From 4fbc32d3d073b48860c8e6603b76d1becc7f4af1 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Tue, 11 May 2021 10:50:15 -0700 Subject: [PATCH 1/4] Fix crop_pct for cait models. --- timm/models/cait.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/timm/models/cait.py b/timm/models/cait.py index b648e712..c5f7742f 100644 --- a/timm/models/cait.py +++ b/timm/models/cait.py @@ -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 From c16d65a8a7a27c0a0418cf601a5ee0fcbb740135 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Tue, 11 May 2021 10:50:46 -0700 Subject: [PATCH 2/4] Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights. --- results/results-imagenet-a-clean.csv | 85 ++- results/results-imagenet-a.csv | 623 +++++++++--------- results/results-imagenet-r-clean.csv | 87 ++- results/results-imagenet-r.csv | 567 ++++++++-------- results/results-imagenet-real.csv | 493 +++++++------- results/results-imagenet.csv | 53 +- .../results-imagenetv2-matched-frequency.csv | 441 +++++++------ results/results-sketch.csv | 545 +++++++-------- 8 files changed, 1539 insertions(+), 1355 deletions(-) diff --git a/results/results-imagenet-a-clean.csv b/results/results-imagenet-a-clean.csv index f4cdcb54..57baa83e 100644 --- a/results/results-imagenet-a-clean.csv +++ b/results/results-imagenet-a-clean.csv @@ -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 diff --git a/results/results-imagenet-a.csv b/results/results-imagenet-a.csv index 4abcb049..267802e7 100644 --- a/results/results-imagenet-a.csv +++ b/results/results-imagenet-a.csv @@ -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 diff --git a/results/results-imagenet-r-clean.csv b/results/results-imagenet-r-clean.csv index f0508ce7..3cc426ed 100644 --- a/results/results-imagenet-r-clean.csv +++ b/results/results-imagenet-r-clean.csv @@ -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 diff --git a/results/results-imagenet-r.csv b/results/results-imagenet-r.csv index a52ab62a..c0a01c3a 100644 --- a/results/results-imagenet-r.csv +++ b/results/results-imagenet-r.csv @@ -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 diff --git a/results/results-imagenet-real.csv b/results/results-imagenet-real.csv index f97c8bd7..fa0fda4a 100644 --- a/results/results-imagenet-real.csv +++ b/results/results-imagenet-real.csv @@ -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 diff --git a/results/results-imagenet.csv b/results/results-imagenet.csv index 611b2e69..321f01c1 100644 --- a/results/results-imagenet.csv +++ b/results/results-imagenet.csv @@ -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 diff --git a/results/results-imagenetv2-matched-frequency.csv b/results/results-imagenetv2-matched-frequency.csv index 63862538..b8238496 100644 --- a/results/results-imagenetv2-matched-frequency.csv +++ b/results/results-imagenetv2-matched-frequency.csv @@ -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 diff --git a/results/results-sketch.csv b/results/results-sketch.csv index 75f39d9f..f66179a6 100644 --- a/results/results-sketch.csv +++ b/results/results-sketch.csv @@ -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 From c4f482a08b369e00a27bbfd266781c98fde45016 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 14 May 2021 15:50:00 -0700 Subject: [PATCH 3/4] EfficientNetV2 official impl w/ weights ported from TF. Cleanup/refactor of related EfficientNet classes and models. --- timm/models/efficientnet.py | 463 +++++++++++++++++++++++----- timm/models/efficientnet_blocks.py | 232 +++++--------- timm/models/efficientnet_builder.py | 110 ++++--- timm/models/ghostnet.py | 5 +- timm/models/hardcorenas.py | 22 +- timm/models/layers/helpers.py | 6 +- timm/models/mobilenetv3.py | 67 ++-- 7 files changed, 587 insertions(+), 318 deletions(-) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index 0c414d50..a64adde6 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -2,6 +2,9 @@ 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 +25,26 @@ An implementation of EfficienNet that covers variety of related models with effi * And likely more... -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 create_conv2d, create_classifier from .registry import register_model -__all__ = ['EfficientNet'] +__all__ = ['EfficientNet', 'EfficientNetFeatures'] def _cfg(url='', **kwargs): @@ -149,9 +155,20 @@ 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 +315,58 @@ 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_21kft1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21kft1k-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_21kft1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21kft1k-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_21kft1k': _cfg( + url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21kft1k-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 +385,12 @@ 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 +401,35 @@ 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 +476,27 @@ 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 +575,8 @@ 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 +611,8 @@ 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 +640,8 @@ 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 +663,14 @@ 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 +700,8 @@ 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 +735,8 @@ 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 +776,14 @@ 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 +806,13 @@ 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 +837,13 @@ def _gen_efficientnet_condconv( ] # NOTE unlike official impl, this one uses `cc` 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 +883,137 @@ 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 - - NOTE: this is a preliminary definition based on paper, awaiting official code release for details - and weights +def _gen_efficientnetv2_base( + variant, channel_multiplier=1.0, depth_multiplier=1.0, pretrained=False, **kwargs): + """ Creates an EfficientNet-V2 base model - 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 = [ + ['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 = [ - # 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_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_chs_fn(num_features), + stem_size=24, + 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=round_channels(1792, channel_multiplier, 8, None), + num_features=1280, 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=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 +1045,8 @@ 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 +1078,8 @@ 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 +1456,35 @@ def efficientnet_b3_pruned(pretrained=False, **kwargs): @register_model -def efficientnet_v2s(pretrained=False, **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_efficientnet_v2s( - 'efficientnet_v2s', channel_multiplier=1.0, depth_multiplier=1.0, pretrained=pretrained, **kwargs) + 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 +1896,127 @@ 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_21kft1k(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_21kft1k', pretrained=pretrained, **kwargs) + return model + + +@register_model +def tf_efficientnetv2_m_21kft1k(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_21kft1k', pretrained=pretrained, **kwargs) + return model + + +@register_model +def tf_efficientnetv2_l_21kft1k(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_21kft1k', 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. diff --git a/timm/models/efficientnet_blocks.py b/timm/models/efficientnet_blocks.py index 040785f6..83b57beb 100644 --- a/timm/models/efficientnet_blocks.py +++ b/timm/models/efficientnet_blocks.py @@ -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) diff --git a/timm/models/efficientnet_builder.py b/timm/models/efficientnet_builder.py index f670aa6c..9d5853c7 100644 --- a/timm/models/efficientnet_builder.py +++ b/timm/models/efficientnet_builder.py @@ -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): diff --git a/timm/models/ghostnet.py b/timm/models/ghostnet.py index 358fb4c7..c132142a 100644 --- a/timm/models/ghostnet.py +++ b/timm/models/ghostnet.py @@ -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 diff --git a/timm/models/hardcorenas.py b/timm/models/hardcorenas.py index 4420172b..231bb4b6 100644 --- a/timm/models/hardcorenas.py +++ b/timm/models/hardcorenas.py @@ -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, ) diff --git a/timm/models/layers/helpers.py b/timm/models/layers/helpers.py index 7a738d5c..64573ef6 100644 --- a/timm/models/layers/helpers.py +++ b/timm/models/layers/helpers.py @@ -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 \ No newline at end of file diff --git a/timm/models/mobilenetv3.py b/timm/models/mobilenetv3.py index 543b33ea..9afa3d75 100644 --- a/timm/models/mobilenetv3.py +++ b/timm/models/mobilenetv3.py @@ -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) From 328249f11a6834150c4064514b703060f4249355 Mon Sep 17 00:00:00 2001 From: Ross Wightman Date: Fri, 14 May 2021 16:37:43 -0700 Subject: [PATCH 4/4] Update README, tweak fine-tune effv2 model names. --- README.md | 9 +++++++ timm/models/efficientnet.py | 48 ++++++++++++++++++++++--------------- timm/version.py | 2 +- 3 files changed, 39 insertions(+), 20 deletions(-) diff --git a/README.md b/README.md index 166957ec..4cd599f1 100644 --- a/README.md +++ b/README.md @@ -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) diff --git a/timm/models/efficientnet.py b/timm/models/efficientnet.py index a64adde6..1716e92d 100644 --- a/timm/models/efficientnet.py +++ b/timm/models/efficientnet.py @@ -1,4 +1,4 @@ -""" PyTorch EfficientNet Family +""" The EfficientNet Family in PyTorch An implementation of EfficienNet that covers variety of related models with efficient architectures: @@ -25,6 +25,10 @@ An implementation of EfficienNet that covers variety of related models with effi * And likely more... +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 @@ -328,16 +332,16 @@ default_cfgs = { 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_21kft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_s_21kft1k-d7dafa41.pth', + '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_21kft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_m_21kft1k-bf41664a.pth', + '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_21kft1k': _cfg( - url='https://github.com/rwightman/pytorch-image-models/releases/download/v0.1-effv2-weights/tf_efficientnetv2_l_21kft1k-60127a9d.pth', + '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), @@ -1925,35 +1929,39 @@ def tf_efficientnetv2_l(pretrained=False, **kwargs): @register_model -def tf_efficientnetv2_s_21kft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Small. Tensorflow compatible variant """ +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_21kft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_s('tf_efficientnetv2_s_21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_m_21kft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Medium. Tensorflow compatible variant """ +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_21kft1k', pretrained=pretrained, **kwargs) + model = _gen_efficientnetv2_m('tf_efficientnetv2_m_21ft1k', pretrained=pretrained, **kwargs) return model @register_model -def tf_efficientnetv2_l_21kft1k(pretrained=False, **kwargs): - """ EfficientNet-V2 Large. Tensorflow compatible variant """ +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_21kft1k', pretrained=pretrained, **kwargs) + 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 """ + """ 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) @@ -1962,7 +1970,8 @@ def tf_efficientnetv2_s_21k(pretrained=False, **kwargs): @register_model def tf_efficientnetv2_m_21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Medium w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ + """ 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) @@ -1971,7 +1980,8 @@ def tf_efficientnetv2_m_21k(pretrained=False, **kwargs): @register_model def tf_efficientnetv2_l_21k(pretrained=False, **kwargs): - """ EfficientNet-V2 Large w/ ImageNet-21k pretrained weights. Tensorflow compatible variant """ + """ 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) diff --git a/timm/version.py b/timm/version.py index 5bf52d50..2d802716 100644 --- a/timm/version.py +++ b/timm/version.py @@ -1 +1 @@ -__version__ = '0.4.8' +__version__ = '0.4.9'