mirrors/pytorch-image-models
1
0
Fork 0
mirror of https://github.com/huggingface/pytorch-image-models.git synced 2025-06-03 15:01:08 +08:00
Code Issues Projects Releases Wiki Activity

Merge branch 'master' into levit_visformer_rednet

Browse Source
This commit is contained in:
Ross Wightman 2021-05-14 23:02:12 -07:00
commit 3bffc701f1
18 changed files with 2150 additions and 1678 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

9
README.md
View File

@ -23,6 +23,15 @@ I'm fortunate to be able to dedicate significant time and money of my own suppor
## What's New
### May 14, 2021
* Add EfficientNet-V2 official model defs w/ ported weights from official [Tensorflow/Keras](https://github.com/google/automl/tree/master/efficientnetv2) impl.
* 1k trained variants: `tf_efficientnetv2_s/m/l`
* 21k trained variants: `tf_efficientnetv2_s/m/l_21k`
* 21k pretrained -> 1k fine-tuned: `tf_efficientnetv2_s/m/l_21ft1k`
* v2 models w/ v1 scaling: `tf_efficientnet_v2_b0` through `b3`
* Rename my prev V2 guess `efficientnet_v2s` -> `efficientnetv2_rw_s`
* Some blank `efficientnetv2_*` models in-place for future native PyTorch training
### May 5, 2021
* Add MLP-Mixer models and port pretrained weights from [Google JAX impl](https://github.com/google-research/vision_transformer/tree/linen)
* Add CaiT models and pretrained weights from [FB](https://github.com/facebookresearch/deit)

85
results/results-imagenet-a-clean.csv
View File

@ -12,44 +12,58 @@ dm_nfnet_f5,97.600,2.400,99.550,0.450,377.21,544,0.954,bicubic
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
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
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
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
vit_large_patch16_384,97.110,2.890,99.640,0.360,304.72,384,1.000,bicubic
tf_efficientnet_b8_ap,97.110,2.890,99.660,0.340,87.41,672,0.954,bicubic
tf_efficientnet_b6_ap,97.080,2.920,99.620,0.380,43.04,528,0.942,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
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
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
dm_nfnet_f1,96.920,3.080,99.390,0.610,132.63,320,0.910,bicubic
resnetrs420,96.910,3.090,99.460,0.540,191.89,416,1.000,bicubic
ig_resnext101_32x16d,96.820,3.180,99.590,0.410,194.03,224,0.875,bilinear
seresnet152d,96.770,3.230,99.450,0.550,66.84,320,1.000,bicubic
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
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
pit_b_distilled_224,96.680,3.320,99.350,0.650,74.79,224,0.900,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_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
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
swsl_resnext101_32x4d,96.420,3.580,99.470,0.530,44.18,224,0.875,bilinear
tf_efficientnet_b3_ns,96.390,3.610,99.350,0.650,12.23,300,0.904,bicubic
cait_s24_224,96.380,3.620,99.150,0.850,46.92,224,1.000,bicubic
resnet152d,96.360,3.640,99.390,0.610,60.21,320,1.000,bicubic
regnety_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
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
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
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
@ -62,28 +76,30 @@ eca_nfnet_l0,95.930,4.070,99.210,0.790,24.14,288,1.000,bicubic
swin_small_patch4_window7_224,95.910,4.090,99.020,0.980,49.61,224,0.900,bicubic
tf_efficientnet_b4,95.900,4.100,99.170,0.830,19.34,380,0.922,bicubic
swsl_resnext50_32x4d,95.870,4.130,99.250,0.750,25.03,224,0.875,bilinear
tresnet_l_448,95.860,4.140,99.120,0.880,55.99,448,0.875,bilinear
resnest101e,95.860,4.140,99.210,0.790,48.28,256,0.875,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
tf_efficientnet_b2_ns,95.770,4.230,99.120,0.880,9.11,260,0.890,bicubic
efficientnet_b3a,95.710,4.290,99.040,0.960,12.23,320,1.000,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
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
efficientnet_b3,95.580,4.420,99.100,0.900,12.23,300,0.904,bicubic
efficientnet_v2s,95.540,4.460,98.960,1.040,23.94,224,1.000,bicubic
ecaresnet101d,95.530,4.470,99.130,0.870,44.57,224,0.875,bicubic
ecaresnet50t,95.510,4.490,99.120,0.880,25.57,320,0.950,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
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
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
tf_efficientnet_b1_ns,95.170,4.830,99.110,0.890,7.79,240,0.882,bicubic
swin_tiny_patch4_window7_224,95.140,4.860,98.850,1.150,28.29,224,0.900,bicubic
@ -93,9 +109,9 @@ ecaresnet101d_pruned,95.080,4.920,98.980,1.020,24.88,224,0.875,bicubic
wide_resnet50_2,95.080,4.920,98.970,1.030,68.88,224,0.875,bicubic
legacy_senet154,95.070,4.930,98.830,1.170,115.09,224,0.875,bilinear
resnetv2_50x1_bitm,95.050,4.950,99.160,0.840,25.55,480,1.000,bilinear
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
gluon_resnet152_v1s,95.040,4.960,98.930,1.070,60.32,224,0.875,bicubic
tf_efficientnet_b3,95.010,4.990,98.910,1.090,12.23,300,0.904,bicubic
tresnet_m_448,94.990,5.010,98.980,1.020,31.39,448,0.875,bilinear
resnest50d_4s2x40d,94.960,5.040,99.070,0.930,30.42,224,0.875,bicubic
@ -111,20 +127,18 @@ resnest50d_1s4x24d,94.750,5.250,98.980,1.020,25.68,224,0.875,bicubic
gluon_resnet152_v1d,94.740,5.260,98.740,1.260,60.21,224,0.875,bicubic
gluon_resnet101_v1s,94.720,5.280,98.820,1.180,44.67,224,0.875,bicubic
vit_deit_small_distilled_patch16_224,94.710,5.290,99.030,0.970,22.44,224,0.900,bicubic
efficientnet_b2,94.700,5.300,98.670,1.330,9.11,260,0.875,bicubic
gluon_resnext101_64x4d,94.670,5.330,98.650,1.350,83.46,224,0.875,bicubic
cspdarknet53,94.660,5.340,98.800,1.200,27.64,256,0.887,bilinear
ecaresnet50d,94.630,5.370,98.890,1.110,25.58,224,0.875,bicubic
efficientnet_b3_pruned,94.630,5.370,98.760,1.240,9.86,300,0.904,bicubic
tresnet_m,94.620,5.380,98.550,1.450,31.39,224,0.875,bilinear
gernet_m,94.620,5.380,98.860,1.140,21.14,224,0.875,bilinear
efficientnet_b2a,94.610,5.390,98.710,1.290,9.11,288,1.000,bicubic
efficientnet_b2,94.610,5.390,98.710,1.290,9.11,288,1.000,bicubic
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
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
inception_resnet_v2,94.540,5.460,98.790,1.210,55.84,299,0.897,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
@ -134,71 +148,75 @@ 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_efficientnet_el,94.410,5.590,98.710,1.290,10.59,300,0.904,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
efficientnet_el_pruned,94.400,5.600,98.740,1.260,10.59,300,0.904,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
gluon_seresnext50_32x4d,94.340,5.660,98.610,1.390,27.56,224,0.875,bicubic
dpn107,94.310,5.690,98.480,1.520,86.92,224,0.875,bicubic
ecaresnet26t,94.310,5.690,98.720,1.280,16.01,320,0.950,bicubic
resnetrs50,94.310,5.690,98.640,1.360,35.69,224,0.910,bicubic
xception71,94.280,5.720,98.640,1.360,42.34,299,0.903,bicubic
gluon_xception65,94.260,5.740,98.570,1.430,39.92,299,0.903,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
regnetx_120,94.240,5.760,98.650,1.350,46.11,224,0.875,bicubic
dpn92,94.230,5.770,98.730,1.270,37.67,224,0.875,bicubic
gluon_resnet101_v1d,94.220,5.780,98.550,1.450,44.57,224,0.875,bicubic
ecaresnet50d_pruned,94.220,5.780,98.730,1.270,19.94,224,0.875,bicubic
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
regnety_080,94.170,5.830,98.680,1.320,39.18,224,0.875,bicubic
ens_adv_inception_resnet_v2,94.160,5.840,98.600,1.400,55.84,299,0.897,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
regnety_064,94.150,5.850,98.730,1.270,30.58,224,0.875,bicubic
efficientnet_b2_pruned,94.140,5.860,98.530,1.470,8.31,260,0.890,bicubic
nf_regnet_b1,94.130,5.870,98.630,1.370,10.22,288,0.900,bicubic
dpn98,94.130,5.870,98.570,1.430,61.57,224,0.875,bicubic
nf_regnet_b1,94.130,5.870,98.630,1.370,10.22,288,0.900,bicubic
regnetx_160,94.120,5.880,98.750,1.250,54.28,224,0.875,bicubic
resnext50_32x4d,94.100,5.900,98.350,1.650,25.03,224,0.875,bicubic
ese_vovnet39b,94.090,5.910,98.660,1.340,24.57,224,0.875,bicubic
gluon_resnet152_v1b,94.080,5.920,98.450,1.550,60.19,224,0.875,bicubic
coat_lite_mini,94.060,5.940,98.560,1.440,11.01,224,0.900,bicubic
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
hrnet_w48,93.920,6.080,98.610,1.390,77.47,224,0.875,bilinear
tf_efficientnet_cc_b1_8e,93.900,6.100,98.260,1.740,39.72,240,0.882,bicubic
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
regnetx_064,93.890,6.110,98.630,1.370,26.21,224,0.875,bicubic
regnetx_080,93.870,6.130,98.520,1.480,39.57,224,0.875,bicubic
regnety_040,93.860,6.140,98.650,1.350,20.65,224,0.875,bicubic
repvgg_b2g4,93.860,6.140,98.590,1.410,61.76,224,0.875,bilinear
efficientnet_em,93.840,6.160,98.810,1.190,6.90,240,0.882,bicubic
resnext101_32x8d,93.830,6.170,98.580,1.420,88.79,224,0.875,bilinear
gluon_resnext50_32x4d,93.810,6.190,98.410,1.590,25.03,224,0.875,bicubic
pit_xs_distilled_224,93.810,6.190,98.670,1.330,11.00,224,0.900,bicubic
resnet50,93.810,6.190,98.390,1.610,25.56,224,0.875,bicubic
pit_xs_distilled_224,93.810,6.190,98.670,1.330,11.00,224,0.900,bicubic
gluon_resnext50_32x4d,93.810,6.190,98.410,1.590,25.03,224,0.875,bicubic
gluon_resnet50_v1d,93.770,6.230,98.390,1.610,25.58,224,0.875,bicubic
xception65,93.760,6.240,98.370,1.630,39.92,299,0.903,bicubic
res2net101_26w_4s,93.750,6.250,98.310,1.690,45.21,224,0.875,bilinear
gluon_resnet101_v1b,93.750,6.250,98.380,1.620,44.55,224,0.875,bicubic
res2net101_26w_4s,93.750,6.250,98.310,1.690,45.21,224,0.875,bilinear
cspresnet50,93.740,6.260,98.640,1.360,21.62,256,0.887,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
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
dpn68b,93.690,6.310,98.510,1.490,12.61,224,0.875,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
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
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
repvgg_b2,93.500,6.500,98.730,1.270,89.02,224,0.875,bilinear
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
@ -207,14 +225,15 @@ 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
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
repvgg_b1,93.330,6.670,98.510,1.490,57.42,224,0.875,bilinear
tf_inception_v3,93.320,6.680,98.030,1.970,23.83,299,0.875,bicubic
tf_mixnet_l,93.310,6.690,98.030,1.970,7.33,224,0.875,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
legacy_seresnet101,93.280,6.720,98.510,1.490,49.33,224,0.875,bilinear
efficientnet_b1,93.260,6.740,98.170,1.830,7.79,240,0.875,bicubic
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
hrnet_w30,93.200,6.800,98.410,1.590,37.71,224,0.875,bilinear
dla60_res2net,93.180,6.820,98.420,1.580,20.85,224,0.875,bilinear
dla60_res2next,93.180,6.820,98.410,1.590,17.03,224,0.875,bilinear
@ -223,10 +242,10 @@ 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
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
rexnet_100,93.030,6.970,98.190,1.810,4.80,224,0.875,bicubic
selecsls60,93.030,6.970,98.300,1.700,30.67,224,0.875,bicubic
regnety_016,93.030,6.970,98.360,1.640,11.20,224,0.875,bicubic
gluon_resnet50_v1c,93.030,6.970,98.390,1.610,25.58,224,0.875,bicubic
repvgg_b1g4,92.980,7.020,98.430,1.570,39.97,224,0.875,bilinear
legacy_seresnet50,92.960,7.040,98.190,1.810,28.09,224,0.875,bilinear
hardcorenas_f,92.950,7.050,98.160,1.840,8.20,224,0.875,bilinear
@ -252,16 +271,17 @@ 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
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
densenet161,92.500,7.500,98.290,1.710,28.68,224,0.875,bicubic
mixnet_m,92.430,7.570,97.870,2.130,5.01,224,0.875,bicubic
mobilenetv2_120d,92.400,7.600,98.050,1.950,5.83,224,0.875,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
skresnet34,92.390,7.610,98.150,1.850,22.28,224,0.875,bicubic
tf_mixnet_m,92.330,7.670,97.890,2.110,5.01,224,0.875,bicubic
hrnet_w18,92.320,7.680,98.240,1.760,21.30,224,0.875,bilinear
ese_vovnet19b_dw,92.290,7.710,98.090,1.910,6.54,224,0.875,bicubic
selecsls42b,92.280,7.720,98.150,1.850,32.46,224,0.875,bicubic
mobilenetv3_large_100_miil,92.260,7.740,97.640,2.360,5.48,224,0.875,bilinear
tf_efficientnet_b0,92.250,7.750,98.000,2.000,5.29,224,0.875,bicubic
dla60,92.230,7.770,98.110,1.890,22.04,224,0.875,bilinear
tf_efficientnet_b0_ap,92.200,7.800,98.020,1.980,5.29,224,0.875,bicubic
@ -275,6 +295,7 @@ 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
tv_resnet50,91.880,8.120,98.040,1.960,25.56,224,0.875,bilinear
mixer_b16_224,91.870,8.130,97.250,2.750,59.88,224,0.875,bicubic
mixnet_s,91.830,8.170,97.690,2.310,4.13,224,0.875,bicubic
mobilenetv2_140,91.830,8.170,97.860,2.140,6.11,224,0.875,bicubic
hardcorenas_b,91.770,8.230,97.780,2.220,5.18,224,0.875,bilinear
@ -307,11 +328,12 @@ vit_deit_tiny_distilled_patch16_224,90.700,9.300,97.570,2.430,5.91,224,0.900,bic
swsl_resnet18,90.690,9.310,97.700,2.300,11.69,224,0.875,bilinear
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
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
regnetx_006,90.350,9.650,97.430,2.570,6.20,224,0.875,bicubic
ssl_resnet18,90.220,9.780,97.550,2.450,11.69,224,0.875,bilinear
vgg16_bn,90.090,9.910,97.370,2.630,138.37,224,0.875,bilinear
vgg19_bn,90.080,9.920,97.580,2.420,143.68,224,0.875,bilinear
ghostnet_100,90.020,9.980,97.370,2.630,5.18,224,0.875,bilinear
pit_ti_224,89.940,10.060,97.450,2.550,4.85,224,0.900,bicubic
tv_resnet34,89.940,10.060,97.340,2.660,21.80,224,0.875,bilinear
tf_mobilenetv3_large_075,89.680,10.320,97.210,2.790,3.99,224,0.875,bilinear
@ -330,6 +352,7 @@ gluon_resnet18_v1b,88.400,11.600,96.680,3.320,11.69,224,0.875,bicubic
vgg11_bn,87.500,12.500,96.820,3.180,132.87,224,0.875,bilinear
resnet18,87.390,12.610,96.290,3.710,11.69,224,0.875,bilinear
regnety_002,87.380,12.620,96.590,3.410,3.16,224,0.875,bicubic
mixer_l16_224,87.150,12.850,93.520,6.480,208.20,224,0.875,bicubic
vgg13,87.050,12.950,96.320,3.680,133.05,224,0.875,bilinear
vgg11,86.550,13.450,96.280,3.720,132.86,224,0.875,bilinear
dla60x_c,86.290,13.710,96.160,3.840,1.32,224,0.875,bilinear

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
12 dm_nfnet_f4 97.570 2.430 99.520 0.480 316.07 512 0.951 bicubic
13 tf_efficientnet_b5_ns 97.500 2.500 99.630 0.370 30.39 456 0.934 bicubic
14 resnetv2_152x4_bitm 97.490 2.510 99.600 0.400 936.53 480 1.000 bilinear
15 cait_m48_448 97.480 2.520 99.550 0.450 356.46 448 1.000 bicubic
16 cait_m36_384 97.400 2.600 99.510 0.490 271.22 384 1.000 bicubic
17 dm_nfnet_f3 97.360 2.640 99.580 0.420 254.92 416 0.940 bicubic
18 ig_resnext101_32x32d 97.360 2.640 99.680 0.320 468.53 224 0.875 bilinear
19 cait_s36_384 97.330 2.670 99.530 0.470 68.37 384 1.000 bicubic
20 swin_base_patch4_window7_224 97.250 2.750 99.530 0.470 87.77 224 0.900 bicubic
21 swsl_resnext101_32x8d 97.200 2.800 99.570 0.430 88.79 224 0.875 bilinear
22 tf_efficientnet_b7_ap 97.200 2.800 99.540 0.460 66.35 600 0.949 bicubic
23 tf_efficientnet_b8 97.200 2.800 99.500 0.500 87.41 672 0.954 bicubic
24 vit_base_r50_s16_384 97.180 2.820 99.560 0.440 98.95 384 1.000 bicubic
25 resnetv2_152x2_bitm 97.150 2.850 99.590 0.410 236.34 480 1.000 bilinear
vit_large_patch16_384 97.110 2.890 99.640 0.360 304.72 384 1.000 bicubic
26 tf_efficientnet_b8_ap 97.110 2.890 99.660 0.340 87.41 672 0.954 bicubic
27 tf_efficientnet_b6_ap vit_large_patch16_384 97.080 97.110 2.920 2.890 99.620 99.640 0.380 0.360 43.04 304.72 528 384 0.942 1.000 bicubic
28 ecaresnet269d 97.080 2.920 99.470 0.530 102.09 352 1.000 bicubic
29 tf_efficientnet_b6_ap 97.080 2.920 99.620 0.380 43.04 528 0.942 bicubic
30 cait_s24_384 97.070 2.930 99.430 0.570 47.06 384 1.000 bicubic
31 resnetv2_101x3_bitm 97.050 2.950 99.520 0.480 387.93 480 1.000 bilinear
32 tf_efficientnet_b7 97.010 2.990 99.520 0.480 66.35 600 0.949 bicubic
33 dm_nfnet_f2 96.960 3.040 99.450 0.550 193.78 352 0.920 bicubic
34 vit_deit_base_distilled_patch16_384 96.960 3.040 99.480 0.520 87.63 384 1.000 bicubic
35 tf_efficientnet_b4_ns 96.950 3.050 99.580 0.420 19.34 380 0.922 bicubic
36 dm_nfnet_f1 96.920 3.080 99.390 0.610 132.63 320 0.910 bicubic
37 resnetrs420 96.910 3.090 99.460 0.540 191.89 416 1.000 bicubic
38 ig_resnext101_32x16d 96.820 3.180 99.590 0.410 194.03 224 0.875 bilinear
seresnet152d 96.770 3.230 99.450 0.550 66.84 320 1.000 bicubic
39 resnetv2_50x3_bitm 96.770 3.230 99.430 0.570 217.32 480 1.000 bilinear
40 seresnet152d 96.770 3.230 99.450 0.550 66.84 320 1.000 bicubic
41 resnetrs350 96.760 3.240 99.370 0.630 163.96 384 1.000 bicubic
42 resnet200d 96.720 3.280 99.330 0.670 64.69 320 1.000 bicubic
43 eca_nfnet_l1 96.700 3.300 99.270 0.730 41.41 320 1.000 bicubic
44 vit_base_patch16_384 96.700 3.300 99.510 0.490 86.86 384 1.000 bicubic
45 pit_b_distilled_224 resnetrs270 96.680 96.690 3.320 3.310 99.350 0.650 74.79 129.86 224 352 0.900 1.000 bicubic
46 tf_efficientnet_b5_ap 96.680 3.320 99.460 0.540 30.39 456 0.934 bicubic
47 pit_b_distilled_224 96.680 3.320 99.350 0.650 74.79 224 0.900 bicubic
48 tf_efficientnet_b6 96.670 3.330 99.370 0.630 43.04 528 0.942 bicubic
49 resnest200e 96.610 3.390 99.350 0.650 70.20 320 0.909 bicubic
50 swsl_resnext101_32x16d 96.600 3.400 99.520 0.480 194.03 224 0.875 bilinear
51 resnetrs152 96.580 3.420 99.240 0.760 86.62 320 1.000 bicubic
52 cait_xs24_384 96.550 3.450 99.420 0.580 26.67 384 1.000 bicubic
53 efficientnet_v2s 96.540 3.460 99.360 0.640 23.94 384 1.000 bicubic
54 resnetrs200 96.530 3.470 99.350 0.650 93.21 320 1.000 bicubic
55 resnest269e 96.520 3.480 99.350 0.650 110.93 416 0.928 bicubic
56 vit_base_patch16_224_miil 96.460 3.540 99.300 0.700 86.54 224 0.875 bilinear
57 swsl_resnext101_32x4d 96.420 3.580 99.470 0.530 44.18 224 0.875 bilinear
58 tf_efficientnet_b3_ns 96.390 3.610 99.350 0.650 12.23 300 0.904 bicubic
59 cait_s24_224 96.380 3.620 99.150 0.850 46.92 224 1.000 bicubic
60 resnet152d 96.360 3.640 99.390 0.610 60.21 320 1.000 bicubic
61 regnety_160 96.350 3.650 99.330 0.670 83.59 288 1.000 bicubic
62 tf_efficientnet_b5 96.350 3.650 99.310 0.690 30.39 456 0.934 bicubic
63 ig_resnext101_32x8d 96.320 3.680 99.430 0.570 88.79 224 0.875 bilinear
64 resnet101d 96.290 3.710 99.230 0.770 44.57 320 1.000 bicubic
65 tf_efficientnet_b4_ap 96.160 3.840 99.280 0.720 19.34 380 0.922 bicubic
66 efficientnet_b4 96.150 3.850 99.200 0.800 19.34 384 1.000 bicubic
67 vit_deit_base_patch16_384 96.150 3.850 99.140 0.860 86.86 384 1.000 bicubic
68 dm_nfnet_f0 96.140 3.860 99.240 0.760 71.49 256 0.900 bicubic
69 vit_deit_base_distilled_patch16_224 96.090 3.910 99.190 0.810 87.34 224 0.900 bicubic
76 swin_small_patch4_window7_224 95.910 4.090 99.020 0.980 49.61 224 0.900 bicubic
77 tf_efficientnet_b4 95.900 4.100 99.170 0.830 19.34 380 0.922 bicubic
78 swsl_resnext50_32x4d 95.870 4.130 99.250 0.750 25.03 224 0.875 bilinear
tresnet_l_448 95.860 4.140 99.120 0.880 55.99 448 0.875 bilinear
79 resnest101e 95.860 4.140 99.210 0.790 48.28 256 0.875 bilinear
80 tresnet_l_448 95.860 4.140 99.120 0.880 55.99 448 0.875 bilinear
81 cait_xxs36_384 95.850 4.150 99.090 0.910 17.37 384 1.000 bicubic
82 vit_large_patch32_384 95.830 4.170 99.150 0.850 306.63 384 1.000 bicubic
83 vit_base_patch32_384 95.810 4.190 99.150 0.850 88.30 384 1.000 bicubic
84 ssl_resnext101_32x16d 95.800 4.200 99.180 0.820 194.03 224 0.875 bilinear
85 tf_efficientnet_b2_ns 95.770 4.230 99.120 0.880 9.11 260 0.890 bicubic
86 efficientnet_b3a tresnet_m 95.710 95.720 4.290 4.280 99.040 99.030 0.960 0.970 12.23 31.39 320 224 1.000 0.875 bicubic bilinear
87 efficientnet_b3 95.710 4.290 99.040 0.960 12.23 320 1.000 bicubic
88 pnasnet5large 95.710 4.290 98.920 1.080 86.06 331 0.911 bicubic
89 nasnetalarge 95.680 4.320 98.930 1.070 88.75 331 0.911 bicubic
90 pit_b_224 95.640 4.360 98.660 1.340 73.76 224 0.900 bicubic
efficientnet_b3 95.580 4.420 99.100 0.900 12.23 300 0.904 bicubic
efficientnet_v2s 95.540 4.460 98.960 1.040 23.94 224 1.000 bicubic
91 ecaresnet101d 95.530 4.470 99.130 0.870 44.57 224 0.875 bicubic
92 ecaresnet50t 95.510 4.490 99.120 0.880 25.57 320 0.950 bicubic
93 ssl_resnext101_32x8d 95.470 4.530 99.110 0.890 88.79 224 0.875 bilinear
94 ssl_resnext101_32x4d 95.440 4.560 99.130 0.870 44.18 224 0.875 bilinear
95 tresnet_xl 95.440 4.560 99.050 0.950 78.44 224 0.875 bilinear
96 vit_deit_base_patch16_224 95.440 4.560 98.840 1.160 86.57 224 0.900 bicubic
97 resnetrs101 95.430 4.570 99.030 0.970 63.62 288 0.940 bicubic
98 swsl_resnet50 95.410 4.590 99.290 0.710 25.56 224 0.875 bilinear
99 vit_base_patch16_224 95.330 4.670 99.000 1.000 86.57 224 0.900 bicubic
100 tf_efficientnet_b3_ap 95.320 4.680 98.900 1.100 12.23 300 0.904 bicubic
101 tresnet_l 95.290 4.710 99.010 0.990 55.99 224 0.875 bilinear
102 cait_xxs24_384 95.260 4.740 98.960 1.040 12.03 384 1.000 bicubic
103 pit_s_distilled_224 95.240 4.760 99.050 0.950 24.04 224 0.900 bicubic
104 tf_efficientnet_b1_ns 95.170 4.830 99.110 0.890 7.79 240 0.882 bicubic
105 swin_tiny_patch4_window7_224 95.140 4.860 98.850 1.150 28.29 224 0.900 bicubic
109 wide_resnet50_2 95.080 4.920 98.970 1.030 68.88 224 0.875 bicubic
110 legacy_senet154 95.070 4.930 98.830 1.170 115.09 224 0.875 bilinear
111 resnetv2_50x1_bitm 95.050 4.950 99.160 0.840 25.55 480 1.000 bilinear
112 gluon_resnet152_v1s 95.040 4.960 98.930 1.070 60.32 224 0.875 bicubic
113 seresnext50_32x4d 95.040 4.960 98.880 1.120 27.56 224 0.875 bicubic
114 tnt_s_patch16_224 95.040 4.960 98.830 1.170 23.76 224 0.900 bicubic
gluon_resnet152_v1s 95.040 4.960 98.930 1.070 60.32 224 0.875 bicubic
115 tf_efficientnet_b3 95.010 4.990 98.910 1.090 12.23 300 0.904 bicubic
116 tresnet_m_448 94.990 5.010 98.980 1.020 31.39 448 0.875 bilinear
117 resnest50d_4s2x40d 94.960 5.040 99.070 0.930 30.42 224 0.875 bicubic
127 gluon_resnet152_v1d 94.740 5.260 98.740 1.260 60.21 224 0.875 bicubic
128 gluon_resnet101_v1s 94.720 5.280 98.820 1.180 44.67 224 0.875 bicubic
129 vit_deit_small_distilled_patch16_224 94.710 5.290 99.030 0.970 22.44 224 0.900 bicubic
efficientnet_b2 94.700 5.300 98.670 1.330 9.11 260 0.875 bicubic
130 gluon_resnext101_64x4d 94.670 5.330 98.650 1.350 83.46 224 0.875 bicubic
131 cspdarknet53 94.660 5.340 98.800 1.200 27.64 256 0.887 bilinear
132 ecaresnet50d 94.630 5.370 98.890 1.110 25.58 224 0.875 bicubic
133 efficientnet_b3_pruned 94.630 5.370 98.760 1.240 9.86 300 0.904 bicubic
tresnet_m 94.620 5.380 98.550 1.450 31.39 224 0.875 bilinear
134 gernet_m 94.620 5.380 98.860 1.140 21.14 224 0.875 bilinear
135 efficientnet_b2a efficientnet_b2 94.610 5.390 98.710 1.290 9.11 288 1.000 bicubic
136 nf_resnet50 94.590 5.410 98.810 1.190 25.56 288 0.940 bicubic
137 pit_s_224 94.590 5.410 98.710 1.290 23.46 224 0.900 bicubic
138 repvgg_b3 94.570 5.430 98.780 1.220 123.09 224 0.875 bilinear
139 seresnet50 94.550 5.450 98.750 1.250 28.09 224 0.875 bicubic
inception_resnet_v2 94.540 5.460 98.790 1.210 55.84 299 0.897 bicubic
140 regnety_320 94.540 5.460 98.850 1.150 145.05 224 0.875 bicubic
141 inception_resnet_v2 94.540 5.460 98.790 1.210 55.84 299 0.897 bicubic
142 gluon_resnext101_32x4d 94.530 5.470 98.630 1.370 44.18 224 0.875 bicubic
143 repvgg_b3g4 94.520 5.480 98.970 1.030 83.83 224 0.875 bilinear
144 tf_efficientnet_b2_ap 94.490 5.510 98.620 1.380 9.11 260 0.890 bicubic
148 regnetx_320 94.460 5.540 98.740 1.260 107.81 224 0.875 bicubic
149 ssl_resnet50 94.450 5.550 98.920 1.080 25.56 224 0.875 bilinear
150 tf_efficientnet_el 94.410 5.590 98.710 1.290 10.59 300 0.904 bicubic
efficientnet_el_pruned 94.400 5.600 98.740 1.260 10.59 300 0.904 bicubic
151 vit_deit_small_patch16_224 94.400 5.600 98.690 1.310 22.05 224 0.900 bicubic
152 efficientnet_el_pruned 94.400 5.600 98.740 1.260 10.59 300 0.904 bicubic
153 inception_v4 94.380 5.620 98.580 1.420 42.68 299 0.875 bicubic
154 legacy_seresnext101_32x4d 94.370 5.630 98.650 1.350 48.96 224 0.875 bilinear
155 tf_efficientnet_b2 94.360 5.640 98.610 1.390 9.11 260 0.890 bicubic
156 gluon_seresnext50_32x4d 94.340 5.660 98.610 1.390 27.56 224 0.875 bicubic
157 dpn107 94.310 5.690 98.480 1.520 86.92 224 0.875 bicubic
158 ecaresnet26t 94.310 5.690 98.720 1.280 16.01 320 0.950 bicubic
159 resnetrs50 94.310 5.690 98.640 1.360 35.69 224 0.910 bicubic
160 xception71 94.280 5.720 98.640 1.360 42.34 299 0.903 bicubic
gluon_xception65 94.260 5.740 98.570 1.430 39.92 299 0.903 bicubic
161 resnet50d 94.260 5.740 98.720 1.280 25.58 224 0.875 bicubic
162 skresnext50_32x4d 94.260 5.740 98.460 1.540 27.48 224 0.875 bicubic
163 cait_xxs36_224 94.260 5.740 98.720 1.280 17.30 224 1.000 bicubic
164 gluon_xception65 94.260 5.740 98.570 1.430 39.92 299 0.903 bicubic
165 regnetx_120 94.240 5.760 98.650 1.350 46.11 224 0.875 bicubic
166 dpn92 94.230 5.770 98.730 1.270 37.67 224 0.875 bicubic
gluon_resnet101_v1d 94.220 5.780 98.550 1.450 44.57 224 0.875 bicubic
167 ecaresnet50d_pruned 94.220 5.780 98.730 1.270 19.94 224 0.875 bicubic
168 gluon_resnet101_v1d 94.220 5.780 98.550 1.450 44.57 224 0.875 bicubic
169 tf_efficientnet_lite3 94.200 5.800 98.640 1.360 8.20 300 0.904 bilinear
170 mixnet_xl 94.190 5.810 98.340 1.660 11.90 224 0.875 bicubic
171 resnext50d_32x4d 94.180 5.820 98.570 1.430 25.05 224 0.875 bicubic
172 regnety_080 94.170 5.830 98.680 1.320 39.18 224 0.875 bicubic
ens_adv_inception_resnet_v2 94.160 5.840 98.600 1.400 55.84 299 0.897 bicubic
173 gluon_resnet152_v1c 94.160 5.840 98.640 1.360 60.21 224 0.875 bicubic
174 ens_adv_inception_resnet_v2 94.160 5.840 98.600 1.400 55.84 299 0.897 bicubic
175 regnety_064 94.150 5.850 98.730 1.270 30.58 224 0.875 bicubic
176 efficientnet_b2_pruned 94.140 5.860 98.530 1.470 8.31 260 0.890 bicubic
nf_regnet_b1 94.130 5.870 98.630 1.370 10.22 288 0.900 bicubic
177 dpn98 94.130 5.870 98.570 1.430 61.57 224 0.875 bicubic
178 nf_regnet_b1 94.130 5.870 98.630 1.370 10.22 288 0.900 bicubic
179 regnetx_160 94.120 5.880 98.750 1.250 54.28 224 0.875 bicubic
180 resnext50_32x4d 94.100 5.900 98.350 1.650 25.03 224 0.875 bicubic
181 ese_vovnet39b 94.090 5.910 98.660 1.340 24.57 224 0.875 bicubic
182 gluon_resnet152_v1b 94.080 5.920 98.450 1.550 60.19 224 0.875 bicubic
183 coat_lite_mini 94.060 5.940 98.560 1.440 11.01 224 0.900 bicubic
184 dpn131 94.010 5.990 98.720 1.280 79.25 224 0.875 bicubic
185 hrnet_w64 94.010 5.990 98.610 1.390 128.06 224 0.875 bilinear
186 resnetblur50 93.960 6.040 98.590 1.410 25.56 224 0.875 bicubic
187 dla102x2 93.950 6.050 98.490 1.510 41.28 224 0.875 bilinear
188 hrnet_w48 93.920 6.080 98.610 1.390 77.47 224 0.875 bilinear
tf_efficientnet_cc_b1_8e 93.900 6.100 98.260 1.740 39.72 240 0.882 bicubic
189 rexnet_130 93.900 6.100 98.400 1.600 7.56 224 0.875 bicubic
190 tf_efficientnet_cc_b1_8e 93.900 6.100 98.260 1.740 39.72 240 0.882 bicubic
191 regnetx_064 93.890 6.110 98.630 1.370 26.21 224 0.875 bicubic
192 regnetx_080 93.870 6.130 98.520 1.480 39.57 224 0.875 bicubic
193 regnety_040 93.860 6.140 98.650 1.350 20.65 224 0.875 bicubic
194 repvgg_b2g4 93.860 6.140 98.590 1.410 61.76 224 0.875 bilinear
195 efficientnet_em 93.840 6.160 98.810 1.190 6.90 240 0.882 bicubic
196 resnext101_32x8d 93.830 6.170 98.580 1.420 88.79 224 0.875 bilinear
gluon_resnext50_32x4d 93.810 6.190 98.410 1.590 25.03 224 0.875 bicubic
pit_xs_distilled_224 93.810 6.190 98.670 1.330 11.00 224 0.900 bicubic
197 resnet50 93.810 6.190 98.390 1.610 25.56 224 0.875 bicubic
198 pit_xs_distilled_224 93.810 6.190 98.670 1.330 11.00 224 0.900 bicubic
199 gluon_resnext50_32x4d 93.810 6.190 98.410 1.590 25.03 224 0.875 bicubic
200 gluon_resnet50_v1d 93.770 6.230 98.390 1.610 25.58 224 0.875 bicubic
201 xception65 93.760 6.240 98.370 1.630 39.92 299 0.903 bicubic
res2net101_26w_4s 93.750 6.250 98.310 1.690 45.21 224 0.875 bilinear
202 gluon_resnet101_v1b 93.750 6.250 98.380 1.620 44.55 224 0.875 bicubic
203 res2net101_26w_4s 93.750 6.250 98.310 1.690 45.21 224 0.875 bilinear
204 cspresnet50 93.740 6.260 98.640 1.360 21.62 256 0.887 bilinear
205 legacy_seresnext50_32x4d 93.730 6.270 98.580 1.420 27.56 224 0.875 bilinear
206 wide_resnet101_2 93.720 6.280 98.540 1.460 126.89 224 0.875 bilinear
dpn68b 93.690 6.310 98.510 1.490 12.61 224 0.875 bicubic
207 tf_efficientnet_b1_ap 93.690 6.310 98.360 1.640 7.79 240 0.882 bicubic
208 dpn68b 93.690 6.310 98.510 1.490 12.61 224 0.875 bicubic
209 gluon_resnet101_v1c 93.670 6.330 98.420 1.580 44.57 224 0.875 bicubic
210 tf_efficientnet_b0_ns 93.630 6.370 98.640 1.360 5.29 224 0.875 bicubic
211 gluon_resnet50_v1s 93.620 6.380 98.460 1.540 25.68 224 0.875 bicubic
212 cait_xxs24_224 93.600 6.400 98.440 1.560 11.96 224 1.000 bicubic
213 regnetx_040 93.560 6.440 98.540 1.460 22.12 224 0.875 bicubic
214 hrnet_w44 93.550 6.450 98.700 1.300 67.06 224 0.875 bilinear
215 res2net50_26w_8s 93.540 6.460 98.260 1.740 48.40 224 0.875 bilinear
216 hrnet_w32 93.530 6.470 98.450 1.550 41.23 224 0.875 bilinear
217 dla102x 93.520 6.480 98.510 1.490 26.31 224 0.875 bilinear
repvgg_b2 93.500 6.500 98.730 1.270 89.02 224 0.875 bilinear
218 tf_efficientnet_b1 93.500 6.500 98.360 1.640 7.79 240 0.882 bicubic
219 repvgg_b2 93.500 6.500 98.730 1.270 89.02 224 0.875 bilinear
220 hrnet_w40 93.490 6.510 98.580 1.420 57.56 224 0.875 bilinear
221 gluon_inception_v3 93.460 6.540 98.570 1.430 23.83 299 0.875 bicubic
222 xception 93.460 6.540 98.530 1.470 22.86 299 0.897 bicubic
225 res2net50_26w_6s 93.410 6.590 98.280 1.720 37.05 224 0.875 bilinear
226 legacy_seresnet152 93.400 6.600 98.350 1.650 66.82 224 0.875 bilinear
227 dla169 93.340 6.660 98.600 1.400 53.39 224 0.875 bilinear
repvgg_b1 93.330 6.670 98.510 1.490 57.42 224 0.875 bilinear
228 resnest26d 93.330 6.670 98.630 1.370 17.07 224 0.875 bilinear
229 repvgg_b1 93.330 6.670 98.510 1.490 57.42 224 0.875 bilinear
230 tf_inception_v3 93.320 6.680 98.030 1.970 23.83 299 0.875 bicubic
231 tf_mixnet_l 93.310 6.690 98.030 1.970 7.33 224 0.875 bicubic
232 selecsls60b 93.300 6.700 98.280 1.720 32.77 224 0.875 bicubic
233 tv_resnet152 93.300 6.700 98.390 1.610 60.19 224 0.875 bilinear
234 legacy_seresnet101 93.280 6.720 98.510 1.490 49.33 224 0.875 bilinear
235 efficientnet_b1 93.260 93.250 6.740 6.750 98.170 98.290 1.830 1.710 7.79 240 256 0.875 1.000 bicubic
236 coat_lite_tiny 93.240 6.760 98.260 1.740 5.72 224 0.900 bicubic
237 hrnet_w30 93.200 6.800 98.410 1.590 37.71 224 0.875 bilinear
238 dla60_res2net 93.180 6.820 98.420 1.580 20.85 224 0.875 bilinear
239 dla60_res2next 93.180 6.820 98.410 1.590 17.03 224 0.875 bilinear
242 regnetx_032 93.120 6.880 98.390 1.610 15.30 224 0.875 bicubic
243 pit_xs_224 93.110 6.890 98.310 1.690 10.62 224 0.900 bicubic
244 dla102 93.060 6.940 98.540 1.460 33.27 224 0.875 bilinear
245 gluon_resnet50_v1c 93.030 6.970 98.390 1.610 25.58 224 0.875 bicubic
246 regnety_016 93.030 6.970 98.360 1.640 11.20 224 0.875 bicubic
247 rexnet_100 93.030 6.970 98.190 1.810 4.80 224 0.875 bicubic
248 selecsls60 93.030 6.970 98.300 1.700 30.67 224 0.875 bicubic
regnety_016 93.030 6.970 98.360 1.640 11.20 224 0.875 bicubic
gluon_resnet50_v1c 93.030 6.970 98.390 1.610 25.58 224 0.875 bicubic
249 repvgg_b1g4 92.980 7.020 98.430 1.570 39.97 224 0.875 bilinear
250 legacy_seresnet50 92.960 7.040 98.190 1.810 28.09 224 0.875 bilinear
251 hardcorenas_f 92.950 7.050 98.160 1.840 8.20 224 0.875 bilinear
271 hardcorenas_e 92.570 7.430 98.110 1.890 8.07 224 0.875 bilinear
272 res2net50_48w_2s 92.550 7.450 98.080 1.920 25.29 224 0.875 bilinear
273 gluon_resnet50_v1b 92.540 7.460 98.170 1.830 25.56 224 0.875 bicubic
densenet161 92.500 7.500 98.290 1.710 28.68 224 0.875 bicubic
274 res2net50_26w_4s 92.500 7.500 98.060 1.940 25.70 224 0.875 bilinear
275 densenet161 92.500 7.500 98.290 1.710 28.68 224 0.875 bicubic
276 mixnet_m 92.430 7.570 97.870 2.130 5.01 224 0.875 bicubic
mobilenetv2_120d 92.400 7.600 98.050 1.950 5.83 224 0.875 bicubic
277 hardcorenas_d 92.400 7.600 98.070 1.930 7.50 224 0.875 bilinear
278 mobilenetv2_120d 92.400 7.600 98.050 1.950 5.83 224 0.875 bicubic
279 skresnet34 92.390 7.610 98.150 1.850 22.28 224 0.875 bicubic
280 tf_mixnet_m 92.330 7.670 97.890 2.110 5.01 224 0.875 bicubic
281 hrnet_w18 92.320 7.680 98.240 1.760 21.30 224 0.875 bilinear
282 ese_vovnet19b_dw 92.290 7.710 98.090 1.910 6.54 224 0.875 bicubic
283 selecsls42b 92.280 7.720 98.150 1.850 32.46 224 0.875 bicubic
284 mobilenetv3_large_100_miil 92.260 7.740 97.640 2.360 5.48 224 0.875 bilinear
285 tf_efficientnet_b0 92.250 7.750 98.000 2.000 5.29 224 0.875 bicubic
286 dla60 92.230 7.770 98.110 1.890 22.04 224 0.875 bilinear
287 tf_efficientnet_b0_ap 92.200 7.800 98.020 1.980 5.29 224 0.875 bicubic
295 densenet169 91.930 8.070 98.100 1.900 14.15 224 0.875 bicubic
296 densenetblur121d 91.910 8.090 98.070 1.930 8.00 224 0.875 bicubic
297 tv_resnet50 91.880 8.120 98.040 1.960 25.56 224 0.875 bilinear
298 mixer_b16_224 91.870 8.130 97.250 2.750 59.88 224 0.875 bicubic
299 mixnet_s 91.830 8.170 97.690 2.310 4.13 224 0.875 bicubic
300 mobilenetv2_140 91.830 8.170 97.860 2.140 6.11 224 0.875 bicubic
301 hardcorenas_b 91.770 8.230 97.780 2.220 5.18 224 0.875 bilinear
328 swsl_resnet18 90.690 9.310 97.700 2.300 11.69 224 0.875 bilinear
329 mnasnet_100 90.510 9.490 97.470 2.530 4.38 224 0.875 bicubic
330 regnety_004 90.500 9.500 97.540 2.460 4.34 224 0.875 bicubic
regnetx_006 90.350 9.650 97.430 2.570 6.20 224 0.875 bicubic
331 spnasnet_100 90.350 9.650 97.190 2.810 4.42 224 0.875 bilinear
332 regnetx_006 90.350 9.650 97.430 2.570 6.20 224 0.875 bicubic
333 ssl_resnet18 90.220 9.780 97.550 2.450 11.69 224 0.875 bilinear
334 vgg16_bn 90.090 9.910 97.370 2.630 138.37 224 0.875 bilinear
335 vgg19_bn 90.080 9.920 97.580 2.420 143.68 224 0.875 bilinear
336 ghostnet_100 90.020 9.980 97.370 2.630 5.18 224 0.875 bilinear
337 pit_ti_224 89.940 10.060 97.450 2.550 4.85 224 0.900 bicubic
338 tv_resnet34 89.940 10.060 97.340 2.660 21.80 224 0.875 bilinear
339 tf_mobilenetv3_large_075 89.680 10.320 97.210 2.790 3.99 224 0.875 bilinear
352 vgg11_bn 87.500 12.500 96.820 3.180 132.87 224 0.875 bilinear
353 resnet18 87.390 12.610 96.290 3.710 11.69 224 0.875 bilinear
354 regnety_002 87.380 12.620 96.590 3.410 3.16 224 0.875 bicubic
355 mixer_l16_224 87.150 12.850 93.520 6.480 208.20 224 0.875 bicubic
356 vgg13 87.050 12.950 96.320 3.680 133.05 224 0.875 bilinear
357 vgg11 86.550 13.450 96.280 3.720 132.86 224 0.875 bilinear
358 dla60x_c 86.290 13.710 96.160 3.840 1.32 224 0.875 bilinear

623
results/results-imagenet-a.csv
View File

@ -4,338 +4,361 @@ tf_efficientnet_l2_ns_475,83.373,16.627,95.453,4.547,480.31,475,0.936,bicubic,-1
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_efficientnet_b6_ns,62.267,37.733,85.173,14.827,43.04,528,0.942,bicubic,-35.363,-14.407,+2
dm_nfnet_f6,62.253,37.747,84.667,15.333,438.36,576,0.956,bicubic,-35.477,-14.913,-1
dm_nfnet_f5,61.587,38.413,84.027,15.973,377.21,544,0.954,bicubic,-36.013,-15.523,+2
ig_resnext101_32x48d,61.013,38.987,83.347,16.653,828.41,224,0.875,bilinear,-36.607,-16.353,0
swin_large_patch4_window7_224,60.893,39.107,85.840,14.160,196.53,224,0.900,bicubic,-36.757,-13.740,-3
resnetv2_152x4_bitm,60.733,39.267,83.600,16.400,936.53,480,1.000,bilinear,-36.757,-16.000,+2
dm_nfnet_f4,60.720,39.280,83.427,16.573,316.07,512,0.951,bicubic,-36.850,-16.093,-1
tf_efficientnet_b5_ns,60.320,39.680,84.493,15.507,30.39,456,0.934,bicubic,-37.180,-15.137,-1
dm_nfnet_f3,58.373,41.627,82.360,17.640,254.92,416,0.940,bicubic,-38.987,-17.220,0
ig_resnext101_32x32d,58.093,41.907,80.653,19.347,468.53,224,0.875,bilinear,-39.267,-19.027,0
resnetv2_152x2_bitm,54.973,45.027,82.813,17.187,236.34,480,1.000,bilinear,-42.177,-16.777,+5
vit_base_r50_s16_384,54.627,45.373,81.213,18.787,98.95,384,1.000,bicubic,-42.553,-18.347,+3
vit_large_patch16_384,53.867,46.133,80.320,19.680,304.72,384,1.000,bicubic,-43.243,-19.340,+4
resnetv2_101x3_bitm,53.813,46.187,81.093,18.907,387.93,480,1.000,bilinear,-43.237,-18.427,+7
ig_resnext101_32x16d,53.067,46.933,76.907,23.093,194.03,224,0.875,bilinear,-43.753,-22.683,+12
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_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
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
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
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,+8
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,+4
vit_base_patch16_384,50.613,49.387,78.200,21.800,86.86,384,1.000,bicubic,-46.087,-21.310,+12
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,-8
resnest269e,48.187,51.813,74.333,25.667,110.93,416,0.928,bicubic,-48.333,-25.017,+15
resnetv2_50x3_bitm,47.787,52.213,77.627,22.373,217.32,480,1.000,bilinear,-48.983,-21.823,+5
tf_efficientnet_b8_ap,46.893,53.107,76.507,23.493,87.41,672,0.954,bicubic,-50.217,-23.133,-7
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_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,+10
ecaresnet269d,45.893,54.107,75.133,24.867,102.09,352,1.000,bicubic,-51.187,-24.487,-8
tf_efficientnet_b7_ap,45.373,54.627,74.213,25.787,66.35,600,0.949,bicubic,-51.827,-25.327,-16
ig_resnext101_32x8d,45.320,54.680,70.867,29.133,88.79,224,0.875,bilinear,-51.000,-28.563,+14
resnest200e,44.147,55.853,73.467,26.533,70.20,320,0.909,bicubic,-52.463,-25.883,+5
tresnet_xl_448,43.480,56.520,72.453,27.547,78.44,448,0.875,bilinear,-52.490,-26.677,+21
tf_efficientnet_b7,42.960,57.040,73.133,26.867,66.35,600,0.949,bicubic,-54.050,-26.387,-11
swsl_resnext101_32x4d,41.560,58.440,71.760,28.240,44.18,224,0.875,bilinear,-54.860,-27.710,+5
tf_efficientnet_b6_ap,40.800,59.200,71.627,28.373,43.04,528,0.942,bicubic,-56.280,-27.843,-16
tresnet_l_448,40.200,59.800,69.893,30.107,55.99,448,0.875,bilinear,-55.660,-29.317,+23
vit_deit_base_patch16_384,40.173,59.827,70.760,29.240,86.86,384,1.000,bicubic,-55.977,-28.380,+10
resnetv2_101x1_bitm,39.307,60.693,71.493,28.507,44.54,480,1.000,bilinear,-56.783,-27.697,+13
vit_large_patch32_384,38.933,61.067,68.920,31.080,306.63,384,1.000,bicubic,-56.897,-30.230,+22
resnet200d,38.147,61.853,68.613,31.387,64.69,320,1.000,bicubic,-58.573,-30.717,-10
eca_nfnet_l1,38.107,61.893,71.293,28.707,41.41,320,1.000,bicubic,-58.593,-27.977,-10
seresnet152d,37.640,62.360,69.480,30.520,66.84,320,1.000,bicubic,-59.130,-29.950,-14
regnety_160,36.747,63.253,69.107,30.893,83.59,288,1.000,bicubic,-59.603,-30.223,-1
pit_b_distilled_224,35.627,64.373,69.120,30.880,74.79,224,0.900,bicubic,-61.053,-30.230,-11
tf_efficientnet_b3_ns,35.520,64.480,67.773,32.227,12.23,300,0.904,bicubic,-60.870,-31.577,-5
vit_large_patch16_224,35.493,64.507,64.427,35.573,304.33,224,0.900,bicubic,-60.457,-34.813,+8
tf_efficientnet_b6,35.213,64.787,67.720,32.280,43.04,528,0.942,bicubic,-61.457,-31.650,-12
tf_efficientnet_b5_ap,34.787,65.213,67.493,32.507,30.39,456,0.934,bicubic,-61.893,-31.967,-14
resnet152d,34.320,65.680,65.907,34.093,60.21,320,1.000,bicubic,-62.040,-33.483,-8
tresnet_m_448,34.107,65.893,64.493,35.507,31.39,448,0.875,bilinear,-60.883,-34.487,+44
vit_base_patch32_384,33.613,66.387,65.240,34.760,88.30,384,1.000,bicubic,-62.197,-33.910,+11
pit_b_224,33.173,66.827,62.320,37.680,73.76,224,0.900,bicubic,-62.467,-36.340,+16
swsl_resnext50_32x4d,33.013,66.987,65.067,34.933,25.03,224,0.875,bilinear,-62.857,-34.183,+5
ssl_resnext101_32x16d,32.600,67.400,64.000,36.000,194.03,224,0.875,bilinear,-63.200,-35.180,+9
swin_small_patch4_window7_224,32.600,67.400,65.440,34.560,49.61,224,0.900,bicubic,-63.310,-33.580,+1
vit_base_patch16_224,32.053,67.947,61.573,38.427,86.57,224,0.900,bicubic,-63.277,-37.427,+22
tf_efficientnet_b5,31.840,68.160,65.293,34.707,30.39,456,0.934,bicubic,-64.510,-34.017,-14
resnest101e,31.413,68.587,64.360,35.640,48.28,256,0.875,bilinear,-64.447,-34.760,+2
dm_nfnet_f0,31.280,68.720,63.347,36.653,71.49,256,0.900,bicubic,-64.860,-35.893,-11
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
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
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
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
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,-12
ssl_resnext101_32x8d,29.040,70.960,60.973,39.027,88.79,224,0.875,bilinear,-66.430,-38.137,+11
resnet101d,28.987,71.013,62.053,37.947,44.57,320,1.000,bicubic,-67.303,-37.177,-18
vit_deit_base_patch16_224,27.440,72.560,58.893,41.107,86.57,224,0.900,bicubic,-68.000,-39.947,+12
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,-16
tf_efficientnet_b4,26.293,73.707,60.107,39.893,19.34,380,0.922,bicubic,-69.607,-39.063,-10
tf_efficientnet_b4_ap,26.240,73.760,60.227,39.773,19.34,380,0.922,bicubic,-69.920,-39.053,-22
regnety_032,26.213,73.787,60.987,39.013,19.44,288,1.000,bicubic,-69.757,-38.203,-17
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,-40.143,0
eca_nfnet_l0,25.013,74.987,60.360,39.640,24.14,288,1.000,bicubic,-70.917,-38.850,-17
tnt_s_patch16_224,24.733,75.267,58.187,41.813,23.76,224,0.900,bicubic,-70.307,-40.693,+18
ssl_resnext101_32x4d,24.173,75.827,57.413,42.587,44.18,224,0.875,bilinear,-71.267,-41.717,0
tf_efficientnet_b2_ns,24.013,75.987,57.293,42.707,9.11,260,0.890,bicubic,-71.757,-41.827,-11
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
efficientnet_b3,23.453,76.547,56.587,43.413,12.23,300,0.904,bicubic,-72.127,-42.513,-8
pnasnet5large,23.333,76.667,53.640,46.360,86.06,331,0.911,bicubic,-72.377,-45.280,-12
efficientnet_b3a,23.213,76.787,55.960,44.040,12.23,320,1.000,bicubic,-72.497,-43.080,-14
pit_s_distilled_224,22.360,77.640,57.120,42.880,24.04,224,0.900,bicubic,-72.880,-41.930,+1
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,+35
efficientnet_v2s,21.013,78.987,52.840,47.160,23.94,224,1.000,bicubic,-74.527,-46.120,-13
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,-12
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,+28
repvgg_b3,19.107,80.893,50.253,49.747,123.09,224,0.875,bilinear,-75.463,-48.527,+26
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,-47.277,+39
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,-47.957,-5
ecaresnet50d,18.227,81.773,51.880,48.120,25.58,224,0.875,bicubic,-76.403,-47.010,+15
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,-17
inception_v4,17.267,82.733,45.920,54.080,42.68,299,0.875,bicubic,-77.113,-52.660,+31
tf_efficientnet_b3_ap,17.187,82.813,49.680,50.320,12.23,300,0.904,bicubic,-78.133,-49.220,-24
tf_efficientnet_b3,17.000,83.000,49.267,50.733,12.23,300,0.904,bicubic,-78.010,-49.643,-11
xception71,17.000,83.000,45.520,54.480,42.34,299,0.903,bicubic,-77.280,-53.120,+34
gluon_resnext101_64x4d,16.853,83.147,44.213,55.787,83.46,224,0.875,bicubic,-77.817,-54.437,+3
tresnet_l,16.600,83.400,49.920,50.080,55.99,224,0.875,bilinear,-78.690,-49.090,-27
gluon_resnet152_v1d,16.573,83.427,44.280,55.720,60.21,224,0.875,bicubic,-78.167,-54.460,-3
gluon_resnet152_v1s,16.573,83.427,44.533,55.467,60.32,224,0.875,bicubic,-78.467,-54.297,-17
inception_resnet_v2,16.573,83.427,44.960,55.040,55.84,299,0.897,bicubic,-77.967,-53.830,+10
gluon_xception65,16.440,83.560,46.027,53.973,39.92,299,0.903,bicubic,-77.820,-52.543,+29
gernet_l,16.373,83.627,47.213,52.787,31.08,256,0.875,bilinear,-78.717,-51.687,-27
wide_resnet50_2,16.280,83.720,48.347,51.653,68.88,224,0.875,bicubic,-78.800,-50.623,-26
ens_adv_inception_resnet_v2,16.240,83.760,43.640,56.360,55.84,299,0.897,bicubic,-77.920,-54.960,+37
repvgg_b3g4,16.213,83.787,47.653,52.347,83.83,224,0.875,bilinear,-78.307,-51.317,+8
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
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
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
ssl_resnet50,15.960,84.040,49.467,50.533,25.56,224,0.875,bilinear,-78.490,-49.453,+12
regnety_320,15.627,84.373,44.827,55.173,145.05,224,0.875,bicubic,-78.913,-54.023,+3
ecaresnet101d_pruned,15.600,84.400,48.027,51.973,24.88,224,0.875,bicubic,-79.480,-50.953,-33
ecaresnet26t,15.467,84.533,47.920,52.080,16.01,320,0.950,bicubic,-78.843,-50.800,+18
skresnext50_32x4d,15.373,84.627,44.493,55.507,27.48,224,0.875,bicubic,-78.887,-53.967,+21
ecaresnetlight,15.160,84.840,45.827,54.173,30.16,224,0.875,bicubic,-79.610,-52.973,-19
rexnet_150,14.720,85.280,46.907,53.093,9.73,224,0.875,bicubic,-79.760,-51.883,+4
efficientnet_el_pruned,14.480,85.520,46.120,53.880,10.59,300,0.904,bicubic,-79.920,-52.620,+7
efficientnet_b2a,14.440,85.560,46.080,53.920,9.11,288,1.000,bicubic,-80.170,-52.630,-10
legacy_seresnext101_32x4d,14.147,85.853,42.973,57.027,48.96,224,0.875,bilinear,-80.223,-55.677,+8
seresnet50,14.147,85.853,45.467,54.533,28.09,224,0.875,bicubic,-80.403,-53.283,-8
gernet_m,14.013,85.987,46.067,53.933,21.14,224,0.875,bilinear,-80.607,-52.483,-14
gluon_resnext101_32x4d,13.867,86.133,41.653,58.347,44.18,224,0.875,bicubic,-80.663,-56.977,-7
gluon_seresnext50_32x4d,13.600,86.400,43.760,56.240,27.56,224,0.875,bicubic,-80.740,-54.850,+6
repvgg_b2g4,13.440,86.560,43.787,56.213,61.76,224,0.875,bilinear,-80.420,-54.803,+40
ese_vovnet39b,13.320,86.680,43.813,56.187,24.57,224,0.875,bicubic,-80.770,-54.847,+27
efficientnet_b2,13.307,86.693,44.440,55.560,9.11,260,0.875,bicubic,-81.393,-54.230,-25
regnetx_320,13.307,86.693,40.720,59.280,107.81,224,0.875,bicubic,-81.153,-58.020,-6
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,-24
gluon_resnet101_v1d,13.160,86.840,41.493,58.507,44.57,224,0.875,bicubic,-81.060,-57.237,+8
mixnet_xl,13.120,86.880,43.253,56.747,11.90,224,0.875,bicubic,-81.070,-55.087,+10
nf_regnet_b1,13.027,86.973,44.413,55.587,10.22,288,0.900,bicubic,-81.103,-54.157,+16
pit_xs_224,12.813,87.187,42.840,57.160,10.62,224,0.900,bicubic,-80.297,-55.470,+78
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
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
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
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
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
tresnet_m,12.600,87.400,41.893,58.107,31.39,224,0.875,bilinear,-82.020,-56.967,-29
regnety_120,12.427,87.573,42.200,57.800,51.82,224,0.875,bicubic,-82.053,-56.610,-17
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,-29
vit_small_patch16_224,12.147,87.853,40.320,59.680,48.75,224,0.900,bicubic,-80.613,-57.610,+88
hrnet_w64,12.027,87.973,40.787,59.213,128.06,224,0.875,bilinear,-81.983,-57.823,+15
cspdarknet53,12.013,87.987,43.253,56.747,27.64,256,0.887,bilinear,-82.647,-55.547,-38
gluon_resnet101_v1s,11.880,88.120,40.973,59.027,44.67,224,0.875,bicubic,-82.840,-57.847,-43
resnet50d,11.693,88.307,42.453,57.547,25.58,224,0.875,bicubic,-82.567,-56.267,-9
dpn92,11.627,88.373,40.267,59.733,37.67,224,0.875,bicubic,-82.603,-58.463,-7
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
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
dla102x2,11.573,88.427,41.293,58.707,41.28,224,0.875,bilinear,-82.377,-57.197,+11
regnety_080,11.413,88.587,40.613,59.387,39.18,224,0.875,bicubic,-82.757,-58.067,-4
efficientnet_b2_pruned,11.360,88.640,42.027,57.973,8.31,260,0.890,bicubic,-82.780,-56.503,-1
tf_efficientnet_el,11.333,88.667,42.040,57.960,10.59,300,0.904,bicubic,-83.077,-56.670,-26
gluon_resnet152_v1c,11.093,88.907,37.120,62.880,60.21,224,0.875,bicubic,-83.067,-61.520,-5
dpn107,11.080,88.920,38.693,61.307,86.92,224,0.875,bicubic,-83.230,-59.787,-21
hrnet_w48,11.080,88.920,40.320,59.680,77.47,224,0.875,bilinear,-82.840,-58.290,+6
ecaresnet50d_pruned,11.027,88.973,41.947,58.053,19.94,224,0.875,bicubic,-83.193,-56.603,-14
adv_inception_v3,11.013,88.987,36.720,63.280,23.83,299,0.875,bicubic,-81.867,-61.420,+67
tf_efficientnet_b0_ns,10.933,89.067,40.067,59.933,5.29,224,0.875,bicubic,-82.697,-58.573,+25
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
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,-6
dpn131,10.787,89.213,37.200,62.800,79.25,224,0.875,bicubic,-83.223,-61.520,-4
tf_efficientnet_b2_ap,10.533,89.467,40.107,59.893,9.11,260,0.890,bicubic,-83.957,-58.513,-42
resnext50d_32x4d,10.413,89.587,39.733,60.267,25.05,224,0.875,bicubic,-83.767,-58.837,-18
rexnet_130,10.400,89.600,41.547,58.453,7.56,224,0.875,bicubic,-83.500,-56.713,-1
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
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,+3
regnetx_160,10.147,89.853,38.000,62.000,54.28,224,0.875,bicubic,-83.973,-60.750,-14
dpn98,10.133,89.867,36.587,63.413,61.57,224,0.875,bicubic,-83.997,-62.043,-16
cspresnext50,10.120,89.880,40.373,59.627,20.57,224,0.875,bilinear,-84.360,-58.307,-48
legacy_seresnext50_32x4d,10.107,89.893,39.200,60.800,27.56,224,0.875,bilinear,-83.623,-59.380,+8
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
xception,9.987,90.013,38.027,61.973,22.86,299,0.897,bicubic,-83.473,-60.503,+22
regnety_064,9.947,90.053,39.067,60.933,30.58,224,0.875,bicubic,-84.203,-59.663,-24
dpn68b,9.787,90.213,38.053,61.947,12.61,224,0.875,bicubic,-83.903,-60.457,+6
gluon_resnet152_v1b,9.747,90.253,36.067,63.933,60.19,224,0.875,bicubic,-84.333,-62.383,-19
tf_efficientnet_lite3,9.667,90.333,39.000,61.000,8.20,300,0.904,bilinear,-84.533,-59.640,-33
tf_efficientnet_b2,9.653,90.347,38.880,61.120,9.11,260,0.890,bicubic,-84.707,-59.730,-46
tf_efficientnet_cc_b1_8e,9.573,90.427,36.773,63.227,39.72,240,0.882,bicubic,-84.327,-61.627,-16
res2net101_26w_4s,9.520,90.480,35.027,64.973,45.21,224,0.875,bilinear,-84.230,-63.353,-4
legacy_seresnet152,9.347,90.653,37.413,62.587,66.82,224,0.875,bilinear,-84.053,-60.937,+18
cspresnet50,9.253,90.747,39.640,60.360,21.62,256,0.887,bilinear,-84.487,-59.000,-4
hrnet_w40,9.227,90.773,36.893,63.107,57.56,224,0.875,bilinear,-84.263,-61.687,+10
regnetx_120,9.187,90.813,37.200,62.800,46.11,224,0.875,bicubic,-85.053,-61.450,-44
seresnext26d_32x4d,9.147,90.853,36.840,63.160,16.81,224,0.875,bicubic,-83.553,-61.310,+51
efficientnet_b1,9.120,90.880,37.360,62.640,7.79,240,0.875,bicubic,-84.140,-60.810,+22
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,-21
gluon_resnext50_32x4d,8.947,91.053,36.333,63.667,25.03,224,0.875,bicubic,-84.863,-62.077,-18
rexnet_100,8.893,91.107,36.373,63.627,4.80,224,0.875,bicubic,-84.137,-62.017,+27
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
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
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
mixnet_l,8.853,91.147,36.187,63.813,7.33,224,0.875,bicubic,-84.597,-62.033,+4
dla169,8.640,91.360,36.040,63.960,53.39,224,0.875,bilinear,-84.700,-62.560,+7
hrnet_w30,8.613,91.387,37.040,62.960,37.71,224,0.875,bilinear,-84.587,-61.370,+15
legacy_seresnet101,8.533,91.467,36.013,63.987,49.33,224,0.875,bilinear,-84.747,-62.497,+12
tf_efficientnet_b1_ap,8.453,91.547,35.253,64.747,7.79,240,0.882,bicubic,-85.237,-63.107,-14
repvgg_b2,8.427,91.573,36.467,63.533,89.02,224,0.875,bilinear,-85.073,-62.263,-6
resnetblur50,8.240,91.760,37.400,62.600,25.56,224,0.875,bicubic,-85.720,-61.190,-38
dla102x,8.200,91.800,37.013,62.987,26.31,224,0.875,bilinear,-85.320,-61.497,-9
hrnet_w32,8.040,91.960,37.507,62.493,41.23,224,0.875,bilinear,-85.490,-60.943,-11
res2net50_26w_8s,8.000,92.000,33.853,66.147,48.40,224,0.875,bilinear,-85.540,-64.407,-13
gluon_resnet101_v1c,7.987,92.013,33.360,66.640,44.57,224,0.875,bicubic,-85.683,-65.060,-19
gluon_resnet50_v1d,7.920,92.080,35.000,65.000,25.58,224,0.875,bicubic,-85.850,-63.390,-29
dla60_res2next,7.787,92.213,34.987,65.013,17.03,224,0.875,bilinear,-85.393,-63.423,+7
densenetblur121d,7.720,92.280,34.733,65.267,8.00,224,0.875,bicubic,-84.190,-63.337,+62
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,+3
efficientnet_b1_pruned,7.440,92.560,34.533,65.467,6.33,240,0.882,bicubic,-85.330,-63.507,+22
wide_resnet101_2,7.360,92.640,34.147,65.853,126.89,224,0.875,bilinear,-86.360,-64.393,-29
regnetx_064,7.333,92.667,34.373,65.627,26.21,224,0.875,bicubic,-86.557,-64.257,-45
vit_deit_tiny_patch16_224,7.307,92.693,30.707,69.293,5.72,224,0.900,bicubic,-82.363,-66.743,+98
hardcorenas_e,7.240,92.760,33.293,66.707,8.07,224,0.875,bilinear,-85.330,-64.817,+31
gluon_resnet101_v1b,7.227,92.773,32.773,67.227,44.55,224,0.875,bicubic,-86.523,-65.537,-36
efficientnet_b0,7.213,92.787,34.013,65.987,5.29,224,0.875,bicubic,-85.477,-64.057,+23
gluon_resnet50_v1s,7.213,92.787,33.507,66.493,25.68,224,0.875,bicubic,-86.407,-64.953,-30
tf_mixnet_l,7.147,92.853,31.613,68.387,7.33,224,0.875,bicubic,-86.163,-66.417,-12
tf_efficientnet_b1,7.133,92.867,33.040,66.960,7.79,240,0.882,bicubic,-86.367,-65.320,-25
tf_efficientnet_cc_b0_8e,7.120,92.880,31.787,68.213,24.01,224,0.875,bicubic,-85.710,-66.393,+9
hardcorenas_f,6.827,93.173,34.093,65.907,8.20,224,0.875,bilinear,-86.123,-64.067,+4
ese_vovnet19b_dw,6.733,93.267,33.413,66.587,6.54,224,0.875,bicubic,-85.557,-64.677,+34
selecsls60b,6.733,93.267,33.267,66.733,32.77,224,0.875,bicubic,-86.567,-65.013,-16
efficientnet_es,6.707,93.293,33.840,66.160,5.44,224,0.875,bicubic,-86.433,-64.580,-10
res2net50_26w_6s,6.693,93.307,31.653,68.347,37.05,224,0.875,bilinear,-86.717,-66.627,-25
legacy_seresnext26_32x4d,6.627,93.373,33.253,66.747,16.79,224,0.875,bicubic,-86.013,-64.877,+16
mixnet_m,6.627,93.373,32.053,67.947,5.01,224,0.875,bicubic,-85.803,-65.817,+23
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
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,+24
repvgg_b1,6.467,93.533,33.827,66.173,57.42,224,0.875,bilinear,-86.863,-64.683,-27
hardcorenas_d,6.440,93.560,32.213,67.787,7.50,224,0.875,bilinear,-85.960,-65.837,+21
dla60x,6.427,93.573,34.080,65.920,17.35,224,0.875,bilinear,-86.693,-64.430,-17
resnet34d,6.400,93.600,31.493,68.507,21.82,224,0.875,bicubic,-86.280,-66.817,+7
regnetx_080,6.307,93.693,32.320,67.680,39.57,224,0.875,bicubic,-87.563,-66.200,-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
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,-12
pit_ti_224,6.120,93.880,30.227,69.773,4.85,224,0.900,bicubic,-83.820,-67.223,+71
tv_resnet152,6.040,93.960,32.053,67.947,60.19,224,0.875,bilinear,-87.260,-66.337,-30
tf_efficientnet_cc_b0_4e,5.973,94.027,29.600,70.400,13.31,224,0.875,bicubic,-86.617,-68.480,+4
regnetx_040,5.973,94.027,31.547,68.453,22.12,224,0.875,bicubic,-87.587,-66.993,-51
resnet50,5.933,94.067,29.093,70.907,25.56,224,0.875,bicubic,-87.877,-69.297,-66
dla102,5.880,94.120,32.707,67.293,33.27,224,0.875,bilinear,-87.180,-65.833,-24
regnety_016,5.680,94.320,30.413,69.587,11.20,224,0.875,bicubic,-87.350,-67.777,-22
selecsls60,5.653,94.347,32.507,67.493,30.67,224,0.875,bicubic,-87.377,-65.853,-24
hardcorenas_c,5.640,94.360,30.400,69.600,5.52,224,0.875,bilinear,-86.380,-67.440,+19
res2next50,5.627,94.373,30.867,69.133,24.67,224,0.875,bilinear,-87.213,-67.313,-18
hrnet_w18,5.493,94.507,30.960,69.040,21.30,224,0.875,bilinear,-86.827,-67.280,+8
resnest14d,5.480,94.520,28.547,71.453,10.61,224,0.875,bilinear,-86.240,-69.323,+27
tf_efficientnet_lite2,5.360,94.640,30.907,69.093,6.09,260,0.890,bicubic,-87.290,-67.323,-8
tf_efficientnet_em,5.347,94.653,31.107,68.893,6.90,240,0.882,bicubic,-87.583,-67.083,-24
gernet_s,5.307,94.693,30.133,69.867,8.17,224,0.875,bilinear,-86.833,-68.057,+11
tf_efficientnet_b0_ap,5.307,94.693,28.813,71.187,5.29,224,0.875,bicubic,-86.893,-69.207,+8
densenet121,5.293,94.707,29.907,70.093,7.98,224,0.875,bicubic,-86.277,-68.123,+23
repvgg_b1g4,5.293,94.707,30.813,69.187,39.97,224,0.875,bilinear,-87.687,-67.617,-31
res2net50_26w_4s,5.160,94.840,29.360,70.640,25.70,224,0.875,bilinear,-87.340,-68.700,-6
tf_mixnet_m,5.080,94.920,28.147,71.853,5.01,224,0.875,bicubic,-87.250,-69.743,-2
mobilenetv3_large_100,5.067,94.933,28.187,71.813,5.48,224,0.875,bicubic,-86.253,-69.523,+24
tf_efficientnet_b0,5.067,94.933,28.800,71.200,5.29,224,0.875,bicubic,-87.183,-69.200,0
res2net50_14w_8s,5.040,94.960,28.773,71.227,25.06,224,0.875,bilinear,-87.700,-69.407,-24
hardcorenas_b,4.947,95.053,28.120,71.880,5.18,224,0.875,bilinear,-86.823,-69.660,+13
mobilenetv3_rw,4.907,95.093,29.853,70.147,5.48,224,0.875,bicubic,-86.303,-67.807,+22
mixnet_s,4.907,95.093,28.573,71.427,4.13,224,0.875,bicubic,-86.923,-69.117,+10
gluon_resnet50_v1c,4.893,95.107,28.147,71.853,25.58,224,0.875,bicubic,-88.137,-70.153,-41
hardcorenas_a,4.867,95.133,28.093,71.907,5.26,224,0.875,bilinear,-86.483,-69.767,+16
regnetx_032,4.853,95.147,30.280,69.720,15.30,224,0.875,bicubic,-88.267,-68.110,-49
tv_resnext50_32x4d,4.840,95.160,30.307,69.693,25.03,224,0.875,bilinear,-87.900,-67.963,-30
tv_resnet101,4.707,95.293,29.333,70.667,44.55,224,0.875,bilinear,-88.103,-68.917,-36
densenet161,4.693,95.307,29.547,70.453,28.68,224,0.875,bicubic,-87.807,-68.743,-20
selecsls42b,4.667,95.333,28.587,71.413,32.46,224,0.875,bicubic,-87.613,-69.563,-12
tf_efficientnet_lite1,4.613,95.387,28.387,71.613,5.42,240,0.882,bicubic,-88.007,-69.693,-27
mobilenetv2_120d,4.533,95.467,29.280,70.720,5.83,224,0.875,bicubic,-87.867,-68.790,-20
efficientnet_es_pruned,4.187,95.813,26.520,73.480,5.44,224,0.875,bicubic,-86.993,-71.230,+14
fbnetc_100,4.133,95.867,25.933,74.067,5.57,224,0.875,bilinear,-86.567,-71.277,+25
densenet201,4.120,95.880,27.547,72.453,20.01,224,0.875,bicubic,-88.630,-70.683,-40
gluon_resnet50_v1b,4.120,95.880,26.933,73.067,25.56,224,0.875,bicubic,-88.420,-71.237,-28
resnet26d,4.040,95.960,28.520,71.480,16.01,224,0.875,bicubic,-88.030,-69.440,-13
semnasnet_100,3.960,96.040,26.947,73.053,3.89,224,0.875,bicubic,-87.320,-70.613,+5
repvgg_a2,3.947,96.053,27.267,72.733,28.21,224,0.875,bilinear,-87.993,-70.883,-11
tf_mixnet_s,3.880,96.120,25.253,74.747,4.13,224,0.875,bicubic,-87.630,-72.367,-2
dpn68,3.867,96.133,26.080,73.920,12.61,224,0.875,bicubic,-88.143,-71.970,-15
tf_efficientnet_es,3.827,96.173,26.107,73.893,5.44,224,0.875,bicubic,-88.153,-71.753,-15
regnety_008,3.813,96.187,27.133,72.867,6.26,224,0.875,bicubic,-87.937,-71.047,-8
dla60,3.773,96.227,27.933,72.067,22.04,224,0.875,bilinear,-88.457,-70.177,-24
ssl_resnet18,3.747,96.253,25.427,74.573,11.69,224,0.875,bilinear,-86.473,-72.123,+21
mobilenetv2_140,3.720,96.280,26.747,73.253,6.11,224,0.875,bicubic,-88.110,-71.113,-13
densenet169,3.707,96.293,25.613,74.387,14.15,224,0.875,bicubic,-88.223,-72.487,-18
regnetx_016,3.627,96.373,26.293,73.707,9.19,224,0.875,bicubic,-88.543,-71.917,-26
res2net50_48w_2s,3.587,96.413,26.613,73.387,25.29,224,0.875,bilinear,-88.963,-71.467,-42
spnasnet_100,3.547,96.453,24.293,75.707,4.42,224,0.875,bilinear,-86.803,-72.897,+15
tf_mobilenetv3_large_100,3.547,96.453,25.053,74.947,5.48,224,0.875,bilinear,-87.693,-72.607,-7
regnety_006,3.467,96.533,24.893,75.107,6.06,224,0.875,bicubic,-87.903,-72.817,-12
legacy_seresnet34,3.333,96.667,23.800,76.200,21.96,224,0.875,bilinear,-87.557,-73.780,+3
efficientnet_lite0,3.253,96.747,25.867,74.133,4.65,224,0.875,bicubic,-87.887,-71.763,-6
dla34,3.227,96.773,23.573,76.427,15.74,224,0.875,bilinear,-87.533,-74.087,+3
regnety_004,3.200,96.800,22.653,77.347,4.34,224,0.875,bicubic,-87.300,-74.887,+7
mobilenetv2_110d,3.173,96.827,24.587,75.413,4.52,224,0.875,bicubic,-87.777,-72.963,-3
mnasnet_100,3.120,96.880,24.227,75.773,4.38,224,0.875,bicubic,-87.390,-73.243,+4
tf_efficientnet_lite0,3.080,96.920,22.907,77.093,4.65,224,0.875,bicubic,-87.960,-74.683,-7
skresnet18,3.013,96.987,22.800,77.200,11.96,224,0.875,bicubic,-86.647,-74.430,+13
vgg19_bn,2.947,97.053,23.480,76.520,143.68,224,0.875,bilinear,-87.133,-74.100,+7
resnet34,2.920,97.080,23.680,76.320,21.80,224,0.875,bilinear,-88.210,-73.940,-13
tf_mobilenetv3_large_075,2.867,97.133,21.573,78.427,3.99,224,0.875,bilinear,-86.813,-75.637,+8
hrnet_w18_small_v2,2.720,97.280,23.693,76.307,15.60,224,0.875,bilinear,-88.470,-74.207,-18
gluon_resnet34_v1b,2.667,97.333,21.680,78.320,21.80,224,0.875,bicubic,-88.293,-75.950,-12
vgg16_bn,2.653,97.347,23.773,76.227,138.37,224,0.875,bilinear,-87.437,-73.597,0
regnetx_008,2.653,97.347,22.453,77.547,7.26,224,0.875,bicubic,-88.397,-75.257,-15
vgg16,2.640,97.360,20.427,79.573,138.36,224,0.875,bilinear,-85.910,-76.363,+14
resnet18d,2.600,97.400,21.613,78.387,11.71,224,0.875,bicubic,-86.680,-75.537,+6
tv_densenet121,2.560,97.440,22.667,77.333,7.98,224,0.875,bicubic,-88.330,-75.043,-13
repvgg_b0,2.547,97.453,24.013,75.987,15.82,224,0.875,bilinear,-88.883,-73.977,-32
regnetx_006,2.507,97.493,20.653,79.347,6.20,224,0.875,bicubic,-87.843,-76.777,-8
legacy_seresnet18,2.493,97.507,20.080,79.920,11.78,224,0.875,bicubic,-86.387,-76.900,+7
resnet26,2.480,97.520,22.987,77.013,16.00,224,0.875,bicubic,-88.630,-74.753,-24
regnety_002,2.147,97.853,18.880,81.120,3.16,224,0.875,bicubic,-85.233,-77.710,+10
mobilenetv2_100,2.147,97.853,19.907,80.093,3.50,224,0.875,bicubic,-87.453,-77.233,-1
vgg19,2.107,97.893,20.733,79.267,143.67,224,0.875,bilinear,-86.933,-76.137,0
vgg13_bn,2.093,97.907,20.307,79.693,133.05,224,0.875,bilinear,-86.667,-76.663,+3
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
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
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
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
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
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
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
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
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
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
vgg13_bn,2.093,97.907,20.307,79.693,133.05,224,0.875,bilinear,-86.667,-76.663,+2
tf_mobilenetv3_small_100,2.013,97.987,15.867,84.133,2.54,224,0.875,bilinear,-83.177,-79.903,+12
tf_mobilenetv3_small_075,2.000,98.000,14.813,85.187,2.04,224,0.875,bilinear,-81.520,-79.977,+14
regnetx_004,1.960,98.040,19.173,80.827,5.16,224,0.875,bicubic,-86.940,-77.947,-2
tv_resnet34,1.867,98.133,20.000,80.000,21.80,224,0.875,bilinear,-88.073,-77.340,-12
regnetx_004,1.960,98.040,19.173,80.827,5.16,224,0.875,bicubic,-86.940,-77.947,-3
tv_resnet34,1.867,98.133,20.000,80.000,21.80,224,0.875,bilinear,-88.073,-77.340,-13
vgg13,1.867,98.133,17.960,82.040,133.05,224,0.875,bilinear,-85.183,-78.360,+4
dla46x_c,1.760,98.240,16.480,83.520,1.07,224,0.875,bilinear,-82.490,-78.790,+8
vgg11_bn,1.720,98.280,18.093,81.907,132.87,224,0.875,bilinear,-85.780,-78.727,-1
tf_mobilenetv3_large_minimal_100,1.627,98.373,17.120,82.880,3.92,224,0.875,bilinear,-87.343,-79.740,-8
vgg11_bn,1.720,98.280,18.093,81.907,132.87,224,0.875,bilinear,-85.780,-78.727,-2
tf_mobilenetv3_large_minimal_100,1.627,98.373,17.120,82.880,3.92,224,0.875,bilinear,-87.343,-79.740,-9
dla60x_c,1.613,98.387,18.040,81.960,1.32,224,0.875,bilinear,-84.677,-78.120,+2
vgg11,1.560,98.440,16.227,83.773,132.86,224,0.875,bilinear,-84.990,-80.053,0
gluon_resnet18_v1b,1.547,98.453,16.613,83.387,11.69,224,0.875,bicubic,-86.853,-80.067,-6
hrnet_w18_small,1.533,98.467,18.120,81.880,13.19,224,0.875,bilinear,-87.517,-78.990,-14
gluon_resnet18_v1b,1.547,98.453,16.613,83.387,11.69,224,0.875,bicubic,-86.853,-80.067,-7
hrnet_w18_small,1.533,98.467,18.120,81.880,13.19,224,0.875,bilinear,-87.517,-78.990,-15
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,-8
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,-64
tv_resnet50,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.880,-83.587,-67

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
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 64.480 35.520 87.493 12.507 87.90 384 1.000 bicubic -33.410 -12.217 0
7 tf_efficientnet_b6_ns cait_m48_448 62.267 62.373 37.733 37.627 85.173 86.453 14.827 13.547 43.04 356.46 528 448 0.942 1.000 bicubic -35.363 -35.107 -14.407 -13.097 +2 +8
8 dm_nfnet_f6 tf_efficientnet_b6_ns 62.253 62.267 37.747 37.733 84.667 85.173 15.333 14.827 438.36 43.04 576 528 0.956 0.942 bicubic -35.477 -35.363 -14.913 -14.407 -1 +1
9 dm_nfnet_f5 dm_nfnet_f6 61.587 62.253 38.413 37.747 84.027 84.667 15.973 15.333 377.21 438.36 544 576 0.954 0.956 bicubic -36.013 -35.477 -15.523 -14.913 +2 -2
10 ig_resnext101_32x48d dm_nfnet_f5 61.013 61.587 38.987 38.413 83.347 84.027 16.653 15.973 828.41 377.21 224 544 0.875 0.954 bilinear bicubic -36.607 -36.013 -16.353 -15.523 0 +1
11 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 -3 -1
12 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 +2 -4
13 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 -1 +1
14 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 -1 -2
15 dm_nfnet_f3 tf_efficientnet_b5_ns 58.373 60.320 41.627 39.680 82.360 84.493 17.640 15.507 254.92 30.39 416 456 0.940 0.934 bicubic -38.987 -37.180 -17.220 -15.137 0 -2
16 ig_resnext101_32x32d dm_nfnet_f3 58.093 58.373 41.907 41.627 80.653 82.360 19.347 17.640 468.53 254.92 224 416 0.875 0.940 bilinear bicubic -39.267 -38.987 -19.027 -17.220 0 +1
17 resnetv2_152x2_bitm ig_resnext101_32x32d 54.973 58.093 45.027 41.907 82.813 80.653 17.187 19.347 236.34 468.53 480 224 1.000 0.875 bilinear -42.177 -39.267 -16.777 -19.027 +5 +1
18 vit_base_r50_s16_384 cait_m36_384 54.627 57.840 45.373 42.160 81.213 84.813 18.787 15.187 98.95 271.22 384 1.000 bicubic -42.553 -39.560 -18.347 -14.697 +3 -2
19 vit_large_patch16_384 resnetv2_152x2_bitm 53.867 54.973 46.133 45.027 80.320 82.813 19.680 17.187 304.72 236.34 384 480 1.000 bicubic bilinear -43.243 -42.177 -19.340 -16.777 +4 +6
20 resnetv2_101x3_bitm vit_base_r50_s16_384 53.813 54.627 46.187 45.373 81.093 81.213 18.907 18.787 387.93 98.95 480 384 1.000 bilinear bicubic -43.237 -42.553 -18.427 -18.347 +7 +4
21 ig_resnext101_32x16d cait_s36_384 53.067 54.413 46.933 45.587 76.907 81.360 23.093 18.640 194.03 68.37 224 384 0.875 1.000 bilinear bicubic -43.753 -42.917 -22.683 -18.170 +12 -2
22 vit_large_patch16_384 53.867 46.133 80.320 19.680 304.72 384 1.000 bicubic -43.243 -19.320 +5
23 resnetv2_101x3_bitm 53.813 46.187 81.093 18.907 387.93 480 1.000 bilinear -43.237 -18.427 +8
24 ig_resnext101_32x16d 53.067 46.933 76.907 23.093 194.03 224 0.875 bilinear -43.753 -22.683 +14
25 swin_base_patch4_window7_224 51.453 48.547 79.973 20.027 87.77 224 0.900 bicubic -45.797 -19.557 -5
26 tf_efficientnet_b4_ns 51.213 48.787 79.187 20.813 19.34 380 0.922 bicubic -45.737 -20.393 +8 +9
27 swsl_resnext101_32x8d 51.187 48.813 78.240 21.760 88.79 224 0.875 bilinear -46.013 -21.330 -6
28 dm_nfnet_f2 50.773 49.227 78.013 21.987 193.78 352 0.920 bicubic -46.187 -21.437 +4 +5
29 vit_base_patch16_384 50.613 49.387 78.200 21.800 86.86 384 1.000 bicubic -46.087 -21.310 +12 +15
30 cait_s24_384 49.733 50.267 78.733 21.267 47.06 384 1.000 bicubic -47.337 -20.697 0
31 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
32 tf_efficientnet_b8 48.947 51.053 77.240 22.760 87.41 672 0.954 bicubic -48.253 -22.260 -8 -9
33 resnest269e 48.187 51.813 74.333 25.667 110.93 416 0.928 bicubic -48.333 -25.017 +15 +22
34 resnetv2_50x3_bitm 47.787 52.213 77.627 22.373 217.32 480 1.000 bilinear -48.983 -21.823 -21.803 +5
35 tf_efficientnet_b8_ap 46.893 53.107 76.507 23.493 87.41 672 0.954 bicubic -50.217 -23.133 -23.153 -7 -9
36 dm_nfnet_f1 46.600 53.400 74.773 25.227 132.63 320 0.910 bicubic -50.320 -24.617 0
37 swsl_resnext101_32x16d 46.200 53.800 72.200 27.800 194.03 224 0.875 bilinear -50.400 -27.320 +10 +13
38 ecaresnet269d 45.893 54.107 75.133 24.867 102.09 352 1.000 bicubic -51.187 -24.487 -24.337 -8 -10
39 tf_efficientnet_b7_ap 45.373 54.627 74.213 25.787 66.35 600 0.949 bicubic -51.827 -25.327 -16 -17
40 ig_resnext101_32x8d 45.320 54.680 70.867 29.133 88.79 224 0.875 bilinear -51.000 -28.563 +14 +23
41 resnest200e 44.147 55.853 73.467 26.533 70.20 320 0.909 bicubic -52.463 -25.883 +5 +8
42 tresnet_xl_448 cait_xs24_384 43.480 43.947 56.520 56.053 72.453 75.187 27.547 24.813 78.44 26.67 448 384 0.875 1.000 bilinear bicubic -52.490 -52.603 -26.677 -24.233 +21 +10
43 tf_efficientnet_b7 tresnet_xl_448 42.960 43.480 57.040 56.520 73.133 72.453 26.867 27.547 66.35 78.44 600 448 0.949 0.875 bicubic bilinear -54.050 -52.490 -26.387 -26.677 -11 +30
44 swsl_resnext101_32x4d resnetrs420 41.560 43.147 58.440 56.853 71.760 70.453 28.240 29.547 44.18 191.89 224 416 0.875 1.000 bilinear bicubic -54.860 -53.763 -27.710 -29.007 +5 -7
45 tf_efficientnet_b6_ap tf_efficientnet_b7 40.800 42.960 59.200 57.040 71.627 73.133 28.373 26.867 43.04 66.35 528 600 0.942 0.949 bicubic -56.280 -54.050 -27.843 -26.387 -16 -13
46 tresnet_l_448 swsl_resnext101_32x4d 40.200 41.560 59.800 58.440 69.893 71.760 30.107 28.240 55.99 44.18 448 224 0.875 bilinear -55.660 -54.860 -29.317 -27.710 +23 +11
47 vit_deit_base_patch16_384 tf_efficientnet_b6_ap 40.173 40.800 59.827 59.200 70.760 71.627 29.240 28.373 86.86 43.04 384 528 1.000 0.942 bicubic -55.977 -56.280 -28.380 -27.993 +10 -18
48 resnetv2_101x1_bitm tresnet_l_448 39.307 40.200 60.693 59.800 71.493 69.893 28.507 30.107 44.54 55.99 480 448 1.000 0.875 bilinear -56.783 -55.660 -27.697 -29.227 +13 +32
49 vit_large_patch32_384 vit_deit_base_patch16_384 38.933 40.173 61.067 59.827 68.920 70.760 31.080 29.240 306.63 86.86 384 1.000 bicubic -56.897 -55.977 -30.230 -28.380 +22 +18
50 resnet200d resnetrs350 38.147 39.960 61.853 60.040 68.613 68.907 31.387 31.093 64.69 163.96 320 384 1.000 bicubic -58.573 -56.800 -30.717 -30.463 -10 -9
51 eca_nfnet_l1 resnetv2_101x1_bitm 38.107 39.307 61.893 60.693 71.293 71.493 28.707 28.507 41.41 44.54 320 480 1.000 bicubic bilinear -58.593 -56.783 -27.977 -27.697 -10 +20
52 seresnet152d vit_large_patch32_384 37.640 38.933 62.360 61.067 69.480 68.920 30.520 31.080 66.84 306.63 320 384 1.000 bicubic -59.130 -56.897 -29.950 -30.230 -14 +30
53 regnety_160 resnet200d 36.747 38.147 63.253 61.853 69.107 68.613 30.893 31.387 83.59 64.69 288 320 1.000 bicubic -59.603 -58.573 -30.223 -30.717 -1 -11
54 pit_b_distilled_224 eca_nfnet_l1 35.627 38.107 64.373 61.893 69.120 71.293 30.880 28.707 74.79 41.41 224 320 0.900 1.000 bicubic -61.053 -58.593 -30.230 -27.977 -11
55 tf_efficientnet_b3_ns seresnet152d 35.520 37.640 64.480 62.360 67.773 69.480 32.227 30.520 12.23 66.84 300 320 0.904 1.000 bicubic -60.870 -59.130 -31.577 -29.970 -5 -15
56 vit_large_patch16_224 efficientnet_v2s 35.493 36.787 64.507 63.213 64.427 68.320 35.573 31.680 304.33 23.94 224 384 0.900 1.000 bicubic -60.457 -59.753 -34.813 -31.040 +8 -3
57 tf_efficientnet_b6 regnety_160 35.213 36.747 64.787 63.253 67.720 69.107 32.280 30.893 43.04 83.59 528 288 0.942 1.000 bicubic -61.457 -59.603 -31.650 -30.223 -12 +4
58 tf_efficientnet_b5_ap cait_xxs36_384 34.787 36.227 65.213 63.773 67.493 67.800 32.507 32.200 30.39 17.37 456 384 0.934 1.000 bicubic -61.893 -59.623 -31.967 -31.290 -14 +23
59 resnet152d pit_b_distilled_224 34.320 35.627 65.680 64.373 65.907 69.120 34.093 30.880 60.21 74.79 320 224 1.000 0.900 bicubic -62.040 -61.053 -33.483 -30.340 -8 -12
60 tresnet_m_448 tf_efficientnet_b3_ns 34.107 35.520 65.893 64.480 64.493 67.773 35.507 32.227 31.39 12.23 448 300 0.875 0.904 bilinear bicubic -60.883 -60.870 -34.487 -31.577 +44 -2
61 vit_base_patch32_384 vit_large_patch16_224 33.613 35.493 66.387 64.507 65.240 64.427 34.760 35.573 88.30 304.33 384 224 1.000 0.900 bicubic -62.197 -60.457 -33.910 -34.813 +11 +13
62 pit_b_224 tf_efficientnet_b6 33.173 35.213 66.827 64.787 62.320 67.720 37.680 32.280 73.76 43.04 224 528 0.900 0.942 bicubic -62.467 -61.457 -36.340 -31.650 +16 -14
63 swsl_resnext50_32x4d resnetrs270 33.013 35.013 66.987 64.987 65.067 65.480 34.933 34.520 25.03 129.86 224 352 0.875 1.000 bilinear bicubic -62.857 -61.677 -34.183 -33.870 +5 -18
64 ssl_resnext101_32x16d tf_efficientnet_b5_ap 32.600 34.787 67.400 65.213 64.000 67.493 36.000 32.507 194.03 30.39 224 456 0.875 0.934 bilinear bicubic -63.200 -61.893 -35.180 -31.857 +9 -18
65 swin_small_patch4_window7_224 vit_base_patch16_224_miil 32.600 34.507 67.400 65.493 65.440 65.000 34.560 35.000 49.61 86.54 224 0.900 0.875 bicubic bilinear -63.310 -61.953 -33.580 -34.300 +1 -9
66 vit_base_patch16_224 resnet152d 32.053 34.320 67.947 65.680 61.573 65.907 38.427 34.093 86.57 60.21 224 320 0.900 1.000 bicubic -63.277 -62.040 -37.427 -33.483 +22 -6
67 tf_efficientnet_b5 tresnet_m_448 31.840 34.107 68.160 65.893 65.293 64.493 34.707 35.507 30.39 31.39 456 448 0.934 0.875 bicubic bilinear -64.510 -60.883 -34.017 -34.487 -14 +49
68 resnest101e vit_base_patch32_384 31.413 33.613 68.587 66.387 64.360 65.240 35.640 34.760 48.28 88.30 256 384 0.875 1.000 bilinear bicubic -64.447 -62.197 -34.760 -33.910 +2 +15
69 dm_nfnet_f0 pit_b_224 31.280 33.173 68.720 66.827 63.347 62.320 36.653 37.680 71.49 73.76 256 224 0.900 bicubic -64.860 -62.467 -35.893 -36.340 -11 +21
70 swsl_resnext50_32x4d 33.013 66.987 65.067 34.933 25.03 224 0.875 bilinear -62.857 -34.183 +8
71 ssl_resnext101_32x16d 32.600 67.400 64.000 36.000 194.03 224 0.875 bilinear -63.200 -35.180 +13
72 swin_small_patch4_window7_224 32.600 67.400 65.440 34.560 49.61 224 0.900 bicubic -63.310 -33.580 +4
73 vit_base_patch16_224 32.053 67.947 61.573 38.427 86.57 224 0.900 bicubic -63.277 -37.427 +26
74 tf_efficientnet_b5 31.840 68.160 65.293 34.707 30.39 456 0.934 bicubic -64.510 -34.017 -12
75 resnest101e 31.413 68.587 64.360 35.640 48.28 256 0.875 bilinear -64.447 -34.850 +4
76 dm_nfnet_f0 31.280 68.720 63.347 36.653 71.49 256 0.900 bicubic -64.860 -35.893 -8
77 cait_s24_224 31.200 68.800 64.560 35.440 46.92 224 1.000 bicubic -65.180 -34.590 -18
78 efficientnet_b4 30.867 69.133 64.600 35.400 19.34 384 1.000 bicubic -65.283 -34.600 -12
79 resnetrs200 30.773 69.227 63.320 36.680 93.21 320 1.000 bicubic -65.757 -36.030 -25
80 cait_xxs24_384 30.027 69.973 63.933 36.067 12.03 384 1.000 bicubic -65.233 -35.027 +22
81 swsl_resnet50 29.867 70.133 63.853 36.147 25.56 224 0.875 bilinear -65.543 -35.437 +17
82 vit_deit_base_distilled_patch16_224 29.600 70.400 64.453 35.547 87.34 224 0.900 bicubic -66.490 -34.807 -12 -13
83 ssl_resnext101_32x8d 29.040 70.960 60.973 39.027 88.79 224 0.875 bilinear -66.430 -38.137 +11 +10
84 resnet101d 28.987 71.013 62.053 37.947 44.57 320 1.000 bicubic -67.303 -37.177 -18 -20
85 vit_deit_base_patch16_224 resnetrs152 27.440 28.920 72.560 71.080 58.893 60.520 41.107 39.480 86.57 86.62 224 320 0.900 1.000 bicubic -68.000 -67.660 -39.947 -38.720 +12 -34
86 vit_deit_base_patch16_224 27.440 72.560 58.893 41.107 86.57 224 0.900 bicubic -68.000 -39.947 +10
87 resnetv2_50x1_bitm 27.347 72.653 63.547 36.453 25.55 480 1.000 bilinear -67.703 -35.613 +24
88 nfnet_l0 26.493 73.507 61.987 38.013 35.07 288 1.000 bicubic -69.597 -37.313 -16 -18
89 tf_efficientnet_b4 26.293 73.707 60.107 39.893 19.34 380 0.922 bicubic -69.607 -39.063 -10 -12
90 tf_efficientnet_b4_ap 26.240 73.760 60.227 39.773 19.34 380 0.922 bicubic -69.920 -39.053 -22 -25
91 regnety_032 26.213 73.787 60.987 39.013 19.44 288 1.000 bicubic -69.757 -38.203 -17 -19
92 ecaresnet50t 26.133 73.867 60.027 39.973 25.57 320 0.950 bicubic -69.377 -39.093 +2 0
93 ecaresnet101d 26.027 73.973 58.987 41.013 44.57 224 0.875 bicubic -69.503 -40.143 0 -2
94 eca_nfnet_l0 25.013 74.987 60.360 39.640 24.14 288 1.000 bicubic -70.917 -38.850 -17 -19
95 tnt_s_patch16_224 24.733 75.267 58.187 41.813 23.76 224 0.900 bicubic -70.307 -40.693 -40.643 +18 +19
96 ssl_resnext101_32x4d 24.173 75.827 57.413 42.587 44.18 224 0.875 bilinear -71.267 -41.717 0 -2
97 tf_efficientnet_b2_ns 24.013 75.987 57.293 42.707 9.11 260 0.890 bicubic -71.757 -41.827 -11 -12
98 nasnetalarge 23.493 76.507 55.027 44.973 88.75 331 0.911 bicubic -72.187 -43.903 -9
99 efficientnet_b3 pnasnet5large 23.453 23.333 76.547 76.667 56.587 53.640 43.413 46.360 12.23 86.06 300 331 0.904 0.911 bicubic -72.127 -72.377 -42.513 -45.280 -8 -11
100 pnasnet5large efficientnet_b3 23.333 23.213 76.667 76.787 53.640 55.960 46.360 44.040 86.06 12.23 331 320 0.911 1.000 bicubic -72.377 -72.497 -45.280 -43.080 -12 -13
101 efficientnet_b3a pit_s_distilled_224 23.213 22.360 76.787 77.640 55.960 57.120 44.040 42.880 12.23 24.04 320 224 1.000 0.900 bicubic -72.497 -72.880 -43.080 -41.930 -14 +2
102 pit_s_distilled_224 tresnet_m 22.360 21.680 77.640 78.320 57.120 53.840 42.880 46.160 24.04 31.39 224 0.900 0.875 bicubic bilinear -72.880 -74.040 -41.930 -45.190 +1 -16
103 swin_tiny_patch4_window7_224 21.173 78.827 55.973 44.027 28.29 224 0.900 bicubic -73.967 -42.877 +2
104 pit_s_224 21.080 78.920 53.573 46.427 23.46 224 0.900 bicubic -73.510 -45.137 +35 +33
105 efficientnet_v2s resnetrs101 21.013 20.893 78.987 79.107 52.840 52.813 47.160 47.187 23.94 63.62 224 288 1.000 0.940 bicubic -74.527 -74.537 -46.120 -46.217 -13 -8
106 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
107 resnest50d_4s2x40d 20.387 79.613 52.800 47.200 30.42 224 0.875 bicubic -74.573 -46.270 +10
108 ssl_resnext50_32x4d 20.000 80.000 53.613 46.387 25.03 224 0.875 bilinear -74.870 -45.267 +15
109 tresnet_xl 19.640 80.360 53.133 46.867 78.44 224 0.875 bilinear -75.800 -45.917 -12 -14
110 gluon_senet154 19.307 80.693 47.533 52.467 115.09 224 0.875 bicubic -75.613 -51.227 +10
111 rexnet_200 19.227 80.773 52.720 47.280 16.37 224 0.875 bicubic -75.713 -46.290 +7
112 repvgg_b3 19.107 80.893 50.253 49.747 123.09 224 0.875 bilinear -75.463 -48.527 +28 +26
113 legacy_senet154 19.053 80.947 47.947 52.053 115.09 224 0.875 bilinear -76.017 -50.883 -3
114 vit_deit_small_patch16_224 18.907 81.093 51.413 48.587 22.05 224 0.900 bicubic -75.493 -47.277 -47.327 +39 +36
115 gluon_seresnext101_64x4d 18.907 81.093 49.187 50.813 88.23 224 0.875 bicubic -76.023 -49.643 +5
116 tf_efficientnet_b1_ns 18.693 81.307 51.667 48.333 7.79 240 0.882 bicubic -76.477 -47.443 -12
117 seresnext50_32x4d 18.360 81.640 50.973 49.027 27.56 224 0.875 bicubic -76.680 -47.957 -47.907 -5 -4
118 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 -49.293 +15 +45
119 tf_efficientnet_lite4 ecaresnet50d 18.133 18.227 81.867 81.773 50.707 51.880 49.293 48.120 13.01 25.58 380 224 0.920 0.875 bilinear bicubic -76.757 -76.403 -48.313 -47.010 +3 +13
120 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 +6 +2
121 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 0 +5
122 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 +2 -1
123 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 -17 +1
124 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 +31 -18
125 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 -24 +28
126 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 -11 -26
127 xception71 tf_efficientnet_b3 17.000 83.000 45.520 49.267 54.480 50.733 42.34 12.23 299 300 0.903 0.904 bicubic -77.280 -78.010 -53.120 -49.643 +34 -12
128 gluon_resnext101_64x4d xception71 16.853 17.000 83.147 83.000 44.213 45.520 55.787 54.480 83.46 42.34 224 299 0.875 0.903 bicubic -77.817 -77.280 -54.437 -53.120 +3 +32
129 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 -27 +1
130 gluon_resnet152_v1d tresnet_l 16.573 16.600 83.427 83.400 44.280 49.920 55.720 50.080 60.21 55.99 224 0.875 bicubic bilinear -78.167 -78.690 -54.460 -49.090 -3 -29
131 gluon_resnet152_v1s gluon_resnet152_v1d 16.573 83.427 44.533 44.280 55.467 55.720 60.32 60.21 224 0.875 bicubic -78.467 -78.167 -54.297 -54.460 -17 -4
132 inception_resnet_v2 gluon_resnet152_v1s 16.573 83.427 44.960 44.533 55.040 55.467 55.84 60.32 299 224 0.897 0.875 bicubic -77.967 -78.467 -53.830 -54.397 +10 -20
133 gluon_xception65 inception_resnet_v2 16.440 16.573 83.560 83.427 46.027 44.960 53.973 55.040 39.92 55.84 299 0.903 0.897 bicubic -77.820 -77.967 -52.543 -53.890 +29 +8
134 gernet_l gluon_xception65 16.373 16.440 83.627 83.560 47.213 46.027 52.787 53.973 31.08 39.92 256 299 0.875 0.903 bilinear bicubic -78.717 -77.820 -51.687 -52.433 -27 +30
135 wide_resnet50_2 gernet_l 16.280 16.373 83.720 83.627 48.347 47.213 51.653 52.787 68.88 31.08 224 256 0.875 bicubic bilinear -78.800 -78.717 -50.623 -51.687 -26 -28
136 ens_adv_inception_resnet_v2 wide_resnet50_2 16.240 16.280 83.760 83.720 43.640 48.347 56.360 51.653 55.84 68.88 299 224 0.897 0.875 bicubic -77.920 -78.800 -54.960 -50.623 +37 -27
137 repvgg_b3g4 ens_adv_inception_resnet_v2 16.213 16.240 83.787 83.760 47.653 43.640 52.347 56.360 83.83 55.84 224 299 0.875 0.897 bilinear bicubic -78.307 -77.920 -51.317 -55.000 +8 +37
138 repvgg_b3g4 16.213 83.787 47.653 52.347 83.83 224 0.875 bilinear -78.307 -51.317 +5
139 xception65 16.027 83.973 43.773 56.227 39.92 299 0.903 bicubic -77.733 -54.597 +62
140 ssl_resnet50 15.960 84.040 49.467 50.533 25.56 224 0.875 bilinear -78.490 -49.453 +12 +9
141 regnety_320 15.627 84.373 44.827 55.173 145.05 224 0.875 bicubic -78.913 -54.023 -53.963 +3 -1
142 ecaresnet101d_pruned 15.600 84.400 48.027 51.973 24.88 224 0.875 bicubic -79.480 -50.953 -33 -34
143 ecaresnet26t 15.467 84.533 47.920 52.080 16.01 320 0.950 bicubic -78.843 -50.800 +18 +15
144 skresnext50_32x4d 15.373 84.627 44.493 55.507 27.48 224 0.875 bicubic -78.887 -53.967 -54.077 +21 +18
145 ecaresnetlight cait_xxs24_224 15.160 84.840 45.827 44.960 54.173 55.040 30.16 11.96 224 0.875 1.000 bicubic -79.610 -78.440 -52.973 -53.480 -19 +67
146 rexnet_150 ecaresnetlight 14.720 15.160 85.280 84.840 46.907 45.827 53.093 54.173 9.73 30.16 224 0.875 bicubic -79.760 -79.610 -51.883 -52.973 +4 -21
147 efficientnet_el_pruned rexnet_150 14.480 14.720 85.520 85.280 46.120 46.907 53.880 53.093 10.59 9.73 300 224 0.904 0.875 bicubic -79.920 -79.760 -52.620 -51.883 +7 0
148 efficientnet_b2a coat_lite_mini 14.440 14.507 85.560 85.493 46.080 44.507 53.920 55.493 9.11 11.01 288 224 1.000 0.900 bicubic -80.170 -79.553 -52.630 -54.053 -10 +35
149 legacy_seresnext101_32x4d efficientnet_el_pruned 14.147 14.480 85.853 85.520 42.973 46.120 57.027 53.880 48.96 10.59 224 300 0.875 0.904 bilinear bicubic -80.223 -79.920 -55.677 -52.570 +8 +3
150 seresnet50 efficientnet_b2 14.147 14.440 85.853 85.560 45.467 46.080 54.533 53.920 28.09 9.11 224 288 0.875 1.000 bicubic -80.403 -80.170 -53.283 -52.630 -8 -15
151 gernet_m legacy_seresnext101_32x4d 14.013 14.147 85.987 85.853 46.067 42.973 53.933 57.027 21.14 48.96 224 0.875 bilinear -80.607 -80.223 -52.483 -55.677 -14 +3
152 gluon_resnext101_32x4d seresnet50 13.867 14.147 86.133 85.853 41.653 45.467 58.347 54.533 44.18 28.09 224 0.875 bicubic -80.663 -80.403 -56.977 -53.283 -7 -13
153 gluon_seresnext50_32x4d gernet_m 13.600 14.013 86.400 85.987 43.760 46.067 56.240 53.933 27.56 21.14 224 0.875 bicubic bilinear -80.740 -80.607 -54.850 -52.793 +6 -19
154 repvgg_b2g4 gluon_resnext101_32x4d 13.440 13.867 86.560 86.133 43.787 41.653 56.213 58.347 61.76 44.18 224 0.875 bilinear bicubic -80.420 -80.663 -54.803 -56.977 +40 -12
155 ese_vovnet39b gluon_seresnext50_32x4d 13.320 13.600 86.680 86.400 43.813 43.760 56.187 56.240 24.57 27.56 224 0.875 bicubic -80.770 -80.740 -54.847 -54.850 +27 +1
156 efficientnet_b2 repvgg_b2g4 13.307 13.440 86.693 86.560 44.440 43.787 55.560 56.213 9.11 61.76 260 224 0.875 bicubic bilinear -81.393 -80.420 -54.230 -54.803 -25 +38
157 regnetx_320 ese_vovnet39b 13.307 13.320 86.693 86.680 40.720 43.813 59.280 56.187 107.81 24.57 224 0.875 bicubic -81.153 -80.770 -58.020 -54.847 -6 +24
158 pit_xs_distilled_224 regnetx_320 13.240 13.307 86.760 86.693 44.573 40.720 55.427 59.280 11.00 107.81 224 0.900 0.875 bicubic -80.570 -81.153 -54.097 -58.020 +40 -10
159 efficientnet_b3_pruned pit_xs_distilled_224 13.173 13.240 86.827 86.760 45.213 44.573 54.787 55.427 9.86 11.00 300 224 0.904 0.900 bicubic -81.457 -80.570 -53.547 -54.097 -24 +39
160 gluon_resnet101_v1d efficientnet_b3_pruned 13.160 13.173 86.840 86.827 41.493 45.213 58.507 54.787 44.57 9.86 224 300 0.875 0.904 bicubic -81.060 -81.457 -57.237 -53.547 +8 -27
161 mixnet_xl gluon_resnet101_v1d 13.120 13.160 86.880 86.840 43.253 41.493 56.747 58.507 11.90 44.57 224 0.875 bicubic -81.070 -81.060 -55.087 -57.057 +10 +7
162 nf_regnet_b1 mixnet_xl 13.027 13.120 86.973 86.880 44.413 43.253 55.587 56.747 10.22 11.90 288 224 0.900 0.875 bicubic -81.103 -81.070 -54.157 -55.087 +16 +8
163 pit_xs_224 nf_regnet_b1 12.813 13.027 87.187 86.973 42.840 44.413 57.160 55.587 10.62 10.22 224 288 0.900 bicubic -80.297 -81.103 -55.470 -54.217 +78 +15
164 pit_xs_224 12.813 87.187 42.840 57.160 10.62 224 0.900 bicubic -80.297 -55.470 +79
165 gluon_inception_v3 12.640 87.360 40.493 59.507 23.83 299 0.875 bicubic -80.820 -58.077 +56
166 tresnet_m coat_lite_tiny 12.600 12.520 87.400 87.480 41.893 41.160 58.107 58.840 31.39 5.72 224 0.875 0.900 bilinear bicubic -82.020 -80.720 -56.967 -57.100 -29 +70
167 regnety_120 12.427 87.573 42.200 57.800 51.82 224 0.875 bicubic -82.053 -56.610 -17 -21
168 efficientnet_em 12.360 87.640 43.880 56.120 6.90 240 0.882 bicubic -81.480 -54.930 +28 +27
169 nf_resnet50 12.320 87.680 46.760 53.240 25.56 288 0.940 bicubic -82.270 -52.050 -29 -33
170 vit_small_patch16_224 12.147 87.853 40.320 59.680 48.75 224 0.900 bicubic -80.613 -57.610 +88 +89
171 hrnet_w64 12.027 87.973 40.787 59.213 128.06 224 0.875 bilinear -81.983 -57.823 +15 +14
172 cspdarknet53 12.013 87.987 43.253 56.747 27.64 256 0.887 bilinear -82.647 -55.547 -38 -41
173 gluon_resnet101_v1s 11.880 88.120 40.973 59.027 44.67 224 0.875 bicubic -82.840 -57.847 -43 -45
174 resnet50d 11.693 88.307 42.453 57.547 25.58 224 0.875 bicubic -82.567 -56.267 -9 -13
175 dpn92 11.627 88.373 40.267 59.733 37.67 224 0.875 bicubic -82.603 -58.463 -7 -9
176 xception41 11.600 88.400 39.133 60.867 26.97 299 0.903 bicubic -81.830 -59.297 +48
177 dla102x2 11.573 88.427 41.293 58.707 41.28 224 0.875 bilinear -82.377 -57.197 +11 +10
178 regnety_080 11.413 88.587 40.613 59.387 39.18 224 0.875 bicubic -82.757 -58.067 -4 -6
179 efficientnet_b2_pruned 11.360 88.640 42.027 57.973 8.31 260 0.890 bicubic -82.780 -56.503 -1 -3
180 tf_efficientnet_el 11.333 88.667 42.040 57.960 10.59 300 0.904 bicubic -83.077 -56.670 -26 -30
181 gluon_resnet152_v1c 11.093 88.907 37.120 62.880 60.21 224 0.875 bicubic -83.067 -61.520 -61.480 -5 -8
182 dpn107 hrnet_w48 11.080 88.920 38.693 40.320 61.307 59.680 86.92 77.47 224 0.875 bicubic bilinear -83.230 -82.840 -59.787 -58.290 -21 +5
183 hrnet_w48 dpn107 11.080 88.920 40.320 38.693 59.680 61.307 77.47 86.92 224 0.875 bilinear bicubic -82.840 -83.230 -58.290 -59.787 +6 -25
184 ecaresnet50d_pruned 11.027 88.973 41.947 58.053 19.94 224 0.875 bicubic -83.193 -56.603 -56.783 -14 -17
185 adv_inception_v3 11.013 88.987 36.720 63.280 23.83 299 0.875 bicubic -81.867 -61.420 +67 +68
186 tf_efficientnet_b0_ns 10.933 89.067 40.067 59.933 5.29 224 0.875 bicubic -82.697 -58.573 +25 +24
187 tf_inception_v3 10.840 89.160 36.853 63.147 23.83 299 0.875 bicubic -82.480 -61.177 +43
188 resnext50_32x4d 10.800 89.200 40.307 59.693 25.03 224 0.875 bicubic -83.300 -58.043 -6 -8
189 dpn131 10.787 89.213 37.200 62.800 79.25 224 0.875 bicubic -83.223 -61.520 -4 -5
190 tf_efficientnet_b2_ap 10.533 89.467 40.107 59.893 9.11 260 0.890 bicubic -83.957 -58.513 -42 -46
191 resnext50d_32x4d 10.413 89.587 39.733 60.267 25.05 224 0.875 bicubic -83.767 -58.837 -18 -20
192 rexnet_130 10.400 89.600 41.547 58.453 7.56 224 0.875 bicubic -83.500 -56.713 -56.853 -1 -3
193 hrnet_w44 10.320 89.680 39.507 60.493 67.06 224 0.875 bilinear -83.230 -59.193 +21
194 resnext101_32x8d 10.187 89.813 37.827 62.173 88.79 224 0.875 bilinear -83.643 -60.753 +3 +2
195 regnetx_160 10.147 89.853 38.000 62.000 54.28 224 0.875 bicubic -83.973 -60.750 -14 -16
196 dpn98 10.133 89.867 36.587 63.413 61.57 224 0.875 bicubic -83.997 -62.043 -61.983 -16 -19
197 cspresnext50 10.120 89.880 40.373 59.627 20.57 224 0.875 bilinear -84.360 -58.307 -48 -52
198 legacy_seresnext50_32x4d 10.107 89.893 39.200 60.800 27.56 224 0.875 bilinear -83.623 -59.380 +8 +7
199 resnetrs50 10.093 89.907 37.507 62.493 35.69 224 0.910 bicubic -84.217 -61.133 -40
200 inception_v3 10.027 89.973 35.227 64.773 23.83 299 0.875 bicubic -82.693 -62.743 +63
201 xception efficientnet_b1 9.987 10.013 90.013 89.987 38.027 37.547 61.973 62.453 22.86 7.79 299 256 0.897 1.000 bicubic -83.473 -83.237 -60.503 -60.743 +22 +34
202 regnety_064 xception 9.947 9.987 90.053 90.013 39.067 38.027 60.933 61.973 30.58 22.86 224 299 0.875 0.897 bicubic -84.203 -83.473 -59.663 -60.503 -24 +20
203 dpn68b regnety_064 9.787 9.947 90.213 90.053 38.053 39.067 61.947 60.933 12.61 30.58 224 0.875 bicubic -83.903 -84.203 -60.457 -59.663 +6 -28
204 gluon_resnet152_v1b dpn68b 9.747 9.787 90.253 90.213 36.067 38.053 63.933 61.947 60.19 12.61 224 0.875 bicubic -84.333 -83.903 -62.383 -60.307 -19 +4
205 tf_efficientnet_lite3 gluon_resnet152_v1b 9.667 9.747 90.333 90.253 39.000 36.067 61.000 63.933 8.20 60.19 300 224 0.904 0.875 bilinear bicubic -84.533 -84.333 -59.640 -62.383 -33 -23
206 tf_efficientnet_b2 tf_efficientnet_lite3 9.653 9.667 90.347 90.333 38.880 39.000 61.120 61.000 9.11 8.20 260 300 0.890 0.904 bicubic bilinear -84.707 -84.533 -59.730 -59.640 -46 -37
207 tf_efficientnet_cc_b1_8e tf_efficientnet_b2 9.573 9.653 90.427 90.347 36.773 38.880 63.227 61.120 39.72 9.11 240 260 0.882 0.890 bicubic -84.327 -84.707 -61.627 -59.730 -16 -52
208 res2net101_26w_4s tf_efficientnet_cc_b1_8e 9.520 9.573 90.480 90.427 35.027 36.773 64.973 63.227 45.21 39.72 224 240 0.875 0.882 bilinear bicubic -84.230 -84.327 -63.353 -61.487 -4 -18
209 legacy_seresnet152 res2net101_26w_4s 9.347 9.520 90.653 90.480 37.413 35.027 62.587 64.973 66.82 45.21 224 0.875 bilinear -84.053 -84.230 -60.937 -63.283 +18 -6
210 cspresnet50 legacy_seresnet152 9.253 9.347 90.747 90.653 39.640 37.413 60.360 62.587 21.62 66.82 256 224 0.887 0.875 bilinear -84.487 -84.053 -59.000 -60.937 -4 +16
211 hrnet_w40 cspresnet50 9.227 9.253 90.773 90.747 36.893 39.640 63.107 60.360 57.56 21.62 224 256 0.875 0.887 bilinear -84.263 -84.487 -61.687 -59.000 +10 -7
212 regnetx_120 hrnet_w40 9.187 9.227 90.813 90.773 37.200 36.893 62.800 63.107 46.11 57.56 224 0.875 bicubic bilinear -85.053 -84.263 -61.450 -61.687 -44 +8
213 seresnext26d_32x4d regnetx_120 9.147 9.187 90.853 90.813 36.840 37.200 63.160 62.800 16.81 46.11 224 0.875 bicubic -83.553 -85.053 -61.310 -61.450 +51 -48
214 efficientnet_b1 seresnext26d_32x4d 9.120 9.147 90.880 90.853 37.360 36.840 62.640 63.160 7.79 16.81 240 224 0.875 bicubic -84.140 -83.553 -60.810 -61.310 +22 +50
215 resnest26d 9.080 90.920 37.853 62.147 17.07 224 0.875 bilinear -84.250 -60.777 -60.657 +15 +13
216 regnety_040 9.000 91.000 37.053 62.947 20.65 224 0.875 bicubic -84.860 -61.597 -21 -23
217 gluon_resnext50_32x4d 8.947 91.053 36.333 63.667 25.03 224 0.875 bicubic -84.863 -62.077 -62.057 -18
218 rexnet_100 8.893 91.107 36.373 63.627 4.80 224 0.875 bicubic -84.137 -62.017 -61.817 +27 +29
219 seresnext26t_32x4d 8.893 91.107 36.907 63.093 16.81 224 0.875 bicubic -83.927 -61.463 +37
220 mixnet_l 8.853 91.147 36.187 63.813 7.33 224 0.875 bicubic -84.597 -62.033 +4 +3
221 dla169 mobilenetv3_large_100_miil 8.640 8.840 91.360 91.160 36.040 32.973 63.960 67.027 53.39 5.48 224 0.875 bilinear -84.700 -83.420 -62.560 -64.667 +7 +63
222 hrnet_w30 dla169 8.613 8.640 91.387 91.360 37.040 36.040 62.960 63.960 37.71 53.39 224 0.875 bilinear -84.587 -84.700 -61.370 -62.560 +15 +5
223 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 +12 +14
224 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 -63.107 -67.837 -14 +74
225 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 -62.263 -62.497 -6 +9
226 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 -63.257 -38 -19
227 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 -61.893 -9 -8
228 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 -11 -42
229 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 -13 -12
230 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 -19 -14
231 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 -29 -16
232 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 +7 -23
233 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 +62 -33
234 dla60_res2next 7.787 92.213 34.987 65.013 17.03 224 0.875 bilinear -85.393 -63.423 +5
235 densenetblur121d 7.720 92.280 34.733 65.267 8.00 224 0.875 bicubic -84.190 -63.337 +61
236 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
237 dla60_res2net 7.560 92.440 34.627 65.373 20.85 224 0.875 bilinear -85.620 -63.793 +3 +1
238 efficientnet_b1_pruned 7.440 92.560 34.533 65.467 6.33 240 0.882 bicubic -85.330 -63.507 +22 +20
239 wide_resnet101_2 7.360 92.640 34.147 65.853 126.89 224 0.875 bilinear -86.360 -64.393 -29 -33
240 regnetx_064 7.333 92.667 34.373 65.627 26.21 224 0.875 bicubic -86.557 -64.257 -45 -49
241 vit_deit_tiny_patch16_224 7.307 92.693 30.707 69.293 5.72 224 0.900 bicubic -82.363 -66.743 +98 +99
242 hardcorenas_e 7.240 92.760 33.293 66.707 8.07 224 0.875 bilinear -85.330 -64.817 +31 +29
243 gluon_resnet101_v1b 7.227 92.773 32.773 67.227 44.55 224 0.875 bicubic -86.523 -65.537 -65.607 -36 -41
244 efficientnet_b0 7.213 92.787 34.013 65.987 5.29 224 0.875 bicubic -85.477 -64.057 +23 +21
245 gluon_resnet50_v1s 7.213 92.787 33.507 66.493 25.68 224 0.875 bicubic -86.407 -64.953 -30 -34
246 tf_mixnet_l 7.147 92.853 31.613 68.387 7.33 224 0.875 bicubic -86.163 -66.417 -12 -15
247 tf_efficientnet_b1 7.133 92.867 33.040 66.960 7.79 240 0.882 bicubic -86.367 -65.320 -65.690 -25 -29
248 tf_efficientnet_cc_b0_8e 7.120 92.880 31.787 68.213 24.01 224 0.875 bicubic -85.710 -66.393 +9 +7
249 hardcorenas_f 6.827 93.173 34.093 65.907 8.20 224 0.875 bilinear -86.123 -64.067 +4 +2
250 ese_vovnet19b_dw selecsls60b 6.733 93.267 33.413 33.267 66.587 66.733 6.54 32.77 224 0.875 bicubic -85.557 -86.567 -64.677 -65.013 +34 -19
251 selecsls60b ese_vovnet19b_dw 6.733 93.267 33.267 33.413 66.733 66.587 32.77 6.54 224 0.875 bicubic -86.567 -85.557 -65.013 -64.677 -16 +32
252 efficientnet_es 6.707 93.293 33.840 66.160 5.44 224 0.875 bicubic -86.433 -64.580 -10 -12
253 res2net50_26w_6s 6.693 93.307 31.653 68.347 37.05 224 0.875 bilinear -86.717 -66.627 -25 -28
254 legacy_seresnext26_32x4d 6.627 93.373 33.253 66.747 16.79 224 0.875 bicubic -86.013 -64.877 +16 +14
255 mixnet_m 6.627 93.373 32.053 67.947 5.01 224 0.875 bicubic -85.803 -65.817 +23 +21
256 pit_ti_distilled_224 6.627 93.373 30.760 69.240 5.10 224 0.900 bicubic -84.273 -66.940 +66
257 skresnet34 6.480 93.520 31.547 68.453 22.28 224 0.875 bicubic -85.910 -66.603 +24 +22
258 repvgg_b1 6.467 93.533 33.827 66.173 57.42 224 0.875 bilinear -86.863 -64.683 -64.803 -27 -29
259 hardcorenas_d 6.440 93.560 32.213 67.787 7.50 224 0.875 bilinear -85.960 -65.837 -65.857 +21 +18
260 dla60x 6.427 93.573 34.080 65.920 17.35 224 0.875 bilinear -86.693 -64.430 -17 -19
261 resnet34d 6.400 93.600 31.493 68.507 21.82 224 0.875 bicubic -86.280 -66.817 +7 +5
262 regnetx_080 6.307 93.693 32.320 67.680 39.57 224 0.875 bicubic -87.563 -66.200 -66 -70
263 swsl_resnet18 6.240 93.760 31.600 68.400 11.69 224 0.875 bilinear -84.450 -66.100 +65
264 legacy_seresnet50 6.187 93.813 32.653 67.347 28.09 224 0.875 bilinear -86.773 -65.537 -12 -14
265 pit_ti_224 6.120 93.880 30.227 69.773 4.85 224 0.900 bicubic -83.820 -67.223 +71 +72
266 tv_resnet152 6.040 93.960 32.053 67.947 60.19 224 0.875 bilinear -87.260 -66.337 -30 -33
267 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 +4 -54
268 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 -51 +2
269 resnet50 5.933 94.067 29.093 70.907 25.56 224 0.875 bicubic -87.877 -69.297 -69.317 -66 -72
270 dla102 5.880 94.120 32.707 67.293 33.27 224 0.875 bilinear -87.180 -65.833 -24 -26
271 regnety_016 mixer_l16_224 5.680 5.867 94.320 94.133 30.413 18.533 69.587 81.467 11.20 208.20 224 0.875 bicubic -87.350 -81.283 -67.777 -74.987 -22 +84
272 selecsls60 regnety_016 5.653 5.680 94.347 94.320 32.507 30.413 67.493 69.587 30.67 11.20 224 0.875 bicubic -87.377 -87.350 -65.853 -67.947 -24 -26
273 hardcorenas_c selecsls60 5.640 5.653 94.360 94.347 30.400 32.507 69.600 67.493 5.52 30.67 224 0.875 bilinear bicubic -86.380 -87.377 -67.440 -65.793 +19 -25
274 res2next50 hardcorenas_c 5.627 5.640 94.373 94.360 30.867 30.400 69.133 69.600 24.67 5.52 224 0.875 bilinear -87.213 -86.380 -67.313 -67.440 -18 +17
275 hrnet_w18 res2next50 5.493 5.627 94.507 94.373 30.960 30.867 69.040 69.133 21.30 24.67 224 0.875 bilinear -86.827 -87.213 -67.280 -67.313 +8 -21
276 resnest14d hrnet_w18 5.480 5.493 94.520 94.507 28.547 30.960 71.453 69.040 10.61 21.30 224 0.875 bilinear -86.240 -86.827 -69.323 -67.280 +27 +5
277 tf_efficientnet_lite2 resnest14d 5.360 5.480 94.640 94.520 30.907 28.547 69.093 71.453 6.09 10.61 260 224 0.890 0.875 bicubic bilinear -87.290 -86.240 -67.323 -69.323 -8 +26
278 tf_efficientnet_em tf_efficientnet_lite2 5.347 5.360 94.653 94.640 31.107 30.907 68.893 69.093 6.90 6.09 240 260 0.882 0.890 bicubic -87.583 -87.290 -67.083 -67.323 -24 -11
279 gernet_s tf_efficientnet_em 5.307 5.347 94.693 94.653 30.133 31.107 69.867 68.893 8.17 6.90 224 240 0.875 0.882 bilinear bicubic -86.833 -87.583 -68.057 -67.083 +11 -27
280 tf_efficientnet_b0_ap gernet_s 5.307 94.693 28.813 30.133 71.187 69.867 5.29 8.17 224 0.875 bicubic bilinear -86.893 -86.833 -69.207 -68.057 +8 +9
281 densenet121 tf_efficientnet_b0_ap 5.293 5.307 94.707 94.693 29.907 28.813 70.093 71.187 7.98 5.29 224 0.875 bicubic -86.277 -86.893 -68.123 -69.207 +23 +6
282 repvgg_b1g4 densenet121 5.293 94.707 30.813 29.907 69.187 70.093 39.97 7.98 224 0.875 bilinear bicubic -87.687 -86.277 -67.617 -68.123 -31 +22
283 res2net50_26w_4s repvgg_b1g4 5.160 5.293 94.840 94.707 29.360 30.813 70.640 69.187 25.70 39.97 224 0.875 bilinear -87.340 -87.687 -68.700 -67.617 -6 -34
284 tf_mixnet_m res2net50_26w_4s 5.080 5.160 94.920 94.840 28.147 29.360 71.853 70.640 5.01 25.70 224 0.875 bicubic bilinear -87.250 -87.340 -69.743 -68.930 -2 -10
285 mobilenetv3_large_100 tf_mixnet_m 5.067 5.080 94.933 94.920 28.187 28.147 71.813 71.853 5.48 5.01 224 0.875 bicubic -86.253 -87.250 -69.523 -69.743 +24 -5
286 tf_efficientnet_b0 5.067 94.933 28.800 71.200 5.29 224 0.875 bicubic -87.183 -69.200 0 -2
287 res2net50_14w_8s mobilenetv3_large_100 5.040 5.067 94.960 94.933 28.773 28.187 71.227 71.813 25.06 5.48 224 0.875 bilinear bicubic -87.700 -86.253 -69.407 -69.523 -24 +23
288 hardcorenas_b res2net50_14w_8s 4.947 5.040 95.053 94.960 28.120 28.773 71.880 71.227 5.18 25.06 224 0.875 bilinear -86.823 -87.700 -69.660 -69.407 +13 -27
289 mobilenetv3_rw hardcorenas_b 4.907 4.947 95.093 95.053 29.853 28.120 70.147 71.880 5.48 5.18 224 0.875 bicubic bilinear -86.303 -86.823 -67.807 -69.660 +22 +12
290 mixnet_s 4.907 95.093 28.573 71.427 4.13 224 0.875 bicubic -86.923 -69.117 +10 +9
291 gluon_resnet50_v1c mobilenetv3_rw 4.893 4.907 95.107 95.093 28.147 29.853 71.853 70.147 25.58 5.48 224 0.875 bicubic -88.137 -86.303 -70.153 -67.807 -41 +21
292 hardcorenas_a gluon_resnet50_v1c 4.867 4.893 95.133 95.107 28.093 28.147 71.907 71.853 5.26 25.58 224 0.875 bilinear bicubic -86.483 -88.137 -69.767 -70.243 +16 -47
293 regnetx_032 hardcorenas_a 4.853 4.867 95.147 95.133 30.280 28.093 69.720 71.907 15.30 5.26 224 0.875 bicubic bilinear -88.267 -86.483 -68.110 -69.767 -49 +15
294 tv_resnext50_32x4d regnetx_032 4.840 4.853 95.160 95.147 30.307 30.280 69.693 69.720 25.03 15.30 224 0.875 bilinear bicubic -87.900 -88.267 -67.963 -68.110 -30 -52
295 tv_resnet101 tv_resnext50_32x4d 4.707 4.840 95.293 95.160 29.333 30.307 70.667 69.693 44.55 25.03 224 0.875 bilinear -88.103 -87.900 -68.917 -67.963 -36 -33
296 densenet161 tv_resnet101 4.693 4.707 95.307 95.293 29.547 29.333 70.453 70.667 28.68 44.55 224 0.875 bicubic bilinear -87.807 -88.103 -68.743 -68.917 -20 -39
297 selecsls42b densenet161 4.667 4.693 95.333 95.307 28.587 29.547 71.413 70.453 32.46 28.68 224 0.875 bicubic -87.613 -87.807 -69.563 -68.513 -12 -22
298 tf_efficientnet_lite1 selecsls42b 4.613 4.667 95.387 95.333 28.387 28.587 71.613 71.413 5.42 32.46 240 224 0.882 0.875 bicubic -88.007 -87.613 -69.693 -69.563 -27 -15
299 mobilenetv2_120d tf_efficientnet_lite1 4.533 4.613 95.467 95.387 29.280 28.387 70.720 71.613 5.83 5.42 224 240 0.875 0.882 bicubic -87.867 -88.007 -68.790 -69.693 -20 -30
300 efficientnet_es_pruned mobilenetv2_120d 4.187 4.533 95.813 95.467 26.520 29.280 73.480 70.720 5.44 5.83 224 0.875 bicubic -86.993 -87.867 -71.230 -68.770 +14 -22
301 fbnetc_100 efficientnet_es_pruned 4.133 4.187 95.867 95.813 25.933 26.520 74.067 73.480 5.57 5.44 224 0.875 bilinear bicubic -86.567 -86.993 -71.277 -71.230 +25 +13
302 densenet201 fbnetc_100 4.120 4.133 95.880 95.867 27.547 25.933 72.453 74.067 20.01 5.57 224 0.875 bicubic bilinear -88.630 -86.567 -70.683 -71.277 -40 +24
303 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 -28 -43
304 resnet26d gluon_resnet50_v1b 4.040 4.120 95.960 95.880 28.520 26.933 71.480 73.067 16.01 25.56 224 0.875 bicubic -88.030 -88.420 -69.440 -71.237 -13 -31
305 semnasnet_100 resnet26d 3.960 4.040 96.040 95.960 26.947 28.520 73.053 71.480 3.89 16.01 224 0.875 bicubic -87.320 -88.030 -70.613 -69.440 +5 -15
306 repvgg_a2 semnasnet_100 3.947 3.960 96.053 96.040 27.267 26.947 72.733 73.053 28.21 3.89 224 0.875 bilinear bicubic -87.993 -87.320 -70.883 -70.613 -11 +4
307 tf_mixnet_s repvgg_a2 3.880 3.947 96.120 96.053 25.253 27.267 74.747 72.733 4.13 28.21 224 0.875 bicubic bilinear -87.630 -87.993 -72.367 -70.883 -2 -13
308 dpn68 tf_mixnet_s 3.867 3.880 96.133 96.120 26.080 25.253 73.920 74.747 12.61 4.13 224 0.875 bicubic -88.143 -87.630 -71.970 -72.367 -15 -3
309 tf_efficientnet_es dpn68 3.827 3.867 96.173 96.133 26.107 26.080 73.893 73.920 5.44 12.61 224 0.875 bicubic -88.153 -88.143 -71.753 -71.970 -15 -17
310 regnety_008 tf_efficientnet_es 3.813 3.827 96.187 96.173 27.133 26.107 72.867 73.893 6.26 5.44 224 0.875 bicubic -87.937 -88.153 -71.047 -71.753 -8 -17
311 dla60 regnety_008 3.773 3.813 96.227 96.187 27.933 27.133 72.067 72.867 22.04 6.26 224 0.875 bilinear bicubic -88.457 -87.937 -70.177 -71.047 -24 -9
312 ssl_resnet18 dla60 3.747 3.773 96.253 96.227 25.427 27.933 74.573 72.067 11.69 22.04 224 0.875 bilinear -86.473 -88.457 -72.123 -70.177 +21 -26
313 mobilenetv2_140 ssl_resnet18 3.720 3.747 96.280 96.253 26.747 25.427 73.253 74.573 6.11 11.69 224 0.875 bicubic bilinear -88.110 -86.473 -71.113 -72.123 -13 +20
314 densenet169 mobilenetv2_140 3.707 3.720 96.293 96.280 25.613 26.747 74.387 73.253 14.15 6.11 224 0.875 bicubic -88.223 -88.110 -72.487 -71.113 -18 -14
315 regnetx_016 densenet169 3.627 3.707 96.373 96.293 26.293 25.613 73.707 74.387 9.19 14.15 224 0.875 bicubic -88.543 -88.223 -71.917 -72.487 -26 -20
316 res2net50_48w_2s regnetx_016 3.587 3.627 96.413 96.373 26.613 26.293 73.387 73.707 25.29 9.19 224 0.875 bilinear bicubic -88.963 -88.543 -71.467 -71.917 -42 -28
317 spnasnet_100 res2net50_48w_2s 3.547 3.587 96.453 96.413 24.293 26.613 75.707 73.387 4.42 25.29 224 0.875 bilinear -86.803 -88.963 -72.897 -71.467 +15 -45
318 tf_mobilenetv3_large_100 spnasnet_100 3.547 96.453 25.053 24.293 74.947 75.707 5.48 4.42 224 0.875 bilinear -87.693 -86.803 -72.607 -73.137 -7 +13
319 regnety_006 tf_mobilenetv3_large_100 3.467 3.547 96.533 96.453 24.893 25.053 75.107 74.947 6.06 5.48 224 0.875 bicubic bilinear -87.903 -87.693 -72.817 -72.607 -12 -8
320 legacy_seresnet34 regnety_006 3.333 3.467 96.667 96.533 23.800 24.893 76.200 75.107 21.96 6.06 224 0.875 bilinear bicubic -87.557 -87.903 -73.780 -72.817 +3 -13
321 efficientnet_lite0 legacy_seresnet34 3.253 3.333 96.747 96.667 25.867 23.800 74.133 76.200 4.65 21.96 224 0.875 bicubic bilinear -87.887 -87.557 -71.763 -73.780 -6 +2
322 dla34 efficientnet_lite0 3.227 3.253 96.773 96.747 23.573 25.867 76.427 74.133 15.74 4.65 224 0.875 bilinear bicubic -87.533 -87.887 -74.087 -71.763 +3 -7
323 regnety_004 dla34 3.200 3.227 96.800 96.773 22.653 23.573 77.347 76.427 4.34 15.74 224 0.875 bicubic bilinear -87.300 -87.533 -74.887 -74.087 +7 +2
324 mobilenetv2_110d ghostnet_100 3.173 3.227 96.827 96.773 24.587 24.853 75.413 75.147 4.52 5.18 224 0.875 bicubic bilinear -87.777 -86.793 -72.963 -72.517 -3 +12
325 mnasnet_100 regnety_004 3.120 3.200 96.880 96.800 24.227 22.653 75.773 77.347 4.38 4.34 224 0.875 bicubic -87.390 -87.300 -73.243 -74.887 +4 +5
326 tf_efficientnet_lite0 mobilenetv2_110d 3.080 3.173 96.920 96.827 22.907 24.587 77.093 75.413 4.65 4.52 224 0.875 bicubic -87.960 -87.777 -74.683 -72.963 -7 -5
327 skresnet18 mnasnet_100 3.013 3.120 96.987 96.880 22.800 24.227 77.200 75.773 11.96 4.38 224 0.875 bicubic -86.647 -87.390 -74.430 -73.243 +13 +2
328 vgg19_bn tf_efficientnet_lite0 2.947 3.080 97.053 96.920 23.480 22.907 76.520 77.093 143.68 4.65 224 0.875 bilinear bicubic -87.133 -87.960 -74.100 -74.683 +7 -9
329 resnet34 skresnet18 2.920 3.013 97.080 96.987 23.680 22.800 76.320 77.200 21.80 11.96 224 0.875 bilinear bicubic -88.210 -86.647 -73.940 -74.430 -13 +12
330 tf_mobilenetv3_large_075 vgg19_bn 2.867 2.947 97.133 97.053 21.573 23.480 78.427 76.520 3.99 143.68 224 0.875 bilinear -86.813 -87.133 -75.637 -74.100 +8 +5
331 hrnet_w18_small_v2 resnet34 2.720 2.920 97.280 97.080 23.693 23.680 76.307 76.320 15.60 21.80 224 0.875 bilinear -88.470 -88.210 -74.207 -73.940 -18 -15
332 gluon_resnet34_v1b tf_mobilenetv3_large_075 2.667 2.867 97.333 97.133 21.680 21.573 78.320 78.427 21.80 3.99 224 0.875 bicubic bilinear -88.293 -86.813 -75.950 -75.637 -12 +7
333 vgg16_bn hrnet_w18_small_v2 2.653 2.720 97.347 97.280 23.773 23.693 76.227 76.307 138.37 15.60 224 0.875 bilinear -87.437 -88.470 -73.597 -74.207 0 -20
334 regnetx_008 gluon_resnet34_v1b 2.653 2.667 97.347 97.333 22.453 21.680 77.547 78.320 7.26 21.80 224 0.875 bicubic -88.397 -88.293 -75.257 -75.950 -15 -14
335 vgg16 regnetx_008 2.640 2.653 97.360 97.347 20.427 22.453 79.573 77.547 138.36 7.26 224 0.875 bilinear bicubic -85.910 -88.397 -76.363 -75.257 +14 -17
336 resnet18d vgg16_bn 2.600 2.653 97.400 97.347 21.613 23.773 78.387 76.227 11.71 138.37 224 0.875 bicubic bilinear -86.680 -87.437 -75.537 -73.597 +6 -2
337 tv_densenet121 vgg16 2.560 2.640 97.440 97.360 22.667 20.427 77.333 79.573 7.98 138.36 224 0.875 bicubic bilinear -88.330 -85.910 -75.043 -76.363 -13 +13
338 repvgg_b0 resnet18d 2.547 2.600 97.453 97.400 24.013 21.613 75.987 78.387 15.82 11.71 224 0.875 bilinear bicubic -88.883 -86.680 -73.977 -75.537 -32 +5
339 regnetx_006 tv_densenet121 2.507 2.560 97.493 97.440 20.653 22.667 79.347 77.333 6.20 7.98 224 0.875 bicubic -87.843 -88.330 -76.777 -75.043 -8 -15
340 legacy_seresnet18 repvgg_b0 2.493 2.547 97.507 97.453 20.080 24.013 79.920 75.987 11.78 15.82 224 0.875 bicubic bilinear -86.387 -88.883 -76.900 -73.977 +7 -34
341 resnet26 regnetx_006 2.480 2.507 97.520 97.493 22.987 20.653 77.013 79.347 16.00 6.20 224 0.875 bicubic -88.630 -87.843 -74.753 -76.537 -24 -9
342 regnety_002 legacy_seresnet18 2.147 2.493 97.853 97.507 18.880 20.080 81.120 79.920 3.16 11.78 224 0.875 bicubic -85.233 -86.387 -77.710 -76.900 +10 +6
343 mobilenetv2_100 resnet26 2.147 2.480 97.853 97.520 19.907 22.987 80.093 77.013 3.50 16.00 224 0.875 bicubic -87.453 -88.630 -77.233 -74.753 -1 -26
344 vgg19 mobilenetv2_100 2.107 2.147 97.893 97.853 20.733 19.907 79.267 80.093 143.67 3.50 224 0.875 bilinear bicubic -86.933 -87.453 -76.137 -77.233 0 -2
345 vgg13_bn regnety_002 2.093 2.147 97.907 97.853 20.307 18.880 79.693 81.120 133.05 3.16 224 0.875 bilinear bicubic -86.667 -85.233 -76.663 -77.710 +3 +9
346 vgg19 2.107 97.893 20.733 79.267 143.67 224 0.875 bilinear -86.933 -76.137 -1
347 vgg13_bn 2.093 97.907 20.307 79.693 133.05 224 0.875 bilinear -86.667 -76.663 +2
348 tf_mobilenetv3_small_100 2.013 97.987 15.867 84.133 2.54 224 0.875 bilinear -83.177 -79.903 +12
349 tf_mobilenetv3_small_075 2.000 98.000 14.813 85.187 2.04 224 0.875 bilinear -81.520 -79.977 +14
350 regnetx_004 1.960 98.040 19.173 80.827 5.16 224 0.875 bicubic -86.940 -77.947 -2 -3
351 tv_resnet34 1.867 98.133 20.000 80.000 21.80 224 0.875 bilinear -88.073 -77.340 -12 -13
352 vgg13 1.867 98.133 17.960 82.040 133.05 224 0.875 bilinear -85.183 -78.360 +4
353 dla46x_c 1.760 98.240 16.480 83.520 1.07 224 0.875 bilinear -82.490 -78.790 +8
354 vgg11_bn 1.720 98.280 18.093 81.907 132.87 224 0.875 bilinear -85.780 -78.727 -1 -2
355 tf_mobilenetv3_large_minimal_100 1.627 98.373 17.120 82.880 3.92 224 0.875 bilinear -87.343 -79.740 -8 -9
356 dla60x_c 1.613 98.387 18.040 81.960 1.32 224 0.875 bilinear -84.677 -78.120 +2
357 vgg11 1.560 98.440 16.227 83.773 132.86 224 0.875 bilinear -84.990 -80.053 0
358 gluon_resnet18_v1b 1.547 98.453 16.613 83.387 11.69 224 0.875 bicubic -86.853 -80.067 -6 -7
359 hrnet_w18_small 1.533 98.467 18.120 81.880 13.19 224 0.875 bilinear -87.517 -78.990 -14 -15
360 dla46_c 1.520 98.480 15.267 84.733 1.30 224 0.875 bilinear -82.130 -79.653 +2
361 regnetx_002 1.373 98.627 15.027 84.973 2.68 224 0.875 bicubic -84.817 -80.953 -2
362 resnet18 1.160 98.840 16.213 83.787 11.69 224 0.875 bilinear -86.230 -80.077 -8 -9
363 tf_mobilenetv3_small_minimal_100 1.013 98.987 11.493 88.507 2.04 224 0.875 bilinear -80.367 -82.177 +1
364 tv_resnet50 0.000 100.000 14.453 85.547 25.56 224 0.875 bilinear -91.880 -83.587 -64 -67

87
results/results-imagenet-r-clean.csv
View File

@ -8,16 +8,20 @@ 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
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
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
ig_resnext101_32x32d,96.780,3.220,99.530,0.470,468.53,224,0.875,bilinear
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
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
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
@ -25,6 +29,7 @@ resnetv2_152x2_bitm,96.500,3.500,99.620,0.380,236.34,480,1.000,bilinear
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
resnetv2_101x3_bitm,96.360,3.640,99.600,0.400,387.93,480,1.000,bilinear
@ -33,6 +38,7 @@ 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
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
vit_base_patch16_384,96.190,3.810,99.530,0.470,86.86,384,1.000,bicubic
resnetv2_50x3_bitm,96.140,3.860,99.620,0.380,217.32,480,1.000,bilinear
@ -40,49 +46,59 @@ resnest269e,96.120,3.880,99.520,0.480,110.93,416,0.928,bicubic
resnet200d,96.110,3.890,99.460,0.540,64.69,320,1.000,bicubic
tf_efficientnet_b3_ns,96.100,3.900,99.480,0.520,12.23,300,0.904,bicubic
tf_efficientnet_b5_ap,96.080,3.920,99.540,0.460,30.39,456,0.934,bicubic
resnest200e,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic
pit_b_distilled_224,96.070,3.930,99.380,0.620,74.79,224,0.900,bicubic
resnest200e,96.070,3.930,99.480,0.520,70.20,320,0.909,bicubic
resnetrs270,96.060,3.940,99.490,0.510,129.86,352,1.000,bicubic
swsl_resnext101_32x4d,96.050,3.950,99.530,0.470,44.18,224,0.875,bilinear
vit_base_patch16_224_miil,96.030,3.970,99.350,0.650,86.54,224,0.875,bilinear
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
eca_nfnet_l1,95.930,4.070,99.500,0.500,41.41,320,1.000,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
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
vit_deit_base_patch16_384,95.650,4.350,99.240,0.760,86.86,384,1.000,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
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
tresnet_l_448,95.410,4.590,99.300,0.700,55.99,448,0.875,bilinear
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
tresnet_m,95.380,4.620,99.150,0.850,31.39,224,0.875,bilinear
pnasnet5large,95.360,4.640,99.130,0.870,86.06,331,0.911,bicubic
ssl_resnext101_32x8d,95.340,4.660,99.320,0.680,88.79,224,0.875,bilinear
vit_large_patch16_224,95.290,4.710,99.310,0.690,304.33,224,0.900,bicubic
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
vit_base_patch16_224,95.210,4.790,99.230,0.770,86.57,224,0.900,bicubic
swsl_resnet50,95.200,4.800,99.390,0.610,25.56,224,0.875,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
ecaresnet101d,95.160,4.840,99.230,0.770,44.57,224,0.875,bicubic
nasnetalarge,95.150,4.850,99.130,0.870,88.75,331,0.911,bicubic
efficientnet_b3a,95.140,4.860,99.210,0.790,12.23,320,1.000,bicubic
efficientnet_b3,95.140,4.860,99.210,0.790,12.23,320,1.000,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
efficientnet_v2s,94.990,5.010,99.080,0.920,23.94,224,1.000,bicubic
efficientnet_b3,94.970,5.030,99.230,0.770,12.23,300,0.904,bicubic
tf_efficientnet_b3_ap,94.970,5.030,99.110,0.890,12.23,300,0.904,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
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
tf_efficientnet_lite4,94.870,5.130,99.090,0.910,13.01,380,0.920,bilinear
@ -95,10 +111,10 @@ 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
efficientnet_el,94.670,5.330,99.130,0.870,10.59,300,0.904,bicubic
wide_resnet50_2,94.670,5.330,99.050,0.950,68.88,224,0.875,bicubic
tresnet_m_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear
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
tresnet_m_448,94.660,5.340,99.150,0.850,31.39,448,0.875,bilinear
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
@ -117,44 +133,44 @@ 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
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_b2a,94.370,5.630,99.050,0.950,9.11,288,1.000,bicubic
efficientnet_b2,94.370,5.630,99.050,0.950,9.11,288,1.000,bicubic
tf_efficientnet_el,94.360,5.640,99.100,0.900,10.59,300,0.904,bicubic
gluon_resnext101_64x4d,94.350,5.650,98.880,1.120,83.46,224,0.875,bicubic
efficientnet_b2,94.340,5.660,99.100,0.900,9.11,260,0.875,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
mixnet_xl,94.230,5.770,98.820,1.180,11.90,224,0.875,bicubic
tf_efficientnet_b2,94.210,5.790,99.030,0.970,9.11,260,0.890,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_resnet101_v1d,94.170,5.830,98.940,1.060,44.57,224,0.875,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
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
regnety_064,94.140,5.860,99.030,0.970,30.58,224,0.875,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
gluon_resnext101_32x4d,94.120,5.880,98.930,1.070,44.18,224,0.875,bicubic
efficientnet_el_pruned,94.090,5.910,99.010,0.990,10.59,300,0.904,bicubic
cspdarknet53,94.090,5.910,98.980,1.020,27.64,256,0.887,bilinear
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
tresnet_m,94.070,5.930,98.830,1.170,31.39,224,0.875,bilinear
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
gluon_xception65,94.010,5.990,99.020,0.980,39.92,299,0.903,bicubic
regnety_120,94.010,5.990,99.030,0.970,51.82,224,0.875,bicubic
vit_deit_small_patch16_224,94.000,6.000,98.960,1.040,22.05,224,0.900,bicubic
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
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
@ -164,23 +180,23 @@ 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
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
ecaresnet50d_pruned,93.820,6.180,99.000,1.000,19.94,224,0.875,bicubic
resnext50d_32x4d,93.810,6.190,98.740,1.260,25.05,224,0.875,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
dla169,93.800,6.200,98.840,1.160,53.39,224,0.875,bilinear
regnetx_080,93.790,6.210,98.910,1.090,39.57,224,0.875,bicubic
resnext101_32x8d,93.770,6.230,98.950,1.050,88.79,224,0.875,bilinear
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
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
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
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
resnetblur50,93.710,6.290,98.810,1.190,25.56,224,0.875,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
@ -188,12 +204,12 @@ 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
regnety_040,93.620,6.380,98.950,1.050,20.65,224,0.875,bicubic
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
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
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
@ -201,8 +217,10 @@ 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
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
resnet50,93.460,6.540,98.600,1.400,25.56,224,0.875,bicubic
coat_lite_mini,93.450,6.550,98.780,1.220,11.01,224,0.900,bicubic
res2net50_26w_8s,93.450,6.550,98.700,1.300,48.40,224,0.875,bilinear
legacy_seresnet152,93.440,6.560,98.850,1.150,66.82,224,0.875,bilinear
legacy_seresnext50_32x4d,93.430,6.570,98.800,1.200,27.56,224,0.875,bilinear
@ -214,32 +232,33 @@ 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
pit_xs_distilled_224,93.240,6.760,98.820,1.180,11.00,224,0.900,bicubic
tv_resnet152,93.240,6.760,98.750,1.250,60.19,224,0.875,bilinear
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
efficientnet_b1,93.060,6.940,98.540,1.460,7.79,240,0.875,bicubic
tf_mixnet_l,93.040,6.960,98.540,1.460,7.33,224,0.875,bicubic
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
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
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
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
pit_xs_224,92.910,7.090,98.780,1.220,10.62,224,0.900,bicubic
gluon_resnet50_v1c,92.910,7.090,98.710,1.290,25.58,224,0.875,bicubic
hardcorenas_e,92.950,7.050,98.570,1.430,8.07,224,0.875,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
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
rexnet_100,92.850,7.150,98.620,1.380,4.80,224,0.875,bicubic
tf_efficientnet_cc_b0_4e,92.840,7.160,98.440,1.560,13.31,224,0.875,bicubic
seresnext26t_32x4d,92.820,7.180,98.560,1.440,16.81,224,0.875,bicubic
@ -252,8 +271,8 @@ legacy_seresnet50,92.670,7.330,98.650,1.350,28.09,224,0.875,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
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
hardcorenas_d,92.600,7.400,98.430,1.570,7.50,224,0.875,bilinear
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
@ -269,6 +288,7 @@ hardcorenas_c,92.330,7.670,98.340,1.660,5.52,224,0.875,bilinear
tf_efficientnet_lite1,92.310,7.690,98.490,1.510,5.42,240,0.882,bicubic
densenet169,92.300,7.700,98.590,1.410,14.15,224,0.875,bicubic
mixnet_m,92.270,7.730,98.350,1.650,5.01,224,0.875,bicubic
mobilenetv3_large_100_miil,92.250,7.750,98.250,1.750,5.48,224,0.875,bilinear
dpn68,92.240,7.760,98.610,1.390,12.61,224,0.875,bicubic
resnet26d,92.230,7.770,98.450,1.550,16.01,224,0.875,bicubic
tf_mixnet_m,92.200,7.800,98.420,1.580,5.01,224,0.875,bicubic
@ -276,8 +296,8 @@ tv_resnet50,92.140,7.860,98.420,1.580,25.56,224,0.875,bilinear
tf_efficientnet_es,92.100,7.900,98.440,1.560,5.44,224,0.875,bicubic
mobilenetv2_140,92.030,7.970,98.250,1.750,6.11,224,0.875,bicubic
ese_vovnet19b_dw,92.010,7.990,98.510,1.490,6.54,224,0.875,bicubic
hardcorenas_b,91.940,8.060,98.400,1.600,5.18,224,0.875,bilinear
densenet121,91.940,8.060,98.280,1.720,7.98,224,0.875,bicubic
hardcorenas_b,91.940,8.060,98.400,1.600,5.18,224,0.875,bilinear
regnety_008,91.900,8.100,98.420,1.580,6.26,224,0.875,bicubic
mixnet_s,91.780,8.220,98.300,1.700,4.13,224,0.875,bicubic
efficientnet_es_pruned,91.700,8.300,98.420,1.580,5.44,224,0.875,bicubic
@ -301,6 +321,7 @@ 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
regnetx_008,91.180,8.820,98.380,1.620,7.26,224,0.875,bicubic
hrnet_w18_small_v2,91.170,8.830,98.340,1.660,15.60,224,0.875,bilinear
mixer_b16_224,91.140,8.860,97.400,2.600,59.88,224,0.875,bicubic
resnest14d,91.130,8.870,98.330,1.670,10.61,224,0.875,bilinear
gluon_resnet34_v1b,91.100,8.900,98.180,1.820,21.80,224,0.875,bicubic
vit_deit_tiny_distilled_patch16_224,91.100,8.900,98.270,1.730,5.91,224,0.900,bicubic
@ -312,6 +333,7 @@ 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
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
tv_resnet34,90.290,9.710,97.980,2.020,21.80,224,0.875,bilinear
@ -334,6 +356,7 @@ vgg13,87.570,12.430,97.120,2.880,133.05,224,0.875,bilinear
regnetx_002,87.380,12.620,96.990,3.010,2.68,224,0.875,bicubic
vgg11,87.340,12.660,97.110,2.890,132.86,224,0.875,bilinear
dla60x_c,87.110,12.890,97.140,2.860,1.32,224,0.875,bilinear
mixer_l16_224,86.970,13.030,94.060,5.940,208.20,224,0.875,bicubic
tf_mobilenetv3_small_100,85.960,14.040,96.400,3.600,2.54,224,0.875,bilinear
dla46x_c,85.480,14.520,96.440,3.560,1.07,224,0.875,bilinear
dla46_c,84.660,15.340,96.200,3.800,1.30,224,0.875,bilinear

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
8 dm_nfnet_f6 96.990 3.010 99.740 0.260 438.36 576 0.956 bicubic
9 ig_resnext101_32x48d 96.970 3.030 99.670 0.330 828.41 224 0.875 bilinear
10 swin_large_patch4_window7_224 96.950 3.050 99.660 0.340 196.53 224 0.900 bicubic
11 cait_m48_448 96.880 3.120 99.620 0.380 356.46 448 1.000 bicubic
12 resnetv2_152x4_bitm 96.880 3.120 99.660 0.340 936.53 480 1.000 bilinear
13 tf_efficientnet_b5_ns 96.870 3.130 99.640 0.360 30.39 456 0.934 bicubic
14 cait_m36_384 96.830 3.170 99.660 0.340 271.22 384 1.000 bicubic
15 dm_nfnet_f4 96.820 3.180 99.600 0.400 316.07 512 0.951 bicubic
16 ig_resnext101_32x32d 96.780 3.220 99.530 0.470 468.53 224 0.875 bilinear
17 dm_nfnet_f5 96.710 3.290 99.680 0.320 377.21 544 0.954 bicubic
18 tf_efficientnet_b4_ns 96.710 3.290 99.640 0.360 19.34 380 0.922 bicubic
19 tf_efficientnet_b8 96.700 3.300 99.530 0.470 87.41 672 0.954 bicubic
20 swin_base_patch4_window7_224 96.680 3.320 99.660 0.340 87.77 224 0.900 bicubic
21 cait_s36_384 96.630 3.370 99.600 0.400 68.37 384 1.000 bicubic
22 dm_nfnet_f3 96.630 3.370 99.640 0.360 254.92 416 0.940 bicubic
23 tf_efficientnet_b7 96.580 3.420 99.510 0.490 66.35 600 0.949 bicubic
24 cait_s24_384 96.570 3.430 99.550 0.450 47.06 384 1.000 bicubic
25 tf_efficientnet_b8_ap 96.550 3.450 99.540 0.460 87.41 672 0.954 bicubic
26 vit_deit_base_distilled_patch16_384 96.510 3.490 99.590 0.410 87.63 384 1.000 bicubic
27 dm_nfnet_f2 96.500 3.500 99.570 0.430 193.78 352 0.920 bicubic
29 ecaresnet269d 96.460 3.540 99.610 0.390 102.09 352 1.000 bicubic
30 vit_base_r50_s16_384 96.450 3.550 99.660 0.340 98.95 384 1.000 bicubic
31 ig_resnext101_32x16d 96.440 3.560 99.540 0.460 194.03 224 0.875 bilinear
32 resnetrs420 96.400 3.600 99.540 0.460 191.89 416 1.000 bicubic
33 dm_nfnet_f1 96.370 3.630 99.470 0.530 132.63 320 0.910 bicubic
34 tf_efficientnet_b6_ap 96.370 3.630 99.550 0.450 43.04 528 0.942 bicubic
35 resnetv2_101x3_bitm 96.360 3.640 99.600 0.400 387.93 480 1.000 bilinear
38 seresnet152d 96.310 3.690 99.510 0.490 66.84 320 1.000 bicubic
39 tf_efficientnet_b6 96.290 3.710 99.520 0.480 43.04 528 0.942 bicubic
40 swsl_resnext101_32x16d 96.270 3.730 99.500 0.500 194.03 224 0.875 bilinear
41 resnetrs350 96.240 3.760 99.470 0.530 163.96 384 1.000 bicubic
42 swsl_resnext101_32x8d 96.240 3.760 99.590 0.410 88.79 224 0.875 bilinear
43 vit_base_patch16_384 96.190 3.810 99.530 0.470 86.86 384 1.000 bicubic
44 resnetv2_50x3_bitm 96.140 3.860 99.620 0.380 217.32 480 1.000 bilinear
46 resnet200d 96.110 3.890 99.460 0.540 64.69 320 1.000 bicubic
47 tf_efficientnet_b3_ns 96.100 3.900 99.480 0.520 12.23 300 0.904 bicubic
48 tf_efficientnet_b5_ap 96.080 3.920 99.540 0.460 30.39 456 0.934 bicubic
resnest200e 96.070 3.930 99.480 0.520 70.20 320 0.909 bicubic
49 pit_b_distilled_224 96.070 3.930 99.380 0.620 74.79 224 0.900 bicubic
50 resnest200e 96.070 3.930 99.480 0.520 70.20 320 0.909 bicubic
51 resnetrs270 96.060 3.940 99.490 0.510 129.86 352 1.000 bicubic
52 swsl_resnext101_32x4d 96.050 3.950 99.530 0.470 44.18 224 0.875 bilinear
53 vit_base_patch16_224_miil 96.030 3.970 99.350 0.650 86.54 224 0.875 bilinear
54 cait_xs24_384 96.010 3.990 99.430 0.570 26.67 384 1.000 bicubic
55 resnetrs200 95.990 4.010 99.440 0.560 93.21 320 1.000 bicubic
56 tf_efficientnet_b5 95.980 4.020 99.450 0.550 30.39 456 0.934 bicubic
57 eca_nfnet_l1 resnetrs152 95.930 95.960 4.070 4.040 99.500 99.380 0.500 0.620 41.41 86.62 320 1.000 bicubic
58 ig_resnext101_32x8d 95.930 4.070 99.380 0.620 88.79 224 0.875 bilinear
59 eca_nfnet_l1 95.930 4.070 99.500 0.500 41.41 320 1.000 bicubic
60 regnety_160 95.880 4.120 99.560 0.440 83.59 288 1.000 bicubic
61 resnet152d 95.870 4.130 99.430 0.570 60.21 320 1.000 bicubic
62 resnet101d 95.750 4.250 99.440 0.560 44.57 320 1.000 bicubic
63 vit_deit_base_distilled_patch16_224 95.750 4.250 99.280 0.720 87.34 224 0.900 bicubic
64 swin_small_patch4_window7_224 95.720 4.280 99.290 0.710 49.61 224 0.900 bicubic
65 efficientnet_v2s 95.710 4.290 99.380 0.620 23.94 384 1.000 bicubic
66 cait_s24_224 95.650 4.350 99.390 0.610 46.92 224 1.000 bicubic
67 vit_deit_base_patch16_384 95.650 4.350 99.240 0.760 86.86 384 1.000 bicubic
68 dm_nfnet_f0 95.630 4.370 99.300 0.700 71.49 256 0.900 bicubic
69 swsl_resnext50_32x4d 95.620 4.380 99.440 0.560 25.03 224 0.875 bilinear
70 tf_efficientnet_b4 95.590 4.410 99.330 0.670 19.34 380 0.922 bicubic
71 resnest101e 95.570 4.430 99.270 0.730 48.28 256 0.875 bilinear
72 efficientnet_b4 95.520 4.480 99.390 0.610 19.34 384 1.000 bicubic
73 tf_efficientnet_b2_ns 95.520 4.480 99.340 0.660 9.11 260 0.890 bicubic
74 resnetv2_101x1_bitm 95.510 4.490 99.510 0.490 44.54 480 1.000 bilinear
75 tresnet_xl_448 95.510 4.490 99.340 0.660 78.44 448 0.875 bilinear
76 tf_efficientnet_b4_ap 95.490 4.510 99.390 0.610 19.34 380 0.922 bicubic
77 eca_nfnet_l0 95.470 4.530 99.380 0.620 24.14 288 1.000 bicubic
78 regnety_032 95.470 4.530 99.320 0.680 19.44 288 1.000 bicubic
tresnet_l_448 95.410 4.590 99.300 0.700 55.99 448 0.875 bilinear
79 ssl_resnext101_32x16d 95.410 4.590 99.410 0.590 194.03 224 0.875 bilinear
80 tresnet_l_448 95.410 4.590 99.300 0.700 55.99 448 0.875 bilinear
81 nfnet_l0 95.390 4.610 99.420 0.580 35.07 288 1.000 bicubic
82 tresnet_m 95.380 4.620 99.150 0.850 31.39 224 0.875 bilinear
83 pnasnet5large 95.360 4.640 99.130 0.870 86.06 331 0.911 bicubic
84 ssl_resnext101_32x8d 95.340 4.660 99.320 0.680 88.79 224 0.875 bilinear
85 vit_large_patch16_224 95.290 4.710 99.310 0.690 304.33 224 0.900 bicubic
86 vit_base_patch32_384 95.260 4.740 99.180 0.820 88.30 384 1.000 bicubic
87 resnetrs101 95.250 4.750 99.210 0.790 63.62 288 0.940 bicubic
88 vit_large_patch32_384 95.240 4.760 99.320 0.680 306.63 384 1.000 bicubic
89 cait_xxs36_384 95.220 4.780 99.320 0.680 17.37 384 1.000 bicubic
90 vit_base_patch16_224 95.210 4.790 99.230 0.770 86.57 224 0.900 bicubic
91 swsl_resnet50 95.200 4.800 99.390 0.610 25.56 224 0.875 bilinear
ecaresnet101d 95.160 4.840 99.230 0.770 44.57 224 0.875 bicubic
92 ssl_resnext101_32x4d 95.160 4.840 99.300 0.700 44.18 224 0.875 bilinear
93 ecaresnet101d 95.160 4.840 99.230 0.770 44.57 224 0.875 bicubic
94 nasnetalarge 95.150 4.850 99.130 0.870 88.75 331 0.911 bicubic
95 efficientnet_b3a efficientnet_b3 95.140 4.860 99.210 0.790 12.23 320 1.000 bicubic
96 ecaresnet50t 95.070 4.930 99.290 0.710 25.57 320 0.950 bicubic
97 tresnet_xl 95.060 4.940 99.260 0.740 78.44 224 0.875 bilinear
98 vit_deit_base_patch16_224 95.010 4.990 98.980 1.020 86.57 224 0.900 bicubic
efficientnet_v2s 94.990 5.010 99.080 0.920 23.94 224 1.000 bicubic
efficientnet_b3 94.970 5.030 99.230 0.770 12.23 300 0.904 bicubic
99 tf_efficientnet_b3_ap 94.970 5.030 99.110 0.890 12.23 300 0.904 bicubic
100 gernet_l 94.930 5.070 99.200 0.800 31.08 256 0.875 bilinear
101 cait_xxs24_384 94.920 5.080 99.140 0.860 12.03 384 1.000 bicubic
102 tf_efficientnet_b3 94.910 5.090 99.110 0.890 12.23 300 0.904 bicubic
103 tresnet_l 94.900 5.100 99.030 0.970 55.99 224 0.875 bilinear
104 tf_efficientnet_lite4 94.870 5.130 99.090 0.910 13.01 380 0.920 bilinear
111 gluon_senet154 94.710 5.290 98.970 1.030 115.09 224 0.875 bicubic
112 resnest50d_4s2x40d 94.710 5.290 99.130 0.870 30.42 224 0.875 bicubic
113 ssl_resnext50_32x4d 94.700 5.300 99.240 0.760 25.03 224 0.875 bilinear
efficientnet_el 94.670 5.330 99.130 0.870 10.59 300 0.904 bicubic
114 wide_resnet50_2 94.670 5.330 99.050 0.950 68.88 224 0.875 bicubic
115 tresnet_m_448 efficientnet_el 94.660 94.670 5.340 5.330 99.150 99.130 0.850 0.870 31.39 10.59 448 300 0.875 0.904 bilinear bicubic
116 rexnet_200 94.660 5.340 99.090 0.910 16.37 224 0.875 bicubic
117 tresnet_m_448 94.660 5.340 99.150 0.850 31.39 448 0.875 bilinear
118 gluon_seresnext101_64x4d 94.650 5.350 98.980 1.020 88.23 224 0.875 bicubic
119 resnest50d 94.620 5.380 99.030 0.970 27.48 224 0.875 bilinear
120 swin_tiny_patch4_window7_224 94.620 5.380 99.120 0.880 28.29 224 0.900 bicubic
133 nf_resnet50 94.410 5.590 99.100 0.900 25.56 288 0.940 bicubic
134 resnest50d_1s4x24d 94.390 5.610 99.070 0.930 25.68 224 0.875 bicubic
135 inception_v4 94.380 5.620 98.820 1.180 42.68 299 0.875 bicubic
136 efficientnet_b2a efficientnet_b2 94.370 5.630 99.050 0.950 9.11 288 1.000 bicubic
137 tf_efficientnet_el 94.360 5.640 99.100 0.900 10.59 300 0.904 bicubic
138 gluon_resnext101_64x4d 94.350 5.650 98.880 1.120 83.46 224 0.875 bicubic
efficientnet_b2 94.340 5.660 99.100 0.900 9.11 260 0.875 bicubic
139 inception_resnet_v2 94.340 5.660 98.800 1.200 55.84 299 0.897 bicubic
140 ssl_resnet50 94.310 5.690 99.150 0.850 25.56 224 0.875 bilinear
141 regnetx_120 94.270 5.730 99.190 0.810 46.11 224 0.875 bicubic
142 rexnet_150 94.270 5.730 99.080 0.920 9.73 224 0.875 bicubic
143 tf_efficientnet_b2_ap 94.270 5.730 98.950 1.050 9.11 260 0.890 bicubic
144 mixnet_xl 94.230 5.770 98.820 1.180 11.90 224 0.875 bicubic
tf_efficientnet_b2 94.210 5.790 99.030 0.970 9.11 260 0.890 bicubic
145 regnetx_320 94.210 5.790 99.050 0.950 107.81 224 0.875 bicubic
146 tf_efficientnet_b2 94.210 5.790 99.030 0.970 9.11 260 0.890 bicubic
147 dpn92 94.190 5.810 98.930 1.070 37.67 224 0.875 bicubic
148 ecaresnet50d 94.190 5.810 99.020 0.980 25.58 224 0.875 bicubic
gluon_resnet101_v1d 94.170 5.830 98.940 1.060 44.57 224 0.875 bicubic
gluon_resnet101_v1s 94.170 5.830 99.010 0.990 44.67 224 0.875 bicubic
149 gluon_seresnext50_32x4d 94.170 5.830 98.910 1.090 27.56 224 0.875 bicubic
150 gluon_resnet101_v1s 94.170 5.830 99.010 0.990 44.67 224 0.875 bicubic
151 gluon_resnet101_v1d 94.170 5.830 98.940 1.060 44.57 224 0.875 bicubic
152 ecaresnetlight 94.140 5.860 98.950 1.050 30.16 224 0.875 bicubic
153 regnety_064 94.140 5.860 99.030 0.970 30.58 224 0.875 bicubic
154 ens_adv_inception_resnet_v2 94.130 5.870 98.790 1.210 55.84 299 0.897 bicubic
155 legacy_seresnext101_32x4d 94.130 5.870 98.970 1.030 48.96 224 0.875 bilinear
156 tf_efficientnet_lite3 94.130 5.870 98.960 1.040 8.20 300 0.904 bilinear
157 gluon_resnext101_32x4d 94.120 5.880 98.930 1.070 44.18 224 0.875 bicubic
efficientnet_el_pruned 94.090 5.910 99.010 0.990 10.59 300 0.904 bicubic
158 cspdarknet53 94.090 5.910 98.980 1.020 27.64 256 0.887 bilinear
159 efficientnet_el_pruned 94.090 5.910 99.010 0.990 10.59 300 0.904 bicubic
160 seresnet50 94.080 5.920 98.970 1.030 28.09 224 0.875 bicubic
161 resnet50d 94.070 5.930 98.920 1.080 25.58 224 0.875 bicubic
tresnet_m 94.070 5.930 98.830 1.170 31.39 224 0.875 bilinear
162 gluon_resnet152_v1b 94.030 5.970 98.740 1.260 60.19 224 0.875 bicubic
163 hrnet_w48 94.030 5.970 99.040 0.960 77.47 224 0.875 bilinear
164 resnetrs50 94.020 5.980 98.850 1.150 35.69 224 0.910 bicubic
165 gluon_xception65 94.010 5.990 99.020 0.980 39.92 299 0.903 bicubic
166 regnety_120 94.010 5.990 99.030 0.970 51.82 224 0.875 bicubic
vit_deit_small_patch16_224 94.000 6.000 98.960 1.040 22.05 224 0.900 bicubic
167 dla102x2 94.000 6.000 99.030 0.970 41.28 224 0.875 bilinear
168 vit_deit_small_patch16_224 94.000 6.000 98.960 1.040 22.05 224 0.900 bicubic
169 dpn107 93.960 6.040 98.840 1.160 86.92 224 0.875 bicubic
170 skresnext50_32x4d 93.950 6.050 98.820 1.180 27.48 224 0.875 bicubic
171 dpn98 93.940 6.060 98.920 1.080 61.57 224 0.875 bicubic
172 ecaresnet26t 93.940 6.060 98.920 1.080 16.01 320 0.950 bicubic
173 cait_xxs36_224 93.940 6.060 98.890 1.110 17.30 224 1.000 bicubic
174 nf_regnet_b1 93.890 6.110 98.750 1.250 10.22 288 0.900 bicubic
175 regnety_080 93.890 6.110 99.000 1.000 39.18 224 0.875 bicubic
176 xception71 93.890 6.110 98.950 1.050 42.34 299 0.903 bicubic
180 ese_vovnet39b 93.850 6.150 98.900 1.100 24.57 224 0.875 bicubic
181 resnext50_32x4d 93.840 6.160 98.830 1.170 25.03 224 0.875 bicubic
182 hrnet_w64 93.830 6.170 98.930 1.070 128.06 224 0.875 bilinear
ecaresnet50d_pruned 93.820 6.180 99.000 1.000 19.94 224 0.875 bicubic
183 repvgg_b2g4 93.820 6.180 98.930 1.070 61.76 224 0.875 bilinear
184 ecaresnet50d_pruned 93.820 6.180 99.000 1.000 19.94 224 0.875 bicubic
185 resnext50d_32x4d 93.810 6.190 98.740 1.260 25.05 224 0.875 bicubic
dla169 93.800 6.200 98.840 1.160 53.39 224 0.875 bilinear
186 efficientnet_b2_pruned 93.800 6.200 98.910 1.090 8.31 260 0.890 bicubic
187 dla169 93.800 6.200 98.840 1.160 53.39 224 0.875 bilinear
188 regnetx_080 93.790 6.210 98.910 1.090 39.57 224 0.875 bicubic
189 resnext101_32x8d 93.770 6.230 98.950 1.050 88.79 224 0.875 bilinear
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
190 cspresnext50 93.760 6.240 98.840 1.160 20.57 224 0.875 bilinear
191 dpn131 93.760 6.240 98.800 1.200 79.25 224 0.875 bicubic
192 gluon_resnet101_v1b 93.760 6.240 98.700 1.300 44.55 224 0.875 bicubic
193 xception65 93.760 6.240 98.860 1.140 39.92 299 0.903 bicubic
194 efficientnet_em 93.740 6.260 98.930 1.070 6.90 240 0.882 bicubic
195 tf_efficientnet_b0_ns 93.740 6.260 98.980 1.020 5.29 224 0.875 bicubic
196 wide_resnet101_2 93.730 6.270 98.810 1.190 126.89 224 0.875 bilinear
197 hrnet_w40 93.710 6.290 98.800 1.200 57.56 224 0.875 bilinear
resnetblur50 93.710 6.290 98.810 1.190 25.56 224 0.875 bicubic
198 tf_efficientnet_b1 93.710 6.290 98.800 1.200 7.79 240 0.882 bicubic
199 resnetblur50 93.710 6.290 98.810 1.190 25.56 224 0.875 bicubic
200 gluon_resnet101_v1c 93.690 6.310 98.760 1.240 44.57 224 0.875 bicubic
201 regnetx_040 93.680 6.320 98.940 1.060 22.12 224 0.875 bicubic
202 rexnet_130 93.670 6.330 98.710 1.290 7.56 224 0.875 bicubic
204 xception 93.640 6.360 98.770 1.230 22.86 299 0.897 bicubic
205 regnetx_064 93.630 6.370 99.050 0.950 26.21 224 0.875 bicubic
206 tf_efficientnet_b1_ap 93.630 6.370 98.800 1.200 7.79 240 0.882 bicubic
207 regnety_040 93.620 6.380 98.950 1.050 20.65 224 0.875 bicubic
208 dpn68b 93.620 6.380 98.700 1.300 12.61 224 0.875 bicubic
209 hrnet_w44 93.620 6.380 98.960 1.040 67.06 224 0.875 bilinear
regnety_040 93.620 6.380 98.950 1.050 20.65 224 0.875 bicubic
res2net50_26w_6s 93.590 6.410 98.750 1.250 37.05 224 0.875 bilinear
210 gluon_resnet50_v1s 93.590 6.410 98.840 1.160 25.68 224 0.875 bicubic
211 repvgg_b2 93.590 6.410 99.070 0.930 89.02 224 0.875 bilinear
212 res2net50_26w_6s 93.590 6.410 98.750 1.250 37.05 224 0.875 bilinear
213 dla60_res2next 93.570 6.430 98.800 1.200 17.03 224 0.875 bilinear
214 tf_efficientnet_cc_b1_8e 93.570 6.430 98.690 1.310 39.72 240 0.882 bicubic
215 gluon_inception_v3 93.540 6.460 98.830 1.170 23.83 299 0.875 bicubic
217 gluon_resnet50_v1d 93.530 6.470 98.710 1.290 25.58 224 0.875 bicubic
218 res2net101_26w_4s 93.520 6.480 98.600 1.400 45.21 224 0.875 bilinear
219 selecsls60b 93.500 6.500 98.840 1.160 32.77 224 0.875 bicubic
220 cait_xxs24_224 93.490 6.510 98.770 1.230 11.96 224 1.000 bicubic
221 xception41 93.480 6.520 98.750 1.250 26.97 299 0.903 bicubic
222 resnet50 93.460 6.540 98.600 1.400 25.56 224 0.875 bicubic
223 coat_lite_mini 93.450 6.550 98.780 1.220 11.01 224 0.900 bicubic
224 res2net50_26w_8s 93.450 6.550 98.700 1.300 48.40 224 0.875 bilinear
225 legacy_seresnet152 93.440 6.560 98.850 1.150 66.82 224 0.875 bilinear
226 legacy_seresnext50_32x4d 93.430 6.570 98.800 1.200 27.56 224 0.875 bilinear
232 mixnet_l 93.260 6.740 98.700 1.300 7.33 224 0.875 bicubic
233 regnetx_032 93.250 6.750 98.730 1.270 15.30 224 0.875 bicubic
234 pit_xs_distilled_224 93.240 6.760 98.820 1.180 11.00 224 0.900 bicubic
tv_resnet152 93.240 6.760 98.750 1.250 60.19 224 0.875 bilinear
235 resnest26d 93.240 6.760 98.850 1.150 17.07 224 0.875 bilinear
236 tv_resnet152 93.240 6.760 98.750 1.250 60.19 224 0.875 bilinear
237 tf_inception_v3 93.200 6.800 98.480 1.520 23.83 299 0.875 bicubic
238 dla60x 93.190 6.810 98.710 1.290 17.35 224 0.875 bilinear
239 res2net50_26w_4s 93.180 6.820 98.670 1.330 25.70 224 0.875 bilinear
240 tf_efficientnet_em 93.170 6.830 98.670 1.330 6.90 240 0.882 bicubic
241 res2next50 93.150 6.850 98.660 1.340 24.67 224 0.875 bilinear
efficientnet_b1 93.060 6.940 98.540 1.460 7.79 240 0.875 bicubic
242 tf_mixnet_l 93.040 6.960 98.540 1.460 7.33 224 0.875 bicubic
243 res2net50_14w_8s efficientnet_b1 93.030 6.970 98.700 98.710 1.300 1.290 25.06 7.79 224 256 0.875 1.000 bilinear bicubic
244 repvgg_b1g4 93.030 6.970 98.820 1.180 39.97 224 0.875 bilinear
245 res2net50_14w_8s 93.030 6.970 98.700 1.300 25.06 224 0.875 bilinear
246 adv_inception_v3 93.010 6.990 98.490 1.510 23.83 299 0.875 bicubic
247 selecsls60 93.010 6.990 98.830 1.170 30.67 224 0.875 bicubic
248 regnety_016 93.000 7.000 98.680 1.320 11.20 224 0.875 bicubic
249 efficientnet_b1_pruned 92.980 7.020 98.530 1.470 6.33 240 0.882 bicubic
250 hardcorenas_f 92.980 7.020 98.620 1.380 8.20 224 0.875 bilinear
hardcorenas_e 92.950 7.050 98.570 1.430 8.07 224 0.875 bilinear
251 hrnet_w32 92.950 7.050 98.840 1.160 41.23 224 0.875 bilinear
252 pit_xs_224 hardcorenas_e 92.910 92.950 7.090 7.050 98.780 98.570 1.220 1.430 10.62 8.07 224 0.900 0.875 bicubic bilinear
gluon_resnet50_v1c 92.910 7.090 98.710 1.290 25.58 224 0.875 bicubic
253 efficientnet_es 92.910 7.090 98.690 1.310 5.44 224 0.875 bicubic
254 gluon_resnet50_v1c 92.910 7.090 98.710 1.290 25.58 224 0.875 bicubic
255 pit_xs_224 92.910 7.090 98.780 1.220 10.62 224 0.900 bicubic
256 densenet161 92.900 7.100 98.810 1.190 28.68 224 0.875 bicubic
257 inception_v3 92.900 7.100 98.330 1.670 23.83 299 0.875 bicubic
258 tv_resnext50_32x4d 92.900 7.100 98.720 1.280 25.03 224 0.875 bilinear
259 tv_resnet101 92.880 7.120 98.660 1.340 44.55 224 0.875 bilinear
260 tf_efficientnet_cc_b0_8e 92.870 7.130 98.460 1.540 24.01 224 0.875 bicubic
261 coat_lite_tiny 92.850 7.150 98.640 1.360 5.72 224 0.900 bicubic
262 rexnet_100 92.850 7.150 98.620 1.380 4.80 224 0.875 bicubic
263 tf_efficientnet_cc_b0_4e 92.840 7.160 98.440 1.560 13.31 224 0.875 bicubic
264 seresnext26t_32x4d 92.820 7.180 98.560 1.440 16.81 224 0.875 bicubic
271 resnet34d 92.640 7.360 98.420 1.580 21.82 224 0.875 bicubic
272 mobilenetv2_120d 92.610 7.390 98.510 1.490 5.83 224 0.875 bicubic
273 tf_efficientnet_b0_ap 92.610 7.390 98.370 1.630 5.29 224 0.875 bicubic
hardcorenas_d 92.600 7.400 98.430 1.570 7.50 224 0.875 bilinear
274 vit_small_patch16_224 92.600 7.400 98.390 1.610 48.75 224 0.900 bicubic
275 hardcorenas_d 92.600 7.400 98.430 1.570 7.50 224 0.875 bilinear
276 tf_efficientnet_lite2 92.590 7.410 98.550 1.450 6.09 260 0.890 bicubic
277 legacy_seresnext26_32x4d 92.570 7.430 98.420 1.580 16.79 224 0.875 bicubic
278 skresnet34 92.570 7.430 98.520 1.480 22.28 224 0.875 bicubic
288 tf_efficientnet_lite1 92.310 7.690 98.490 1.510 5.42 240 0.882 bicubic
289 densenet169 92.300 7.700 98.590 1.410 14.15 224 0.875 bicubic
290 mixnet_m 92.270 7.730 98.350 1.650 5.01 224 0.875 bicubic
291 mobilenetv3_large_100_miil 92.250 7.750 98.250 1.750 5.48 224 0.875 bilinear
292 dpn68 92.240 7.760 98.610 1.390 12.61 224 0.875 bicubic
293 resnet26d 92.230 7.770 98.450 1.550 16.01 224 0.875 bicubic
294 tf_mixnet_m 92.200 7.800 98.420 1.580 5.01 224 0.875 bicubic
296 tf_efficientnet_es 92.100 7.900 98.440 1.560 5.44 224 0.875 bicubic
297 mobilenetv2_140 92.030 7.970 98.250 1.750 6.11 224 0.875 bicubic
298 ese_vovnet19b_dw 92.010 7.990 98.510 1.490 6.54 224 0.875 bicubic
hardcorenas_b 91.940 8.060 98.400 1.600 5.18 224 0.875 bilinear
299 densenet121 91.940 8.060 98.280 1.720 7.98 224 0.875 bicubic
300 hardcorenas_b 91.940 8.060 98.400 1.600 5.18 224 0.875 bilinear
301 regnety_008 91.900 8.100 98.420 1.580 6.26 224 0.875 bicubic
302 mixnet_s 91.780 8.220 98.300 1.700 4.13 224 0.875 bicubic
303 efficientnet_es_pruned 91.700 8.300 98.420 1.580 5.44 224 0.875 bicubic
321 mnasnet_100 91.200 8.800 98.050 1.950 4.38 224 0.875 bicubic
322 regnetx_008 91.180 8.820 98.380 1.620 7.26 224 0.875 bicubic
323 hrnet_w18_small_v2 91.170 8.830 98.340 1.660 15.60 224 0.875 bilinear
324 mixer_b16_224 91.140 8.860 97.400 2.600 59.88 224 0.875 bicubic
325 resnest14d 91.130 8.870 98.330 1.670 10.61 224 0.875 bilinear
326 gluon_resnet34_v1b 91.100 8.900 98.180 1.820 21.80 224 0.875 bicubic
327 vit_deit_tiny_distilled_patch16_224 91.100 8.900 98.270 1.730 5.91 224 0.900 bicubic
333 ssl_resnet18 90.700 9.300 98.020 1.980 11.69 224 0.875 bilinear
334 spnasnet_100 90.610 9.390 97.950 2.050 4.42 224 0.875 bilinear
335 vgg16_bn 90.540 9.460 97.990 2.010 138.37 224 0.875 bilinear
336 ghostnet_100 90.440 9.560 97.830 2.170 5.18 224 0.875 bilinear
337 pit_ti_224 90.420 9.580 98.010 1.990 4.85 224 0.900 bicubic
338 tf_mobilenetv3_large_075 90.320 9.680 97.870 2.130 3.99 224 0.875 bilinear
339 tv_resnet34 90.290 9.710 97.980 2.020 21.80 224 0.875 bilinear
356 regnetx_002 87.380 12.620 96.990 3.010 2.68 224 0.875 bicubic
357 vgg11 87.340 12.660 97.110 2.890 132.86 224 0.875 bilinear
358 dla60x_c 87.110 12.890 97.140 2.860 1.32 224 0.875 bilinear
359 mixer_l16_224 86.970 13.030 94.060 5.940 208.20 224 0.875 bicubic
360 tf_mobilenetv3_small_100 85.960 14.040 96.400 3.600 2.54 224 0.875 bilinear
361 dla46x_c 85.480 14.520 96.440 3.560 1.07 224 0.875 bilinear
362 dla46_c 84.660 15.340 96.200 3.800 1.30 224 0.875 bilinear

567
results/results-imagenet-r.csv
View File

@ -1,330 +1,352 @@
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,+11
ig_resnext101_32x16d,78.837,21.163,88.480,11.520,194.03,224,0.875,bilinear,-17.603,-11.060,+23
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
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,+29
ig_resnext101_32x8d,75.813,24.187,86.200,13.800,88.79,224,0.875,bilinear,-20.117,-13.180,+41
swsl_resnext101_32x8d,75.590,24.410,86.937,13.063,88.79,224,0.875,bilinear,-20.650,-12.653,+28
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
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,+35
swsl_resnext50_32x4d,68.977,31.023,82.810,17.190,25.03,224,0.875,bilinear,-26.643,-16.630,+45
swsl_resnet50,68.297,31.703,83.313,16.687,25.56,224,0.875,bilinear,-26.903,-16.077,+62
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
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
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,-6
tf_efficientnet_b4_ns,61.230,38.770,76.173,23.827,19.34,380,0.922,bicubic,-35.480,-23.467,-3
swin_base_patch4_window7_224,59.537,40.463,74.247,25.753,87.77,224,0.900,bicubic,-37.143,-25.413,-2
tf_efficientnet_b8_ap,57.830,42.170,72.957,27.043,87.41,672,0.954,bicubic,-38.720,-26.583,0
tf_efficientnet_b3_ns,57.417,42.583,72.387,27.613,12.23,300,0.904,bicubic,-38.683,-27.093,+19
vit_large_patch16_384,54.750,45.250,70.007,29.993,304.72,384,1.000,bicubic,-41.610,-29.623,+8
vit_base_r50_s16_384,54.400,45.600,69.560,30.440,98.95,384,1.000,bicubic,-42.050,-30.100,+2
resnetv2_152x4_bitm,54.263,45.737,70.137,29.863,936.53,480,1.000,bilinear,-42.617,-29.523,-14
dm_nfnet_f6,54.073,45.927,69.110,30.890,438.36,576,0.956,bicubic,-42.917,-30.630,-18
tf_efficientnet_b5_ap,53.870,46.130,69.160,30.840,30.39,456,0.934,bicubic,-42.210,-30.380,+15
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
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
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,+30
tf_efficientnet_b6_ap,53.560,46.440,68.550,31.450,43.04,528,0.942,bicubic,-42.810,-31.000,-1
tf_efficientnet_b8,53.410,46.590,69.090,30.910,87.41,672,0.954,bicubic,-43.290,-30.440,-14
tf_efficientnet_b7_ap,53.260,46.740,68.873,31.127,66.35,600,0.949,bicubic,-43.090,-30.717,0
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_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,+28
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_b7,52.393,47.607,68.233,31.767,66.35,600,0.949,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,+271
dm_nfnet_f4,52.260,47.740,67.120,32.880,316.07,512,0.951,bicubic,-44.560,-32.480,-24
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
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
ecaresnet269d,51.670,48.330,66.047,33.953,102.09,352,1.000,bicubic,-44.790,-33.563,-14
pit_b_distilled_224,51.153,48.847,66.770,33.230,74.79,224,0.900,bicubic,-44.917,-32.710,+4
resnetv2_152x2_bitm,51.040,48.960,68.527,31.473,236.34,480,1.000,bilinear,-45.460,-31.093,-17
tf_efficientnet_b1_ns,50.883,49.117,67.910,32.090,7.79,240,0.882,bicubic,-43.977,-31.340,+47
vit_base_patch16_384,50.883,49.117,65.270,34.730,86.86,384,1.000,bicubic,-45.307,-34.260,-6
vit_large_patch16_224,50.877,49.123,66.227,33.773,304.33,224,0.900,bicubic,-44.413,-33.083,+26
ssl_resnext101_32x16d,50.257,49.743,66.033,33.967,194.03,224,0.875,bilinear,-45.153,-33.267,+21
resnest269e,50.153,49.847,64.670,35.330,110.93,416,0.928,bicubic,-45.967,-34.850,-7
vit_deit_base_distilled_patch16_224,50.063,49.937,66.227,33.773,87.34,224,0.900,bicubic,-45.687,-33.053,+5
tf_efficientnet_b3_ap,50.057,49.943,65.210,34.790,12.23,300,0.904,bicubic,-44.913,-33.900,+36
resnest200e,49.873,50.127,64.743,35.257,70.20,320,0.909,bicubic,-46.197,-34.637,-6
resnetv2_101x3_bitm,49.823,50.177,66.917,33.083,387.93,480,1.000,bilinear,-46.537,-32.683,-20
tf_efficientnet_b5,49.510,50.490,65.657,34.343,30.39,456,0.934,bicubic,-46.470,-33.793,-5
resnet200d,49.470,50.530,64.330,35.670,64.69,320,1.000,bicubic,-46.640,-35.130,-12
efficientnet_v2s,49.367,50.633,64.203,35.797,23.94,224,1.000,bicubic,-45.623,-34.877,+29
resnest101e,49.367,50.633,65.587,34.413,48.28,256,0.875,bilinear,-46.203,-33.683,+4
resnet152d,49.253,50.747,64.413,35.587,60.21,320,1.000,bicubic,-46.617,-35.017,-5
seresnet152d,49.247,50.753,64.170,35.830,66.84,320,1.000,bicubic,-47.063,-35.340,-23
ssl_resnext101_32x8d,49.067,50.933,65.480,34.520,88.79,224,0.875,bilinear,-46.273,-33.840,+12
repvgg_b3,48.917,51.083,64.887,35.113,123.09,224,0.875,bilinear,-45.633,-34.023,+52
dm_nfnet_f2,48.623,51.377,63.537,36.463,193.78,352,0.920,bicubic,-47.877,-36.033,-36
efficientnet_b3a,48.563,51.437,64.250,35.750,12.23,320,1.000,bicubic,-46.577,-34.960,+18
ecaresnet101d,48.527,51.473,64.100,35.900,44.57,224,0.875,bicubic,-46.633,-35.130,+14
repvgg_b3g4,48.310,51.690,64.800,35.200,83.83,224,0.875,bilinear,-46.180,-34.220,+51
vit_large_patch32_384,48.250,51.750,61.830,38.170,306.63,384,1.000,bicubic,-46.990,-37.490,+9
efficientnet_b3,48.170,51.830,64.133,35.867,12.23,300,0.904,bicubic,-46.800,-35.097,+19
repvgg_b2g4,47.787,52.213,64.390,35.610,61.76,224,0.875,bilinear,-46.033,-34.540,+103
eca_nfnet_l1,47.663,52.337,62.767,37.233,41.41,320,1.000,bicubic,-48.267,-36.733,-19
pit_s_distilled_224,47.543,52.457,63.493,36.507,24.04,224,0.900,bicubic,-47.187,-35.697,+26
resnest50d_4s2x40d,47.483,52.517,63.807,36.193,30.42,224,0.875,bicubic,-47.227,-35.323,+28
efficientnet_b3_pruned,47.447,52.553,62.793,37.207,9.86,300,0.904,bicubic,-47.133,-36.277,+38
vit_base_patch16_224,47.340,52.660,61.607,38.393,86.57,224,0.900,bicubic,-47.870,-37.623,+3
tf_efficientnet_b6,47.213,52.787,63.110,36.890,43.04,528,0.942,bicubic,-49.077,-36.410,-37
ssl_resnext101_32x4d,47.177,52.823,63.367,36.633,44.18,224,0.875,bilinear,-47.983,-35.933,+4
tf_efficientnet_b4,47.083,52.917,62.867,37.133,19.34,380,0.922,bicubic,-48.507,-36.463,-16
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
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
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
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
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
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
resnetv2_50x3_bitm,46.827,53.173,64.873,35.127,217.32,480,1.000,bilinear,-49.313,-34.747,-37
dm_nfnet_f1,46.693,53.307,61.560,38.440,132.63,320,0.910,bicubic,-49.677,-37.910,-48
gluon_seresnext101_64x4d,46.677,53.323,61.303,38.697,88.23,224,0.875,bicubic,-47.973,-37.677,+25
tresnet_xl,46.283,53.717,61.943,38.057,78.44,224,0.875,bilinear,-48.777,-37.317,+2
vit_deit_small_distilled_patch16_224,46.160,53.840,62.417,37.583,22.44,224,0.900,bicubic,-48.430,-36.683,+27
regnety_160,46.153,53.847,61.837,38.163,83.59,288,1.000,bicubic,-49.727,-37.723,-31
gernet_m,46.150,53.850,62.700,37.300,21.14,224,0.875,bilinear,-48.400,-36.550,+30
resnest50d_1s4x24d,46.083,53.917,62.377,37.623,25.68,224,0.875,bicubic,-48.307,-36.693,+36
tf_efficientnet_b0_ns,46.047,53.953,63.253,36.747,5.29,224,0.875,bicubic,-47.693,-35.727,+96
resnest50d,45.937,54.063,62.623,37.377,27.48,224,0.875,bilinear,-48.683,-36.407,+19
regnety_032,45.893,54.107,61.537,38.463,19.44,288,1.000,bicubic,-49.577,-37.783,-21
gluon_seresnext101_32x4d,45.590,54.410,61.143,38.857,48.96,224,0.875,bicubic,-48.860,-37.947,+29
gluon_resnet152_v1d,45.430,54.570,60.077,39.923,60.21,224,0.875,bicubic,-49.010,-38.933,+29
dm_nfnet_f0,45.420,54.580,60.990,39.010,71.49,256,0.900,bicubic,-50.210,-38.310,-33
ssl_resnext50_32x4d,45.407,54.593,62.047,37.953,25.03,224,0.875,bilinear,-49.293,-37.193,+8
nfnet_l0,45.390,54.610,62.057,37.943,35.07,288,1.000,bicubic,-50.000,-37.363,-23
tresnet_xl_448,45.223,54.777,61.437,38.563,78.44,448,0.875,bilinear,-50.287,-37.903,-30
nasnetalarge,45.210,54.790,57.883,42.117,88.75,331,0.911,bicubic,-49.940,-41.247,-15
swin_small_patch4_window7_224,45.163,54.837,60.330,39.670,49.61,224,0.900,bicubic,-50.557,-38.960,-40
tf_efficientnet_b3,45.107,54.893,60.650,39.350,12.23,300,0.904,bicubic,-49.803,-38.460,-8
rexnet_200,45.047,54.953,62.317,37.683,16.37,224,0.875,bicubic,-49.613,-36.833,+6
ecaresnetlight,44.890,55.110,60.770,39.230,30.16,224,0.875,bicubic,-49.250,-38.180,+41
vit_deit_base_patch16_224,44.870,55.130,59.177,40.823,86.57,224,0.900,bicubic,-50.140,-39.803,-16
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
gernet_l,44.740,55.260,58.943,41.057,31.08,256,0.875,bilinear,-50.190,-40.257,-14
tf_efficientnet_b2_ap,44.700,55.300,60.680,39.320,9.11,260,0.890,bicubic,-49.570,-38.270,+28
vit_base_patch32_384,44.693,55.307,58.530,41.470,88.30,384,1.000,bicubic,-50.567,-40.650,-30
ens_adv_inception_resnet_v2,44.393,55.607,58.117,41.883,55.84,299,0.897,bicubic,-49.737,-40.673,+37
tresnet_l,44.363,55.637,59.953,40.047,55.99,224,0.875,bilinear,-50.537,-39.077,-16
gluon_resnext101_32x4d,44.290,55.710,59.090,40.910,44.18,224,0.875,bicubic,-49.830,-39.840,+38
wide_resnet50_2,44.177,55.823,59.727,40.273,68.88,224,0.875,bicubic,-50.493,-39.323,-6
cspresnext50,44.147,55.853,60.533,39.467,20.57,224,0.875,bilinear,-49.613,-38.167,+70
seresnext50_32x4d,44.127,55.873,59.490,40.510,27.56,224,0.875,bicubic,-50.693,-39.640,-17
gluon_resnet152_v1s,44.073,55.927,58.703,41.297,60.32,224,0.875,bicubic,-50.647,-40.357,-14
pit_b_224,44.070,55.930,58.017,41.983,73.76,224,0.900,bicubic,-50.720,-40.803,-18
ssl_resnet50,44.010,55.990,61.887,38.113,25.56,224,0.875,bilinear,-50.300,-37.263,+15
inception_resnet_v2,44.003,55.997,57.907,42.093,55.84,299,0.897,bicubic,-50.337,-40.893,+13
pnasnet5large,43.950,56.050,56.730,43.270,86.06,331,0.911,bicubic,-51.410,-42.400,-44
pit_s_224,43.890,56.110,58.627,41.373,23.46,224,0.900,bicubic,-50.700,-40.303,-8
gluon_resnext101_64x4d,43.877,56.123,58.710,41.290,83.46,224,0.875,bicubic,-50.473,-40.170,+8
tnt_s_patch16_224,43.773,56.227,59.197,40.803,23.76,224,0.900,bicubic,-50.807,-39.983,-7
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,-3
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,+97
gluon_resnet101_v1d,43.440,56.560,58.613,41.387,44.57,224,0.875,bicubic,-50.730,-40.327,+14
ecaresnet50t,43.407,56.593,59.300,40.700,25.57,320,0.950,bicubic,-51.663,-39.990,-42
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,-39.580,+21
dpn68b,43.287,56.713,58.673,41.327,12.61,224,0.875,bicubic,-50.333,-40.027,+67
eca_nfnet_l0,43.230,56.770,59.913,40.087,24.14,288,1.000,bicubic,-52.240,-39.467,-62
resnest26d,43.140,56.860,60.623,39.377,17.07,224,0.875,bilinear,-50.100,-38.127,+92
resnetv2_101x1_bitm,43.113,56.887,60.950,39.050,44.54,480,1.000,bilinear,-52.397,-38.560,-67
dpn131,43.047,56.953,57.440,42.560,79.25,224,0.875,bicubic,-50.713,-41.420,+49
cspresnet50,43.030,56.970,59.153,40.847,21.62,256,0.887,bilinear,-50.830,-39.717,+34
tf_efficientnet_lite4,42.967,57.033,57.620,42.380,13.01,380,0.920,bilinear,-51.903,-41.470,-42
gluon_resnet152_v1b,42.903,57.097,57.750,42.250,60.19,224,0.875,bicubic,-51.127,-40.990,+17
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
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,+57
gluon_resnet152_v1c,42.800,57.200,57.737,42.263,60.21,224,0.875,bicubic,-51.080,-41.063,+27
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,-40.463,-71
resnet50d,42.707,57.293,58.697,41.303,25.58,224,0.875,bicubic,-51.363,-40.223,+9
tresnet_m,42.687,57.313,58.153,41.847,31.39,224,0.875,bilinear,-51.383,-40.677,+9
gluon_seresnext50_32x4d,42.683,57.317,58.710,41.290,27.56,224,0.875,bicubic,-51.487,-40.200,-3
resnext101_32x8d,42.557,57.443,58.317,41.683,88.79,224,0.875,bilinear,-51.213,-40.633,+33
seresnet50,42.510,57.490,58.667,41.333,28.09,224,0.875,bicubic,-51.570,-40.303,+4
nf_resnet50,42.400,57.600,59.540,40.460,25.56,288,0.940,bicubic,-52.010,-39.560,-25
dpn98,42.280,57.720,56.880,43.120,61.57,224,0.875,bicubic,-51.660,-42.040,+13
vit_deit_small_patch16_224,42.263,57.737,58.020,41.980,22.05,224,0.900,bicubic,-51.737,-41.010,+8
tf_efficientnet_cc_b1_8e,42.233,57.767,58.420,41.580,39.72,240,0.882,bicubic,-51.337,-40.270,+53
legacy_senet154,42.207,57.793,56.597,43.403,115.09,224,0.875,bilinear,-52.523,-42.503,-54
tf_efficientnet_b2,42.120,57.880,58.197,41.803,9.11,260,0.890,bicubic,-52.090,-40.853,-17
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,+56
ecaresnet50d_pruned,41.953,58.047,58.217,41.783,19.94,224,0.875,bicubic,-51.867,-40.783,+17
efficientnet_b2a,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,-40.993,+1
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
efficientnet_b2,41.627,58.373,58.033,41.967,9.11,260,0.875,bicubic,-52.713,-41.067,-31
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,-37
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,+20
tf_efficientnet_cc_b0_8e,41.487,58.513,57.377,42.623,24.01,224,0.875,bicubic,-51.383,-41.083,+83
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,+4
tv_resnet152,41.327,58.673,57.520,42.480,60.19,224,0.875,bilinear,-51.913,-41.330,+55
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,-32
adv_inception_v3,41.263,58.737,56.317,43.683,23.83,299,0.875,bicubic,-51.747,-42.173,+63
gernet_s,41.247,58.753,58.830,41.170,8.17,224,0.875,bilinear,-51.193,-39.670,+98
resnetblur50,41.053,58.947,57.077,42.923,25.56,224,0.875,bicubic,-52.657,-41.733,+15
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,+79
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,-20
gluon_resnet101_v1b,40.683,59.317,56.117,43.883,44.55,224,0.875,bicubic,-53.077,-42.723,-2
hrnet_w40,40.660,59.340,56.753,43.247,57.56,224,0.875,bilinear,-53.050,-42.047,+4
repvgg_b1,40.593,59.407,57.837,42.163,57.42,224,0.875,bilinear,-52.817,-40.953,+31
tf_efficientnet_lite3,40.563,59.437,56.477,43.523,8.20,300,0.904,bilinear,-53.567,-42.483,-38
tresnet_m_448,40.530,59.470,56.700,43.300,31.39,448,0.875,bilinear,-54.130,-42.390,-80
pit_xs_224,40.497,59.503,56.530,43.470,10.62,224,0.900,bicubic,-52.413,-42.160,+54
dla169,40.493,59.507,57.263,42.737,53.39,224,0.875,bilinear,-53.307,-41.577,-12
repvgg_b2,40.467,59.533,57.780,42.220,89.02,224,0.875,bilinear,-53.123,-40.970,+13
regnetx_320,40.443,59.557,55.660,44.340,107.81,224,0.875,bicubic,-53.767,-43.370,-53
skresnet34,40.397,59.603,56.737,43.263,22.28,224,0.875,bicubic,-52.173,-41.783,+74
efficientnet_el_pruned,40.390,59.610,56.903,43.097,10.59,300,0.904,bicubic,-53.700,-42.077,-43
efficientnet_b2_pruned,40.383,59.617,56.537,43.463,8.31,260,0.890,bicubic,-53.417,-42.373,-16
legacy_seresnext101_32x4d,40.360,59.640,54.817,45.183,48.96,224,0.875,bilinear,-53.770,-44.153,-48
wide_resnet101_2,40.360,59.640,55.780,44.220,126.89,224,0.875,bilinear,-53.370,-43.030,-9
tf_efficientnet_b0_ap,40.337,59.663,56.787,43.213,5.29,224,0.875,bicubic,-52.273,-41.583,+64
xception65,40.273,59.727,55.283,44.717,39.92,299,0.903,bicubic,-53.487,-43.517,-16
regnetx_160,40.270,59.730,56.050,43.950,54.28,224,0.875,bicubic,-53.610,-43.040,-30
densenet201,40.267,59.733,56.710,43.290,20.01,224,0.875,bicubic,-52.423,-41.940,+55
resnext50d_32x4d,40.170,59.830,55.487,44.513,25.05,224,0.875,bicubic,-53.640,-43.253,-25
vit_small_patch16_224,40.130,59.870,56.543,43.457,48.75,224,0.900,bicubic,-52.470,-41.847,+61
hrnet_w48,40.093,59.907,56.640,43.360,77.47,224,0.875,bilinear,-53.937,-42.400,-47
legacy_seresnet152,40.043,59.957,55.820,44.180,66.82,224,0.875,bilinear,-53.397,-43.030,+10
hrnet_w30,40.030,59.970,57.093,42.907,37.71,224,0.875,bilinear,-53.340,-41.737,+13
regnetx_080,40.000,60.000,55.977,44.023,39.57,224,0.875,bicubic,-53.790,-42.933,-27
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,-17
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,-77
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,-42.677,+33
mixnet_xl,39.617,60.383,55.887,44.113,11.90,224,0.875,bicubic,-54.613,-42.933,-77
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
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
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
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
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
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
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,-2
dla102x,39.553,60.447,56.323,43.677,26.31,224,0.875,bilinear,-53.977,-42.527,-9
rexnet_130,39.487,60.513,56.640,43.360,7.56,224,0.875,bicubic,-54.183,-42.070,-24
hrnet_w32,39.463,60.537,56.123,43.877,41.23,224,0.875,bilinear,-53.487,-42.717,+23
regnety_064,39.403,60.597,55.773,44.227,30.58,224,0.875,bicubic,-54.737,-43.257,-74
densenetblur121d,39.380,60.620,56.640,43.360,8.00,224,0.875,bicubic,-53.020,-41.770,+53
regnety_120,39.347,60.653,55.277,44.723,51.82,224,0.875,bicubic,-54.663,-43.753,-63
tv_resnet101,39.307,60.693,55.803,44.197,44.55,224,0.875,bilinear,-53.573,-42.857,+26
tf_efficientnet_el,39.303,60.697,55.387,44.613,10.59,300,0.904,bicubic,-55.057,-43.713,-95
tf_inception_v3,39.237,60.763,54.300,45.700,23.83,299,0.875,bicubic,-53.963,-44.180,+2
gluon_resnet50_v1s,39.233,60.767,55.010,44.990,25.68,224,0.875,bicubic,-54.357,-44.060,-23
densenet169,39.167,60.833,55.843,44.157,14.15,224,0.875,bicubic,-53.133,-42.747,+51
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,+10
repvgg_b1g4,38.990,61.010,56.350,43.650,39.97,224,0.875,bilinear,-54.040,-42.350,+5
inception_v3,38.960,61.040,53.853,46.147,23.83,299,0.875,bicubic,-53.940,-44.477,+16
dpn68,38.933,61.067,54.933,45.067,12.61,224,0.875,bicubic,-53.307,-43.677,+48
regnety_080,38.917,61.083,55.213,44.787,39.18,224,0.875,bicubic,-54.973,-43.787,-66
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,-35
densenet121,38.783,61.217,56.273,43.727,7.98,224,0.875,bicubic,-53.157,-42.127,+51
res2net50_14w_8s,38.710,61.290,54.077,45.923,25.06,224,0.875,bilinear,-54.320,-44.743,-4
regnetx_040,38.703,61.297,55.340,44.660,22.12,224,0.875,bicubic,-54.977,-43.600,-46
res2net50_26w_6s,38.687,61.313,53.743,46.257,37.05,224,0.875,bilinear,-54.903,-45.097,-38
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,-5
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,+31
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,-35
repvgg_a2,38.563,61.437,55.770,44.230,28.21,224,0.875,bilinear,-54.117,-42.750,+10
hardcorenas_f,38.500,61.500,55.657,44.343,8.20,224,0.875,bilinear,-54.480,-42.963,-8
dla60_res2next,38.450,61.550,54.950,45.050,17.03,224,0.875,bilinear,-55.120,-43.850,-44
regnetx_064,38.430,61.570,54.990,45.010,26.21,224,0.875,bicubic,-55.200,-44.060,-53
tf_efficientnet_cc_b0_4e,38.413,61.587,55.150,44.850,13.31,224,0.875,bicubic,-54.427,-43.290,+1
gluon_resnet50_v1b,38.407,61.593,54.833,45.167,25.56,224,0.875,bicubic,-54.153,-43.717,+16
resnetv2_50x1_bitm,38.287,61.713,56.967,43.033,25.55,480,1.000,bilinear,-56.263,-41.963,-136
hrnet_w18,38.277,61.723,55.643,44.357,21.30,224,0.875,bilinear,-54.483,-43.017,+1
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,-15
hardcorenas_c,37.883,62.117,55.717,44.283,5.52,224,0.875,bilinear,-54.447,-42.623,+19
efficientnet_b1,37.843,62.157,53.640,46.360,7.79,240,0.875,bicubic,-55.217,-44.900,-26
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,-31
efficientnet_es,37.770,62.230,54.967,45.033,5.44,224,0.875,bicubic,-55.140,-43.813,-16
resnest14d,37.767,62.233,56.470,43.530,10.61,224,0.875,bilinear,-53.363,-41.860,+50
tv_resnext50_32x4d,37.750,62.250,54.113,45.887,25.03,224,0.875,bilinear,-55.150,-44.607,-15
ecaresnet26t,37.650,62.350,54.350,45.650,16.01,320,0.950,bicubic,-56.290,-44.570,-99
hardcorenas_d,37.550,62.450,54.723,45.277,7.50,224,0.875,bilinear,-55.050,-43.707,-2
res2next50,37.477,62.523,52.853,47.147,24.67,224,0.875,bilinear,-55.673,-45.807,-35
resnet34,37.443,62.557,54.297,45.703,21.80,224,0.875,bilinear,-53.757,-43.753,+41
pit_ti_distilled_224,37.337,62.663,55.137,44.863,5.10,224,0.900,bicubic,-53.563,-43.083,+49
hardcorenas_b,37.243,62.757,55.073,44.927,5.18,224,0.875,bilinear,-54.697,-43.207,+18
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,-44.430,-13
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,-35
tf_mixnet_l,36.987,63.013,52.583,47.417,7.33,224,0.875,bicubic,-56.053,-45.957,-41
legacy_seresnet50,36.873,63.127,53.487,46.513,28.09,224,0.875,bilinear,-55.797,-45.163,-16
res2net50_26w_8s,39.603,60.397,54.550,45.450,48.40,224,0.875,bilinear,-53.847,-44.150,-1
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
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,-12
mobilenetv2_120d,36.780,63.220,54.047,45.953,5.83,224,0.875,bicubic,-55.830,-44.463,-17
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
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
efficientnet_b0,36.600,63.400,53.497,46.503,5.29,224,0.875,bicubic,-55.880,-45.183,-12
tf_efficientnet_em,36.380,63.620,52.840,47.160,6.90,240,0.882,bicubic,-56.790,-45.830,-53
skresnet18,36.320,63.680,54.197,45.803,11.96,224,0.875,bicubic,-53.840,-43.583,+42
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
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,+37
vit_deit_tiny_distilled_patch16_224,36.023,63.977,54.240,45.760,5.91,224,0.900,bicubic,-55.077,-44.030,+25
tv_resnet34,36.087,63.913,53.533,46.467,21.80,224,0.875,bilinear,-54.203,-44.447,+39
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
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,-21
gluon_resnet34_v1b,35.763,64.237,52.187,47.813,21.80,224,0.875,bicubic,-55.337,-45.993,+20
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
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,-16
mixnet_m,35.640,64.360,52.430,47.570,5.01,224,0.875,bicubic,-56.630,-45.920,-17
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,+23
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
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
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,+24
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
pit_ti_224,34.670,65.330,52.170,47.830,4.85,224,0.900,bicubic,-55.750,-45.840,+17
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,-35
seresnext26t_32x4d,34.540,65.460,51.377,48.623,16.81,224,0.875,bicubic,-58.280,-47.183,-56
resnet26d,34.273,65.727,51.687,48.313,16.01,224,0.875,bicubic,-57.957,-46.763,-29
tf_efficientnet_es,34.263,65.737,51.350,48.650,5.44,224,0.875,bicubic,-57.837,-47.090,-27
fbnetc_100,34.253,65.747,51.180,48.820,5.57,224,0.875,bilinear,-57.017,-46.650,-7
regnety_006,34.150,65.850,51.277,48.723,6.06,224,0.875,bicubic,-57.420,-47.153,-17
tf_mobilenetv3_large_100,33.950,66.050,51.490,48.510,5.48,224,0.875,bilinear,-57.470,-46.770,-13
regnetx_008,33.770,66.230,50.547,49.453,7.26,224,0.875,bicubic,-57.410,-47.833,-5
mnasnet_100,33.763,66.237,51.170,48.830,4.38,224,0.875,bicubic,-57.437,-47.070,-7
semnasnet_100,33.520,66.480,50.787,49.213,3.89,224,0.875,bicubic,-58.140,-47.483,-23
resnet26,33.500,66.500,50.927,49.073,16.00,224,0.875,bicubic,-57.940,-47.353,-18
mixnet_s,33.480,66.520,50.997,49.003,4.13,224,0.875,bicubic,-58.300,-47.303,-29
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
regnetx_006,33.157,66.843,50.250,49.750,6.20,224,0.875,bicubic,-57.603,-47.850,-3
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
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,-58
legacy_seresnext26_32x4d,32.757,67.243,49.237,50.763,16.79,224,0.875,bicubic,-59.813,-49.183,-61
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,-13
tf_mixnet_s,32.183,67.817,48.493,51.507,4.13,224,0.875,bicubic,-59.497,-49.747,-39
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
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,-13
vgg16_bn,30.357,69.643,47.260,52.740,138.37,224,0.875,bilinear,-60.183,-50.730,-14
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
@ -332,9 +354,10 @@ vgg19,28.580,71.420,45.170,54.830,143.67,224,0.875,bilinear,-61.100,-52.380,-9
dla60x_c,28.447,71.553,46.193,53.807,1.32,224,0.875,bilinear,-58.663,-50.947,+4
vgg11_bn,28.423,71.577,46.453,53.547,132.87,224,0.875,bilinear,-59.967,-50.817,-3
vgg16,27.877,72.123,44.673,55.327,138.36,224,0.875,bilinear,-61.483,-52.847,-9
tf_mobilenetv3_small_100,27.297,72.703,44.420,55.580,2.54,224,0.875,bilinear,-58.663,-51.980,+2
vgg11,26.533,73.467,43.460,56.540,132.86,224,0.875,bilinear,-60.807,-53.650,-1
vgg13,26.267,73.733,43.370,56.630,133.05,224,0.875,bilinear,-61.303,-53.750,-4
tf_mobilenetv3_small_100,27.297,72.703,44.420,55.580,2.54,224,0.875,bilinear,-58.663,-51.980,+3
mixer_l16_224,26.853,73.147,37.923,62.077,208.20,224,0.875,bicubic,-60.117,-56.137,+1
vgg11,26.533,73.467,43.460,56.540,132.86,224,0.875,bilinear,-60.807,-53.650,-2
vgg13,26.267,73.733,43.370,56.630,133.05,224,0.875,bilinear,-61.303,-53.750,-5
dla46x_c,26.217,73.783,43.780,56.220,1.07,224,0.875,bilinear,-59.263,-52.660,0
tf_mobilenetv3_small_075,26.200,73.800,43.637,56.363,2.04,224,0.875,bilinear,-58.330,-52.253,+1
dla46_c,25.490,74.510,43.800,56.200,1.30,224,0.875,bilinear,-59.170,-52.400,-1

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
3 ig_resnext101_32x32d 79.457 20.543 89.183 10.817 468.53 224 0.875 bilinear -17.323 -10.347 +11 +13
4 ig_resnext101_32x16d 78.837 21.163 88.480 11.520 194.03 224 0.875 bilinear -17.603 -11.060 +23 +27
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 +29 +34
7 ig_resnext101_32x8d 75.813 24.187 86.200 13.800 88.79 224 0.875 bilinear -20.117 -13.180 -13.300 +41 +51
8 swsl_resnext101_32x8d 75.590 24.410 86.937 13.063 88.79 224 0.875 bilinear -20.650 -12.653 +28 +34
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 +35 +42
11 swsl_resnext50_32x4d 68.977 31.023 82.810 17.190 25.03 224 0.875 bilinear -26.643 -16.630 +45 +58
12 swsl_resnet50 68.297 31.703 83.313 16.687 25.56 224 0.875 bilinear -26.903 -16.077 +62 +79
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
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
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 -6 -5
19 tf_efficientnet_b4_ns 61.230 38.770 76.173 23.827 19.34 380 0.922 bicubic -35.480 -23.467 -3 -1
20 swin_base_patch4_window7_224 59.537 40.463 74.247 25.753 87.77 224 0.900 bicubic -37.143 -25.413 -2 0
21 tf_efficientnet_b8_ap 57.830 42.170 72.957 27.043 87.41 672 0.954 bicubic -38.720 -26.583 0 +4
22 tf_efficientnet_b3_ns cait_m48_448 57.417 57.470 42.583 42.530 72.387 71.860 27.613 28.140 12.23 356.46 300 448 0.904 1.000 bicubic -38.683 -39.410 -27.093 -27.760 +19 -11
23 vit_large_patch16_384 cait_m36_384 54.750 57.467 45.250 42.533 70.007 72.313 29.993 27.687 304.72 271.22 384 1.000 bicubic -41.610 -39.363 -29.623 -27.347 +8 -9
24 vit_base_r50_s16_384 tf_efficientnet_b3_ns 54.400 57.417 45.600 42.583 69.560 72.387 30.440 27.613 98.95 12.23 384 300 1.000 0.904 bicubic -42.050 -38.683 -30.100 -27.093 +2 +23
25 resnetv2_152x4_bitm vit_large_patch16_384 54.263 54.750 45.737 45.250 70.137 70.007 29.863 29.993 936.53 304.72 480 384 1.000 bilinear bicubic -42.617 -41.610 -29.523 -29.623 -14 +11
26 dm_nfnet_f6 vit_base_r50_s16_384 54.073 54.400 45.927 45.600 69.110 69.560 30.890 30.440 438.36 98.95 576 384 0.956 1.000 bicubic -42.917 -42.050 -30.630 -30.100 -18 +4
27 tf_efficientnet_b5_ap resnetv2_152x4_bitm 53.870 54.263 46.130 45.737 69.160 70.137 30.840 29.863 30.39 936.53 456 480 0.934 1.000 bicubic bilinear -42.210 -42.617 -30.380 -29.523 +15 -15
28 dm_nfnet_f6 54.073 45.927 69.110 30.890 438.36 576 0.956 bicubic -42.917 -30.630 -20
29 tf_efficientnet_b5_ap 53.870 46.130 69.160 30.840 30.39 456 0.934 bicubic -42.210 -30.380 +19
30 dm_nfnet_f5 53.773 46.227 68.500 31.500 377.21 544 0.954 bicubic -42.937 -31.180 -13
31 tf_efficientnet_b2_ns 53.600 46.400 70.270 29.730 9.11 260 0.890 bicubic -41.920 -29.070 +30 +42
32 tf_efficientnet_b6_ap 53.560 46.440 68.550 31.450 43.04 528 0.942 bicubic -42.810 -31.000 -1 +2
33 tf_efficientnet_b8 cait_s36_384 53.410 53.550 46.590 46.450 69.090 68.000 30.910 32.000 87.41 68.37 672 384 0.954 1.000 bicubic -43.290 -43.080 -30.440 -31.600 -14 -12
34 tf_efficientnet_b7_ap tf_efficientnet_b8 53.260 53.410 46.740 46.590 68.873 69.090 31.127 30.910 66.35 87.41 600 672 0.949 0.954 bicubic -43.090 -43.290 -30.717 -30.440 0 -15
35 tf_efficientnet_b7_ap 53.260 46.740 68.873 31.127 66.35 600 0.949 bicubic -43.090 -30.717 +2
36 dm_nfnet_f3 53.190 46.810 68.083 31.917 254.92 416 0.940 bicubic -43.440 -31.557 -14
37 tf_efficientnet_b4_ap 53.090 46.910 68.210 31.790 19.34 380 0.922 bicubic -42.400 -31.180 +28 +39
38 tf_efficientnet_b7 52.393 47.607 68.233 31.767 66.35 600 0.949 bicubic -44.187 -31.277 -15
39 swsl_resnet18 52.327 47.673 70.480 29.520 11.69 224 0.875 bilinear -38.763 -27.730 +271 +289
40 dm_nfnet_f4 52.260 47.740 67.120 32.880 316.07 512 0.951 bicubic -44.560 -32.480 -24 -25
41 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 -15
42 cait_s24_384 51.783 48.217 66.313 33.687 47.06 384 1.000 bicubic -44.787 -33.237 -18
43 ecaresnet269d 51.670 48.330 66.047 33.953 102.09 352 1.000 bicubic -44.790 -33.563 -14
44 pit_b_distilled_224 vit_base_patch16_224_miil 51.153 51.557 48.847 48.443 66.770 65.207 33.230 34.793 74.79 86.54 224 0.900 0.875 bicubic bilinear -44.917 -44.473 -32.710 -34.143 +4 +9
45 resnetv2_152x2_bitm pit_b_distilled_224 51.040 51.153 48.960 48.847 68.527 66.770 31.473 33.230 236.34 74.79 480 224 1.000 0.900 bilinear bicubic -45.460 -44.917 -31.093 -32.610 -17 +4
46 tf_efficientnet_b1_ns resnetv2_152x2_bitm 50.883 51.040 49.117 48.960 67.910 68.527 32.090 31.473 7.79 236.34 240 480 0.882 1.000 bicubic bilinear -43.977 -45.460 -31.340 -31.093 +47 -18
47 vit_base_patch16_384 50.883 49.117 65.270 34.730 86.86 384 1.000 bicubic -45.307 -34.260 -6 -5
48 vit_large_patch16_224 tf_efficientnet_b1_ns 50.877 50.883 49.123 49.117 66.227 67.910 33.773 32.090 304.33 7.79 224 240 0.900 0.882 bicubic -44.413 -43.977 -33.083 -31.340 +26 +58
49 ssl_resnext101_32x16d vit_large_patch16_224 50.257 50.877 49.743 49.123 66.033 66.227 33.967 33.773 194.03 304.33 224 0.875 0.900 bilinear bicubic -45.153 -44.413 -33.267 -33.083 +21 +36
50 resnest269e efficientnet_b4 50.153 50.510 49.847 49.490 64.670 65.703 35.330 34.297 110.93 19.34 416 384 0.928 1.000 bicubic -45.967 -45.010 -34.850 -33.687 -7 +22
51 vit_deit_base_distilled_patch16_224 ssl_resnext101_32x16d 50.063 50.257 49.937 49.743 66.227 66.033 33.773 33.967 87.34 194.03 224 0.900 0.875 bicubic bilinear -45.687 -45.153 -33.053 -33.377 +5 +28
52 tf_efficientnet_b3_ap cait_s24_224 50.057 50.243 49.943 49.757 65.210 65.027 34.790 34.973 12.23 46.92 300 224 0.904 1.000 bicubic -44.913 -45.407 -33.900 -34.363 +36 +14
53 resnest200e resnest269e 49.873 50.153 50.127 49.847 64.743 64.670 35.257 35.330 70.20 110.93 320 416 0.909 0.928 bicubic -46.197 -45.967 -34.637 -34.850 -6 -8
54 resnetv2_101x3_bitm vit_deit_base_distilled_patch16_224 49.823 50.063 50.177 49.937 66.917 66.227 33.083 33.773 387.93 87.34 480 224 1.000 0.900 bilinear bicubic -46.537 -45.687 -32.683 -33.053 -20 +9
55 tf_efficientnet_b5 tf_efficientnet_b3_ap 49.510 50.057 50.490 49.943 65.657 65.210 34.343 34.790 30.39 12.23 456 300 0.934 0.904 bicubic -46.470 -44.913 -33.793 -33.900 -5 +44
56 resnet200d resnest200e 49.470 49.873 50.530 50.127 64.330 64.743 35.670 35.257 64.69 70.20 320 1.000 0.909 bicubic -46.640 -46.197 -35.130 -34.737 -12 -6
57 efficientnet_v2s resnetv2_101x3_bitm 49.367 49.823 50.633 50.177 64.203 66.917 35.797 33.083 23.94 387.93 224 480 1.000 bicubic bilinear -45.623 -46.537 -34.877 -32.683 +29 -22
58 resnest101e cait_xs24_384 49.367 49.527 50.633 50.473 65.587 64.900 34.413 35.100 48.28 26.67 256 384 0.875 1.000 bilinear bicubic -46.203 -46.483 -33.683 -34.530 +4 -4
59 resnet152d tf_efficientnet_b5 49.253 49.510 50.747 50.490 64.413 65.657 35.587 34.343 60.21 30.39 320 456 1.000 0.934 bicubic -46.617 -46.470 -35.017 -33.793 -5 -3
60 seresnet152d resnet200d 49.247 49.470 50.753 50.530 64.170 64.330 35.830 35.670 66.84 64.69 320 1.000 bicubic -47.063 -46.640 -35.340 -35.130 -23 -14
61 ssl_resnext101_32x8d resnest101e 49.067 49.367 50.933 50.633 65.480 65.587 34.520 34.413 88.79 48.28 224 256 0.875 bilinear -46.273 -46.203 -33.840 -33.683 +12 +10
62 repvgg_b3 resnet152d 48.917 49.253 51.083 50.747 64.887 64.413 35.113 35.587 123.09 60.21 224 320 0.875 1.000 bilinear bicubic -45.633 -46.617 -34.023 -35.017 +52 -1
63 dm_nfnet_f2 seresnet152d 48.623 49.247 51.377 50.753 63.537 64.170 36.463 35.830 193.78 66.84 352 320 0.920 1.000 bicubic -47.877 -47.063 -36.033 -35.340 -36 -25
64 efficientnet_b3a ssl_resnext101_32x8d 48.563 49.067 51.437 50.933 64.250 65.480 35.750 34.520 12.23 88.79 320 224 1.000 0.875 bicubic bilinear -46.577 -46.273 -34.960 -33.840 +18 +20
65 ecaresnet101d repvgg_b3 48.527 48.917 51.473 51.083 64.100 64.887 35.900 35.113 44.57 123.09 224 0.875 bicubic bilinear -46.633 -45.633 -35.130 -34.023 +14 +61
66 repvgg_b3g4 resnetrs420 48.310 48.857 51.690 51.143 64.800 63.427 35.200 36.573 83.83 191.89 224 416 0.875 1.000 bilinear bicubic -46.180 -47.543 -34.220 -36.113 +51 -34
67 vit_large_patch32_384 dm_nfnet_f2 48.250 48.623 51.750 51.377 61.830 63.537 38.170 36.463 306.63 193.78 384 352 1.000 0.920 bicubic -46.990 -47.877 -37.490 -36.033 +9 -40
68 efficientnet_b3 efficientnet_v2s 48.170 48.603 51.830 51.397 64.133 63.840 35.867 36.160 12.23 23.94 300 384 0.904 1.000 bicubic -46.800 -47.107 -35.097 -35.540 +19 -3
69 repvgg_b2g4 efficientnet_b3 47.787 48.563 52.213 51.437 64.390 64.250 35.610 35.750 61.76 12.23 224 320 0.875 1.000 bilinear bicubic -46.033 -46.577 -34.540 -34.960 +103 +26
70 eca_nfnet_l1 ecaresnet101d 47.663 48.527 52.337 51.473 62.767 64.100 37.233 35.900 41.41 44.57 320 224 1.000 0.875 bicubic -48.267 -46.633 -36.733 -35.200 -19 +23
71 pit_s_distilled_224 repvgg_b3g4 47.543 48.310 52.457 51.690 63.493 64.800 36.507 35.200 24.04 83.83 224 0.900 0.875 bicubic bilinear -47.187 -46.180 -35.697 -34.220 +26 +58
72 resnest50d_4s2x40d vit_large_patch32_384 47.483 48.250 52.517 51.750 63.807 61.830 36.193 38.170 30.42 306.63 224 384 0.875 1.000 bicubic -47.227 -46.990 -35.323 -37.490 +28 +16
73 efficientnet_b3_pruned resnetrs350 47.447 48.050 52.553 51.950 62.793 62.653 37.207 37.347 9.86 163.96 300 384 0.904 1.000 bicubic -47.133 -48.190 -36.277 -36.817 +38 -32
74 vit_base_patch16_224 repvgg_b2g4 47.340 47.787 52.660 52.213 61.607 64.390 38.393 35.610 86.57 61.76 224 0.900 0.875 bicubic bilinear -47.870 -46.033 -37.623 -34.610 +3 +109
75 tf_efficientnet_b6 eca_nfnet_l1 47.213 47.663 52.787 52.337 63.110 62.767 36.890 37.233 43.04 41.41 528 320 0.942 1.000 bicubic -49.077 -48.267 -36.410 -36.613 -37 -16
76 ssl_resnext101_32x4d pit_s_distilled_224 47.177 47.543 52.823 52.457 63.367 63.493 36.633 36.507 44.18 24.04 224 0.875 0.900 bilinear bicubic -47.983 -47.187 -35.933 -35.697 +4 +33
77 tf_efficientnet_b4 resnest50d_4s2x40d 47.083 47.483 52.917 52.517 62.867 63.807 37.133 36.193 19.34 30.42 380 224 0.922 0.875 bicubic -48.507 -47.227 -36.463 -35.323 -16 +35
78 efficientnet_b3_pruned 47.447 52.553 62.793 37.207 9.86 300 0.904 bicubic -47.133 -36.277 +45
79 vit_base_patch16_224 47.340 52.660 61.607 38.393 86.57 224 0.900 bicubic -47.870 -37.623 +11
80 tresnet_m 47.230 52.770 61.993 38.007 31.39 224 0.875 bilinear -48.150 -37.157 +2
81 tf_efficientnet_b6 47.213 52.787 63.110 36.890 43.04 528 0.942 bicubic -49.077 -36.410 -42
82 ssl_resnext101_32x4d 47.177 52.823 63.367 36.633 44.18 224 0.875 bilinear -47.983 -35.863 +10
83 resnetrs270 47.107 52.893 62.010 37.990 129.86 352 1.000 bicubic -48.953 -37.480 -32
84 tf_efficientnet_b4 47.083 52.917 62.867 37.133 19.34 380 0.922 bicubic -48.507 -36.463 -14
85 resnet101d 46.893 53.107 62.317 37.683 44.57 320 1.000 bicubic -48.857 -37.123 -23
86 resnetv2_50x3_bitm resnetrs200 46.827 46.837 53.173 53.163 64.873 62.487 35.127 37.513 217.32 93.21 480 320 1.000 bilinear bicubic -49.313 -49.153 -34.747 -36.953 -37 -31
87 dm_nfnet_f1 resnetv2_50x3_bitm 46.693 46.827 53.307 53.173 61.560 64.873 38.440 35.127 132.63 217.32 320 480 0.910 1.000 bicubic bilinear -49.677 -49.313 -37.910 -34.747 -48 -43
88 gluon_seresnext101_64x4d dm_nfnet_f1 46.677 46.693 53.323 53.307 61.303 61.560 38.697 38.440 88.23 132.63 224 320 0.875 0.910 bicubic -47.973 -49.677 -37.677 -37.910 +25 -55
89 tresnet_xl gluon_seresnext101_64x4d 46.283 46.677 53.717 53.323 61.943 61.303 38.057 38.697 78.44 88.23 224 0.875 bilinear bicubic -48.777 -47.973 -37.317 -37.677 +2 +29
90 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 +27 +7
91 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 -31 +31
92 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 +30 -32
93 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 -36.550 +36 +34
94 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 +96 +40
95 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 +19 +100
96 regnety_032 resnest50d 45.893 45.937 54.107 54.063 61.537 62.623 38.463 37.377 19.44 27.48 288 224 1.000 0.875 bicubic bilinear -49.577 -48.683 -37.783 -36.407 -21 +23
97 gluon_seresnext101_32x4d regnety_032 45.590 45.893 54.410 54.107 61.143 61.537 38.857 38.463 48.96 19.44 224 288 0.875 1.000 bicubic -48.860 -49.577 -37.947 -37.783 +29 -19
98 gluon_resnet152_v1d gluon_seresnext101_32x4d 45.430 45.590 54.570 54.410 60.077 61.143 39.923 38.857 60.21 48.96 224 0.875 bicubic -49.010 -48.860 -38.933 -37.947 +29 +33
99 dm_nfnet_f0 gluon_resnet152_v1d 45.420 45.430 54.580 54.570 60.990 60.077 39.010 39.923 71.49 60.21 256 224 0.900 0.875 bicubic -50.210 -49.010 -38.310 -38.933 -33 +33
100 ssl_resnext50_32x4d dm_nfnet_f0 45.407 45.420 54.593 54.580 62.047 60.990 37.953 39.010 25.03 71.49 224 256 0.875 0.900 bilinear bicubic -49.293 -50.210 -37.193 -38.310 +8 -32
101 nfnet_l0 ssl_resnext50_32x4d 45.390 45.407 54.610 54.593 62.057 62.047 37.943 37.953 35.07 25.03 288 224 1.000 0.875 bicubic bilinear -50.000 -49.293 -37.363 -37.193 -23 +12
102 tresnet_xl_448 nfnet_l0 45.223 45.390 54.777 54.610 61.437 62.057 38.563 37.943 78.44 35.07 448 288 0.875 1.000 bilinear bicubic -50.287 -50.000 -37.903 -37.363 -30 -21
103 nasnetalarge tresnet_xl_448 45.210 45.223 54.790 54.777 57.883 61.437 42.117 38.563 88.75 78.44 331 448 0.911 0.875 bicubic bilinear -49.940 -50.287 -41.247 -37.903 -15 -28
104 swin_small_patch4_window7_224 nasnetalarge 45.163 45.210 54.837 54.790 60.330 57.883 39.670 42.117 49.61 88.75 224 331 0.900 0.911 bicubic -50.557 -49.940 -38.960 -41.247 -40 -10
105 tf_efficientnet_b3 swin_small_patch4_window7_224 45.107 45.163 54.893 54.837 60.650 60.330 39.350 39.670 12.23 49.61 300 224 0.904 0.900 bicubic -49.803 -50.557 -38.460 -38.960 -8 -41
106 rexnet_200 tf_efficientnet_b3 45.047 45.107 54.953 54.893 62.317 60.650 37.683 39.350 16.37 12.23 224 300 0.875 0.904 bicubic -49.613 -49.803 -36.833 -38.460 +6 -4
107 ecaresnetlight rexnet_200 44.890 45.047 55.110 54.953 60.770 62.317 39.230 37.683 30.16 16.37 224 0.875 bicubic -49.250 -49.613 -38.180 -36.773 +41 +9
108 vit_deit_base_patch16_224 resnetrs152 44.870 44.943 55.130 55.057 59.177 59.713 40.823 40.287 86.57 86.62 224 320 0.900 1.000 bicubic -50.140 -51.017 -39.803 -39.667 -16 -51
109 ecaresnetlight 44.890 55.110 60.770 39.230 30.16 224 0.875 bicubic -49.250 -38.180 +43
110 vit_deit_base_patch16_224 44.870 55.130 59.177 40.823 86.57 224 0.900 bicubic -50.140 -39.803 -12
111 vit_deit_base_patch16_384 44.777 55.223 59.617 40.383 86.86 384 1.000 bicubic -50.873 -39.623 -44
112 gernet_l cait_xxs36_384 44.740 44.773 55.260 55.227 58.943 59.380 41.057 40.620 31.08 17.37 256 384 0.875 1.000 bilinear bicubic -50.190 -50.447 -40.257 -39.940 -14 -23
113 tf_efficientnet_b2_ap gernet_l 44.700 44.740 55.300 55.260 60.680 58.943 39.320 41.057 9.11 31.08 260 256 0.890 0.875 bicubic bilinear -49.570 -50.190 -38.270 -40.257 +28 -13
114 vit_base_patch32_384 tf_efficientnet_b2_ap 44.693 44.700 55.307 55.300 58.530 60.680 41.470 39.320 88.30 9.11 384 260 1.000 0.890 bicubic -50.567 -49.570 -40.650 -38.270 -30 +29
115 ens_adv_inception_resnet_v2 vit_base_patch32_384 44.393 44.693 55.607 55.307 58.117 58.530 41.883 41.470 55.84 88.30 299 384 0.897 1.000 bicubic -49.737 -50.567 -40.673 -40.650 +37 -29
116 tresnet_l ens_adv_inception_resnet_v2 44.363 44.393 55.637 55.607 59.953 58.117 40.047 41.883 55.99 55.84 224 299 0.875 0.897 bilinear bicubic -50.537 -49.737 -39.077 -40.673 -16 +38
117 gluon_resnext101_32x4d tresnet_l 44.290 44.363 55.710 55.637 59.090 59.953 40.910 40.047 44.18 55.99 224 0.875 bicubic bilinear -49.830 -50.537 -39.840 -39.077 +38 -14
118 wide_resnet50_2 gluon_resnext101_32x4d 44.177 44.290 55.823 55.710 59.727 59.090 40.273 40.910 68.88 44.18 224 0.875 bicubic -50.493 -49.830 -39.323 -39.840 -6 +39
119 cspresnext50 wide_resnet50_2 44.147 44.177 55.853 55.823 60.533 59.727 39.467 40.273 20.57 68.88 224 0.875 bilinear bicubic -49.613 -50.493 -38.167 -39.403 +70 -5
120 seresnext50_32x4d cspresnext50 44.127 44.147 55.873 55.853 59.490 60.533 40.510 39.467 27.56 20.57 224 0.875 bicubic bilinear -50.693 -49.613 -39.640 -38.307 -17 +70
121 gluon_resnet152_v1s seresnext50_32x4d 44.073 44.127 55.927 55.873 58.703 59.490 41.297 40.510 60.32 27.56 224 0.875 bicubic -50.647 -50.693 -40.357 -39.640 -14 -15
122 pit_b_224 gluon_resnet152_v1s 44.070 44.073 55.930 55.927 58.017 58.703 41.983 41.297 73.76 60.32 224 0.900 0.875 bicubic -50.720 -50.647 -40.803 -40.357 -18 -12
123 ssl_resnet50 pit_b_224 44.010 44.070 55.990 55.930 61.887 58.017 38.113 41.983 25.56 73.76 224 0.875 0.900 bilinear bicubic -50.300 -50.720 -37.263 -40.803 +15 -16
124 inception_resnet_v2 ssl_resnet50 44.003 44.010 55.997 55.990 57.907 61.887 42.093 38.113 55.84 25.56 299 224 0.897 0.875 bicubic bilinear -50.337 -50.300 -40.893 -37.263 +13 +16
125 pnasnet5large inception_resnet_v2 43.950 44.003 56.050 55.997 56.730 57.907 43.270 42.093 86.06 55.84 331 299 0.911 0.897 bicubic -51.410 -50.337 -42.400 -40.893 -44 +14
126 pit_s_224 pnasnet5large 43.890 43.950 56.110 56.050 58.627 56.730 41.373 43.270 23.46 86.06 224 331 0.900 0.911 bicubic -50.700 -51.410 -40.303 -42.400 -8 -43
127 gluon_resnext101_64x4d pit_s_224 43.877 43.890 56.123 56.110 58.710 58.627 41.290 41.373 83.46 23.46 224 0.875 0.900 bicubic -50.473 -50.700 -40.170 -40.303 +8 -6
128 tnt_s_patch16_224 gluon_resnext101_64x4d 43.773 43.877 56.227 56.123 59.197 58.710 40.803 41.290 23.76 83.46 224 0.900 0.875 bicubic -50.807 -50.473 -39.983 -40.170 -7 +10
129 tnt_s_patch16_224 43.773 56.227 59.197 40.803 23.76 224 0.900 bicubic -50.807 -39.983 -5
130 cait_xxs36_224 43.760 56.240 58.720 41.280 17.30 224 1.000 bicubic -50.180 -40.200 +43
131 ecaresnet50d 43.750 56.250 60.387 39.613 25.58 224 0.875 bicubic -50.440 -38.633 +17
132 ecaresnet101d_pruned 43.737 56.263 59.607 40.393 24.88 224 0.875 bicubic -50.713 -39.493 -3 -2
133 rexnet_150 43.690 56.310 60.897 39.103 9.73 224 0.875 bicubic -50.580 -38.183 +9
134 pit_xs_distilled_224 43.663 56.337 60.703 39.297 11.00 224 0.900 bicubic -49.577 -38.117 +97 +100
135 gluon_resnet101_v1d 43.440 56.560 58.613 41.387 44.57 224 0.875 bicubic -50.730 -40.327 -40.297 +14 +16
136 ecaresnet50t 43.407 56.593 59.300 40.700 25.57 320 0.950 bicubic -51.663 -39.990 -42 -40
137 gluon_resnet101_v1s 43.363 56.637 58.503 41.497 44.67 224 0.875 bicubic -50.807 -40.507 +13
138 cspdarknet53 43.357 56.643 59.430 40.570 27.64 256 0.887 bilinear -50.733 -39.580 -39.550 +21 +20
139 dpn68b 43.287 56.713 58.673 41.327 12.61 224 0.875 bicubic -50.333 -40.027 -40.287 +67 +69
140 eca_nfnet_l0 43.230 56.770 59.913 40.087 24.14 288 1.000 bicubic -52.240 -39.467 -62 -63
141 resnest26d 43.140 56.860 60.623 39.377 17.07 224 0.875 bilinear -50.100 -38.127 -38.227 +92 +94
142 resnetv2_101x1_bitm 43.113 56.887 60.950 39.050 44.54 480 1.000 bilinear -52.397 -38.560 -67 -68
143 dpn131 43.047 56.953 57.440 42.560 79.25 224 0.875 bicubic -50.713 -41.420 -41.360 +49 +48
144 cspresnet50 43.030 56.970 59.153 40.847 21.62 256 0.887 bilinear -50.830 -39.717 +34 +35
145 tf_efficientnet_lite4 42.967 57.033 57.620 42.380 13.01 380 0.920 bilinear -51.903 -41.470 -42 -41
146 gluon_resnet152_v1b 42.903 57.097 57.750 42.250 60.19 224 0.875 bicubic -51.127 -40.990 +17 +16
147 dpn107 42.857 57.143 57.367 42.633 86.92 224 0.875 bicubic -51.103 -41.473 +22
148 tf_efficientnet_b1_ap 42.803 57.197 58.813 41.187 7.79 240 0.882 bicubic -50.827 -39.987 +57 +58
149 gluon_resnet152_v1c 42.800 57.200 57.737 42.263 60.21 224 0.875 bicubic -51.080 -41.063 +27 +28
150 gluon_xception65 42.793 57.207 58.820 41.180 39.92 299 0.903 bicubic -51.217 -40.200 +15
151 tresnet_l_448 42.753 57.247 58.947 41.053 55.99 448 0.875 bilinear -52.657 -40.463 -40.353 -71
152 resnet50d 42.707 42.703 57.293 57.297 58.697 41.303 25.58 224 0.875 bicubic -51.363 -51.367 -40.223 +9
153 tresnet_m gluon_seresnext50_32x4d 42.687 42.683 57.313 57.317 58.153 58.710 41.847 41.290 31.39 27.56 224 0.875 bilinear bicubic -51.383 -51.487 -40.677 -40.230 +9 -4
154 gluon_seresnext50_32x4d resnext101_32x8d 42.683 42.557 57.317 57.443 58.710 58.317 41.290 41.683 27.56 88.79 224 0.875 bicubic bilinear -51.487 -51.213 -40.200 -40.633 -3 +35
155 resnext101_32x8d seresnet50 42.557 42.510 57.443 57.490 58.317 58.667 41.683 41.333 88.79 28.09 224 0.875 bilinear bicubic -51.213 -51.570 -40.633 -40.303 +33 +5
156 seresnet50 resnetrs101 42.510 42.437 57.490 57.563 58.667 57.300 41.333 42.700 28.09 63.62 224 288 0.875 0.940 bicubic -51.570 -52.813 -40.303 -41.910 +4 -69
157 nf_resnet50 42.400 57.600 59.540 40.460 25.56 288 0.940 bicubic -52.010 -39.560 -25 -24
158 dpn98 42.280 57.720 56.880 43.120 61.57 224 0.875 bicubic -51.660 -42.040 -42.010 +13
159 vit_deit_small_patch16_224 42.263 57.737 58.020 41.980 22.05 224 0.900 bicubic -51.737 -41.010 -40.940 +8 +9
160 tf_efficientnet_cc_b1_8e 42.233 57.767 58.420 41.580 39.72 240 0.882 bicubic -51.337 -40.270 +53 +54
161 legacy_senet154 42.207 57.793 56.597 43.403 115.09 224 0.875 bilinear -52.523 -42.503 -54 -53
162 tf_efficientnet_b2 cait_xxs24_384 42.120 42.187 57.880 57.813 58.197 57.460 41.803 42.540 9.11 12.03 260 384 0.890 1.000 bicubic -52.090 -52.733 -40.853 -41.680 -17 -61
163 tf_efficientnet_b2 42.120 57.880 58.197 41.803 9.11 260 0.890 bicubic -52.090 -40.833 -17
164 gluon_resnext50_32x4d 42.043 57.957 57.667 42.333 25.03 224 0.875 bicubic -51.607 -41.023 +39
165 resnet50 42.013 57.987 56.000 44.000 25.56 224 0.875 bicubic -51.447 -42.600 +56 +57
166 ecaresnet50d_pruned 41.953 58.047 58.217 41.783 19.94 224 0.875 bicubic -51.867 -40.783 -40.713 +17 +18
167 efficientnet_b2a efficientnet_b2 41.933 58.067 58.300 41.700 9.11 288 1.000 bicubic -52.437 -40.750 -31
168 dla102x2 41.647 58.353 57.967 42.033 41.28 224 0.875 bilinear -52.353 -40.993 -41.063 +1 -1
169 hrnet_w64 41.637 58.363 57.130 42.870 128.06 224 0.875 bilinear -52.193 -41.800 +13
170 efficientnet_b2 gluon_senet154 41.627 58.373 58.033 56.373 41.967 43.627 9.11 115.09 260 224 0.875 bicubic -52.713 -53.083 -41.067 -42.597 -31 -59
171 gluon_senet154 inception_v4 41.627 41.577 58.373 58.423 56.373 55.383 43.627 44.617 115.09 42.68 224 299 0.875 bicubic -53.083 -52.803 -42.597 -43.437 -60 -36
172 inception_v4 efficientnet_el 41.577 41.497 58.423 58.503 55.383 58.303 44.617 41.697 42.68 10.59 299 300 0.875 0.904 bicubic -52.803 -53.173 -43.437 -40.747 -37 -57
173 efficientnet_el efficientnet_em 41.497 41.493 58.503 58.507 58.303 58.877 41.697 41.123 10.59 6.90 300 240 0.904 0.882 bicubic -53.173 -52.247 -40.827 -40.053 -59 +21
174 efficientnet_em tf_efficientnet_cc_b0_8e 41.493 41.487 58.507 58.513 58.877 57.377 41.123 42.623 6.90 24.01 240 224 0.882 0.875 bicubic -52.247 -51.383 -40.053 -41.083 +20 +86
175 tf_efficientnet_cc_b0_8e swin_tiny_patch4_window7_224 41.487 41.457 58.513 58.543 57.377 57.303 42.623 42.697 24.01 28.29 224 0.875 0.900 bicubic -51.383 -53.163 -41.083 -41.817 +83 -55
176 swin_tiny_patch4_window7_224 resnext50_32x4d 41.457 41.443 58.543 58.557 57.303 56.997 42.697 43.003 28.29 25.03 224 0.900 0.875 bicubic -53.163 -52.397 -41.817 -41.833 -56 +5
177 resnext50_32x4d cait_xxs24_224 41.443 41.383 58.557 58.617 56.997 57.527 43.003 42.473 25.03 11.96 224 0.875 1.000 bicubic -52.397 -52.107 -41.833 -41.243 +4 +43
178 tv_resnet152 41.327 41.330 58.673 58.670 57.520 42.480 60.19 224 0.875 bilinear -51.913 -51.910 -41.330 -41.230 +55 +58
179 xception71 41.270 58.730 55.873 44.127 42.34 299 0.903 bicubic -52.620 -43.077 -3
180 dpn92 41.267 58.733 56.333 43.667 37.67 224 0.875 bicubic -52.923 -42.597 -32 -33
181 adv_inception_v3 41.263 58.737 56.317 43.683 23.83 299 0.875 bicubic -51.747 -42.173 +63 +65
182 gernet_s 41.247 58.753 58.830 41.170 8.17 224 0.875 bilinear -51.193 -39.670 +98 +101
183 resnetblur50 41.053 58.947 57.077 42.923 25.56 224 0.875 bicubic -52.657 -41.733 -41.723 +15 +16
184 nf_regnet_b1 41.010 58.990 58.117 41.883 10.22 288 0.900 bicubic -52.880 -40.633 -10
185 gluon_resnet50_v1d 40.970 59.030 57.137 42.863 25.58 224 0.875 bicubic -52.560 -41.573 +32
186 gluon_inception_v3 40.907 59.093 55.617 44.383 23.83 299 0.875 bicubic -52.633 -43.213 +29
187 ese_vovnet39b 40.867 59.133 56.950 43.050 24.57 224 0.875 bicubic -52.983 -41.950 -7
188 regnety_320 40.813 59.187 56.117 43.883 145.05 224 0.875 bicubic -53.707 -43.053 -60
189 resnet34d 40.810 59.190 56.530 43.470 21.82 224 0.875 bicubic -51.830 -41.890 +79 +82
190 xception 40.763 59.237 56.387 43.613 22.86 299 0.897 bicubic -52.877 -42.383 +14
191 skresnext50_32x4d 40.700 59.300 56.023 43.977 27.48 224 0.875 bicubic -53.250 -42.797 -20 -21
192 gluon_resnet101_v1b 40.683 59.317 56.117 43.883 44.55 224 0.875 bicubic -53.077 -42.723 -42.583 -2 0
193 hrnet_w40 40.660 59.340 56.753 56.757 43.247 43.243 57.56 224 0.875 bilinear -53.050 -42.047 -42.043 +4
194 repvgg_b1 40.593 59.407 57.837 42.163 57.42 224 0.875 bilinear -52.817 -40.953 +31 +33
195 tf_efficientnet_lite3 40.563 59.437 56.477 43.523 8.20 300 0.904 bilinear -53.567 -42.483 -38 -39
196 tresnet_m_448 40.530 59.470 56.700 43.300 31.39 448 0.875 bilinear -54.130 -42.390 -42.450 -80 -79
197 pit_xs_224 40.497 59.503 56.530 43.470 10.62 224 0.900 bicubic -52.413 -42.160 -42.250 +54 +58
198 dla169 40.493 59.507 57.263 42.737 53.39 224 0.875 bilinear -53.307 -41.577 -41.647 -12 -11
199 repvgg_b2 40.467 59.533 57.780 42.220 89.02 224 0.875 bilinear -53.123 -40.970 -41.290 +13 +12
200 regnetx_320 40.443 59.557 55.660 44.340 107.81 224 0.875 bicubic -53.767 -43.370 -43.390 -53 -55
201 skresnet34 40.397 59.603 56.737 43.263 22.28 224 0.875 bicubic -52.173 -41.783 +74 +77
202 efficientnet_el_pruned 40.390 59.610 56.903 43.097 10.59 300 0.904 bicubic -53.700 -42.077 -42.107 -43
203 efficientnet_b2_pruned 40.383 59.617 56.537 43.463 8.31 260 0.890 bicubic -53.417 -42.373 -42.303 -16 -17
204 legacy_seresnext101_32x4d 40.360 59.640 54.817 45.183 48.96 224 0.875 bilinear -53.770 -44.153 -48 -50
205 wide_resnet101_2 40.360 59.640 55.780 44.220 126.89 224 0.875 bilinear -53.370 -43.030 -9 -10
206 tf_efficientnet_b0_ap coat_lite_mini 40.337 40.360 59.663 59.640 56.787 55.717 43.213 44.283 5.29 11.01 224 0.875 0.900 bicubic -52.273 -53.090 -41.583 -43.063 +64 +19
207 xception65 tf_efficientnet_b0_ap 40.273 40.337 59.727 59.663 55.283 56.787 44.717 43.213 39.92 5.29 299 224 0.903 0.875 bicubic -53.487 -52.273 -43.517 -41.583 -16 +66
208 regnetx_160 xception65 40.270 40.273 59.730 59.727 56.050 55.283 43.950 44.717 54.28 39.92 224 299 0.875 0.903 bicubic -53.610 -53.487 -43.040 -43.577 -30 -15
209 densenet201 regnetx_160 40.267 40.270 59.733 59.730 56.710 56.050 43.290 43.950 20.01 54.28 224 0.875 bicubic -52.423 -53.610 -41.940 -43.040 +55 -31
210 resnext50d_32x4d densenet201 40.170 40.267 59.830 59.733 55.487 56.710 44.513 43.290 25.05 20.01 224 0.875 bicubic -53.640 -52.423 -43.253 -41.940 -25 +57
211 vit_small_patch16_224 resnext50d_32x4d 40.130 40.170 59.870 59.830 56.543 55.487 43.457 44.513 48.75 25.05 224 0.900 0.875 bicubic -52.470 -53.640 -41.847 -43.253 +61 -26
212 hrnet_w48 vit_small_patch16_224 40.093 40.130 59.907 59.870 56.640 56.543 43.360 43.457 77.47 48.75 224 0.875 0.900 bilinear bicubic -53.937 -52.470 -42.400 -41.887 -47 +62
213 legacy_seresnet152 hrnet_w48 40.043 40.093 59.957 59.907 55.820 56.640 44.180 43.360 66.82 77.47 224 0.875 bilinear -53.397 -53.937 -43.030 -42.400 +10 -50
214 hrnet_w30 legacy_seresnet152 40.030 40.043 59.970 59.957 57.093 55.820 42.907 44.180 37.71 66.82 224 0.875 bilinear -53.340 -53.397 -41.737 -43.030 +13 +11
215 regnetx_080 hrnet_w30 40.000 40.030 60.000 59.970 55.977 57.093 44.023 42.907 39.57 37.71 224 0.875 bicubic bilinear -53.790 -53.340 -42.933 -41.737 -27 +14
216 tf_efficientnet_b1 regnetx_080 39.977 40.000 60.023 60.000 56.137 55.977 43.863 44.023 7.79 39.57 240 224 0.882 0.875 bicubic -53.733 -53.790 -42.663 -42.933 -17 -28
217 gluon_resnet101_v1c tf_efficientnet_b1 39.953 39.977 60.047 60.023 55.300 56.137 44.700 43.863 44.57 7.79 224 240 0.875 0.882 bicubic -53.737 -53.733 -43.460 -42.673 -17 -19
218 res2net101_26w_4s gluon_resnet101_v1c 39.717 39.953 60.283 60.047 54.550 55.300 45.450 44.700 45.21 44.57 224 0.875 bilinear bicubic -53.803 -53.737 -44.050 -43.460 0 -18
219 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 -77 -1
220 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 -43.627 -43.557 -12 -79
221 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 -43.617 +33 -12
222 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 -42.677 -77 +34
223 mixnet_xl 39.617 60.383 55.887 44.113 11.90 224 0.875 bicubic -54.613 -42.933 -79
224 xception41 39.610 60.390 55.037 44.963 26.97 299 0.903 bicubic -53.870 -43.713 -3
225 res2net50_26w_8s 39.603 60.397 54.550 45.450 48.40 224 0.875 bilinear -53.847 -44.150 -2 -1
226 dla102x 39.553 60.447 56.323 43.677 26.31 224 0.875 bilinear -53.977 -42.527 -9 -10
227 rexnet_130 39.487 60.513 56.640 43.360 7.56 224 0.875 bicubic -54.183 -42.070 -24 -25
228 hrnet_w32 39.463 60.537 56.123 43.877 41.23 224 0.875 bilinear -53.487 -42.717 -42.447 +23
229 regnety_064 39.403 60.597 55.773 44.227 30.58 224 0.875 bicubic -54.737 -43.257 -74 -76
230 densenetblur121d 39.380 60.620 56.640 43.360 8.00 224 0.875 bicubic -53.020 -41.770 +53 +55
231 regnety_120 39.347 60.653 55.277 44.723 51.82 224 0.875 bicubic -54.663 -43.753 -63 -65
232 tv_resnet101 39.307 60.693 55.803 44.197 44.55 224 0.875 bilinear -53.573 -42.857 +26 +27
233 tf_efficientnet_el 39.303 60.697 55.387 44.613 10.59 300 0.904 bicubic -55.057 -43.713 -95 -96
234 tf_inception_v3 39.237 60.763 54.300 45.700 23.83 299 0.875 bicubic -53.963 -44.180 +2 +3
235 gluon_resnet50_v1s 39.233 60.767 55.010 44.990 25.68 224 0.875 bicubic -54.357 -44.060 -43.830 -23 -25
236 densenet169 39.167 60.833 55.843 44.157 14.15 224 0.875 bicubic -53.133 -42.747 +51 +53
237 legacy_seresnet101 39.037 60.963 55.003 44.997 49.33 224 0.875 bilinear -54.223 -43.737 -7 -6
238 efficientnet_b1_pruned 39.010 60.990 55.647 44.353 6.33 240 0.882 bicubic -53.970 -42.883 +10 +11
239 repvgg_b1g4 38.990 61.010 56.350 43.650 39.97 224 0.875 bilinear -54.040 -42.350 -42.470 +5
240 inception_v3 38.960 61.040 53.853 46.147 23.83 299 0.875 bicubic -53.940 -44.477 +16 +17
241 dpn68 38.933 61.067 54.933 45.067 12.61 224 0.875 bicubic -53.307 -43.677 +48 +51
242 regnety_080 38.917 61.083 55.213 44.787 39.18 224 0.875 bicubic -54.973 -43.787 -66 -67
243 legacy_seresnext50_32x4d 38.877 61.123 54.593 45.407 27.56 224 0.875 bilinear -54.553 -44.207 -18 -17
244 dla102 38.833 61.167 55.323 44.677 33.27 224 0.875 bilinear -54.427 -43.457 -15 -14
245 regnety_040 38.820 61.180 55.557 44.443 20.65 224 0.875 bicubic -54.800 -43.393 -43.143 -35 -38
246 densenet121 38.783 61.217 56.273 43.727 7.98 224 0.875 bicubic -53.157 -42.127 -42.007 +51 +53
247 res2net50_14w_8s 38.710 61.290 54.077 45.923 25.06 224 0.875 bilinear -54.320 -44.743 -44.623 -4 -2
248 regnetx_040 38.703 61.297 55.340 44.660 22.12 224 0.875 bicubic -54.977 -43.600 -46 -47
249 res2net50_26w_6s 38.687 61.313 53.743 46.257 37.05 224 0.875 bilinear -54.903 -45.097 -45.007 -38 -37
250 regnetx_032 38.680 61.320 55.157 44.843 15.30 224 0.875 bicubic -54.570 -43.573 -18 -17
251 selecsls60 38.623 61.377 55.630 44.370 30.67 224 0.875 bicubic -54.387 -43.200 -5 -4
252 dla60x 38.617 61.383 55.383 44.617 17.35 224 0.875 bilinear -54.573 -43.327 -15 -14
253 tf_efficientnet_b0 38.600 61.400 55.957 44.043 5.29 224 0.875 bicubic -53.800 -42.513 +31 +33
254 dla60_res2net 38.590 61.410 54.560 45.440 20.85 224 0.875 bilinear -54.790 -44.300 -27 -26
255 selecsls60b 38.573 61.427 55.307 44.693 32.77 224 0.875 bicubic -54.927 -43.533 -35 -36
256 repvgg_a2 38.563 61.437 55.770 44.230 28.21 224 0.875 bilinear -54.117 -42.750 +10 +12
257 hardcorenas_f 38.500 61.500 55.657 44.343 8.20 224 0.875 bilinear -54.480 -42.963 -8 -7
258 dla60_res2next 38.450 61.550 54.950 45.050 17.03 224 0.875 bilinear -55.120 -43.850 -44 -45
259 regnetx_064 38.430 61.570 54.990 45.010 26.21 224 0.875 bicubic -55.200 -44.060 -53 -54
260 tf_efficientnet_cc_b0_4e 38.413 61.587 55.150 44.850 13.31 224 0.875 bicubic -54.427 -43.290 +1 +3
261 gluon_resnet50_v1b 38.407 61.593 54.833 45.167 25.56 224 0.875 bicubic -54.153 -43.717 +16 +18
262 resnetv2_50x1_bitm 38.287 61.713 56.967 43.033 25.55 480 1.000 bilinear -56.263 -41.963 -136 -137
263 hrnet_w18 38.277 61.723 55.643 44.357 21.30 224 0.875 bilinear -54.483 -43.017 +1 +3
264 mixnet_l 38.160 61.840 54.757 45.243 7.33 224 0.875 bicubic -55.100 -43.943 -33 -32
265 hardcorenas_e 38.137 61.863 55.173 44.827 8.07 224 0.875 bilinear -54.813 -43.397 -43.667 -15 -13
266 hardcorenas_c efficientnet_b1 37.883 38.087 62.117 61.913 55.717 54.010 44.283 45.990 5.52 7.79 224 256 0.875 1.000 bilinear bicubic -54.447 -54.943 -42.623 -44.700 +19 -23
267 efficientnet_b1 coat_lite_tiny 37.843 38.070 62.157 61.930 53.640 53.453 46.360 46.547 7.79 5.72 240 224 0.875 0.900 bicubic -55.217 -54.780 -44.900 -45.187 -26 -6
268 gluon_resnet50_v1c resnetrs50 37.843 37.957 62.157 62.043 54.123 53.310 45.877 46.690 25.58 35.69 224 0.875 0.910 bicubic -55.067 -56.063 -44.587 -45.540 -15 -104
269 res2net50_26w_4s hardcorenas_c 37.827 37.883 62.173 62.117 53.073 55.717 46.927 44.283 25.70 5.52 224 0.875 bilinear -55.353 -54.447 -45.597 -42.623 -31 +18
270 efficientnet_es gluon_resnet50_v1c 37.770 37.843 62.230 62.157 54.967 54.123 45.033 45.877 5.44 25.58 224 0.875 bicubic -55.140 -55.067 -43.813 -44.587 -16
271 resnest14d res2net50_26w_4s 37.767 37.827 62.233 62.173 56.470 53.073 43.530 46.927 10.61 25.70 224 0.875 bilinear -53.363 -55.353 -41.860 -45.597 +50 -32
272 tv_resnext50_32x4d efficientnet_es 37.750 37.770 62.250 62.230 54.113 54.967 45.887 45.033 25.03 5.44 224 0.875 bilinear bicubic -55.150 -55.140 -44.607 -43.723 -15 -19
273 ecaresnet26t resnest14d 37.650 37.767 62.350 62.233 54.350 56.470 45.650 43.530 16.01 10.61 320 224 0.950 0.875 bicubic bilinear -56.290 -53.363 -44.570 -41.860 -99 +52
274 hardcorenas_d tv_resnext50_32x4d 37.550 37.750 62.450 62.250 54.723 54.113 45.277 45.887 7.50 25.03 224 0.875 bilinear -55.050 -55.150 -43.707 -44.607 -2 -16
275 res2next50 ecaresnet26t 37.477 37.650 62.523 62.350 52.853 54.350 47.147 45.650 24.67 16.01 224 320 0.875 0.950 bilinear bicubic -55.673 -56.290 -45.807 -44.570 -35 -103
276 resnet34 hardcorenas_d 37.443 37.550 62.557 62.450 54.297 54.723 45.703 45.277 21.80 7.50 224 0.875 bilinear -53.757 -55.050 -43.753 -43.667 +41 -1
277 pit_ti_distilled_224 res2next50 37.337 37.477 62.663 62.523 55.137 52.853 44.863 47.147 5.10 24.67 224 0.900 0.875 bicubic bilinear -53.563 -55.673 -43.083 -45.807 +49 -36
278 hardcorenas_b resnet34 37.243 37.443 62.757 62.557 55.073 54.297 44.927 45.703 5.18 21.80 224 0.875 bilinear -54.697 -53.757 -43.207 -43.753 +18 +42
279 res2net50_48w_2s pit_ti_distilled_224 37.117 37.337 62.883 62.663 53.333 55.137 46.667 44.863 25.29 5.10 224 0.875 0.900 bilinear bicubic -55.673 -53.563 -45.137 -43.083 -16 +51
280 dla60 hardcorenas_b 37.073 37.243 62.927 62.757 54.200 55.073 45.800 44.927 22.04 5.18 224 0.875 bilinear -55.597 -54.697 -44.430 -43.327 -13 +20
281 rexnet_100 mobilenetv3_large_100_miil 37.063 37.210 62.937 62.790 54.020 53.513 45.980 46.487 4.80 5.48 224 0.875 bicubic bilinear -55.787 -55.040 -44.600 -44.737 -21 +10
282 regnety_016 res2net50_48w_2s 37.017 37.117 62.983 62.883 54.093 53.333 45.907 46.667 11.20 25.29 224 0.875 bicubic bilinear -55.983 -55.673 -44.587 -45.137 -35 -17
283 tf_mixnet_l dla60 36.987 37.073 63.013 62.927 52.583 54.200 47.417 45.800 7.33 22.04 224 0.875 bicubic bilinear -56.053 -55.597 -45.957 -44.430 -41 -14
284 legacy_seresnet50 rexnet_100 36.873 37.063 63.127 62.937 53.487 54.020 46.513 45.980 28.09 4.80 224 0.875 bilinear bicubic -55.797 -55.787 -45.163 -44.600 -16 -22
285 regnety_016 37.017 62.983 54.093 45.907 11.20 224 0.875 bicubic -55.983 -44.587 -37
286 tf_mixnet_l 36.987 63.013 52.583 47.417 7.33 224 0.875 bicubic -56.053 -45.957 -44
287 legacy_seresnet50 36.873 63.127 53.487 46.513 28.09 224 0.875 bilinear -55.797 -45.163 -17
288 tv_densenet121 36.810 63.190 54.033 45.967 7.98 224 0.875 bicubic -54.590 -44.217 +26
289 tf_efficientnet_lite2 36.807 63.193 53.320 46.680 6.09 260 0.890 bicubic -55.783 -45.230 -12 -13
290 mobilenetv2_120d 36.780 63.220 54.047 45.953 5.83 224 0.875 bicubic -55.830 -44.463 -17 -18
291 tf_efficientnet_lite1 36.737 63.263 53.590 46.410 5.42 240 0.882 bicubic -55.573 -44.900 -2 -3
292 regnetx_016 36.683 63.317 53.297 46.703 9.19 224 0.875 bicubic -55.857 -45.253 -11 -12
293 hardcorenas_a 36.640 63.360 54.910 45.090 5.26 224 0.875 bilinear -54.980 -43.260 +14
294 efficientnet_b0 36.600 63.400 53.497 46.503 5.29 224 0.875 bicubic -55.880 -45.183 -12 -13
295 tf_efficientnet_em 36.380 63.620 52.840 47.160 6.90 240 0.882 bicubic -56.790 -45.830 -53 -55
296 skresnet18 36.320 63.680 54.197 45.803 11.96 224 0.875 bicubic -53.840 -43.583 +42 +44
297 repvgg_b0 36.287 63.713 54.057 45.943 15.82 224 0.875 bilinear -55.393 -44.393 +7
298 tv_resnet50 36.177 63.823 52.803 47.197 25.56 224 0.875 bilinear -55.963 -45.617 -3
299 legacy_seresnet34 36.143 63.857 52.553 47.447 21.96 224 0.875 bilinear -55.337 -45.767 +12
300 tv_resnet34 36.087 63.913 53.533 46.467 21.80 224 0.875 bilinear -54.203 -44.447 +37 +39
301 vit_deit_tiny_distilled_patch16_224 36.023 63.977 54.240 45.760 5.91 224 0.900 bicubic -55.077 -44.030 +25 +26
302 mobilenetv2_140 36.000 64.000 53.943 46.057 6.11 224 0.875 bicubic -56.030 -44.307 -5
303 tf_efficientnet_lite0 35.930 64.070 53.480 46.520 4.65 224 0.875 bicubic -55.370 -44.610 +13
304 selecsls42b 35.813 64.187 52.487 47.513 32.46 224 0.875 bicubic -56.667 -45.953 -21 -22
305 gluon_resnet34_v1b 35.763 35.760 64.237 64.240 52.187 47.813 21.80 224 0.875 bicubic -55.337 -55.340 -45.993 +20 +21
306 dla34 35.643 64.357 52.783 47.217 15.74 224 0.875 bilinear -55.597 -45.397 +13
307 mixnet_m 35.640 64.360 52.430 47.570 5.01 224 0.875 bicubic -56.630 -45.920 -16 -17
308 efficientnet_lite0 35.620 64.380 53.657 46.343 4.65 224 0.875 bicubic -55.640 -44.593 +10
309 ssl_resnet18 35.597 64.403 53.740 46.260 11.69 224 0.875 bilinear -55.103 -44.280 +23 +24
310 mobilenetv3_rw 35.547 64.453 53.713 46.287 5.48 224 0.875 bicubic -56.003 -44.557 -1
311 efficientnet_es_pruned 35.390 64.610 52.850 47.150 5.44 224 0.875 bicubic -56.310 -45.570 -8
312 mobilenetv2_110d 35.293 64.707 52.830 47.170 4.52 224 0.875 bicubic -56.057 -45.360 +3
313 tf_mixnet_m 35.180 64.820 50.987 49.013 5.01 224 0.875 bicubic -57.020 -47.433 -19
314 hrnet_w18_small_v2 35.173 64.827 52.440 47.560 15.60 224 0.875 bilinear -55.997 -45.900 +9
315 resnet18d 35.127 64.873 52.890 47.110 11.71 224 0.875 bicubic -54.863 -44.940 +24 +26
316 ese_vovnet19b_dw 34.840 65.160 52.030 47.970 6.54 224 0.875 bicubic -57.170 -46.480 -18
317 regnety_008 34.807 65.193 51.743 48.257 6.26 224 0.875 bicubic -57.093 -46.677 -16
318 pit_ti_224 34.670 65.330 52.170 47.830 4.85 224 0.900 bicubic -55.750 -45.840 +17 +19
319 mobilenetv3_large_100 34.603 65.397 52.860 47.140 5.48 224 0.875 bicubic -56.877 -45.340 -9
320 seresnext26d_32x4d 34.543 65.457 51.543 48.457 16.81 224 0.875 bicubic -57.897 -46.997 -35 -36
321 seresnext26t_32x4d 34.540 65.460 51.377 48.623 16.81 224 0.875 bicubic -58.280 -47.183 -56 -57
322 resnet26d mixer_b16_224 34.273 34.423 65.727 65.577 51.687 48.093 48.313 51.907 16.01 59.88 224 0.875 bicubic -57.957 -56.717 -46.763 -49.307 -29 +2
323 tf_efficientnet_es resnet26d 34.263 34.273 65.737 65.727 51.350 51.687 48.650 48.313 5.44 16.01 224 0.875 bicubic -57.837 -57.957 -47.090 -46.763 -27 -30
324 fbnetc_100 tf_efficientnet_es 34.253 34.263 65.747 65.737 51.180 51.350 48.820 48.650 5.57 5.44 224 0.875 bilinear bicubic -57.017 -57.837 -46.650 -47.090 -7 -28
325 regnety_006 fbnetc_100 34.150 34.253 65.850 65.747 51.277 51.180 48.723 48.820 6.06 5.57 224 0.875 bicubic bilinear -57.420 -57.017 -47.153 -46.650 -17 -8
326 tf_mobilenetv3_large_100 regnety_006 33.950 34.150 66.050 65.850 51.490 51.277 48.510 48.723 5.48 6.06 224 0.875 bilinear bicubic -57.470 -57.420 -46.770 -47.153 -13 -18
327 regnetx_008 tf_mobilenetv3_large_100 33.770 33.950 66.230 66.050 50.547 51.490 49.453 48.510 7.26 5.48 224 0.875 bicubic bilinear -57.410 -57.470 -47.833 -46.770 -5 -14
328 mnasnet_100 regnetx_008 33.763 33.770 66.237 66.230 51.170 50.547 48.830 49.453 4.38 7.26 224 0.875 bicubic -57.437 -57.410 -47.070 -47.833 -7 -6
329 semnasnet_100 mnasnet_100 33.520 33.763 66.480 66.237 50.787 51.170 49.213 48.830 3.89 4.38 224 0.875 bicubic -58.140 -57.437 -47.483 -47.070 -23 -8
330 resnet26 semnasnet_100 33.500 33.520 66.500 66.480 50.927 50.787 49.073 49.213 16.00 3.89 224 0.875 bicubic -57.940 -58.140 -47.353 -47.483 -18 -24
331 mixnet_s resnet26 33.480 33.500 66.520 66.500 50.997 50.927 49.003 49.073 4.13 16.00 224 0.875 bicubic -58.300 -57.940 -47.303 -47.353 -29 -19
332 mixnet_s 33.480 66.520 50.997 49.003 4.13 224 0.875 bicubic -58.300 -47.303 -30
333 spnasnet_100 33.477 66.523 51.267 48.733 4.42 224 0.875 bilinear -57.133 -46.683 +1
334 vgg19_bn 33.230 66.770 50.803 49.197 143.68 224 0.875 bilinear -57.760 -47.307 -5
335 regnetx_006 ghostnet_100 33.157 33.207 66.843 66.793 50.250 51.163 49.750 48.837 6.20 5.18 224 0.875 bicubic bilinear -57.603 -57.233 -47.850 -46.667 -3 +1
336 regnetx_006 33.157 66.843 50.250 49.750 6.20 224 0.875 bicubic -57.603 -47.850 -4
337 resnet18 33.067 66.933 51.170 48.830 11.69 224 0.875 bilinear -55.083 -45.950 +17
338 legacy_seresnext26_32x4d 32.757 67.243 49.237 50.763 16.79 224 0.875 bicubic -59.813 -49.183 -58 -61
339 hrnet_w18_small 32.667 67.333 50.587 49.413 13.19 224 0.875 bilinear -57.213 -47.313 +3
340 vit_deit_tiny_patch16_224 32.667 67.333 50.273 49.727 5.72 224 0.900 bicubic -56.953 -47.687 +5
341 legacy_seresnet18 32.600 67.400 50.340 49.660 11.78 224 0.875 bicubic -56.670 -47.340 +7
342 mobilenetv2_100 32.523 67.477 50.800 49.200 3.50 224 0.875 bicubic -57.307 -47.030 +1
343 regnetx_004 32.517 67.483 49.343 50.657 5.16 224 0.875 bicubic -56.943 -48.427 +3
344 gluon_resnet18_v1b 32.407 67.593 49.727 50.273 11.69 224 0.875 bicubic -56.253 -47.373 +7
345 regnety_004 32.333 67.667 49.453 50.547 4.34 224 0.875 bicubic -58.447 -48.627 -13 -14
346 tf_mixnet_s 32.183 67.817 48.493 51.507 4.13 224 0.875 bicubic -59.497 -49.747 -39 -41
347 tf_mobilenetv3_large_075 31.867 68.133 49.110 50.890 3.99 224 0.875 bilinear -58.453 -48.760 -9
348 tf_mobilenetv3_large_minimal_100 31.597 68.403 49.337 50.663 3.92 224 0.875 bilinear -57.583 -47.983 +2
349 vgg16_bn 30.357 69.643 47.260 52.740 138.37 224 0.875 bilinear -60.183 -50.730 -13 -14
350 regnety_002 29.687 70.313 46.787 53.213 3.16 224 0.875 bicubic -58.513 -50.643 +3
351 vgg13_bn 28.883 71.117 46.737 53.263 133.05 224 0.875 bilinear -60.317 -50.793 -2
352 regnetx_002 28.860 71.140 45.420 54.580 2.68 224 0.875 bicubic -58.520 -51.570 +4
354 dla60x_c 28.447 71.553 46.193 53.807 1.32 224 0.875 bilinear -58.663 -50.947 +4
355 vgg11_bn 28.423 71.577 46.453 53.547 132.87 224 0.875 bilinear -59.967 -50.817 -3
356 vgg16 27.877 72.123 44.673 55.327 138.36 224 0.875 bilinear -61.483 -52.847 -9
357 tf_mobilenetv3_small_100 27.297 72.703 44.420 55.580 2.54 224 0.875 bilinear -58.663 -51.980 +2 +3
358 vgg11 mixer_l16_224 26.533 26.853 73.467 73.147 43.460 37.923 56.540 62.077 132.86 208.20 224 0.875 bilinear bicubic -60.807 -60.117 -53.650 -56.137 -1 +1
359 vgg13 vgg11 26.267 26.533 73.733 73.467 43.370 43.460 56.630 56.540 133.05 132.86 224 0.875 bilinear -61.303 -60.807 -53.750 -53.650 -4 -2
360 vgg13 26.267 73.733 43.370 56.630 133.05 224 0.875 bilinear -61.303 -53.750 -5
361 dla46x_c 26.217 73.783 43.780 56.220 1.07 224 0.875 bilinear -59.263 -52.660 0
362 tf_mobilenetv3_small_075 26.200 73.800 43.637 56.363 2.04 224 0.875 bilinear -58.330 -52.253 +1
363 dla46_c 25.490 74.510 43.800 56.200 1.30 224 0.875 bilinear -59.170 -52.400 -1

493
results/results-imagenet-real.csv
View File

@ -1,297 +1,318 @@
model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation,top1_diff,top5_diff,rank_diff
tf_efficientnet_l2_ns,90.563,9.437,98.779,1.221,480.31,800,0.960,bicubic,+2.211,+0.129,0
tf_efficientnet_l2_ns_475,90.537,9.463,98.710,1.290,480.31,475,0.936,bicubic,+2.303,+0.164,0
tf_efficientnet_b7_ns,90.100,9.900,98.614,1.386,66.35,600,0.949,bicubic,+3.260,+0.520,+1
swin_large_patch4_window12_384,90.027,9.973,98.657,1.343,196.74,384,1.000,bicubic,+2.879,+0.423,-1
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,+2
swin_large_patch4_window7_224,89.796,10.204,98.640,1.360,196.53,224,0.900,bicubic,+3.477,+0.744,0
tf_efficientnet_b6_ns,89.782,10.218,98.510,1.490,43.04,528,0.942,bicubic,+3.330,+0.628,-3
tf_efficientnet_b5_ns,89.651,10.349,98.482,1.518,30.39,456,0.934,bicubic,+3.563,+0.730,0
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
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
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
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,-1
tf_efficientnet_b8,89.355,10.645,98.303,1.697,87.41,672,0.954,bicubic,+3.985,+0.913,+1
tf_efficientnet_b6_ap,89.342,10.658,98.281,1.719,43.04,528,0.942,bicubic,+4.554,+1.143,+12
tf_efficientnet_b4_ns,89.305,10.694,98.347,1.653,19.34,380,0.922,bicubic,+4.143,+0.877,+2
dm_nfnet_f4,89.299,10.701,98.224,1.776,316.07,512,0.951,bicubic,+3.641,+0.714,-6
dm_nfnet_f5,89.184,10.816,98.232,1.768,377.21,544,0.954,bicubic,+3.470,+0.790,-8
swin_base_patch4_window7_224,89.145,10.855,98.429,1.571,87.77,224,0.900,bicubic,+3.893,+0.867,-2
ig_resnext101_32x48d,89.120,10.880,98.130,1.870,828.41,224,0.875,bilinear,+3.692,+0.558,-7
ig_resnext101_32x32d,89.111,10.889,98.181,1.819,468.53,224,0.875,bilinear,+4.017,+0.743,0
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
dm_nfnet_f3,89.393,10.607,98.315,1.685,254.92,416,0.940,bicubic,+3.833,+0.909,-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_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
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,+10
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,+2
ig_resnext101_32x16d,88.834,11.166,98.049,1.951,194.03,224,0.875,bilinear,+4.664,+0.853,+9
vit_base_r50_s16_384,88.808,11.192,98.232,1.768,98.95,384,1.000,bicubic,+3.836,+0.944,-4
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
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
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,+7
resnetv2_152x2_bitm,88.699,11.301,98.337,1.663,236.34,480,1.000,bilinear,+4.259,+0.891,-2
regnety_160,88.697,11.303,98.068,1.932,83.59,288,1.000,bicubic,+5.011,+1.292,+12
pit_b_distilled_224,88.676,11.324,98.093,1.907,74.79,224,0.900,bicubic,+4.532,+1.237,+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
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
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,-10
resnetv2_152x4_bitm,88.565,11.435,98.185,1.815,936.53,480,1.000,bilinear,+3.633,+0.749,-15
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,-9
resnetv2_101x3_bitm,88.492,11.508,98.162,1.838,387.93,480,1.000,bilinear,+4.098,+0.800,-8
resnest200e,88.432,11.568,98.042,1.958,70.20,320,0.909,bicubic,+4.600,+1.148,+2
tf_efficientnet_b3_ns,88.426,11.574,98.029,1.971,12.23,300,0.904,bicubic,+4.378,+1.119,-2
vit_large_patch16_384,88.407,11.593,98.187,1.813,304.72,384,1.000,bicubic,+3.249,+0.831,-23
vit_base_patch16_384,88.389,11.611,98.155,1.845,86.86,384,1.000,bicubic,+4.180,+0.937,-8
resnet152d,88.355,11.645,97.935,2.065,60.21,320,1.000,bicubic,+4.675,+1.197,+2
resnetv2_50x3_bitm,88.349,11.651,98.108,1.892,217.32,480,1.000,bilinear,+4.565,+1.002,-1
tf_efficientnet_b4_ap,88.349,11.651,97.893,2.107,19.34,380,0.922,bicubic,+5.101,+1.501,+4
tf_efficientnet_b5,88.321,11.679,97.912,2.088,30.39,456,0.934,bicubic,+4.509,+1.164,-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
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
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_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,+13
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
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,-4
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
swin_small_patch4_window7_224,87.664,12.336,97.566,2.434,49.61,224,0.900,bicubic,+4.452,+1.244,-7
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,-2
swsl_resnext101_32x16d,87.615,12.386,97.820,2.180,194.03,224,0.875,bilinear,+4.269,+0.974,-14
swsl_resnext50_32x4d,87.600,12.400,97.651,2.349,25.03,224,0.875,bilinear,+5.418,+1.421,+8
tf_efficientnet_b2_ns,87.557,12.443,97.628,2.372,9.11,260,0.890,bicubic,+5.177,+1.380,+2
ecaresnet50t,87.538,12.462,97.643,2.357,25.57,320,0.950,bicubic,+5.192,+1.505,+2
efficientnet_b3a,87.435,12.565,97.681,2.319,12.23,320,1.000,bicubic,+5.193,+1.567,+3
tresnet_l_448,87.377,12.623,97.485,2.515,55.99,448,0.875,bilinear,+5.109,+1.509,+1
nasnetalarge,87.350,12.650,97.417,2.583,88.75,331,0.911,bicubic,+4.730,+1.371,-5
efficientnet_b3,87.313,12.687,97.602,2.398,12.23,300,0.904,bicubic,+5.237,+1.582,+4
ecaresnet101d,87.288,12.712,97.562,2.438,44.57,224,0.875,bicubic,+5.116,+1.516,+2
efficientnet_v2s,87.286,12.714,97.470,2.530,23.94,224,1.000,bicubic,+5.216,+1.516,+3
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
tresnet_xl,87.224,12.776,97.400,2.600,78.44,224,0.875,bilinear,+5.170,+1.463,+1
tf_efficientnet_b3_ap,87.192,12.808,97.380,2.620,12.23,300,0.904,bicubic,+5.370,+1.756,+4
vit_base_patch32_384,87.019,12.981,97.654,2.346,88.30,384,1.000,bicubic,+5.367,+1.526,+6
vit_large_patch16_224,87.006,12.994,97.690,2.310,304.33,224,0.900,bicubic,+3.944,+1.252,-23
vit_deit_small_distilled_patch16_224,86.993,13.007,97.316,2.684,22.44,224,0.900,bicubic,+5.793,+1.938,+20
tnt_s_patch16_224,86.903,13.097,97.368,2.632,23.76,224,0.900,bicubic,+5.385,+1.620,+8
ssl_resnext101_32x16d,86.856,13.143,97.517,2.483,194.03,224,0.875,bilinear,+5.013,+1.421,-2
rexnet_200,86.846,13.154,97.276,2.724,16.37,224,0.875,bicubic,+5.214,+1.608,+3
tf_efficientnet_b3,86.835,13.165,97.297,2.703,12.23,300,0.904,bicubic,+5.199,+1.579,+1
vit_deit_base_patch16_224,86.829,13.171,97.049,2.951,86.57,224,0.900,bicubic,+4.831,+1.315,-7
tresnet_m_448,86.820,13.180,97.212,2.788,31.39,448,0.875,bilinear,+5.106,+1.640,-3
swsl_resnet50,86.807,13.193,97.498,2.502,25.56,224,0.875,bilinear,+5.641,+1.526,+13
ssl_resnext101_32x8d,86.807,13.193,97.466,2.534,88.79,224,0.875,bilinear,+5.191,+1.428,0
tf_efficientnet_lite4,86.803,13.197,97.263,2.737,13.01,380,0.920,bilinear,+5.267,+1.595,-1
vit_base_patch16_224,86.778,13.223,97.438,2.562,86.57,224,0.900,bicubic,+4.992,+1.316,-8
tresnet_l,86.767,13.233,97.271,2.729,55.99,224,0.875,bilinear,+5.277,+1.647,0
seresnext50_32x4d,86.699,13.301,97.214,2.786,27.56,224,0.875,bicubic,+5.433,+1.594,+6
pit_b_224,86.686,13.314,96.898,3.102,73.76,224,0.900,bicubic,+4.240,+1.188,-26
tf_efficientnet_b1_ns,86.669,13.331,97.378,2.622,7.79,240,0.882,bicubic,+5.281,+1.640,-1
swin_tiny_patch4_window7_224,86.664,13.336,97.197,2.803,28.29,224,0.900,bicubic,+5.286,+1.657,-1
gernet_l,86.654,13.346,97.186,2.814,31.08,256,0.875,bilinear,+5.300,+1.650,-1
wide_resnet50_2,86.647,13.353,97.214,2.786,68.88,224,0.875,bicubic,+5.191,+1.682,-5
efficientnet_el,86.635,13.366,97.175,2.825,10.59,300,0.904,bicubic,+5.319,+1.649,-2
nf_resnet50,86.617,13.383,97.282,2.718,25.56,288,0.940,bicubic,+5.923,+1.926,+16
resnest50d_4s2x40d,86.592,13.408,97.269,2.731,30.42,224,0.875,bicubic,+5.484,+1.711,+2
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_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
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
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
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
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
tresnet_l,86.767,13.233,97.271,2.729,55.99,224,0.875,bilinear,+5.277,+1.647,-1
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
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
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,+18
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,+14
gluon_resnet152_v1s,86.468,13.532,97.109,2.891,60.32,224,0.875,bicubic,+5.452,+1.697,-1
resnest50d_1s4x24d,86.447,13.553,97.148,2.852,25.68,224,0.875,bicubic,+5.459,+1.826,-1
repvgg_b3g4,86.363,13.637,97.054,2.946,83.83,224,0.875,bilinear,+6.151,+1.944,+31
legacy_senet154,86.342,13.658,96.928,3.072,115.09,224,0.875,bilinear,+5.032,+1.432,-11
gernet_m,86.319,13.681,97.096,2.904,21.14,224,0.875,bilinear,+5.587,+1.912,+5
pit_s_224,86.316,13.684,97.045,2.955,23.46,224,0.900,bicubic,+5.222,+1.713,-7
efficientnet_b2a,86.304,13.696,96.990,3.010,9.11,288,1.000,bicubic,+5.692,+1.672,+5
gluon_senet154,86.278,13.722,96.949,3.051,115.09,224,0.875,bicubic,+5.044,+1.601,-13
resnest50d,86.240,13.761,97.073,2.927,27.48,224,0.875,bilinear,+5.266,+1.695,-7
ecaresnet101d_pruned,86.210,13.790,97.335,2.665,24.88,224,0.875,bicubic,+5.394,+1.707,-3
tresnet_m,86.199,13.801,96.667,3.333,31.39,224,0.875,bilinear,+5.397,+1.807,-2
efficientnet_el_pruned,86.192,13.807,97.026,2.974,10.59,300,0.904,bicubic,+5.892,+1.808,+16
cspdarknet53,86.182,13.818,97.013,2.987,27.64,256,0.887,bilinear,+6.124,+1.929,+27
inception_v4,86.169,13.831,96.919,3.081,42.68,299,0.875,bicubic,+6.001,+1.951,+22
rexnet_150,86.154,13.846,97.058,2.942,9.73,224,0.875,bicubic,+5.844,+1.892,+10
resnetv2_50x1_bitm,86.154,13.846,97.560,2.440,25.55,480,1.000,bilinear,+5.982,+1.934,+20
inception_resnet_v2,86.133,13.867,97.043,2.957,55.84,299,0.897,bicubic,+5.675,+1.737,+3
ssl_resnext50_32x4d,86.086,13.914,97.212,2.788,25.03,224,0.875,bilinear,+5.768,+1.806,+7
tf_efficientnet_el,86.084,13.916,96.964,3.036,10.59,300,0.904,bicubic,+5.834,+1.836,+12
efficientnet_b2,86.056,13.944,96.917,3.083,9.11,260,0.875,bicubic,+5.664,+1.841,+2
gluon_resnet101_v1s,86.054,13.946,97.022,2.978,44.67,224,0.875,bicubic,+5.752,+1.862,+6
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
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
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
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
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
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
tf_efficientnet_el,86.084,13.916,96.964,3.036,10.59,300,0.904,bicubic,+5.834,+1.836,+10
gluon_resnet101_v1s,86.054,13.946,97.022,2.978,44.67,224,0.875,bicubic,+5.752,+1.862,+5
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,-19
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
tf_efficientnet_b2_ap,85.975,14.025,96.810,3.190,9.11,260,0.890,bicubic,+5.675,+1.782,+3
gluon_seresnext101_64x4d,85.960,14.040,96.979,3.021,88.23,224,0.875,bicubic,+5.066,+1.671,-22
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,+9
seresnet50,85.857,14.143,97.004,2.995,28.09,224,0.875,bicubic,+5.583,+1.934,-1
repvgg_b2g4,85.855,14.145,96.812,3.188,61.76,224,0.875,bilinear,+6.489,+2.124,+36
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,+56
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
ens_adv_inception_resnet_v2,85.781,14.220,96.759,3.241,55.84,299,0.897,bicubic,+5.799,+1.823,+7
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,+32
gluon_resnext101_32x4d,85.746,14.254,96.635,3.365,44.18,224,0.875,bicubic,+5.412,+1.709,-15
legacy_seresnext101_32x4d,85.746,14.254,96.757,3.243,48.96,224,0.875,bilinear,+5.518,+1.739,-7
cspresnext50,85.740,14.260,96.840,3.160,20.57,224,0.875,bilinear,+5.700,+1.896,0
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,+19
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
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
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,+33
efficientnet_em,85.684,14.316,96.938,3.062,6.90,240,0.882,bicubic,+6.432,+2.144,+34
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,+27
efficientnet_b2_pruned,85.642,14.358,96.746,3.254,8.31,260,0.890,bicubic,+5.726,+1.890,-4
dpn107,85.640,14.360,96.729,3.271,86.92,224,0.875,bicubic,+5.484,+2.087,-12
ecaresnet50d_pruned,85.580,14.420,96.936,3.064,19.94,224,0.875,bicubic,+5.864,+2.056,+5
gluon_resnet152_v1c,85.580,14.420,96.646,3.354,60.21,224,0.875,bicubic,+5.670,+1.806,-6
resnext50d_32x4d,85.569,14.431,96.748,3.252,25.05,224,0.875,bicubic,+5.893,+1.882,+6
regnety_120,85.543,14.457,96.785,3.215,51.82,224,0.875,bicubic,+5.177,+1.659,-31
regnetx_320,85.524,14.476,96.669,3.331,107.81,224,0.875,bicubic,+5.278,+1.643,-23
nf_regnet_b1,85.499,14.501,96.799,3.200,10.22,288,0.900,bicubic,+6.193,+2.051,+18
dpn92,85.494,14.506,96.635,3.365,37.67,224,0.875,bicubic,+5.486,+1.797,-15
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
dpn131,85.398,14.602,96.639,3.361,79.25,224,0.875,bicubic,+5.576,+1.929,-8
regnetx_160,85.390,14.610,96.637,3.363,54.28,224,0.875,bicubic,+5.534,+1.807,-12
pit_xs_distilled_224,85.657,14.343,96.667,3.333,11.00,224,0.900,bicubic,+6.351,+2.303,+28
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
gluon_resnet152_v1c,85.580,14.420,96.646,3.354,60.21,224,0.875,bicubic,+5.670,+1.806,-7
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
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
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,-19
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,+1.480,-28
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
dpn68b,85.291,14.709,96.464,3.536,12.61,224,0.875,bicubic,+6.076,+2.050,+14
resnetblur50,85.283,14.717,96.531,3.470,25.56,224,0.875,bicubic,+5.997,+1.892,+7
regnety_064,85.283,14.717,96.639,3.361,30.58,224,0.875,bicubic,+5.561,+1.871,-14
regnety_080,85.245,14.755,96.633,3.367,39.18,224,0.875,bicubic,+5.369,+1.803,-24
resnext50_32x4d,85.221,14.779,96.526,3.474,25.03,224,0.875,bicubic,+5.453,+1.928,-18
resnext101_32x8d,85.187,14.813,96.445,3.555,88.79,224,0.875,bilinear,+5.879,+1.927,-2
gluon_inception_v3,85.183,14.817,96.526,3.474,23.83,299,0.875,bicubic,+6.377,+2.156,+22
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,-19
gluon_resnet101_v1b,85.142,14.858,96.366,3.634,44.55,224,0.875,bicubic,+5.836,+1.842,-4
regnetx_120,85.131,14.869,96.477,3.523,46.11,224,0.875,bicubic,+5.535,+1.739,-17
xception,85.129,14.871,96.471,3.529,22.86,299,0.897,bicubic,+6.077,+2.079,+10
tf_efficientnet_b1_ap,85.127,14.873,96.405,3.595,7.79,240,0.882,bicubic,+5.847,+2.099,-2
hrnet_w64,85.119,14.881,96.744,3.256,128.06,224,0.875,bilinear,+5.645,+2.092,-15
ssl_resnet50,85.097,14.903,96.866,3.134,25.56,224,0.875,bilinear,+5.875,+2.034,-2
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,-12
res2net50_26w_8s,85.029,14.971,96.419,3.580,48.40,224,0.875,bilinear,+5.831,+2.052,-2
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,-18
tf_efficientnet_b0_ns,84.984,15.016,96.503,3.497,5.29,224,0.875,bicubic,+6.326,+2.127,+15
regnety_040,84.948,15.052,96.612,3.388,20.65,224,0.875,bicubic,+5.728,+1.956,-8
dla169,84.920,15.080,96.535,3.465,53.39,224,0.875,bilinear,+6.232,+2.199,+11
tf_efficientnet_b1,84.918,15.082,96.364,3.636,7.79,240,0.882,bicubic,+6.092,+2.166,+4
legacy_seresnext50_32x4d,84.901,15.099,96.434,3.566,27.56,224,0.875,bilinear,+5.823,+1.998,-6
hrnet_w44,84.884,15.116,96.434,3.566,67.06,224,0.875,bilinear,+5.988,+2.066,0
gluon_resnet50_v1s,84.862,15.138,96.443,3.557,25.68,224,0.875,bicubic,+6.152,+2.205,+5
regnetx_080,84.862,15.138,96.434,3.566,39.57,224,0.875,bicubic,+5.668,+1.874,-10
gluon_resnet50_v1d,84.832,15.168,96.398,3.602,25.58,224,0.875,bicubic,+5.758,+1.928,-9
dla60_res2next,84.830,15.170,96.411,3.589,17.03,224,0.875,bilinear,+6.390,+2.259,+14
mixnet_l,84.822,15.178,96.328,3.672,7.33,224,0.875,bicubic,+5.846,+2.146,-7
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
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_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
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
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
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,+9
dla102x,84.813,15.187,96.552,3.448,26.31,224,0.875,bilinear,+6.303,+2.324,+5
xception41,84.792,15.208,96.413,3.587,26.97,299,0.903,bicubic,+6.276,+2.135,+2
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
regnetx_064,84.781,15.219,96.490,3.510,26.21,224,0.875,bicubic,+5.709,+2.032,-16
hrnet_w40,84.743,15.257,96.554,3.446,57.56,224,0.875,bilinear,+5.823,+2.084,-13
res2net50_26w_6s,84.726,15.274,96.281,3.719,37.05,224,0.875,bilinear,+6.156,+2.157,-2
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,-6
selecsls60b,84.657,15.343,96.300,3.700,32.77,224,0.875,bicubic,+6.245,+2.126,+3
hrnet_w32,84.651,15.349,96.407,3.593,41.23,224,0.875,bilinear,+6.201,+2.221,0
regnetx_040,84.600,15.400,96.383,3.617,22.12,224,0.875,bicubic,+6.118,+2.139,-4
efficientnet_es,84.591,15.409,96.311,3.689,5.44,224,0.875,bicubic,+6.525,+2.385,+13
hrnet_w30,84.572,15.428,96.388,3.612,37.71,224,0.875,bilinear,+6.366,+2.166,+6
tf_mixnet_l,84.564,15.437,96.244,3.756,7.33,224,0.875,bicubic,+5.790,+2.246,-16
wide_resnet101_2,84.557,15.443,96.349,3.651,126.89,224,0.875,bilinear,+5.701,+2.067,-21
efficientnet_b1,84.531,15.469,96.153,3.847,7.79,240,0.875,bicubic,+5.834,+2.009,-16
legacy_seresnet152,84.704,15.296,96.417,3.583,66.82,224,0.875,bilinear,+6.044,+2.047,-7
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
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
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,+4
repvgg_b1,84.416,15.584,96.221,3.779,57.42,224,0.875,bilinear,+6.050,+2.123,-6
efficientnet_b1_pruned,84.393,15.607,96.140,3.860,6.33,240,0.882,bicubic,+6.157,+2.306,-3
res2net50_26w_4s,84.365,15.635,96.082,3.918,25.70,224,0.875,bilinear,+6.401,+2.228,+8
hardcorenas_f,84.326,15.674,96.025,3.975,8.20,224,0.875,bilinear,+6.222,+2.222,+1
res2net50_14w_8s,84.309,15.691,96.072,3.929,25.06,224,0.875,bilinear,+6.159,+2.224,-2
selecsls60,84.288,15.712,96.095,3.905,30.67,224,0.875,bicubic,+6.306,+2.267,+4
regnetx_032,84.237,15.763,96.247,3.753,15.30,224,0.875,bicubic,+6.065,+2.159,-5
res2next50,84.226,15.774,95.997,4.003,24.67,224,0.875,bilinear,+5.980,+2.105,-10
gluon_resnet50_v1c,84.207,15.793,96.161,3.839,25.58,224,0.875,bicubic,+6.195,+2.173,-1
dla102,84.190,15.810,96.206,3.794,33.27,224,0.875,bilinear,+6.158,+2.260,-3
rexnet_100,84.162,15.838,96.255,3.745,4.80,224,0.875,bicubic,+6.304,+2.617,+4
tf_inception_v3,84.132,15.868,95.920,4.080,23.83,299,0.875,bicubic,+6.274,+2.504,+4
tf_efficientnet_em,84.450,15.550,96.180,3.820,6.90,240,0.882,bicubic,+6.320,+2.136,+3
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
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
res2net50_14w_8s,84.309,15.691,96.072,3.929,25.06,224,0.875,bilinear,+6.159,+2.224,-3
selecsls60,84.288,15.712,96.095,3.905,30.67,224,0.875,bicubic,+6.306,+2.267,+3
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
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,+22
tf_efficientnet_lite2,84.094,15.906,96.069,3.931,6.09,260,0.890,bicubic,+6.626,+2.315,+11
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
efficientnet_b0,84.038,15.962,95.956,4.044,5.29,224,0.875,bicubic,+6.340,+2.424,+2
hardcorenas_e,83.968,16.032,95.898,4.101,8.07,224,0.875,bilinear,+6.174,+2.204,0
tf_efficientnet_cc_b0_8e,83.966,16.034,96.065,3.935,24.01,224,0.875,bicubic,+6.058,+2.411,-6
tv_resnext50_32x4d,83.959,16.041,95.960,4.040,25.03,224,0.875,bilinear,+6.339,+2.264,+1
regnety_016,83.955,16.045,96.005,3.995,11.20,224,0.875,bicubic,+6.093,+2.285,-7
gluon_resnet50_v1b,83.940,16.060,96.012,3.988,25.56,224,0.875,bicubic,+6.360,+2.296,+3
densenet161,83.906,16.094,96.010,3.990,28.68,224,0.875,bicubic,+6.548,+2.372,+8
densenet161,83.906,16.094,96.010,3.990,28.68,224,0.875,bicubic,+6.548,+2.372,+9
adv_inception_v3,83.902,16.098,95.935,4.065,23.83,299,0.875,bicubic,+6.320,+2.199,0
mobilenetv2_120d,83.893,16.107,95.909,4.091,5.83,224,0.875,bicubic,+6.609,+2.417,+9
seresnext26t_32x4d,83.878,16.122,95.931,4.069,16.81,224,0.875,bicubic,+5.892,+2.185,-16
tv_resnet101,83.848,16.152,95.892,4.108,44.55,224,0.875,bilinear,+6.474,+2.352,+3
inception_v3,83.761,16.239,95.879,4.121,23.83,299,0.875,bicubic,+6.323,+2.403,0
hardcorenas_d,83.759,16.241,95.734,4.266,7.50,224,0.875,bilinear,+6.327,+2.250,0
mobilenetv2_120d,83.893,16.107,95.909,4.091,5.83,224,0.875,bicubic,+6.609,+2.417,+10
seresnext26t_32x4d,83.878,16.122,95.931,4.069,16.81,224,0.875,bicubic,+5.892,+2.185,-17
tv_resnet101,83.848,16.152,95.892,4.108,44.55,224,0.875,bilinear,+6.474,+2.352,+4
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,+1.888,-16
dla60,83.729,16.271,95.933,4.067,22.04,224,0.875,bilinear,+6.697,+2.615,+9
vit_small_patch16_224,83.735,16.265,95.758,4.242,48.75,224,0.900,bicubic,+5.877,+2.342,-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
tf_efficientnet_b0_ap,83.650,16.350,95.779,4.221,5.29,224,0.875,bicubic,+6.564,+2.523,+4
skresnet34,83.641,16.359,95.933,4.067,22.28,224,0.875,bicubic,+6.729,+2.611,+9
tf_efficientnet_cc_b0_4e,83.639,16.361,95.740,4.260,13.31,224,0.875,bicubic,+6.333,+2.406,-5
densenet201,83.556,16.444,95.811,4.189,20.01,224,0.875,bicubic,+6.270,+2.333,-5
tf_efficientnet_b0_ap,83.650,16.350,95.779,4.221,5.29,224,0.875,bicubic,+6.564,+2.523,+5
skresnet34,83.641,16.359,95.933,4.067,22.28,224,0.875,bicubic,+6.729,+2.611,+10
tf_efficientnet_cc_b0_4e,83.639,16.361,95.740,4.260,13.31,224,0.875,bicubic,+6.333,+2.406,-4
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
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,+8
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,+5
dpn68,83.178,16.822,95.597,4.402,12.61,224,0.875,bicubic,+6.860,+2.620,+6
tf_efficientnet_es,83.178,16.822,95.585,4.415,5.44,224,0.875,bicubic,+6.584,+2.383,0
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
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
repvgg_a2,83.001,16.999,95.589,4.411,28.21,224,0.875,bilinear,+6.541,+2.585,0
tv_resnet50,82.958,17.042,95.467,4.533,25.56,224,0.875,bilinear,+6.820,+2.603,+2
hardcorenas_b,82.873,17.128,95.392,4.607,5.18,224,0.875,bilinear,+6.335,+2.638,-4
densenet121,82.823,17.177,95.585,4.415,7.98,224,0.875,bicubic,+7.245,+2.933,+7
densenet169,82.683,17.317,95.600,4.400,14.15,224,0.875,bicubic,+6.776,+2.574,+2
mixnet_s,82.525,17.476,95.356,4.644,4.13,224,0.875,bicubic,+6.532,+2.560,-1
regnety_008,82.493,17.508,95.487,4.513,6.26,224,0.875,bicubic,+6.177,+2.421,-4
efficientnet_lite0,82.382,17.619,95.279,4.721,4.65,224,0.875,bicubic,+6.898,+2.769,+6
resnest14d,82.352,17.648,95.339,4.661,10.61,224,0.875,bilinear,+6.848,+2.821,+4
hardcorenas_a,82.313,17.687,95.294,4.706,5.26,224,0.875,bilinear,+6.397,+2.780,-4
efficientnet_es_pruned,82.296,17.704,95.303,4.697,5.44,224,0.875,bicubic,+7.296,+2.855,+13
mobilenetv3_rw,82.275,17.725,95.234,4.766,5.48,224,0.875,bicubic,+6.641,+2.526,-2
semnasnet_100,82.251,17.749,95.230,4.770,3.89,224,0.875,bicubic,+6.803,+2.626,+2
mobilenetv3_large_100,82.177,17.823,95.196,4.804,5.48,224,0.875,bicubic,+6.410,+2.654,-6
resnet34,82.138,17.862,95.130,4.870,21.80,224,0.875,bilinear,+7.028,+2.846,+6
mobilenetv2_110d,82.070,17.930,95.076,4.923,4.52,224,0.875,bicubic,+7.034,+2.890,+7
tf_mixnet_s,82.038,17.962,95.121,4.879,4.13,224,0.875,bicubic,+6.388,+2.493,-8
repvgg_b0,82.001,17.999,95.100,4.900,15.82,224,0.875,bilinear,+6.849,+2.682,0
vit_deit_tiny_distilled_patch16_224,81.997,18.003,95.141,4.859,5.91,224,0.900,bicubic,+7.487,+3.251,+13
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
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
regnety_008,82.493,17.508,95.487,4.513,6.26,224,0.875,bicubic,+6.177,+2.421,-3
efficientnet_lite0,82.382,17.619,95.279,4.721,4.65,224,0.875,bicubic,+6.898,+2.769,+7
resnest14d,82.352,17.648,95.339,4.661,10.61,224,0.875,bilinear,+6.846,+2.821,+5
hardcorenas_a,82.313,17.687,95.294,4.706,5.26,224,0.875,bilinear,+6.397,+2.780,-3
efficientnet_es_pruned,82.296,17.704,95.303,4.697,5.44,224,0.875,bicubic,+7.296,+2.855,+14
mobilenetv3_rw,82.275,17.725,95.234,4.766,5.48,224,0.875,bicubic,+6.641,+2.526,-1
semnasnet_100,82.251,17.749,95.230,4.770,3.89,224,0.875,bicubic,+6.803,+2.626,+3
mobilenetv3_large_100,82.177,17.823,95.196,4.804,5.48,224,0.875,bicubic,+6.410,+2.654,-5
resnet34,82.138,17.862,95.130,4.870,21.80,224,0.875,bilinear,+7.028,+2.846,+7
mobilenetv2_110d,82.070,17.930,95.076,4.923,4.52,224,0.875,bicubic,+7.034,+2.890,+8
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
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
@ -305,9 +326,10 @@ legacy_seresnet34,81.534,18.466,94.899,5.101,21.96,224,0.875,bilinear,+6.726,+2.
gluon_resnet34_v1b,81.500,18.500,94.810,5.190,21.80,224,0.875,bicubic,+6.912,+2.820,0
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,81.446,18.554,94.763,5.237,143.68,224,0.875,bilinear,+7.232,+2.921,0
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
regnety_004,80.659,19.341,94.686,5.314,4.34,224,0.875,bicubic,+6.624,+2.934,0
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
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
pit_ti_224,80.605,19.395,94.618,5.383,4.85,224,0.900,bicubic,+7.693,+3.216,+5
@ -319,16 +341,17 @@ mobilenetv2_100,80.257,19.743,94.195,5.805,3.50,224,0.875,bicubic,+7.287,+3.179,
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
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,79.555,20.445,93.898,6.102,13.19,224,0.875,bilinear,+7.211,+3.220,0
hrnet_w18_small,79.557,20.443,93.898,6.102,13.19,224,0.875,bilinear,+7.215,+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
legacy_seresnet18,79.153,20.847,93.783,6.217,11.78,224,0.875,bicubic,+7.411,+3.449,0
vgg16,79.038,20.962,93.646,6.354,138.36,224,0.875,bilinear,+7.444,+3.264,+1
vgg13_bn,79.006,20.994,93.655,6.345,133.05,224,0.875,bilinear,+7.412,+3.279,-1
gluon_resnet18_v1b,78.372,21.628,93.138,6.862,11.69,224,0.875,bicubic,+7.536,+3.376,0
vgg11_bn,77.926,22.074,93.230,6.770,132.87,224,0.875,bilinear,+7.566,+3.428,0
regnety_002,77.405,22.595,92.914,7.086,3.16,224,0.875,bicubic,+7.153,+3.374,0
legacy_seresnet18,79.153,20.847,93.783,6.217,11.78,224,0.875,bicubic,+7.411,+3.449,+1
vgg16,79.038,20.962,93.646,6.354,138.36,224,0.875,bilinear,+7.444,+3.264,+2
vgg13_bn,79.006,20.994,93.655,6.345,133.05,224,0.875,bilinear,+7.412,+3.279,0
gluon_resnet18_v1b,78.372,21.628,93.138,6.862,11.69,224,0.875,bicubic,+7.536,+3.376,+1
vgg11_bn,77.926,22.074,93.230,6.770,132.87,224,0.875,bilinear,+7.566,+3.428,+1
regnety_002,77.405,22.595,92.914,7.086,3.16,224,0.875,bicubic,+7.153,+3.374,+1
mixer_l16_224,77.285,22.715,90.582,9.418,208.20,224,0.875,bicubic,+5.227,+2.914,-6
resnet18,77.276,22.724,92.756,7.244,11.69,224,0.875,bilinear,+7.528,+3.678,+1
vgg13,77.230,22.770,92.689,7.311,133.05,224,0.875,bilinear,+7.303,+3.444,-1
vgg11,76.384,23.616,92.154,7.846,132.86,224,0.875,bilinear,+7.360,+3.526,0

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
2 tf_efficientnet_l2_ns 90.563 9.437 98.779 1.221 480.31 800 0.960 bicubic +2.211 +0.129 0
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 tf_efficientnet_b7_ns cait_m48_448 90.100 90.196 9.900 9.804 98.614 98.484 1.386 1.516 66.35 356.46 600 448 0.949 1.000 bicubic +3.260 +3.712 +0.520 +0.730 +1 +2
5 swin_large_patch4_window12_384 tf_efficientnet_b7_ns 90.027 90.100 9.973 9.900 98.657 98.614 1.343 1.386 196.74 66.35 384 600 1.000 0.949 bicubic +2.879 +3.260 +0.423 +0.520 -1 0
6 swin_base_patch4_window12_384 cait_m36_384 89.995 90.046 10.005 9.954 98.695 98.493 1.304 1.507 87.90 271.22 384 1.000 bicubic +3.563 +3.992 +0.637 +0.763 +1 +6
7 dm_nfnet_f6 swin_large_patch4_window12_384 89.901 90.027 10.099 9.973 98.529 98.657 1.471 1.343 438.36 196.74 576 384 0.956 1.000 bicubic +3.605 +2.879 +0.785 +0.423 +2 -3
8 swin_large_patch4_window7_224 swin_base_patch4_window12_384 89.796 89.995 10.204 10.005 98.640 98.695 1.360 1.304 196.53 87.90 224 384 0.900 1.000 bicubic +3.477 +3.563 +0.744 +0.637 0
9 tf_efficientnet_b6_ns dm_nfnet_f6 89.782 89.901 10.218 10.099 98.510 98.529 1.490 1.471 43.04 438.36 528 576 0.942 0.956 bicubic +3.330 +3.605 +0.628 +0.785 -3 +1
10 tf_efficientnet_b5_ns cait_s36_384 89.651 89.844 10.349 10.156 98.482 98.427 1.518 1.573 30.39 68.37 456 384 0.934 1.000 bicubic +3.563 +4.384 +0.730 +0.947 0 +6
11 swin_large_patch4_window7_224 89.796 10.204 98.640 1.360 196.53 224 0.900 bicubic +3.477 +0.744 -2
12 tf_efficientnet_b6_ns 89.782 10.218 98.510 1.490 43.04 528 0.942 bicubic +3.330 +0.628 -5
13 tf_efficientnet_b5_ns 89.651 10.349 98.482 1.518 30.39 456 0.934 bicubic +3.563 +0.730 -2
14 tf_efficientnet_b8_ap 89.581 10.419 98.305 1.695 87.41 672 0.954 bicubic +4.211 +1.011 +6
15 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 +9 +11
16 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 +2 +8
17 dm_nfnet_f3 vit_deit_base_distilled_patch16_384 89.393 89.429 10.607 10.571 98.315 98.441 1.685 1.559 254.92 87.63 416 384 0.940 1.000 bicubic +3.833 +4.007 +0.909 +1.109 -1 +1
18 tf_efficientnet_b8 dm_nfnet_f3 89.355 89.393 10.645 10.607 98.303 98.315 1.697 1.685 87.41 254.92 672 416 0.954 0.940 bicubic +3.985 +3.833 +0.913 +0.909 +1 -3
19 tf_efficientnet_b6_ap tf_efficientnet_b8 89.342 89.355 10.658 10.645 98.281 98.303 1.719 1.697 43.04 87.41 528 672 0.942 0.954 bicubic +4.554 +3.985 +1.143 +0.913 +12 0
20 tf_efficientnet_b4_ns tf_efficientnet_b6_ap 89.305 89.342 10.694 10.658 98.347 98.281 1.653 1.719 19.34 43.04 380 528 0.922 0.942 bicubic +4.143 +4.554 +0.877 +1.143 +2 +13
21 dm_nfnet_f4 tf_efficientnet_b4_ns 89.299 89.305 10.701 10.694 98.224 98.347 1.776 1.653 316.07 19.34 512 380 0.951 0.922 bicubic +3.641 +4.143 +0.714 +0.877 -6 +1
22 dm_nfnet_f5 dm_nfnet_f4 89.184 89.299 10.816 10.701 98.232 98.224 1.768 1.776 377.21 316.07 544 512 0.954 0.951 bicubic +3.470 +3.641 +0.790 +0.714 -8
23 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 -2 -10
24 ig_resnext101_32x48d swin_base_patch4_window7_224 89.120 89.145 10.880 10.855 98.130 98.429 1.870 1.571 828.41 87.77 224 0.875 0.900 bilinear bicubic +3.692 +3.893 +0.558 +0.867 -7 -3
25 ig_resnext101_32x32d cait_xs24_384 89.111 89.139 10.889 10.861 98.181 98.290 1.819 1.710 468.53 26.67 224 384 0.875 1.000 bilinear bicubic +4.017 +5.077 +0.743 +1.402 0 +24
26 ig_resnext101_32x48d 89.120 10.880 98.130 1.870 828.41 224 0.875 bilinear +3.692 +0.558 -9
27 ig_resnext101_32x32d 89.111 10.889 98.181 1.819 468.53 224 0.875 bilinear +4.017 +0.743 -2
28 tf_efficientnet_b7 89.086 10.914 98.183 1.817 66.35 600 0.949 bicubic +4.150 +0.979 +3
29 ecaresnet269d 89.069 10.931 98.234 1.766 102.09 352 1.000 bicubic +4.093 +1.008 0
30 tf_efficientnet_b5_ap 88.938 11.062 98.164 1.836 30.39 456 0.934 bicubic +4.686 +1.190 +10 +13
31 dm_nfnet_f2 88.889 11.111 98.117 1.883 193.78 352 0.920 bicubic +3.899 +0.973 -3
32 dm_nfnet_f1 88.853 11.147 98.093 1.907 132.63 320 0.910 bicubic +4.249 +1.025 +2 +3
33 ig_resnext101_32x16d resnetrs420 88.834 88.840 11.166 11.160 98.049 98.034 1.951 1.966 194.03 191.89 224 416 0.875 1.000 bilinear bicubic +4.664 +3.832 +0.853 +0.910 +9 -6
34 vit_base_r50_s16_384 ig_resnext101_32x16d 88.808 88.834 11.192 11.166 98.232 98.049 1.768 1.951 98.95 194.03 384 224 1.000 0.875 bicubic bilinear +3.836 +4.664 +0.944 +0.853 -4 +11
35 resnetrs270 88.834 11.166 98.136 1.864 129.86 352 1.000 bicubic +4.400 +1.166 +3
36 vit_base_r50_s16_384 88.808 11.192 98.232 1.768 98.95 384 1.000 bicubic +3.836 +0.944 -6
37 seresnet152d 88.795 11.205 98.172 1.828 66.84 320 1.000 bicubic +4.433 +1.132 +3
38 swsl_resnext101_32x8d 88.770 11.230 98.147 1.853 88.79 224 0.875 bilinear +4.486 +0.971 +3
39 tf_efficientnet_b6 88.761 11.239 98.064 1.937 43.04 528 0.942 bicubic +4.651 +1.178 +7 +8
40 resnetv2_152x2_bitm resnetrs350 88.699 88.759 11.301 11.241 98.337 98.029 1.663 1.971 236.34 163.96 480 384 1.000 bilinear bicubic +4.259 +4.039 +0.891 +1.041 -2 -6
41 regnety_160 vit_base_patch16_224_miil 88.697 88.737 11.303 11.262 98.068 98.027 1.932 1.973 83.59 86.54 288 224 1.000 0.875 bicubic bilinear +5.011 +4.469 +1.292 +1.225 +12 +1
42 pit_b_distilled_224 resnetv2_152x2_bitm 88.676 88.699 11.324 11.301 98.093 98.337 1.907 1.663 74.79 236.34 224 480 0.900 1.000 bicubic bilinear +4.532 +4.259 +1.237 +0.891 +3 -5
43 regnety_160 88.697 11.303 98.068 1.932 83.59 288 1.000 bicubic +5.011 +1.292 +15
44 pit_b_distilled_224 88.676 11.324 98.093 1.907 74.79 224 0.900 bicubic +4.532 +1.237 +2
45 resnetrs200 88.605 11.395 98.034 1.966 93.21 320 1.000 bicubic +4.539 +1.160 +3
46 eca_nfnet_l1 88.575 11.425 98.130 1.870 41.41 320 1.000 bicubic +4.567 +1.102 +5
47 resnetv2_152x4_bitm 88.565 11.435 98.185 1.815 936.53 480 1.000 bilinear +3.633 +0.749 -10 -15
48 resnet200d 88.543 11.457 97.959 2.041 64.69 320 1.000 bicubic +4.581 +1.135 +4
49 resnest269e 88.522 11.478 98.027 1.973 110.93 416 0.928 bicubic +4.004 +1.041 -9 -13
50 resnetv2_101x3_bitm 88.492 11.508 98.162 1.838 387.93 480 1.000 bilinear +4.098 +0.800 -8 -11
51 resnest200e efficientnet_v2s 88.432 88.473 11.568 11.527 98.042 97.974 1.958 2.026 70.20 23.94 320 384 0.909 1.000 bicubic +4.600 +4.665 +1.148 +1.250 +2 +4
52 tf_efficientnet_b3_ns cait_s24_224 88.426 88.447 11.574 11.553 98.029 97.957 1.971 2.043 12.23 46.92 300 224 0.904 1.000 bicubic +4.378 +4.995 +1.119 +1.393 -2 +8
53 vit_large_patch16_384 resnest200e 88.407 88.432 11.593 11.568 98.187 98.042 1.813 1.958 304.72 70.20 384 320 1.000 0.909 bicubic +3.249 +4.600 +0.831 +1.148 -23 0
54 vit_base_patch16_384 tf_efficientnet_b3_ns 88.389 88.426 11.611 11.574 98.155 98.029 1.845 1.971 86.86 12.23 384 300 1.000 0.904 bicubic +4.180 +4.378 +0.937 +1.119 -8 -4
55 resnet152d vit_large_patch16_384 88.355 88.407 11.645 11.593 97.935 98.187 2.065 1.813 60.21 304.72 320 384 1.000 bicubic +4.675 +3.249 +1.197 +0.831 +2 -32
56 resnetv2_50x3_bitm vit_base_patch16_384 88.349 88.389 11.651 11.611 98.108 98.155 1.892 1.845 217.32 86.86 480 384 1.000 bilinear bicubic +4.565 +4.180 +1.002 +0.937 -1 -12
57 tf_efficientnet_b4_ap efficientnet_b4 88.349 88.372 11.651 11.628 97.893 97.961 2.107 2.039 19.34 380 384 0.922 1.000 bicubic +5.101 +4.944 +1.501 +1.365 +4
58 tf_efficientnet_b5 resnet152d 88.321 88.355 11.679 11.645 97.912 97.935 2.088 2.065 30.39 60.21 456 320 0.934 1.000 bicubic +4.509 +4.675 +1.164 +1.197 -4 +1
59 tf_efficientnet_b4_ap 88.349 11.651 97.893 2.107 19.34 380 0.922 bicubic +5.101 +1.501 +5
60 resnetv2_50x3_bitm 88.349 11.651 98.108 1.892 217.32 480 1.000 bilinear +4.565 +1.002 -3
61 tf_efficientnet_b5 88.321 11.679 97.912 2.088 30.39 456 0.934 bicubic +4.509 +1.164 -7
62 resnetrs152 88.251 11.749 97.737 2.263 86.62 320 1.000 bicubic +4.539 +1.123 -5
63 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
64 ig_resnext101_32x8d 88.146 11.854 97.856 2.144 88.79 224 0.875 bilinear +5.458 +1.220 +13 +14
65 dm_nfnet_f0 cait_xxs36_384 88.112 88.140 11.888 11.860 97.837 97.908 2.163 2.092 71.49 17.37 256 384 0.900 1.000 bicubic +4.770 +5.946 +1.277 +1.760 -1 +23
66 swsl_resnext101_32x4d dm_nfnet_f0 88.099 88.112 11.901 11.888 97.967 97.837 2.033 2.163 44.18 71.49 224 256 0.875 0.900 bilinear bicubic +4.869 +4.770 +1.207 +1.277 0 -2
67 swsl_resnext101_32x4d 88.099 11.901 97.967 2.033 44.18 224 0.875 bilinear +4.869 +1.207 -1
68 tf_efficientnet_b4 87.963 12.037 97.739 2.261 19.34 380 0.922 bicubic +4.941 +1.439 +5
69 nfnet_l0 87.948 12.052 97.850 2.150 35.07 288 1.000 bicubic +5.188 +1.352 +7
70 eca_nfnet_l0 87.943 12.057 97.861 2.139 24.14 288 1.000 bicubic +5.355 +1.387 +10
71 resnet101d 87.941 12.059 97.908 2.092 44.57 320 1.000 bicubic +4.919 +1.462 +1
72 regnety_032 87.937 12.063 97.891 2.109 19.44 288 1.000 bicubic +5.213 +1.467 +5
73 vit_deit_base_patch16_384 87.845 12.155 97.510 2.490 86.86 384 1.000 bicubic +4.739 +1.138 -4 -5
74 tresnet_xl_448 87.796 12.204 97.459 2.541 78.44 448 0.875 bilinear +4.746 +1.285 -3
75 swin_small_patch4_window7_224 tresnet_m 87.664 87.736 12.336 12.264 97.566 97.523 2.434 2.477 49.61 31.39 224 0.900 0.875 bicubic bilinear +4.452 +4.656 +1.244 +1.405 -7 -6
76 swin_small_patch4_window7_224 87.664 12.336 97.566 2.434 49.61 224 0.900 bicubic +4.452 +1.244 -9
77 resnetv2_101x1_bitm 87.638 12.362 97.955 2.045 44.54 480 1.000 bilinear +5.426 +1.483 +10
78 pnasnet5large 87.636 12.364 97.485 2.515 86.06 331 0.911 bicubic +4.854 +1.445 -2 -3
79 swsl_resnext101_32x16d 87.615 12.386 97.820 2.180 194.03 224 0.875 bilinear +4.269 +0.974 -14 -16
80 swsl_resnext50_32x4d 87.600 12.400 97.651 2.349 25.03 224 0.875 bilinear +5.418 +1.421 +8 +9
81 tf_efficientnet_b2_ns 87.557 12.443 97.628 2.372 9.11 260 0.890 bicubic +5.177 +1.380 +2 +1
82 ecaresnet50t 87.538 12.462 97.643 2.357 25.57 320 0.950 bicubic +5.192 +1.505 +2 +1
83 efficientnet_b3a efficientnet_b3 87.435 12.565 97.681 2.319 12.23 320 1.000 bicubic +5.193 +1.567 +3
84 tresnet_l_448 cait_xxs24_384 87.377 87.416 12.623 12.584 97.485 97.619 2.515 2.381 55.99 12.03 448 384 0.875 1.000 bilinear bicubic +5.109 +6.450 +1.509 +1.973 +1 +37
85 nasnetalarge tresnet_l_448 87.350 87.377 12.650 12.623 97.417 97.485 2.583 2.515 88.75 55.99 331 448 0.911 0.875 bicubic bilinear +4.730 +5.109 +1.371 +1.509 -5 0
86 efficientnet_b3 nasnetalarge 87.313 87.350 12.687 12.650 97.602 97.417 2.398 2.583 12.23 88.75 300 331 0.904 0.911 bicubic +5.237 +4.730 +1.582 +1.371 +4 -7
87 ecaresnet101d 87.288 12.712 97.562 2.438 44.57 224 0.875 bicubic +5.116 +1.516 +2 +3
efficientnet_v2s 87.286 12.714 97.470 2.530 23.94 224 1.000 bicubic +5.216 +1.516 +3
88 resnest101e 87.284 12.716 97.560 2.440 48.28 256 0.875 bilinear +4.394 +1.240 -14
89 pit_s_distilled_224 87.277 12.723 97.500 2.500 24.04 224 0.900 bicubic +5.281 +1.702 +4
90 tresnet_xl resnetrs101 87.224 87.247 12.776 12.753 97.400 97.457 2.600 2.543 78.44 63.62 224 288 0.875 0.940 bilinear bicubic +5.170 +4.959 +1.463 +1.449 +1 -6
91 tf_efficientnet_b3_ap tresnet_xl 87.192 87.224 12.808 12.776 97.380 97.400 2.620 2.600 12.23 78.44 300 224 0.904 0.875 bicubic bilinear +5.370 +5.170 +1.756 +1.463 +4 0
92 vit_base_patch32_384 tf_efficientnet_b3_ap 87.019 87.192 12.981 12.808 97.654 97.380 2.346 2.620 88.30 12.23 384 300 1.000 0.904 bicubic +5.367 +5.370 +1.526 +1.756 +6 +3
93 vit_large_patch16_224 vit_base_patch32_384 87.006 87.019 12.994 12.981 97.690 97.654 2.310 2.346 304.33 88.30 224 384 0.900 1.000 bicubic +3.944 +5.367 +1.252 +1.526 -23 +5
94 vit_deit_small_distilled_patch16_224 vit_large_patch16_224 86.993 87.006 13.007 12.994 97.316 97.690 2.684 2.310 22.44 304.33 224 0.900 bicubic +5.793 +3.944 +1.938 +1.252 +20 -24
95 tnt_s_patch16_224 vit_deit_small_distilled_patch16_224 86.903 86.993 13.097 13.007 97.368 97.316 2.632 2.684 23.76 22.44 224 0.900 bicubic +5.385 +5.793 +1.620 +1.938 +8 +19
96 ssl_resnext101_32x16d tnt_s_patch16_224 86.856 86.903 13.143 13.097 97.517 97.368 2.483 2.632 194.03 23.76 224 0.875 0.900 bilinear bicubic +5.013 +5.385 +1.421 +1.620 -2 +7
97 rexnet_200 ssl_resnext101_32x16d 86.846 86.856 13.154 13.143 97.276 97.517 2.724 2.483 16.37 194.03 224 0.875 bicubic bilinear +5.214 +5.013 +1.608 +1.421 +3 -3
98 tf_efficientnet_b3 rexnet_200 86.835 86.846 13.165 13.154 97.297 97.276 2.703 2.724 12.23 16.37 300 224 0.904 0.875 bicubic +5.199 +5.214 +1.579 +1.608 +1 +2
99 vit_deit_base_patch16_224 tf_efficientnet_b3 86.829 86.835 13.171 13.165 97.049 97.297 2.951 2.703 86.57 12.23 224 300 0.900 0.904 bicubic +4.831 +5.199 +1.315 +1.579 -7 0
100 tresnet_m_448 vit_deit_base_patch16_224 86.820 86.829 13.180 13.171 97.212 97.049 2.788 2.951 31.39 86.57 448 224 0.875 0.900 bilinear bicubic +5.106 +4.831 +1.640 +1.315 -3 -8
101 swsl_resnet50 tresnet_m_448 86.807 86.820 13.193 13.180 97.498 97.212 2.502 2.788 25.56 31.39 224 448 0.875 bilinear +5.641 +5.106 +1.526 +1.640 +13 -4
102 ssl_resnext101_32x8d 86.807 13.193 97.466 2.534 88.79 224 0.875 bilinear +5.191 +1.428 0 -1
103 tf_efficientnet_lite4 swsl_resnet50 86.803 86.807 13.197 13.193 97.263 97.498 2.737 2.502 13.01 25.56 380 224 0.920 0.875 bilinear +5.267 +5.641 +1.595 +1.526 -1 +12
104 vit_base_patch16_224 tf_efficientnet_lite4 86.778 86.803 13.223 13.197 97.438 97.263 2.562 2.737 86.57 13.01 224 380 0.900 0.920 bicubic bilinear +4.992 +5.267 +1.316 +1.595 -8 -2
105 tresnet_l vit_base_patch16_224 86.767 86.778 13.233 13.223 97.271 97.438 2.729 2.562 55.99 86.57 224 0.875 0.900 bilinear bicubic +5.277 +4.992 +1.647 +1.316 0 -9
106 seresnext50_32x4d tresnet_l 86.699 86.767 13.301 13.233 97.214 97.271 2.786 2.729 27.56 55.99 224 0.875 bicubic bilinear +5.433 +5.277 +1.594 +1.647 +6 -1
107 pit_b_224 seresnext50_32x4d 86.686 86.699 13.314 13.301 96.898 97.214 3.102 2.786 73.76 27.56 224 0.900 0.875 bicubic +4.240 +5.433 +1.188 +1.594 -26 +5
108 tf_efficientnet_b1_ns pit_b_224 86.669 86.686 13.331 13.314 97.378 96.898 2.622 3.102 7.79 73.76 240 224 0.882 0.900 bicubic +5.281 +4.240 +1.640 +1.188 -1 -27
109 swin_tiny_patch4_window7_224 tf_efficientnet_b1_ns 86.664 86.669 13.336 13.331 97.197 97.378 2.803 2.622 28.29 7.79 224 240 0.900 0.882 bicubic +5.286 +5.281 +1.657 +1.640 -1 -2
110 gernet_l swin_tiny_patch4_window7_224 86.654 86.664 13.346 13.336 97.186 97.197 2.814 2.803 31.08 28.29 256 224 0.875 0.900 bilinear bicubic +5.300 +5.286 +1.650 +1.657 -1 -2
111 wide_resnet50_2 gernet_l 86.647 86.654 13.353 13.346 97.214 97.186 2.786 2.814 68.88 31.08 224 256 0.875 bicubic bilinear +5.191 +5.300 +1.682 +1.650 -5 -2
112 efficientnet_el wide_resnet50_2 86.635 86.647 13.366 13.353 97.175 97.214 2.825 2.786 10.59 68.88 300 224 0.904 0.875 bicubic +5.319 +5.191 +1.649 +1.682 -2 -6
113 nf_resnet50 efficientnet_el 86.617 86.635 13.383 13.366 97.282 97.175 2.718 2.825 25.56 10.59 288 300 0.940 0.904 bicubic +5.923 +5.319 +1.926 +1.649 +16 -3
114 resnest50d_4s2x40d nf_resnet50 86.592 86.617 13.408 13.383 97.269 97.282 2.731 2.718 30.42 25.56 224 288 0.875 0.940 bicubic +5.484 +5.923 +1.711 +1.926 +2 +15
115 resnest50d_4s2x40d 86.592 13.408 97.269 2.731 30.42 224 0.875 bicubic +5.484 +1.711 +1
116 efficientnet_b3_pruned 86.581 13.419 97.190 2.810 9.86 300 0.904 bicubic +5.723 +1.948 +9
117 repvgg_b3 86.566 13.434 97.139 2.861 123.09 224 0.875 bilinear +6.074 +1.879 +18 +17
118 ssl_resnext101_32x4d 86.479 13.521 97.468 2.532 44.18 224 0.875 bilinear +5.555 +1.740 +4
119 ecaresnet50d 86.470 13.530 97.186 2.814 25.58 224 0.875 bicubic +5.878 +1.866 +14 +13
120 gluon_resnet152_v1s 86.468 13.532 97.109 2.891 60.32 224 0.875 bicubic +5.452 +1.697 -1 -2
121 resnest50d_1s4x24d 86.447 13.553 97.148 2.852 25.68 224 0.875 bicubic +5.459 +1.826 -1 -2
122 repvgg_b3g4 86.363 86.361 13.637 13.639 97.054 2.946 83.83 224 0.875 bilinear +6.151 +6.149 +1.944 +31 +29
123 legacy_senet154 86.342 13.658 96.928 3.072 115.09 224 0.875 bilinear +5.032 +1.432 -11 -12
124 gernet_m cait_xxs36_224 86.319 86.340 13.681 13.660 97.096 97.111 2.904 2.889 21.14 17.30 224 0.875 1.000 bilinear bicubic +5.587 +6.590 +1.912 +2.245 +5 +49
125 pit_s_224 gernet_m 86.316 86.319 13.684 13.681 97.045 97.096 2.955 2.904 23.46 21.14 224 0.900 0.875 bicubic bilinear +5.222 +5.587 +1.713 +1.912 -7 +3
126 efficientnet_b2a pit_s_224 86.304 86.316 13.696 13.684 96.990 97.045 3.010 2.955 9.11 23.46 288 224 1.000 0.900 bicubic +5.692 +5.222 +1.672 +1.713 +5 -9
127 gluon_senet154 efficientnet_b2 86.278 86.304 13.722 13.696 96.949 96.990 3.051 3.010 115.09 9.11 224 288 0.875 1.000 bicubic +5.044 +5.692 +1.601 +1.672 -13 +3
128 resnest50d gluon_senet154 86.240 86.278 13.761 13.722 97.073 96.949 2.927 3.051 27.48 115.09 224 0.875 bilinear bicubic +5.266 +5.044 +1.695 +1.601 -7 -15
129 ecaresnet101d_pruned resnest50d 86.210 86.240 13.790 13.761 97.335 97.073 2.665 2.927 24.88 27.48 224 0.875 bicubic bilinear +5.394 +5.266 +1.707 +1.695 -3 -9
130 tresnet_m ecaresnet101d_pruned 86.199 86.210 13.801 13.790 96.667 97.335 3.333 2.665 31.39 24.88 224 0.875 bilinear bicubic +5.397 +5.392 +1.807 +1.707 -2 -4
131 efficientnet_el_pruned 86.192 13.807 97.026 2.974 10.59 300 0.904 bicubic +5.892 +1.808 +16 +14
132 cspdarknet53 86.182 13.818 97.013 2.987 27.64 256 0.887 bilinear +6.124 +1.929 +27 +25
133 inception_v4 86.169 13.831 96.919 3.081 42.68 299 0.875 bicubic +6.001 +1.951 +22 +20
134 rexnet_150 resnetv2_50x1_bitm 86.154 13.846 97.058 97.560 2.942 2.440 9.73 25.55 224 480 0.875 1.000 bicubic bilinear +5.844 +5.982 +1.892 +1.934 +10 +18
135 resnetv2_50x1_bitm rexnet_150 86.154 13.846 97.560 97.058 2.440 2.942 25.55 9.73 480 224 1.000 0.875 bilinear bicubic +5.982 +5.844 +1.934 +1.892 +20 +8
136 inception_resnet_v2 86.133 13.867 97.043 2.957 55.84 299 0.897 bicubic +5.675 +1.737 +3 +2
137 ssl_resnext50_32x4d 86.086 13.914 97.212 2.788 25.03 224 0.875 bilinear +5.768 +1.806 +7 +5
138 tf_efficientnet_el 86.084 13.916 96.964 3.036 10.59 300 0.904 bicubic +5.834 +1.836 +12 +10
139 efficientnet_b2 gluon_resnet101_v1s 86.056 86.054 13.944 13.946 96.917 97.022 3.083 2.978 9.11 44.67 260 224 0.875 bicubic +5.664 +5.752 +1.841 +1.862 +2 +5
gluon_resnet101_v1s 86.054 13.946 97.022 2.978 44.67 224 0.875 bicubic +5.752 +1.862 +6
140 ecaresnetlight 86.052 13.948 97.069 2.931 30.16 224 0.875 bicubic +5.590 +1.819 -3
141 gluon_seresnext101_32x4d 86.032 13.968 96.977 3.023 48.96 224 0.875 bicubic +5.128 +1.683 -19 -18
142 resnet50d 86.009 13.991 96.979 3.021 25.58 224 0.875 bicubic +5.479 +1.819 -9
143 ecaresnet26t 85.983 14.017 97.041 2.959 16.01 320 0.950 bicubic +6.129 +1.957 +26
144 tf_efficientnet_b2_ap 85.975 14.025 96.810 3.190 9.11 260 0.890 bicubic +5.675 +1.782 +3 +2
145 gluon_seresnext101_64x4d 85.960 14.040 96.979 3.021 88.23 224 0.875 bicubic +5.066 +1.671 -22 -21
146 gluon_resnet152_v1d 85.917 14.083 96.812 3.188 60.21 224 0.875 bicubic +5.443 +1.606 -10
147 vit_large_patch32_384 85.909 14.091 97.368 2.632 306.63 384 1.000 bicubic +4.403 +1.276 -43
148 tf_efficientnet_b2 85.902 14.098 96.862 3.139 9.11 260 0.890 bicubic +5.816 +1.954 +9 +8
149 seresnet50 85.857 14.143 97.004 2.995 28.09 224 0.875 bicubic +5.583 +1.934 -1 -2
150 repvgg_b2g4 85.855 14.145 96.812 3.188 61.76 224 0.875 bilinear +6.489 +2.124 +36 +37
151 gluon_resnet101_v1d 85.849 14.151 96.663 3.337 44.57 224 0.875 bicubic +5.435 +1.649 -12
152 resnet50 85.804 14.196 96.712 3.288 25.56 224 0.875 bicubic +6.766 +2.322 +56 +58
153 mixnet_xl 85.798 14.202 96.712 3.288 11.90 224 0.875 bicubic +5.322 +1.776 -18
154 ens_adv_inception_resnet_v2 85.781 14.220 96.759 3.241 55.84 299 0.897 bicubic +5.799 +1.823 +7 +6
155 tf_efficientnet_lite3 85.755 14.245 96.887 3.113 8.20 300 0.904 bilinear +5.935 +1.973 +16
156 ese_vovnet39b 85.751 14.249 96.891 3.109 24.57 224 0.875 bicubic +6.431 +2.179 +32 +33
157 gluon_resnext101_32x4d 85.746 14.254 96.635 3.365 44.18 224 0.875 bicubic +5.412 +1.709 -15 -16
158 legacy_seresnext101_32x4d 85.746 14.254 96.757 3.243 48.96 224 0.875 bilinear +5.518 +1.739 -7 -8
159 cspresnext50 85.740 14.260 96.840 3.160 20.57 224 0.875 bilinear +5.700 +1.896 0 -1
160 regnety_320 85.727 14.273 96.725 3.275 145.05 224 0.875 bicubic +4.915 +1.481 -34 -33
161 cspresnet50 85.721 14.279 96.795 3.205 21.62 256 0.887 bilinear +6.147 +2.083 +19 +20
162 xception71 85.697 14.303 96.776 3.224 42.34 299 0.903 bicubic +5.823 +1.854 +4
163 gluon_resnext101_64x4d 85.693 14.307 96.644 3.356 83.46 224 0.875 bicubic +5.089 +1.656 -32
164 efficientnet_em 85.684 14.316 96.938 3.062 6.90 240 0.882 bicubic +6.432 +2.144 +33 +34
165 vit_deit_small_patch16_224 85.678 14.322 96.906 3.094 22.05 224 0.900 bicubic +5.822 +1.854 +3
166 pit_xs_distilled_224 85.657 14.343 96.667 3.333 11.00 224 0.900 bicubic +6.351 +2.303 +27 +28
167 efficientnet_b2_pruned 85.642 14.358 96.746 3.254 8.31 260 0.890 bicubic +5.726 +1.890 -4 -5
168 dpn107 85.640 14.360 96.729 3.271 86.92 224 0.875 bicubic +5.484 +2.087 +1.819 -12 -14
169 ecaresnet50d_pruned 85.580 14.420 96.936 3.064 19.94 224 0.875 bicubic +5.864 +2.056 +5 +6
170 gluon_resnet152_v1c 85.580 14.420 96.646 3.354 60.21 224 0.875 bicubic +5.670 +1.806 -6 -7
171 resnext50d_32x4d 85.569 14.431 96.748 3.252 25.05 224 0.875 bicubic +5.893 +1.882 +6 +7
172 regnety_120 85.543 14.457 96.785 3.215 51.82 224 0.875 bicubic +5.177 +1.659 -31 -32
173 regnetx_320 85.524 14.476 96.669 3.331 107.81 224 0.875 bicubic +5.278 +1.643 -23 -24
174 nf_regnet_b1 85.499 14.501 96.799 3.200 10.22 288 0.900 bicubic +6.193 +2.051 +18 +19
175 dpn92 85.494 14.506 96.635 3.365 37.67 224 0.875 bicubic +5.486 +1.797 +1.799 -15 -16
176 gluon_resnet152_v1b 85.475 14.525 96.550 3.450 60.19 224 0.875 bicubic +5.789 +1.814 0 +1
177 rexnet_130 85.473 14.527 96.684 3.316 7.56 224 0.875 bicubic +5.973 +2.002 +6 +7
178 dpn131 resnetrs50 85.398 85.462 14.602 14.538 96.639 96.736 3.361 3.264 79.25 35.69 224 0.875 0.910 bicubic +5.576 +5.570 +1.929 +1.767 -8 -14
179 regnetx_160 dpn131 85.390 85.398 14.610 14.602 96.637 96.639 3.363 3.361 54.28 79.25 224 0.875 bicubic +5.534 +5.576 +1.807 +1.929 -12 -9
180 regnetx_160 85.390 14.610 96.637 3.363 54.28 224 0.875 bicubic +5.534 +1.807 -13
181 dla102x2 85.366 14.634 96.629 3.371 41.28 224 0.875 bilinear +5.918 +1.989 +5
182 gluon_seresnext50_32x4d 85.336 14.664 96.667 3.333 27.56 224 0.875 bicubic +5.418 +1.845 -19 -21
183 xception65 85.315 14.685 96.637 3.363 39.92 299 0.903 bicubic +5.763 +1.983 -1
184 skresnext50_32x4d 85.313 14.687 96.390 3.610 27.48 224 0.875 bicubic +5.157 +1.480 +1.748 -28 -29
185 dpn98 85.311 14.689 96.469 3.531 61.57 224 0.875 bicubic +5.669 +1.871 -6
186 gluon_resnet101_v1c 85.304 14.696 96.405 3.595 44.57 224 0.875 bicubic +5.770 +1.827 -3
187 dpn68b 85.291 14.709 96.464 3.536 12.61 224 0.875 bicubic +6.076 +2.050 +14
resnetblur50 85.283 14.717 96.531 3.470 25.56 224 0.875 bicubic +5.997 +1.892 +7
188 regnety_064 85.283 14.717 96.639 3.361 30.58 224 0.875 bicubic +5.561 +1.871 -14
189 regnety_080 resnetblur50 85.245 85.283 14.755 14.717 96.633 96.531 3.367 3.470 39.18 25.56 224 0.875 bicubic +5.369 +5.997 +1.803 +1.892 -24 +7
190 resnext50_32x4d coat_lite_mini 85.221 85.251 14.779 14.749 96.526 96.680 3.474 3.320 25.03 11.01 224 0.875 0.900 bicubic +5.453 +6.163 +1.928 +2.076 -18 +15
191 resnext101_32x8d regnety_080 85.187 85.245 14.813 14.755 96.445 96.633 3.555 3.367 88.79 39.18 224 0.875 bilinear bicubic +5.879 +5.369 +1.927 +1.803 -2 -26
192 gluon_inception_v3 cait_xxs24_224 85.183 85.228 14.817 14.773 96.526 96.712 3.474 3.288 23.83 11.96 299 224 0.875 1.000 bicubic +6.377 +6.842 +2.156 +2.402 +22 +41
193 hrnet_w48 resnext50_32x4d 85.151 85.221 14.849 14.779 96.492 96.526 3.508 3.474 77.47 25.03 224 0.875 bilinear bicubic +5.851 +5.453 +1.980 +1.928 +1 -21
194 gluon_xception65 resnext101_32x8d 85.148 85.187 14.851 14.813 96.597 96.445 3.403 3.555 39.92 88.79 299 224 0.903 0.875 bicubic bilinear +5.433 +5.879 +1.737 +1.927 -19 -4
195 gluon_resnet101_v1b gluon_inception_v3 85.142 85.183 14.858 14.817 96.366 96.526 3.634 3.474 44.55 23.83 224 299 0.875 bicubic +5.836 +6.377 +1.842 +2.156 -4 +21
196 regnetx_120 hrnet_w48 85.131 85.151 14.869 14.849 96.477 96.492 3.523 3.508 46.11 77.47 224 0.875 bicubic bilinear +5.535 +5.851 +1.739 +1.980 -17 -1
197 xception gluon_xception65 85.129 85.148 14.871 14.851 96.471 96.597 3.529 3.403 22.86 39.92 299 0.897 0.903 bicubic +6.077 +5.433 +2.079 +1.737 +10 -21
198 tf_efficientnet_b1_ap gluon_resnet101_v1b 85.127 85.142 14.873 14.858 96.405 96.366 3.595 3.634 7.79 44.55 240 224 0.882 0.875 bicubic +5.847 +5.836 +2.099 +1.842 -2 -6
199 hrnet_w64 regnetx_120 85.119 85.131 14.881 14.869 96.744 96.477 3.256 3.523 128.06 46.11 224 0.875 bilinear bicubic +5.645 +5.535 +2.092 +1.739 -15 -19
200 ssl_resnet50 xception 85.097 85.129 14.903 14.871 96.866 96.471 3.134 3.529 25.56 22.86 224 299 0.875 0.897 bilinear bicubic +5.875 +6.077 +2.034 +2.079 -2 +9
201 res2net101_26w_4s tf_efficientnet_b1_ap 85.093 85.127 14.907 14.873 96.381 96.405 3.619 3.595 45.21 7.79 224 240 0.875 0.882 bilinear bicubic +5.895 +5.847 +1.949 +2.099 +1 -4
202 tf_efficientnet_cc_b1_8e hrnet_w64 85.063 85.119 14.937 14.881 96.422 96.744 3.578 3.256 39.72 128.06 240 224 0.882 0.875 bicubic bilinear +5.755 +5.645 +2.052 +2.092 -12 -17
203 res2net50_26w_8s ssl_resnet50 85.029 85.097 14.971 14.903 96.419 96.866 3.580 3.134 48.40 25.56 224 0.875 bilinear +5.831 +5.875 +2.052 +2.034 -2 -4
204 resnest26d res2net101_26w_4s 85.008 85.093 14.992 14.907 96.637 96.381 3.363 3.619 17.07 45.21 224 0.875 bilinear +6.530 +5.895 +2.339 +1.949 +22 -1
205 gluon_resnext50_32x4d tf_efficientnet_cc_b1_8e 84.995 85.063 15.005 14.937 96.426 96.422 3.574 3.578 25.03 39.72 224 240 0.875 0.882 bicubic +5.641 +5.755 +2.000 +2.052 -18 -14
206 tf_efficientnet_b0_ns res2net50_26w_8s 84.984 85.029 15.016 14.971 96.503 96.419 3.497 3.580 5.29 48.40 224 0.875 bicubic bilinear +6.326 +5.831 +2.127 +2.052 +15 -4
207 regnety_040 resnest26d 84.948 85.008 15.052 14.992 96.612 96.637 3.388 3.363 20.65 17.07 224 0.875 bicubic bilinear +5.728 +6.530 +1.956 +2.339 -8 +21
208 dla169 gluon_resnext50_32x4d 84.920 84.995 15.080 15.005 96.535 96.426 3.465 3.574 53.39 25.03 224 0.875 bilinear bicubic +6.232 +5.641 +2.199 +2.000 +11 -20
209 tf_efficientnet_b1 tf_efficientnet_b0_ns 84.918 84.984 15.082 15.016 96.364 96.503 3.636 3.497 7.79 5.29 240 224 0.882 0.875 bicubic +6.092 +6.326 +2.166 +2.127 +4 +14
210 legacy_seresnext50_32x4d regnety_040 84.901 84.948 15.099 15.052 96.434 96.612 3.566 3.388 27.56 20.65 224 0.875 bilinear bicubic +5.823 +5.728 +1.998 +1.956 -6 -10
211 hrnet_w44 dla169 84.884 84.920 15.116 15.080 96.434 96.535 3.566 3.465 67.06 53.39 224 0.875 bilinear +5.988 +6.232 +2.066 +2.199 0 +10
212 gluon_resnet50_v1s tf_efficientnet_b1 84.862 84.918 15.138 15.082 96.443 96.364 3.557 3.636 25.68 7.79 224 240 0.875 0.882 bicubic +6.152 +6.092 +2.205 +2.166 +5 +3
213 regnetx_080 legacy_seresnext50_32x4d 84.862 84.901 15.138 15.099 96.434 3.566 39.57 27.56 224 0.875 bicubic bilinear +5.668 +5.823 +1.874 +1.998 -10 -7
214 gluon_resnet50_v1d hrnet_w44 84.832 84.884 15.168 15.116 96.398 96.434 3.602 3.566 25.58 67.06 224 0.875 bicubic bilinear +5.758 +5.988 +1.928 +2.066 -9 -1
215 dla60_res2next regnetx_080 84.830 84.862 15.170 15.138 96.411 96.434 3.589 3.566 17.03 39.57 224 0.875 bilinear bicubic +6.390 +5.668 +2.259 +1.874 +14 -11
216 mixnet_l gluon_resnet50_v1s 84.822 84.860 15.178 15.140 96.328 96.443 3.672 3.557 7.33 25.68 224 0.875 bicubic +5.846 +6.148 +2.146 +2.205 -7 +4
217 gluon_resnet50_v1d 84.832 15.168 96.398 3.602 25.58 224 0.875 bicubic +5.758 +1.928 -10
218 dla60_res2next 84.830 15.170 96.411 3.589 17.03 224 0.875 bilinear +6.390 +2.259 +13
219 mixnet_l 84.822 15.178 96.328 3.672 7.33 224 0.875 bicubic +5.846 +2.146 -8
220 tv_resnet152 84.815 15.185 96.225 3.775 60.19 224 0.875 bilinear +6.503 +2.187 +16
221 dla60_res2net 84.813 15.187 96.481 3.519 20.85 224 0.875 bilinear +6.349 +2.275 +9 +8
222 dla102x 84.813 15.187 96.552 3.448 26.31 224 0.875 bilinear +6.303 +2.324 +5 +4
xception41 84.792 15.208 96.413 3.587 26.97 299 0.903 bicubic +6.276 +2.135 +2
223 pit_xs_224 84.792 15.208 96.492 3.508 10.62 224 0.900 bicubic +6.610 +2.324 +18
224 regnetx_064 xception41 84.781 84.792 15.219 15.208 96.490 96.413 3.510 3.587 26.21 26.97 224 299 0.875 0.903 bicubic +5.709 +6.276 +2.032 +2.135 -16 +1
225 hrnet_w40 regnetx_064 84.743 84.781 15.257 15.219 96.554 96.490 3.446 3.510 57.56 26.21 224 0.875 bilinear bicubic +5.823 +5.709 +2.084 +2.032 -13 -17
226 res2net50_26w_6s hrnet_w40 84.726 84.743 15.274 15.257 96.281 96.554 3.719 3.446 37.05 57.56 224 0.875 bilinear +6.156 +5.823 +2.157 +2.084 -2 -14
227 res2net50_26w_6s 84.726 15.274 96.281 3.719 37.05 224 0.875 bilinear +6.156 +2.157 -3
228 repvgg_b2 84.724 15.276 96.469 3.531 89.02 224 0.875 bilinear +5.932 +2.055 -10
229 legacy_seresnet152 84.704 15.296 96.417 3.583 66.82 224 0.875 bilinear +6.044 +2.047 -6 -7
230 selecsls60b 84.657 15.343 96.300 3.700 32.77 224 0.875 bicubic +6.245 +2.126 +3 +2
231 hrnet_w32 84.651 15.349 96.407 3.593 41.23 224 0.875 bilinear +6.201 +2.221 0 -1
232 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 -4 -15
233 efficientnet_es regnetx_040 84.591 84.600 15.409 15.400 96.311 96.383 3.689 3.617 5.44 22.12 224 0.875 bicubic +6.525 +6.118 +2.385 +2.139 +13 -6
234 hrnet_w30 efficientnet_es 84.572 84.591 15.428 15.409 96.388 96.311 3.612 3.689 37.71 5.44 224 0.875 bilinear bicubic +6.366 +6.525 +2.166 +2.385 +6 +12
235 tf_mixnet_l hrnet_w30 84.564 84.572 15.437 15.428 96.244 96.388 3.756 3.612 7.33 37.71 224 0.875 bicubic bilinear +5.790 +6.366 +2.246 +2.166 -16 +5
236 wide_resnet101_2 tf_mixnet_l 84.557 84.564 15.443 15.437 96.349 96.244 3.651 3.756 126.89 7.33 224 0.875 bilinear bicubic +5.701 +5.790 +2.067 +2.246 -21 -17
237 efficientnet_b1 wide_resnet101_2 84.531 84.557 15.469 15.443 96.153 96.349 3.847 3.651 7.79 126.89 240 224 0.875 bicubic bilinear +5.834 +5.701 +2.009 +2.067 -16 -23
238 dla60x 84.523 15.477 96.285 3.715 17.35 224 0.875 bilinear +6.277 +2.267 -1
239 legacy_seresnet101 84.504 15.496 96.330 3.670 49.33 224 0.875 bilinear +6.122 +2.066 -5
240 tf_efficientnet_em 84.450 15.550 96.180 3.820 6.90 240 0.882 bicubic +6.320 +2.136 +4 +3
241 repvgg_b1 coat_lite_tiny 84.416 84.450 15.584 15.550 96.221 96.368 3.779 3.632 57.42 5.72 224 0.875 0.900 bilinear bicubic +6.050 +6.938 +2.123 +2.452 -6 +27
242 efficientnet_b1_pruned repvgg_b1 84.393 84.416 15.607 15.584 96.140 96.221 3.860 3.779 6.33 57.42 240 224 0.882 0.875 bicubic bilinear +6.157 +6.050 +2.306 +2.123 -3 -7
243 res2net50_26w_4s efficientnet_b1_pruned 84.365 84.393 15.635 15.607 96.082 96.140 3.918 3.860 25.70 6.33 224 240 0.875 0.882 bilinear bicubic +6.401 +6.157 +2.228 +2.306 +8 -4
244 hardcorenas_f res2net50_26w_4s 84.326 84.365 15.674 15.635 96.025 96.082 3.975 3.918 8.20 25.70 224 0.875 bilinear +6.222 +6.401 +2.222 +2.228 +1 +7
245 res2net50_14w_8s hardcorenas_f 84.309 84.326 15.691 15.674 96.072 96.025 3.929 3.975 25.06 8.20 224 0.875 bilinear +6.159 +6.222 +2.224 +2.222 -2 0
246 selecsls60 res2net50_14w_8s 84.288 84.309 15.712 15.691 96.095 96.072 3.905 3.929 30.67 25.06 224 0.875 bicubic bilinear +6.306 +6.159 +2.267 +2.224 +4 -3
247 regnetx_032 selecsls60 84.237 84.288 15.763 15.712 96.247 96.095 3.753 3.905 15.30 30.67 224 0.875 bicubic +6.065 +6.306 +2.159 +2.267 -5 +3
248 res2next50 regnetx_032 84.226 84.237 15.774 15.763 95.997 96.247 4.003 3.753 24.67 15.30 224 0.875 bilinear bicubic +5.980 +6.065 +2.105 +2.159 -10 -6
249 gluon_resnet50_v1c res2next50 84.207 84.226 15.793 15.774 96.161 95.997 3.839 4.003 25.58 24.67 224 0.875 bicubic bilinear +6.195 +5.980 +2.173 +2.105 -1 -11
250 dla102 gluon_resnet50_v1c 84.190 84.207 15.810 15.793 96.206 96.161 3.794 3.839 33.27 25.58 224 0.875 bilinear bicubic +6.158 +6.195 +2.260 +2.173 -3 -2
251 rexnet_100 dla102 84.162 84.190 15.838 15.810 96.255 96.206 3.745 3.794 4.80 33.27 224 0.875 bicubic bilinear +6.304 +6.158 +2.617 +2.260 +4 -4
252 tf_inception_v3 rexnet_100 84.132 84.162 15.868 15.838 95.920 96.255 4.080 3.745 23.83 4.80 299 224 0.875 bicubic +6.274 +6.304 +2.504 +2.385 +4 +3
253 tf_inception_v3 84.132 15.868 95.920 4.080 23.83 299 0.875 bicubic +6.276 +2.280 +4
254 res2net50_48w_2s 84.126 15.874 95.965 4.035 25.29 224 0.875 bilinear +6.604 +2.411 +12
255 resnet34d 84.098 15.902 95.978 4.022 21.82 224 0.875 bicubic +6.982 +2.596 +22 +23
256 tf_efficientnet_lite2 84.094 15.906 96.069 3.931 6.09 260 0.890 bicubic +6.626 +2.315 +11 +12
257 efficientnet_b0 84.038 15.962 95.956 4.044 5.29 224 0.875 bicubic +6.340 +2.424 +2
258 hardcorenas_e 83.968 16.032 95.898 4.101 8.07 224 0.875 bilinear +6.174 +2.204 0
259 tf_efficientnet_cc_b0_8e 83.966 16.034 96.065 3.935 24.01 224 0.875 bicubic +6.058 +2.411 -6
260 tv_resnext50_32x4d 83.959 16.041 95.960 4.040 25.03 224 0.875 bilinear +6.339 +2.264 +1
261 regnety_016 83.955 16.045 96.005 3.995 11.20 224 0.875 bicubic +6.093 +2.285 -7
262 gluon_resnet50_v1b 83.940 16.060 96.012 3.988 25.56 224 0.875 bicubic +6.360 +2.296 +3
263 densenet161 83.906 16.094 96.010 3.990 28.68 224 0.875 bicubic +6.548 +2.372 +8 +9
264 adv_inception_v3 83.902 16.098 95.935 4.065 23.83 299 0.875 bicubic +6.320 +2.199 0
265 mobilenetv2_120d 83.893 16.107 95.909 4.091 5.83 224 0.875 bicubic +6.609 +2.417 +9 +10
266 seresnext26t_32x4d 83.878 16.122 95.931 4.069 16.81 224 0.875 bicubic +5.892 +2.185 -16 -17
267 tv_resnet101 83.848 16.152 95.892 4.108 44.55 224 0.875 bilinear +6.474 +2.352 +3 +4
268 inception_v3 83.761 16.239 95.879 4.121 23.83 299 0.875 bicubic +6.323 +2.403 0 +1
269 hardcorenas_d 83.759 16.241 95.734 4.266 7.50 224 0.875 bilinear +6.327 +2.250 0 +1
270 seresnext26d_32x4d 83.754 16.246 95.849 4.151 16.81 224 0.875 bicubic +6.152 +2.241 -8
271 vit_small_patch16_224 83.735 16.265 95.758 4.242 48.75 224 0.900 bicubic +5.877 +1.888 +2.342 -16 -15
272 dla60 83.729 16.271 95.933 4.067 22.04 224 0.875 bilinear +6.697 +2.615 +9 +10
273 repvgg_b1g4 83.699 16.301 96.020 3.980 39.97 224 0.875 bilinear +6.105 +2.194 -10
274 legacy_seresnet50 83.662 16.337 95.973 4.027 28.09 224 0.875 bilinear +6.032 +2.225 -14
275 tf_efficientnet_b0_ap 83.650 16.350 95.779 4.221 5.29 224 0.875 bicubic +6.564 +2.523 +4 +5
276 skresnet34 83.641 16.359 95.933 4.067 22.28 224 0.875 bicubic +6.729 +2.611 +9 +10
277 tf_efficientnet_cc_b0_4e 83.639 16.361 95.740 4.260 13.31 224 0.875 bicubic +6.333 +2.406 -5 -4
278 densenet201 83.556 16.444 95.811 4.189 20.01 224 0.875 bicubic +6.270 +2.333 -5 -4
279 mobilenetv3_large_100_miil 83.556 16.444 95.452 4.548 5.48 224 0.875 bilinear +5.640 +2.542 -27
280 gernet_s 83.522 16.478 95.794 4.206 8.17 224 0.875 bilinear +6.606 +2.662 +5
281 legacy_seresnext26_32x4d 83.517 16.483 95.719 4.281 16.79 224 0.875 bicubic +6.413 +2.403 -2
282 mixnet_m 83.515 16.485 95.689 4.311 5.01 224 0.875 bicubic +6.255 +2.265 -6
283 tf_efficientnet_b0 83.515 16.485 95.719 4.281 5.29 224 0.875 bicubic +6.667 +2.491 +4
284 hrnet_w18 83.500 16.500 95.907 4.093 21.30 224 0.875 bilinear +6.742 +2.463 +5
285 densenetblur121d 83.472 16.527 95.822 4.178 8.00 224 0.875 bicubic +6.885 +2.630 +8 +9
286 selecsls42b 83.457 16.543 95.745 4.255 32.46 224 0.875 bicubic +6.283 +2.355 -9
287 tf_efficientnet_lite1 83.344 16.656 95.642 4.358 5.42 240 0.882 bicubic +6.702 +2.416 +4
288 hardcorenas_c 83.342 16.658 95.706 4.294 5.52 224 0.875 bilinear +6.288 +2.548 -7
289 regnetx_016 83.195 16.805 95.740 4.260 9.19 224 0.875 bicubic +6.245 +2.320 -6
290 mobilenetv2_140 83.182 16.818 95.689 4.311 6.11 224 0.875 bicubic +6.666 +2.693 +5 +6
291 dpn68 83.178 16.822 95.597 4.402 12.61 224 0.875 bicubic +6.860 +2.620 +6 +7
292 tf_efficientnet_es 83.178 16.822 95.585 4.415 5.44 224 0.875 bicubic +6.584 +2.383 0 +1
293 tf_mixnet_m 83.176 16.824 95.461 4.539 5.01 224 0.875 bicubic +6.234 +2.309 -9
294 ese_vovnet19b_dw 83.109 16.890 95.779 4.221 6.54 224 0.875 bicubic +6.311 +2.511 -6
295 resnet26d 83.050 16.950 95.604 4.396 16.01 224 0.875 bicubic +6.354 +2.454 -5
296 repvgg_a2 83.001 16.999 95.589 4.411 28.21 224 0.875 bilinear +6.541 +2.585 0 +1
297 tv_resnet50 82.958 17.042 95.467 4.533 25.56 224 0.875 bilinear +6.820 +2.603 +2 +3
298 hardcorenas_b 82.873 17.128 95.392 4.607 5.18 224 0.875 bilinear +6.335 +2.638 -4 -3
299 densenet121 82.823 17.177 95.585 4.415 7.98 224 0.875 bicubic +7.245 +2.933 +7 +8
300 densenet169 82.683 17.317 95.600 4.400 14.15 224 0.875 bicubic +6.776 +2.574 +2 +3
301 mixnet_s 82.525 17.476 95.356 4.644 4.13 224 0.875 bicubic +6.532 +2.560 -1 0
302 regnety_008 82.493 17.508 95.487 4.513 6.26 224 0.875 bicubic +6.177 +2.421 -4 -3
303 efficientnet_lite0 82.382 17.619 95.279 4.721 4.65 224 0.875 bicubic +6.898 +2.769 +6 +7
304 resnest14d 82.352 17.648 95.339 4.661 10.61 224 0.875 bilinear +6.848 +6.846 +2.821 +4 +5
305 hardcorenas_a 82.313 17.687 95.294 4.706 5.26 224 0.875 bilinear +6.397 +2.780 -4 -3
306 efficientnet_es_pruned 82.296 17.704 95.303 4.697 5.44 224 0.875 bicubic +7.296 +2.855 +13 +14
307 mobilenetv3_rw 82.275 17.725 95.234 4.766 5.48 224 0.875 bicubic +6.641 +2.526 -2 -1
308 semnasnet_100 82.251 17.749 95.230 4.770 3.89 224 0.875 bicubic +6.803 +2.626 +2 +3
309 mobilenetv3_large_100 82.177 17.823 95.196 4.804 5.48 224 0.875 bicubic +6.410 +2.654 -6 -5
310 resnet34 82.138 17.862 95.130 4.870 21.80 224 0.875 bilinear +7.028 +2.846 +6 +7
311 mobilenetv2_110d 82.070 17.930 95.076 4.923 4.52 224 0.875 bicubic +7.034 +2.890 +7 +8
312 tf_mixnet_s 82.038 17.962 95.121 4.879 4.13 224 0.875 bicubic +6.388 +2.493 -8 -7
313 repvgg_b0 82.001 17.999 95.100 4.900 15.82 224 0.875 bilinear +6.849 +2.682 0 +1
314 vit_deit_tiny_distilled_patch16_224 81.997 18.003 95.141 4.859 5.91 224 0.900 bicubic +7.487 +3.251 +13 +14
315 mixer_b16_224 81.978 18.022 94.449 5.551 59.88 224 0.875 bicubic +5.376 +2.221 -23
316 pit_ti_distilled_224 81.967 18.033 95.145 4.855 5.10 224 0.900 bicubic +7.437 +3.049 +11
317 hrnet_w18_small_v2 81.961 18.039 95.164 4.836 15.60 224 0.875 bilinear +6.847 +2.748 -1
318 tf_efficientnet_lite0 81.952 18.048 95.168 4.832 4.65 224 0.875 bicubic +7.122 +2.992 +3
326 gluon_resnet34_v1b 81.500 18.500 94.810 5.190 21.80 224 0.875 bicubic +6.912 +2.820 0
327 regnetx_008 81.485 18.515 95.059 4.941 7.26 224 0.875 bicubic +6.447 +2.724 -9
328 mnasnet_100 81.459 18.541 94.899 5.101 4.38 224 0.875 bicubic +6.801 +2.785 -4
329 vgg19_bn 81.446 81.444 18.554 18.556 94.763 5.237 143.68 224 0.875 bilinear +7.232 +7.230 +2.921 0
330 spnasnet_100 80.878 19.122 94.526 5.474 4.42 224 0.875 bilinear +6.794 +2.708 0
331 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 0 +1
332 regnety_004 80.659 19.341 94.686 5.314 4.34 224 0.875 bicubic +6.624 +2.934 -1
333 skresnet18 80.637 19.363 94.378 5.622 11.96 224 0.875 bicubic +7.599 +3.210 +5
334 regnetx_006 80.629 19.371 94.524 5.476 6.20 224 0.875 bicubic +6.777 +2.852 -1
335 pit_ti_224 80.605 19.395 94.618 5.383 4.85 224 0.900 bicubic +7.693 +3.216 +5
341 ssl_resnet18 80.101 19.899 94.590 5.410 11.69 224 0.875 bilinear +7.491 +3.174 0
342 tf_mobilenetv3_large_075 80.093 19.907 94.184 5.816 3.99 224 0.875 bilinear +6.655 +2.834 -8
343 vit_deit_tiny_patch16_224 80.018 19.982 94.449 5.551 5.72 224 0.900 bicubic +7.850 +3.331 +4
344 hrnet_w18_small 79.555 79.557 20.445 20.443 93.898 6.102 13.19 224 0.875 bilinear +7.211 +7.215 +3.220 0
345 vgg19 79.480 20.520 93.870 6.130 143.67 224 0.875 bilinear +7.112 +2.998 -2
346 regnetx_004 79.435 20.565 93.853 6.147 5.16 224 0.875 bicubic +7.039 +3.023 -4
347 tf_mobilenetv3_large_minimal_100 79.222 20.778 93.706 6.294 3.92 224 0.875 bilinear +6.974 +3.076 -1
348 legacy_seresnet18 79.153 20.847 93.783 6.217 11.78 224 0.875 bicubic +7.411 +3.449 0 +1
349 vgg16 79.038 20.962 93.646 6.354 138.36 224 0.875 bilinear +7.444 +3.264 +1 +2
350 vgg13_bn 79.006 20.994 93.655 6.345 133.05 224 0.875 bilinear +7.412 +3.279 -1 0
351 gluon_resnet18_v1b 78.372 21.628 93.138 6.862 11.69 224 0.875 bicubic +7.536 +3.376 0 +1
352 vgg11_bn 77.926 22.074 93.230 6.770 132.87 224 0.875 bilinear +7.566 +3.428 0 +1
353 regnety_002 77.405 22.595 92.914 7.086 3.16 224 0.875 bicubic +7.153 +3.374 0 +1
354 mixer_l16_224 77.285 22.715 90.582 9.418 208.20 224 0.875 bicubic +5.227 +2.914 -6
355 resnet18 77.276 22.724 92.756 7.244 11.69 224 0.875 bilinear +7.528 +3.678 +1
356 vgg13 77.230 22.770 92.689 7.311 133.05 224 0.875 bilinear +7.303 +3.444 -1
357 vgg11 76.384 23.616 92.154 7.846 132.86 224 0.875 bilinear +7.360 +3.526 0

53
results/results-imagenet.csv
View File

@ -3,14 +3,17 @@ tf_efficientnet_l2_ns,88.352,11.648,98.650,1.350,480.31,800,0.960,bicubic
tf_efficientnet_l2_ns_475,88.234,11.766,98.546,1.454,480.31,475,0.936,bicubic
swin_large_patch4_window12_384,87.148,12.852,98.234,1.766,196.74,384,1.000,bicubic
tf_efficientnet_b7_ns,86.840,13.160,98.094,1.906,66.35,600,0.949,bicubic
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
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
dm_nfnet_f3,85.560,14.440,97.406,2.594,254.92,416,0.940,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
tf_efficientnet_b8,85.370,14.630,97.390,2.610,87.41,672,0.954,bicubic
@ -20,31 +23,42 @@ tf_efficientnet_b4_ns,85.162,14.838,97.470,2.530,19.34,380,0.922,bicubic
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
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
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
resnest269e,84.518,15.482,96.986,3.014,110.93,416,0.928,bicubic
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
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
vit_base_patch16_384,84.210,15.790,97.218,2.782,86.86,384,1.000,bicubic
ig_resnext101_32x16d,84.170,15.830,97.196,2.804,194.03,224,0.875,bilinear
pit_b_distilled_224,84.144,15.856,96.856,3.144,74.79,224,0.900,bicubic
tf_efficientnet_b6,84.110,15.890,96.886,3.114,43.04,528,0.942,bicubic
resnetrs200,84.066,15.934,96.874,3.126,93.21,320,1.000,bicubic
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
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
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
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
swsl_resnext101_32x16d,83.346,16.654,96.846,3.154,194.03,224,0.875,bilinear
dm_nfnet_f0,83.342,16.658,96.560,3.440,71.49,256,0.900,bicubic
@ -52,6 +66,7 @@ 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
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
tresnet_xl_448,83.050,16.950,96.174,3.826,78.44,448,0.875,bilinear
resnet101d,83.022,16.978,96.446,3.554,44.57,320,1.000,bicubic
@ -66,13 +81,13 @@ eca_nfnet_l0,82.588,17.412,96.474,3.526,24.14,288,1.000,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
ecaresnet50t,82.346,17.654,96.138,3.862,25.57,320,0.950,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_b3a,82.242,17.758,96.114,3.886,12.23,320,1.000,bicubic
efficientnet_b3,82.242,17.758,96.114,3.886,12.23,320,1.000,bicubic
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
efficientnet_b3,82.076,17.924,96.020,3.980,12.23,300,0.904,bicubic
efficientnet_v2s,82.070,17.930,95.954,4.046,23.94,224,1.000,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
@ -103,16 +118,16 @@ pit_s_224,81.094,18.906,95.332,4.668,23.46,224,0.900,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
cait_xxs24_384,80.966,19.034,95.646,4.354,12.03,384,1.000,bicubic
ssl_resnext101_32x4d,80.924,19.076,95.728,4.272,44.18,224,0.875,bilinear
gluon_seresnext101_32x4d,80.904,19.096,95.294,4.706,48.96,224,0.875,bicubic
gluon_seresnext101_64x4d,80.894,19.106,95.308,4.692,88.23,224,0.875,bicubic
efficientnet_b3_pruned,80.858,19.142,95.242,4.758,9.86,300,0.904,bicubic
ecaresnet101d_pruned,80.816,19.184,95.628,4.372,24.88,224,0.875,bicubic
ecaresnet101d_pruned,80.818,19.182,95.628,4.372,24.88,224,0.875,bicubic
regnety_320,80.812,19.188,95.244,4.756,145.05,224,0.875,bicubic
tresnet_m,80.802,19.198,94.860,5.140,31.39,224,0.875,bilinear
gernet_m,80.732,19.268,95.184,4.816,21.14,224,0.875,bilinear
nf_resnet50,80.694,19.306,95.356,4.644,25.56,288,0.940,bicubic
efficientnet_b2a,80.612,19.388,95.318,4.682,9.11,288,1.000,bicubic
efficientnet_b2,80.612,19.388,95.318,4.682,9.11,288,1.000,bicubic
gluon_resnext101_64x4d,80.604,19.396,94.988,5.012,83.46,224,0.875,bicubic
ecaresnet50d,80.592,19.408,95.320,4.680,25.58,224,0.875,bicubic
resnet50d,80.530,19.470,95.160,4.840,25.58,224,0.875,bicubic
@ -122,7 +137,6 @@ gluon_resnet152_v1d,80.474,19.526,95.206,4.794,60.21,224,0.875,bicubic
ecaresnetlight,80.462,19.538,95.250,4.750,30.16,224,0.875,bicubic
inception_resnet_v2,80.458,19.542,95.306,4.694,55.84,299,0.897,bicubic
gluon_resnet101_v1d,80.414,19.586,95.014,4.986,44.57,224,0.875,bicubic
efficientnet_b2,80.392,19.608,95.076,4.924,9.11,260,0.875,bicubic
regnety_120,80.366,19.634,95.126,4.874,51.82,224,0.875,bicubic
gluon_resnext101_32x4d,80.334,19.666,94.926,5.074,44.18,224,0.875,bicubic
ssl_resnext50_32x4d,80.318,19.682,95.406,4.594,25.03,224,0.875,bilinear
@ -137,16 +151,17 @@ 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
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
skresnext50_32x4d,80.156,19.844,94.642,5.358,27.48,224,0.875,bicubic
dpn107,80.156,19.844,94.910,5.090,86.92,224,0.875,bicubic
skresnext50_32x4d,80.156,19.844,94.642,5.358,27.48,224,0.875,bicubic
tf_efficientnet_b2,80.086,19.914,94.908,5.092,9.11,260,0.890,bicubic
cspdarknet53,80.058,19.942,95.084,4.916,27.64,256,0.887,bilinear
cspresnext50,80.040,19.960,94.944,5.056,20.57,224,0.875,bilinear
dpn92,80.008,19.992,94.838,5.162,37.67,224,0.875,bicubic
dpn92,80.008,19.992,94.836,5.164,37.67,224,0.875,bicubic
ens_adv_inception_resnet_v2,79.982,20.018,94.936,5.064,55.84,299,0.897,bicubic
gluon_seresnext50_32x4d,79.918,20.082,94.822,5.178,27.56,224,0.875,bicubic
efficientnet_b2_pruned,79.916,20.084,94.856,5.144,8.31,260,0.890,bicubic
gluon_resnet152_v1c,79.910,20.090,94.840,5.160,60.21,224,0.875,bicubic
resnetrs50,79.892,20.108,94.968,5.032,35.69,224,0.910,bicubic
regnety_080,79.876,20.124,94.830,5.170,39.18,224,0.875,bicubic
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
@ -155,6 +170,7 @@ ecaresnet26t,79.854,20.146,95.084,4.916,16.01,320,0.950,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
cait_xxs36_224,79.750,20.250,94.866,5.134,17.30,224,1.000,bicubic
regnety_064,79.722,20.278,94.768,5.232,30.58,224,0.875,bicubic
ecaresnet50d_pruned,79.716,20.284,94.880,5.120,19.94,224,0.875,bicubic
gluon_xception65,79.716,20.284,94.860,5.140,39.92,299,0.903,bicubic
@ -186,6 +202,7 @@ dpn68b,79.216,20.784,94.414,5.586,12.61,224,0.875,bicubic
res2net50_26w_8s,79.198,20.802,94.368,5.632,48.40,224,0.875,bilinear
res2net101_26w_4s,79.198,20.802,94.432,5.568,45.21,224,0.875,bilinear
regnetx_080,79.194,20.806,94.560,5.440,39.57,224,0.875,bicubic
coat_lite_mini,79.088,20.912,94.604,5.396,11.01,224,0.900,bicubic
legacy_seresnext50_32x4d,79.078,20.922,94.436,5.564,27.56,224,0.875,bilinear
gluon_resnet50_v1d,79.074,20.926,94.470,5.530,25.58,224,0.875,bicubic
regnetx_064,79.072,20.928,94.458,5.542,26.21,224,0.875,bicubic
@ -197,10 +214,10 @@ hrnet_w44,78.896,21.104,94.368,5.632,67.06,224,0.875,bilinear
wide_resnet101_2,78.856,21.144,94.282,5.718,126.89,224,0.875,bilinear
tf_efficientnet_b1,78.826,21.174,94.198,5.802,7.79,240,0.882,bicubic
gluon_inception_v3,78.806,21.194,94.370,5.630,23.83,299,0.875,bicubic
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,78.710,21.290,94.238,5.762,25.68,224,0.875,bicubic
efficientnet_b1,78.698,21.302,94.144,5.856,7.79,240,0.875,bicubic
gluon_resnet50_v1s,78.712,21.288,94.238,5.762,25.68,224,0.875,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
@ -213,6 +230,7 @@ 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
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
tv_resnet152,78.312,21.688,94.038,5.962,60.19,224,0.875,bilinear
@ -231,11 +249,12 @@ gluon_resnet50_v1c,78.012,21.988,93.988,6.012,25.58,224,0.875,bicubic
seresnext26t_32x4d,77.986,22.014,93.746,6.254,16.81,224,0.875,bicubic
selecsls60,77.982,22.018,93.828,6.172,30.67,224,0.875,bicubic
res2net50_26w_4s,77.964,22.036,93.854,6.146,25.70,224,0.875,bilinear
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
vit_small_patch16_224,77.858,22.142,93.416,6.584,48.75,224,0.900,bicubic
rexnet_100,77.858,22.142,93.870,6.130,4.80,224,0.875,bicubic
tf_inception_v3,77.858,22.142,93.638,6.362,23.83,299,0.875,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
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
@ -245,6 +264,7 @@ repvgg_b1g4,77.594,22.406,93.826,6.174,39.97,224,0.875,bilinear
adv_inception_v3,77.582,22.418,93.736,6.264,23.83,299,0.875,bicubic
gluon_resnet50_v1b,77.580,22.420,93.716,6.284,25.56,224,0.875,bicubic
res2net50_48w_2s,77.522,22.478,93.554,6.446,25.29,224,0.875,bilinear
coat_lite_tiny,77.512,22.488,93.916,6.084,5.72,224,0.900,bicubic
tf_efficientnet_lite2,77.468,22.532,93.754,6.246,6.09,260,0.890,bicubic
inception_v3,77.438,22.562,93.476,6.524,23.83,299,0.875,bicubic
hardcorenas_d,77.432,22.568,93.484,6.516,7.50,224,0.875,bilinear
@ -269,6 +289,7 @@ ese_vovnet19b_dw,76.798,23.202,93.268,6.732,6.54,224,0.875,bicubic
hrnet_w18,76.758,23.242,93.444,6.556,21.30,224,0.875,bilinear
resnet26d,76.696,23.304,93.150,6.850,16.01,224,0.875,bicubic
tf_efficientnet_lite1,76.642,23.358,93.226,6.774,5.42,240,0.882,bicubic
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
@ -285,7 +306,7 @@ tf_mixnet_s,75.650,24.350,92.628,7.372,4.13,224,0.875,bicubic
mobilenetv3_rw,75.634,24.366,92.708,7.292,5.48,224,0.875,bicubic
densenet121,75.578,24.422,92.652,7.348,7.98,224,0.875,bicubic
tf_mobilenetv3_large_100,75.518,24.482,92.606,7.394,5.48,224,0.875,bilinear
resnest14d,75.504,24.496,92.518,7.482,10.61,224,0.875,bilinear
resnest14d,75.506,24.494,92.518,7.482,10.61,224,0.875,bilinear
efficientnet_lite0,75.484,24.516,92.510,7.490,4.65,224,0.875,bicubic
semnasnet_100,75.448,24.552,92.604,7.396,3.89,224,0.875,bicubic
resnet26,75.292,24.708,92.570,7.430,16.00,224,0.875,bicubic
@ -308,6 +329,7 @@ vit_deit_tiny_distilled_patch16_224,74.510,25.490,91.890,8.110,5.91,224,0.900,bi
vgg19_bn,74.214,25.786,91.842,8.158,143.68,224,0.875,bilinear
spnasnet_100,74.084,25.916,91.818,8.182,4.42,224,0.875,bilinear
regnety_004,74.034,25.966,91.752,8.248,4.34,224,0.875,bicubic
ghostnet_100,73.978,26.022,91.456,8.544,5.18,224,0.875,bilinear
regnetx_006,73.852,26.148,91.672,8.328,6.20,224,0.875,bicubic
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
@ -319,10 +341,11 @@ 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,72.344,27.656,90.678,9.322,13.19,224,0.875,bilinear
hrnet_w18_small,72.342,27.658,90.678,9.322,13.19,224,0.875,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
mixer_l16_224,72.058,27.942,87.668,12.332,208.20,224,0.875,bicubic
legacy_seresnet18,71.742,28.258,90.334,9.666,11.78,224,0.875,bicubic
vgg13_bn,71.594,28.406,90.376,9.624,133.05,224,0.875,bilinear
vgg16,71.594,28.406,90.382,9.618,138.36,224,0.875,bilinear

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation
3 tf_efficientnet_l2_ns_475 88.234 11.766 98.546 1.454 480.31 475 0.936 bicubic
4 swin_large_patch4_window12_384 87.148 12.852 98.234 1.766 196.74 384 1.000 bicubic
5 tf_efficientnet_b7_ns 86.840 13.160 98.094 1.906 66.35 600 0.949 bicubic
6 cait_m48_448 86.484 13.516 97.754 2.246 356.46 448 1.000 bicubic
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 dm_nfnet_f6 86.296 13.704 97.744 2.256 438.36 576 0.956 bicubic
11 tf_efficientnet_b5_ns 86.088 13.912 97.752 2.248 30.39 456 0.934 bicubic
12 cait_m36_384 86.054 13.946 97.730 2.270 271.22 384 1.000 bicubic
13 dm_nfnet_f5 85.714 14.286 97.442 2.558 377.21 544 0.954 bicubic
14 dm_nfnet_f4 85.658 14.342 97.510 2.490 316.07 512 0.951 bicubic
15 dm_nfnet_f3 85.560 14.440 97.406 2.594 254.92 416 0.940 bicubic
16 cait_s36_384 85.460 14.540 97.480 2.520 68.37 384 1.000 bicubic
17 ig_resnext101_32x48d 85.428 14.572 97.572 2.428 828.41 224 0.875 bilinear
18 vit_deit_base_distilled_patch16_384 85.422 14.578 97.332 2.668 87.63 384 1.000 bicubic
19 tf_efficientnet_b8 85.370 14.630 97.390 2.610 87.41 672 0.954 bicubic
23 vit_large_patch16_384 85.158 14.842 97.356 2.644 304.72 384 1.000 bicubic
24 tf_efficientnet_b7_ap 85.120 14.880 97.252 2.748 66.35 600 0.949 bicubic
25 ig_resnext101_32x32d 85.094 14.906 97.438 2.562 468.53 224 0.875 bilinear
26 cait_s24_384 85.046 14.954 97.346 2.654 47.06 384 1.000 bicubic
27 resnetrs420 85.008 14.992 97.124 2.876 191.89 416 1.000 bicubic
28 dm_nfnet_f2 84.990 15.010 97.144 2.856 193.78 352 0.920 bicubic
29 ecaresnet269d 84.976 15.024 97.226 2.774 102.09 352 1.000 bicubic
30 vit_base_r50_s16_384 84.972 15.028 97.288 2.712 98.95 384 1.000 bicubic
31 tf_efficientnet_b7 84.936 15.064 97.204 2.796 66.35 600 0.949 bicubic
32 resnetv2_152x4_bitm 84.932 15.068 97.436 2.564 936.53 480 1.000 bilinear
33 tf_efficientnet_b6_ap 84.788 15.212 97.138 2.862 43.04 528 0.942 bicubic
34 resnetrs350 84.720 15.280 96.988 3.012 163.96 384 1.000 bicubic
35 dm_nfnet_f1 84.604 15.396 97.068 2.932 132.63 320 0.910 bicubic
36 resnest269e 84.518 15.482 96.986 3.014 110.93 416 0.928 bicubic
37 resnetv2_152x2_bitm 84.440 15.560 97.446 2.554 236.34 480 1.000 bilinear
38 resnetrs270 84.434 15.566 96.970 3.030 129.86 352 1.000 bicubic
39 resnetv2_101x3_bitm 84.394 15.606 97.362 2.638 387.93 480 1.000 bilinear
40 seresnet152d 84.362 15.638 97.040 2.960 66.84 320 1.000 bicubic
41 swsl_resnext101_32x8d 84.284 15.716 97.176 2.824 88.79 224 0.875 bilinear
42 vit_base_patch16_224_miil 84.268 15.732 96.802 3.198 86.54 224 0.875 bilinear
43 tf_efficientnet_b5_ap 84.252 15.748 96.974 3.026 30.39 456 0.934 bicubic
44 vit_base_patch16_384 84.210 15.790 97.218 2.782 86.86 384 1.000 bicubic
45 ig_resnext101_32x16d 84.170 15.830 97.196 2.804 194.03 224 0.875 bilinear
46 pit_b_distilled_224 84.144 15.856 96.856 3.144 74.79 224 0.900 bicubic
47 tf_efficientnet_b6 84.110 15.890 96.886 3.114 43.04 528 0.942 bicubic
48 resnetrs200 84.066 15.934 96.874 3.126 93.21 320 1.000 bicubic
49 cait_xs24_384 84.062 15.938 96.888 3.112 26.67 384 1.000 bicubic
50 tf_efficientnet_b3_ns 84.048 15.952 96.910 3.090 12.23 300 0.904 bicubic
51 eca_nfnet_l1 84.008 15.992 97.028 2.972 41.41 320 1.000 bicubic
52 resnet200d 83.962 16.038 96.824 3.176 64.69 320 1.000 bicubic
53 resnest200e 83.832 16.168 96.894 3.106 70.20 320 0.909 bicubic
54 tf_efficientnet_b5 83.812 16.188 96.748 3.252 30.39 456 0.934 bicubic
55 efficientnet_v2s 83.808 16.192 96.724 3.276 23.94 384 1.000 bicubic
56 resnetv2_50x3_bitm 83.784 16.216 97.106 2.894 217.32 480 1.000 bilinear
57 resnetrs152 83.712 16.288 96.614 3.386 86.62 320 1.000 bicubic
58 regnety_160 83.686 16.314 96.776 3.224 83.59 288 1.000 bicubic
59 resnet152d 83.680 16.320 96.738 3.262 60.21 320 1.000 bicubic
60 cait_s24_224 83.452 16.548 96.564 3.436 46.92 224 1.000 bicubic
61 efficientnet_b4 83.428 16.572 96.596 3.404 19.34 384 1.000 bicubic
62 vit_deit_base_distilled_patch16_224 83.388 16.612 96.488 3.512 87.34 224 0.900 bicubic
63 swsl_resnext101_32x16d 83.346 16.654 96.846 3.154 194.03 224 0.875 bilinear
64 dm_nfnet_f0 83.342 16.658 96.560 3.440 71.49 256 0.900 bicubic
66 swsl_resnext101_32x4d 83.230 16.770 96.760 3.240 44.18 224 0.875 bilinear
67 swin_small_patch4_window7_224 83.212 16.788 96.322 3.678 49.61 224 0.900 bicubic
68 vit_deit_base_patch16_384 83.106 16.894 96.372 3.628 86.86 384 1.000 bicubic
69 tresnet_m 83.080 16.920 96.118 3.882 31.39 224 0.875 bilinear
70 vit_large_patch16_224 83.062 16.938 96.438 3.562 304.33 224 0.900 bicubic
71 tresnet_xl_448 83.050 16.950 96.174 3.826 78.44 448 0.875 bilinear
72 resnet101d 83.022 16.978 96.446 3.554 44.57 320 1.000 bicubic
81 pit_b_224 82.446 17.554 95.710 4.290 73.76 224 0.900 bicubic
82 tf_efficientnet_b2_ns 82.380 17.620 96.248 3.752 9.11 260 0.890 bicubic
83 ecaresnet50t 82.346 17.654 96.138 3.862 25.57 320 0.950 bicubic
84 resnetrs101 82.288 17.712 96.008 3.992 63.62 288 0.940 bicubic
85 tresnet_l_448 82.268 17.732 95.976 4.024 55.99 448 0.875 bilinear
86 efficientnet_b3a efficientnet_b3 82.242 17.758 96.114 3.886 12.23 320 1.000 bicubic
87 resnetv2_101x1_bitm 82.212 17.788 96.472 3.528 44.54 480 1.000 bilinear
88 cait_xxs36_384 82.194 17.806 96.148 3.852 17.37 384 1.000 bicubic
89 swsl_resnext50_32x4d 82.182 17.818 96.230 3.770 25.03 224 0.875 bilinear
90 ecaresnet101d 82.172 17.828 96.046 3.954 44.57 224 0.875 bicubic
efficientnet_b3 82.076 17.924 96.020 3.980 12.23 300 0.904 bicubic
efficientnet_v2s 82.070 17.930 95.954 4.046 23.94 224 1.000 bicubic
91 tresnet_xl 82.054 17.946 95.936 4.064 78.44 224 0.875 bilinear
92 vit_deit_base_patch16_224 81.998 18.002 95.734 4.266 86.57 224 0.900 bicubic
93 pit_s_distilled_224 81.996 18.004 95.798 4.202 24.04 224 0.900 bicubic
118 gluon_resnet152_v1s 81.016 18.984 95.412 4.588 60.32 224 0.875 bicubic
119 resnest50d_1s4x24d 80.988 19.012 95.322 4.678 25.68 224 0.875 bicubic
120 resnest50d 80.974 19.026 95.378 4.622 27.48 224 0.875 bilinear
121 cait_xxs24_384 80.966 19.034 95.646 4.354 12.03 384 1.000 bicubic
122 ssl_resnext101_32x4d 80.924 19.076 95.728 4.272 44.18 224 0.875 bilinear
123 gluon_seresnext101_32x4d 80.904 19.096 95.294 4.706 48.96 224 0.875 bicubic
124 gluon_seresnext101_64x4d 80.894 19.106 95.308 4.692 88.23 224 0.875 bicubic
125 efficientnet_b3_pruned 80.858 19.142 95.242 4.758 9.86 300 0.904 bicubic
126 ecaresnet101d_pruned 80.816 80.818 19.184 19.182 95.628 4.372 24.88 224 0.875 bicubic
127 regnety_320 80.812 19.188 95.244 4.756 145.05 224 0.875 bicubic
tresnet_m 80.802 19.198 94.860 5.140 31.39 224 0.875 bilinear
128 gernet_m 80.732 19.268 95.184 4.816 21.14 224 0.875 bilinear
129 nf_resnet50 80.694 19.306 95.356 4.644 25.56 288 0.940 bicubic
130 efficientnet_b2a efficientnet_b2 80.612 19.388 95.318 4.682 9.11 288 1.000 bicubic
131 gluon_resnext101_64x4d 80.604 19.396 94.988 5.012 83.46 224 0.875 bicubic
132 ecaresnet50d 80.592 19.408 95.320 4.680 25.58 224 0.875 bicubic
133 resnet50d 80.530 19.470 95.160 4.840 25.58 224 0.875 bicubic
137 ecaresnetlight 80.462 19.538 95.250 4.750 30.16 224 0.875 bicubic
138 inception_resnet_v2 80.458 19.542 95.306 4.694 55.84 299 0.897 bicubic
139 gluon_resnet101_v1d 80.414 19.586 95.014 4.986 44.57 224 0.875 bicubic
efficientnet_b2 80.392 19.608 95.076 4.924 9.11 260 0.875 bicubic
140 regnety_120 80.366 19.634 95.126 4.874 51.82 224 0.875 bicubic
141 gluon_resnext101_32x4d 80.334 19.666 94.926 5.074 44.18 224 0.875 bicubic
142 ssl_resnext50_32x4d 80.318 19.682 95.406 4.594 25.03 224 0.875 bilinear
151 repvgg_b3g4 80.212 19.788 95.110 4.890 83.83 224 0.875 bilinear
152 resnetv2_50x1_bitm 80.172 19.828 95.626 4.374 25.55 480 1.000 bilinear
153 inception_v4 80.168 19.832 94.968 5.032 42.68 299 0.875 bicubic
skresnext50_32x4d 80.156 19.844 94.642 5.358 27.48 224 0.875 bicubic
154 dpn107 80.156 19.844 94.910 5.090 86.92 224 0.875 bicubic
155 skresnext50_32x4d 80.156 19.844 94.642 5.358 27.48 224 0.875 bicubic
156 tf_efficientnet_b2 80.086 19.914 94.908 5.092 9.11 260 0.890 bicubic
157 cspdarknet53 80.058 19.942 95.084 4.916 27.64 256 0.887 bilinear
158 cspresnext50 80.040 19.960 94.944 5.056 20.57 224 0.875 bilinear
159 dpn92 80.008 19.992 94.838 94.836 5.162 5.164 37.67 224 0.875 bicubic
160 ens_adv_inception_resnet_v2 79.982 20.018 94.936 5.064 55.84 299 0.897 bicubic
161 gluon_seresnext50_32x4d 79.918 20.082 94.822 5.178 27.56 224 0.875 bicubic
162 efficientnet_b2_pruned 79.916 20.084 94.856 5.144 8.31 260 0.890 bicubic
163 gluon_resnet152_v1c 79.910 20.090 94.840 5.160 60.21 224 0.875 bicubic
164 resnetrs50 79.892 20.108 94.968 5.032 35.69 224 0.910 bicubic
165 regnety_080 79.876 20.124 94.830 5.170 39.18 224 0.875 bicubic
166 xception71 79.874 20.126 94.922 5.078 42.34 299 0.903 bicubic
167 regnetx_160 79.856 20.144 94.830 5.170 54.28 224 0.875 bicubic
170 dpn131 79.822 20.178 94.710 5.290 79.25 224 0.875 bicubic
171 tf_efficientnet_lite3 79.820 20.180 94.914 5.086 8.20 300 0.904 bilinear
172 resnext50_32x4d 79.768 20.232 94.598 5.402 25.03 224 0.875 bicubic
173 cait_xxs36_224 79.750 20.250 94.866 5.134 17.30 224 1.000 bicubic
174 regnety_064 79.722 20.278 94.768 5.232 30.58 224 0.875 bicubic
175 ecaresnet50d_pruned 79.716 20.284 94.880 5.120 19.94 224 0.875 bicubic
176 gluon_xception65 79.716 20.284 94.860 5.140 39.92 299 0.903 bicubic
202 res2net50_26w_8s 79.198 20.802 94.368 5.632 48.40 224 0.875 bilinear
203 res2net101_26w_4s 79.198 20.802 94.432 5.568 45.21 224 0.875 bilinear
204 regnetx_080 79.194 20.806 94.560 5.440 39.57 224 0.875 bicubic
205 coat_lite_mini 79.088 20.912 94.604 5.396 11.01 224 0.900 bicubic
206 legacy_seresnext50_32x4d 79.078 20.922 94.436 5.564 27.56 224 0.875 bilinear
207 gluon_resnet50_v1d 79.074 20.926 94.470 5.530 25.58 224 0.875 bicubic
208 regnetx_064 79.072 20.928 94.458 5.542 26.21 224 0.875 bicubic
214 wide_resnet101_2 78.856 21.144 94.282 5.718 126.89 224 0.875 bilinear
215 tf_efficientnet_b1 78.826 21.174 94.198 5.802 7.79 240 0.882 bicubic
216 gluon_inception_v3 78.806 21.194 94.370 5.630 23.83 299 0.875 bicubic
217 efficientnet_b1 78.794 21.206 94.342 5.658 7.79 256 1.000 bicubic
218 repvgg_b2 78.792 21.208 94.414 5.586 89.02 224 0.875 bilinear
219 tf_mixnet_l 78.774 21.226 93.998 6.002 7.33 224 0.875 bicubic
220 gluon_resnet50_v1s 78.710 78.712 21.290 21.288 94.238 5.762 25.68 224 0.875 bicubic
efficientnet_b1 78.698 21.302 94.144 5.856 7.79 240 0.875 bicubic
221 dla169 78.688 21.312 94.336 5.664 53.39 224 0.875 bilinear
222 legacy_seresnet152 78.660 21.340 94.370 5.630 66.82 224 0.875 bilinear
223 tf_efficientnet_b0_ns 78.658 21.342 94.376 5.624 5.29 224 0.875 bicubic
230 hrnet_w32 78.450 21.550 94.186 5.814 41.23 224 0.875 bilinear
231 dla60_res2next 78.440 21.560 94.152 5.848 17.03 224 0.875 bilinear
232 selecsls60b 78.412 21.588 94.174 5.826 32.77 224 0.875 bicubic
233 cait_xxs24_224 78.386 21.614 94.310 5.690 11.96 224 1.000 bicubic
234 legacy_seresnet101 78.382 21.618 94.264 5.736 49.33 224 0.875 bilinear
235 repvgg_b1 78.366 21.634 94.098 5.902 57.42 224 0.875 bilinear
236 tv_resnet152 78.312 21.688 94.038 5.962 60.19 224 0.875 bilinear
249 seresnext26t_32x4d 77.986 22.014 93.746 6.254 16.81 224 0.875 bicubic
250 selecsls60 77.982 22.018 93.828 6.172 30.67 224 0.875 bicubic
251 res2net50_26w_4s 77.964 22.036 93.854 6.146 25.70 224 0.875 bilinear
252 mobilenetv3_large_100_miil 77.916 22.084 92.910 7.090 5.48 224 0.875 bilinear
253 tf_efficientnet_cc_b0_8e 77.908 22.092 93.654 6.346 24.01 224 0.875 bicubic
254 regnety_016 77.862 22.138 93.720 6.280 11.20 224 0.875 bicubic
vit_small_patch16_224 77.858 22.142 93.416 6.584 48.75 224 0.900 bicubic
255 rexnet_100 77.858 22.142 93.870 6.130 4.80 224 0.875 bicubic
256 tf_inception_v3 vit_small_patch16_224 77.858 22.142 93.638 93.416 6.362 6.584 23.83 48.75 299 224 0.875 0.900 bicubic
257 tf_inception_v3 77.856 22.144 93.640 6.360 23.83 299 0.875 bicubic
258 hardcorenas_e 77.794 22.206 93.694 6.306 8.07 224 0.875 bilinear
259 efficientnet_b0 77.698 22.302 93.532 6.468 5.29 224 0.875 bicubic
260 legacy_seresnet50 77.630 22.370 93.748 6.252 28.09 224 0.875 bilinear
264 adv_inception_v3 77.582 22.418 93.736 6.264 23.83 299 0.875 bicubic
265 gluon_resnet50_v1b 77.580 22.420 93.716 6.284 25.56 224 0.875 bicubic
266 res2net50_48w_2s 77.522 22.478 93.554 6.446 25.29 224 0.875 bilinear
267 coat_lite_tiny 77.512 22.488 93.916 6.084 5.72 224 0.900 bicubic
268 tf_efficientnet_lite2 77.468 22.532 93.754 6.246 6.09 260 0.890 bicubic
269 inception_v3 77.438 22.562 93.476 6.524 23.83 299 0.875 bicubic
270 hardcorenas_d 77.432 22.568 93.484 6.516 7.50 224 0.875 bilinear
289 hrnet_w18 76.758 23.242 93.444 6.556 21.30 224 0.875 bilinear
290 resnet26d 76.696 23.304 93.150 6.850 16.01 224 0.875 bicubic
291 tf_efficientnet_lite1 76.642 23.358 93.226 6.774 5.42 240 0.882 bicubic
292 mixer_b16_224 76.602 23.398 92.228 7.772 59.88 224 0.875 bicubic
293 tf_efficientnet_es 76.594 23.406 93.202 6.798 5.44 224 0.875 bicubic
294 densenetblur121d 76.588 23.412 93.192 6.808 8.00 224 0.875 bicubic
295 hardcorenas_b 76.538 23.462 92.754 7.246 5.18 224 0.875 bilinear
306 mobilenetv3_rw 75.634 24.366 92.708 7.292 5.48 224 0.875 bicubic
307 densenet121 75.578 24.422 92.652 7.348 7.98 224 0.875 bicubic
308 tf_mobilenetv3_large_100 75.518 24.482 92.606 7.394 5.48 224 0.875 bilinear
309 resnest14d 75.504 75.506 24.496 24.494 92.518 7.482 10.61 224 0.875 bilinear
310 efficientnet_lite0 75.484 24.516 92.510 7.490 4.65 224 0.875 bicubic
311 semnasnet_100 75.448 24.552 92.604 7.396 3.89 224 0.875 bicubic
312 resnet26 75.292 24.708 92.570 7.430 16.00 224 0.875 bicubic
329 vgg19_bn 74.214 25.786 91.842 8.158 143.68 224 0.875 bilinear
330 spnasnet_100 74.084 25.916 91.818 8.182 4.42 224 0.875 bilinear
331 regnety_004 74.034 25.966 91.752 8.248 4.34 224 0.875 bicubic
332 ghostnet_100 73.978 26.022 91.456 8.544 5.18 224 0.875 bilinear
333 regnetx_006 73.852 26.148 91.672 8.328 6.20 224 0.875 bicubic
334 tf_mobilenetv3_large_075 73.438 26.562 91.350 8.650 3.99 224 0.875 bilinear
335 vgg16_bn 73.350 26.650 91.506 8.494 138.37 224 0.875 bilinear
341 ssl_resnet18 72.610 27.390 91.416 8.584 11.69 224 0.875 bilinear
342 regnetx_004 72.396 27.604 90.830 9.170 5.16 224 0.875 bicubic
343 vgg19 72.368 27.632 90.872 9.128 143.67 224 0.875 bilinear
344 hrnet_w18_small 72.344 72.342 27.656 27.658 90.678 9.322 13.19 224 0.875 bilinear
345 resnet18d 72.260 27.740 90.696 9.304 11.71 224 0.875 bicubic
346 tf_mobilenetv3_large_minimal_100 72.248 27.752 90.630 9.370 3.92 224 0.875 bilinear
347 vit_deit_tiny_patch16_224 72.168 27.832 91.118 8.882 5.72 224 0.900 bicubic
348 mixer_l16_224 72.058 27.942 87.668 12.332 208.20 224 0.875 bicubic
349 legacy_seresnet18 71.742 28.258 90.334 9.666 11.78 224 0.875 bicubic
350 vgg13_bn 71.594 28.406 90.376 9.624 133.05 224 0.875 bilinear
351 vgg16 71.594 28.406 90.382 9.618 138.36 224 0.875 bilinear

441
results/results-imagenetv2-matched-frequency.csv
View File

@ -2,260 +2,280 @@ model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation,t
tf_efficientnet_l2_ns_475,80.460,19.540,95.730,4.270,480.31,475,0.936,bicubic,-7.774,-2.816,+1
tf_efficientnet_l2_ns,80.250,19.750,95.840,4.160,480.31,800,0.960,bicubic,-8.102,-2.810,-1
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,+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
ig_resnext101_32x48d,76.870,23.130,93.310,6.690,828.41,224,0.875,bilinear,-8.558,-4.262,+7
ig_resnext101_32x32d,76.840,23.160,93.200,6.800,468.53,224,0.875,bilinear,-8.254,-4.238,+14
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_efficientnet_b5_ns,76.810,23.190,93.580,6.420,30.39,456,0.934,bicubic,-9.278,-4.172,+1
swin_base_patch4_window12_384,76.280,23.720,93.320,6.680,87.90,384,1.000,bicubic,-10.152,-4.738,-3
swin_large_patch4_window7_224,76.270,23.730,93.420,6.580,196.53,224,0.900,bicubic,-10.050,-4.476,-3
dm_nfnet_f6,76.180,23.820,93.220,6.780,438.36,576,0.956,bicubic,-10.116,-4.524,-3
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,-3
ig_resnext101_32x16d,75.720,24.280,92.910,7.090,194.03,224,0.875,bilinear,-8.450,-4.286,+21
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_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,+7
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_deit_base_distilled_patch16_384,75.550,24.450,92.500,7.500,87.63,384,1.000,bicubic,-9.872,-4.832,-4
swsl_resnext101_32x8d,75.430,24.570,92.760,7.240,88.79,224,0.875,bilinear,-8.854,-4.416,+14
dm_nfnet_f3,75.410,24.590,92.830,7.170,254.92,416,0.940,bicubic,-10.150,-4.576,-8
tf_efficientnet_b6_ap,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.408,-4.698,+6
vit_large_patch16_384,75.150,24.850,92.660,7.340,304.72,384,1.000,bicubic,-10.008,-4.696,-3
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,-9
dm_nfnet_f5,74.790,25.210,92.460,7.540,377.21,544,0.954,bicubic,-10.924,-4.982,-15
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,+7
swin_base_patch4_window7_224,74.570,25.430,92.560,7.440,87.77,224,0.900,bicubic,-10.682,-5.002,-11
seresnet152d,74.510,25.490,92.080,7.920,66.84,320,1.000,bicubic,-9.852,-4.960,+3
resnest200e,74.480,25.520,91.860,8.140,70.20,320,0.909,bicubic,-9.352,-5.034,+12
dm_nfnet_f2,74.450,25.550,92.230,7.770,193.78,352,0.920,bicubic,-10.540,-4.914,-9
dm_nfnet_f1,74.400,25.600,92.350,7.650,132.63,320,0.910,bicubic,-10.204,-4.718,-4
resnest269e,74.170,25.830,91.950,8.050,110.93,416,0.928,bicubic,-10.348,-5.036,-4
pit_b_distilled_224,74.160,25.840,91.680,8.320,74.79,224,0.900,bicubic,-9.984,-5.176,+3
swsl_resnext101_32x4d,74.140,25.860,91.990,8.010,44.18,224,0.875,bilinear,-9.090,-4.770,+16
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,+3
swsl_resnext101_32x16d,74.020,25.980,92.160,7.840,194.03,224,0.875,bilinear,-9.326,-4.686,+10
resnetv2_152x4_bitm,74.000,26.000,92.340,7.660,936.53,480,1.000,bilinear,-10.932,-5.096,-13
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
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
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
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,-2
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,+19
resnetv2_152x2_bitm,73.630,26.370,92.590,7.410,236.34,480,1.000,bilinear,-10.810,-4.856,-14
tf_efficientnet_b5,73.550,26.450,91.460,8.540,30.39,456,0.934,bicubic,-10.262,-5.288,-2
resnetv2_101x3_bitm,73.530,26.470,92.570,7.430,387.93,480,1.000,bilinear,-10.864,-4.792,-15
resnet152d,73.520,26.480,91.230,8.770,60.21,320,1.000,bicubic,-10.160,-5.508,-1
regnety_160,73.360,26.640,91.690,8.310,83.59,288,1.000,bicubic,-10.326,-5.086,-3
vit_deit_base_distilled_patch16_224,73.240,26.760,91.000,9.000,87.34,224,0.900,bicubic,-10.148,-5.488,-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
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
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
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
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,+9
pnasnet5large,72.610,27.390,90.510,9.490,86.06,331,0.911,bicubic,-10.172,-5.530,+5
nfnet_l0,72.610,27.390,91.010,8.990,35.07,288,1.000,bicubic,-10.150,-5.488,+7
resnest101e,72.570,27.430,90.820,9.180,48.28,256,0.875,bilinear,-10.320,-5.500,+3
swsl_resnext50_32x4d,72.560,27.440,90.870,9.130,25.03,224,0.875,bilinear,-9.622,-5.360,+15
tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.500,-5.864,-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
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,-3
tf_efficientnet_b4,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.732,-5.710,-3
tf_efficientnet_b2_ns,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.100,-5.158,+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
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
resnetv2_50x3_bitm,72.180,27.820,91.790,8.210,217.32,480,1.000,bilinear,-11.604,-5.316,-20
eca_nfnet_l0,71.850,28.150,91.130,8.870,24.14,288,1.000,bicubic,-10.738,-5.344,-1
swin_small_patch4_window7_224,71.740,28.260,90.240,9.760,49.61,224,0.900,bicubic,-11.472,-6.082,-14
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,+31
tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.394,-6.306,+6
efficientnet_v2s,71.610,28.390,90.200,9.800,23.94,224,1.000,bicubic,-10.460,-5.754,+4
tresnet_l_448,71.600,28.400,90.050,9.950,55.99,448,0.875,bilinear,-10.668,-5.926,-3
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
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_b3a,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.762,-6.054,-5
efficientnet_b3,71.460,28.540,90.090,9.910,12.23,300,0.904,bicubic,-10.616,-5.930,-2
ssl_resnext101_32x16d,71.410,28.590,90.560,9.440,194.03,224,0.875,bilinear,-10.434,-5.536,+2
pit_s_distilled_224,71.380,28.620,89.780,10.220,24.04,224,0.900,bicubic,-10.616,-6.018,0
vit_base_patch16_224,71.330,28.670,90.460,9.540,86.57,224,0.900,bicubic,-10.456,-5.662,+2
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,+2
vit_deit_base_patch16_224,71.170,28.830,89.200,10.800,86.57,224,0.900,bicubic,-10.828,-6.534,-5
tresnet_m_448,70.990,29.010,88.680,11.320,31.39,448,0.875,bilinear,-10.724,-6.892,-1
resnest50d_4s2x40d,70.950,29.050,89.710,10.290,30.42,224,0.875,bicubic,-10.158,-5.848,+17
wide_resnet50_2,70.950,29.050,89.230,10.770,68.88,224,0.875,bicubic,-10.506,-6.302,+6
tnt_s_patch16_224,70.930,29.070,89.600,10.400,23.76,224,0.900,bicubic,-10.588,-6.148,+2
tf_efficientnet_b3_ap,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.902,-6.194,-7
tf_efficientnet_b1_ns,70.870,29.130,90.120,9.880,7.79,240,0.882,bicubic,-10.518,-5.618,+4
vit_large_patch32_384,70.860,29.140,90.570,9.430,306.63,384,1.000,bicubic,-10.646,-5.522,0
rexnet_200,70.840,29.160,89.700,10.300,16.37,224,0.875,bicubic,-10.792,-5.968,-5
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
resnetv2_101x1_bitm,70.710,29.290,90.800,9.200,44.54,480,1.000,bilinear,-11.502,-5.672,-21
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
gluon_senet154,70.600,29.400,88.920,11.080,115.09,224,0.875,bicubic,-10.634,-6.428,+4
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,+3
legacy_senet154,70.500,29.500,89.010,10.990,115.09,224,0.875,bilinear,-10.810,-6.486,-1
tf_efficientnet_lite4,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.106,-6.558,-12
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
resnest50d,70.410,29.590,88.760,11.240,27.48,224,0.875,bilinear,-10.564,-6.618,+5
resnest50d_1s4x24d,70.400,29.600,89.220,10.780,25.68,224,0.875,bicubic,-10.588,-6.102,+3
seresnext50_32x4d,70.400,29.600,89.110,10.890,27.56,224,0.875,bicubic,-10.866,-6.510,-5
gernet_l,70.350,29.650,88.980,11.020,31.08,256,0.875,bilinear,-11.004,-6.556,-9
gluon_resnet152_v1s,70.290,29.710,88.850,11.150,60.32,224,0.875,bicubic,-10.726,-6.562,-1
repvgg_b3,70.250,29.750,88.730,11.270,123.09,224,0.875,bilinear,-10.242,-6.530,+14
ecaresnet101d_pruned,70.130,29.870,89.590,10.410,24.88,224,0.875,bicubic,-10.686,-6.038,+4
efficientnet_el,70.120,29.880,89.290,10.710,10.59,300,0.904,bicubic,-11.196,-6.236,-12
inception_resnet_v2,70.120,29.880,88.700,11.300,55.84,299,0.897,bicubic,-10.338,-6.606,+15
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
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_v1d,69.960,30.040,88.490,11.510,60.21,224,0.875,bicubic,-10.514,-6.716,+10
pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.204,-6.402,-10
ecaresnet50d,69.840,30.160,89.400,10.600,25.58,224,0.875,bicubic,-10.752,-5.920,+4
ssl_resnext50_32x4d,69.710,30.290,89.440,10.560,25.03,224,0.875,bilinear,-10.608,-5.966,+14
gluon_resnext101_64x4d,69.680,30.320,88.270,11.730,83.46,224,0.875,bicubic,-10.924,-6.718,+1
tresnet_m,69.660,30.340,87.990,12.010,31.39,224,0.875,bilinear,-11.142,-6.870,-4
efficientnet_b3_pruned,69.580,30.420,88.980,11.020,9.86,300,0.904,bicubic,-11.278,-6.262,-8
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
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
efficientnet_b3_pruned,69.580,30.420,88.980,11.020,9.86,300,0.904,bicubic,-11.278,-6.262,-7
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
repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.692,-6.660,+15
efficientnet_el_pruned,69.520,30.480,88.930,11.070,10.59,300,0.904,bicubic,-10.780,-6.288,+11
ens_adv_inception_resnet_v2,69.520,30.480,88.510,11.490,55.84,299,0.897,bicubic,-10.462,-6.426,+25
efficientnet_b2a,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,+5
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
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
regnetx_320,69.440,30.560,88.270,11.730,107.81,224,0.875,bicubic,-10.806,-6.756,+9
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,+8
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
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,+28
gluon_resnet152_v1c,69.140,30.860,87.870,12.130,60.21,224,0.875,bicubic,-10.770,-6.970,+16
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
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,+35
seresnet50,68.980,31.020,88.710,11.290,28.09,224,0.875,bicubic,-11.294,-6.360,-4
xception65,68.980,31.020,88.480,11.520,39.92,299,0.903,bicubic,-10.572,-6.174,+28
efficientnet_b2,68.970,31.030,88.630,11.370,9.11,260,0.875,bicubic,-11.422,-6.446,-14
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
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,+17
dpn131,68.770,31.230,87.470,12.530,79.25,224,0.875,bicubic,-11.052,-7.240,+10
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
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,+14
vit_deit_small_patch16_224,68.720,31.280,88.200,11.800,22.05,224,0.900,bicubic,-11.136,-6.852,+4
resnext50d_32x4d,68.740,31.260,88.300,11.700,25.05,224,0.875,bicubic,-10.936,-6.566,+16
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
regnety_080,68.700,31.300,87.970,12.030,39.18,224,0.875,bicubic,-11.176,-6.860,-1
dpn107,68.690,31.310,88.130,11.870,86.92,224,0.875,bicubic,-11.466,-6.512,-11
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,+15
resnext50_32x4d,68.640,31.360,87.570,12.430,25.03,224,0.875,bicubic,-11.128,-7.028,+2
dpn98,68.590,31.410,87.680,12.320,61.57,224,0.875,bicubic,-11.052,-6.918,+7
regnetx_160,68.530,31.470,88.450,11.550,54.28,224,0.875,bicubic,-11.326,-6.380,-5
cspresnet50,68.460,31.540,88.010,11.990,21.62,256,0.887,bilinear,-11.114,-6.702,+7
rexnet_130,68.450,31.550,88.040,11.960,7.56,224,0.875,bicubic,-11.050,-6.642,+9
regnety_064,68.420,31.580,88.080,11.920,30.58,224,0.875,bicubic,-11.302,-6.688,-3
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
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
ecaresnet50d_pruned,68.420,31.580,88.370,11.630,19.94,224,0.875,bicubic,-11.296,-6.510,-1
ssl_resnet50,68.410,31.590,88.560,11.440,25.56,224,0.875,bilinear,-10.812,-6.272,+20
skresnext50_32x4d,68.350,31.650,87.570,12.430,27.48,224,0.875,bicubic,-11.806,-7.340,-24
dla102x2,68.330,31.670,87.890,12.110,41.28,224,0.875,bilinear,-11.118,-6.750,+5
efficientnet_b2_pruned,68.320,31.680,88.100,11.900,8.31,260,0.890,bicubic,-11.596,-6.756,-18
gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.044,-7.126,+5
ecaresnet26t,68.230,31.770,88.790,11.210,16.01,320,0.950,bicubic,-11.624,-6.294,-14
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
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
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
ese_vovnet39b,68.210,31.790,88.250,11.750,24.57,224,0.875,bicubic,-11.110,-6.462,+3
regnetx_120,68.150,31.850,87.660,12.340,46.11,224,0.875,bicubic,-11.446,-7.078,-7
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
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
dpn92,67.990,32.010,87.580,12.420,37.67,224,0.875,bicubic,-12.018,-7.258,-28
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,+15
gluon_resnet50_v1d,67.940,32.060,87.130,12.870,25.58,224,0.875,bicubic,-11.134,-7.340,+16
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
legacy_seresnext50_32x4d,67.840,32.160,87.620,12.380,27.56,224,0.875,bilinear,-11.238,-6.816,+10
legacy_seresnext50_32x4d,67.840,32.160,87.620,12.380,27.56,224,0.875,bilinear,-11.238,-6.816,+11
hrnet_w48,67.770,32.230,87.420,12.580,77.47,224,0.875,bilinear,-11.530,-7.092,-1
hrnet_w44,67.740,32.260,87.560,12.440,67.06,224,0.875,bilinear,-11.156,-6.808,+15
hrnet_w44,67.740,32.260,87.560,12.440,67.06,224,0.875,bilinear,-11.156,-6.808,+16
coat_lite_mini,67.720,32.280,87.700,12.300,11.01,224,0.900,bicubic,-11.368,-6.904,+7
tf_efficientnet_b0_ns,67.710,32.290,88.070,11.930,5.29,224,0.875,bicubic,-10.948,-6.306,+24
regnetx_064,67.680,32.320,87.520,12.480,26.21,224,0.875,bicubic,-11.392,-6.938,+8
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,-6.754,0
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,-21
regnety_040,67.580,32.420,87.510,12.490,20.65,224,0.875,bicubic,-11.640,-7.146,-5
res2net50_26w_8s,67.570,32.430,87.280,12.720,48.40,224,0.875,bilinear,-11.628,-7.088,-4
gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.954,-7.398,-22
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
resnetv2_50x1_bitm,67.520,32.480,89.250,10.750,25.55,480,1.000,bilinear,-12.652,-6.376,-55
tf_efficientnet_b1_ap,67.520,32.480,87.760,12.240,7.79,240,0.882,bicubic,-11.760,-6.546,-13
legacy_seresnet152,67.520,32.480,87.390,12.610,66.82,224,0.875,bilinear,-11.140,-6.980,+13
gluon_resnet101_v1b,67.460,32.540,87.240,12.760,44.55,224,0.875,bicubic,-11.846,-7.284,-19
tf_efficientnet_cc_b1_8e,67.450,32.550,87.310,12.690,39.72,240,0.882,bicubic,-11.858,-7.060,-21
res2net101_26w_4s,67.440,32.560,87.010,12.990,45.21,224,0.875,bilinear,-11.758,-7.422,-10
resnet50,67.440,32.560,87.420,12.580,25.56,224,0.875,bicubic,-11.598,-6.970,-5
resnetblur50,67.430,32.570,87.440,12.560,25.56,224,0.875,bicubic,-11.856,-7.198,-19
regnetx_032,67.290,32.710,87.000,13.000,15.30,224,0.875,bicubic,-10.882,-7.088,+24
xception41,67.250,32.750,87.200,12.800,26.97,299,0.903,bicubic,-11.266,-7.078,+7
resnest26d,67.200,32.800,87.170,12.830,17.07,224,0.875,bilinear,-11.278,-7.128,+9
efficientnet_b1,67.170,32.830,87.150,12.850,7.79,240,0.875,bicubic,-11.528,-6.994,0
repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.632,-7.084,-5
resnetv2_50x1_bitm,67.520,32.480,89.250,10.750,25.55,480,1.000,bilinear,-12.652,-6.376,-58
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
tf_efficientnet_cc_b1_8e,67.450,32.550,87.310,12.690,39.72,240,0.882,bicubic,-11.858,-7.060,-23
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
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,-11.650,-7.378,-5
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
dla60_res2net,67.020,32.980,87.160,12.840,20.85,224,0.875,bilinear,-11.444,-7.046,+3
dla102x,67.010,32.990,86.770,13.230,26.31,224,0.875,bilinear,-11.500,-7.458,-1
mixnet_l,66.940,33.060,86.910,13.090,7.33,224,0.875,bicubic,-12.036,-7.272,-17
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,-6
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
tf_efficientnet_b1,66.880,33.120,87.010,12.990,7.79,240,0.882,bicubic,-11.946,-7.188,-18
efficientnet_es,66.880,33.120,86.730,13.270,5.44,224,0.875,bicubic,-11.186,-7.196,+13
regnetx_040,66.840,33.160,86.730,13.270,22.12,224,0.875,bicubic,-11.642,-7.514,-7
tf_efficientnet_b1,66.880,33.120,87.010,12.990,7.79,240,0.882,bicubic,-11.946,-7.188,-19
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,-5
hrnet_w32,66.750,33.250,87.300,12.700,41.23,224,0.875,bilinear,-11.700,-6.886,-8
wide_resnet101_2,66.730,33.270,87.030,12.970,126.89,224,0.875,bilinear,-12.126,-7.252,-25
adv_inception_v3,66.650,33.350,86.540,13.460,23.83,299,0.875,bicubic,-10.932,-7.196,+22
dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.800,-7.122,-10
selecsls60b,66.760,33.240,86.530,13.470,32.77,224,0.875,bicubic,-11.652,-7.644,-6
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
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,-11.448,-6.756,+11
tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.446,-6.980,+12
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
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,+1
tf_efficientnet_em,66.180,33.820,86.360,13.640,6.90,240,0.882,bicubic,-11.950,-7.684,-6
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
tv_resnext50_32x4d,66.180,33.820,86.040,13.960,25.03,224,0.875,bilinear,-11.440,-7.656,+9
tf_efficientnet_cc_b0_8e,66.170,33.830,86.240,13.760,24.01,224,0.875,bicubic,-11.738,-7.414,0
inception_v3,66.160,33.840,86.320,13.680,23.83,299,0.875,bicubic,-11.278,-7.156,+14
res2net50_26w_4s,66.140,33.860,86.600,13.400,25.70,224,0.875,bilinear,-11.824,-7.254,-3
efficientnet_b1_pruned,66.090,33.910,86.570,13.430,6.33,240,0.882,bicubic,-12.146,-7.264,-16
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
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,-7.148,-2
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,-16
seresnext26t_32x4d,65.880,34.120,85.680,14.320,16.81,224,0.875,bicubic,-12.106,-8.066,-11
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,-24
densenet161,65.840,34.160,86.450,13.550,28.68,224,0.875,bicubic,-11.518,-7.188,+7
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
resnet34d,65.780,34.220,86.710,13.290,21.82,224,0.875,bicubic,-11.336,-6.672,+11
densenet161,65.840,34.160,86.450,13.550,28.68,224,0.875,bicubic,-11.518,-7.188,+8
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,-7.750,-13
vit_small_patch16_224,65.740,34.260,86.120,13.880,48.75,224,0.900,bicubic,-12.118,-7.296,-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
tf_efficientnet_b0_ap,65.490,34.510,85.580,14.420,5.29,224,0.875,bicubic,-11.596,-7.676,+7
seresnext26d_32x4d,65.410,34.590,85.970,14.030,16.81,224,0.875,bicubic,-12.192,-7.638,-11
seresnext26d_32x4d,65.410,34.590,85.970,14.030,16.81,224,0.875,bicubic,-12.192,-7.638,-12
tf_efficientnet_lite2,65.380,34.620,85.990,14.010,6.09,260,0.890,bicubic,-12.088,-7.764,-7
res2net50_48w_2s,65.350,34.650,85.960,14.040,25.29,224,0.875,bilinear,-12.172,-7.594,-9
res2net50_48w_2s,65.350,34.650,85.960,14.040,25.29,224,0.875,bilinear,-12.172,-7.594,-10
densenet201,65.290,34.710,85.690,14.310,20.01,224,0.875,bicubic,-11.996,-7.788,-3
densenetblur121d,65.280,34.720,85.710,14.290,8.00,224,0.875,bicubic,-11.308,-7.482,+15
densenetblur121d,65.280,34.720,85.710,14.290,8.00,224,0.875,bicubic,-11.308,-7.482,+16
dla60,65.200,34.800,85.760,14.240,22.04,224,0.875,bilinear,-11.832,-7.558,+3
ese_vovnet19b_dw,65.190,34.810,85.470,14.530,6.54,224,0.875,bicubic,-11.608,-7.798,+8
tf_efficientnet_cc_b0_4e,65.150,34.850,85.160,14.840,13.31,224,0.875,bicubic,-12.156,-8.174,-8
@ -264,28 +284,29 @@ 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,+15
densenet169,64.760,35.240,85.240,14.760,14.15,224,0.875,bicubic,-11.146,-7.786,+16
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
repvgg_a2,64.450,35.550,85.130,14.870,28.21,224,0.875,bilinear,-12.010,-7.874,+6
hardcorenas_b,64.420,35.580,84.870,15.130,5.18,224,0.875,bilinear,-12.118,-7.884,+3
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
dpn68,64.230,35.770,85.180,14.820,12.61,224,0.875,bicubic,-12.088,-7.798,+1
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,-7.796,0
mobilenetv2_140,64.060,35.940,85.040,14.960,6.11,224,0.875,bicubic,-12.456,-7.956,-4
densenet121,63.750,36.250,84.590,15.410,7.98,224,0.875,bicubic,-11.828,-8.062,+6
hardcorenas_a,63.710,36.290,84.400,15.600,5.26,224,0.875,bilinear,-12.206,-8.114,0
resnest14d,63.590,36.410,84.250,15.750,10.61,224,0.875,bilinear,-11.914,-8.268,+6
tf_mixnet_s,63.560,36.440,84.270,15.730,4.13,224,0.875,bicubic,-12.090,-8.358,+1
resnet26,63.470,36.530,84.260,15.740,16.00,224,0.875,bicubic,-11.822,-8.310,+7
mixnet_s,63.390,36.610,84.740,15.260,4.13,224,0.875,bicubic,-12.602,-8.056,-5
mobilenetv3_large_100,63.360,36.640,84.090,15.910,5.48,224,0.875,bicubic,-12.406,-8.452,-3
efficientnet_es_pruned,63.330,36.670,84.950,15.050,5.44,224,0.875,bicubic,-11.670,-7.498,+12
tv_resnet50,63.330,36.670,84.640,15.360,25.56,224,0.875,bilinear,-12.808,-8.224,-9
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
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
hardcorenas_a,63.710,36.290,84.400,15.600,5.26,224,0.875,bilinear,-12.206,-8.114,+1
resnest14d,63.590,36.410,84.250,15.750,10.61,224,0.875,bilinear,-11.916,-8.268,+7
tf_mixnet_s,63.560,36.440,84.270,15.730,4.13,224,0.875,bicubic,-12.090,-8.358,+2
resnet26,63.470,36.530,84.260,15.740,16.00,224,0.875,bicubic,-11.822,-8.310,+8
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
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
@ -297,7 +318,7 @@ legacy_seresnet34,62.850,37.150,84.210,15.790,21.96,224,0.875,bilinear,-11.958,-
mobilenetv2_110d,62.830,37.170,84.500,15.500,4.52,224,0.875,bicubic,-12.206,-7.686,+1
vit_deit_tiny_distilled_patch16_224,62.810,37.190,83.930,16.070,5.91,224,0.900,bicubic,-11.700,-7.960,+9
hrnet_w18_small_v2,62.800,37.200,83.980,16.020,15.60,224,0.875,bilinear,-12.314,-8.436,-4
swsl_resnet18,62.760,37.240,84.300,15.700,11.69,224,0.875,bilinear,-10.516,-7.434,+15
swsl_resnet18,62.760,37.240,84.300,15.700,11.69,224,0.875,bilinear,-10.516,-7.434,+16
repvgg_b0,62.720,37.280,83.860,16.140,15.82,224,0.875,bilinear,-12.432,-8.558,-8
gluon_resnet34_v1b,62.570,37.430,83.990,16.010,21.80,224,0.875,bicubic,-12.018,-8.000,+3
tf_efficientnet_lite0,62.550,37.450,84.220,15.780,4.65,224,0.875,bicubic,-12.280,-7.956,-3
@ -308,29 +329,31 @@ 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
ssl_resnet18,61.480,38.520,83.300,16.700,11.69,224,0.875,bilinear,-11.130,-8.116,+8
regnetx_006,61.350,38.650,83.450,16.550,6.20,224,0.875,bicubic,-12.502,-8.222,-1
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,-8.716,0
pit_ti_224,60.980,39.020,83.860,16.140,4.85,224,0.900,bicubic,-11.932,-7.542,+3
skresnet18,60.860,39.140,82.880,17.120,11.96,224,0.875,bicubic,-12.178,-8.288,0
tv_resnet34,61.190,38.810,82.710,17.290,21.80,224,0.875,bilinear,-12.122,-8.716,+1
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
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
legacy_seresnet18,59.800,40.200,81.690,18.310,11.78,224,0.875,bicubic,-11.942,-8.644,+4
legacy_seresnet18,59.800,40.200,81.690,18.310,11.78,224,0.875,bicubic,-11.942,-8.644,+5
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,+1
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,-8.722,0
gluon_resnet18_v1b,58.340,41.660,80.970,19.030,11.69,224,0.875,bicubic,-12.496,-8.792,0
vgg11_bn,57.410,42.590,80.020,19.980,132.87,224,0.875,bilinear,-12.950,-9.782,0
resnet18,57.170,42.830,80.200,19.800,11.69,224,0.875,bilinear,-12.578,-8.878,+2
vgg13,57.150,42.850,79.540,20.460,133.05,224,0.875,bilinear,-12.776,-9.706,0
regnety_002,57.000,43.000,79.840,20.160,3.16,224,0.875,bicubic,-13.252,-9.700,-2
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
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
resnet18,57.170,42.830,80.200,19.800,11.69,224,0.875,bilinear,-12.578,-8.878,+3
vgg13,57.150,42.850,79.540,20.460,133.05,224,0.875,bilinear,-12.776,-9.706,+1
regnety_002,57.000,43.000,79.840,20.160,3.16,224,0.875,bicubic,-13.252,-9.700,-1
mixer_l16_224,56.690,43.310,75.990,24.010,208.20,224,0.875,bicubic,-15.368,-11.678,-8
regnetx_002,56.050,43.950,79.210,20.790,2.68,224,0.875,bicubic,-12.712,-9.346,+1
dla60x_c,56.000,44.000,78.930,21.070,1.32,224,0.875,bilinear,-11.892,-9.496,+2
vgg11,55.800,44.200,78.830,21.170,132.86,224,0.875,bilinear,-13.224,-9.798,-2

1 model top1 top1_err top5 top5_err param_count img_size cropt_pct interpolation top1_diff top5_diff rank_diff
2 tf_efficientnet_l2_ns_475 80.460 19.540 95.730 4.270 480.31 475 0.936 bicubic -7.774 -2.816 +1
3 tf_efficientnet_l2_ns 80.250 19.750 95.840 4.160 480.31 800 0.960 bicubic -8.102 -2.810 -1
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 +1 +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 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 +7 -1
8 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 +14 +9
9 ig_resnext101_32x32d 76.840 23.160 93.200 6.800 468.53 224 0.875 bilinear -8.254 -4.238 +16
10 tf_efficientnet_b5_ns 76.810 23.190 93.580 6.420 30.39 456 0.934 bicubic -9.278 -4.172 +1
11 swin_base_patch4_window12_384 cait_m36_384 76.280 76.320 23.720 23.680 93.320 93.050 6.680 6.950 87.90 271.22 384 1.000 bicubic -10.152 -9.734 -4.738 -4.680 -3 +1
12 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 -3 -4
13 dm_nfnet_f6 swin_large_patch4_window7_224 76.180 76.270 23.820 23.730 93.220 93.420 6.780 6.580 438.36 196.53 576 224 0.956 0.900 bicubic -10.116 -10.050 -4.524 -4.476 -3 -4
14 cait_s36_384 76.210 23.790 92.970 7.030 68.37 384 1.000 bicubic -9.250 -4.510 +2
15 dm_nfnet_f6 76.180 23.820 93.220 6.780 438.36 576 0.956 bicubic -10.116 -4.524 -5
16 tf_efficientnet_b7_ap 76.090 23.910 92.970 7.030 66.35 600 0.949 bicubic -9.030 -4.282 +8
17 tf_efficientnet_b8_ap 76.090 23.910 92.730 7.270 87.41 672 0.954 bicubic -9.280 -4.564 +3
18 dm_nfnet_f4 75.750 24.250 92.790 7.210 316.07 512 0.951 bicubic -9.908 -4.720 -3 -4
19 ig_resnext101_32x16d 75.720 24.280 92.910 7.090 194.03 224 0.875 bilinear -8.450 -4.286 +21 +26
20 tf_efficientnet_b4_ns 75.670 24.330 93.050 6.950 19.34 380 0.922 bicubic -9.492 -4.420 +2
21 vit_base_r50_s16_384 75.590 24.410 92.790 7.210 98.95 384 1.000 bicubic -9.382 -4.498 +7 +9
22 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
23 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 +14 +3
24 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 -8 +17
25 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 +6 -10
26 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 -3 +7
27 ecaresnet269d vit_large_patch16_384 75.120 75.150 24.880 24.850 92.840 92.660 7.160 7.340 102.09 304.72 352 384 1.000 bicubic -9.856 -10.008 -4.386 -4.696 0 -4
28 tf_efficientnet_b8 ecaresnet269d 74.940 75.120 25.060 24.880 92.310 92.840 7.690 7.160 87.41 102.09 672 352 0.954 1.000 bicubic -10.430 -9.856 -5.080 -4.386 -9 +1
29 dm_nfnet_f5 tf_efficientnet_b8 74.790 74.940 25.210 25.060 92.460 92.310 7.540 7.690 377.21 87.41 544 672 0.954 bicubic -10.924 -10.430 -4.982 -5.080 -15 -10
30 tf_efficientnet_b7 dm_nfnet_f5 74.720 74.790 25.280 25.210 92.220 92.460 7.780 7.540 66.35 377.21 600 544 0.949 0.954 bicubic -10.216 -10.924 -4.984 -4.982 -1 -17
31 tf_efficientnet_b5_ap tf_efficientnet_b7 74.600 74.720 25.400 25.280 91.990 92.220 8.010 7.780 30.39 66.35 456 600 0.934 0.949 bicubic -9.652 -10.216 -4.984 +7 0
32 swin_base_patch4_window7_224 tf_efficientnet_b5_ap 74.570 74.600 25.430 25.400 92.560 91.990 7.440 8.010 87.77 30.39 224 456 0.900 0.934 bicubic -10.682 -9.652 -5.002 -4.984 -11 +11
33 seresnet152d swin_base_patch4_window7_224 74.510 74.570 25.490 25.430 92.080 92.560 7.920 7.440 66.84 87.77 320 224 1.000 0.900 bicubic -9.852 -10.682 -4.960 -5.002 +3 -12
34 resnest200e seresnet152d 74.480 74.510 25.520 25.490 91.860 92.080 8.140 7.920 70.20 66.84 320 0.909 1.000 bicubic -9.352 -9.852 -5.034 -4.960 +12 +6
35 dm_nfnet_f2 resnest200e 74.450 74.480 25.550 25.520 92.230 91.860 7.770 8.140 193.78 70.20 352 320 0.920 0.909 bicubic -10.540 -9.352 -4.914 -5.034 -9 +18
36 dm_nfnet_f1 dm_nfnet_f2 74.400 74.450 25.600 25.550 92.350 92.230 7.650 7.770 132.63 193.78 320 352 0.910 0.920 bicubic -10.204 -10.540 -4.718 -4.914 -4 -8
37 resnest269e dm_nfnet_f1 74.170 74.400 25.830 25.600 91.950 92.350 8.050 7.650 110.93 132.63 416 320 0.928 0.910 bicubic -10.348 -10.204 -5.036 -4.718 -4 -2
38 pit_b_distilled_224 efficientnet_v2s 74.160 74.170 25.840 25.830 91.680 91.710 8.320 8.290 74.79 23.94 224 384 0.900 1.000 bicubic -9.984 -9.638 -5.176 -5.014 +3 +17
39 swsl_resnext101_32x4d resnest269e 74.140 74.170 25.860 25.830 91.990 91.950 8.010 8.050 44.18 110.93 224 416 0.875 0.928 bilinear bicubic -9.090 -10.348 -4.770 -5.036 +16 -3
40 vit_base_patch16_384 cait_xs24_384 74.130 74.160 25.870 25.840 92.360 91.910 7.640 8.090 86.86 26.67 384 1.000 bicubic -10.080 -9.902 -4.858 -4.978 -1 +9
41 eca_nfnet_l1 pit_b_distilled_224 74.060 74.160 25.940 25.840 92.120 91.680 7.880 8.320 41.41 74.79 320 224 1.000 0.900 bicubic -9.948 -9.984 -4.908 -5.176 +3 +5
42 swsl_resnext101_32x16d swsl_resnext101_32x4d 74.020 74.140 25.980 25.860 92.160 91.990 7.840 8.010 194.03 44.18 224 0.875 bilinear -9.326 -9.090 -4.686 -4.770 +10 +24
43 resnetv2_152x4_bitm vit_base_patch16_384 74.000 74.130 26.000 25.870 92.340 92.360 7.660 7.640 936.53 86.86 480 384 1.000 bilinear bicubic -10.932 -10.080 -5.096 -4.858 -13 +1
44 eca_nfnet_l1 74.060 25.940 92.120 7.880 41.41 320 1.000 bicubic -9.948 -4.908 +7
45 vit_base_patch16_224_miil 74.040 25.960 91.700 8.300 86.54 224 0.875 bilinear -10.228 -5.102 -3
46 swsl_resnext101_32x16d 74.020 25.980 92.160 7.840 194.03 224 0.875 bilinear -9.326 -4.686 +17
47 resnetv2_152x4_bitm 74.000 26.000 92.340 7.660 936.53 480 1.000 bilinear -10.932 -5.096 -15
48 resnetrs420 73.920 26.080 91.760 8.240 191.89 416 1.000 bicubic -11.088 -5.364 -21
49 tf_efficientnet_b6 73.900 26.100 91.750 8.250 43.04 528 0.942 bicubic -10.210 -5.136 -2
50 tf_efficientnet_b3_ns 73.890 26.110 91.870 8.130 12.23 300 0.904 bicubic -10.158 -5.040 -2 0
51 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 -1 -13
52 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 +19 0
53 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 -14 +25
54 tf_efficientnet_b5 resnetv2_152x2_bitm 73.550 73.630 26.450 26.370 91.460 92.590 8.540 7.410 30.39 236.34 456 480 0.934 1.000 bicubic bilinear -10.262 -10.810 -5.288 -4.856 -2 -17
55 resnetv2_101x3_bitm tf_efficientnet_b5 73.530 73.550 26.470 26.450 92.570 91.460 7.430 8.540 387.93 30.39 480 456 1.000 0.934 bilinear bicubic -10.864 -10.262 -4.792 -5.288 -15 -1
56 resnet152d resnetv2_101x3_bitm 73.520 73.530 26.480 26.470 91.230 92.570 8.770 7.430 60.21 387.93 320 480 1.000 bicubic bilinear -10.160 -10.864 -5.508 -4.792 -1 -17
57 regnety_160 resnet152d 73.360 73.520 26.640 26.480 91.690 91.230 8.310 8.770 83.59 60.21 288 320 1.000 bicubic -10.326 -10.160 -5.086 -5.508 -3 +2
58 vit_deit_base_distilled_patch16_224 resnetrs200 73.240 73.500 26.760 26.500 91.000 91.250 9.000 8.750 87.34 93.21 224 320 0.900 1.000 bicubic -10.148 -10.566 -5.488 -5.624 -2 -10
59 resnetrs350 73.400 26.600 91.310 8.690 163.96 384 1.000 bicubic -11.320 -5.678 -25
60 regnety_160 73.360 26.640 91.690 8.310 83.59 288 1.000 bicubic -10.326 -5.086 -2
61 efficientnet_b4 73.320 26.680 91.280 8.720 19.34 384 1.000 bicubic -10.108 -5.316 0
62 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
63 resnetrs152 73.200 26.800 91.260 8.740 86.62 320 1.000 bicubic -10.512 -5.354 -6
64 cait_s24_224 73.070 26.930 91.130 8.870 46.92 224 1.000 bicubic -10.382 -5.434 -4
65 tf_efficientnet_b4_ap 72.890 27.110 90.980 9.020 19.34 380 0.922 bicubic -10.358 -5.412 0
66 dm_nfnet_f0 72.790 27.210 91.040 8.960 71.49 256 0.900 bicubic -10.552 -5.520 -2
67 regnety_032 72.770 27.230 90.950 9.050 19.44 288 1.000 bicubic -9.954 -5.474 +9 +10
68 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 +5 +8
69 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 +7 +6
70 resnest101e 72.570 27.430 90.820 9.180 48.28 256 0.875 bilinear -10.320 -5.500 +3 +4
71 swsl_resnext50_32x4d 72.560 27.440 90.870 9.130 25.03 224 0.875 bilinear -9.622 -5.360 +15 +18
72 tresnet_xl_448 72.550 27.450 90.310 9.690 78.44 448 0.875 bilinear -10.500 -5.864 -2 -1
73 vit_deit_base_patch16_384 72.530 27.470 90.250 9.750 86.86 384 1.000 bicubic -10.576 -6.122 -5
74 resnet101d 72.410 27.590 90.650 9.350 44.57 320 1.000 bicubic -10.612 -5.796 -3 -2
75 tf_efficientnet_b4 72.290 27.710 90.590 9.410 19.34 380 0.922 bicubic -10.732 -5.710 -3 -2
76 tf_efficientnet_b2_ns 72.280 27.720 91.090 8.910 9.11 260 0.890 bicubic -10.100 -5.158 +5 +6
77 tresnet_m 72.270 27.730 90.240 9.760 31.39 224 0.875 bilinear -10.810 -5.878 -8
78 vit_large_patch16_224 72.250 27.750 90.990 9.010 304.33 224 0.900 bicubic -10.812 -5.448 -8
79 nasnetalarge 72.230 27.770 90.470 9.530 88.75 331 0.911 bicubic -10.390 -5.576 0
80 resnetv2_50x3_bitm cait_xxs36_384 72.180 72.190 27.820 27.810 91.790 90.840 8.210 9.160 217.32 17.37 480 384 1.000 bilinear bicubic -11.604 -10.004 -5.316 -5.308 -20 +8
81 eca_nfnet_l0 resnetv2_50x3_bitm 71.850 72.180 28.150 27.820 91.130 91.790 8.870 8.210 24.14 217.32 288 480 1.000 bicubic bilinear -10.738 -11.604 -5.344 -5.316 -1 -25
82 swin_small_patch4_window7_224 eca_nfnet_l0 71.740 71.850 28.260 28.150 90.240 91.130 9.760 8.870 49.61 24.14 224 288 0.900 1.000 bicubic -11.472 -10.738 -6.082 -5.344 -14 -2
83 pit_b_224 swin_small_patch4_window7_224 71.700 71.740 28.300 28.260 89.250 90.240 10.750 9.760 73.76 49.61 224 0.900 bicubic -10.746 -11.472 -6.460 -6.082 -2 -16
84 swsl_resnet50 pit_b_224 71.700 28.300 90.500 89.250 9.500 10.750 25.56 73.76 224 0.875 0.900 bilinear bicubic -9.466 -10.746 -5.472 -6.460 +31 -3
85 tresnet_xl swsl_resnet50 71.660 71.700 28.340 28.300 89.630 90.500 10.370 9.500 78.44 25.56 224 0.875 bilinear -10.394 -9.466 -6.306 -5.472 +6 +30
86 efficientnet_v2s tresnet_xl 71.610 71.660 28.390 28.340 90.200 89.630 9.800 10.370 23.94 78.44 224 1.000 0.875 bicubic bilinear -10.460 -10.394 -5.754 -6.306 +4 +5
87 tresnet_l_448 71.600 28.400 90.050 9.950 55.99 448 0.875 bilinear -10.668 -5.926 -3 -2
88 ssl_resnext101_32x8d 71.500 28.500 90.460 9.540 88.79 224 0.875 bilinear -10.116 -5.578 +13
89 ecaresnet101d 71.490 28.510 90.330 9.670 44.57 224 0.875 bicubic -10.682 -5.716 -1 +1
90 efficientnet_b3a efficientnet_b3 71.480 28.520 90.060 9.940 12.23 320 1.000 bicubic -10.762 -6.054 -5 -4
91 efficientnet_b3 ssl_resnext101_32x16d 71.460 71.410 28.540 28.590 90.090 90.560 9.910 9.440 12.23 194.03 300 224 0.904 0.875 bicubic bilinear -10.616 -10.434 -5.930 -5.536 -2 +3
92 ssl_resnext101_32x16d pit_s_distilled_224 71.410 71.380 28.590 28.620 90.560 89.780 9.440 10.220 194.03 24.04 224 0.875 0.900 bilinear bicubic -10.434 -10.616 -5.536 -6.018 +2 +1
93 pit_s_distilled_224 vit_base_patch16_224 71.380 71.330 28.620 28.670 89.780 90.460 10.220 9.540 24.04 86.57 224 0.900 bicubic -10.616 -10.456 -6.018 -5.662 0 +3
94 vit_base_patch16_224 ecaresnet50t 71.330 71.280 28.670 28.720 90.460 90.420 9.540 9.580 86.57 25.57 224 320 0.900 0.950 bicubic -10.456 -11.066 -5.662 -5.718 +2 -11
95 ecaresnet50t vit_base_patch32_384 71.280 71.180 28.720 28.820 90.420 90.630 9.580 9.370 25.57 88.30 320 384 0.950 1.000 bicubic -11.066 -10.472 -5.718 -5.498 -12 +3
96 vit_base_patch32_384 vit_deit_base_patch16_224 71.180 71.170 28.820 28.830 90.630 89.200 9.370 10.800 88.30 86.57 384 224 1.000 0.900 bicubic -10.472 -10.828 -5.498 -6.534 +2 -4
97 vit_deit_base_patch16_224 tresnet_m_448 71.170 70.990 28.830 29.010 89.200 88.680 10.800 11.320 86.57 31.39 224 448 0.900 0.875 bicubic bilinear -10.828 -10.724 -6.534 -6.892 -5 0
98 tresnet_m_448 resnest50d_4s2x40d 70.990 70.950 29.010 29.050 88.680 89.710 11.320 10.290 31.39 30.42 448 224 0.875 bilinear bicubic -10.724 -10.158 -6.892 -5.848 -1 +18
99 resnest50d_4s2x40d wide_resnet50_2 70.950 29.050 89.710 89.230 10.290 10.770 30.42 68.88 224 0.875 bicubic -10.158 -10.506 -5.848 -6.302 +17 +7
100 wide_resnet50_2 tnt_s_patch16_224 70.950 70.930 29.050 29.070 89.230 89.600 10.770 10.400 68.88 23.76 224 0.875 0.900 bicubic -10.506 -10.588 -6.302 -6.148 +6 +3
101 tnt_s_patch16_224 tf_efficientnet_b3_ap 70.930 70.920 29.070 29.080 89.600 89.430 10.400 10.570 23.76 12.23 224 300 0.900 0.904 bicubic -10.588 -10.902 -6.148 -6.194 +2 -6
102 tf_efficientnet_b3_ap tf_efficientnet_b1_ns 70.920 70.870 29.080 29.130 89.430 90.120 10.570 9.880 12.23 7.79 300 240 0.904 0.882 bicubic -10.902 -10.518 -6.194 -5.618 -7 +5
103 tf_efficientnet_b1_ns vit_large_patch32_384 70.870 70.860 29.130 29.140 90.120 90.570 9.880 9.430 7.79 306.63 240 384 0.882 1.000 bicubic -10.518 -10.646 -5.618 -5.522 +4 +1
vit_large_patch32_384 70.860 29.140 90.570 9.430 306.63 384 1.000 bicubic -10.646 -5.522 0
rexnet_200 70.840 29.160 89.700 10.300 16.37 224 0.875 bicubic -10.792 -5.968 -5
104 tresnet_l 70.840 29.160 89.630 10.370 55.99 224 0.875 bilinear -10.650 -5.994 -1
105 resnetv2_101x1_bitm rexnet_200 70.710 70.840 29.290 29.160 90.800 89.700 9.200 10.300 44.54 16.37 480 224 1.000 0.875 bilinear bicubic -11.502 -10.792 -5.672 -5.968 -21 -5
106 resnetrs101 70.840 29.160 89.830 10.170 63.62 288 0.940 bicubic -11.448 -6.178 -20
107 resnetv2_101x1_bitm 70.710 29.290 90.800 9.200 44.54 480 1.000 bilinear -11.502 -5.672 -20
108 tf_efficientnet_b3 70.640 29.360 89.440 10.560 12.23 300 0.904 bicubic -10.996 -6.278 -9
109 gluon_senet154 cait_xxs24_384 70.600 29.400 88.920 89.720 11.080 10.280 115.09 12.03 224 384 0.875 1.000 bicubic -10.634 -10.366 -6.428 -5.926 +4 +12
110 gluon_senet154 70.600 29.400 88.920 11.080 115.09 224 0.875 bicubic -10.634 -6.428 +3
111 ssl_resnext101_32x4d 70.530 29.470 89.760 10.240 44.18 224 0.875 bilinear -10.394 -5.968 +11
112 vit_deit_small_distilled_patch16_224 70.520 29.480 89.470 10.530 22.44 224 0.900 bicubic -10.680 -5.908 +3 +2
113 legacy_senet154 70.500 29.500 89.010 10.990 115.09 224 0.875 bilinear -10.810 -6.486 -1 -2
tf_efficientnet_lite4 70.430 29.570 89.110 10.890 13.01 380 0.920 bilinear -11.106 -6.558 -12
114 gluon_seresnext101_64x4d 70.430 29.570 89.350 10.650 88.23 224 0.875 bicubic -10.464 -5.958 +10
115 resnest50d tf_efficientnet_lite4 70.410 70.430 29.590 29.570 88.760 89.110 11.240 10.890 27.48 13.01 224 380 0.875 0.920 bilinear -10.564 -11.106 -6.618 -6.558 +5 -13
116 resnest50d_1s4x24d resnest50d 70.400 70.410 29.600 29.590 89.220 88.760 10.780 11.240 25.68 27.48 224 0.875 bicubic bilinear -10.588 -10.564 -6.102 -6.618 +3 +4
117 seresnext50_32x4d resnest50d_1s4x24d 70.400 29.600 89.110 89.220 10.890 10.780 27.56 25.68 224 0.875 bicubic -10.866 -10.588 -6.510 -6.102 -5 +2
118 gernet_l seresnext50_32x4d 70.350 70.400 29.650 29.600 88.980 89.110 11.020 10.890 31.08 27.56 256 224 0.875 bilinear bicubic -11.004 -10.866 -6.556 -6.510 -9 -6
119 gluon_resnet152_v1s gernet_l 70.290 70.350 29.710 29.650 88.850 88.980 11.150 11.020 60.32 31.08 224 256 0.875 bicubic bilinear -10.726 -11.004 -6.562 -6.556 -1 -10
120 repvgg_b3 gluon_resnet152_v1s 70.250 70.290 29.750 29.710 88.730 88.850 11.270 11.150 123.09 60.32 224 0.875 bilinear bicubic -10.242 -10.726 -6.530 -6.562 +14 -2
121 ecaresnet101d_pruned repvgg_b3 70.130 70.250 29.870 29.750 89.590 88.730 10.410 11.270 24.88 123.09 224 0.875 bicubic bilinear -10.686 -10.242 -6.038 -6.530 +4 +13
122 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 -12 +4
123 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 +15 -13
124 inception_resnet_v2 70.120 29.880 88.700 11.300 55.84 299 0.897 bicubic -10.338 -6.606 +14
125 gluon_seresnext101_32x4d 70.010 29.990 88.900 11.100 48.96 224 0.875 bicubic -10.894 -6.394 -2
126 regnety_320 70.000 30.000 88.890 11.110 145.05 224 0.875 bicubic -10.812 -6.354 +1
127 gluon_resnet152_v1d 69.960 30.040 88.490 11.510 60.21 224 0.875 bicubic -10.514 -6.716 +10 +9
128 pit_s_224 69.890 30.110 88.930 11.070 23.46 224 0.900 bicubic -11.204 -6.402 -10 -11
129 ecaresnet50d 69.840 30.160 89.400 10.600 25.58 224 0.875 bicubic -10.752 -5.920 +4 +3
130 ssl_resnext50_32x4d 69.710 30.290 89.440 10.560 25.03 224 0.875 bilinear -10.608 -5.966 +14 +12
131 gluon_resnext101_64x4d 69.680 30.320 88.270 11.730 83.46 224 0.875 bicubic -10.924 -6.718 +1 0
132 tresnet_m efficientnet_b3_pruned 69.660 69.580 30.340 30.420 87.990 88.980 12.010 11.020 31.39 9.86 224 300 0.875 0.904 bilinear bicubic -11.142 -11.278 -6.870 -6.262 -4 -7
efficientnet_b3_pruned 69.580 30.420 88.980 11.020 9.86 300 0.904 bicubic -11.278 -6.262 -8
133 nf_resnet50 69.580 30.420 88.730 11.270 25.56 288 0.940 bicubic -11.114 -6.626 -4
134 gernet_m 69.530 30.470 88.690 11.310 21.14 224 0.875 bilinear -11.202 -6.494 -6
135 repvgg_b3g4 efficientnet_el_pruned 69.520 30.480 88.450 88.930 11.550 11.070 83.83 10.59 224 300 0.875 0.904 bilinear bicubic -10.692 -10.780 -6.660 -6.288 +15 +10
136 efficientnet_el_pruned ens_adv_inception_resnet_v2 69.520 30.480 88.930 88.510 11.070 11.490 10.59 55.84 300 299 0.904 0.897 bicubic -10.780 -10.462 -6.288 -6.426 +11 +24
137 ens_adv_inception_resnet_v2 repvgg_b3g4 69.520 30.480 88.510 88.450 11.490 11.550 55.84 83.83 299 224 0.897 0.875 bicubic bilinear -10.462 -10.692 -6.426 -6.660 +25 +14
138 efficientnet_b2a efficientnet_b2 69.500 30.500 88.680 11.320 9.11 288 1.000 bicubic -11.112 -6.638 -8
139 rexnet_150 69.470 30.530 88.980 11.020 9.73 224 0.875 bicubic -10.840 -6.186 +5 +4
140 swin_tiny_patch4_window7_224 69.450 30.550 89.020 10.980 28.29 224 0.900 bicubic -11.928 -6.520 -32
141 regnetx_320 69.440 30.560 88.270 11.730 107.81 224 0.875 bicubic -10.806 -6.756 +9 +8
142 inception_v4 69.360 30.640 88.780 11.220 42.68 299 0.875 bicubic -10.808 -6.188 +12 +11
143 legacy_seresnext101_32x4d 69.360 30.640 88.070 11.930 48.96 224 0.875 bilinear -10.868 -6.948 +8 +7
144 ecaresnetlight 69.340 30.660 89.220 10.780 30.16 224 0.875 bicubic -11.122 -6.030 -7
145 resnet50d 69.330 30.670 88.220 11.780 25.58 224 0.875 bicubic -11.200 -6.940 -12
146 xception71 69.320 30.680 88.260 11.740 42.34 299 0.903 bicubic -10.554 -6.662 +20
147 gluon_xception65 69.160 30.840 88.090 11.910 39.92 299 0.903 bicubic -10.556 -6.770 +28 +29
148 gluon_resnet152_v1c 69.140 30.860 87.870 12.130 60.21 224 0.875 bicubic -10.770 -6.970 +16 +15
149 mixnet_xl 69.100 30.900 88.310 11.690 11.90 224 0.875 bicubic -11.376 -6.626 -14
150 gluon_resnet101_v1d 69.010 30.990 88.100 11.900 44.57 224 0.875 bicubic -11.404 -6.914 -11
151 repvgg_b2g4 69.000 31.000 88.360 11.640 61.76 224 0.875 bilinear -10.366 -6.328 +35 +36
152 seresnet50 68.980 31.020 88.710 11.290 28.09 224 0.875 bicubic -11.294 -6.360 -4 -5
153 xception65 68.980 31.020 88.480 11.520 39.92 299 0.903 bicubic -10.572 -6.174 +28 +29
efficientnet_b2 68.970 31.030 88.630 11.370 9.11 260 0.875 bicubic -11.422 -6.446 -14
154 gluon_resnext101_32x4d 68.960 31.040 88.360 11.640 44.18 224 0.875 bicubic -11.374 -6.566 -13
155 tf_efficientnet_b2_ap 68.920 31.080 88.350 11.650 9.11 260 0.890 bicubic -11.380 -6.678 -9
156 cspdarknet53 68.890 31.110 88.600 11.400 27.64 256 0.887 bilinear -11.168 -6.484 +1
157 regnety_120 68.850 31.150 88.330 11.670 51.82 224 0.875 bicubic -11.516 -6.796 -17
158 gluon_resnet152_v1b 68.820 31.180 87.710 12.290 60.19 224 0.875 bicubic -10.866 -7.026 +17 +19
159 dpn131 68.770 31.230 87.470 12.530 79.25 224 0.875 bicubic -11.052 -7.240 +10 +11
160 cspresnext50 68.760 31.240 87.950 12.050 20.57 224 0.875 bilinear -11.280 -6.994 -2
161 tf_efficientnet_b2 68.750 31.250 87.990 12.010 9.11 260 0.890 bicubic -11.336 -6.918 -5
162 resnext50d_32x4d 68.740 31.260 88.300 11.700 25.05 224 0.875 bicubic -10.936 -6.566 +14 +16
163 vit_deit_small_patch16_224 68.720 31.280 88.200 11.800 22.05 224 0.900 bicubic -11.136 -6.852 +4 +5
164 gluon_resnet101_v1s 68.710 31.290 87.910 12.090 44.67 224 0.875 bicubic -11.592 -7.250 -20
165 regnety_080 68.700 31.300 87.970 12.030 39.18 224 0.875 bicubic -11.176 -6.860 -1 0
166 dpn107 68.690 31.310 88.130 11.870 86.92 224 0.875 bicubic -11.466 -6.512 -6.780 -11 -12
167 gluon_seresnext50_32x4d 68.670 31.330 88.310 11.690 27.56 224 0.875 bicubic -11.248 -6.512 -6
168 hrnet_w64 68.640 31.360 88.050 11.950 128.06 224 0.875 bilinear -10.834 -6.602 +15 +17
169 resnext50_32x4d 68.640 31.360 87.570 12.430 25.03 224 0.875 bicubic -11.128 -7.028 +2 +3
170 dpn98 68.590 31.410 87.680 12.320 61.57 224 0.875 bicubic -11.052 -6.918 +7 +9
171 regnetx_160 68.530 31.470 88.450 11.550 54.28 224 0.875 bicubic -11.326 -6.380 -5 -4
172 cspresnet50 68.460 31.540 88.010 11.990 21.62 256 0.887 bilinear -11.114 -6.702 +7 +9
173 rexnet_130 68.450 31.550 88.040 11.960 7.56 224 0.875 bicubic -11.050 -6.642 +9 +11
174 regnety_064 ecaresnet50d_pruned 68.420 31.580 88.080 88.370 11.920 11.630 30.58 19.94 224 0.875 bicubic -11.302 -11.296 -6.688 -6.510 -3 +1
175 regnety_064 68.420 31.580 88.080 11.920 30.58 224 0.875 bicubic -11.302 -6.688 -1
176 tf_efficientnet_el 68.420 31.580 88.210 11.790 10.59 300 0.904 bicubic -11.830 -6.918 -28
177 ecaresnet50d_pruned cait_xxs36_224 68.420 68.410 31.580 31.590 88.370 88.630 11.630 11.370 19.94 17.30 224 0.875 1.000 bicubic -11.296 -11.340 -6.510 -6.236 -1 -4
178 ssl_resnet50 68.410 31.590 88.560 11.440 25.56 224 0.875 bilinear -10.812 -6.272 +20 +21
179 skresnext50_32x4d 68.350 31.650 87.570 12.430 27.48 224 0.875 bicubic -11.806 -7.340 -7.072 -24
180 dla102x2 68.330 31.670 87.890 12.110 41.28 224 0.875 bilinear -11.118 -6.750 +5 +6
181 efficientnet_b2_pruned 68.320 31.680 88.100 11.900 8.31 260 0.890 bicubic -11.596 -6.756 -18 -19
182 gluon_resnext50_32x4d 68.310 31.690 87.300 12.700 25.03 224 0.875 bicubic -11.044 -7.126 +5 +6
ecaresnet26t 68.230 31.770 88.790 11.210 16.01 320 0.950 bicubic -11.624 -6.294 -14
183 tf_efficientnet_lite3 68.230 31.770 87.740 12.260 8.20 300 0.904 bilinear -11.590 -7.174 -13
184 ese_vovnet39b ecaresnet26t 68.210 68.230 31.790 31.770 88.250 88.790 11.750 11.210 24.57 16.01 224 320 0.875 0.950 bicubic -11.110 -11.624 -6.462 -6.294 +3 -14
185 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 -7 +4
186 regnetx_120 68.150 31.850 87.660 12.340 46.11 224 0.875 bicubic -11.446 -7.078 -6
187 resnetrs50 68.030 31.970 87.710 12.290 35.69 224 0.910 bicubic -11.862 -7.258 -23
188 pit_xs_distilled_224 68.020 31.980 87.720 12.280 11.00 224 0.900 bicubic -11.286 -6.644 +6
189 dpn92 67.990 32.010 87.580 12.420 37.67 224 0.875 bicubic -12.018 -7.258 -7.256 -28 -30
190 nf_regnet_b1 67.980 32.020 88.180 11.820 10.22 288 0.900 bicubic -11.326 -6.568 +3
191 gluon_resnet50_v1d 67.940 32.060 87.130 12.870 25.58 224 0.875 bicubic -11.134 -7.340 +15 +16
192 regnetx_080 67.880 32.120 86.990 13.010 39.57 224 0.875 bicubic -11.314 -7.570 +12
193 resnext101_32x8d 67.860 32.140 87.490 12.510 88.79 224 0.875 bilinear -11.448 -7.028 -3
194 efficientnet_em 67.840 32.160 88.120 11.880 6.90 240 0.882 bicubic -11.412 -6.674 +4
195 legacy_seresnext50_32x4d 67.840 32.160 87.620 12.380 27.56 224 0.875 bilinear -11.238 -6.816 +10 +11
196 hrnet_w48 67.770 32.230 87.420 12.580 77.47 224 0.875 bilinear -11.530 -7.092 -1
197 hrnet_w44 67.740 32.260 87.560 12.440 67.06 224 0.875 bilinear -11.156 -6.808 +15 +16
198 coat_lite_mini 67.720 32.280 87.700 12.300 11.01 224 0.900 bicubic -11.368 -6.904 +7
199 tf_efficientnet_b0_ns 67.710 32.290 88.070 11.930 5.29 224 0.875 bicubic -10.948 -6.306 +24
200 regnetx_064 67.680 32.320 87.520 12.480 26.21 224 0.875 bicubic -11.392 -6.938 +8
201 xception 67.650 32.350 87.570 12.430 22.86 299 0.897 bicubic -11.402 -6.822 +8
202 dpn68b 67.630 32.370 87.660 12.340 12.61 224 0.875 bicubic -11.586 -6.754 0 -1
203 dla169 67.610 32.390 87.590 12.410 53.39 224 0.875 bilinear -11.078 -6.746 +18
204 gluon_inception_v3 67.590 32.410 87.470 12.530 23.83 299 0.875 bicubic -11.216 -6.900 +12
205 gluon_resnet101_v1c 67.580 32.420 87.180 12.820 44.57 224 0.875 bicubic -11.954 -7.398 -21 -22
206 regnety_040 67.580 32.420 87.510 12.490 20.65 224 0.875 bicubic -11.640 -7.146 -5 -6
207 res2net50_26w_8s 67.570 32.430 87.280 12.720 48.40 224 0.875 bilinear -11.628 -7.088 -4 -5
208 hrnet_w40 67.560 32.440 87.140 12.860 57.56 224 0.875 bilinear -11.360 -7.330 +4
resnetv2_50x1_bitm 67.520 32.480 89.250 10.750 25.55 480 1.000 bilinear -12.652 -6.376 -55
tf_efficientnet_b1_ap 67.520 32.480 87.760 12.240 7.79 240 0.882 bicubic -11.760 -6.546 -13
209 legacy_seresnet152 67.520 32.480 87.390 12.610 66.82 224 0.875 bilinear -11.140 -6.980 +13
210 gluon_resnet101_v1b resnetv2_50x1_bitm 67.460 67.520 32.540 32.480 87.240 89.250 12.760 10.750 44.55 25.55 224 480 0.875 1.000 bicubic bilinear -11.846 -12.652 -7.284 -6.376 -19 -58
211 tf_efficientnet_cc_b1_8e tf_efficientnet_b1_ap 67.450 67.520 32.550 32.480 87.310 87.760 12.690 12.240 39.72 7.79 240 0.882 bicubic -11.858 -11.760 -7.060 -6.546 -21 -14
212 res2net101_26w_4s efficientnet_b1 67.440 67.470 32.560 32.530 87.010 87.510 12.990 12.490 45.21 7.79 224 256 0.875 1.000 bilinear bicubic -11.758 -11.324 -7.422 -6.832 -10 +5
213 resnet50 gluon_resnet101_v1b 67.440 67.460 32.560 32.540 87.420 87.240 12.580 12.760 25.56 44.55 224 0.875 bicubic -11.598 -11.846 -6.970 -7.284 -5 -21
214 resnetblur50 tf_efficientnet_cc_b1_8e 67.430 67.450 32.570 32.550 87.440 87.310 12.560 12.690 25.56 39.72 224 240 0.875 0.882 bicubic -11.856 -11.858 -7.198 -7.060 -19 -23
215 regnetx_032 res2net101_26w_4s 67.290 67.440 32.710 32.560 87.000 87.010 13.000 12.990 15.30 45.21 224 0.875 bicubic bilinear -10.882 -11.758 -7.088 -7.422 +24 -12
216 xception41 resnet50 67.250 67.440 32.750 32.560 87.200 87.420 12.800 12.580 26.97 25.56 299 224 0.903 0.875 bicubic -11.266 -11.598 -7.078 -6.970 +7 -6
217 resnest26d resnetblur50 67.200 67.430 32.800 32.570 87.170 87.440 12.830 12.560 17.07 25.56 224 0.875 bilinear bicubic -11.278 -11.856 -7.128 -7.198 +9 -21
218 efficientnet_b1 cait_xxs24_224 67.170 67.330 32.830 32.670 87.150 87.510 12.850 12.490 7.79 11.96 240 224 0.875 1.000 bicubic -11.528 -11.056 -6.994 -6.800 0 +15
219 repvgg_b2 regnetx_032 67.160 67.290 32.840 32.710 87.330 87.000 12.670 13.000 89.02 15.30 224 0.875 bilinear bicubic -11.632 -10.882 -7.084 -7.088 -5 +23
220 xception41 67.250 32.750 87.200 12.800 26.97 299 0.903 bicubic -11.266 -7.078 +5
221 resnest26d 67.200 32.800 87.170 12.830 17.07 224 0.875 bilinear -11.278 -7.128 +7
222 legacy_seresnet101 67.160 32.840 87.060 12.940 49.33 224 0.875 bilinear -11.222 -7.204 +12
223 repvgg_b2 67.160 32.840 87.330 12.670 89.02 224 0.875 bilinear -11.632 -7.084 -5
224 dla60x 67.100 32.900 87.190 12.810 17.35 224 0.875 bilinear -11.146 -6.828 +13
225 gluon_resnet50_v1s 67.060 32.940 86.860 13.140 25.68 224 0.875 bicubic -11.650 -11.652 -7.378 -5
226 tv_resnet152 67.050 32.950 87.550 12.450 60.19 224 0.875 bilinear -11.262 -6.488 +10
227 dla60_res2net 67.020 32.980 87.160 12.840 20.85 224 0.875 bilinear -11.444 -7.046 +3 +2
228 dla102x 67.010 32.990 86.770 13.230 26.31 224 0.875 bilinear -11.500 -7.458 -1 -2
229 mixnet_l 66.940 33.060 86.910 13.090 7.33 224 0.875 bicubic -12.036 -7.272 -17 -18
230 pit_xs_224 66.920 33.080 87.280 12.720 10.62 224 0.900 bicubic -11.262 -6.888 +11
231 res2net50_26w_6s 66.910 33.090 86.860 13.140 37.05 224 0.875 bilinear -11.660 -7.264 -6 -7
232 repvgg_b1 66.900 33.100 86.780 13.220 57.42 224 0.875 bilinear -11.466 -7.318 +3
tf_efficientnet_b1 66.880 33.120 87.010 12.990 7.79 240 0.882 bicubic -11.946 -7.188 -18
233 efficientnet_es 66.880 33.120 86.730 13.270 5.44 224 0.875 bicubic -11.186 -7.196 +13
234 regnetx_040 tf_efficientnet_b1 66.840 66.880 33.160 33.120 86.730 87.010 13.270 12.990 22.12 7.79 224 240 0.875 0.882 bicubic -11.642 -11.946 -7.514 -7.188 -7 -19
235 regnetx_040 66.840 33.160 86.730 13.270 22.12 224 0.875 bicubic -11.642 -7.514 -8
236 hrnet_w30 66.780 33.220 86.800 13.200 37.71 224 0.875 bilinear -11.426 -7.422 +4
237 tf_mixnet_l 66.780 33.220 86.470 13.530 7.33 224 0.875 bicubic -11.994 -7.528 -18
238 selecsls60b 66.760 33.240 86.530 13.470 32.77 224 0.875 bicubic -11.652 -7.644 -5 -6
239 hrnet_w32 66.750 33.250 87.300 12.700 41.23 224 0.875 bilinear -11.700 -6.886 -8 -9
240 wide_resnet101_2 66.730 33.270 87.030 12.970 126.89 224 0.875 bilinear -12.126 -7.252 -25 -26
241 adv_inception_v3 66.650 33.350 86.540 13.460 23.83 299 0.875 bicubic -10.932 -7.196 +22 +23
242 dla60_res2next 66.640 33.360 87.030 12.970 17.03 224 0.875 bilinear -11.800 -7.122 -10 -11
243 gluon_resnet50_v1c 66.560 33.440 86.180 13.820 25.58 224 0.875 bicubic -11.452 -7.808 +5
244 dla102 66.540 33.460 86.910 13.090 33.27 224 0.875 bilinear -11.492 -7.036 +3
245 tf_inception_v3 66.410 33.590 86.660 13.340 23.83 299 0.875 bicubic -11.448 -11.446 -6.756 -6.980 +11 +12
246 hardcorenas_f 66.370 33.630 86.200 13.800 8.20 224 0.875 bilinear -11.734 -7.602 -1
247 coat_lite_tiny 66.290 33.710 86.980 13.020 5.72 224 0.900 bicubic -11.222 -6.936 +20
248 efficientnet_b0 66.290 33.710 85.960 14.040 5.29 224 0.875 bicubic -11.408 -7.572 +11
249 legacy_seresnet50 66.250 33.750 86.330 13.670 28.09 224 0.875 bilinear -11.380 -7.418 +11
250 selecsls60 66.210 33.790 86.340 13.660 30.67 224 0.875 bicubic -11.772 -7.488 +1 0
251 tf_efficientnet_em 66.180 33.820 86.360 13.640 6.90 240 0.882 bicubic -11.950 -7.684 -6 -7
252 tv_resnext50_32x4d 66.180 33.820 86.040 13.960 25.03 224 0.875 bilinear -11.440 -7.656 +9
253 tf_efficientnet_cc_b0_8e 66.170 33.830 86.240 13.760 24.01 224 0.875 bicubic -11.738 -7.414 0
254 inception_v3 66.160 33.840 86.320 13.680 23.83 299 0.875 bicubic -11.278 -7.156 +14 +15
255 res2net50_26w_4s 66.140 33.860 86.600 13.400 25.70 224 0.875 bilinear -11.824 -7.254 -3 -4
256 efficientnet_b1_pruned 66.090 33.910 86.570 13.430 6.33 240 0.882 bicubic -12.146 -7.264 -16 -17
257 gluon_resnet50_v1b 66.070 33.930 86.260 13.740 25.56 224 0.875 bicubic -11.510 -7.456 +8
258 rexnet_100 66.070 33.930 86.490 13.510 4.80 224 0.875 bicubic -11.788 -7.148 -7.380 -2 -3
259 regnety_016 66.060 33.940 86.380 13.620 11.20 224 0.875 bicubic -11.802 -7.340 -5
260 res2net50_14w_8s 66.020 33.980 86.250 13.750 25.06 224 0.875 bilinear -12.130 -7.598 -16 -17
261 seresnext26t_32x4d 65.880 34.120 85.680 14.320 16.81 224 0.875 bicubic -12.106 -8.066 -11 -12
262 repvgg_b1g4 65.850 34.150 86.120 13.880 39.97 224 0.875 bilinear -11.744 -7.706 +1
263 res2next50 65.850 34.150 85.840 14.160 24.67 224 0.875 bilinear -12.396 -8.052 -24 -25
densenet161 65.840 34.160 86.450 13.550 28.68 224 0.875 bicubic -11.518 -7.188 +7
264 hardcorenas_e 65.840 34.160 85.980 14.020 8.07 224 0.875 bilinear -11.954 -7.714 -7
265 resnet34d densenet161 65.780 65.840 34.220 34.160 86.710 86.450 13.290 13.550 21.82 28.68 224 0.875 bicubic -11.336 -11.518 -6.672 -7.188 +11 +8
266 resnet34d 65.780 34.220 86.720 13.280 21.82 224 0.875 bicubic -11.336 -6.662 +12
267 mobilenetv3_large_100_miil 65.760 34.240 85.200 14.800 5.48 224 0.875 bilinear -12.156 -7.710 -15
268 skresnet34 65.750 34.250 85.960 14.040 22.28 224 0.875 bicubic -11.162 -7.362 +18
269 vit_small_patch16_224 65.740 34.260 86.120 13.880 48.75 224 0.900 bicubic -12.118 -7.750 -7.296 -13
270 tv_resnet101 65.690 34.310 85.980 14.020 44.55 224 0.875 bilinear -11.684 -7.560 +1
271 hardcorenas_d 65.630 34.370 85.460 14.540 7.50 224 0.875 bilinear -11.802 -8.024 -1
272 selecsls42b 65.610 34.390 85.810 14.190 32.46 224 0.875 bicubic -11.564 -7.580 +5
273 tf_efficientnet_b0_ap 65.490 34.510 85.580 14.420 5.29 224 0.875 bicubic -11.596 -7.676 +7
274 seresnext26d_32x4d 65.410 34.590 85.970 14.030 16.81 224 0.875 bicubic -12.192 -7.638 -11 -12
275 tf_efficientnet_lite2 65.380 34.620 85.990 14.010 6.09 260 0.890 bicubic -12.088 -7.764 -7
276 res2net50_48w_2s 65.350 34.650 85.960 14.040 25.29 224 0.875 bilinear -12.172 -7.594 -9 -10
277 densenet201 65.290 34.710 85.690 14.310 20.01 224 0.875 bicubic -11.996 -7.788 -3
278 densenetblur121d 65.280 34.720 85.710 14.290 8.00 224 0.875 bicubic -11.308 -7.482 +15 +16
279 dla60 65.200 34.800 85.760 14.240 22.04 224 0.875 bilinear -11.832 -7.558 +3
280 ese_vovnet19b_dw 65.190 34.810 85.470 14.530 6.54 224 0.875 bicubic -11.608 -7.798 +8
281 tf_efficientnet_cc_b0_4e 65.150 34.850 85.160 14.840 13.31 224 0.875 bicubic -12.156 -8.174 -8
284 mobilenetv2_120d 65.030 34.970 85.960 14.040 5.83 224 0.875 bicubic -12.254 -7.532 -9
285 hrnet_w18 64.920 35.080 85.740 14.260 21.30 224 0.875 bilinear -11.838 -7.704 +4
286 hardcorenas_c 64.860 35.140 85.250 14.750 5.52 224 0.875 bilinear -12.194 -7.908 -5
287 densenet169 64.760 35.240 85.240 14.760 14.15 224 0.875 bicubic -11.146 -7.786 +15 +16
288 mixnet_m 64.700 35.300 85.450 14.550 5.01 224 0.875 bicubic -12.560 -7.974 -12
289 resnet26d 64.680 35.320 85.120 14.880 16.01 224 0.875 bicubic -12.016 -8.030 +1
290 repvgg_a2 64.450 35.550 85.130 14.870 28.21 224 0.875 bilinear -12.010 -7.874 +6 +7
291 hardcorenas_b 64.420 35.580 84.870 15.130 5.18 224 0.875 bilinear -12.118 -7.884 +3 +4
292 regnetx_016 64.380 35.620 85.470 14.530 9.19 224 0.875 bicubic -12.570 -7.950 -9
293 tf_efficientnet_lite1 64.380 35.620 85.470 14.530 5.42 240 0.882 bicubic -12.262 -7.756 -2
294 tf_efficientnet_b0 64.310 35.690 85.280 14.720 5.29 224 0.875 bicubic -12.538 -7.948 -7
295 tf_mixnet_m 64.270 35.730 85.090 14.910 5.01 224 0.875 bicubic -12.672 -8.062 -11
296 dpn68 64.230 35.770 85.180 14.820 12.61 224 0.875 bicubic -12.088 -7.798 +1 +2
297 tf_efficientnet_es 64.230 35.770 84.740 15.260 5.44 224 0.875 bicubic -12.364 -8.462 -5 -4
298 regnety_008 64.160 35.840 85.270 14.730 6.26 224 0.875 bicubic -12.156 -7.796 0 +1
299 mobilenetv2_140 64.060 35.940 85.040 14.960 6.11 224 0.875 bicubic -12.456 -7.956 -4 -3
300 densenet121 63.750 36.250 84.590 15.410 7.98 224 0.875 bicubic -11.828 -8.062 +6 +7
301 hardcorenas_a 63.710 36.290 84.400 15.600 5.26 224 0.875 bilinear -12.206 -8.114 0 +1
302 resnest14d 63.590 36.410 84.250 15.750 10.61 224 0.875 bilinear -11.914 -11.916 -8.268 +6 +7
303 tf_mixnet_s 63.560 36.440 84.270 15.730 4.13 224 0.875 bicubic -12.090 -8.358 +1 +2
304 resnet26 63.470 36.530 84.260 15.740 16.00 224 0.875 bicubic -11.822 -8.310 +7 +8
305 mixnet_s 63.390 36.610 84.740 15.260 4.13 224 0.875 bicubic -12.602 -8.056 -5 -4
306 mobilenetv3_large_100 63.360 36.640 84.090 15.910 5.48 224 0.875 bicubic -12.406 -8.452 -3 -2
307 efficientnet_es_pruned 63.330 36.670 84.950 15.050 5.44 224 0.875 bicubic -11.670 -7.498 +12 +13
308 tv_resnet50 63.330 36.670 84.640 15.360 25.56 224 0.875 bilinear -12.808 -8.224 -9 -8
309 mixer_b16_224 63.280 36.720 83.310 16.690 59.88 224 0.875 bicubic -13.322 -8.918 -17
310 efficientnet_lite0 63.240 36.760 84.440 15.560 4.65 224 0.875 bicubic -12.244 -8.070 0
311 mobilenetv3_rw 63.220 36.780 84.510 15.490 5.48 224 0.875 bicubic -12.414 -8.198 -5
312 pit_ti_distilled_224 63.150 36.850 83.960 16.040 5.10 224 0.900 bicubic -11.380 -8.136 +15
318 mobilenetv2_110d 62.830 37.170 84.500 15.500 4.52 224 0.875 bicubic -12.206 -7.686 +1
319 vit_deit_tiny_distilled_patch16_224 62.810 37.190 83.930 16.070 5.91 224 0.900 bicubic -11.700 -7.960 +9
320 hrnet_w18_small_v2 62.800 37.200 83.980 16.020 15.60 224 0.875 bilinear -12.314 -8.436 -4
321 swsl_resnet18 62.760 37.240 84.300 15.700 11.69 224 0.875 bilinear -10.516 -7.434 +15 +16
322 repvgg_b0 62.720 37.280 83.860 16.140 15.82 224 0.875 bilinear -12.432 -8.558 -8
323 gluon_resnet34_v1b 62.570 37.430 83.990 16.010 21.80 224 0.875 bicubic -12.018 -8.000 +3
324 tf_efficientnet_lite0 62.550 37.450 84.220 15.780 4.65 224 0.875 bicubic -12.280 -7.956 -3
329 mnasnet_100 61.900 38.100 83.710 16.290 4.38 224 0.875 bicubic -12.758 -8.404 -5
330 regnety_004 61.870 38.130 83.430 16.570 4.34 224 0.875 bicubic -12.164 -8.322 +1
331 vgg19_bn 61.860 38.140 83.450 16.550 143.68 224 0.875 bilinear -12.354 -8.392 -2
332 ssl_resnet18 61.480 38.520 83.300 16.700 11.69 224 0.875 bilinear -11.130 -8.116 +8 +9
333 regnetx_006 61.350 38.650 83.450 16.550 6.20 224 0.875 bicubic -12.502 -8.222 -1 0
334 spnasnet_100 61.220 38.780 82.790 17.210 4.42 224 0.875 bilinear -12.864 -9.028 -4
335 tv_resnet34 61.190 38.810 82.710 17.290 21.80 224 0.875 bilinear -12.122 -8.716 0 +1
336 pit_ti_224 60.980 39.020 83.860 16.140 4.85 224 0.900 bicubic -11.932 -7.542 +3 +4
337 skresnet18 60.860 39.140 82.880 17.120 11.96 224 0.875 bicubic -12.178 -8.288 0 +1
338 ghostnet_100 60.830 39.170 82.360 17.640 5.18 224 0.875 bilinear -13.148 -9.096 -6
339 vgg16_bn 60.760 39.240 82.950 17.050 138.37 224 0.875 bilinear -12.590 -8.556 -4
340 tf_mobilenetv3_large_075 60.400 39.600 81.950 18.050 3.99 224 0.875 bilinear -13.038 -9.400 -6
341 mobilenetv2_100 60.190 39.810 82.240 17.760 3.50 224 0.875 bicubic -12.780 -8.776 -2
342 resnet18d 60.160 39.840 82.300 17.700 11.71 224 0.875 bicubic -12.100 -8.396 +3
343 vit_deit_tiny_patch16_224 59.830 40.170 82.670 17.330 5.72 224 0.900 bicubic -12.338 -8.448 +4
344 legacy_seresnet18 59.800 40.200 81.690 18.310 11.78 224 0.875 bicubic -11.942 -8.644 +4 +5
345 vgg19 59.710 40.290 81.450 18.550 143.67 224 0.875 bilinear -12.658 -9.422 -2
346 regnetx_004 59.410 40.590 81.690 18.310 5.16 224 0.875 bicubic -12.986 -9.140 -4
347 tf_mobilenetv3_large_minimal_100 59.070 40.930 81.150 18.850 3.92 224 0.875 bilinear -13.178 -9.480 -1
348 vgg13_bn 59.000 41.000 81.070 18.930 133.05 224 0.875 bilinear -12.594 -9.306 +1 +2
349 hrnet_w18_small 58.950 41.050 81.340 18.660 13.19 224 0.875 bilinear -13.394 -13.392 -9.338 -5
350 vgg16 58.830 41.170 81.660 18.340 138.36 224 0.875 bilinear -12.764 -8.722 0 +1
351 gluon_resnet18_v1b 58.340 41.660 80.970 19.030 11.69 224 0.875 bicubic -12.496 -8.792 0 +1
352 vgg11_bn 57.410 42.590 80.020 19.980 132.87 224 0.875 bilinear -12.950 -9.782 0 +1
353 resnet18 57.170 42.830 80.200 19.800 11.69 224 0.875 bilinear -12.578 -8.878 +2 +3
354 vgg13 57.150 42.850 79.540 20.460 133.05 224 0.875 bilinear -12.776 -9.706 0 +1
355 regnety_002 57.000 43.000 79.840 20.160 3.16 224 0.875 bicubic -13.252 -9.700 -2 -1
356 mixer_l16_224 56.690 43.310 75.990 24.010 208.20 224 0.875 bicubic -15.368 -11.678 -8
357 regnetx_002 56.050 43.950 79.210 20.790 2.68 224 0.875 bicubic -12.712 -9.346 +1
358 dla60x_c 56.000 44.000 78.930 21.070 1.32 224 0.875 bilinear -11.892 -9.496 +2
359 vgg11 55.800 44.200 78.830 21.170 132.86 224 0.875 bilinear -13.224 -9.798 -2

545
results/results-sketch.csv
View File

@ -1,293 +1,314 @@
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,+12
ig_resnext101_32x32d,58.386,41.614,80.381,19.619,468.53,224,0.875,bilinear,-26.708,-17.057,+19
ig_resnext101_32x16d,57.690,42.310,79.905,20.095,194.03,224,0.875,bilinear,-26.480,-17.291,+33
swsl_resnext101_32x16d,57.458,42.542,80.385,19.615,194.03,224,0.875,bilinear,-25.888,-16.461,+44
swsl_resnext101_32x8d,56.438,43.562,78.944,21.056,88.79,224,0.875,bilinear,-27.846,-18.232,+28
ig_resnext101_32x8d,54.918,45.082,77.534,22.466,88.79,224,0.875,bilinear,-27.770,-19.102,+56
swsl_resnext101_32x4d,53.603,46.397,76.347,23.653,44.18,224,0.875,bilinear,-29.627,-20.413,+44
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
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,+62
swsl_resnext50_32x4d,50.437,49.563,73.368,26.633,25.03,224,0.875,bilinear,-31.745,-22.862,+79
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,+88
swin_large_patch4_window7_224,48.991,51.009,71.391,28.609,196.53,224,0.900,bicubic,-37.329,-26.505,-5
swin_base_patch4_window12_384,48.553,51.447,71.813,28.187,87.90,384,1.000,bicubic,-37.879,-26.245,-7
swsl_resnet50,49.541,50.459,72.334,27.666,25.56,224,0.875,bilinear,-31.625,-23.638,+103
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,-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,-1
tf_efficientnet_b5_ns,45.615,54.385,67.842,32.158,30.39,456,0.934,bicubic,-40.473,-29.910,-9
swin_base_patch4_window7_224,45.560,54.440,68.512,31.488,87.77,224,0.900,bicubic,-39.692,-29.050,-2
vit_base_r50_s16_384,43.512,56.488,66.781,33.219,98.95,384,1.000,bicubic,-41.460,-30.507,+4
tf_efficientnet_b4_ns,43.450,56.550,65.519,34.481,19.34,380,0.922,bicubic,-41.713,-31.951,-3
vit_large_patch16_384,43.300,56.700,66.454,33.546,304.72,384,1.000,bicubic,-41.858,-30.902,-3
tf_efficientnet_b8,42.508,57.492,64.857,35.143,87.41,672,0.954,bicubic,-42.862,-32.533,-8
dm_nfnet_f6,41.593,58.407,63.192,36.808,438.36,576,0.956,bicubic,-44.704,-34.552,-16
tf_efficientnet_b7,41.431,58.569,63.017,36.983,66.35,600,0.949,bicubic,-43.505,-34.186,0
tf_efficientnet_b7_ap,41.429,58.571,62.874,37.126,66.35,600,0.949,bicubic,-43.691,-34.378,-6
tf_efficientnet_b5_ap,41.418,58.582,62.084,37.916,30.39,456,0.934,bicubic,-42.834,-34.890,+7
resnetv2_152x4_bitm,41.241,58.759,64.238,35.762,936.53,480,1.000,bilinear,-43.691,-33.198,-2
tf_efficientnet_b6_ap,41.099,58.901,62.355,37.645,43.04,528,0.942,bicubic,-43.689,-34.783,-2
tf_efficientnet_b8_ap,45.774,54.226,67.911,32.089,87.41,672,0.954,bicubic,-39.596,-29.383,+2
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,+22
tf_efficientnet_b4_ap,40.484,59.516,61.723,38.277,19.34,380,0.922,bicubic,-42.764,-34.669,+17
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,+4
dm_nfnet_f4,39.474,60.526,60.420,39.580,316.07,512,0.951,bicubic,-46.184,-37.090,-25
tf_efficientnet_b5,38.356,61.644,59.913,40.087,30.39,456,0.934,bicubic,-45.456,-36.835,+6
vit_deit_base_distilled_patch16_384,38.260,61.740,57.783,42.217,87.63,384,1.000,bicubic,-47.162,-39.549,-24
vit_base_patch16_384,38.099,61.901,60.428,39.572,86.86,384,1.000,bicubic,-46.111,-36.790,-4
resnet152d,37.857,62.143,58.356,41.644,60.21,320,1.000,bicubic,-45.823,-38.382,+6
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
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,-14
efficientnet_v2s,37.130,62.870,56.486,43.514,23.94,224,1.000,bicubic,-44.940,-39.468,+30
tf_efficientnet_b3_ap,37.055,62.945,57.240,42.760,12.23,300,0.904,bicubic,-44.767,-38.384,+34
resnetv2_152x2_bitm,36.847,63.153,59.899,40.101,236.34,480,1.000,bilinear,-47.593,-37.547,-16
seresnet152d,36.790,63.210,56.718,43.282,66.84,320,1.000,bicubic,-47.572,-40.322,-15
efficientnet_b3a,36.420,63.580,56.845,43.155,12.23,320,1.000,bicubic,-45.822,-39.269,+21
vit_deit_base_distilled_patch16_224,36.397,63.603,56.617,43.383,87.34,224,0.900,bicubic,-46.991,-39.871,-2
dm_nfnet_f2,36.257,63.743,55.847,44.153,193.78,352,0.920,bicubic,-48.733,-41.297,-28
tf_efficientnet_b2_ns,36.183,63.817,57.551,42.449,9.11,260,0.890,bicubic,-46.197,-38.697,+15
efficientnet_b3,36.037,63.963,56.370,43.630,12.23,300,0.904,bicubic,-46.039,-39.650,+21
ecaresnet101d,36.004,63.996,56.165,43.835,44.57,224,0.875,bicubic,-46.168,-39.881,+19
resnest200e,35.931,64.069,55.849,44.151,70.20,320,0.909,bicubic,-47.901,-41.045,-12
swsl_resnet18,35.858,64.142,58.455,41.545,11.69,224,0.875,bilinear,-37.418,-33.279,+259
eca_nfnet_l1,35.856,64.144,55.955,44.045,41.41,320,1.000,bicubic,-48.151,-41.073,-16
vit_base_patch16_224,35.768,64.232,57.390,42.610,86.57,224,0.900,bicubic,-46.018,-38.732,+23
resnest101e,35.373,64.627,55.780,44.220,48.28,256,0.875,bilinear,-47.517,-40.540,0
resnetv2_101x3_bitm,35.261,64.739,57.851,42.149,387.93,480,1.000,bilinear,-49.133,-39.511,-28
dm_nfnet_f1,35.192,64.808,54.413,45.587,132.63,320,0.910,bicubic,-49.412,-42.655,-32
repvgg_b3,35.043,64.957,54.542,45.458,123.09,224,0.875,bilinear,-45.449,-40.718,+57
repvgg_b3g4,35.043,64.957,54.772,45.228,83.83,224,0.875,bilinear,-45.169,-40.338,+74
resnet101d,34.872,65.128,54.202,45.798,44.57,320,1.000,bicubic,-48.150,-42.244,-7
vit_large_patch32_384,34.673,65.326,55.729,44.271,306.63,384,1.000,bicubic,-46.833,-40.363,+24
dm_nfnet_f0,34.642,65.358,54.762,45.238,71.49,256,0.900,bicubic,-48.700,-41.798,-16
ssl_resnext101_32x16d,34.605,65.395,55.931,44.069,194.03,224,0.875,bilinear,-47.239,-40.165,+12
repvgg_b2g4,34.587,65.413,54.782,45.218,61.76,224,0.875,bilinear,-44.779,-39.906,+103
resnest50d_4s2x40d,34.355,65.645,54.725,45.275,30.42,224,0.875,bicubic,-46.753,-40.833,+32
tf_efficientnet_b1_ns,34.157,65.843,55.489,44.511,7.79,240,0.882,bicubic,-47.231,-40.249,+22
tf_efficientnet_b4,34.064,65.936,54.198,45.802,19.34,380,0.922,bicubic,-48.958,-42.102,-13
nfnet_l0,34.029,65.971,54.418,45.582,35.07,288,1.000,bicubic,-48.731,-42.080,-11
ssl_resnext101_32x8d,34.017,65.983,55.601,44.399,88.79,224,0.875,bilinear,-47.599,-40.437,+13
tf_efficientnet_b6,33.998,66.002,54.544,45.456,43.04,528,0.942,bicubic,-50.112,-42.342,-35
efficientnet_b3_pruned,33.996,66.004,54.108,45.892,9.86,300,0.904,bicubic,-46.862,-41.134,+34
regnety_160,33.976,66.024,53.546,46.454,83.59,288,1.000,bicubic,-49.710,-43.230,-30
pit_s_distilled_224,33.939,66.061,53.265,46.735,24.04,224,0.900,bicubic,-48.057,-42.533,+1
regnety_032,33.412,66.588,52.754,47.246,19.44,288,1.000,bicubic,-49.312,-43.670,-16
gernet_l,33.357,66.643,51.901,48.099,31.08,256,0.875,bilinear,-47.997,-43.635,+15
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,+23
rexnet_200,32.987,67.013,52.939,47.061,16.37,224,0.875,bicubic,-48.645,-42.729,+3
resnest50d,32.972,67.028,52.713,47.287,27.48,224,0.875,bilinear,-48.002,-42.665,+22
tf_efficientnet_b3,32.860,67.140,52.950,47.050,12.23,300,0.904,bicubic,-48.776,-42.768,0
pnasnet5large,32.848,67.152,50.500,49.500,86.06,331,0.911,bicubic,-49.934,-45.540,-25
nasnetalarge,32.775,67.225,50.141,49.859,88.75,331,0.911,bicubic,-49.845,-45.906,-22
gernet_m,32.740,67.260,51.913,48.087,21.14,224,0.875,bilinear,-47.992,-43.271,+26
inception_resnet_v2,32.738,67.262,50.648,49.352,55.84,299,0.897,bicubic,-47.720,-44.658,+35
gluon_resnet152_v1d,32.734,67.266,51.088,48.912,60.21,224,0.875,bicubic,-47.740,-44.118,+32
pit_b_224,32.718,67.282,49.852,50.148,73.76,224,0.900,bicubic,-49.728,-45.858,-24
tf_efficientnet_b2_ap,32.681,67.319,52.239,47.761,9.11,260,0.890,bicubic,-47.619,-42.789,+41
tresnet_l,32.559,67.441,51.139,48.861,55.99,224,0.875,bilinear,-48.931,-44.485,-2
vit_base_patch32_384,32.461,67.539,52.444,47.556,88.30,384,1.000,bicubic,-49.191,-43.684,-10
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,-50
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
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
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
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
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,-20
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,+4
vit_deit_small_distilled_patch16_224,32.284,67.716,52.102,47.898,22.44,224,0.900,bicubic,-48.916,-43.276,-1
gluon_seresnext101_64x4d,32.205,67.795,50.319,49.681,88.23,224,0.875,bicubic,-48.689,-44.989,+7
gluon_seresnext101_32x4d,32.107,67.893,51.237,48.763,48.96,224,0.875,bicubic,-48.797,-44.057,+5
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,-7
cspresnext50,31.822,68.178,51.602,48.398,20.57,224,0.875,bilinear,-48.218,-43.342,+39
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,+85
ssl_resnext101_32x4d,31.423,68.577,52.121,47.879,44.18,224,0.875,bilinear,-49.501,-43.607,-3
inception_v4,31.378,68.622,49.244,50.756,42.68,299,0.875,bicubic,-48.790,-45.724,+29
rexnet_150,31.366,68.634,51.288,48.712,9.73,224,0.875,bicubic,-48.944,-43.878,+18
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,+8
gluon_resnet101_v1s,31.115,68.885,49.793,50.207,44.67,224,0.875,bicubic,-49.187,-45.367,+15
tf_efficientnet_cc_b0_8e,31.087,68.913,50.761,49.239,24.01,224,0.875,bicubic,-46.821,-42.892,+118
ecaresnet50d,31.058,68.942,50.848,49.152,25.58,224,0.875,bicubic,-49.534,-44.472,0
ecaresnet50t,31.058,68.942,50.577,49.423,25.57,320,0.950,bicubic,-51.288,-45.561,-50
resnet50d,31.020,68.980,49.808,50.192,25.58,224,0.875,bicubic,-49.510,-45.352,-1
cspdarknet53,31.018,68.981,50.390,49.610,27.64,256,0.887,bilinear,-49.040,-44.694,+23
tresnet_m,30.997,69.003,48.682,51.318,31.39,224,0.875,bilinear,-49.805,-46.178,-9
gluon_resnet152_v1c,30.991,69.009,48.924,51.076,60.21,224,0.875,bicubic,-48.919,-45.916,+27
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,+51
ecaresnet101d_pruned,30.897,69.103,50.013,49.987,24.88,224,0.875,bicubic,-49.919,-45.615,-15
gluon_resnext101_32x4d,30.877,69.123,48.537,51.463,44.18,224,0.875,bicubic,-49.457,-46.389,+1
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,-45.832,+12
ese_vovnet39b,30.657,69.343,49.875,50.125,24.57,224,0.875,bicubic,-48.663,-44.837,+43
gluon_resnet152_v1b,30.623,69.376,48.521,51.479,60.19,224,0.875,bicubic,-49.063,-46.215,+30
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,-5
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,30.517,69.483,49.162,50.838,12.61,224,0.875,bicubic,-48.699,-45.252,+50
resnest26d,30.490,69.510,50.677,49.323,17.07,224,0.875,bilinear,-47.988,-43.621,+75
efficientnet_b2a,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,+43
pit_xs_distilled_224,30.278,69.722,49.836,50.164,11.00,224,0.900,bicubic,-49.028,-44.528,+39
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,+22
tf_efficientnet_b2,30.026,69.974,49.581,50.419,9.11,260,0.890,bicubic,-50.060,-45.328,0
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,-45.676,-1
dpn92,29.953,70.047,49.162,50.838,37.67,224,0.875,bicubic,-50.055,-45.674,-2
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,+44
adv_inception_v3,29.816,70.184,47.847,52.153,23.83,299,0.875,bicubic,-47.766,-45.889,+96
gluon_xception65,29.784,70.216,47.755,52.245,39.92,299,0.903,bicubic,-49.932,-47.105,+10
resnetblur50,29.625,70.375,48.248,51.752,25.56,224,0.875,bicubic,-49.661,-46.390,+29
efficientnet_b2,29.615,70.385,48.777,51.223,9.11,260,0.875,bicubic,-50.777,-46.299,-27
efficientnet_em,29.486,70.514,48.946,51.054,6.90,240,0.882,bicubic,-49.766,-45.848,+29
resnext101_32x8d,29.439,70.561,48.486,51.514,88.79,224,0.875,bilinear,-49.869,-46.032,+20
ssl_resnet50,29.423,70.577,49.781,50.219,25.56,224,0.875,bilinear,-49.799,-45.051,+28
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,+20
swin_tiny_patch4_window7_224,29.334,70.666,47.602,52.398,28.29,224,0.900,bicubic,-52.044,-47.938,-65
resnext50_32x4d,29.331,70.669,47.397,52.603,25.03,224,0.875,bicubic,-50.438,-47.201,-2
resnet34d,29.328,70.671,48.409,51.591,21.82,224,0.875,bicubic,-47.788,-44.973,+98
ecaresnet50d_pruned,29.215,70.785,48.453,51.547,19.94,224,0.875,bicubic,-50.501,-46.427,-2
tresnet_l_448,29.165,70.835,47.232,52.768,55.99,448,0.875,bilinear,-53.103,-48.744,-93
gluon_inception_v3,29.122,70.878,46.957,53.043,23.83,299,0.875,bicubic,-49.684,-47.413,+36
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,+4
resnetv2_101x1_bitm,28.910,71.090,49.502,50.498,44.54,480,1.000,bilinear,-53.302,-46.970,-95
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,-2
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,+6
skresnext50_32x4d,28.818,71.182,46.497,53.503,27.48,224,0.875,bicubic,-51.338,-48.413,-31
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,-26
skresnet34,28.645,71.355,47.953,52.047,22.28,224,0.875,bicubic,-48.267,-45.369,+92
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,+42
repvgg_b2,28.427,71.573,47.038,52.962,89.02,224,0.875,bilinear,-50.365,-47.376,+23
hrnet_w48,28.413,71.587,47.586,52.414,77.47,224,0.875,bilinear,-50.887,-46.926,+1
gluon_resnext50_32x4d,28.375,71.624,45.328,54.672,25.03,224,0.875,bicubic,-50.978,-49.098,-7
efficientnet_b2_pruned,28.362,71.638,47.051,52.949,8.31,260,0.890,bicubic,-51.554,-47.805,-32
tf_efficientnet_b0_ap,28.346,71.654,47.531,52.469,5.29,224,0.875,bicubic,-48.740,-45.725,+79
tf_efficientnet_cc_b0_4e,28.315,71.685,47.364,52.636,13.31,224,0.875,bicubic,-48.991,-45.970,+71
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
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
dla102x2,28.313,71.687,46.761,53.239,41.28,224,0.875,bilinear,-51.135,-47.879,-13
mixnet_xl,28.287,71.713,46.702,53.298,11.90,224,0.875,bicubic,-52.189,-48.234,-65
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,-21
regnetx_320,28.093,71.907,45.126,54.874,107.81,224,0.875,bicubic,-52.153,-49.900,-54
densenet161,28.081,71.919,46.641,53.359,28.68,224,0.875,bicubic,-49.277,-46.997,+62
regnety_320,28.059,71.941,45.444,54.556,145.05,224,0.875,bicubic,-52.753,-49.800,-80
gernet_s,28.022,71.978,46.723,53.277,8.17,224,0.875,bilinear,-48.894,-46.409,+73
efficientnet_el_pruned,28.016,71.984,46.790,53.210,10.59,300,0.904,bicubic,-52.284,-48.428,-62
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,-43
tf_inception_v3,27.780,72.220,45.721,54.279,23.83,299,0.875,bicubic,-50.078,-47.695,+42
res2net101_26w_4s,27.768,72.232,45.179,54.821,45.21,224,0.875,bilinear,-51.430,-49.253,-10
repvgg_b1,27.656,72.344,46.531,53.469,57.42,224,0.875,bilinear,-50.710,-47.567,+19
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
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,+49
pit_xs_224,27.491,72.509,45.900,54.100,10.62,224,0.900,bicubic,-50.691,-48.268,+22
regnetx_080,27.405,72.595,45.002,54.998,39.57,224,0.875,bicubic,-51.789,-49.558,-14
hrnet_w30,27.381,72.619,46.554,53.446,37.71,224,0.875,bilinear,-50.825,-47.668,+19
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
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,-51.384,-49.016,-3
densenet201,27.265,72.735,46.222,53.778,20.01,224,0.875,bicubic,-50.021,-47.256,+48
densenetblur121d,27.228,72.772,46.299,53.701,8.00,224,0.875,bicubic,-49.360,-46.893,+66
regnety_064,27.220,72.780,44.847,55.153,30.58,224,0.875,bicubic,-52.502,-49.921,-50
efficientnet_b1_pruned,27.181,72.819,45.872,54.128,6.33,240,0.882,bicubic,-51.055,-47.962,+12
rexnet_130,27.094,72.906,45.933,54.067,7.56,224,0.875,bicubic,-52.406,-48.749,-42
vit_small_patch16_224,27.086,72.914,45.701,54.299,48.75,224,0.900,bicubic,-50.772,-48.169,+25
res2net50_26w_8s,27.078,72.921,44.428,55.572,48.40,224,0.875,bilinear,-52.119,-49.940,-26
dla102x,27.061,72.939,45.475,54.525,26.31,224,0.875,bilinear,-51.449,-48.753,-4
tv_resnet101,26.963,73.037,45.234,54.766,44.55,224,0.875,bilinear,-50.411,-48.306,+37
resnext50d_32x4d,26.876,73.124,44.436,55.564,25.05,224,0.875,bicubic,-52.800,-50.430,-53
regnetx_120,26.868,73.132,44.682,55.318,46.11,224,0.875,bicubic,-52.728,-50.056,-52
rexnet_100,26.831,73.169,45.369,54.631,4.80,224,0.875,bicubic,-51.027,-48.269,+20
densenet169,26.829,73.171,45.373,54.627,14.15,224,0.875,bicubic,-49.077,-47.653,+64
legacy_seresnext101_32x4d,26.811,73.189,43.497,56.503,48.96,224,0.875,bilinear,-53.417,-51.521,-83
regnety_120,26.788,73.212,44.454,55.546,51.82,224,0.875,bicubic,-53.578,-50.672,-94
regnetx_064,26.784,73.216,44.927,55.073,26.21,224,0.875,bicubic,-52.288,-49.531,-30
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,-18
densenet121,26.664,73.336,45.900,54.100,7.98,224,0.875,bicubic,-48.914,-46.752,+62
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,-19
repvgg_b1g4,26.579,73.421,45.084,54.916,39.97,224,0.875,bilinear,-51.015,-48.742,+17
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,-79
tf_efficientnet_b0,26.485,73.515,45.646,54.354,5.29,224,0.875,bicubic,-50.363,-47.582,+37
res2net50_14w_8s,26.483,73.517,44.371,55.629,25.06,224,0.875,bilinear,-51.667,-49.477,-6
gluon_resnet50_v1b,26.436,73.564,44.035,55.965,25.56,224,0.875,bicubic,-51.144,-49.681,+14
tf_efficientnet_el,26.357,73.643,44.175,55.825,10.59,300,0.904,bicubic,-53.893,-50.953,-99
regnetx_040,26.243,73.757,44.438,55.562,22.12,224,0.875,bicubic,-52.239,-49.806,-24
dpn68,26.129,73.871,44.228,55.772,12.61,224,0.875,bicubic,-50.189,-48.750,+42
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,-10
regnety_040,25.923,74.077,43.848,56.152,20.65,224,0.875,bicubic,-53.297,-50.808,-54
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,-7
tresnet_m_448,25.852,74.148,42.874,57.126,31.39,448,0.875,bilinear,-55.862,-52.698,-159
hardcorenas_c,25.815,74.185,44.772,55.228,5.52,224,0.875,bilinear,-51.239,-48.386,+19
gluon_resnet50_v1c,25.784,74.216,43.031,56.969,25.58,224,0.875,bicubic,-52.228,-50.957,-13
selecsls60,25.729,74.272,44.065,55.935,30.67,224,0.875,bicubic,-52.254,-49.764,-12
hardcorenas_e,25.662,74.338,43.412,56.588,8.07,224,0.875,bilinear,-52.132,-50.282,-6
dla60_res2net,25.652,74.348,43.599,56.401,20.85,224,0.875,bilinear,-52.812,-50.607,-34
dla60_res2next,25.640,74.360,43.670,56.330,17.03,224,0.875,bilinear,-52.800,-50.482,-33
ecaresnet26t,25.538,74.462,43.660,56.340,16.01,320,0.950,bicubic,-54.316,-51.424,-94
mixnet_l,25.512,74.488,43.455,56.545,7.33,224,0.875,bicubic,-53.464,-50.727,-55
tf_efficientnet_lite1,25.499,74.501,43.585,56.415,5.42,240,0.882,bicubic,-51.143,-49.641,+21
efficientnet_b1,25.469,74.531,43.284,56.716,7.79,240,0.875,bicubic,-53.229,-50.860,-48
tv_resnext50_32x4d,25.455,74.545,42.787,57.213,25.03,224,0.875,bilinear,-52.165,-50.909,-10
repvgg_a2,25.436,74.564,43.939,56.061,28.21,224,0.875,bilinear,-51.024,-49.065,+23
tf_mixnet_l,25.422,74.578,42.534,57.466,7.33,224,0.875,bicubic,-53.352,-51.464,-53
hardcorenas_b,25.402,74.598,44.190,55.810,5.18,224,0.875,bilinear,-51.136,-48.564,+19
res2next50,25.389,74.611,42.508,57.492,24.67,224,0.875,bilinear,-52.857,-51.384,-36
legacy_seresnet101,25.334,74.666,42.825,57.175,49.33,224,0.875,bilinear,-53.048,-51.439,-41
selecsls60b,25.332,74.668,43.559,56.441,32.77,224,0.875,bicubic,-53.080,-50.615,-43
dla102,25.316,74.684,43.827,56.173,33.27,224,0.875,bilinear,-52.716,-50.119,-30
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
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
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
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
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,-50.220,-48.404,+27
legacy_seresnext50_32x4d,25.210,74.790,41.936,58.064,27.56,224,0.875,bilinear,-53.868,-52.500,-73
res2net50_48w_2s,25.027,74.973,42.208,57.792,25.29,224,0.875,bilinear,-52.495,-51.346,-16
efficientnet_b0,25.015,74.985,42.787,57.213,5.29,224,0.875,bicubic,-52.683,-50.745,-24
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
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
mobilenetv2_120d,24.937,75.063,43.058,56.942,5.83,224,0.875,bicubic,-52.347,-50.434,-11
dla60,24.933,75.067,43.296,56.704,22.04,224,0.875,bilinear,-52.099,-50.022,-5
regnety_016,24.811,75.189,42.616,57.384,11.20,224,0.875,bicubic,-53.051,-51.104,-33
tf_efficientnet_lite2,24.530,75.470,42.280,57.720,6.09,260,0.890,bicubic,-52.938,-51.474,-21
skresnet18,24.483,75.517,42.536,57.464,11.96,224,0.875,bicubic,-48.555,-48.632,+46
regnetx_016,24.473,75.527,42.514,57.486,9.19,224,0.875,bicubic,-52.477,-50.906,-8
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
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,24.233,75.767,43.477,56.523,25.55,480,1.000,bilinear,-55.939,-52.149,-137
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
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,+45
resnet18d,23.929,76.071,42.300,57.700,11.71,224,0.875,bicubic,-48.331,-48.396,+46
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,-27
mixnet_m,23.710,76.290,41.141,58.859,5.01,224,0.875,bicubic,-53.550,-52.284,-28
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,-44
ese_vovnet19b_dw,23.535,76.465,41.288,58.712,6.54,224,0.875,bicubic,-53.263,-51.980,-18
tf_mixnet_m,23.484,76.516,40.989,59.011,5.01,224,0.875,bicubic,-53.458,-52.163,-23
tv_resnet34,23.473,76.527,41.367,58.633,21.80,224,0.875,bilinear,-49.839,-50.059,+26
tf_efficientnet_em,23.359,76.641,40.404,59.596,6.90,240,0.882,bicubic,-54.771,-53.640,-63
selecsls42b,23.357,76.643,40.677,59.323,32.46,224,0.875,bicubic,-53.817,-52.713,-33
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
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
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
@ -297,35 +318,37 @@ tf_mobilenetv3_large_100,22.569,77.431,39.767,60.233,5.48,224,0.875,bilinear,-52
tf_efficientnet_es,22.413,77.587,39.095,60.905,5.44,224,0.875,bicubic,-54.180,-54.107,-25
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,-69
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,-33
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
pit_ti_224,21.875,78.125,39.541,60.459,4.85,224,0.900,bicubic,-51.037,-51.861,+13
regnetx_006,21.738,78.263,38.904,61.096,6.20,224,0.875,bicubic,-52.115,-52.768,+5
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
gluon_resnet18_v1b,21.549,78.451,38.869,61.131,11.69,224,0.875,bicubic,-49.287,-50.893,+21
fbnetc_100,21.484,78.516,38.161,61.839,5.57,224,0.875,bilinear,-53.640,-54.224,-15
mnasnet_100,21.350,78.650,37.719,62.281,4.38,224,0.875,bicubic,-53.308,-54.395,-7
resnet26,21.295,78.705,38.018,61.982,16.00,224,0.875,bicubic,-53.997,-54.552,-20
ghostnet_100,21.620,78.380,38.692,61.308,5.18,224,0.875,bilinear,-52.358,-52.764,+3
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
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,-33
seresnext26d_32x4d,21.252,78.748,37.311,62.689,16.81,224,0.875,bicubic,-56.350,-56.297,-71
legacy_seresnext26_32x4d,21.093,78.907,37.633,62.367,16.79,224,0.875,bicubic,-56.011,-55.683,-56
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
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,-8
legacy_seresnet18,20.837,79.162,37.619,62.381,11.78,224,0.875,bicubic,-50.905,-52.715,+8
spnasnet_100,20.863,79.137,37.896,62.104,4.42,224,0.875,bilinear,-53.221,-53.922,-9
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,-36
regnety_004,20.415,79.585,37.002,62.998,4.34,224,0.875,bicubic,-53.619,-54.750,-11
hrnet_w18_small,20.368,79.632,37.093,62.907,13.19,224,0.875,bilinear,-51.976,-53.585,0
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
resnet18,20.228,79.772,37.261,62.739,11.69,224,0.875,bilinear,-49.520,-51.817,+9
vit_deit_tiny_patch16_224,20.162,79.838,37.546,62.454,5.72,224,0.900,bicubic,-52.007,-53.572,0
tf_mobilenetv3_large_minimal_100,20.122,79.878,36.908,63.092,3.92,224,0.875,bilinear,-52.126,-53.722,-2
vgg16_bn,19.959,80.041,36.301,63.699,138.37,224,0.875,bilinear,-53.391,-55.205,-14
vgg19,17.929,82.071,33.054,66.946,143.67,224,0.875,bilinear,-54.439,-57.818,-7
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
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
regnety_002,17.450,82.550,32.431,67.569,3.16,224,0.875,bicubic,-52.802,-57.109,0

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 +12 +15
3 ig_resnext101_32x32d 58.386 41.614 80.381 19.619 468.53 224 0.875 bilinear -26.708 -17.057 +19 +22
4 ig_resnext101_32x16d 57.690 42.310 79.905 20.095 194.03 224 0.875 bilinear -26.480 -17.291 +33 +41
5 swsl_resnext101_32x16d 57.458 42.542 80.385 19.615 194.03 224 0.875 bilinear -25.888 -16.461 +44 +58
6 swsl_resnext101_32x8d 56.438 43.562 78.944 21.056 88.79 224 0.875 bilinear -27.846 -18.232 +28 +35
7 ig_resnext101_32x8d 54.918 45.082 77.534 22.466 88.79 224 0.875 bilinear -27.770 -19.102 +56 +71
8 swsl_resnext101_32x4d 53.603 46.397 76.347 23.653 44.18 224 0.875 bilinear -29.627 -20.413 +44 +58
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 +62 +79
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 +88 +103
13 swin_large_patch4_window7_224 48.991 51.009 71.391 28.609 196.53 224 0.900 bicubic -37.329 -26.505 -5 -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 -7 -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 -10 -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 45.774 54.226 67.911 32.089 87.41 672 0.954 bicubic -39.596 -29.383 -1 +2
19 tf_efficientnet_b5_ns 45.615 54.385 67.842 32.158 30.39 456 0.934 bicubic -40.473 -29.910 -9 -8
20 swin_base_patch4_window7_224 45.560 54.440 68.512 31.488 87.77 224 0.900 bicubic -39.692 -29.050 -2 +1
21 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 +4 -15
22 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 -3 +8
23 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 -3 -1
24 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 -8 -1
25 dm_nfnet_f6 tf_efficientnet_b8 41.593 42.508 58.407 57.492 63.192 64.857 36.808 35.143 438.36 87.41 576 672 0.956 0.954 bicubic -44.704 -42.862 -34.552 -32.533 -16 -6
26 tf_efficientnet_b7 cait_m36_384 41.431 42.398 58.569 57.602 63.017 63.324 36.983 36.676 66.35 271.22 600 384 0.949 1.000 bicubic -43.505 -43.656 -34.186 -34.406 0 -14
27 tf_efficientnet_b7_ap dm_nfnet_f6 41.429 41.593 58.571 58.407 62.874 63.192 37.126 36.808 66.35 438.36 600 576 0.949 0.956 bicubic -43.691 -44.704 -34.378 -34.552 -6 -17
28 tf_efficientnet_b5_ap tf_efficientnet_b7 41.418 41.431 58.582 58.569 62.084 63.017 37.916 36.983 30.39 66.35 456 600 0.934 0.949 bicubic -42.834 -43.505 -34.890 -34.186 +7 +3
29 resnetv2_152x4_bitm tf_efficientnet_b7_ap 41.241 41.429 58.759 58.571 64.238 62.874 35.762 37.126 936.53 66.35 480 600 1.000 0.949 bilinear bicubic -43.691 -33.198 -34.378 -2 -5
30 tf_efficientnet_b6_ap tf_efficientnet_b5_ap 41.099 41.418 58.901 58.582 62.355 62.084 37.645 37.916 43.04 30.39 528 456 0.942 0.934 bicubic -43.689 -42.834 -34.783 -34.890 -2 +13
31 resnetv2_152x4_bitm 41.241 58.759 64.238 35.762 936.53 480 1.000 bilinear -43.691 -33.198 +1
32 tf_efficientnet_b6_ap 41.099 58.901 62.355 37.645 43.04 528 0.942 bicubic -43.689 -34.783 +1
33 dm_nfnet_f5 41.003 58.997 61.911 38.089 377.21 544 0.954 bicubic -44.711 -35.531 -20
34 dm_nfnet_f3 40.920 59.080 61.949 38.051 254.92 416 0.940 bicubic -44.640 -35.457 -19
35 vit_large_patch16_224 40.732 59.268 63.593 36.407 304.33 224 0.900 bicubic -42.330 -32.845 +22 +35
36 tf_efficientnet_b4_ap 40.484 59.516 61.723 38.277 19.34 380 0.922 bicubic -42.764 -34.669 +17 +29
37 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 -11 +5
38 tf_efficientnet_b3_ns cait_s36_384 39.584 39.765 60.416 60.235 61.453 60.475 38.547 39.525 12.23 68.37 300 384 0.904 1.000 bicubic -44.464 -45.695 -35.457 -37.005 +4 -22
39 dm_nfnet_f4 ecaresnet269d 39.474 39.594 60.526 60.406 60.420 60.343 39.580 39.657 316.07 102.09 512 352 0.951 1.000 bicubic -46.184 -45.382 -37.090 -36.883 -25 -10
40 tf_efficientnet_b5 tf_efficientnet_b3_ns 38.356 39.584 61.644 60.416 59.913 61.453 40.087 38.547 30.39 12.23 456 300 0.934 0.904 bicubic -45.456 -44.464 -36.835 -35.457 +6 +10
41 vit_deit_base_distilled_patch16_384 dm_nfnet_f4 38.260 39.474 61.740 60.526 57.783 60.420 42.217 39.580 87.63 316.07 384 512 1.000 0.951 bicubic -47.162 -46.184 -39.549 -37.090 -24 -27
42 vit_base_patch16_384 efficientnet_b4 38.099 39.079 61.901 60.921 60.428 59.608 39.572 40.392 86.86 19.34 384 1.000 bicubic -46.111 -44.349 -36.790 -36.988 -4 +19
43 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 +6 +11
44 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
45 vit_base_patch16_384 38.099 61.901 60.428 39.572 86.86 384 1.000 bicubic -46.111 -36.790 -1
46 cait_s24_384 37.873 62.127 58.079 41.921 47.06 384 1.000 bicubic -47.173 -39.267 -20
47 resnet152d 37.857 62.143 58.356 41.644 60.21 320 1.000 bicubic -45.823 -38.382 +12
48 resnetrs420 37.747 62.253 58.215 41.785 191.89 416 1.000 bicubic -47.261 -38.909 -21
49 resnetrs350 37.676 62.324 58.083 41.917 163.96 384 1.000 bicubic -47.044 -38.905 -15
50 pit_b_distilled_224 37.590 62.410 57.238 42.762 74.79 224 0.900 bicubic -46.554 -39.618 -4
51 resnet200d 37.505 62.495 58.297 41.703 64.69 320 1.000 bicubic -46.457 -38.526 -1 +1
52 resnest269e 37.315 62.685 57.468 42.532 110.93 416 0.928 bicubic -47.203 -39.518 -14 -16
53 efficientnet_v2s cait_s24_224 37.130 37.153 62.870 62.847 56.486 56.724 43.514 43.276 23.94 46.92 224 1.000 bicubic -44.940 -46.299 -39.468 -39.840 +30 +7
54 tf_efficientnet_b3_ap 37.055 62.945 57.240 42.760 12.23 300 0.904 bicubic -44.767 -38.384 +34 +41
55 resnetv2_152x2_bitm efficientnet_v2s 36.847 37.049 63.153 62.951 59.899 56.814 40.101 43.186 236.34 23.94 480 384 1.000 bilinear bicubic -47.593 -46.759 -37.547 -39.910 -16 0
56 seresnet152d resnetv2_152x2_bitm 36.790 36.847 63.210 63.153 56.718 59.899 43.282 40.101 66.84 236.34 320 480 1.000 bicubic bilinear -47.572 -47.593 -40.322 -37.547 -15 -19
57 efficientnet_b3a seresnet152d 36.420 36.790 63.580 63.210 56.845 56.718 43.155 43.282 12.23 66.84 320 1.000 bicubic -45.822 -47.572 -39.269 -40.322 +21 -17
58 vit_deit_base_distilled_patch16_224 resnetrs200 36.397 36.639 63.603 63.361 56.617 56.828 43.383 43.172 87.34 93.21 224 320 0.900 1.000 bicubic -46.991 -47.427 -39.871 -40.046 -2 -10
59 dm_nfnet_f2 efficientnet_b3 36.257 36.420 63.743 63.580 55.847 56.845 44.153 43.155 193.78 12.23 352 320 0.920 1.000 bicubic -48.733 -45.822 -41.297 -39.269 -28 +27
60 tf_efficientnet_b2_ns cait_xs24_384 36.183 36.416 63.817 63.584 57.551 56.944 42.449 43.056 9.11 26.67 260 384 0.890 1.000 bicubic -46.197 -47.645 -38.697 -39.945 +15 -11
61 efficientnet_b3 vit_deit_base_distilled_patch16_224 36.037 36.397 63.963 63.603 56.370 56.617 43.630 43.383 12.23 87.34 300 224 0.904 0.900 bicubic -46.039 -46.991 -39.650 -39.871 +21 +1
62 ecaresnet101d resnetrs270 36.004 36.320 63.996 63.680 56.165 56.562 43.835 43.438 44.57 129.86 224 352 0.875 1.000 bicubic -46.168 -48.114 -39.881 -40.408 +19 -24
63 resnest200e tresnet_m 35.931 36.285 64.069 63.715 55.849 55.796 44.151 44.204 70.20 31.39 320 224 0.909 0.875 bicubic bilinear -47.901 -46.795 -41.045 -40.322 -12 +6
64 swsl_resnet18 dm_nfnet_f2 35.858 36.257 64.142 63.743 58.455 55.847 41.545 44.153 11.69 193.78 224 352 0.875 0.920 bilinear bicubic -37.418 -48.733 -33.279 -41.297 +259 -36
65 eca_nfnet_l1 tf_efficientnet_b2_ns 35.856 36.183 64.144 63.817 55.955 57.551 44.045 42.449 41.41 9.11 320 260 1.000 0.890 bicubic -48.151 -46.197 -41.073 -38.697 -16 +17
66 vit_base_patch16_224 ecaresnet101d 35.768 36.004 64.232 63.996 57.390 56.165 42.610 43.835 86.57 44.57 224 0.900 0.875 bicubic -46.018 -46.168 -38.732 -39.881 +23 +24
67 resnest101e resnest200e 35.373 35.931 64.627 64.069 55.780 55.849 44.220 44.151 48.28 70.20 256 320 0.875 0.909 bilinear bicubic -47.517 -47.901 -40.540 -41.045 0 -14
68 resnetv2_101x3_bitm swsl_resnet18 35.261 35.858 64.739 64.142 57.851 58.455 42.149 41.545 387.93 11.69 480 224 1.000 0.875 bilinear -49.133 -37.418 -39.511 -33.279 -28 +269
69 dm_nfnet_f1 eca_nfnet_l1 35.192 35.856 64.808 64.144 54.413 55.955 45.587 44.045 132.63 41.41 320 0.910 1.000 bicubic -49.412 -48.151 -42.655 -41.073 -32 -18
70 repvgg_b3 vit_base_patch16_224 35.043 35.768 64.957 64.232 54.542 57.390 45.458 42.610 123.09 86.57 224 0.875 0.900 bilinear bicubic -45.449 -46.018 -40.718 -38.732 +57 +26
71 repvgg_b3g4 resnest101e 35.043 35.373 64.957 64.627 54.772 55.780 45.228 44.220 83.83 48.28 224 256 0.875 bilinear -45.169 -47.517 -40.338 -40.540 +74 +3
72 resnet101d resnetv2_101x3_bitm 34.872 35.261 65.128 64.739 54.202 57.851 45.798 42.149 44.57 387.93 320 480 1.000 bicubic bilinear -48.150 -49.133 -42.244 -39.511 -7 -33
73 vit_large_patch32_384 dm_nfnet_f1 34.673 35.192 65.326 64.808 55.729 54.413 44.271 45.587 306.63 132.63 384 320 1.000 0.910 bicubic -46.833 -49.412 -40.363 -42.655 +24 -38
74 dm_nfnet_f0 repvgg_b3 34.642 35.043 65.358 64.957 54.762 54.542 45.238 45.458 71.49 123.09 256 224 0.900 0.875 bicubic bilinear -48.700 -45.449 -41.798 -40.718 -16 +60
75 ssl_resnext101_32x16d repvgg_b3g4 34.605 35.043 65.395 64.957 55.931 54.772 44.069 45.228 194.03 83.83 224 0.875 bilinear -47.239 -45.169 -40.165 -40.338 +12 +76
76 repvgg_b2g4 resnet101d 34.587 34.872 65.413 65.128 54.782 54.202 45.218 45.798 61.76 44.57 224 320 0.875 1.000 bilinear bicubic -44.779 -48.150 -39.906 -42.244 +103 -4
77 resnest50d_4s2x40d vit_large_patch32_384 34.355 34.673 65.645 65.326 54.725 55.729 45.275 44.271 30.42 306.63 224 384 0.875 1.000 bicubic -46.753 -46.833 -40.833 -40.363 +32 +27
78 tf_efficientnet_b1_ns dm_nfnet_f0 34.157 34.642 65.843 65.358 55.489 54.762 44.511 45.238 7.79 71.49 240 256 0.882 0.900 bicubic -47.231 -48.700 -40.249 -41.798 +22 -14
79 tf_efficientnet_b4 ssl_resnext101_32x16d 34.064 34.603 65.936 65.397 54.198 55.931 45.802 44.069 19.34 194.03 380 224 0.922 0.875 bicubic bilinear -48.958 -47.241 -42.102 -40.165 -13 +15
80 nfnet_l0 repvgg_b2g4 34.029 34.587 65.971 65.413 54.418 54.782 45.582 45.218 35.07 61.76 288 224 1.000 0.875 bicubic bilinear -48.731 -44.779 -42.080 -39.906 -11 +107
81 ssl_resnext101_32x8d resnetrs152 34.017 34.355 65.983 65.645 55.601 53.562 44.399 46.438 88.79 86.62 224 320 0.875 1.000 bilinear bicubic -47.599 -49.357 -40.437 -43.052 +13 -25
82 tf_efficientnet_b6 resnest50d_4s2x40d 33.998 34.355 66.002 65.645 54.544 54.725 45.456 45.275 43.04 30.42 528 224 0.942 0.875 bicubic -50.112 -46.753 -42.342 -40.833 -35 +35
83 efficientnet_b3_pruned tf_efficientnet_b1_ns 33.996 34.157 66.004 65.843 54.108 55.489 45.892 44.511 9.86 7.79 300 240 0.904 0.882 bicubic -46.862 -47.231 -41.134 -40.249 +34 +24
84 regnety_160 tf_efficientnet_b4 33.976 34.064 66.024 65.936 53.546 54.198 46.454 45.802 83.59 19.34 288 380 1.000 0.922 bicubic -49.710 -48.958 -43.230 -42.102 -30 -11
85 pit_s_distilled_224 nfnet_l0 33.939 34.029 66.061 65.971 53.265 54.418 46.735 45.582 24.04 35.07 224 288 0.900 1.000 bicubic -48.057 -48.731 -42.533 -42.080 +1 -9
86 regnety_032 ssl_resnext101_32x8d 33.412 34.017 66.588 65.983 52.754 55.601 47.246 44.399 19.44 88.79 288 224 1.000 0.875 bicubic bilinear -49.312 -47.599 -43.670 -40.437 -16 +15
87 gernet_l tf_efficientnet_b6 33.357 33.998 66.643 66.002 51.901 54.544 48.099 45.456 31.08 43.04 256 528 0.875 0.942 bilinear bicubic -47.997 -50.112 -43.635 -42.342 +15 -40
88 tresnet_xl efficientnet_b3_pruned 33.257 33.996 66.743 66.004 52.294 54.108 47.706 45.892 78.44 9.86 224 300 0.875 0.904 bilinear bicubic -48.797 -46.862 -43.642 -41.134 -4 +37
89 resnest50d_1s4x24d regnety_160 33.147 33.976 66.853 66.024 52.839 53.546 47.161 46.454 25.68 83.59 224 288 0.875 1.000 bicubic -47.841 -49.710 -42.483 -43.230 +23 -31
90 rexnet_200 pit_s_distilled_224 32.987 33.939 67.013 66.061 52.939 53.265 47.061 46.735 16.37 24.04 224 0.875 0.900 bicubic -48.645 -48.057 -42.729 -42.533 +3
91 resnest50d regnety_032 32.972 33.412 67.028 66.588 52.713 52.754 47.287 47.246 27.48 19.44 224 288 0.875 1.000 bilinear bicubic -48.002 -49.312 -42.665 -43.670 +22 -14
92 tf_efficientnet_b3 gernet_l 32.860 33.357 67.140 66.643 52.950 51.901 47.050 48.099 12.23 31.08 300 256 0.904 0.875 bicubic bilinear -48.776 -47.997 -42.768 -43.635 0 +17
93 pnasnet5large tresnet_xl 32.848 33.257 67.152 66.743 50.500 52.294 49.500 47.706 86.06 78.44 331 224 0.911 0.875 bicubic bilinear -49.934 -48.797 -45.540 -43.642 -25 -2
94 nasnetalarge resnest50d_1s4x24d 32.775 33.147 67.225 66.853 50.141 52.839 49.859 47.161 88.75 25.68 331 224 0.911 0.875 bicubic -49.845 -47.841 -45.906 -42.483 -22 +25
95 gernet_m rexnet_200 32.740 32.987 67.260 67.013 51.913 52.939 48.087 47.061 21.14 16.37 224 0.875 bilinear bicubic -47.992 -48.645 -43.271 -42.729 +26 +5
96 inception_resnet_v2 resnest50d 32.738 32.972 67.262 67.028 50.648 52.713 49.352 47.287 55.84 27.48 299 224 0.897 0.875 bicubic bilinear -47.720 -48.002 -44.658 -42.665 +35 +24
97 gluon_resnet152_v1d tf_efficientnet_b3 32.734 32.860 67.266 67.140 51.088 52.950 48.912 47.050 60.21 12.23 224 300 0.875 0.904 bicubic -47.740 -48.776 -44.118 -42.768 +32 +2
98 pit_b_224 pnasnet5large 32.718 32.848 67.282 67.152 49.852 50.500 50.148 49.500 73.76 86.06 224 331 0.900 0.911 bicubic -49.728 -49.934 -45.858 -45.540 -24 -23
99 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 +41 -20
100 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 -2 +28
101 vit_base_patch32_384 inception_resnet_v2 32.461 32.738 67.539 67.262 52.444 50.648 47.556 49.352 88.30 55.84 384 299 1.000 0.897 bicubic -49.191 -47.720 -43.684 -44.658 -10 +37
102 wide_resnet50_2 gluon_resnet152_v1d 32.439 32.734 67.561 67.266 51.459 51.088 48.541 48.912 68.88 60.21 224 0.875 bicubic -49.017 -47.740 -44.073 -44.118 -3 +34
103 resnetv2_50x3_bitm pit_b_224 32.410 32.718 67.590 67.282 54.314 49.852 45.686 50.148 217.32 73.76 480 224 1.000 0.900 bilinear bicubic -51.374 -49.728 -42.792 -45.858 -50 -22
104 tf_efficientnet_b2_ap 32.681 67.319 52.239 47.761 9.11 260 0.890 bicubic -47.619 -42.789 +42
105 tresnet_l 32.559 67.441 51.139 48.861 55.99 224 0.875 bilinear -48.931 -44.485 0
106 cait_xxs36_384 32.549 67.451 52.233 47.767 17.37 384 1.000 bicubic -49.645 -43.915 -18
107 vit_base_patch32_384 32.461 67.539 52.444 47.556 88.30 384 1.000 bicubic -49.191 -43.684 -9
108 wide_resnet50_2 32.439 67.561 51.459 48.541 68.88 224 0.875 bicubic -49.017 -44.073 -2
109 resnetv2_50x3_bitm 32.410 67.590 54.314 45.686 217.32 480 1.000 bilinear -51.374 -42.792 -53
110 ens_adv_inception_resnet_v2 32.370 67.629 50.427 49.573 55.84 299 0.897 bicubic -47.611 -44.509 +50
111 vit_deit_base_patch16_224 32.363 67.637 51.011 48.989 86.57 224 0.900 bicubic -49.635 -44.723 -20 -19
112 swin_small_patch4_window7_224 32.341 67.659 50.905 49.095 49.61 224 0.900 bicubic -50.871 -45.417 -45
113 gluon_resnet152_v1s 32.331 67.669 50.526 49.474 60.32 224 0.875 bicubic -48.685 -44.886 +4 +5
114 vit_deit_small_distilled_patch16_224 32.284 67.716 52.102 47.898 22.44 224 0.900 bicubic -48.916 -43.276 -1 0
115 gluon_seresnext101_64x4d 32.205 67.795 50.319 49.681 88.23 224 0.875 bicubic -48.689 -44.989 +7 +9
116 gluon_seresnext101_32x4d 32.107 67.893 51.237 48.763 48.96 224 0.875 bicubic -48.797 -44.057 +5 +7
117 vit_deit_base_patch16_384 31.989 68.011 50.547 49.453 86.86 384 1.000 bicubic -51.117 -45.825 -49
118 seresnext50_32x4d 31.985 68.015 51.231 48.769 27.56 224 0.875 bicubic -49.281 -44.389 -7 -6
119 cspresnext50 resnetrs101 31.822 31.858 68.178 68.142 51.602 51.017 48.398 48.983 20.57 63.62 224 288 0.875 0.940 bilinear bicubic -48.218 -50.430 -43.342 -44.991 +39 -35
120 cspresnext50 31.822 68.178 51.602 48.398 20.57 224 0.875 bilinear -48.218 -43.342 +38
121 eca_nfnet_l0 31.657 68.343 51.654 48.346 24.14 288 1.000 bicubic -50.931 -44.820 -41
122 tnt_s_patch16_224 31.643 68.357 51.143 48.857 23.76 224 0.900 bicubic -49.875 -44.605 -19
123 resnet50 31.547 68.453 50.170 49.830 25.56 224 0.875 bicubic -47.491 -44.220 +85 +87
124 ssl_resnext101_32x4d 31.423 68.577 52.121 47.879 44.18 224 0.875 bilinear -49.501 -43.607 -3 -2
125 inception_v4 31.378 68.622 49.244 50.756 42.68 299 0.875 bicubic -48.790 -45.724 +29 +28
126 rexnet_150 31.366 68.634 51.288 48.712 9.73 224 0.875 bicubic -48.944 -43.878 +18 +17
127 pit_s_224 31.333 68.667 49.661 50.339 23.46 224 0.900 bicubic -49.761 -45.671 -10
128 cait_xxs36_224 31.278 68.722 50.616 49.384 17.30 224 1.000 bicubic -48.472 -44.250 +45
129 cspresnet50 31.270 68.730 51.223 48.777 21.62 256 0.887 bilinear -48.304 -43.489 +52
130 ecaresnetlight 31.121 68.879 50.243 49.757 30.16 224 0.875 bicubic -49.341 -45.007 +8 +7
131 gluon_resnet101_v1s 31.115 68.885 49.793 50.207 44.67 224 0.875 bicubic -49.187 -45.367 +15 +13
132 tf_efficientnet_cc_b0_8e 31.087 68.913 50.761 49.239 24.01 224 0.875 bicubic -46.821 -42.892 +118 +121
133 ecaresnet50d 31.058 68.942 50.848 49.152 25.58 224 0.875 bicubic -49.534 -44.472 0 -1
134 ecaresnet50t 31.058 68.942 50.577 49.423 25.57 320 0.950 bicubic -51.288 -45.561 -50 -51
135 resnet50d 31.020 68.980 49.808 50.192 25.58 224 0.875 bicubic -49.510 -45.352 -1 -2
136 cspdarknet53 31.018 68.981 50.390 49.610 27.64 256 0.887 bilinear -49.040 -44.694 +23 +21
137 tresnet_m gluon_resnet152_v1c 30.997 30.991 69.003 69.009 48.682 48.924 51.318 51.076 31.39 60.21 224 0.875 bilinear bicubic -49.805 -48.919 -46.178 -45.916 -9 +26
gluon_resnet152_v1c 30.991 69.009 48.924 51.076 60.21 224 0.875 bicubic -48.919 -45.916 +27
138 gluon_resnext101_64x4d 30.987 69.013 48.549 51.451 83.46 224 0.875 bicubic -49.617 -46.439 -7
139 tf_efficientnet_cc_b1_8e 30.899 69.101 50.080 49.920 39.72 240 0.882 bicubic -48.409 -44.290 +51 +52
140 ecaresnet101d_pruned 30.897 69.103 50.013 49.987 24.88 224 0.875 bicubic -49.919 -49.921 -45.615 -15 -14
141 gluon_resnext101_32x4d 30.877 69.123 48.537 51.463 44.18 224 0.875 bicubic -49.457 -46.389 +1 0
142 tf_efficientnet_lite4 30.830 69.170 50.386 49.614 13.01 380 0.920 bilinear -50.706 -45.282 -40
143 nf_resnet50 30.775 69.225 50.074 49.926 25.56 288 0.940 bicubic -49.919 -45.282 -14
144 dpn107 30.678 69.322 48.810 51.190 86.92 224 0.875 bicubic -49.478 -45.832 -46.100 +12 +10
145 ese_vovnet39b 30.657 69.343 49.875 50.125 24.57 224 0.875 bicubic -48.663 -44.837 +43 +44
146 gluon_resnet152_v1b 30.623 69.376 48.521 51.479 60.19 224 0.875 bicubic -49.063 -46.215 +30 +31
147 tresnet_xl_448 30.614 69.386 49.069 50.931 78.44 448 0.875 bilinear -52.436 -47.105 -76
148 ssl_resnext50_32x4d 30.594 69.406 50.657 49.343 25.03 224 0.875 bilinear -49.724 -44.749 -5 -6
149 gluon_resnet101_v1d 30.523 69.477 47.950 52.050 44.57 224 0.875 bicubic -49.891 -47.064 -10
150 dpn68b 30.517 69.483 49.162 49.158 50.838 50.842 12.61 224 0.875 bicubic -48.699 -45.252 -45.256 +50 +51
151 resnest26d 30.490 69.510 50.677 49.323 17.07 224 0.875 bilinear -47.988 -43.621 +75 +77
152 efficientnet_b2a efficientnet_b2 30.435 69.565 49.698 50.302 9.11 288 1.000 bicubic -50.177 -45.620 -22
153 tf_efficientnet_b1_ap 30.421 69.579 49.553 50.447 7.79 240 0.882 bicubic -48.859 -44.753 +43 +44
154 pit_xs_distilled_224 30.278 69.722 49.836 50.164 11.00 224 0.900 bicubic -49.028 -44.528 +39 +40
155 seresnet50 30.077 69.923 49.292 50.708 28.09 224 0.875 bicubic -50.197 -45.778 -7 -8
156 dpn98 30.067 69.933 48.244 51.756 61.57 224 0.875 bicubic -49.575 -46.354 +22 +23
157 tf_efficientnet_b2 30.026 69.974 49.581 50.419 9.11 260 0.890 bicubic -50.060 -45.328 0 -1
158 dpn131 30.024 69.976 48.146 51.854 79.25 224 0.875 bicubic -49.798 -46.564 +12
159 efficientnet_el 30.018 69.982 48.834 51.166 10.59 300 0.904 bicubic -51.298 -46.692 -49
160 legacy_senet154 30.001 69.999 48.034 51.966 115.09 224 0.875 bilinear -51.309 -47.462 -49
161 dpn92 29.953 70.047 49.162 50.838 37.67 224 0.875 bicubic -50.055 -45.676 -45.674 -1 -2
162 gluon_senet154 29.877 70.123 47.894 52.106 115.09 224 0.875 bicubic -51.357 -47.454 -49
163 xception 29.865 70.135 48.686 51.314 22.86 299 0.897 bicubic -49.187 -45.706 +44 +46
164 adv_inception_v3 29.816 70.184 47.847 52.153 23.83 299 0.875 bicubic -47.766 -45.889 +96 +100
165 gluon_xception65 29.784 70.216 47.755 52.245 39.92 299 0.903 bicubic -49.932 -47.105 +10 +11
166 resnetblur50 29.625 70.375 48.248 51.752 25.56 224 0.875 bicubic -49.661 -46.390 +29 +30
167 efficientnet_b2 efficientnet_em 29.615 29.486 70.385 70.514 48.777 48.946 51.223 51.054 9.11 6.90 260 240 0.875 0.882 bicubic -50.777 -49.766 -46.299 -45.848 -27 +31
168 efficientnet_em resnext101_32x8d 29.486 29.439 70.514 70.561 48.946 48.486 51.054 51.514 6.90 88.79 240 224 0.882 0.875 bicubic bilinear -49.766 -49.869 -45.848 -46.032 +29 +22
169 resnext101_32x8d coat_lite_mini 29.439 29.433 70.561 70.567 48.486 47.724 51.514 52.276 88.79 11.01 224 0.875 0.900 bilinear bicubic -49.869 -49.655 -46.032 -46.880 +20 +36
170 ssl_resnet50 29.423 70.577 49.781 50.219 25.56 224 0.875 bilinear -49.799 -45.051 +28 +29
171 vit_deit_small_patch16_224 29.421 70.579 48.256 51.744 22.05 224 0.900 bicubic -50.435 -46.796 -3
172 nf_regnet_b1 29.397 70.603 49.445 50.555 10.22 288 0.900 bicubic -49.909 -45.303 +20 +21
173 swin_tiny_patch4_window7_224 cait_xxs24_384 29.334 29.387 70.666 70.612 47.602 48.753 52.398 51.247 28.29 12.03 224 384 0.900 1.000 bicubic -52.044 -51.578 -47.938 -46.893 -65 -52
174 resnext50_32x4d swin_tiny_patch4_window7_224 29.331 29.334 70.669 70.666 47.397 47.602 52.603 52.398 25.03 28.29 224 0.875 0.900 bicubic -50.438 -52.044 -47.201 -47.938 -2 -66
175 resnet34d resnext50_32x4d 29.328 29.331 70.671 70.669 48.409 47.397 51.591 52.603 21.82 25.03 224 0.875 bicubic -47.788 -50.438 -44.973 -47.201 +98 -3
176 ecaresnet50d_pruned resnet34d 29.215 29.328 70.785 70.671 48.453 48.409 51.547 51.591 19.94 21.82 224 0.875 bicubic -50.501 -47.788 -46.427 -44.973 -2 +102
177 tresnet_l_448 cait_xxs24_224 29.165 29.303 70.835 70.697 47.232 48.535 52.768 51.465 55.99 11.96 448 224 0.875 1.000 bilinear bicubic -53.103 -49.083 -48.744 -45.775 -93 +56
178 gluon_inception_v3 ecaresnet50d_pruned 29.122 29.215 70.878 70.785 46.957 48.453 53.043 51.547 23.83 19.94 299 224 0.875 bicubic -49.684 -50.501 -47.413 -46.427 +36 -3
179 xception71 tresnet_l_448 29.047 29.165 70.953 70.835 47.405 47.232 52.595 52.768 42.34 55.99 299 448 0.903 0.875 bicubic bilinear -50.826 -53.103 -47.517 -48.744 -13 -94
180 hrnet_w64 gluon_inception_v3 28.989 29.124 71.011 70.876 47.142 46.957 52.858 53.043 128.06 23.83 224 299 0.875 bilinear bicubic -50.485 -49.682 -47.510 -47.413 +4 +36
181 resnetv2_101x1_bitm xception71 28.910 29.047 71.090 70.953 49.502 47.405 50.498 52.595 44.54 42.34 480 299 1.000 0.903 bilinear bicubic -53.302 -50.826 -46.970 -47.517 -95 -15
182 hrnet_w64 28.989 71.011 47.142 52.858 128.06 224 0.875 bilinear -50.485 -47.510 +3
183 resnetv2_101x1_bitm 28.910 71.090 49.502 50.498 44.54 480 1.000 bilinear -53.302 -46.970 -96
184 tf_efficientnet_b0_ns 28.902 71.098 49.011 50.989 5.29 224 0.875 bicubic -49.756 -45.365 +39
185 xception65 28.896 71.104 47.167 52.833 39.92 299 0.903 bicubic -50.656 -47.487 -2 -3
186 tf_efficientnet_b1 28.886 71.114 47.503 52.497 7.79 240 0.882 bicubic -49.940 -46.695 +29
187 gluon_resnet101_v1b 28.878 71.121 46.389 53.611 44.55 224 0.875 bicubic -50.427 -48.135 +6 +5
188 skresnext50_32x4d 28.818 71.182 46.497 53.503 27.48 224 0.875 bicubic -51.338 -48.413 -48.145 -31 -33
189 tf_efficientnet_lite3 28.660 71.340 47.354 52.646 8.20 300 0.904 bilinear -51.160 -47.560 -16 -18
190 gluon_seresnext50_32x4d 28.651 71.349 46.436 53.564 27.56 224 0.875 bicubic -51.267 -48.386 -26 -29
191 skresnet34 28.645 71.355 47.953 52.047 22.28 224 0.875 bicubic -48.267 -45.369 +92 +95
192 hrnet_w40 28.641 71.359 47.454 52.546 57.56 224 0.875 bilinear -50.279 -47.016 +20
193 tv_resnet152 28.533 71.467 47.118 52.882 60.19 224 0.875 bilinear -49.779 -46.920 +42 +43
194 repvgg_b2 28.427 71.573 47.038 52.962 89.02 224 0.875 bilinear -50.365 -47.376 +23 +24
195 hrnet_w48 28.413 71.587 47.586 52.414 77.47 224 0.875 bilinear -50.887 -46.926 +1 0
196 gluon_resnext50_32x4d 28.375 71.624 45.328 54.672 25.03 224 0.875 bicubic -50.978 -49.098 -7 -8
197 efficientnet_b2_pruned 28.362 71.638 47.051 52.949 8.31 260 0.890 bicubic -51.554 -47.805 -32 -35
198 tf_efficientnet_b0_ap 28.346 71.654 47.531 52.469 5.29 224 0.875 bicubic -48.740 -45.725 +79 +82
199 tf_efficientnet_cc_b0_4e 28.315 71.685 47.364 52.636 13.31 224 0.875 bicubic -48.991 -45.970 +71 +74
200 dla102x2 28.313 71.687 46.761 53.239 41.28 224 0.875 bilinear -51.135 -47.879 -14
201 dla169 28.313 71.687 47.391 52.609 53.39 224 0.875 bilinear -50.375 -46.945 +20
202 dla102x2 mixnet_xl 28.313 28.287 71.687 71.713 46.761 46.702 53.239 53.298 41.28 11.90 224 0.875 bilinear bicubic -51.135 -52.189 -47.879 -48.234 -13 -67
mixnet_xl 28.287 71.713 46.702 53.298 11.90 224 0.875 bicubic -52.189 -48.234 -65
203 gluon_resnet50_v1d 28.246 71.754 45.878 54.122 25.58 224 0.875 bicubic -50.828 -48.592 +4
204 wide_resnet101_2 28.108 71.892 46.401 53.599 126.89 224 0.875 bilinear -50.748 -47.881 +10
205 gluon_resnet101_v1c 28.104 71.896 45.961 54.039 44.57 224 0.875 bicubic -51.430 -48.617 -21 -22
206 regnetx_320 28.093 71.907 45.126 54.874 107.81 224 0.875 bicubic -52.153 -49.900 -54 -57
207 densenet161 28.081 71.919 46.641 53.359 28.68 224 0.875 bicubic -49.277 -46.997 +62 +65
208 regnety_320 28.059 71.941 45.444 54.556 145.05 224 0.875 bicubic -52.753 -49.800 -80 -81
209 gernet_s 28.022 71.978 46.723 53.277 8.17 224 0.875 bilinear -48.894 -46.409 +73 +76
210 efficientnet_el_pruned 28.016 71.984 46.790 53.210 10.59 300 0.904 bicubic -52.284 -48.428 -62 -65
211 xception41 27.888 72.112 45.890 54.110 26.97 299 0.903 bicubic -50.628 -48.388 +14
212 regnetx_160 27.817 72.183 45.617 54.383 54.28 224 0.875 bicubic -52.039 -49.213 -43 -45
213 tf_inception_v3 27.780 27.782 72.220 72.218 45.721 45.719 54.279 54.281 23.83 299 0.875 bicubic -50.078 -50.074 -47.695 -47.921 +42 +44
214 res2net101_26w_4s 27.768 72.232 45.179 54.821 45.21 224 0.875 bilinear -51.430 -49.253 -10 -11
215 repvgg_b1 27.656 72.344 46.531 53.469 57.42 224 0.875 bilinear -50.710 -47.567 +19 +20
216 hrnet_w44 27.621 72.379 45.837 54.163 67.06 224 0.875 bilinear -51.275 -48.531 -3
217 inception_v3 27.556 72.444 45.263 54.737 23.83 299 0.875 bicubic -49.882 -48.213 +49 +52
218 pit_xs_224 27.491 72.509 45.900 54.100 10.62 224 0.900 bicubic -50.691 -48.268 +22 +23
219 regnetx_080 27.405 72.595 45.002 54.998 39.57 224 0.875 bicubic -51.789 -49.558 -14 -15
220 hrnet_w30 27.381 72.619 46.554 53.446 37.71 224 0.875 bilinear -50.825 -47.668 +19 +20
221 hrnet_w32 27.369 72.631 45.994 54.006 41.23 224 0.875 bilinear -51.081 -48.192 +9
222 gluon_resnet50_v1s 27.326 72.674 45.222 54.778 25.68 224 0.875 bicubic -51.384 -51.386 -49.016 -3 -2
223 densenet201 27.265 72.735 46.222 53.778 20.01 224 0.875 bicubic -50.021 -47.256 +48 +51
224 densenetblur121d 27.228 72.772 46.299 53.701 8.00 224 0.875 bicubic -49.360 -46.893 +66 +70
225 regnety_064 27.220 72.780 44.847 55.153 30.58 224 0.875 bicubic -52.502 -49.921 -50 -51
226 efficientnet_b1_pruned 27.181 72.819 45.872 54.128 6.33 240 0.882 bicubic -51.055 -47.962 +12 +13
227 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 -42 -63
228 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 -48.169 -48.749 +25 -44
229 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 -47.715 -26 +27
230 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 -4 -28
231 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 +37 -5
232 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 -53 +39
233 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 -52 -55
234 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 -48.269 -50.056 +20 -54
235 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 -48.501 +64 +20
236 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 -83 +67
237 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 -94 -87
238 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 -30 -98
239 regnetx_064 26.784 73.216 44.927 55.073 26.21 224 0.875 bicubic -52.288 -49.531 -31
240 regnetx_032 26.703 73.297 45.236 54.764 15.30 224 0.875 bicubic -51.469 -48.852 +2
241 legacy_seresnet152 26.676 73.324 43.947 56.053 66.82 224 0.875 bilinear -51.984 -50.423 -18 -19
242 densenet121 26.664 73.336 45.900 54.100 7.98 224 0.875 bicubic -48.914 -46.752 +62 +65
243 efficientnet_es 26.621 73.379 45.112 54.888 5.44 224 0.875 bicubic -51.445 -48.814 +3
244 res2net50_26w_6s 26.595 73.405 43.990 56.010 37.05 224 0.875 bilinear -51.975 -50.134 -19 -20
245 repvgg_b1g4 26.579 73.421 45.084 54.916 39.97 224 0.875 bilinear -51.015 -48.742 +17 +18
246 dla60x 26.552 73.448 45.023 54.977 17.35 224 0.875 bilinear -51.694 -48.995 -9
247 regnety_080 26.524 73.476 44.359 55.641 39.18 224 0.875 bicubic -53.352 -50.471 -79 -82
248 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 +37 +19
249 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 -6 +38
250 gluon_resnet50_v1b res2net50_14w_8s 26.436 26.483 73.564 73.517 44.035 44.371 55.965 55.629 25.56 25.06 224 0.875 bicubic bilinear -51.144 -51.667 -49.681 -49.477 +14 -7
251 tf_efficientnet_el mobilenetv3_large_100_miil 26.357 26.481 73.643 73.519 44.175 44.473 55.825 55.527 10.59 5.48 300 224 0.904 0.875 bicubic bilinear -53.893 -51.435 -50.953 -48.437 -99 +1
252 regnetx_040 gluon_resnet50_v1b 26.243 26.436 73.757 73.564 44.438 44.035 55.562 55.965 22.12 25.56 224 0.875 bicubic -52.239 -51.144 -49.806 -49.681 -24 +13
253 dpn68 tf_efficientnet_el 26.129 26.357 73.871 73.643 44.228 44.175 55.772 55.825 12.61 10.59 224 300 0.875 0.904 bicubic -50.189 -53.893 -48.750 -50.953 +42 -105
254 hrnet_w18 regnetx_040 25.986 26.243 74.014 73.757 44.813 44.438 55.187 55.562 21.30 22.12 224 0.875 bilinear bicubic -50.772 -52.239 -48.631 -49.806 +33 -27
255 hardcorenas_f dpn68 25.951 26.129 74.049 73.871 44.220 44.228 55.780 55.772 8.20 12.61 224 0.875 bilinear bicubic -52.153 -50.189 -49.582 -48.750 -10 +43
256 regnety_040 efficientnet_b1 25.923 26.061 74.077 73.939 43.848 44.080 56.152 55.920 20.65 7.79 224 256 0.875 1.000 bicubic -53.297 -52.733 -50.808 -50.262 -54 -39
257 hrnet_w18 25.986 74.014 44.813 55.187 21.30 224 0.875 bilinear -50.772 -48.631 +32
258 hardcorenas_f 25.951 74.049 44.220 55.780 8.20 224 0.875 bilinear -52.153 -49.582 -13
259 regnety_040 25.923 74.077 43.848 56.152 20.65 224 0.875 bicubic -53.297 -50.808 -59
260 resnet34 25.888 74.112 43.982 56.018 21.80 224 0.875 bilinear -49.222 -48.302 +57
261 res2net50_26w_4s 25.866 74.134 43.155 56.845 25.70 224 0.875 bilinear -52.098 -50.699 -7 -10
262 tresnet_m_448 25.852 74.148 42.874 57.126 31.39 448 0.875 bilinear -55.862 -52.698 -159 -165
263 hardcorenas_c 25.815 74.185 44.772 55.228 5.52 224 0.875 bilinear -51.239 -48.386 +19 +18
264 gluon_resnet50_v1c 25.784 74.216 43.031 56.969 25.58 224 0.875 bicubic -52.228 -50.957 -13 -16
265 selecsls60 25.729 74.272 44.065 55.935 30.67 224 0.875 bicubic -52.254 -49.764 -12 -15
266 hardcorenas_e 25.662 74.338 43.412 56.588 8.07 224 0.875 bilinear -52.132 -50.282 -6 -8
267 dla60_res2net 25.652 74.348 43.599 56.401 20.85 224 0.875 bilinear -52.812 -50.607 -34 -38
268 dla60_res2next 25.640 74.360 43.670 56.330 17.03 224 0.875 bilinear -52.800 -50.482 -33 -37
269 ecaresnet26t 25.538 74.462 43.660 56.340 16.01 320 0.950 bicubic -54.316 -51.424 -94 -100
270 mixnet_l 25.512 74.488 43.455 56.545 7.33 224 0.875 bicubic -53.464 -50.727 -55 -59
271 tf_efficientnet_lite1 25.499 74.501 43.585 56.415 5.42 240 0.882 bicubic -51.143 -49.641 +21 +20
272 efficientnet_b1 tv_resnext50_32x4d 25.469 25.455 74.531 74.545 43.284 42.787 56.716 57.213 7.79 25.03 240 224 0.875 bicubic bilinear -53.229 -52.165 -50.860 -50.909 -48 -11
273 tv_resnext50_32x4d repvgg_a2 25.455 25.436 74.545 74.564 42.787 43.939 57.213 56.061 25.03 28.21 224 0.875 bilinear -52.165 -51.024 -50.909 -49.065 -10 +24
274 repvgg_a2 tf_mixnet_l 25.436 25.422 74.564 74.578 43.939 42.534 56.061 57.466 28.21 7.33 224 0.875 bilinear bicubic -51.024 -53.352 -49.065 -51.464 +23 -55
275 tf_mixnet_l hardcorenas_b 25.422 25.402 74.578 74.598 42.534 44.190 57.466 55.810 7.33 5.18 224 0.875 bicubic bilinear -53.352 -51.136 -51.464 -48.564 -53 +20
276 hardcorenas_b res2next50 25.402 25.389 74.598 74.611 44.190 42.508 55.810 57.492 5.18 24.67 224 0.875 bilinear -51.136 -52.857 -48.564 -51.384 +19 -38
277 res2next50 legacy_seresnet101 25.389 25.334 74.611 74.666 42.508 42.825 57.492 57.175 24.67 49.33 224 0.875 bilinear -52.857 -53.048 -51.384 -51.439 -36 -43
278 legacy_seresnet101 selecsls60b 25.334 25.332 74.666 74.668 42.825 43.559 57.175 56.441 49.33 32.77 224 0.875 bilinear bicubic -53.048 -53.080 -51.439 -50.615 -41 -46
279 selecsls60b dla102 25.332 25.316 74.668 74.684 43.559 43.827 56.441 56.173 32.77 33.27 224 0.875 bicubic bilinear -53.080 -52.716 -50.615 -50.119 -43 -32
dla102 25.316 74.684 43.827 56.173 33.27 224 0.875 bilinear -52.716 -50.119 -30
280 hardcorenas_d 25.300 74.700 43.121 56.879 7.50 224 0.875 bilinear -52.132 -50.363 -10
281 resnest14d 25.284 74.716 44.114 55.886 10.61 224 0.875 bilinear -50.220 -50.222 -48.404 +27 +28
282 legacy_seresnext50_32x4d 25.210 74.790 41.936 58.064 27.56 224 0.875 bilinear -53.868 -52.500 -73 -76
283 res2net50_48w_2s mixer_b16_224 25.027 25.121 74.973 74.879 42.208 41.227 57.792 58.773 25.29 59.88 224 0.875 bilinear bicubic -52.495 -51.481 -51.346 -51.001 -16 +9
284 efficientnet_b0 res2net50_48w_2s 25.015 25.027 74.985 74.973 42.787 42.208 57.213 57.792 5.29 25.29 224 0.875 bicubic bilinear -52.683 -52.495 -50.745 -51.346 -24 -18
285 efficientnet_b0 25.015 74.985 42.787 57.213 5.29 224 0.875 bicubic -52.683 -50.745 -26
286 gluon_resnet34_v1b 24.939 75.061 42.243 57.757 21.80 224 0.875 bicubic -49.649 -49.747 +40
287 mobilenetv2_120d 24.937 75.063 43.058 56.942 5.83 224 0.875 bicubic -52.347 -50.434 -11 -12
288 dla60 24.933 75.067 43.296 56.704 22.04 224 0.875 bilinear -52.099 -50.022 -5 -6
289 regnety_016 24.811 75.189 42.616 57.384 11.20 224 0.875 bicubic -53.051 -51.104 -33 -35
290 tf_efficientnet_lite2 24.530 75.470 42.280 57.720 6.09 260 0.890 bicubic -52.938 -51.474 -21 -22
291 skresnet18 24.483 75.517 42.536 57.464 11.96 224 0.875 bicubic -48.555 -48.632 +46 +47
292 regnetx_016 24.473 75.527 42.514 57.486 9.19 224 0.875 bicubic -52.477 -50.906 -8 -9
293 pit_ti_distilled_224 24.406 75.594 42.730 57.270 5.10 224 0.900 bicubic -50.124 -49.366 +34
294 tf_efficientnet_lite0 24.373 75.627 42.487 57.513 4.65 224 0.875 bicubic -50.457 -49.689 +27
295 hardcorenas_a 24.369 75.631 43.284 56.716 5.26 224 0.875 bilinear -51.547 -49.230 +7
296 resnetv2_50x1_bitm 24.233 24.231 75.767 75.769 43.477 56.523 25.55 480 1.000 bilinear -55.939 -55.941 -52.149 -137 -144
297 tv_resnet50 24.070 75.930 41.313 58.687 25.56 224 0.875 bilinear -52.068 -51.551 +3
298 legacy_seresnet34 24.027 75.973 41.909 58.091 21.96 224 0.875 bilinear -50.781 -50.215 +24
299 resnet18d 23.929 76.071 42.300 57.700 11.71 224 0.875 bicubic -48.331 -48.396 +45 +46
300 efficientnet_lite0 23.909 76.091 42.088 57.912 4.65 224 0.875 bicubic -51.575 -50.422 +10
301 tv_densenet121 23.844 76.156 41.925 58.075 7.98 224 0.875 bicubic -50.894 -50.225 +22
302 efficientnet_es_pruned 23.828 76.172 41.995 58.005 5.44 224 0.875 bicubic -51.172 -50.453 +18
303 mobilenetv2_140 23.712 76.288 41.477 58.523 6.11 224 0.875 bicubic -52.804 -51.519 -7
304 mixnet_m 23.710 76.290 41.141 58.859 5.01 224 0.875 bicubic -53.550 -52.284 -27 -28
305 dla34 23.669 76.331 41.551 58.449 15.74 224 0.875 bilinear -50.961 -50.527 +20
306 legacy_seresnet50 23.651 76.349 40.091 59.909 28.09 224 0.875 bilinear -53.978 -53.657 -44 -46
307 ese_vovnet19b_dw 23.535 76.465 41.288 58.712 6.54 224 0.875 bicubic -53.263 -51.980 -18 -19
308 tf_mixnet_m 23.484 76.516 40.989 59.011 5.01 224 0.875 bicubic -53.458 -52.163 -23 -24
309 tv_resnet34 23.473 76.527 41.367 58.633 21.80 224 0.875 bilinear -49.839 -50.059 +26 +27
310 tf_efficientnet_em 23.359 76.641 40.404 59.596 6.90 240 0.882 bicubic -54.771 -53.640 -63 -66
311 selecsls42b 23.357 76.643 40.677 59.323 32.46 224 0.875 bicubic -53.817 -52.713 -33 -34
312 repvgg_b0 23.316 76.684 41.182 58.818 15.82 224 0.875 bilinear -51.837 -51.236 +2
313 mobilenetv2_110d 23.066 76.934 40.716 59.284 4.52 224 0.875 bicubic -51.970 -51.470 +6
314 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
318 tf_efficientnet_es 22.413 77.587 39.095 60.905 5.44 224 0.875 bicubic -54.180 -54.107 -25
319 hrnet_w18_small_v2 22.337 77.663 39.861 60.139 15.60 224 0.875 bilinear -52.777 -52.555 -3
320 regnety_008 22.119 77.881 38.900 61.100 6.26 224 0.875 bicubic -54.197 -54.166 -21
321 seresnext26t_32x4d 21.991 78.009 38.482 61.518 16.81 224 0.875 bicubic -55.995 -55.264 -69 -72
322 regnety_006 21.971 78.029 38.955 61.045 6.06 224 0.875 bicubic -53.275 -53.577 -9
323 regnetx_008 21.940 78.060 38.928 61.072 7.26 224 0.875 bicubic -53.098 -53.408 -5
324 resnet26d 21.907 78.094 38.619 61.381 16.01 224 0.875 bicubic -54.789 -54.531 -33 -34
325 semnasnet_100 21.903 78.097 38.600 61.400 3.89 224 0.875 bicubic -53.545 -54.004 -14
326 pit_ti_224 21.875 78.125 39.541 60.459 4.85 224 0.900 bicubic -51.037 -51.861 +13 +14
327 regnetx_006 21.738 78.263 38.904 61.096 6.20 224 0.875 bicubic -52.115 -52.768 +5 +6
328 vgg19_bn 21.628 78.373 39.283 60.717 143.68 224 0.875 bilinear -52.587 -52.559 +1
329 gluon_resnet18_v1b ghostnet_100 21.549 21.620 78.451 78.380 38.869 38.692 61.131 61.308 11.69 5.18 224 0.875 bicubic bilinear -49.287 -52.358 -50.893 -52.764 +21 +3
330 fbnetc_100 gluon_resnet18_v1b 21.484 21.549 78.516 78.451 38.161 38.869 61.839 61.131 5.57 11.69 224 0.875 bilinear bicubic -53.640 -49.287 -54.224 -50.893 -15 +22
331 mnasnet_100 fbnetc_100 21.350 21.484 78.650 78.516 37.719 38.161 62.281 61.839 4.38 5.57 224 0.875 bicubic bilinear -53.308 -53.640 -54.395 -54.224 -7 -16
332 resnet26 mnasnet_100 21.295 21.350 78.705 78.650 38.018 37.719 61.982 62.281 16.00 4.38 224 0.875 bicubic -53.997 -53.308 -54.552 -54.395 -20 -8
333 resnet26 21.295 78.705 38.018 61.982 16.00 224 0.875 bicubic -53.997 -54.552 -21
334 ssl_resnet18 21.278 78.722 39.113 60.887 11.69 224 0.875 bilinear -51.332 -52.303 +7
335 mixnet_s 21.254 78.746 38.187 61.813 4.13 224 0.875 bicubic -54.738 -54.609 -33 -34
336 seresnext26d_32x4d 21.252 78.748 37.311 62.689 16.81 224 0.875 bicubic -56.350 -56.297 -71 -74
337 legacy_seresnext26_32x4d 21.093 78.907 37.633 62.367 16.79 224 0.875 bicubic -56.011 -55.683 -56 -58
338 regnetx_004 20.898 79.102 37.566 62.434 5.16 224 0.875 bicubic -51.498 -53.264 +4
339 spnasnet_100 20.863 79.137 37.896 62.104 4.42 224 0.875 bilinear -53.221 -53.922 -8 -9
340 legacy_seresnet18 20.837 79.162 37.619 62.381 11.78 224 0.875 bicubic -50.905 -52.715 +8 +9
341 mobilenetv2_100 20.773 79.227 37.759 62.241 3.50 224 0.875 bicubic -52.197 -53.257 -2
342 tf_mixnet_s 20.470 79.530 36.607 63.393 4.13 224 0.875 bicubic -55.180 -56.021 -36 -37
343 regnety_004 20.415 79.585 37.002 62.998 4.34 224 0.875 bicubic -53.619 -54.750 -11 -12
344 hrnet_w18_small 20.368 79.632 37.093 62.907 13.19 224 0.875 bilinear -51.976 -51.974 -53.585 0
345 tf_mobilenetv3_large_075 20.366 79.634 36.764 63.236 3.99 224 0.875 bilinear -53.072 -54.586 -11
346 resnet18 20.228 79.772 37.261 62.739 11.69 224 0.875 bilinear -49.520 -51.817 +9 +10
347 vit_deit_tiny_patch16_224 mixer_l16_224 20.162 20.171 79.838 79.829 37.546 32.956 62.454 67.044 5.72 208.20 224 0.900 0.875 bicubic -52.007 -51.887 -53.572 -54.712 0 +1
348 tf_mobilenetv3_large_minimal_100 vit_deit_tiny_patch16_224 20.122 20.162 79.878 79.838 36.908 37.546 63.092 62.454 3.92 5.72 224 0.875 0.900 bilinear bicubic -52.126 -52.007 -53.722 -53.572 -2 -1
349 vgg16_bn tf_mobilenetv3_large_minimal_100 19.959 20.122 80.041 79.878 36.301 36.908 63.699 63.092 138.37 3.92 224 0.875 bilinear -53.391 -52.126 -55.205 -53.722 -14 -3
350 vgg19 vgg16_bn 17.929 19.959 82.071 80.041 33.054 36.301 66.946 63.699 143.67 138.37 224 0.875 bilinear -54.439 -53.391 -57.818 -55.205 -7 -15
351 vgg19 17.929 82.071 33.054 66.946 143.67 224 0.875 bilinear -54.439 -57.818 -8
352 vgg13_bn 17.802 82.198 34.039 65.961 133.05 224 0.875 bilinear -53.792 -56.337 -2
353 vgg16 17.540 82.460 32.773 67.227 138.36 224 0.875 bilinear -54.054 -57.609 -2
354 regnety_002 17.450 82.550 32.431 67.569 3.16 224 0.875 bicubic -52.802 -57.109 0

2
timm/models/cait.py
View File

@ -26,7 +26,7 @@ def _cfg(url='', **kwargs):
return {
'url': url,
'num_classes': 1000, 'input_size': (3, 384, 384), 'pool_size': None,
'crop_pct': .9, 'interpolation': 'bicubic', 'fixed_input_size': True,
'crop_pct': 1.0, 'interpolation': 'bicubic', 'fixed_input_size': True,
'mean': IMAGENET_DEFAULT_MEAN, 'std': IMAGENET_DEFAULT_STD,
'first_conv': 'patch_embed.proj', 'classifier': 'head',
**kwargs

479
timm/models/efficientnet.py
View File

@ -1,7 +1,10 @@
""" PyTorch EfficientNet Family
""" The EfficientNet Family in PyTorch
An implementation of EfficienNet that covers variety of related models with efficient architectures:
* EfficientNet-V2
- `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
* EfficientNet (B0-B8, L2 + Tensorflow pretrained AutoAug/RandAug/AdvProp/NoisyStudent weight ports)
- EfficientNet: Rethinking Model Scaling for CNNs - https://arxiv.org/abs/1905.11946
- CondConv: Conditionally Parameterized Convolutions for Efficient Inference - https://arxiv.org/abs/1904.04971
@ -22,23 +25,30 @@ An implementation of EfficienNet that covers variety of related models with effi
* And likely more...
Hacked together by / Copyright 2020 Ross Wightman
The majority of the above models (EfficientNet*, MixNet, MnasNet) and original weights were made available
by Mingxing Tan, Quoc Le, and other members of their Google Brain team. Thanks for consistently releasing
the models and weights open source!
Hacked together by / Copyright 2021 Ross Wightman
"""
from functools import partial
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
from .efficientnet_blocks import SqueezeExcite
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights,\
round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .features import FeatureInfo, FeatureHooks
from .helpers import build_model_with_cfg, default_cfg_for_features
from .layers import create_conv2d, create_classifier
from .registry import register_model
__all__ = ['EfficientNet']
__all__ = ['EfficientNet', 'EfficientNetFeatures']
def _cfg(url='', **kwargs):
@ -149,9 +159,20 @@ default_cfgs = {
url='https://imvl-automl-sh.oss-cn-shanghai.aliyuncs.com/darts/hyperml/hyperml/job_45403/outputs/effnetb3_pruned_5abcc29f.pth',
input_size=(3, 300, 300), pool_size=(10, 10), crop_pct=0.904, mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
'efficientnet_v2s': _cfg(
'efficientnetv2_rw_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/efficientnet_v2s_ra2_288-a6477665.pth',
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0), # FIXME WIP
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
'efficientnetv2_s': _cfg(
url='',
input_size=(3, 288, 288), test_input_size=(3, 384, 384), pool_size=(9, 9), crop_pct=1.0),
'efficientnetv2_m': _cfg(
url='',
input_size=(3, 320, 320), test_input_size=(3, 416, 416), pool_size=(10, 10), crop_pct=1.0),
'efficientnetv2_l': _cfg(
url='',
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnet_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_efficientnet_b0_aa-827b6e33.pth',
@ -298,6 +319,58 @@ default_cfgs = {
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 380, 380), pool_size=(12, 12), crop_pct=0.920, interpolation='bilinear'),
'tf_efficientnetv2_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s-eb54923e.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m-cc09e0cd.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_l': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l-d664b728.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_s_21ft1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21ft1k-d7dafa41.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m_21ft1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21ft1k-bf41664a.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_l_21ft1k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21ft1k-60127a9d.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_s_21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21k-6337ad01.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 300, 300), test_input_size=(3, 384, 384), pool_size=(10, 10), crop_pct=1.0),
'tf_efficientnetv2_m_21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21k-361418a2.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_l_21k': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21k-91a19ec9.pth',
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), num_classes=21843,
input_size=(3, 384, 384), test_input_size=(3, 480, 480), pool_size=(12, 12), crop_pct=1.0),
'tf_efficientnetv2_b0': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b0-c7cc451f.pth',
input_size=(3, 192, 192), test_input_size=(3, 224, 224), pool_size=(6, 6)),
'tf_efficientnetv2_b1': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b1-be6e41b0.pth',
input_size=(3, 192, 192), test_input_size=(3, 240, 240), pool_size=(6, 6), crop_pct=0.882),
'tf_efficientnetv2_b2': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b2-847de54e.pth',
input_size=(3, 208, 208), test_input_size=(3, 260, 260), pool_size=(7, 7), crop_pct=0.890),
'tf_efficientnetv2_b3': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_b3-57773f13.pth',
input_size=(3, 240, 240), test_input_size=(3, 300, 300), pool_size=(8, 8), crop_pct=0.904),
'mixnet_s': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mixnet_s-a907afbc.pth'),
'mixnet_m': _cfg(
@ -316,13 +389,12 @@ default_cfgs = {
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mixnet_l-6c92e0c8.pth'),
}
_DEBUG = False
class EfficientNet(nn.Module):
""" (Generic) EfficientNet
A flexible and performant PyTorch implementation of efficient network architectures, including:
* EfficientNet-V2 Small, Medium, Large & B0-B3
* EfficientNet B0-B8, L2
* EfficientNet-EdgeTPU
* EfficientNet-CondConv
@ -333,35 +405,35 @@ class EfficientNet(nn.Module):
"""
def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32,
channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
def __init__(self, block_args, num_classes=1000, num_features=1280, in_chans=3, stem_size=32, fix_stem=False,
output_stride=32, pad_type='', round_chs_fn=round_channels, act_layer=None, norm_layer=None,
se_layer=None, drop_rate=0., drop_path_rate=0., global_pool='avg'):
super(EfficientNet, self).__init__()
norm_kwargs = norm_kwargs or {}
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
# Head + Pooling
self.conv_head = create_conv2d(head_chs, self.num_features, 1, padding=pad_type)
self.bn2 = norm_layer(self.num_features, **norm_kwargs)
self.bn2 = norm_layer(self.num_features)
self.act2 = act_layer(inplace=True)
self.global_pool, self.classifier = create_classifier(
self.num_features, self.num_classes, pool_type=global_pool)
@ -408,25 +480,27 @@ class EfficientNetFeatures(nn.Module):
and object detection models.
"""
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck',
in_chans=3, stem_size=32, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', fix_stem=False, act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None):
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck', in_chans=3,
stem_size=32, fix_stem=False, output_stride=32, pad_type='', round_chs_fn=round_channels,
act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
super(EfficientNetFeatures, self).__init__()
norm_kwargs = norm_kwargs or {}
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
# Stem
if not fix_stem:
stem_size = round_channels(stem_size, channel_multiplier, channel_divisor, channel_min)
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, channel_divisor, channel_min, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate,
feature_location=feature_location)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
@ -505,8 +579,8 @@ def _gen_mnasnet_a1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -541,8 +615,8 @@ def _gen_mnasnet_b1(variant, channel_multiplier=1.0, pretrained=False, **kwargs)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -570,8 +644,8 @@ def _gen_mnasnet_small(variant, channel_multiplier=1.0, pretrained=False, **kwar
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=8,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -593,13 +667,14 @@ def _gen_mobilenet_v2(
['ir_r3_k3_s2_e6_c160'],
['ir_r1_k3_s1_e6_c320'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier=depth_multiplier, fix_first_last=fix_stem_head),
num_features=1280 if fix_stem_head else round_channels(1280, channel_multiplier, 8, None),
num_features=1280 if fix_stem_head else round_chs_fn(1280),
stem_size=32,
fix_stem=fix_stem_head,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu6'),
**kwargs
)
@ -629,8 +704,8 @@ def _gen_fbnetc(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
block_args=decode_arch_def(arch_def),
stem_size=16,
num_features=1984, # paper suggests this, but is not 100% clear
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -664,8 +739,8 @@ def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -705,13 +780,14 @@ def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, pre
['ir_r4_k5_s2_e6_c192_se0.25'],
['ir_r1_k3_s1_e6_c320_se0.25'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
num_features=round_chs_fn(1280),
stem_size=32,
channel_multiplier=channel_multiplier,
round_chs_fn=round_chs_fn,
act_layer=resolve_act_layer(kwargs, 'swish'),
norm_kwargs=resolve_bn_args(kwargs),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -734,12 +810,13 @@ def _gen_efficientnet_edge(variant, channel_multiplier=1.0, depth_multiplier=1.0
['ir_r4_k5_s1_e8_c144'],
['ir_r2_k5_s2_e8_c192'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
num_features=round_chs_fn(1280),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'relu'),
**kwargs,
)
@ -764,12 +841,13 @@ def _gen_efficientnet_condconv(
]
# NOTE unlike official impl, this one uses `cc<x>` option where x is the base number of experts for each stage and
# the expert_multiplier increases that on a per-model basis as with depth/channel multipliers
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier, experts_multiplier=experts_multiplier),
num_features=round_channels(1280, channel_multiplier, 8, None),
num_features=round_chs_fn(1280),
stem_size=32,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'swish'),
**kwargs,
)
@ -809,45 +887,137 @@ def _gen_efficientnet_lite(variant, channel_multiplier=1.0, depth_multiplier=1.0
num_features=1280,
stem_size=32,
fix_stem=True,
channel_multiplier=channel_multiplier,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
act_layer=resolve_act_layer(kwargs, 'relu6'),
norm_kwargs=resolve_bn_args(kwargs),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnet_v2s(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2s model
def _gen_efficientnetv2_base(
variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 base model
NOTE: this is a preliminary definition based on paper, awaiting official code release for details
and weights
Ref impl:
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
# FIXME it's not clear if the FusedMBConv layers have SE enabled for the Small variant,
# Table 4 suggests no. 23.94M params w/o, 23.98 with which is closer to 24M.
# ['er_r2_k3_s1_e1_c24_se0.25'],
# ['er_r4_k3_s2_e4_c48_se0.25'],
# ['er_r4_k3_s2_e4_c64_se0.25'],
['er_r2_k3_s1_e1_c24'],
['cn_r1_k3_s1_e1_c16_skip'],
['er_r2_k3_s2_e4_c32'],
['er_r2_k3_s2_e4_c48'],
['ir_r3_k3_s2_e4_c96_se0.25'],
['ir_r5_k3_s1_e6_c112_se0.25'],
['ir_r8_k3_s2_e6_c192_se0.25'],
]
round_chs_fn = partial(round_channels, multiplier=channel_multiplier, round_limit=0.)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_chs_fn(1280),
stem_size=32,
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_s(
variant, channel_multiplier=1.0, depth_multiplier=1.0, rw=False, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Small model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
NOTE: `rw` flag sets up 'small' variant to behave like my initial v2 small model,
before ref the impl was released.
"""
arch_def = [
['cn_r2_k3_s1_e1_c24_skip'],
['er_r4_k3_s2_e4_c48'],
['er_r4_k3_s2_e4_c64'],
['ir_r6_k3_s2_e4_c128_se0.25'],
['ir_r9_k3_s1_e6_c160_se0.25'],
['ir_r15_k3_s2_e6_c272_se0.25'],
['ir_r15_k3_s2_e6_c256_se0.25'],
]
num_features = 1280
if rw:
# my original variant, based on paper figure differs from the official release
arch_def[0] = ['er_r2_k3_s1_e1_c24']
arch_def[-1] = ['ir_r15_k3_s2_e6_c272_se0.25']
num_features = 1792
round_chs_fn = partial(round_channels, multiplier=channel_multiplier)
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=round_channels(1792, channel_multiplier, 8, None),
num_features=round_chs_fn(num_features),
stem_size=24,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
act_layer=resolve_act_layer(kwargs, 'silu'), # FIXME this is an assumption, paper does not mention
round_chs_fn=round_chs_fn,
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Medium model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r3_k3_s1_e1_c24_skip'],
['er_r5_k3_s2_e4_c48'],
['er_r5_k3_s2_e4_c80'],
['ir_r7_k3_s2_e4_c160_se0.25'],
['ir_r14_k3_s1_e6_c176_se0.25'],
['ir_r18_k3_s2_e6_c304_se0.25'],
['ir_r5_k3_s1_e6_c512_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=1280,
stem_size=24,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
return model
def _gen_efficientnetv2_l(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs):
""" Creates an EfficientNet-V2 Large model
Ref impl: https://github.com/google/automl/tree/master/efficientnetv2
Paper: `EfficientNetV2: Smaller Models and Faster Training` - https://arxiv.org/abs/2104.00298
"""
arch_def = [
['cn_r4_k3_s1_e1_c32_skip'],
['er_r7_k3_s2_e4_c64'],
['er_r7_k3_s2_e4_c96'],
['ir_r10_k3_s2_e4_c192_se0.25'],
['ir_r19_k3_s1_e6_c224_se0.25'],
['ir_r25_k3_s2_e6_c384_se0.25'],
['ir_r7_k3_s1_e6_c640_se0.25'],
]
model_kwargs = dict(
block_args=decode_arch_def(arch_def, depth_multiplier),
num_features=1280,
stem_size=32,
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'silu'),
**kwargs,
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -879,8 +1049,8 @@ def _gen_mixnet_s(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
block_args=decode_arch_def(arch_def),
num_features=1536,
stem_size=16,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -912,8 +1082,8 @@ def _gen_mixnet_m(variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrai
block_args=decode_arch_def(arch_def, depth_multiplier, depth_trunc='round'),
num_features=1536,
stem_size=24,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
**kwargs
)
model = _create_effnet(variant, pretrained, **model_kwargs)
@ -1290,13 +1460,35 @@ def efficientnet_b3_pruned(pretrained=False, **kwargs):
@register_model
def efficientnet_v2s(pretrained=False, **kwargs):
""" EfficientNet-V2 Small. """
model = _gen_efficientnet_v2s(
'efficientnet_v2s', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs)
def efficientnetv2_rw_s(pretrained=False, **kwargs):
""" EfficientNet-V2 Small.
NOTE: This is my initial (pre official code release) w/ some differences.
See efficientnetv2_s and tf_efficientnetv2_s for versions that match the official w/ PyTorch vs TF padding
"""
model = _gen_efficientnetv2_s('efficientnetv2_rw_s', rw=True, pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_s(pretrained=False, **kwargs):
""" EfficientNet-V2 Small. """
model = _gen_efficientnetv2_s('efficientnetv2_s', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_m(pretrained=False, **kwargs):
""" EfficientNet-V2 Medium. """
model = _gen_efficientnetv2_m('efficientnetv2_m', pretrained=pretrained, **kwargs)
return model
@register_model
def efficientnetv2_l(pretrained=False, **kwargs):
""" EfficientNet-V2 Large. """
model = _gen_efficientnetv2_l('efficientnetv2_l', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnet_b0(pretrained=False, **kwargs):
@ -1708,6 +1900,133 @@ def tf_efficientnet_lite4(pretrained=False, **kwargs):
return model
@register_model
def tf_efficientnetv2_s(pretrained=False, **kwargs):
""" EfficientNet-V2 Small. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_s('tf_efficientnetv2_s', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_m(pretrained=False, **kwargs):
""" EfficientNet-V2 Medium. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_m('tf_efficientnetv2_m', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_l(pretrained=False, **kwargs):
""" EfficientNet-V2 Large. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_l('tf_efficientnetv2_l', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_s_21ft1k(pretrained=False, **kwargs):
""" EfficientNet-V2 Small. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21ft1k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_m_21ft1k(pretrained=False, **kwargs):
""" EfficientNet-V2 Medium. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21ft1k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_l_21ft1k(pretrained=False, **kwargs):
""" EfficientNet-V2 Large. Pretrained on ImageNet-21k, fine-tuned on 1k. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21ft1k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_s_21k(pretrained=False, **kwargs):
""" EfficientNet-V2 Small w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_m_21k(pretrained=False, **kwargs):
""" EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_l_21k(pretrained=False, **kwargs):
""" EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant
"""
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_l('tf_efficientnetv2_l_21k', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b0(pretrained=False, **kwargs):
""" EfficientNet-V2-B0. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_base('tf_efficientnetv2_b0', pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b1(pretrained=False, **kwargs):
""" EfficientNet-V2-B1. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b1', channel_multiplier=1.0, depth_multiplier=1.1, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b2(pretrained=False, **kwargs):
""" EfficientNet-V2-B2. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b2', channel_multiplier=1.1, depth_multiplier=1.2, pretrained=pretrained, **kwargs)
return model
@register_model
def tf_efficientnetv2_b3(pretrained=False, **kwargs):
""" EfficientNet-V2-B3. Tensorflow compatible variant """
kwargs['bn_eps'] = BN_EPS_TF_DEFAULT
kwargs['pad_type'] = 'same'
model = _gen_efficientnetv2_base(
'tf_efficientnetv2_b3', channel_multiplier=1.2, depth_multiplier=1.4, pretrained=pretrained, **kwargs)
return model
@register_model
def mixnet_s(pretrained=False, **kwargs):
"""Creates a MixNet Small model.

232
timm/models/efficientnet_blocks.py
View File

@ -7,106 +7,34 @@ import torch
import torch.nn as nn
from torch.nn import functional as F
from .layers import create_conv2d, drop_path, get_act_layer
from .layers import create_conv2d, drop_path, make_divisible
from .layers.activations import sigmoid
# Defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
# papers and TF reference implementations. PT momentum equiv for TF decay is (1 - TF decay)
# NOTE: momentum varies btw .99 and .9997 depending on source
# .99 in official TF TPU impl
# .9997 (/w .999 in search space) for paper
BN_MOMENTUM_TF_DEFAULT = 1 - 0.99
BN_EPS_TF_DEFAULT = 1e-3
_BN_ARGS_TF = dict(momentum=BN_MOMENTUM_TF_DEFAULT, eps=BN_EPS_TF_DEFAULT)
def get_bn_args_tf():
return _BN_ARGS_TF.copy()
def resolve_bn_args(kwargs):
bn_args = get_bn_args_tf() if kwargs.pop('bn_tf', False) else {}
bn_momentum = kwargs.pop('bn_momentum', None)
if bn_momentum is not None:
bn_args['momentum'] = bn_momentum
bn_eps = kwargs.pop('bn_eps', None)
if bn_eps is not None:
bn_args['eps'] = bn_eps
return bn_args
_SE_ARGS_DEFAULT = dict(
gate_fn=sigmoid,
act_layer=None,
reduce_mid=False,
divisor=1)
def resolve_se_args(kwargs, in_chs, act_layer=None):
se_kwargs = kwargs.copy() if kwargs is not None else {}
# fill in args that aren't specified with the defaults
for k, v in _SE_ARGS_DEFAULT.items():
se_kwargs.setdefault(k, v)
# some models, like MobilNetV3, calculate SE reduction chs from the containing block's mid_ch instead of in_ch
if not se_kwargs.pop('reduce_mid'):
se_kwargs['reduced_base_chs'] = in_chs
# act_layer override, if it remains None, the containing block's act_layer will be used
if se_kwargs['act_layer'] is None:
assert act_layer is not None
se_kwargs['act_layer'] = act_layer
return se_kwargs
def resolve_act_layer(kwargs, default='relu'):
act_layer = kwargs.pop('act_layer', default)
if isinstance(act_layer, str):
act_layer = get_act_layer(act_layer)
return act_layer
def make_divisible(v, divisor=8, min_value=None):
min_value = min_value or divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
new_v += divisor
return new_v
def round_channels(channels, multiplier=1.0, divisor=8, channel_min=None):
"""Round number of filters based on depth multiplier."""
if not multiplier:
return channels
channels *= multiplier
return make_divisible(channels, divisor, channel_min)
class ChannelShuffle(nn.Module):
# FIXME haven't used yet
def __init__(self, groups):
super(ChannelShuffle, self).__init__()
self.groups = groups
def forward(self, x):
"""Channel shuffle: [N,C,H,W] -> [N,g,C/g,H,W] -> [N,C/g,g,H,w] -> [N,C,H,W]"""
N, C, H, W = x.size()
g = self.groups
assert C % g == 0, "Incompatible group size {} for input channel {}".format(
g, C
)
return (
x.view(N, g, int(C / g), H, W)
.permute(0, 2, 1, 3, 4)
.contiguous()
.view(N, C, H, W)
)
__all__ = [
'SqueezeExcite', 'ConvBnAct', 'DepthwiseSeparableConv', 'InvertedResidual', 'CondConvResidual', 'EdgeResidual']
class SqueezeExcite(nn.Module):
def __init__(self, in_chs, se_ratio=0.25, reduced_base_chs=None,
act_layer=nn.ReLU, gate_fn=sigmoid, divisor=1, **_):
""" Squeeze-and-Excitation w/ specific features for EfficientNet/MobileNet family
Args:
in_chs (int): input channels to layer
se_ratio (float): ratio of squeeze reduction
act_layer (nn.Module): activation layer of containing block
gate_fn (Callable): attention gate function
block_in_chs (int): input channels of containing block (for calculating reduction from)
reduce_from_block (bool): calculate reduction from block input channels if True
force_act_layer (nn.Module): override block's activation fn if this is set/bound
divisor (int): make reduction channels divisible by this
"""
def __init__(
self, in_chs, se_ratio=0.25, act_layer=nn.ReLU, gate_fn=sigmoid,
block_in_chs=None, reduce_from_block=True, force_act_layer=None, divisor=1):
super(SqueezeExcite, self).__init__()
reduced_chs = make_divisible((reduced_base_chs or in_chs) * se_ratio, divisor)
reduced_chs = (block_in_chs or in_chs) if reduce_from_block else in_chs
reduced_chs = make_divisible(reduced_chs * se_ratio, divisor)
act_layer = force_act_layer or act_layer
self.conv_reduce = nn.Conv2d(in_chs, reduced_chs, 1, bias=True)
self.act1 = act_layer(inplace=True)
self.conv_expand = nn.Conv2d(reduced_chs, in_chs, 1, bias=True)
@ -121,13 +49,16 @@ class SqueezeExcite(nn.Module):
class ConvBnAct(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU,
norm_layer=nn.BatchNorm2d, norm_kwargs=None):
""" Conv + Norm Layer + Activation w/ optional skip connection
"""
def __init__(
self, in_chs, out_chs, kernel_size, stride=1, dilation=1, pad_type='',
skip=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_path_rate=0.):
super(ConvBnAct, self).__init__()
norm_kwargs = norm_kwargs or {}
self.has_residual = skip and stride == 1 and in_chs == out_chs
self.drop_path_rate = drop_path_rate
self.conv = create_conv2d(in_chs, out_chs, kernel_size, stride=stride, dilation=dilation, padding=pad_type)
self.bn1 = norm_layer(out_chs, **norm_kwargs)
self.bn1 = norm_layer(out_chs)
self.act1 = act_layer(inplace=True)
def feature_info(self, location):
@ -138,9 +69,14 @@ class ConvBnAct(nn.Module):
return info
def forward(self, x):
shortcut = x
x = self.conv(x)
x = self.bn1(x)
x = self.act1(x)
if self.has_residual:
if self.drop_path_rate > 0.:
x = drop_path(x, self.drop_path_rate, self.training)
x += shortcut
return x
@ -149,31 +85,26 @@ class DepthwiseSeparableConv(nn.Module):
Used for DS convs in MobileNet-V1 and in the place of IR blocks that have no expansion
(factor of 1.0). This is an alternative to having a IR with an optional first pw conv.
"""
def __init__(self, in_chs, out_chs, dw_kernel_size=3,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
pw_kernel_size=1, pw_act=False, se_ratio=0., se_kwargs=None,
norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0.):
def __init__(
self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
noskip=False, pw_kernel_size=1, pw_act=False, se_ratio=0.,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, se_layer=None, drop_path_rate=0.):
super(DepthwiseSeparableConv, self).__init__()
norm_kwargs = norm_kwargs or {}
has_se = se_ratio is not None and se_ratio > 0.
has_se = se_layer is not None and se_ratio > 0.
self.has_residual = (stride == 1 and in_chs == out_chs) and not noskip
self.has_pw_act = pw_act # activation after point-wise conv
self.drop_path_rate = drop_path_rate
self.conv_dw = create_conv2d(
in_chs, in_chs, dw_kernel_size, stride=stride, dilation=dilation, padding=pad_type, depthwise=True)
self.bn1 = norm_layer(in_chs, **norm_kwargs)
self.bn1 = norm_layer(in_chs)
self.act1 = act_layer(inplace=True)
# Squeeze-and-excitation
if has_se:
se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
self.se = SqueezeExcite(in_chs, se_ratio=se_ratio, **se_kwargs)
else:
self.se = None
self.se = se_layer(in_chs, se_ratio=se_ratio, act_layer=act_layer) if has_se else nn.Identity()
self.conv_pw = create_conv2d(in_chs, out_chs, pw_kernel_size, padding=pad_type)
self.bn2 = norm_layer(out_chs, **norm_kwargs)
self.bn2 = norm_layer(out_chs)
self.act2 = act_layer(inplace=True) if self.has_pw_act else nn.Identity()
def feature_info(self, location):
@ -190,8 +121,7 @@ class DepthwiseSeparableConv(nn.Module):
x = self.bn1(x)
x = self.act1(x)
if self.se is not None:
x = self.se(x)
x = self.se(x)
x = self.conv_pw(x)
x = self.bn2(x)
@ -214,41 +144,36 @@ class InvertedResidual(nn.Module):
* MobileNet-V3 - https://arxiv.org/abs/1905.02244
"""
def __init__(self, in_chs, out_chs, dw_kernel_size=3,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
conv_kwargs=None, drop_path_rate=0.):
def __init__(
self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
noskip=False, exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1, se_ratio=0.,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, se_layer=None, conv_kwargs=None, drop_path_rate=0.):
super(InvertedResidual, self).__init__()
norm_kwargs = norm_kwargs or {}
conv_kwargs = conv_kwargs or {}
mid_chs = make_divisible(in_chs * exp_ratio)
has_se = se_ratio is not None and se_ratio > 0.
has_se = se_layer is not None and se_ratio > 0.
self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
self.drop_path_rate = drop_path_rate
# Point-wise expansion
self.conv_pw = create_conv2d(in_chs, mid_chs, exp_kernel_size, padding=pad_type, **conv_kwargs)
self.bn1 = norm_layer(mid_chs, **norm_kwargs)
self.bn1 = norm_layer(mid_chs)
self.act1 = act_layer(inplace=True)
# Depth-wise convolution
self.conv_dw = create_conv2d(
mid_chs, mid_chs, dw_kernel_size, stride=stride, dilation=dilation,
padding=pad_type, depthwise=True, **conv_kwargs)
self.bn2 = norm_layer(mid_chs, **norm_kwargs)
self.bn2 = norm_layer(mid_chs)
self.act2 = act_layer(inplace=True)
# Squeeze-and-excitation
if has_se:
se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio, **se_kwargs)
else:
self.se = None
self.se = se_layer(
mid_chs, se_ratio=se_ratio, act_layer=act_layer, block_in_chs=in_chs) if has_se else nn.Identity()
# Point-wise linear projection
self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type, **conv_kwargs)
self.bn3 = norm_layer(out_chs, **norm_kwargs)
self.bn3 = norm_layer(out_chs)
def feature_info(self, location):
if location == 'expansion': # after SE, input to PWL
@ -271,8 +196,7 @@ class InvertedResidual(nn.Module):
x = self.act2(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
x = self.se(x)
# Point-wise linear projection
x = self.conv_pwl(x)
@ -289,11 +213,10 @@ class InvertedResidual(nn.Module):
class CondConvResidual(InvertedResidual):
""" Inverted residual block w/ CondConv routing"""
def __init__(self, in_chs, out_chs, dw_kernel_size=3,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False,
exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1,
se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
num_experts=0, drop_path_rate=0.):
def __init__(
self, in_chs, out_chs, dw_kernel_size=3, stride=1, dilation=1, pad_type='',
noskip=False, exp_ratio=1.0, exp_kernel_size=1, pw_kernel_size=1, se_ratio=0.,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, se_layer=None, num_experts=0, drop_path_rate=0.):
self.num_experts = num_experts
conv_kwargs = dict(num_experts=self.num_experts)
@ -301,9 +224,8 @@ class CondConvResidual(InvertedResidual):
super(CondConvResidual, self).__init__(
in_chs, out_chs, dw_kernel_size=dw_kernel_size, stride=stride, dilation=dilation, pad_type=pad_type,
act_layer=act_layer, noskip=noskip, exp_ratio=exp_ratio, exp_kernel_size=exp_kernel_size,
pw_kernel_size=pw_kernel_size, se_ratio=se_ratio, se_kwargs=se_kwargs,
norm_layer=norm_layer, norm_kwargs=norm_kwargs, conv_kwargs=conv_kwargs,
drop_path_rate=drop_path_rate)
pw_kernel_size=pw_kernel_size, se_ratio=se_ratio, se_layer=se_layer,
norm_layer=norm_layer, conv_kwargs=conv_kwargs, drop_path_rate=drop_path_rate)
self.routing_fn = nn.Linear(in_chs, self.num_experts)
@ -325,8 +247,7 @@ class CondConvResidual(InvertedResidual):
x = self.act2(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
x = self.se(x)
# Point-wise linear projection
x = self.conv_pwl(x, routing_weights)
@ -351,36 +272,32 @@ class EdgeResidual(nn.Module):
* EfficientNet-V2 - https://arxiv.org/abs/2104.00298
"""
def __init__(self, in_chs, out_chs, exp_kernel_size=3, exp_ratio=1.0, fake_in_chs=0,
stride=1, dilation=1, pad_type='', act_layer=nn.ReLU, noskip=False, pw_kernel_size=1,
se_ratio=0., se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None,
drop_path_rate=0.):
def __init__(
self, in_chs, out_chs, exp_kernel_size=3, stride=1, dilation=1, pad_type='',
force_in_chs=0, noskip=False, exp_ratio=1.0, pw_kernel_size=1, se_ratio=0.,
act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, se_layer=None, drop_path_rate=0.):
super(EdgeResidual, self).__init__()
norm_kwargs = norm_kwargs or {}
if fake_in_chs > 0:
mid_chs = make_divisible(fake_in_chs * exp_ratio)
if force_in_chs > 0:
mid_chs = make_divisible(force_in_chs * exp_ratio)
else:
mid_chs = make_divisible(in_chs * exp_ratio)
has_se = se_ratio is not None and se_ratio > 0.
has_se = se_layer is not None and se_ratio > 0.
self.has_residual = (in_chs == out_chs and stride == 1) and not noskip
self.drop_path_rate = drop_path_rate
# Expansion convolution
self.conv_exp = create_conv2d(
in_chs, mid_chs, exp_kernel_size, stride=stride, dilation=dilation, padding=pad_type)
self.bn1 = norm_layer(mid_chs, **norm_kwargs)
self.bn1 = norm_layer(mid_chs)
self.act1 = act_layer(inplace=True)
# Squeeze-and-excitation
if has_se:
se_kwargs = resolve_se_args(se_kwargs, in_chs, act_layer)
self.se = SqueezeExcite(mid_chs, se_ratio=se_ratio, **se_kwargs)
else:
self.se = None
self.se = SqueezeExcite(
mid_chs, se_ratio=se_ratio, act_layer=act_layer, block_in_chs=in_chs) if has_se else nn.Identity()
# Point-wise linear projection
self.conv_pwl = create_conv2d(mid_chs, out_chs, pw_kernel_size, padding=pad_type)
self.bn2 = norm_layer(out_chs, **norm_kwargs)
self.bn2 = norm_layer(out_chs)
def feature_info(self, location):
if location == 'expansion': # after SE, before PWL
@ -398,8 +315,7 @@ class EdgeResidual(nn.Module):
x = self.act1(x)
# Squeeze-and-excitation
if self.se is not None:
x = self.se(x)
x = self.se(x)
# Point-wise linear projection
x = self.conv_pwl(x)

110
timm/models/efficientnet_builder.py
View File

@ -14,13 +14,55 @@ from copy import deepcopy
import torch.nn as nn
from .efficientnet_blocks import *
from .layers import CondConv2d, get_condconv_initializer
from .layers import CondConv2d, get_condconv_initializer, get_act_layer, make_divisible
__all__ = ["EfficientNetBuilder", "decode_arch_def", "efficientnet_init_weights"]
__all__ = ["EfficientNetBuilder", "decode_arch_def", "efficientnet_init_weights",
'resolve_bn_args', 'resolve_act_layer', 'round_channels', 'BN_MOMENTUM_TF_DEFAULT', 'BN_EPS_TF_DEFAULT']
_logger = logging.getLogger(__name__)
_DEBUG_BUILDER = False
# Defaults used for Google/Tensorflow training of mobile networks /w RMSprop as per
# papers and TF reference implementations. PT momentum equiv for TF decay is (1 - TF decay)
# NOTE: momentum varies btw .99 and .9997 depending on source
# .99 in official TF TPU impl
# .9997 (/w .999 in search space) for paper
BN_MOMENTUM_TF_DEFAULT = 1 - 0.99
BN_EPS_TF_DEFAULT = 1e-3
_BN_ARGS_TF = dict(momentum=BN_MOMENTUM_TF_DEFAULT, eps=BN_EPS_TF_DEFAULT)
def get_bn_args_tf():
return _BN_ARGS_TF.copy()
def resolve_bn_args(kwargs):
bn_args = get_bn_args_tf() if kwargs.pop('bn_tf', False) else {}
bn_momentum = kwargs.pop('bn_momentum', None)
if bn_momentum is not None:
bn_args['momentum'] = bn_momentum
bn_eps = kwargs.pop('bn_eps', None)
if bn_eps is not None:
bn_args['eps'] = bn_eps
return bn_args
def resolve_act_layer(kwargs, default='relu'):
act_layer = kwargs.pop('act_layer', default)
if isinstance(act_layer, str):
act_layer = get_act_layer(act_layer)
return act_layer
def round_channels(channels, multiplier=1.0, divisor=8, channel_min=None, round_limit=0.9):
"""Round number of filters based on depth multiplier."""
if not multiplier:
return channels
return make_divisible(channels * multiplier, divisor, channel_min, round_limit=round_limit)
def _log_info_if(msg, condition):
if condition:
_logger.info(msg)
@ -63,11 +105,13 @@ def _decode_block_str(block_str):
block_type = ops[0] # take the block type off the front
ops = ops[1:]
options = {}
noskip = False
skip = None
for op in ops:
# string options being checked on individual basis, combine if they grow
if op == 'noskip':
noskip = True
skip = False # force no skip connection
elif op == 'skip':
skip = True # force a skip connection
elif op.startswith('n'):
# activation fn
key = op[0]
@ -94,7 +138,7 @@ def _decode_block_str(block_str):
act_layer = options['n'] if 'n' in options else None
exp_kernel_size = _parse_ksize(options['a']) if 'a' in options else 1
pw_kernel_size = _parse_ksize(options['p']) if 'p' in options else 1
fake_in_chs = int(options['fc']) if 'fc' in options else 0 # FIXME hack to deal with in_chs issue in TPU def
force_in_chs = int(options['fc']) if 'fc' in options else 0 # FIXME hack to deal with in_chs issue in TPU def
num_repeat = int(options['r'])
# each type of block has different valid arguments, fill accordingly
@ -106,10 +150,10 @@ def _decode_block_str(block_str):
pw_kernel_size=pw_kernel_size,
out_chs=int(options['c']),
exp_ratio=float(options['e']),
se_ratio=float(options['se']) if 'se' in options else None,
se_ratio=float(options['se']) if 'se' in options else 0.,
stride=int(options['s']),
act_layer=act_layer,
noskip=noskip,
noskip=skip is False,
)
if 'cc' in options:
block_args['num_experts'] = int(options['cc'])
@ -119,11 +163,11 @@ def _decode_block_str(block_str):
dw_kernel_size=_parse_ksize(options['k']),
pw_kernel_size=pw_kernel_size,
out_chs=int(options['c']),
se_ratio=float(options['se']) if 'se' in options else None,
se_ratio=float(options['se']) if 'se' in options else 0.,
stride=int(options['s']),
act_layer=act_layer,
pw_act=block_type == 'dsa',
noskip=block_type == 'dsa' or noskip,
noskip=block_type == 'dsa' or skip is False,
)
elif block_type == 'er':
block_args = dict(
@ -132,11 +176,11 @@ def _decode_block_str(block_str):
pw_kernel_size=pw_kernel_size,
out_chs=int(options['c']),
exp_ratio=float(options['e']),
fake_in_chs=fake_in_chs,
se_ratio=float(options['se']) if 'se' in options else None,
force_in_chs=force_in_chs,
se_ratio=float(options['se']) if 'se' in options else 0.,
stride=int(options['s']),
act_layer=act_layer,
noskip=noskip,
noskip=skip is False,
)
elif block_type == 'cn':
block_args = dict(
@ -145,6 +189,7 @@ def _decode_block_str(block_str):
out_chs=int(options['c']),
stride=int(options['s']),
act_layer=act_layer,
skip=skip is True,
)
else:
assert False, 'Unknown block type (%s)' % block_type
@ -219,19 +264,14 @@ class EfficientNetBuilder:
https://github.com/facebookresearch/maskrcnn-benchmark/blob/master/maskrcnn_benchmark/modeling/backbone/fbnet_builder.py
"""
def __init__(self, channel_multiplier=1.0, channel_divisor=8, channel_min=None,
output_stride=32, pad_type='', act_layer=None, se_kwargs=None,
norm_layer=nn.BatchNorm2d, norm_kwargs=None, drop_path_rate=0., feature_location='',
verbose=False):
self.channel_multiplier = channel_multiplier
self.channel_divisor = channel_divisor
self.channel_min = channel_min
def __init__(self, output_stride=32, pad_type='', round_chs_fn=round_channels,
act_layer=None, norm_layer=None, se_layer=None, drop_path_rate=0., feature_location=''):
self.output_stride = output_stride
self.pad_type = pad_type
self.round_chs_fn = round_chs_fn
self.act_layer = act_layer
self.se_kwargs = se_kwargs
self.norm_layer = norm_layer
self.norm_kwargs = norm_kwargs
self.se_layer = se_layer
self.drop_path_rate = drop_path_rate
if feature_location == 'depthwise':
# old 'depthwise' mode renamed 'expansion' to match TF impl, old expansion mode didn't make sense
@ -239,45 +279,39 @@ class EfficientNetBuilder:
feature_location = 'expansion'
self.feature_location = feature_location
assert feature_location in ('bottleneck', 'expansion', '')
self.verbose = verbose
self.verbose = _DEBUG_BUILDER
# state updated during build, consumed by model
self.in_chs = None
self.features = []
def _round_channels(self, chs):
return round_channels(chs, self.channel_multiplier, self.channel_divisor, self.channel_min)
def _make_block(self, ba, block_idx, block_count):
drop_path_rate = self.drop_path_rate * block_idx / block_count
bt = ba.pop('block_type')
ba['in_chs'] = self.in_chs
ba['out_chs'] = self._round_channels(ba['out_chs'])
if 'fake_in_chs' in ba and ba['fake_in_chs']:
# FIXME this is a hack to work around mismatch in origin impl input filters
ba['fake_in_chs'] = self._round_channels(ba['fake_in_chs'])
ba['norm_layer'] = self.norm_layer
ba['norm_kwargs'] = self.norm_kwargs
ba['out_chs'] = self.round_chs_fn(ba['out_chs'])
if 'force_in_chs' in ba and ba['force_in_chs']:
# NOTE this is a hack to work around mismatch in TF EdgeEffNet impl
ba['force_in_chs'] = self.round_chs_fn(ba['force_in_chs'])
ba['pad_type'] = self.pad_type
# block act fn overrides the model default
ba['act_layer'] = ba['act_layer'] if ba['act_layer'] is not None else self.act_layer
assert ba['act_layer'] is not None
if bt == 'ir':
ba['norm_layer'] = self.norm_layer
if bt != 'cn':
ba['se_layer'] = self.se_layer
ba['drop_path_rate'] = drop_path_rate
ba['se_kwargs'] = self.se_kwargs
if bt == 'ir':
_log_info_if(' InvertedResidual {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
if ba.get('num_experts', 0) > 0:
block = CondConvResidual(**ba)
else:
block = InvertedResidual(**ba)
elif bt == 'ds' or bt == 'dsa':
ba['drop_path_rate'] = drop_path_rate
ba['se_kwargs'] = self.se_kwargs
_log_info_if(' DepthwiseSeparable {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = DepthwiseSeparableConv(**ba)
elif bt == 'er':
ba['drop_path_rate'] = drop_path_rate
ba['se_kwargs'] = self.se_kwargs
_log_info_if(' EdgeResidual {}, Args: {}'.format(block_idx, str(ba)), self.verbose)
block = EdgeResidual(**ba)
elif bt == 'cn':
@ -285,8 +319,8 @@ class EfficientNetBuilder:
block = ConvBnAct(**ba)
else:
assert False, 'Uknkown block type (%s) while building model.' % bt
self.in_chs = ba['out_chs'] # update in_chs for arg of next block
self.in_chs = ba['out_chs'] # update in_chs for arg of next block
return block
def __call__(self, in_chs, model_block_args):

5
timm/models/ghostnet.py
View File

@ -13,8 +13,8 @@ import torch.nn.functional as F
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .layers import SelectAdaptivePool2d, Linear, hard_sigmoid
from .efficientnet_blocks import SqueezeExcite, ConvBnAct, make_divisible
from .layers import SelectAdaptivePool2d, Linear, hard_sigmoid, make_divisible
from .efficientnet_blocks import SqueezeExcite, ConvBnAct
from .helpers import build_model_with_cfg
from .registry import register_model
@ -110,7 +110,6 @@ class GhostBottleneck(nn.Module):
nn.BatchNorm2d(out_chs),
)
def forward(self, x):
shortcut = x

22
timm/models/hardcorenas.py
View File

@ -1,10 +1,14 @@
from functools import partial
import torch.nn as nn
from .efficientnet_builder import decode_arch_def, resolve_bn_args
from .mobilenetv3 import MobileNetV3, MobileNetV3Features, build_model_with_cfg, default_cfg_for_features
from .layers import hard_sigmoid
from .efficientnet_blocks import resolve_act_layer
from .registry import register_model
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from .efficientnet_blocks import SqueezeExcite
from .efficientnet_builder import decode_arch_def, resolve_act_layer, resolve_bn_args
from .helpers import build_model_with_cfg, default_cfg_for_features
from .layers import get_act_fn
from .mobilenetv3 import MobileNetV3, MobileNetV3Features
from .registry import register_model
def _cfg(url='', **kwargs):
@ -35,15 +39,15 @@ def _gen_hardcorenas(pretrained, variant, arch_def, **kwargs):
"""
num_features = 1280
se_layer = partial(
SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, reduce_from_block=False, divisor=8)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=32,
channel_multiplier=1,
norm_kwargs=resolve_bn_args(kwargs),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisor=8),
se_layer=se_layer,
**kwargs,
)

6
timm/models/layers/helpers.py
View File

@ -22,10 +22,10 @@ to_4tuple = _ntuple(4)
to_ntuple = _ntuple
def make_divisible(v, divisor=8, min_value=None):
def make_divisible(v, divisor=8, min_value=None, round_limit=.9):
min_value = min_value or divisor
new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
# Make sure that round down does not go down by more than 10%.
if new_v < 0.9 * v:
if new_v < round_limit * v:
new_v += divisor
return new_v
return new_v

67
timm/models/mobilenetv3.py
View File

@ -5,23 +5,25 @@ A PyTorch impl of MobileNet-V3, compatible with TF weights from official impl.
Paper: Searching for MobileNetV3 - https://arxiv.org/abs/1905.02244
Hacked together by / Copyright 2020 Ross Wightman
Hacked together by / Copyright 2021 Ross Wightman
"""
from functools import partial
from typing import List
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List
from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
from .efficientnet_blocks import round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights
from .efficientnet_blocks import SqueezeExcite
from .efficientnet_builder import EfficientNetBuilder, decode_arch_def, efficientnet_init_weights,\
round_channels, resolve_bn_args, resolve_act_layer, BN_EPS_TF_DEFAULT
from .features import FeatureInfo, FeatureHooks
from .helpers import build_model_with_cfg, default_cfg_for_features
from .layers import SelectAdaptivePool2d, Linear, create_conv2d, get_act_fn, hard_sigmoid
from .registry import register_model
__all__ = ['MobileNetV3']
__all__ = ['MobileNetV3', 'MobileNetV3Features']
def _cfg(url='', **kwargs):
@ -47,9 +49,11 @@ default_cfgs = {
url='https://miil-public-eu.oss-eu-central-1.aliyuncs.com/model-zoo/ImageNet_21K_P/models/timm/mobilenetv3_large_100_in21k_miil.pth', num_classes=11221),
'mobilenetv3_small_075': _cfg(url=''),
'mobilenetv3_small_100': _cfg(url=''),
'mobilenetv3_rw': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/mobilenetv3_100-35495452.pth',
interpolation='bicubic'),
'tf_mobilenetv3_large_075': _cfg(
url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-weights/tf_mobilenetv3_large_075-150ee8b0.pth',
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
@ -70,8 +74,6 @@ default_cfgs = {
mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD),
}
_DEBUG = False
class MobileNetV3(nn.Module):
""" MobiletNet-V3
@ -84,24 +86,26 @@ class MobileNetV3(nn.Module):
"""
def __init__(self, block_args, num_classes=1000, in_chans=3, stem_size=16, num_features=1280, head_bias=True,
channel_multiplier=1.0, pad_type='', act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0.,
se_kwargs=None, norm_layer=nn.BatchNorm2d, norm_kwargs=None, global_pool='avg'):
pad_type='', act_layer=None, norm_layer=None, se_layer=None,
round_chs_fn=round_channels, drop_rate=0., drop_path_rate=0., global_pool='avg'):
super(MobileNetV3, self).__init__()
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.num_classes = num_classes
self.num_features = num_features
self.drop_rate = drop_rate
# Stem
stem_size = round_channels(stem_size, channel_multiplier)
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, 8, None, 32, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, verbose=_DEBUG)
output_stride=32, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer, drop_path_rate=drop_path_rate)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = builder.features
head_chs = builder.in_chs
@ -158,23 +162,25 @@ class MobileNetV3Features(nn.Module):
"""
def __init__(self, block_args, out_indices=(0, 1, 2, 3, 4), feature_location='bottleneck',
in_chans=3, stem_size=16, channel_multiplier=1.0, output_stride=32, pad_type='',
act_layer=nn.ReLU, drop_rate=0., drop_path_rate=0., se_kwargs=None,
norm_layer=nn.BatchNorm2d, norm_kwargs=None):
in_chans=3, stem_size=16, output_stride=32, pad_type='', round_chs_fn=round_channels,
act_layer=None, norm_layer=None, se_layer=None, drop_rate=0., drop_path_rate=0.):
super(MobileNetV3Features, self).__init__()
norm_kwargs = norm_kwargs or {}
act_layer = act_layer or nn.ReLU
norm_layer = norm_layer or nn.BatchNorm2d
se_layer = se_layer or SqueezeExcite
self.drop_rate = drop_rate
# Stem
stem_size = round_channels(stem_size, channel_multiplier)
stem_size = round_chs_fn(stem_size)
self.conv_stem = create_conv2d(in_chans, stem_size, 3, stride=2, padding=pad_type)
self.bn1 = norm_layer(stem_size, **norm_kwargs)
self.bn1 = norm_layer(stem_size)
self.act1 = act_layer(inplace=True)
# Middle stages (IR/ER/DS Blocks)
builder = EfficientNetBuilder(
channel_multiplier, 8, None, output_stride, pad_type, act_layer, se_kwargs,
norm_layer, norm_kwargs, drop_path_rate, feature_location=feature_location, verbose=_DEBUG)
output_stride=output_stride, pad_type=pad_type, round_chs_fn=round_chs_fn,
act_layer=act_layer, norm_layer=norm_layer, se_layer=se_layer,
drop_path_rate=drop_path_rate, feature_location=feature_location)
self.blocks = nn.Sequential(*builder(stem_size, block_args))
self.feature_info = FeatureInfo(builder.features, out_indices)
self._stage_out_idx = {v['stage']: i for i, v in enumerate(self.feature_info) if i in out_indices}
@ -253,10 +259,10 @@ def _gen_mobilenet_v3_rw(variant, channel_multiplier=1.0, pretrained=False, **kw
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
head_bias=False,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=resolve_act_layer(kwargs, 'hard_swish'),
se_kwargs=dict(gate_fn=get_act_fn('hard_sigmoid'), reduce_mid=True, divisor=1),
se_layer=partial(SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), reduce_from_block=False),
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)
@ -344,15 +350,16 @@ def _gen_mobilenet_v3(variant, channel_multiplier=1.0, pretrained=False, **kwarg
# stage 6, 7x7 in
['cn_r1_k1_s1_c960'], # hard-swish
]
se_layer = partial(
SqueezeExcite, gate_fn=get_act_fn('hard_sigmoid'), force_act_layer=nn.ReLU, reduce_from_block=False, divisor=8)
model_kwargs = dict(
block_args=decode_arch_def(arch_def),
num_features=num_features,
stem_size=16,
channel_multiplier=channel_multiplier,
norm_kwargs=resolve_bn_args(kwargs),
round_chs_fn=partial(round_channels, multiplier=channel_multiplier),
norm_layer=partial(nn.BatchNorm2d, **resolve_bn_args(kwargs)),
act_layer=act_layer,
se_kwargs=dict(act_layer=nn.ReLU, gate_fn=hard_sigmoid, reduce_mid=True, divisor=8),
se_layer=se_layer,
**kwargs,
)
model = _create_mnv3(variant, pretrained, **model_kwargs)

2
timm/version.py
View File

@ -1 +1 @@
__version__ = '0.4.8'
__version__ = '0.4.9'