:], std=1e-6)
def forward(self, x):
x = self.fc1(x)
x, gates = x.chunk(2, dim=-1)

8
timm/models/layers/non_local_attn.py
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@ -81,7 +81,7 @@ class BilinearAttnTransform(nn.Module):
self.groups = groups
self.in_channels = in_channels
def resize_mat(self, x, t):
def resize_mat(self, x, t: int):
B, C, block_size, block_size1 = x.shape
assert block_size == block_size1
if t <= 1:
@ -100,10 +100,8 @@ class BilinearAttnTransform(nn.Module):
out = self.conv1(x)
rp = F.adaptive_max_pool2d(out, (self.block_size, 1))
cp = F.adaptive_max_pool2d(out, (1, self.block_size))
p = self.conv_p(rp).view(B, self.groups, self.block_size, self.block_size)
q = self.conv_q(cp).view(B, self.groups, self.block_size, self.block_size)
p = F.sigmoid(p)
q = F.sigmoid(q)
p = self.conv_p(rp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
q = self.conv_q(cp).view(B, self.groups, self.block_size, self.block_size).sigmoid()
p = p / p.sum(dim=3, keepdim=True)
q = q / q.sum(dim=2, keepdim=True)
p = p.view(B, self.groups, 1, self.block_size, self.block_size).expand(x.size(

10
timm/models/layers/pool2d_same.py
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@ -27,7 +27,8 @@ class AvgPool2dSame(nn.AvgPool2d):
super(AvgPool2dSame, self).__init__(kernel_size, stride, (0, 0), ceil_mode, count_include_pad)
def forward(self, x):
return avg_pool2d_same(
x = pad_same(x, self.kernel_size, self.stride)
return F.avg_pool2d(
x, self.kernel_size, self.stride, self.padding, self.ceil_mode, self.count_include_pad)
@ -41,14 +42,15 @@ def max_pool2d_same(
class MaxPool2dSame(nn.MaxPool2d):
""" Tensorflow like 'SAME' wrapper for 2D max pooling
"""
def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False, count_include_pad=True):
def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False):
kernel_size = to_2tuple(kernel_size)
stride = to_2tuple(stride)
dilation = to_2tuple(dilation)
super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode, count_include_pad)
super(MaxPool2dSame, self).__init__(kernel_size, stride, (0, 0), dilation, ceil_mode)
def forward(self, x):
return max_pool2d_same(x, self.kernel_size, self.stride, self.padding, self.dilation, self.ceil_mode)
x = pad_same(x, self.kernel_size, self.stride, value=-float('inf'))
return F.max_pool2d(x, self.kernel_size, self.stride, (0, 0), self.dilation, self.ceil_mode)
def create_pool2d(pool_type, kernel_size, stride=None, **kwargs):

102
timm/models/layers/std_conv.py
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@ -1,3 +1,21 @@
""" Convolution with Weight Standardization (StdConv and ScaledStdConv)
StdConv:
@article{weightstandardization,
author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
title = {Weight Standardization},
journal = {arXiv preprint arXiv:1903.10520},
year = {2019},
}
Code: https://github.com/joe-siyuan-qiao/WeightStandardization
ScaledStdConv:
Paper: `Characterizing signal propagation to close the performance gap in unnormalized ResNets`
- https://arxiv.org/abs/2101.08692
Official Deepmind JAX code: https://github.com/deepmind/deepmind-research/tree/master/nfnets
Hacked together by / copyright Ross Wightman, 2021.
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
@ -5,12 +23,6 @@ import torch.nn.functional as F
from .padding import get_padding, get_padding_value, pad_same
def get_weight(module):
std, mean = torch.std_mean(module.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (module.weight - mean) / (std + module.eps)
return weight
class StdConv2d(nn.Conv2d):
"""Conv2d with Weight Standardization. Used for BiT ResNet-V2 models.
@ -18,8 +30,8 @@ class StdConv2d(nn.Conv2d):
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding=None, dilation=1,
groups=1, bias=False, eps=1e-5):
self, in_channel, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=False, eps=1e-6):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
@ -27,13 +39,11 @@ class StdConv2d(nn.Conv2d):
padding=padding, dilation=dilation, groups=groups, bias=bias)
self.eps = eps
def get_weight(self):
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
return weight
def forward(self, x):
x = F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
weight = F.batch_norm(
self.weight.view(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
@ -44,8 +54,8 @@ class StdConv2dSame(nn.Conv2d):
https://arxiv.org/abs/1903.10520v2
"""
def __init__(
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME', dilation=1,
groups=1, bias=False, eps=1e-5):
self, in_channel, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=False, eps=1e-6):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channel, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
@ -53,15 +63,13 @@ class StdConv2dSame(nn.Conv2d):
self.same_pad = is_dynamic
self.eps = eps
def get_weight(self):
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = (self.weight - mean) / (std + self.eps)
return weight
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
x = F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
weight = F.batch_norm(
self.weight.view(1, self.out_channels, -1), None, None,
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
x = F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
return x
@ -75,8 +83,8 @@ class ScaledStdConv2d(nn.Conv2d):
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding=None, dilation=1, groups=1,
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=False):
self, in_channels, out_channels, kernel_size, stride=1, padding=None,
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
if padding is None:
padding = get_padding(kernel_size, stride, dilation)
super().__init__(
@ -84,19 +92,14 @@ class ScaledStdConv2d(nn.Conv2d):
groups=groups, bias=bias)
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5 # gamma * 1 / sqrt(fan-in)
self.eps = eps ** 2 if use_layernorm else eps
self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
def get_weight(self):
if self.use_layernorm:
weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = self.scale * (self.weight - mean) / (std + self.eps)
return self.gain * weight
self.eps = eps
def forward(self, x):
return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
weight = F.batch_norm(
self.weight.view(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
class ScaledStdConv2dSame(nn.Conv2d):
@ -109,8 +112,8 @@ class ScaledStdConv2dSame(nn.Conv2d):
"""
def __init__(
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME', dilation=1, groups=1,
bias=True, gamma=1.0, eps=1e-5, gain_init=1.0, use_layernorm=False):
self, in_channels, out_channels, kernel_size, stride=1, padding='SAME',
dilation=1, groups=1, bias=True, gamma=1.0, eps=1e-6, gain_init=1.0):
padding, is_dynamic = get_padding_value(padding, kernel_size, stride=stride, dilation=dilation)
super().__init__(
in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation,
@ -118,26 +121,13 @@ class ScaledStdConv2dSame(nn.Conv2d):
self.gain = nn.Parameter(torch.full((self.out_channels, 1, 1, 1), gain_init))
self.scale = gamma * self.weight[0].numel() ** -0.5
self.same_pad = is_dynamic
self.eps = eps ** 2 if use_layernorm else eps
self.use_layernorm = use_layernorm # experimental, slightly faster/less GPU memory to hijack LN kernel
# NOTE an alternate formulation to consider, closer to DeepMind Haiku impl but doesn't seem
# to make much numerical difference (+/- .002 to .004) in top-1 during eval.
# def get_weight(self):
# var, mean = torch.var_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
# scale = torch.rsqrt((self.weight[0].numel() * var).clamp_(self.eps)) * self.gain
# weight = (self.weight - mean) * scale
# return self.gain * weight
def get_weight(self):
if self.use_layernorm:
weight = self.scale * F.layer_norm(self.weight, self.weight.shape[1:], eps=self.eps)
else:
std, mean = torch.std_mean(self.weight, dim=[1, 2, 3], keepdim=True, unbiased=False)
weight = self.scale * (self.weight - mean) / (std + self.eps)
return self.gain * weight
self.eps = eps
def forward(self, x):
if self.same_pad:
x = pad_same(x, self.kernel_size, self.stride, self.dilation)
return F.conv2d(x, self.get_weight(), self.bias, self.stride, self.padding, self.dilation, self.groups)
weight = F.batch_norm(
self.weight.view(1, self.out_channels, -1), None, None,
weight=(self.gain * self.scale).view(-1),
training=True, momentum=0., eps=self.eps).reshape_as(self.weight)
return F.conv2d(x, weight, self.bias, self.stride, self.padding, self.dilation, self.groups)

89
timm/models/levit.py
Unescape View File

@ -84,63 +84,33 @@ __all__ = ['Levit']
@register_model
def levit_128s(pretrained=False, fuse=False,distillation=True, use_conv=False, **kwargs):
def levit_128s(pretrained=False, use_conv=False, **kwargs):
return create_levit(
'levit_128s', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
'levit_128s', pretrained=pretrained, use_conv=use_conv, **kwargs)
@register_model
def levit_128(pretrained=False, fuse=False, distillation=True, use_conv=False, **kwargs):
def levit_128(pretrained=False, use_conv=False, **kwargs):
return create_levit(
'levit_128', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
'levit_128', pretrained=pretrained, use_conv=use_conv, **kwargs)
@register_model
def levit_192(pretrained=False, fuse=False, distillation=True, use_conv=False, **kwargs):
def levit_192(pretrained=False, use_conv=False, **kwargs):
return create_levit(
'levit_192', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
'levit_192', pretrained=pretrained, use_conv=use_conv, **kwargs)
@register_model
def levit_256(pretrained=False, fuse=False, distillation=True, use_conv=False, **kwargs):
def levit_256(pretrained=False, use_conv=False, **kwargs):
return create_levit(
'levit_256', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
'levit_256', pretrained=pretrained, use_conv=use_conv, **kwargs)
@register_model
def levit_384(pretrained=False, fuse=False, distillation=True, use_conv=False, **kwargs):
def levit_384(pretrained=False, use_conv=False, **kwargs):
return create_levit(
'levit_384', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
@register_model
def levit_c_128s(pretrained=False, fuse=False, distillation=True, use_conv=True,**kwargs):
return create_levit(
'levit_128s', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
@register_model
def levit_c_128(pretrained=False, fuse=False,distillation=True, use_conv=True, **kwargs):
return create_levit(
'levit_128', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
@register_model
def levit_c_192(pretrained=False, fuse=False, distillation=True, use_conv=True, **kwargs):
return create_levit(
'levit_192', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
@register_model
def levit_c_256(pretrained=False, fuse=False, distillation=True, use_conv=True, **kwargs):
return create_levit(
'levit_256', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
@register_model
def levit_c_384(pretrained=False, fuse=False, distillation=True, use_conv=True, **kwargs):
return create_levit(
'levit_384', pretrained=pretrained, fuse=fuse, distillation=distillation, use_conv=use_conv, **kwargs)
'levit_384', pretrained=pretrained, use_conv=use_conv, **kwargs)
class ConvNorm(nn.Sequential):
@ -427,6 +397,9 @@ class AttentionSubsample(nn.Module):
class Levit(nn.Module):
""" Vision Transformer with support for patch or hybrid CNN input stage
NOTE: distillation is defaulted to True since pretrained weights use it, will cause problems
w/ train scripts that don't take tuple outputs,
"""
def __init__(
@ -447,7 +420,8 @@ class Levit(nn.Module):
attn_act_layer='hard_swish',
distillation=True,
use_conv=False,
drop_path=0):
drop_rate=0.,
drop_path_rate=0.):
super().__init__()
act_layer = get_act_layer(act_layer)
attn_act_layer = get_act_layer(attn_act_layer)
@ -486,7 +460,7 @@ class Levit(nn.Module):
Attention(
ed, kd, nh, attn_ratio=ar, act_layer=attn_act_layer,
resolution=resolution, use_conv=use_conv),
drop_path))
drop_path_rate))
if mr > 0:
h = int(ed * mr)
self.blocks.append(
@ -494,7 +468,7 @@ class Levit(nn.Module):
ln_layer(ed, h, resolution=resolution),
act_layer(),
ln_layer(h, ed, bn_weight_init=0, resolution=resolution),
), drop_path))
), drop_path_rate))
if do[0] == 'Subsample':
# ('Subsample',key_dim, num_heads, attn_ratio, mlp_ratio, stride)
resolution_ = (resolution - 1) // do[5] + 1
@ -511,26 +485,45 @@ class Levit(nn.Module):
ln_layer(embed_dim[i + 1], h, resolution=resolution),
act_layer(),
ln_layer(h, embed_dim[i + 1], bn_weight_init=0, resolution=resolution),
), drop_path))
), drop_path_rate))
self.blocks = nn.Sequential(*self.blocks)
# Classifier head
self.head = NormLinear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = None
if distillation:
self.head_dist = NormLinear(embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
else:
self.head_dist = None
@torch.jit.ignore
def no_weight_decay(self):
return {x for x in self.state_dict().keys() if 'attention_biases' in x}
def forward(self, x):
def get_classifier(self):
if self.head_dist is None:
return self.head
else:
return self.head, self.head_dist
def reset_classifier(self, num_classes, global_pool='', distillation=None):
self.num_classes = num_classes
self.head = NormLinear(self.embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
if distillation is not None:
self.distillation = distillation
if self.distillation:
self.head_dist = NormLinear(self.embed_dim[-1], num_classes) if num_classes > 0 else nn.Identity()
else:
self.head_dist = None
def forward_features(self, x):
x = self.patch_embed(x)
if not self.use_conv:
x = x.flatten(2).transpose(1, 2)
x = self.blocks(x)
x = x.mean((-2, -1)) if self.use_conv else x.mean(1)
return x
def forward(self, x):
x = self.forward_features(x)
if self.head_dist is not None:
x, x_dist = self.head(x), self.head_dist(x)
if self.training and not torch.jit.is_scripting():
@ -549,7 +542,7 @@ def checkpoint_filter_fn(state_dict, model):
state_dict = state_dict['model']
D = model.state_dict()
for k in state_dict.keys():
if D[k].ndim == 4 and state_dict[k].ndim == 2:
if k in D and D[k].ndim == 4 and state_dict[k].ndim == 2:
state_dict[k] = state_dict[k][:, :, None, None]
return state_dict

275
timm/models/mlp_mixer.py
Unescape View File

@ -14,8 +14,9 @@ Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2
year={2021}
}
Also supporting preliminary (not verified) implementations of ResMlp, gMLP, and possibly more...
Also supporting ResMlp, and a preliminary (not verified) implementations of gMLP
Code: https://github.com/facebookresearch/deit
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
@misc{touvron2021resmlp,
title={ResMLP: Feedforward networks for image classification with data-efficient training},
@ -45,7 +46,7 @@ import torch
import torch.nn as nn
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .helpers import build_model_with_cfg, overlay_external_default_cfg, named_apply
from .layers import PatchEmbed, Mlp, GluMlp, GatedMlp, DropPath, lecun_normal_, to_2tuple
from .registry import register_model
@ -80,25 +81,57 @@ default_cfgs = dict(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_mixer_l16_224_in21k-846aa33c.pth',
num_classes=21843
),
# Mixer ImageNet-21K-P pretraining
mixer_b16_224_miil_in21k=_cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth',
mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil_in21k.pth',
mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
),
mixer_b16_224_miil=_cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth',
mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mixer_b16_224_miil.pth',
mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear',
),
gmixer_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
gmixer_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_12_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_24_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_36_224=_cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
gmixer_24_224=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmixer_24_224_raa-7daf7ae6.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_12_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_12_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_24_no_dist.pth',
#url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/resmlp_24_224_raa-a8256759.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_36_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_36_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_big_24_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_no_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_12_distilled_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_12_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_24_distilled_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_24_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_36_distilled_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlp_36_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_big_24_distilled_224=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_dist.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
resmlp_big_24_224_in22ft1k=_cfg(
url='https://dl.fbaipublicfiles.com/deit/resmlpB_24_22k.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
gmlp_ti16_224=_cfg(),
gmlp_s16_224=_cfg(),
gmlp_s16_224=_cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/gmlp_s16_224_raa-10536d42.pth',
),
gmlp_b16_224=_cfg(),
)
@ -169,6 +202,11 @@ class SpatialGatingUnit(nn.Module):
self.norm = norm_layer(gate_dim)
self.proj = nn.Linear(seq_len, seq_len)
def init_weights(self):
# special init for the projection gate, called as override by base model init
nn.init.normal_(self.proj.weight, std=1e-6)
nn.init.ones_(self.proj.bias)
def forward(self, x):
u, v = x.chunk(2, dim=-1)
v = self.norm(v)
@ -205,7 +243,7 @@ class MlpMixer(nn.Module):
in_chans=3,
patch_size=16,
num_blocks=8,
hidden_dim=512,
embed_dim=512,
mlp_ratio=(0.5, 4.0),
block_layer=MixerBlock,
mlp_layer=Mlp,
@ -218,59 +256,95 @@ class MlpMixer(nn.Module):
):
super().__init__()
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim # num_features for consistency with other models
self.stem = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans, embed_dim=hidden_dim,
norm_layer=norm_layer if stem_norm else None)
img_size=img_size, patch_size=patch_size, in_chans=in_chans,
embed_dim=embed_dim, norm_layer=norm_layer if stem_norm else None)
# FIXME drop_path (stochastic depth scaling rule or all the same?)
self.blocks = nn.Sequential(*[
block_layer(
hidden_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer,
embed_dim, self.stem.num_patches, mlp_ratio, mlp_layer=mlp_layer, norm_layer=norm_layer,
act_layer=act_layer, drop=drop_rate, drop_path=drop_path_rate)
for _ in range(num_blocks)])
self.norm = norm_layer(hidden_dim)
self.head = nn.Linear(hidden_dim, self.num_classes) # zero init
self.norm = norm_layer(embed_dim)
self.head = nn.Linear(embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
self.init_weights(nlhb=nlhb)
def init_weights(self, nlhb=False):
head_bias = -math.log(self.num_classes) if nlhb else 0.
for n, m in self.named_modules():
_init_weights(m, n, head_bias=head_bias)
named_apply(partial(_init_weights, head_bias=head_bias), module=self) # depth-first
def forward(self, x):
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.stem(x)
x = self.blocks(x)
x = self.norm(x)
x = x.mean(dim=1)
return x
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
return x
def _init_weights(m, n: str, head_bias: float = 0.):
def _init_weights(module: nn.Module, name: str, head_bias: float = 0., flax=False):
""" Mixer weight initialization (trying to match Flax defaults)
"""
if isinstance(m, nn.Linear):
if n.startswith('head'):
nn.init.zeros_(m.weight)
nn.init.constant_(m.bias, head_bias)
elif n.endswith('gate.proj'):
nn.init.normal_(m.weight, std=1e-4)
nn.init.ones_(m.bias)
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
else:
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
if 'mlp' in n:
nn.init.normal_(m.bias, std=1e-6)
else:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Conv2d):
lecun_normal_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
nn.init.zeros_(m.bias)
nn.init.ones_(m.weight)
if flax:
# Flax defaults
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
else:
# like MLP init in vit (my original init)
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.LayerNorm, nn.BatchNorm2d, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif hasattr(module, 'init_weights'):
# NOTE if a parent module contains init_weights method, it can override the init of the
# child modules as this will be called in depth-first order.
module.init_weights()
def checkpoint_filter_fn(state_dict, model):
""" Remap checkpoints if needed """
if 'patch_embed.proj.weight' in state_dict:
# Remap FB ResMlp models -> timm
out_dict = {}
for k, v in state_dict.items():
k = k.replace('patch_embed.', 'stem.')
k = k.replace('attn.', 'linear_tokens.')
k = k.replace('mlp.', 'mlp_channels.')
k = k.replace('gamma_', 'ls')
if k.endswith('.alpha') or k.endswith('.beta'):
v = v.reshape(1, 1, -1)
out_dict[k] = v
return out_dict
return state_dict
def _create_mixer(variant, pretrained=False, **kwargs):
@ -280,6 +354,7 @@ def _create_mixer(variant, pretrained=False, **kwargs):
model = build_model_with_cfg(
MlpMixer, variant, pretrained,
default_cfg=default_cfgs[variant],
pretrained_filter_fn=checkpoint_filter_fn,
**kwargs)
return model
@ -289,7 +364,7 @@ def mixer_s32_224(pretrained=False, **kwargs):
""" Mixer-S/32 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=8, hidden_dim=512, **kwargs)
model_args = dict(patch_size=32, num_blocks=8, embed_dim=512, **kwargs)
model = _create_mixer('mixer_s32_224', pretrained=pretrained, **model_args)
return model
@ -299,7 +374,7 @@ def mixer_s16_224(pretrained=False, **kwargs):
""" Mixer-S/16 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=8, hidden_dim=512, **kwargs)
model_args = dict(patch_size=16, num_blocks=8, embed_dim=512, **kwargs)
model = _create_mixer('mixer_s16_224', pretrained=pretrained, **model_args)
return model
@ -309,7 +384,7 @@ def mixer_b32_224(pretrained=False, **kwargs):
""" Mixer-B/32 224x224
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=12, hidden_dim=768, **kwargs)
model_args = dict(patch_size=32, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b32_224', pretrained=pretrained, **model_args)
return model
@ -319,7 +394,7 @@ def mixer_b16_224(pretrained=False, **kwargs):
""" Mixer-B/16 224x224. ImageNet-1k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b16_224', pretrained=pretrained, **model_args)
return model
@ -329,7 +404,7 @@ def mixer_b16_224_in21k(pretrained=False, **kwargs):
""" Mixer-B/16 224x224. ImageNet-21k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b16_224_in21k', pretrained=pretrained, **model_args)
return model
@ -339,7 +414,7 @@ def mixer_l32_224(pretrained=False, **kwargs):
""" Mixer-L/32 224x224.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=32, num_blocks=24, hidden_dim=1024, **kwargs)
model_args = dict(patch_size=32, num_blocks=24, embed_dim=1024, **kwargs)
model = _create_mixer('mixer_l32_224', pretrained=pretrained, **model_args)
return model
@ -349,7 +424,7 @@ def mixer_l16_224(pretrained=False, **kwargs):
""" Mixer-L/16 224x224. ImageNet-1k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs)
model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
model = _create_mixer('mixer_l16_224', pretrained=pretrained, **model_args)
return model
@ -359,35 +434,38 @@ def mixer_l16_224_in21k(pretrained=False, **kwargs):
""" Mixer-L/16 224x224. ImageNet-21k pretrained weights.
Paper: 'MLP-Mixer: An all-MLP Architecture for Vision' - https://arxiv.org/abs/2105.01601
"""
model_args = dict(patch_size=16, num_blocks=24, hidden_dim=1024, **kwargs)
model_args = dict(patch_size=16, num_blocks=24, embed_dim=1024, **kwargs)
model = _create_mixer('mixer_l16_224_in21k', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_b16_224_miil(pretrained=False, **kwargs):
""" Mixer-B/16 224x224. ImageNet-21k pretrained weights.
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
"""
model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b16_224_miil', pretrained=pretrained, **model_args)
return model
@register_model
def mixer_b16_224_miil_in21k(pretrained=False, **kwargs):
""" Mixer-B/16 224x224. ImageNet-1k pretrained weights.
Weights taken from: https://github.com/Alibaba-MIIL/ImageNet21K
"""
model_args = dict(patch_size=16, num_blocks=12, hidden_dim=768, **kwargs)
model_args = dict(patch_size=16, num_blocks=12, embed_dim=768, **kwargs)
model = _create_mixer('mixer_b16_224_miil_in21k', pretrained=pretrained, **model_args)
return model
@register_model
def gmixer_12_224(pretrained=False, **kwargs):
""" Glu-Mixer-12 224x224 (short & fat)
""" Glu-Mixer-12 224x224
Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
"""
model_args = dict(
patch_size=20, num_blocks=12, hidden_dim=512, mlp_ratio=(1.0, 6.0),
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=(1.0, 4.0),
mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
model = _create_mixer('gmixer_12_224', pretrained=pretrained, **model_args)
return model
@ -395,11 +473,11 @@ def gmixer_12_224(pretrained=False, **kwargs):
@register_model
def gmixer_24_224(pretrained=False, **kwargs):
""" Glu-Mixer-24 224x224 (tall & slim)
""" Glu-Mixer-24 224x224
Experiment by Ross Wightman, adding (Si)GLU to MLP-Mixer
"""
model_args = dict(
patch_size=20, num_blocks=24, hidden_dim=384, mlp_ratio=(1.0, 6.0),
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=(1.0, 4.0),
mlp_layer=GluMlp, act_layer=nn.SiLU, **kwargs)
model = _create_mixer('gmixer_24_224', pretrained=pretrained, **model_args)
return model
@ -411,7 +489,7 @@ def resmlp_12_224(pretrained=False, **kwargs):
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=12, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_12_224', pretrained=pretrained, **model_args)
return model
@ -422,7 +500,8 @@ def resmlp_24_224(pretrained=False, **kwargs):
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=24, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_24_224', pretrained=pretrained, **model_args)
return model
@ -433,18 +512,90 @@ def resmlp_36_224(pretrained=False, **kwargs):
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=36, hidden_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_36_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_big_24_224(pretrained=False, **kwargs):
""" ResMLP-B-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_12_distilled_224(pretrained=False, **kwargs):
""" ResMLP-12
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=12, embed_dim=384, mlp_ratio=4, block_layer=ResBlock, norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_12_distilled_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_24_distilled_224(pretrained=False, **kwargs):
""" ResMLP-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=24, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-5), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_24_distilled_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_36_distilled_224(pretrained=False, **kwargs):
""" ResMLP-36
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=16, num_blocks=36, embed_dim=384, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_36_distilled_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_big_24_distilled_224(pretrained=False, **kwargs):
""" ResMLP-B-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_distilled_224', pretrained=pretrained, **model_args)
return model
@register_model
def resmlp_big_24_224_in22ft1k(pretrained=False, **kwargs):
""" ResMLP-B-24
Paper: `ResMLP: Feedforward networks for image classification...` - https://arxiv.org/abs/2105.03404
"""
model_args = dict(
patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4,
block_layer=partial(ResBlock, init_values=1e-6), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_224_in22ft1k', pretrained=pretrained, **model_args)
return model
@register_model
def gmlp_ti16_224(pretrained=False, **kwargs):
""" gMLP-Tiny
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, hidden_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock,
patch_size=16, num_blocks=30, embed_dim=128, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_ti16_224', pretrained=pretrained, **model_args)
return model
@ -456,7 +607,7 @@ def gmlp_s16_224(pretrained=False, **kwargs):
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, hidden_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock,
patch_size=16, num_blocks=30, embed_dim=256, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_s16_224', pretrained=pretrained, **model_args)
return model
@ -468,7 +619,7 @@ def gmlp_b16_224(pretrained=False, **kwargs):
Paper: `Pay Attention to MLPs` - https://arxiv.org/abs/2105.08050
"""
model_args = dict(
patch_size=16, num_blocks=30, hidden_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock,
patch_size=16, num_blocks=30, embed_dim=512, mlp_ratio=6, block_layer=SpatialGatingBlock,
mlp_layer=GatedMlp, **kwargs)
model = _create_mixer('gmlp_b16_224', pretrained=pretrained, **model_args)
return model

5
timm/models/mobilenetv3.py
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@ -119,6 +119,7 @@ class MobileNetV3(nn.Module):
num_pooled_chs = head_chs * self.global_pool.feat_mult()
self.conv_head = create_conv2d(num_pooled_chs, self.num_features, 1, padding=pad_type, bias=head_bias)
self.act2 = act_layer(inplace=True)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
efficientnet_init_weights(self)
@ -137,6 +138,7 @@ class MobileNetV3(nn.Module):
self.num_classes = num_classes
# cannot meaningfully change pooling of efficient head after creation
self.global_pool = SelectAdaptivePool2d(pool_type=global_pool)
self.flatten = nn.Flatten(1) if global_pool else nn.Identity() # don't flatten if pooling disabled
self.classifier = Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
@ -151,8 +153,7 @@ class MobileNetV3(nn.Module):
def forward(self, x):
x = self.forward_features(x)
if not self.global_pool.is_identity():
x = x.flatten(1)
x = self.flatten(x)
if self.drop_rate > 0.:
x = F.dropout(x, p=self.drop_rate, training=self.training)
return self.classifier(x)

12
timm/models/nfnet.py
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@ -111,11 +111,11 @@ default_cfgs = dict(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l1_ra2-7dce93cd.pth',
pool_size=(8, 8), input_size=(3, 256, 256), test_input_size=(3, 320, 320), crop_pct=1.0),
eca_nfnet_l2=_dcfg(
url='',
pool_size=(9, 9), input_size=(3, 288, 288), test_input_size=(3, 352, 352), crop_pct=1.0),
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/ecanfnet_l2_ra3-da781a61.pth',
pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), crop_pct=1.0),
eca_nfnet_l3=_dcfg(
url='',
pool_size=(10, 10), input_size=(3, 320, 320), test_input_size=(3, 384, 384), crop_pct=1.0),
pool_size=(11, 11), input_size=(3, 352, 352), test_input_size=(3, 448, 448), crop_pct=1.0),
nf_regnet_b0=_dcfg(
url='', pool_size=(6, 6), input_size=(3, 192, 192), test_input_size=(3, 256, 256), first_conv='stem.conv'),
@ -166,6 +166,7 @@ class NfCfg:
extra_conv: bool = False # extra 3x3 bottleneck convolution for NFNet models
gamma_in_act: bool = False
same_padding: bool = False
std_conv_eps: float = 1e-5
skipinit: bool = False # disabled by default, non-trivial performance impact
zero_init_fc: bool = False
act_layer: str = 'silu'
@ -209,6 +210,7 @@ def _dm_nfnet_cfg(depths, channels=(256, 512, 1536, 1536), act_layer='gelu', ski
return cfg
model_cfgs = dict(
# NFNet-F models w/ GELU compatible with DeepMind weights
dm_nfnet_f0=_dm_nfnet_cfg(depths=(1, 2, 6, 3)),
@ -482,10 +484,10 @@ class NormFreeNet(nn.Module):
conv_layer = ScaledStdConv2dSame if cfg.same_padding else ScaledStdConv2d
if cfg.gamma_in_act:
act_layer = act_with_gamma(cfg.act_layer, gamma=_nonlin_gamma[cfg.act_layer])
conv_layer = partial(conv_layer, eps=1e-4) # DM weights better with higher eps
conv_layer = partial(conv_layer, eps=cfg.std_conv_eps)
else:
act_layer = get_act_layer(cfg.act_layer)
conv_layer = partial(conv_layer, gamma=_nonlin_gamma[cfg.act_layer])
conv_layer = partial(conv_layer, gamma=_nonlin_gamma[cfg.act_layer], eps=cfg.std_conv_eps)
attn_layer = partial(get_attn(cfg.attn_layer), **cfg.attn_kwargs) if cfg.attn_layer else None
stem_chs = make_divisible((cfg.stem_chs or cfg.channels[0]) * cfg.width_factor, cfg.ch_div)

32
timm/models/pit.py
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@ -186,12 +186,13 @@ class PoolingVisionTransformer(nn.Module):
]
self.transformers = SequentialTuple(*transformers)
self.norm = nn.LayerNorm(base_dims[-1] * heads[-1], eps=1e-6)
self.embed_dim = base_dims[-1] * heads[-1]
self.num_features = self.embed_dim = base_dims[-1] * heads[-1]
# Classifier head
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) \
if num_classes > 0 and distilled else nn.Identity()
self.head_dist = None
if distilled:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
trunc_normal_(self.pos_embed, std=.02)
trunc_normal_(self.cls_token, std=.02)
@ -207,13 +208,16 @@ class PoolingVisionTransformer(nn.Module):
return {'pos_embed', 'cls_token'}
def get_classifier(self):
return self.head
if self.head_dist is not None:
return self.head, self.head_dist
else:
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) \
if num_classes > 0 and self.num_tokens == 2 else nn.Identity()
if self.head_dist is not None:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
@ -221,19 +225,21 @@ class PoolingVisionTransformer(nn.Module):
cls_tokens = self.cls_token.expand(x.shape[0], -1, -1)
x, cls_tokens = self.transformers((x, cls_tokens))
cls_tokens = self.norm(cls_tokens)
return cls_tokens
if self.head_dist is not None:
return cls_tokens[:, 0], cls_tokens[:, 1]
else:
return cls_tokens[:, 0]
def forward(self, x):
x = self.forward_features(x)
x_cls = self.head(x[:, 0])
if self.num_tokens > 1:
x_dist = self.head_dist(x[:, 1])
if self.head_dist is not None:
x, x_dist = self.head(x[0]), self.head_dist(x[1]) # x must be a tuple
if self.training and not torch.jit.is_scripting():
return x_cls, x_dist
return x, x_dist
else:
return (x_cls + x_dist) / 2
return (x + x_dist) / 2
else:
return x_cls
return self.head(x)
def checkpoint_filter_fn(state_dict, model):

13
timm/models/registry.py
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@ -65,11 +65,18 @@ def list_models(filter='', module='', pretrained=False, exclude_filters='', name
model_list('*resnext*, 'resnet') -- returns all models with 'resnext' in 'resnet' module
"""
if module:
models = list(_module_to_models[module])
all_models = list(_module_to_models[module])
else:
models = _model_entrypoints.keys()
all_models = _model_entrypoints.keys()
if filter:
models = fnmatch.filter(models, filter) # include these models
models = []
include_filters = filter if isinstance(filter, (tuple, list)) else [filter]
for f in include_filters:
include_models = fnmatch.filter(all_models, f) # include these models
if len(include_models):
models = set(models).union(include_models)
else:
models = all_models
if exclude_filters:
if not isinstance(exclude_filters, (tuple, list)):
exclude_filters = [exclude_filters]

7
timm/models/resnet.py
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@ -638,12 +638,15 @@ class ResNet(nn.Module):
self.num_features = 512 * block.expansion
self.global_pool, self.fc = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
self.init_weights(zero_init_last_bn=zero_init_last_bn)
def init_weights(self, zero_init_last_bn=True):
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.constant_(m.weight, 1.)
nn.init.constant_(m.bias, 0.)
nn.init.ones_(m.weight)
nn.init.zeros_(m.bias)
if zero_init_last_bn:
for m in self.modules():
if hasattr(m, 'zero_init_last_bn'):

325
timm/models/resnetv2.py
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@ -11,6 +11,7 @@ https://github.com/google-research/vision_transformer
Thanks to the Google team for the above two repositories and associated papers:
* Big Transfer (BiT): General Visual Representation Learning - https://arxiv.org/abs/1912.11370
* An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale - https://arxiv.org/abs/2010.11929
* Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
Original copyright of Google code below, modifications by Ross Wightman, Copyright 2020.
"""
@ -35,16 +36,16 @@ import torch.nn as nn
from functools import partial
from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg
from .helpers import build_model_with_cfg, named_apply, adapt_input_conv
from .registry import register_model
from .layers import GroupNormAct, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d
from .layers import GroupNormAct, ClassifierHead, DropPath, AvgPool2dSame, create_pool2d, StdConv2d, create_conv2d
def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 480, 480), 'pool_size': (7, 7),
'crop_pct': 1.0, 'interpolation': 'bilinear',
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': (7, 7),
'crop_pct': 0.875, 'interpolation': 'bilinear',
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'stem.conv', 'classifier': 'head.fc',
**kwargs
@ -54,17 +55,23 @@ def _cfg(url='', **kwargs):
default_cfgs = {
# pretrained on imagenet21k, finetuned on imagenet1k
'resnetv2_50x1_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R50x1-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
'resnetv2_50x3_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R50x3-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
'resnetv2_101x1_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R101x1-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
'resnetv2_101x3_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R101x3-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
'resnetv2_152x2_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R152x2-ILSVRC2012.npz',
input_size=(3, 448, 448), pool_size=(14, 14), crop_pct=1.0),
'resnetv2_152x4_bitm': _cfg(
url='https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz'),
url='https://storage.googleapis.com/bit_models/BiT-M-R152x4-ILSVRC2012.npz',
input_size=(3, 480, 480), pool_size=(15, 15), crop_pct=1.0), # only one at 480x480?
# trained on imagenet-21k
'resnetv2_50x1_bitm_in21k': _cfg(
@ -86,20 +93,20 @@ default_cfgs = {
url='https://storage.googleapis.com/bit_models/BiT-M-R152x4.npz',
num_classes=21843),
# trained on imagenet-1k, NOTE not overly interesting set of weights, leaving disabled for now
# 'resnetv2_50x1_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R50x1.npz'),
# 'resnetv2_50x3_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R50x3.npz'),
# 'resnetv2_101x1_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R101x3.npz'),
# 'resnetv2_101x3_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R101x3.npz'),
# 'resnetv2_152x2_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R152x2.npz'),
# 'resnetv2_152x4_bits': _cfg(
# url='https://storage.googleapis.com/bit_models/BiT-S-R152x4.npz'),
'resnetv2_50x1_bit_distilled': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R50x1_224.npz',
interpolation='bicubic'),
'resnetv2_152x2_bit_teacher': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R152x2_T_224.npz',
interpolation='bicubic'),
'resnetv2_152x2_bit_teacher_384': _cfg(
url='https://storage.googleapis.com/bit_models/distill/R152x2_T_384.npz',
input_size=(3, 384, 384), pool_size=(12, 12), crop_pct=1.0, interpolation='bicubic'),
'resnetv2_50': _cfg(
interpolation='bicubic'),
'resnetv2_50d': _cfg(
interpolation='bicubic', first_conv='stem.conv1'),
}
@ -111,13 +118,6 @@ def make_div(v, divisor=8):
return new_v
def tf2th(conv_weights):
"""Possibly convert HWIO to OIHW."""
if conv_weights.ndim == 4:
conv_weights = conv_weights.transpose([3, 2, 0, 1])
return torch.from_numpy(conv_weights)
class PreActBottleneck(nn.Module):
"""Pre-activation (v2) bottleneck block.
@ -152,6 +152,9 @@ class PreActBottleneck(nn.Module):
self.conv3 = conv_layer(mid_chs, out_chs, 1)
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
def zero_init_last_bn(self):
nn.init.zeros_(self.norm3.weight)
def forward(self, x):
x_preact = self.norm1(x)
@ -198,6 +201,9 @@ class Bottleneck(nn.Module):
self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0 else nn.Identity()
self.act3 = act_layer(inplace=True)
def zero_init_last_bn(self):
nn.init.zeros_(self.norm3.weight)
def forward(self, x):
# shortcut branch
shortcut = x
@ -285,14 +291,17 @@ def create_resnetv2_stem(
# A 3 deep 3x3 conv stack as in ResNet V1D models
mid_chs = out_chs // 2
stem['conv1'] = conv_layer(in_chs, mid_chs, kernel_size=3, stride=2)
stem['norm1'] = norm_layer(mid_chs)
stem['conv2'] = conv_layer(mid_chs, mid_chs, kernel_size=3, stride=1)
stem['norm2'] = norm_layer(mid_chs)
stem['conv3'] = conv_layer(mid_chs, out_chs, kernel_size=3, stride=1)
if not preact:
stem['norm3'] = norm_layer(out_chs)
else:
# The usual 7x7 stem conv
stem['conv'] = conv_layer(in_chs, out_chs, kernel_size=7, stride=2)
if not preact:
stem['norm'] = norm_layer(out_chs)
if not preact:
stem['norm'] = norm_layer(out_chs)
if 'fixed' in stem_type:
# 'fixed' SAME padding approximation that is used in BiT models
@ -312,11 +321,12 @@ class ResNetV2(nn.Module):
"""Implementation of Pre-activation (v2) ResNet mode.
"""
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.):
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_bn=True):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
@ -354,12 +364,14 @@ class ResNetV2(nn.Module):
self.head = ClassifierHead(
self.num_features, num_classes, pool_type=global_pool, drop_rate=self.drop_rate, use_conv=True)
for n, m in self.named_modules():
if isinstance(m, nn.Linear) or ('.fc' in n and isinstance(m, nn.Conv2d)):
nn.init.normal_(m.weight, mean=0.0, std=0.01)
nn.init.zeros_(m.bias)
elif isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
self.init_weights(zero_init_last_bn=zero_init_last_bn)
def init_weights(self, zero_init_last_bn=True):
named_apply(partial(_init_weights, zero_init_last_bn=zero_init_last_bn), self)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix='resnet/'):
_load_weights(self, checkpoint_path, prefix)
def get_classifier(self):
return self.head.fc
@ -378,41 +390,60 @@ class ResNetV2(nn.Module):
def forward(self, x):
x = self.forward_features(x)
x = self.head(x)
if not self.head.global_pool.is_identity():
x = x.flatten(1) # conv classifier, flatten if pooling isn't pass-through (disabled)
return x
def load_pretrained(self, checkpoint_path, prefix='resnet/'):
import numpy as np
weights = np.load(checkpoint_path)
with torch.no_grad():
stem_conv_w = tf2th(weights[f'{prefix}root_block/standardized_conv2d/kernel'])
if self.stem.conv.weight.shape[1] == 1:
self.stem.conv.weight.copy_(stem_conv_w.sum(dim=1, keepdim=True))
# FIXME handle > 3 in_chans?
else:
self.stem.conv.weight.copy_(stem_conv_w)
self.norm.weight.copy_(tf2th(weights[f'{prefix}group_norm/gamma']))
self.norm.bias.copy_(tf2th(weights[f'{prefix}group_norm/beta']))
if self.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
self.head.fc.weight.copy_(tf2th(weights[f'{prefix}head/conv2d/kernel']))
self.head.fc.bias.copy_(tf2th(weights[f'{prefix}head/conv2d/bias']))
for i, (sname, stage) in enumerate(self.stages.named_children()):
for j, (bname, block) in enumerate(stage.blocks.named_children()):
convname = 'standardized_conv2d'
block_prefix = f'{prefix}block{i + 1}/unit{j + 1:02d}/'
block.conv1.weight.copy_(tf2th(weights[f'{block_prefix}a/{convname}/kernel']))
block.conv2.weight.copy_(tf2th(weights[f'{block_prefix}b/{convname}/kernel']))
block.conv3.weight.copy_(tf2th(weights[f'{block_prefix}c/{convname}/kernel']))
block.norm1.weight.copy_(tf2th(weights[f'{block_prefix}a/group_norm/gamma']))
block.norm2.weight.copy_(tf2th(weights[f'{block_prefix}b/group_norm/gamma']))
block.norm3.weight.copy_(tf2th(weights[f'{block_prefix}c/group_norm/gamma']))
block.norm1.bias.copy_(tf2th(weights[f'{block_prefix}a/group_norm/beta']))
block.norm2.bias.copy_(tf2th(weights[f'{block_prefix}b/group_norm/beta']))
block.norm3.bias.copy_(tf2th(weights[f'{block_prefix}c/group_norm/beta']))
if block.downsample is not None:
w = weights[f'{block_prefix}a/proj/{convname}/kernel']
block.downsample.conv.weight.copy_(tf2th(w))
def _init_weights(module: nn.Module, name: str = '', zero_init_last_bn=True):
if isinstance(module, nn.Linear) or ('head.fc' in name and isinstance(module, nn.Conv2d)):
nn.init.normal_(module.weight, mean=0.0, std=0.01)
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Conv2d):
nn.init.kaiming_normal_(module.weight, mode='fan_out', nonlinearity='relu')
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.BatchNorm2d, nn.LayerNorm, nn.GroupNorm)):
nn.init.ones_(module.weight)
nn.init.zeros_(module.bias)
elif zero_init_last_bn and hasattr(module, 'zero_init_last_bn'):
module.zero_init_last_bn()
@torch.no_grad()
def _load_weights(model: nn.Module, checkpoint_path: str, prefix: str = 'resnet/'):
import numpy as np
def t2p(conv_weights):
"""Possibly convert HWIO to OIHW."""
if conv_weights.ndim == 4:
conv_weights = conv_weights.transpose([3, 2, 0, 1])
return torch.from_numpy(conv_weights)
weights = np.load(checkpoint_path)
stem_conv_w = adapt_input_conv(
model.stem.conv.weight.shape[1], t2p(weights[f'{prefix}root_block/standardized_conv2d/kernel']))
model.stem.conv.weight.copy_(stem_conv_w)
model.norm.weight.copy_(t2p(weights[f'{prefix}group_norm/gamma']))
model.norm.bias.copy_(t2p(weights[f'{prefix}group_norm/beta']))
if isinstance(getattr(model.head, 'fc', None), nn.Conv2d) and \
model.head.fc.weight.shape[0] == weights[f'{prefix}head/conv2d/kernel'].shape[-1]:
model.head.fc.weight.copy_(t2p(weights[f'{prefix}head/conv2d/kernel']))
model.head.fc.bias.copy_(t2p(weights[f'{prefix}head/conv2d/bias']))
for i, (sname, stage) in enumerate(model.stages.named_children()):
for j, (bname, block) in enumerate(stage.blocks.named_children()):
cname = 'standardized_conv2d'
block_prefix = f'{prefix}block{i + 1}/unit{j + 1:02d}/'
block.conv1.weight.copy_(t2p(weights[f'{block_prefix}a/{cname}/kernel']))
block.conv2.weight.copy_(t2p(weights[f'{block_prefix}b/{cname}/kernel']))
block.conv3.weight.copy_(t2p(weights[f'{block_prefix}c/{cname}/kernel']))
block.norm1.weight.copy_(t2p(weights[f'{block_prefix}a/group_norm/gamma']))
block.norm2.weight.copy_(t2p(weights[f'{block_prefix}b/group_norm/gamma']))
block.norm3.weight.copy_(t2p(weights[f'{block_prefix}c/group_norm/gamma']))
block.norm1.bias.copy_(t2p(weights[f'{block_prefix}a/group_norm/beta']))
block.norm2.bias.copy_(t2p(weights[f'{block_prefix}b/group_norm/beta']))
block.norm3.bias.copy_(t2p(weights[f'{block_prefix}c/group_norm/beta']))
if block.downsample is not None:
w = weights[f'{block_prefix}a/proj/{cname}/kernel']
block.downsample.conv.weight.copy_(t2p(w))
def _create_resnetv2(variant, pretrained=False, **kwargs):
@ -425,130 +456,126 @@ def _create_resnetv2(variant, pretrained=False, **kwargs):
**kwargs)
def _create_resnetv2_bit(variant, pretrained=False, **kwargs):
return _create_resnetv2(
variant, pretrained=pretrained, stem_type='fixed', conv_layer=partial(StdConv2d, eps=1e-8), **kwargs)
@register_model
def resnetv2_50x1_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50x1_bitm', pretrained=pretrained,
layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_50x1_bitm', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_50x3_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50x3_bitm', pretrained=pretrained,
layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_50x3_bitm', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_101x1_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101x1_bitm', pretrained=pretrained,
layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_101x1_bitm', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_101x3_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101x3_bitm', pretrained=pretrained,
layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_101x3_bitm', pretrained=pretrained, layers=[3, 4, 23, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_152x2_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_152x2_bitm', pretrained=pretrained,
layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_152x2_bitm', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x4_bitm(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_152x4_bitm', pretrained=pretrained,
layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)
return _create_resnetv2_bit(
'resnetv2_152x4_bitm', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=4, **kwargs)
@register_model
def resnetv2_50x1_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
return _create_resnetv2_bit(
'resnetv2_50x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)
layers=[3, 4, 6, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_50x3_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
return _create_resnetv2_bit(
'resnetv2_50x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)
layers=[3, 4, 6, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_101x1_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_101x1_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)
layers=[3, 4, 23, 3], width_factor=1, **kwargs)
@register_model
def resnetv2_101x3_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
return _create_resnetv2_bit(
'resnetv2_101x3_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)
layers=[3, 4, 23, 3], width_factor=3, **kwargs)
@register_model
def resnetv2_152x2_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
return _create_resnetv2_bit(
'resnetv2_152x2_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)
layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x4_bitm_in21k(pretrained=False, **kwargs):
return _create_resnetv2(
return _create_resnetv2_bit(
'resnetv2_152x4_bitm_in21k', pretrained=pretrained, num_classes=kwargs.pop('num_classes', 21843),
layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)
layers=[3, 8, 36, 3], width_factor=4, **kwargs)
@register_model
def resnetv2_50x1_bit_distilled(pretrained=False, **kwargs):
""" ResNetV2-50x1-BiT Distilled
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_50x1_bit_distilled', pretrained=pretrained, layers=[3, 4, 6, 3], width_factor=1, **kwargs)
# NOTE the 'S' versions of the model weights arent as interesting as original 21k or transfer to 1K M.
# @register_model
# def resnetv2_50x1_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_50x1_bits', pretrained=pretrained,
# layers=[3, 4, 6, 3], width_factor=1, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_50x3_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_50x3_bits', pretrained=pretrained,
# layers=[3, 4, 6, 3], width_factor=3, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_101x1_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_101x1_bits', pretrained=pretrained,
# layers=[3, 4, 23, 3], width_factor=1, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_101x3_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_101x3_bits', pretrained=pretrained,
# layers=[3, 4, 23, 3], width_factor=3, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_152x2_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_152x2_bits', pretrained=pretrained,
# layers=[3, 8, 36, 3], width_factor=2, stem_type='fixed', **kwargs)
#
#
# @register_model
# def resnetv2_152x4_bits(pretrained=False, **kwargs):
# return _create_resnetv2(
# 'resnetv2_152x4_bits', pretrained=pretrained,
# layers=[3, 8, 36, 3], width_factor=4, stem_type='fixed', **kwargs)
#
@register_model
def resnetv2_152x2_bit_teacher(pretrained=False, **kwargs):
""" ResNetV2-152x2-BiT Teacher
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_152x2_bit_teacher', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **kwargs)
@register_model
def resnetv2_152x2_bit_teacher_384(pretrained=False, **kwargs):
""" ResNetV2-152xx-BiT Teacher @ 384x384
Paper: Knowledge distillation: A good teacher is patient and consistent - https://arxiv.org/abs/2106.05237
"""
return _create_resnetv2_bit(
'resnetv2_152x2_bit_teacher_384', pretrained=pretrained, layers=[3, 8, 36, 3], width_factor=2, **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=nn.BatchNorm2d, **kwargs)
@register_model
def resnetv2_50d(pretrained=False, **kwargs):
return _create_resnetv2(
'resnetv2_50d', pretrained=pretrained,
layers=[3, 4, 6, 3], conv_layer=create_conv2d, norm_layer=nn.BatchNorm2d,
stem_type='deep', avg_down=True, **kwargs)

4
timm/models/rexnet.py
Unescape View File

@ -89,9 +89,9 @@ class LinearBottleneck(nn.Module):
x = self.se(x)
x = self.act_dw(x)
x = self.conv_pwl(x)
if self.drop_path is not None:
x = self.drop_path(x)
if self.use_shortcut:
if self.drop_path is not None:
x = self.drop_path(x)
x[:, 0:self.in_channels] += shortcut
return x

43
timm/models/swin_transformer.py
Unescape View File

@ -126,19 +126,18 @@ class WindowAttention(nn.Module):
window_size (tuple[int]): The height and width of the window.
num_heads (int): Number of attention heads.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
proj_drop (float, optional): Dropout ratio of output. Default: 0.0
"""
def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
def __init__(self, dim, window_size, num_heads, qkv_bias=True, attn_drop=0., proj_drop=0.):
super().__init__()
self.dim = dim
self.window_size = window_size # Wh, Ww
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
# define a parameter table of relative position bias
self.relative_position_bias_table = nn.Parameter(
@ -210,7 +209,6 @@ class SwinTransformerBlock(nn.Module):
shift_size (int): Shift size for SW-MSA.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float, optional): Stochastic depth rate. Default: 0.0
@ -219,7 +217,7 @@ class SwinTransformerBlock(nn.Module):
"""
def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0., drop_path=0.,
act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.dim = dim
@ -236,8 +234,8 @@ class SwinTransformerBlock(nn.Module):
self.norm1 = norm_layer(dim)
self.attn = WindowAttention(
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
dim, window_size=to_2tuple(self.window_size), num_heads=num_heads, qkv_bias=qkv_bias,
attn_drop=attn_drop, proj_drop=drop)
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
@ -369,7 +367,6 @@ class BasicLayer(nn.Module):
window_size (int): Local window size.
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
drop (float, optional): Dropout rate. Default: 0.0
attn_drop (float, optional): Attention dropout rate. Default: 0.0
drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
@ -379,7 +376,7 @@ class BasicLayer(nn.Module):
"""
def __init__(self, dim, input_resolution, depth, num_heads, window_size,
mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
mlp_ratio=4., qkv_bias=True, drop=0., attn_drop=0.,
drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
super().__init__()
@ -390,14 +387,11 @@ class BasicLayer(nn.Module):
# build blocks
self.blocks = nn.ModuleList([
SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2,
mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
norm_layer=norm_layer)
SwinTransformerBlock(
dim=dim, input_resolution=input_resolution, num_heads=num_heads, window_size=window_size,
shift_size=0 if (i % 2 == 0) else window_size // 2, mlp_ratio=mlp_ratio,
qkv_bias=qkv_bias, drop=drop, attn_drop=attn_drop,
drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path, norm_layer=norm_layer)
for i in range(depth)])
# patch merging layer
@ -436,7 +430,6 @@ class SwinTransformer(nn.Module):
window_size (int): Window size. Default: 7
mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4
qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
qk_scale (float): Override default qk scale of head_dim ** -0.5 if set. Default: None
drop_rate (float): Dropout rate. Default: 0
attn_drop_rate (float): Attention dropout rate. Default: 0
drop_path_rate (float): Stochastic depth rate. Default: 0.1
@ -448,7 +441,7 @@ class SwinTransformer(nn.Module):
def __init__(self, img_size=224, patch_size=4, in_chans=3, num_classes=1000,
embed_dim=96, depths=(2, 2, 6, 2), num_heads=(3, 6, 12, 24),
window_size=7, mlp_ratio=4., qkv_bias=True, qk_scale=None,
window_size=7, mlp_ratio=4., qkv_bias=True,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0.1,
norm_layer=nn.LayerNorm, ape=False, patch_norm=True,
use_checkpoint=False, weight_init='', **kwargs):
@ -491,8 +484,9 @@ class SwinTransformer(nn.Module):
num_heads=num_heads[i_layer],
window_size=window_size,
mlp_ratio=self.mlp_ratio,
qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate,
qkv_bias=qkv_bias,
drop=drop_rate,
attn_drop=attn_drop_rate,
drop_path=dpr[sum(depths[:i_layer]):sum(depths[:i_layer + 1])],
norm_layer=norm_layer,
downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
@ -520,6 +514,13 @@ class SwinTransformer(nn.Module):
def no_weight_decay_keywords(self):
return {'relative_position_bias_table'}
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def forward_features(self, x):
x = self.patch_embed(x)
if self.absolute_pos_embed is not None:

8
timm/models/twins.py
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@ -278,6 +278,8 @@ class Twins(nn.Module):
super().__init__()
self.num_classes = num_classes
self.depths = depths
self.embed_dims = embed_dims
self.num_features = embed_dims[-1]
img_size = to_2tuple(img_size)
prev_chs = in_chans
@ -303,10 +305,10 @@ class Twins(nn.Module):
self.pos_block = nn.ModuleList([PosConv(embed_dim, embed_dim) for embed_dim in embed_dims])
self.norm = norm_layer(embed_dims[-1])
self.norm = norm_layer(self.num_features)
# classification head
self.head = nn.Linear(embed_dims[-1], num_classes) if num_classes > 0 else nn.Identity()
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
# init weights
self.apply(self._init_weights)
@ -320,7 +322,7 @@ class Twins(nn.Module):
def reset_classifier(self, num_classes, global_pool=''):
self.num_classes = num_classes
self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()
self.head = nn.Linear(self.num_features, num_classes) if num_classes > 0 else nn.Identity()
def _init_weights(self, m):
if isinstance(m, nn.Linear):

62
timm/models/visformer.py
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@ -13,7 +13,7 @@ import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from .layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d
from .layers import to_2tuple, trunc_normal_, DropPath, PatchEmbed, LayerNorm2d, create_classifier
from .registry import register_model
@ -140,14 +140,14 @@ class Visformer(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, init_channels=32, embed_dim=384,
depth=12, num_heads=6, mlp_ratio=4., drop_rate=0., attn_drop_rate=0., drop_path_rate=0.,
norm_layer=LayerNorm2d, attn_stage='111', pos_embed=True, spatial_conv='111',
vit_stem=False, group=8, pool=True, conv_init=False, embed_norm=None):
vit_stem=False, group=8, global_pool='avg', conv_init=False, embed_norm=None):
super().__init__()
img_size = to_2tuple(img_size)
self.num_classes = num_classes
self.num_features = self.embed_dim = embed_dim
self.embed_dim = embed_dim
self.init_channels = init_channels
self.img_size = img_size
self.vit_stem = vit_stem
self.pool = pool
self.conv_init = conv_init
if isinstance(depth, (list, tuple)):
self.stage_num1, self.stage_num2, self.stage_num3 = depth
@ -164,31 +164,31 @@ class Visformer(nn.Module):
self.patch_embed1 = PatchEmbed(
img_size=img_size, patch_size=patch_size, in_chans=in_chans,
embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
img_size //= 16
img_size = [x // 16 for x in img_size]
else:
if self.init_channels is None:
self.stem = None
self.patch_embed1 = PatchEmbed(
img_size=img_size, patch_size=patch_size // 2, in_chans=in_chans,
embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
img_size //= 8
img_size = [x // 8 for x in img_size]
else:
self.stem = nn.Sequential(
nn.Conv2d(in_chans, self.init_channels, 7, stride=2, padding=3, bias=False),
nn.BatchNorm2d(self.init_channels),
nn.ReLU(inplace=True)
)
img_size //= 2
img_size = [x // 2 for x in img_size]
self.patch_embed1 = PatchEmbed(
img_size=img_size, patch_size=patch_size // 4, in_chans=self.init_channels,
embed_dim=embed_dim // 2, norm_layer=embed_norm, flatten=False)
img_size //= 4
img_size = [x // 4 for x in img_size]
if self.pos_embed:
if self.vit_stem:
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim, img_size, img_size))
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
else:
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim//2, img_size, img_size))
self.pos_embed1 = nn.Parameter(torch.zeros(1, embed_dim//2, *img_size))
self.pos_drop = nn.Dropout(p=drop_rate)
self.stage1 = nn.ModuleList([
Block(
@ -199,14 +199,14 @@ class Visformer(nn.Module):
for i in range(self.stage_num1)
])
#stage2
# stage2
if not self.vit_stem:
self.patch_embed2 = PatchEmbed(
img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim // 2,
embed_dim=embed_dim, norm_layer=embed_norm, flatten=False)
img_size //= 2
img_size = [x // 2 for x in img_size]
if self.pos_embed:
self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, img_size, img_size))
self.pos_embed2 = nn.Parameter(torch.zeros(1, embed_dim, *img_size))
self.stage2 = nn.ModuleList([
Block(
dim=embed_dim, num_heads=num_heads, head_dim_ratio=1.0, mlp_ratio=mlp_ratio,
@ -221,9 +221,9 @@ class Visformer(nn.Module):
self.patch_embed3 = PatchEmbed(
img_size=img_size, patch_size=patch_size // 8, in_chans=embed_dim,
embed_dim=embed_dim * 2, norm_layer=embed_norm, flatten=False)
img_size //= 2
img_size = [x // 2 for x in img_size]
if self.pos_embed:
self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, img_size, img_size))
self.pos_embed3 = nn.Parameter(torch.zeros(1, embed_dim*2, *img_size))
self.stage3 = nn.ModuleList([
Block(
dim=embed_dim*2, num_heads=num_heads, head_dim_ratio=1.0, mlp_ratio=mlp_ratio,
@ -234,11 +234,9 @@ class Visformer(nn.Module):
])
# head
if self.pool:
self.global_pooling = nn.AdaptiveAvgPool2d(1)
head_dim = embed_dim if self.vit_stem else embed_dim * 2
self.norm = norm_layer(head_dim)
self.head = nn.Linear(head_dim, num_classes)
self.num_features = embed_dim if self.vit_stem else embed_dim * 2
self.norm = norm_layer(self.num_features)
self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
# weights init
if self.pos_embed:
@ -267,7 +265,14 @@ class Visformer(nn.Module):
if m.bias is not None:
nn.init.constant_(m.bias, 0.)
def forward(self, x):
def get_classifier(self):
return self.head
def reset_classifier(self, num_classes, global_pool='avg'):
self.num_classes = num_classes
self.global_pool, self.head = create_classifier(self.num_features, self.num_classes, pool_type=global_pool)
def forward_features(self, x):
if self.stem is not None:
x = self.stem(x)
@ -297,14 +302,13 @@ class Visformer(nn.Module):
for b in self.stage3:
x = b(x)
# head
x = self.norm(x)
if self.pool:
x = self.global_pooling(x)
else:
x = x[:, :, 0, 0]
return x
x = self.head(x.view(x.size(0), -1))
def forward(self, x):
x = self.forward_features(x)
x = self.global_pool(x)
x = self.head(x)
return x
@ -321,7 +325,7 @@ def _create_visformer(variant, pretrained=False, default_cfg=None, **kwargs):
@register_model
def visformer_tiny(pretrained=False, **kwargs):
model_cfg = dict(
img_size=224, init_channels=16, embed_dim=192, depth=(7, 4, 4), num_heads=3, mlp_ratio=4., group=8,
init_channels=16, embed_dim=192, depth=(7, 4, 4), num_heads=3, mlp_ratio=4., group=8,
attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
embed_norm=nn.BatchNorm2d, **kwargs)
model = _create_visformer('visformer_tiny', pretrained=pretrained, **model_cfg)
@ -331,7 +335,7 @@ def visformer_tiny(pretrained=False, **kwargs):
@register_model
def visformer_small(pretrained=False, **kwargs):
model_cfg = dict(
img_size=224, init_channels=32, embed_dim=384, depth=(7, 4, 4), num_heads=6, mlp_ratio=4., group=8,
init_channels=32, embed_dim=384, depth=(7, 4, 4), num_heads=6, mlp_ratio=4., group=8,
attn_stage='011', spatial_conv='100', norm_layer=nn.BatchNorm2d, conv_init=True,
embed_norm=nn.BatchNorm2d, **kwargs)
model = _create_visformer('visformer_small', pretrained=pretrained, **model_cfg)

509
timm/models/vision_transformer.py
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@ -1,7 +1,12 @@
""" Vision Transformer (ViT) in PyTorch
A PyTorch implement of Vision Transformers as described in
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale' - https://arxiv.org/abs/2010.11929
A PyTorch implement of Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
`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
@ -15,7 +20,7 @@ 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
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
import math
import logging
@ -27,8 +32,8 @@ import torch
import torch.nn as nn
import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .helpers import build_model_with_cfg, overlay_external_default_cfg
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .helpers import build_model_with_cfg, named_apply, adapt_input_conv
from .layers import PatchEmbed, Mlp, DropPath, trunc_normal_, lecun_normal_
from .registry import register_model
@ -40,86 +45,118 @@ def _cfg(url='', **kwargs):
'url': url,
'num_classes': 1000, 'input_size': (3, 224, 224), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'mean': IMAGENET_INCEPTION_MEAN, 'std': IMAGENET_INCEPTION_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs
}
default_cfgs = {
# patch models (my experiments)
# patch models (weights from official Google JAX impl)
'vit_tiny_patch16_224': _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'),
'vit_tiny_patch16_384': _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',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch32_224': _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'),
'vit_small_patch32_384': _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',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_small_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/vit_small_p16_224-15ec54c9.pth',
),
# patch models (weights ported from official Google JAX impl)
'vit_base_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_224-80ecf9dd.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
),
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'),
'vit_small_patch16_384': _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',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch32_224': _cfg(
url='', # no official model weights for this combo, only for in21k
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'vit_base_patch16_384': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p16_384-83fb41ba.pth',
input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
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'),
'vit_base_patch32_384': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_p32_384-830016f5.pth',
input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
'vit_large_patch16_224': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_224-4ee7a4dc.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
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',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_base_patch16_224': _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'),
'vit_base_patch16_384': _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',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch32_224': _cfg(
url='', # no official model weights for this combo, only for in21k
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'vit_large_patch16_384': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p16_384-b3be5167.pth',
input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
),
'vit_large_patch32_384': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_p32_384-9b920ba8.pth',
input_size=(3, 384, 384), mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), crop_pct=1.0),
input_size=(3, 384, 384), crop_pct=1.0),
'vit_large_patch16_224': _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'),
'vit_large_patch16_384': _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',
input_size=(3, 384, 384), crop_pct=1.0),
# patch models, imagenet21k (weights ported from official Google JAX impl)
'vit_base_patch16_224_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch16_224_in21k-e5005f0a.pth',
num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
# patch models, imagenet21k (weights from official Google JAX impl)
'vit_tiny_patch16_224_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',
num_classes=21843),
'vit_small_patch32_224_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',
num_classes=21843),
'vit_small_patch16_224_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',
num_classes=21843),
'vit_base_patch32_224_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_patch32_224_in21k-8db57226.pth',
num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
'vit_large_patch16_224_in21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_large_patch16_224_in21k-606da67d.pth',
num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
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',
num_classes=21843),
'vit_base_patch16_224_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',
num_classes=21843),
'vit_large_patch32_224_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, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
num_classes=21843),
'vit_large_patch16_224_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',
num_classes=21843),
'vit_huge_patch14_224_in21k': _cfg(
url='https://storage.googleapis.com/vit_models/imagenet21k/ViT-H_14.npz',
hf_hub='timm/vit_huge_patch14_224_in21k',
num_classes=21843, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5)),
num_classes=21843),
# deit models (FB weights)
'vit_deit_tiny_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth'),
'vit_deit_small_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth'),
'vit_deit_base_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',),
'vit_deit_base_patch16_384': _cfg(
'deit_tiny_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_patch16_224-a1311bcf.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'deit_small_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_small_patch16_224-cd65a155.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'deit_base_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_224-b5f2ef4d.pth',
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'deit_base_patch16_384': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_base_patch16_384-8de9b5d1.pth',
input_size=(3, 384, 384), crop_pct=1.0),
'vit_deit_tiny_distilled_patch16_224': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0),
'deit_tiny_distilled_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_tiny_distilled_patch16_224-b40b3cf7.pth',
classifier=('head', 'head_dist')),
'vit_deit_small_distilled_patch16_224': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
'deit_small_distilled_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_small_distilled_patch16_224-649709d9.pth',
classifier=('head', 'head_dist')),
'vit_deit_base_distilled_patch16_224': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
'deit_base_distilled_patch16_224': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth',
classifier=('head', 'head_dist')),
'vit_deit_base_distilled_patch16_384': _cfg(
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, classifier=('head', 'head_dist')),
'deit_base_distilled_patch16_384': _cfg(
url='https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_384-d0272ac0.pth',
input_size=(3, 384, 384), crop_pct=1.0, classifier=('head', 'head_dist')),
mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(3, 384, 384), crop_pct=1.0,
classifier=('head', 'head_dist')),
# ViT ImageNet-21K-P pretraining
# ViT ImageNet-21K-P pretraining by MILL
'vit_base_patch16_224_miil_in21k': _cfg(
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/vit_base_patch16_224_in21k_miil.pth',
mean=(0, 0, 0), std=(1, 1, 1), crop_pct=0.875, interpolation='bilinear', num_classes=11221,
@ -133,11 +170,11 @@ default_cfgs = {
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
def __init__(self, dim, num_heads=8, qkv_bias=False, attn_drop=0., proj_drop=0.):
super().__init__()
self.num_heads = num_heads
head_dim = dim // num_heads
self.scale = qk_scale or head_dim ** -0.5
self.scale = head_dim ** -0.5
self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
self.attn_drop = nn.Dropout(attn_drop)
@ -161,12 +198,11 @@ class Attention(nn.Module):
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
def __init__(self, dim, num_heads, mlp_ratio=4., qkv_bias=False, drop=0., attn_drop=0.,
drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
super().__init__()
self.norm1 = norm_layer(dim)
self.attn = Attention(
dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
self.attn = Attention(dim, num_heads=num_heads, qkv_bias=qkv_bias, attn_drop=attn_drop, proj_drop=drop)
# NOTE: drop path for stochastic depth, we shall see if this is better than dropout here
self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
self.norm2 = norm_layer(dim)
@ -190,7 +226,7 @@ class VisionTransformer(nn.Module):
"""
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12,
num_heads=12, mlp_ratio=4., qkv_bias=True, qk_scale=None, representation_size=None, distilled=False,
num_heads=12, mlp_ratio=4., qkv_bias=True, representation_size=None, distilled=False,
drop_rate=0., attn_drop_rate=0., drop_path_rate=0., embed_layer=PatchEmbed, norm_layer=None,
act_layer=None, weight_init=''):
"""
@ -204,7 +240,6 @@ class VisionTransformer(nn.Module):
num_heads (int): number of attention heads
mlp_ratio (int): ratio of mlp hidden dim to embedding dim
qkv_bias (bool): enable bias for qkv if True
qk_scale (float): override default qk scale of head_dim ** -0.5 if set
representation_size (Optional[int]): enable and set representation layer (pre-logits) to this value if set
distilled (bool): model includes a distillation token and head as in DeiT models
drop_rate (float): dropout rate
@ -233,8 +268,8 @@ class VisionTransformer(nn.Module):
dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule
self.blocks = nn.Sequential(*[
Block(
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale,
drop=drop_rate, attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, drop=drop_rate,
attn_drop=attn_drop_rate, drop_path=dpr[i], norm_layer=norm_layer, act_layer=act_layer)
for i in range(depth)])
self.norm = norm_layer(embed_dim)
@ -254,16 +289,17 @@ class VisionTransformer(nn.Module):
if distilled:
self.head_dist = nn.Linear(self.embed_dim, self.num_classes) if num_classes > 0 else nn.Identity()
# Weight init
assert weight_init in ('jax', 'jax_nlhb', 'nlhb', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in weight_init else 0.
self.init_weights(weight_init)
def init_weights(self, mode=''):
assert mode in ('jax', 'jax_nlhb', 'nlhb', '')
head_bias = -math.log(self.num_classes) if 'nlhb' in mode else 0.
trunc_normal_(self.pos_embed, std=.02)
if self.dist_token is not None:
trunc_normal_(self.dist_token, std=.02)
if weight_init.startswith('jax'):
if mode.startswith('jax'):
# leave cls token as zeros to match jax impl
for n, m in self.named_modules():
_init_vit_weights(m, n, head_bias=head_bias, jax_impl=True)
named_apply(partial(_init_vit_weights, head_bias=head_bias, jax_impl=True), self)
else:
trunc_normal_(self.cls_token, std=.02)
self.apply(_init_vit_weights)
@ -272,6 +308,10 @@ class VisionTransformer(nn.Module):
# this fn left here for compat with downstream users
_init_vit_weights(m)
@torch.jit.ignore()
def load_pretrained(self, checkpoint_path, prefix=''):
_load_weights(self, checkpoint_path, prefix)
@torch.jit.ignore
def no_weight_decay(self):
return {'pos_embed', 'cls_token', 'dist_token'}
@ -317,39 +357,119 @@ class VisionTransformer(nn.Module):
return x
def _init_vit_weights(m, n: str = '', head_bias: float = 0., jax_impl: bool = False):
def _init_vit_weights(module: nn.Module, name: str = '', head_bias: float = 0., jax_impl: bool = False):
""" ViT weight initialization
* When called without n, head_bias, jax_impl args it will behave exactly the same
as my original init for compatibility with prev hparam / downstream use cases (ie DeiT).
* When called w/ valid n (module name) and jax_impl=True, will (hopefully) match JAX impl
"""
if isinstance(m, nn.Linear):
if n.startswith('head'):
nn.init.zeros_(m.weight)
nn.init.constant_(m.bias, head_bias)
elif n.startswith('pre_logits'):
lecun_normal_(m.weight)
nn.init.zeros_(m.bias)
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
elif name.startswith('pre_logits'):
lecun_normal_(module.weight)
nn.init.zeros_(module.bias)
else:
if jax_impl:
nn.init.xavier_uniform_(m.weight)
if m.bias is not None:
if 'mlp' in n:
nn.init.normal_(m.bias, std=1e-6)
nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
if 'mlp' in name:
nn.init.normal_(module.bias, std=1e-6)
else:
nn.init.zeros_(m.bias)
nn.init.zeros_(module.bias)
else:
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif jax_impl and isinstance(m, nn.Conv2d):
trunc_normal_(module.weight, std=.02)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif jax_impl and isinstance(module, nn.Conv2d):
# NOTE conv was left to pytorch default in my original init
lecun_normal_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
elif isinstance(m, nn.LayerNorm):
nn.init.zeros_(m.bias)
nn.init.ones_(m.weight)
lecun_normal_(module.weight)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, (nn.LayerNorm, nn.GroupNorm, nn.BatchNorm2d)):
nn.init.zeros_(module.bias)
nn.init.ones_(module.weight)
@torch.no_grad()
def _load_weights(model: VisionTransformer, checkpoint_path: str, prefix: str = ''):
""" Load weights from .npz checkpoints for official Google Brain Flax implementation
"""
import numpy as np
def _n2p(w, t=True):
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
w = w.flatten()
if t:
if w.ndim == 4:
w = w.transpose([3, 2, 0, 1])
elif w.ndim == 3:
w = w.transpose([2, 0, 1])
elif w.ndim == 2:
w = w.transpose([1, 0])
return torch.from_numpy(w)
w = np.load(checkpoint_path)
if not prefix and 'opt/target/embedding/kernel' in w:
prefix = 'opt/target/'
if hasattr(model.patch_embed, 'backbone'):
# hybrid
backbone = model.patch_embed.backbone
stem_only = not hasattr(backbone, 'stem')
stem = backbone if stem_only else backbone.stem
stem.conv.weight.copy_(adapt_input_conv(stem.conv.weight.shape[1], _n2p(w[f'{prefix}conv_root/kernel'])))
stem.norm.weight.copy_(_n2p(w[f'{prefix}gn_root/scale']))
stem.norm.bias.copy_(_n2p(w[f'{prefix}gn_root/bias']))
if not stem_only:
for i, stage in enumerate(backbone.stages):
for j, block in enumerate(stage.blocks):
bp = f'{prefix}block{i + 1}/unit{j + 1}/'
for r in range(3):
getattr(block, f'conv{r + 1}').weight.copy_(_n2p(w[f'{bp}conv{r + 1}/kernel']))
getattr(block, f'norm{r + 1}').weight.copy_(_n2p(w[f'{bp}gn{r + 1}/scale']))
getattr(block, f'norm{r + 1}').bias.copy_(_n2p(w[f'{bp}gn{r + 1}/bias']))
if block.downsample is not None:
block.downsample.conv.weight.copy_(_n2p(w[f'{bp}conv_proj/kernel']))
block.downsample.norm.weight.copy_(_n2p(w[f'{bp}gn_proj/scale']))
block.downsample.norm.bias.copy_(_n2p(w[f'{bp}gn_proj/bias']))
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
else:
embed_conv_w = adapt_input_conv(
model.patch_embed.proj.weight.shape[1], _n2p(w[f'{prefix}embedding/kernel']))
model.patch_embed.proj.weight.copy_(embed_conv_w)
model.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
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 pos_embed_w.shape != model.pos_embed.shape:
pos_embed_w = resize_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w, model.pos_embed, getattr(model, 'num_tokens', 1), model.patch_embed.grid_size)
model.pos_embed.copy_(pos_embed_w)
model.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
model.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
if isinstance(model.head, nn.Linear) and model.head.bias.shape[0] == w[f'{prefix}head/bias'].shape[-1]:
model.head.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
model.head.bias.copy_(_n2p(w[f'{prefix}head/bias']))
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']))
for i, block in enumerate(model.blocks.children()):
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
mha_prefix = block_prefix + 'MultiHeadDotProductAttention_1/'
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([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
block.attn.qkv.bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
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_tokens=1, gs_new=()):
@ -413,34 +533,64 @@ def _create_vision_transformer(variant, pretrained=False, default_cfg=None, **kw
default_cfg=default_cfg,
representation_size=repr_size,
pretrained_filter_fn=checkpoint_filter_fn,
pretrained_custom_load='npz' in default_cfg['url'],
**kwargs)
return model
@register_model
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)
return model
@register_model
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)
return model
@register_model
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)
return model
@register_model
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)
return model
@register_model
def vit_small_patch16_224(pretrained=False, **kwargs):
""" My custom 'small' ViT model. embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.
NOTE:
* this differs from the DeiT based 'small' definitions with embed_dim=384, depth=12, num_heads=6
* this model does not have a bias for QKV (unlike the official ViT and DeiT models)
""" ViT-Small (ViT-S/16)
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=8, num_heads=8, mlp_ratio=3.,
qkv_bias=False, norm_layer=nn.LayerNorm, **kwargs)
if pretrained:
# NOTE my scale was wrong for original weights, leaving this here until I have better ones for this model
model_kwargs.setdefault('qk_scale', 768 ** -0.5)
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)
return model
@register_model
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.
def vit_small_patch16_384(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
NOTE I've replaced my previous 'small' model definition and weights with the small variant from the DeiT paper
"""
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=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@ -453,6 +603,26 @@ def vit_base_patch32_224(pretrained=False, **kwargs):
return model
@register_model
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)
return model
@register_model
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)
return model
@register_model
def vit_base_patch16_384(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
@ -464,31 +634,31 @@ def vit_base_patch16_384(pretrained=False, **kwargs):
@register_model
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.
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=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=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_large_patch32_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_large_patch16_224(pretrained=False, **kwargs):
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 @ 224x224, source https://github.com/google-research/vision_transformer.
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_224', pretrained=pretrained, **model_kwargs)
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)
return model
@register_model
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.
def vit_large_patch16_224(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 @ 224x224, 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_224', pretrained=pretrained, **model_kwargs)
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)
return model
@ -503,23 +673,35 @@ def vit_large_patch16_384(pretrained=False, **kwargs):
@register_model
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.
def vit_tiny_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Tiny (Vit-Ti/16).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
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=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer('vit_tiny_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
def vit_small_patch32_224_in21k(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
model_kwargs = dict(
patch_size=16, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
model_kwargs = dict(patch_size=32, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Small (ViT-S/16)
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_small_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@ -527,21 +709,23 @@ def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
def vit_base_patch32_224_in21k(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
model_kwargs = dict(
patch_size=32, embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
patch_size=32, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch32_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
def vit_base_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Base model (ViT-B/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_base_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@ -549,6 +733,7 @@ def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/32) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
"""
model_kwargs = dict(
patch_size=32, embed_dim=1024, depth=24, num_heads=16, representation_size=1024, **kwargs)
@ -556,11 +741,23 @@ def vit_large_patch32_224_in21k(pretrained=False, **kwargs):
return model
@register_model
def vit_large_patch16_224_in21k(pretrained=False, **kwargs):
""" ViT-Large model (ViT-L/16) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: this model has valid 21k classifier head and no representation (pre-logits) layer
"""
model_kwargs = dict(
patch_size=16, embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer('vit_large_patch16_224_in21k', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
""" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
NOTE: converted weights not currently available, too large for github release hosting.
NOTE: this model has a representation layer but the 21k classifier head is zero'd out in original weights
"""
model_kwargs = dict(
patch_size=14, embed_dim=1280, depth=32, num_heads=16, representation_size=1280, **kwargs)
@ -569,86 +766,86 @@ def vit_huge_patch14_224_in21k(pretrained=False, **kwargs):
@register_model
def vit_deit_tiny_patch16_224(pretrained=False, **kwargs):
def deit_tiny_patch16_224(pretrained=False, **kwargs):
""" DeiT-tiny model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer('vit_deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
model = _create_vision_transformer('deit_tiny_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_deit_small_patch16_224(pretrained=False, **kwargs):
def deit_small_patch16_224(pretrained=False, **kwargs):
""" DeiT-small model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer('vit_deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
model = _create_vision_transformer('deit_small_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_deit_base_patch16_224(pretrained=False, **kwargs):
def deit_base_patch16_224(pretrained=False, **kwargs):
""" DeiT base model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
model = _create_vision_transformer('deit_base_patch16_224', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_deit_base_patch16_384(pretrained=False, **kwargs):
def deit_base_patch16_384(pretrained=False, **kwargs):
""" DeiT base model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer('vit_deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
model = _create_vision_transformer('deit_base_patch16_384', pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
def deit_tiny_distilled_patch16_224(pretrained=False, **kwargs):
""" DeiT-tiny distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer(
'vit_deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
'deit_tiny_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
return model
@register_model
def vit_deit_small_distilled_patch16_224(pretrained=False, **kwargs):
def deit_small_distilled_patch16_224(pretrained=False, **kwargs):
""" DeiT-small distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer(
'vit_deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
'deit_small_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
return model
@register_model
def vit_deit_base_distilled_patch16_224(pretrained=False, **kwargs):
def deit_base_distilled_patch16_224(pretrained=False, **kwargs):
""" DeiT-base distilled model @ 224x224 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer(
'vit_deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
'deit_base_distilled_patch16_224', pretrained=pretrained, distilled=True, **model_kwargs)
return model
@register_model
def vit_deit_base_distilled_patch16_384(pretrained=False, **kwargs):
def deit_base_distilled_patch16_384(pretrained=False, **kwargs):
""" DeiT-base distilled model @ 384x384 from paper (https://arxiv.org/abs/2012.12877).
ImageNet-1k weights from https://github.com/facebookresearch/deit.
"""
model_kwargs = dict(patch_size=16, embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer(
'vit_deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
'deit_base_distilled_patch16_384', pretrained=pretrained, distilled=True, **model_kwargs)
return model

239
timm/models/vision_transformer_hybrid.py
Unescape View File

@ -1,13 +1,17 @@
""" Hybrid Vision Transformer (ViT) in PyTorch
A PyTorch implement of the Hybrid Vision Transformers as described in
A PyTorch implement of the Hybrid Vision Transformers as described in:
'An Image Is Worth 16 x 16 Words: Transformers for Image Recognition at Scale'
- https://arxiv.org/abs/2010.11929
NOTE This relies on code in vision_transformer.py. The hybrid model definitions were moved here to
keep file sizes sane.
`How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers`
- https://arxiv.org/abs/2106.TODO
NOTE These hybrid model definitions depend on code in vision_transformer.py.
They were moved here to keep file sizes sane.
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
from copy import deepcopy
from functools import partial
@ -35,32 +39,61 @@ def _cfg(url='', **kwargs):
default_cfgs = {
# hybrid in-21k models (weights ported from official Google JAX impl where they exist)
'vit_base_r50_s16_224_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),
# hybrid in-1k models (weights ported from official JAX impl)
'vit_base_r50_s16_384': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-vitjx/jx_vit_base_resnet50_384-9fd3c705.pth',
# hybrid in-1k models (weights from official JAX impl where they exist)
'vit_tiny_r_s16_p8_224': _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',
first_conv='patch_embed.backbone.conv'),
'vit_tiny_r_s16_p8_384': _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),
'vit_small_r26_s32_224': _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',
),
'vit_small_r26_s32_384': _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),
# hybrid in-1k models (mostly untrained, experimental configs w/ resnetv2 stdconv backbones)
'vit_tiny_r_s16_p8_224': _cfg(),
'vit_small_r_s16_p8_224': _cfg(),
'vit_small_r20_s16_p2_224': _cfg(),
'vit_small_r20_s16_224': _cfg(),
'vit_small_r26_s32_224': _cfg(),
'vit_base_r20_s16_224': _cfg(),
'vit_base_r26_s32_224': _cfg(),
'vit_base_r50_s16_224': _cfg(),
'vit_large_r50_s32_224': _cfg(),
'vit_base_r50_s16_384': _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': _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'
),
'vit_large_r50_s32_384': _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
),
# hybrid in-21k models (weights from official Google JAX impl where they exist)
'vit_tiny_r_s16_p8_224_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'),
'vit_small_r26_s32_224_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),
'vit_base_r50_s16_224_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_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),
# hybrid models (using timm resnet backbones)
'vit_small_resnet26d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'vit_small_resnet50d_s16_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'vit_base_resnet26d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'vit_base_resnet50d_224': _cfg(mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD),
'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'),
}
@ -95,7 +128,8 @@ class HybridEmbed(nn.Module):
else:
feature_dim = self.backbone.num_features
assert feature_size[0] % patch_size[0] == 0 and feature_size[1] % patch_size[1] == 0
self.num_patches = feature_size[0] // patch_size[0] * feature_size[1] // patch_size[1]
self.grid_size = (feature_size[0] // patch_size[0], feature_size[1] // patch_size[1])
self.num_patches = self.grid_size[0] * self.grid_size[1]
self.proj = nn.Conv2d(feature_dim, embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
@ -116,12 +150,8 @@ def _create_vision_transformer_hybrid(variant, backbone, pretrained=False, **kwa
def _resnetv2(layers=(3, 4, 9), **kwargs):
""" ResNet-V2 backbone helper"""
padding_same = kwargs.get('padding_same', True)
if padding_same:
stem_type = 'same'
conv_layer = StdConv2dSame
else:
stem_type = ''
conv_layer = StdConv2d
stem_type = 'same' if padding_same else ''
conv_layer = partial(StdConv2dSame, eps=1e-8) if padding_same else partial(StdConv2d, eps=1e-8)
if len(layers):
backbone = ResNetV2(
layers=layers, num_classes=0, global_pool='', in_chans=kwargs.get('in_chans', 3),
@ -133,138 +163,159 @@ def _resnetv2(layers=(3, 4, 9), **kwargs):
@register_model
def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
""" R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
"""
backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_base_r50_s16_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_tiny_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
# NOTE this is forwarding to model def above for backwards compatibility
return vit_base_r50_s16_224_in21k(pretrained=pretrained, **kwargs)
def vit_tiny_r_s16_p8_384(pretrained=False, **kwargs):
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 384 x 384.
"""
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_tiny_r_s16_p8_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_r50_s16_384(pretrained=False, **kwargs):
""" R50+ViT-B/16 hybrid 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.
def vit_small_r26_s32_224(pretrained=False, **kwargs):
""" R26+ViT-S/S32 hybrid.
"""
backbone = _resnetv2((3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_base_r50_s16_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_small_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_resnet50_384(pretrained=False, **kwargs):
# NOTE this is forwarding to model def above for backwards compatibility
return vit_base_r50_s16_384(pretrained=pretrained, **kwargs)
def vit_small_r26_s32_384(pretrained=False, **kwargs):
""" R26+ViT-S/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_small_r26_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_tiny_r_s16_p8_224(pretrained=False, **kwargs):
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid @ 224 x 224.
def vit_base_r26_s32_224(pretrained=False, **kwargs):
""" R26+ViT-B/S32 hybrid.
"""
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_tiny_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_base_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_r_s16_p8_224(pretrained=False, **kwargs):
""" R+ViT-S/S16 w/ 8x8 patch hybrid @ 224 x 224.
def vit_base_r50_s16_224(pretrained=False, **kwargs):
""" R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
"""
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=384, depth=12, num_heads=6, **kwargs)
backbone = _resnetv2((3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_small_r_s16_p8_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_base_r50_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_r20_s16_p2_224(pretrained=False, **kwargs):
""" R52+ViT-S/S16 w/ 2x2 patch hybrid @ 224 x 224.
def vit_base_r50_s16_384(pretrained=False, **kwargs):
""" R50+ViT-B/16 hybrid 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.
"""
backbone = _resnetv2((2, 4), **kwargs)
model_kwargs = dict(patch_size=2, embed_dim=384, depth=12, num_heads=6, **kwargs)
backbone = _resnetv2((3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_small_r20_s16_p2_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_base_r50_s16_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_r20_s16_224(pretrained=False, **kwargs):
""" R20+ViT-S/S16 hybrid.
def vit_base_resnet50_384(pretrained=False, **kwargs):
# DEPRECATED this is forwarding to model def above for backwards compatibility
return vit_base_r50_s16_384(pretrained=pretrained, **kwargs)
@register_model
def vit_large_r50_s32_224(pretrained=False, **kwargs):
""" R50+ViT-L/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
backbone = _resnetv2((3, 4, 6, 3), **kwargs)
model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_small_r20_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_large_r50_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_small_r26_s32_224(pretrained=False, **kwargs):
""" R26+ViT-S/S32 hybrid.
def vit_large_r50_s32_384(pretrained=False, **kwargs):
""" R50+ViT-L/S32 hybrid.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
backbone = _resnetv2((3, 4, 6, 3), **kwargs)
model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_small_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_large_r50_s32_384', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_r20_s16_224(pretrained=False, **kwargs):
""" R20+ViT-B/S16 hybrid.
def vit_tiny_r_s16_p8_224_in21k(pretrained=False, **kwargs):
""" R+ViT-Ti/S16 w/ 8x8 patch hybrid. ImageNet-21k.
"""
backbone = _resnetv2((2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
backbone = _resnetv2(layers=(), **kwargs)
model_kwargs = dict(patch_size=8, embed_dim=192, depth=12, num_heads=3, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_base_r20_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_tiny_r_s16_p8_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_r26_s32_224(pretrained=False, **kwargs):
""" R26+ViT-B/S32 hybrid.
def vit_small_r26_s32_224_in21k(pretrained=False, **kwargs):
""" R26+ViT-S/S32 hybrid. ImageNet-21k.
"""
backbone = _resnetv2((2, 2, 2, 2), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
model_kwargs = dict(embed_dim=384, depth=12, num_heads=6, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_base_r26_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_small_r26_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_base_r50_s16_224(pretrained=False, **kwargs):
""" R50+ViT-B/S16 hybrid from original paper (https://arxiv.org/abs/2010.11929).
def vit_base_r50_s16_224_in21k(pretrained=False, **kwargs):
""" R50+ViT-B/16 hybrid model from original paper (https://arxiv.org/abs/2010.11929).
ImageNet-21k weights @ 224x224, source https://github.com/google-research/vision_transformer.
"""
backbone = _resnetv2((3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
backbone = _resnetv2(layers=(3, 4, 9), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, representation_size=768, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_base_r50_s16_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_base_r50_s16_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model
@register_model
def vit_large_r50_s32_224(pretrained=False, **kwargs):
""" R50+ViT-L/S32 hybrid.
def vit_base_resnet50_224_in21k(pretrained=False, **kwargs):
# DEPRECATED this is forwarding to model def above for backwards compatibility
return vit_base_r50_s16_224_in21k(pretrained=pretrained, **kwargs)
@register_model
def vit_large_r50_s32_224_in21k(pretrained=False, **kwargs):
""" R50+ViT-L/S32 hybrid. ImageNet-21k.
"""
backbone = _resnetv2((3, 4, 6, 3), **kwargs)
model_kwargs = dict(embed_dim=768, depth=12, num_heads=12, **kwargs)
model_kwargs = dict(embed_dim=1024, depth=24, num_heads=16, **kwargs)
model = _create_vision_transformer_hybrid(
'vit_large_r50_s32_224', backbone=backbone, pretrained=pretrained, **model_kwargs)
'vit_large_r50_s32_224_in21k', backbone=backbone, pretrained=pretrained, **model_kwargs)
return model

2
timm/version.py
Unescape View File

@ -1 +1 @@
__version__ = '0.4.11'
__version__ = '0.4.12'

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