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pytorch-image-models/results/results-imagenet-a.csv

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Re-ran batch validation on all models across all sets
4 years ago
model,top1,top1_err,top5,top5_err,param_count,img_size,cropt_pct,interpolation,top1_diff,top5_diff,rank_diff
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
tf_efficientnet_l2_ns,84.760,15.240,96.147,3.853,480.31,800,0.960,bicubic,-13.790,-3.673,0
tf_efficientnet_l2_ns_475,83.373,16.627,95.453,4.547,480.31,475,0.936,bicubic,-15.127,-4.377,0
swin_large_patch4_window12_384,69.627,30.373,89.560,10.440,196.74,384,1.000,bicubic,-28.413,-10.130,0
tf_efficientnet_b7_ns,67.040,32.960,88.667,11.333,66.35,600,0.949,bicubic,-30.870,-11.053,0
swin_base_patch4_window12_384,64.480,35.520,87.493,12.507,87.90,384,1.000,bicubic,-33.410,-12.217,0
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
swin_base_patch4_window7_224,51.453,48.547,79.973,20.027,87.77,224,0.900,bicubic,-45.797,-19.557,-5
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
tf_efficientnet_b4_ns,51.213,48.787,79.187,20.813,19.34,380,0.922,bicubic,-45.737,-20.393,+9
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
swsl_resnext101_32x8d,51.187,48.813,78.240,21.760,88.79,224,0.875,bilinear,-46.013,-21.330,-6
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
dm_nfnet_f1,46.600,53.400,74.773,25.227,132.63,320,0.910,bicubic,-50.320,-24.617,0
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
swsl_resnet50,29.867,70.133,63.853,36.147,25.56,224,0.875,bilinear,-65.543,-35.437,+17
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
resnetv2_50x1_bitm,27.347,72.653,63.547,36.453,25.55,480,1.000,bilinear,-67.703,-35.613,+24
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
nasnetalarge,23.493,76.507,55.027,44.973,88.75,331,0.911,bicubic,-72.187,-43.903,-9
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
swin_tiny_patch4_window7_224,21.173,78.827,55.973,44.027,28.29,224,0.900,bicubic,-73.967,-42.877,+2
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
tresnet_xl,19.640,80.360,53.133,46.867,78.44,224,0.875,bilinear,-75.800,-45.917,-14
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
repvgg_b3,19.107,80.893,50.253,49.747,123.09,224,0.875,bilinear,-75.463,-48.527,+26
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
legacy_senet154,19.053,80.947,47.947,52.053,115.09,224,0.875,bilinear,-76.017,-50.883,-3
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
vit_deit_small_patch16_224,18.907,81.093,51.413,48.587,22.05,224,0.900,bicubic,-75.493,-47.327,+36
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
xception65,16.027,83.973,43.773,56.227,39.92,299,0.903,bicubic,-77.733,-54.597,+62
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
gluon_inception_v3,12.640,87.360,40.493,59.507,23.83,299,0.875,bicubic,-80.820,-58.077,+56
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
xception41,11.600,88.400,39.133,60.867,26.97,299,0.903,bicubic,-81.830,-59.297,+48
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
tf_inception_v3,10.840,89.160,36.853,63.147,23.83,299,0.875,bicubic,-82.480,-61.177,+43
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
hrnet_w44,10.320,89.680,39.507,60.493,67.06,224,0.875,bilinear,-83.230,-59.193,+21
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
inception_v3,10.027,89.973,35.227,64.773,23.83,299,0.875,bicubic,-82.693,-62.743,+63
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
seresnext26t_32x4d,8.893,91.107,36.907,63.093,16.81,224,0.875,bicubic,-83.927,-61.463,+37
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
pit_ti_distilled_224,6.627,93.373,30.760,69.240,5.10,224,0.900,bicubic,-84.273,-66.940,+66
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
swsl_resnet18,6.240,93.760,31.600,68.400,11.69,224,0.875,bilinear,-84.450,-66.100,+65
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
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
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
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
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
resnet18,1.160,98.840,16.213,83.787,11.69,224,0.875,bilinear,-86.230,-80.077,-9
Update results csv, includes latest transformer models, swin, pit, tnt... run on pytorch 1.8.1 cuda 10.2 (11.x rel crashes).
3 years ago
tf_mobilenetv3_small_minimal_100,1.013,98.987,11.493,88.507,2.04,224,0.875,bilinear,-80.367,-82.177,+1
Update results.csv files with cait, mlp-mixer, resnetrs, miil imagenet21k pretrained, and my latest efficientnet v2s/b4 weights.
3 years ago
tv_resnet50,0.000,100.000,14.453,85.547,25.56,224,0.875,bilinear,-91.880,-83.587,-67