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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 csvs for latest release
3 years ago
tf_efficientnet_l2_ns_475,80.460,19.540,95.730,4.270,480.31,475,0.936,bicubic,-7.772,-2.816,+3
tf_efficientnet_l2_ns,80.250,19.750,95.840,4.160,480.31,800,0.960,bicubic,-8.098,-2.808,+1
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
beit_large_patch16_512,79.940,20.060,95.350,4.650,305.67,512,1.000,bicubic,-8.660,-3.306,-2
Update results csvs for latest release
3 years ago
beit_large_patch16_384,79.500,20.500,95.170,4.830,305.00,384,1.000,bicubic,-8.904,-3.438,-2
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
beit_large_patch16_224,78.810,21.190,94.600,5.400,304.43,224,0.900,bicubic,-8.664,-3.704,+1
tf_efficientnet_b7_ns,78.510,21.490,94.380,5.620,66.35,600,0.949,bicubic,-8.328,-3.716,+7
volo_d5_512,77.970,22.030,94.170,5.830,296.09,512,1.150,bicubic,-9.072,-3.798,+3
vit_large_patch16_384,77.940,22.060,94.440,5.560,304.72,384,1.000,bicubic,-9.140,-3.860,+1
volo_d5_448,77.770,22.230,94.060,5.940,295.91,448,1.150,bicubic,-9.182,-3.880,+3
volo_d4_448,77.750,22.250,93.930,6.070,193.41,448,1.150,bicubic,-9.040,-3.952,+5
convnext_large_384_in22ft1k,77.730,22.270,94.080,5.920,197.77,384,1.000,bicubic,-9.666,-4.288,-4
convnext_xlarge_384_in22ft1k,77.710,22.290,94.200,5.800,350.20,384,1.000,bicubic,-9.836,-4.286,-7
tf_efficientnet_b6_ns,77.280,22.720,93.900,6.100,43.04,528,0.942,bicubic,-9.174,-3.982,+7
volo_d3_448,77.080,22.920,94.110,5.890,86.63,448,1.000,bicubic,-9.416,-3.600,+4
vit_large_r50_s32_384,77.060,22.940,93.720,6.280,329.09,384,1.000,bicubic,-9.122,-4.200,+10
swin_large_patch4_window12_384,77.040,22.960,93.750,6.250,196.74,384,1.000,bicubic,-10.110,-4.490,-8
Update results csvs for latest release
3 years ago
tf_efficientnetv2_xl_in21ft1k,77.040,22.960,93.270,6.730,208.12,512,1.000,bicubic,-9.378,-4.596,+5
tf_efficientnetv2_l_in21ft1k,76.940,23.060,93.960,6.040,118.52,480,1.000,bicubic,-9.364,-4.018,+6
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
beit_base_patch16_384,76.890,23.110,93.910,6.090,86.74,384,1.000,bicubic,-9.908,-4.226,-5
ig_resnext101_32x48d,76.880,23.120,93.310,6.690,828.41,224,0.875,bilinear,-8.556,-4.266,+26
cait_m48_448,76.860,23.140,93.370,6.630,356.46,448,1.000,bicubic,-9.626,-4.382,-2
ig_resnext101_32x32d,76.820,23.180,93.210,6.790,468.53,224,0.875,bilinear,-8.280,-4.224,+37
Update results csvs for latest release
3 years ago
tf_efficientnet_b5_ns,76.810,23.190,93.580,6.420,30.39,456,0.934,bicubic,-9.280,-4.170,+4
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
convnext_xlarge_in22ft1k,76.770,23.230,93.550,6.450,350.20,224,0.875,bicubic,-10.232,-4.662,-13
xcit_large_24_p8_384_dist,76.620,23.380,93.090,6.910,188.93,384,1.000,bicubic,-9.378,-4.594,+7
convnext_base_384_in22ft1k,76.570,23.430,93.720,6.280,88.59,384,1.000,bicubic,-9.974,-4.470,-9
volo_d5_224,76.570,23.430,93.300,6.700,295.46,224,0.960,bicubic,-9.502,-4.278,+1
vit_base_patch16_384,76.490,23.510,93.770,6.230,86.86,384,1.000,bicubic,-9.520,-4.232,+3
convnext_large_in22ft1k,76.440,23.560,93.470,6.530,197.77,224,0.875,bicubic,-10.196,-4.558,-13
Update results csvs for latest release
3 years ago
cait_m36_384,76.320,23.680,93.060,6.940,271.22,384,1.000,bicubic,-9.732,-4.670,-1
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
vit_large_patch16_224,76.300,23.700,93.600,6.400,304.33,224,0.900,bicubic,-9.538,-4.220,+3
swin_large_patch4_window7_224,76.290,23.710,93.400,6.600,196.53,224,0.900,bicubic,-10.024,-4.492,-9
swin_base_patch4_window12_384,76.280,23.720,93.310,6.690,87.90,384,1.000,bicubic,-10.152,-4.746,-12
tf_efficientnetv2_l,76.280,23.720,92.970,7.030,118.52,480,1.000,bicubic,-9.210,-4.402,+10
cait_s36_384,76.220,23.780,92.970,7.030,68.37,384,1.000,bicubic,-9.242,-4.510,+10
xcit_medium_24_p8_384_dist,76.140,23.860,92.970,7.030,84.32,384,1.000,bicubic,-9.674,-4.622,+1
dm_nfnet_f6,76.130,23.870,93.110,6.890,438.36,576,0.956,bicubic,-10.012,-4.620,-11
tf_efficientnet_b7_ap,76.090,23.910,92.970,7.030,66.35,600,0.949,bicubic,-9.030,-4.282,+20
tf_efficientnet_b8_ap,76.090,23.910,92.730,7.270,87.41,672,0.954,bicubic,-9.282,-4.562,+12
volo_d2_384,76.090,23.910,93.130,6.870,58.87,384,1.000,bicubic,-9.950,-4.442,-10
vit_base_patch8_224,76.010,23.990,93.380,6.620,86.58,224,0.900,bicubic,-9.784,-4.414,-3
volo_d4_224,76.000,24.000,93.010,6.990,192.96,224,0.960,bicubic,-9.874,-4.458,-9
xcit_large_24_p8_224_dist,75.990,24.010,92.730,7.270,188.93,224,1.000,bicubic,-9.408,-4.680,+7
tf_efficientnetv2_m_in21ft1k,75.910,24.090,93.290,6.710,54.14,480,1.000,bicubic,-9.680,-4.454,-3
Update results csvs for latest release
3 years ago
dm_nfnet_f4,75.850,24.150,92.970,7.030,316.07,512,0.951,bicubic,-9.864,-4.552,-5
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
xcit_large_24_p16_384_dist,75.820,24.180,92.750,7.250,189.10,384,1.000,bicubic,-9.934,-4.788,-7
xcit_small_24_p8_384_dist,75.770,24.230,92.970,7.030,47.63,384,1.000,bicubic,-9.786,-4.602,-5
ig_resnext101_32x16d,75.750,24.250,92.880,7.120,194.03,224,0.875,bilinear,-8.420,-4.318,+53
tf_efficientnet_b4_ns,75.680,24.320,93.050,6.950,19.34,380,0.922,bicubic,-9.482,-4.420,+8
volo_d1_384,75.620,24.380,93.070,6.930,26.78,384,1.000,bicubic,-9.628,-4.144,+4
volo_d3_224,75.610,24.390,93.000,7.000,86.33,224,0.960,bicubic,-9.802,-4.280,-2
vit_base_r50_s16_384,75.590,24.410,92.790,7.210,98.95,384,1.000,bicubic,-9.380,-4.500,+17
convnext_base_in22ft1k,75.570,24.430,93.130,6.870,88.59,224,0.875,bicubic,-10.254,-4.736,-18
deit_base_distilled_patch16_384,75.540,24.460,92.500,7.500,87.63,384,1.000,bicubic,-9.882,-4.906,-6
tf_efficientnetv2_m,75.520,24.480,92.620,7.380,54.14,480,1.000,bicubic,-9.518,-4.658,+10
regnetz_e8,75.490,24.510,92.690,7.310,57.70,320,1.000,bicubic,-9.540,-4.574,+10
cait_s24_384,75.480,24.520,92.590,7.410,47.06,384,1.000,bicubic,-9.566,-4.756,+7
xcit_medium_24_p8_224_dist,75.470,24.530,92.900,7.100,84.32,224,1.000,bicubic,-9.602,-4.380,+4
swsl_resnext101_32x8d,75.420,24.580,92.750,7.250,88.79,224,0.875,bilinear,-8.870,-4.430,+34
tf_efficientnet_b6_ap,75.380,24.620,92.440,7.560,43.04,528,0.942,bicubic,-9.406,-4.698,+15
beit_base_patch16_224,75.370,24.630,93.040,6.960,86.53,224,0.900,bicubic,-9.858,-4.618,-6
volo_d2_224,75.310,24.690,92.510,7.490,58.68,224,0.960,bicubic,-9.884,-4.678,-6
dm_nfnet_f3,75.210,24.790,92.940,7.060,254.92,416,0.940,bicubic,-10.312,-4.522,-20
efficientnetv2_rw_m,75.160,24.840,92.570,7.430,53.24,416,1.000,bicubic,-9.652,-4.576,+10
ecaresnet269d,75.120,24.880,92.840,7.160,102.09,352,1.000,bicubic,-9.856,-4.386,+3
xcit_medium_24_p16_384_dist,75.100,24.900,92.450,7.550,84.40,384,1.000,bicubic,-10.322,-4.882,-19
dm_nfnet_f5,75.000,25.000,92.600,7.400,377.21,544,0.954,bicubic,-10.816,-4.886,-31
xcit_small_24_p8_224_dist,74.970,25.030,92.300,7.700,47.63,224,1.000,bicubic,-9.906,-4.888,+5
tf_efficientnet_b8,74.920,25.080,92.330,7.670,87.41,672,0.954,bicubic,-10.448,-5.060,-17
xcit_small_12_p8_384_dist,74.860,25.140,92.460,7.540,26.21,384,1.000,bicubic,-10.220,-4.820,-9
Update results csvs for latest release
3 years ago
eca_nfnet_l2,74.830,25.170,92.650,7.350,56.72,384,1.000,bicubic,-9.866,-4.614,+7
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
tf_efficientnet_b7,74.720,25.280,92.220,7.780,66.35,600,0.949,bicubic,-10.216,-4.984,-2
xcit_large_24_p16_224_dist,74.670,25.330,91.860,8.140,189.10,224,1.000,bicubic,-10.252,-5.272,-2
dm_nfnet_f2,74.620,25.380,92.260,7.740,193.78,352,0.920,bicubic,-10.442,-4.980,-11
Update results csvs for latest release
3 years ago
xcit_small_24_p16_384_dist,74.600,25.400,92.450,7.550,47.67,384,1.000,bicubic,-10.494,-4.860,-15
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
tf_efficientnet_b5_ap,74.590,25.410,91.990,8.010,30.39,456,0.934,bicubic,-9.666,-4.986,+20
dm_nfnet_f1,74.570,25.430,92.260,7.740,132.63,320,0.910,bicubic,-10.054,-4.840,+2
swin_base_patch4_window7_224,74.550,25.450,92.560,7.440,87.77,224,0.900,bicubic,-10.698,-5.002,-25
seresnet152d,74.520,25.480,92.080,7.920,66.84,320,1.000,bicubic,-9.840,-4.960,+11
regnetz_040,74.460,25.540,91.890,8.110,27.12,320,1.000,bicubic,-9.774,-5.042,+18
resnest200e,74.460,25.540,91.860,8.140,70.20,320,0.909,bicubic,-9.368,-5.032,+41
tf_efficientnetv2_s_in21ft1k,74.460,25.540,92.500,7.500,21.46,384,1.000,bicubic,-9.838,-4.754,+9
regnetz_040h,74.440,25.560,92.240,7.760,28.94,320,1.000,bicubic,-10.056,-4.766,0
resnetrs200,74.380,25.620,91.940,8.060,93.21,320,1.000,bicubic,-10.058,-5.140,+1
efficientnetv2_rw_s,74.180,25.820,91.710,8.290,23.94,384,1.000,bicubic,-9.630,-5.012,+39
cait_xs24_384,74.170,25.830,91.910,8.090,26.67,384,1.000,bicubic,-9.892,-4.980,+19
resnest269e,74.170,25.830,91.930,8.070,110.93,416,0.928,bicubic,-10.350,-5.056,-6
pit_b_distilled_224,74.160,25.840,91.670,8.330,74.79,224,0.900,bicubic,-9.980,-5.186,+15
swsl_resnext101_32x4d,74.140,25.860,92.000,8.000,44.18,224,0.875,bilinear,-9.096,-4.764,+57
eca_nfnet_l1,74.120,25.880,92.070,7.930,41.41,320,1.000,bicubic,-9.892,-4.958,+22
volo_d1_224,74.120,25.880,92.030,7.970,26.63,224,0.960,bicubic,-10.042,-4.746,+11
xcit_small_12_p16_384_dist,74.120,25.880,92.080,7.920,26.25,384,1.000,bicubic,-10.590,-5.038,-15
vit_large_r50_s32_224,74.110,25.890,92.380,7.620,328.99,224,0.900,bicubic,-10.314,-4.786,-6
convnext_large,74.070,25.930,91.560,8.440,197.77,224,0.875,bicubic,-10.222,-5.334,-2
xcit_large_24_p8_224,74.070,25.930,90.890,9.110,188.93,224,1.000,bicubic,-10.324,-5.766,-6
vit_base_patch16_224_miil,74.040,25.960,91.700,8.300,86.54,224,0.875,bilinear,-10.232,-5.102,-1
resnetv2_152x4_bitm,74.010,25.990,92.330,7.670,936.53,480,1.000,bilinear,-10.906,-5.112,-25
tf_efficientnetv2_s,74.000,26.000,91.530,8.470,21.46,384,1.000,bicubic,-9.886,-5.166,+18
vit_base_patch16_224,74.000,26.000,92.470,7.530,86.57,224,0.900,bicubic,-10.528,-4.824,-19
swsl_resnext101_32x16d,73.980,26.020,92.170,7.830,194.03,224,0.875,bilinear,-9.370,-4.674,+41
regnetz_d32,73.970,26.030,91.950,8.050,27.58,320,0.950,bicubic,-10.052,-4.916,+9
crossvit_18_dagger_408,73.960,26.040,91.410,8.590,44.61,408,1.000,bicubic,-10.234,-5.408,-2
seresnext101_32x8d,73.940,26.060,91.450,8.550,93.57,288,1.000,bicubic,-10.264,-5.426,-4
resnetrs420,73.920,26.080,91.760,8.240,191.89,416,1.000,bicubic,-11.090,-5.364,-37
resnetv2_152x2_bitm,73.920,26.080,92.670,7.330,236.34,448,1.000,bilinear,-10.586,-4.764,-23
xcit_small_12_p8_224_dist,73.920,26.080,91.720,8.280,26.21,224,1.000,bicubic,-10.316,-5.154,-9
resmlp_big_24_224_in22ft1k,73.900,26.100,91.750,8.250,129.14,224,0.875,bicubic,-10.494,-5.370,-19
tf_efficientnet_b6,73.900,26.100,91.750,8.250,43.04,528,0.942,bicubic,-10.210,-5.138,-4
Update results csvs for latest release
3 years ago
tf_efficientnet_b3_ns,73.890,26.110,91.870,8.130,12.23,300,0.904,bicubic,-10.158,-5.038,-1
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
convnext_base,73.870,26.130,91.320,8.680,88.59,224,0.875,bicubic,-9.968,-5.430,+9
vit_small_r26_s32_384,73.800,26.200,92.300,7.700,36.47,384,1.000,bicubic,-10.242,-5.028,-2
Update results csvs for latest release
3 years ago
regnetz_d8,73.760,26.240,92.020,7.980,23.37,320,1.000,bicubic,-10.292,-4.974,-5
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
regnety_080,73.740,26.260,91.810,8.190,39.18,288,1.000,bicubic,-10.186,-5.078,+2
resnetrs270,73.690,26.310,91.570,8.430,129.86,352,1.000,bicubic,-10.746,-5.400,-28
resnetv2_101x3_bitm,73.690,26.310,92.470,7.530,387.93,448,1.000,bilinear,-10.752,-4.912,-31
resnet200d,73.680,26.320,91.570,8.430,64.69,320,1.000,bicubic,-10.284,-5.254,-3
ig_resnext101_32x8d,73.660,26.340,92.160,7.840,88.79,224,0.875,bilinear,-9.040,-4.470,+54
xcit_medium_24_p16_224_dist,73.660,26.340,91.580,8.420,84.40,224,1.000,bicubic,-10.614,-5.360,-24
regnety_064,73.570,26.430,91.350,8.650,30.58,288,1.000,bicubic,-10.150,-5.372,+10
Update results csvs for latest release
3 years ago
tf_efficientnet_b5,73.560,26.440,91.460,8.540,30.39,456,0.934,bicubic,-10.252,-5.288,+3
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
resnet152d,73.520,26.480,91.230,8.770,60.21,320,1.000,bicubic,-10.158,-5.508,+13
regnetv_064,73.480,26.520,91.600,8.400,30.58,288,1.000,bicubic,-10.234,-5.014,+9
xcit_tiny_24_p8_384_dist,73.420,26.580,91.560,8.440,12.11,384,1.000,bicubic,-10.322,-5.150,+4
resnetrs350,73.400,26.600,91.310,8.690,163.96,384,1.000,bicubic,-11.318,-5.678,-48
twins_svt_large,73.390,26.610,90.910,9.090,99.27,224,0.900,bicubic,-10.290,-5.684,+8
regnetz_d8_evos,73.380,26.620,91.640,8.360,23.46,320,0.950,bicubic,-10.674,-5.356,-20
regnety_160,73.370,26.630,91.700,8.300,83.59,288,1.000,bicubic,-10.320,-5.076,+5
efficientnet_b4,73.310,26.690,91.280,8.720,19.34,384,1.000,bicubic,-10.114,-5.316,+10
swin_s3_base_224,73.310,26.690,91.190,8.810,71.13,224,0.900,bicubic,-10.622,-5.470,-15
xcit_small_24_p16_224_dist,73.290,26.710,91.440,8.560,47.67,224,1.000,bicubic,-10.578,-5.284,-14
resmlp_big_24_distilled_224,73.290,26.710,91.170,8.830,129.14,224,0.875,bicubic,-10.298,-5.478,+5
vit_small_patch16_384,73.280,26.720,92.000,8.000,22.20,384,1.000,bicubic,-10.524,-5.102,-7
deit_base_distilled_patch16_224,73.240,26.760,91.000,9.000,87.34,224,0.900,bicubic,-10.152,-5.486,+6
resnetrs152,73.200,26.800,91.270,8.730,86.62,320,1.000,bicubic,-10.514,-5.476,-4
xcit_medium_24_p8_224,73.150,26.850,90.280,9.720,84.32,224,1.000,bicubic,-10.586,-6.114,-7
vit_base_patch32_384,73.130,26.870,91.250,8.750,88.30,384,1.000,bicubic,-10.218,-5.584,+7
jx_nest_base,73.120,26.880,91.060,8.940,67.72,224,0.875,bicubic,-10.436,-5.302,-1
xcit_small_24_p8_224,73.080,26.920,91.150,8.850,47.63,224,1.000,bicubic,-10.758,-5.486,-18
Update results csvs for latest release
3 years ago
cait_s24_224,73.070,26.930,91.120,8.880,46.92,224,1.000,bicubic,-10.388,-5.444,-2
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
crossvit_15_dagger_408,72.960,27.040,91.090,8.910,28.50,408,1.000,bicubic,-10.876,-5.694,-19
resnetv2_152x2_bit_teacher_384,72.900,27.100,91.550,8.450,236.34,384,1.000,bicubic,-10.944,-5.568,-23
dm_nfnet_f0,72.880,27.120,91.080,8.920,71.49,256,0.900,bicubic,-10.506,-5.494,-2
regnetv_040,72.880,27.120,91.100,8.900,20.64,288,1.000,bicubic,-10.320,-5.562,+5
tf_efficientnet_b4_ap,72.880,27.120,90.980,9.020,19.34,380,0.922,bicubic,-10.372,-5.414,+1
xception65p,72.790,27.210,90.910,9.090,39.82,299,0.940,bicubic,-10.336,-5.568,+9
regnety_032,72.770,27.230,90.950,9.050,19.44,288,1.000,bicubic,-9.956,-5.474,+23
regnety_040,72.720,27.280,90.720,9.280,20.65,288,1.000,bicubic,-10.316,-5.786,+12
swin_s3_small_224,72.690,27.310,90.550,9.450,49.74,224,0.900,bicubic,-11.078,-5.902,-22
resnext101_64x4d,72.620,27.380,90.840,9.160,83.46,288,1.000,bicubic,-10.520,-5.530,+2
xcit_small_12_p8_224,72.610,27.390,90.670,9.330,26.21,224,1.000,bicubic,-10.734,-5.810,-8
nfnet_l0,72.610,27.390,91.000,9.000,35.07,288,1.000,bicubic,-10.142,-5.518,+18
pnasnet5large,72.610,27.390,90.500,9.500,86.06,331,0.911,bicubic,-10.180,-5.540,+16
xception65,72.600,27.400,90.820,9.180,39.92,299,0.940,bicubic,-10.580,-5.772,-4
resnest101e,72.580,27.420,90.820,9.180,48.28,256,0.875,bilinear,-10.310,-5.498,+9
twins_pcpvt_large,72.580,27.420,90.700,9.300,60.99,224,0.900,bicubic,-10.554,-5.904,-2
swsl_resnext50_32x4d,72.560,27.440,90.850,9.150,25.03,224,0.875,bilinear,-9.616,-5.382,+47
twins_svt_base,72.560,27.440,90.460,9.540,56.07,224,0.900,bicubic,-10.578,-5.960,-5
tresnet_xl_448,72.550,27.450,90.310,9.690,78.44,448,0.875,bilinear,-10.504,-5.862,0
deit_base_patch16_384,72.540,27.460,90.270,9.730,86.86,384,1.000,bicubic,-10.566,-6.098,-3
gc_efficientnetv2_rw_t,72.530,27.470,90.820,9.180,13.68,288,1.000,bicubic,-9.936,-5.476,+23
resnetv2_50x3_bitm,72.510,27.490,91.760,8.240,217.32,448,1.000,bilinear,-11.504,-5.364,-50
xcit_small_12_p16_224_dist,72.500,27.500,91.120,8.880,26.25,224,1.000,bicubic,-10.850,-5.294,-20
xcit_tiny_24_p8_224_dist,72.450,27.550,90.920,9.080,12.11,224,1.000,bicubic,-10.114,-5.250,+16
resnet101d,72.410,27.590,90.660,9.340,44.57,320,1.000,bicubic,-10.612,-5.788,-3
jx_nest_small,72.370,27.630,90.700,9.300,38.35,224,0.875,bicubic,-10.748,-5.630,-10
convnext_small,72.320,27.680,90.850,9.150,50.22,224,0.875,bicubic,-10.830,-5.582,-16
regnetz_c16,72.300,27.700,90.820,9.180,13.46,320,0.940,bicubic,-10.216,-5.540,+15
tf_efficientnet_b4,72.290,27.710,90.590,9.410,19.34,380,0.922,bicubic,-10.734,-5.710,-8
tf_efficientnet_b2_ns,72.280,27.720,91.090,8.910,9.11,260,0.890,bicubic,-10.100,-5.158,+19
resnetv2_50x1_bit_distilled,72.260,27.740,91.010,8.990,25.55,224,0.875,bicubic,-10.562,-5.518,-5
tresnet_m,72.260,27.740,90.230,9.770,31.39,224,0.875,bilinear,-10.810,-5.890,-14
crossvit_18_240,72.250,27.750,90.270,9.730,43.27,240,0.875,bicubic,-10.148,-5.788,+14
efficientnetv2_rw_t,72.250,27.750,90.420,9.580,13.65,288,1.000,bicubic,-10.094,-5.776,+18
nasnetalarge,72.230,27.770,90.460,9.540,88.75,331,0.911,bicubic,-10.396,-5.586,0
regnetz_c16_evos,72.230,27.770,91.220,8.780,13.49,320,0.950,bicubic,-10.402,-5.256,-2
cait_xxs36_384,72.190,27.810,90.840,9.160,17.37,384,1.000,bicubic,-10.004,-5.304,+26
twins_pcpvt_base,72.190,27.810,90.510,9.490,43.83,224,0.900,bicubic,-10.518,-5.840,-7
crossvit_18_dagger_240,72.140,27.860,90.070,9.930,44.27,240,0.875,bicubic,-10.380,-6.000,+3
xcit_tiny_24_p16_384_dist,72.080,27.920,90.580,9.420,12.12,384,1.000,bicubic,-10.492,-5.708,-1
resnet152,72.060,27.940,90.320,9.680,60.19,224,0.950,bicubic,-10.760,-5.810,-14
eca_nfnet_l0,71.840,28.160,91.110,8.890,24.14,288,1.000,bicubic,-10.736,-5.380,-4
swin_small_patch4_window7_224,71.740,28.260,90.240,9.760,49.61,224,0.900,bicubic,-11.476,-6.084,-35
pit_b_224,71.720,28.280,89.250,10.750,73.76,224,0.900,bicubic,-10.726,-6.460,+2
swsl_resnet50,71.700,28.300,90.480,9.520,25.56,224,0.875,bilinear,-9.474,-5.498,+70
xcit_large_24_p16_224,71.700,28.300,89.170,10.830,189.10,224,1.000,bicubic,-11.194,-6.712,-23
Update results csvs for latest release
3 years ago
resnet61q,71.680,28.320,90.280,9.720,36.85,288,1.000,bicubic,-10.846,-5.854,-6
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3 years ago
tresnet_xl,71.660,28.340,89.630,10.370,78.44,224,0.875,bilinear,-10.398,-6.306,+23
convit_base,71.610,28.390,90.160,9.840,86.54,224,0.875,bicubic,-10.676,-5.778,+9
tresnet_l_448,71.590,28.410,90.060,9.940,55.99,448,0.875,bilinear,-10.678,-5.922,+9
xcit_tiny_12_p8_384_dist,71.580,28.420,90.710,9.290,6.71,384,1.000,bicubic,-10.814,-5.510,-3
poolformer_m48,71.550,28.450,89.760,10.240,73.47,224,0.950,bicubic,-10.912,-6.198,-7
crossvit_15_dagger_240,71.510,28.490,89.860,10.140,28.21,240,0.875,bicubic,-10.820,-6.098,+1
ssl_resnext101_32x8d,71.510,28.490,90.470,9.530,88.79,224,0.875,bilinear,-10.098,-5.572,+37
fbnetv3_g,71.490,28.510,90.370,9.630,16.62,288,0.950,bilinear,-10.556,-5.694,+17
efficientnet_b3,71.480,28.520,90.060,9.940,12.23,320,1.000,bicubic,-10.760,-6.054,+4
ecaresnet101d,71.470,28.530,90.330,9.670,44.57,224,0.875,bicubic,-10.702,-5.716,+8
ssl_resnext101_32x16d,71.430,28.570,90.520,9.480,194.03,224,0.875,bilinear,-10.424,-5.576,+25
resnet51q,71.420,28.580,90.180,9.820,35.70,288,1.000,bilinear,-10.942,-6.000,-9
pit_s_distilled_224,71.370,28.630,89.780,10.220,24.04,224,0.900,bicubic,-10.624,-6.018,+15
xcit_tiny_24_p8_224,71.330,28.670,90.240,9.760,12.11,224,1.000,bicubic,-10.562,-5.736,+20
mixer_b16_224_miil,71.310,28.690,89.650,10.350,59.88,224,0.875,bilinear,-10.998,-6.068,-7
resnetv2_152x2_bit_teacher,71.290,28.710,90.430,9.570,236.34,224,0.875,bicubic,-11.582,-6.140,-38
convnext_tiny_hnf,71.280,28.720,89.400,10.600,28.59,224,0.950,bicubic,-10.942,-6.466,-3
resnetv2_101,71.260,28.740,89.920,10.080,44.54,224,0.950,bicubic,-10.782,-5.944,+7
Update results csvs for latest release
3 years ago
ecaresnet50t,71.260,28.740,90.420,9.580,25.57,320,0.950,bicubic,-11.088,-5.718,-14
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
convmixer_1536_20,71.230,28.770,89.440,10.560,51.63,224,0.960,bicubic,-10.136,-6.174,+39
xcit_small_12_p16_224,71.210,28.790,89.750,10.250,26.25,224,1.000,bicubic,-10.766,-6.068,+9
deit_base_patch16_224,71.190,28.810,89.190,10.810,86.57,224,0.900,bicubic,-10.806,-6.542,+5
crossvit_base_240,71.180,28.820,89.830,10.170,105.03,240,0.875,bicubic,-11.036,-6.004,-8
swin_s3_tiny_224,71.130,28.870,89.720,10.280,28.33,224,0.900,bicubic,-10.996,-6.230,-5
resnetv2_50d_evos,71.120,28.880,90.010,9.990,25.59,288,0.950,bicubic,-10.860,-5.900,+4
halo2botnet50ts_256,71.100,28.900,89.630,10.370,22.64,256,0.950,bicubic,-10.960,-6.012,-3
xcit_tiny_12_p8_224_dist,71.030,28.970,89.880,10.120,6.71,224,1.000,bicubic,-10.178,-5.720,+40
xcit_medium_24_p16_224,71.020,28.980,89.520,10.480,84.40,224,1.000,bicubic,-11.618,-6.454,-42
visformer_small,71.010,28.990,89.450,10.550,40.22,224,0.900,bicubic,-11.096,-6.424,-8
lamhalobotnet50ts_256,71.000,29.000,89.070,10.930,22.57,256,0.950,bicubic,-10.546,-6.432,+15
xcit_small_24_p16_224,71.000,29.000,89.700,10.300,47.67,224,1.000,bicubic,-11.580,-6.306,-41
resnetv2_50d_gn,70.990,29.010,89.770,10.230,25.57,288,0.950,bicubic,-10.828,-6.152,+6
resnetv2_101x1_bitm,70.990,29.010,91.090,8.910,44.54,448,1.000,bilinear,-11.346,-5.426,-27
tresnet_m_448,70.990,29.010,88.680,11.320,31.39,448,0.875,bilinear,-10.714,-6.892,+5
wide_resnet50_2,70.940,29.060,89.230,10.770,68.88,224,0.875,bicubic,-10.512,-6.300,+17
tnt_s_patch16_224,70.930,29.070,89.590,10.410,23.76,224,0.900,bicubic,-10.590,-6.154,+10
convnext_tiny,70.930,29.070,89.750,10.250,28.59,224,0.875,bicubic,-11.134,-6.102,-14
resnest50d_4s2x40d,70.930,29.070,89.710,10.290,30.42,224,0.875,bicubic,-10.180,-5.854,+35
tf_efficientnet_b3_ap,70.920,29.080,89.430,10.570,12.23,300,0.904,bicubic,-10.906,-6.192,-2
vit_small_patch16_224,70.910,29.090,90.150,9.850,22.05,224,0.900,bicubic,-10.486,-5.982,+16
resnet101,70.870,29.130,89.520,10.480,44.55,224,0.950,bicubic,-11.062,-6.250,-8
vit_large_patch32_384,70.870,29.130,90.570,9.430,306.63,384,1.000,bicubic,-10.636,-5.524,+8
jx_nest_tiny,70.860,29.140,89.940,10.060,17.06,224,0.875,bicubic,-10.560,-5.678,+11
resnetrs101,70.860,29.140,89.830,10.170,63.62,288,0.940,bicubic,-11.428,-6.180,-34
tf_efficientnet_b1_ns,70.860,29.140,90.120,9.880,7.79,240,0.882,bicubic,-10.528,-5.616,+12
rexnet_200,70.840,29.160,89.710,10.290,16.37,224,0.875,bicubic,-10.790,-5.958,-3
tresnet_l,70.840,29.160,89.620,10.380,55.99,224,0.875,bilinear,-10.652,-6.004,+4
tf_efficientnetv2_b3,70.830,29.170,89.510,10.490,14.36,300,0.904,bicubic,-11.136,-6.270,-17
poolformer_m36,70.800,29.200,89.510,10.490,56.17,224,0.950,bicubic,-11.312,-6.180,-29
coat_lite_small,70.790,29.210,89.570,10.430,19.84,224,0.900,bicubic,-11.514,-6.278,-41
levit_384,70.750,29.250,89.300,10.700,39.13,224,0.900,bicubic,-11.838,-6.722,-62
tf_efficientnet_b3,70.640,29.360,89.440,10.560,12.23,300,0.904,bicubic,-10.996,-6.278,-10
gluon_senet154,70.620,29.380,88.920,11.080,115.09,224,0.875,bicubic,-10.612,-6.428,+12
crossvit_small_240,70.610,29.390,89.360,10.640,26.86,240,0.875,bicubic,-10.408,-6.096,+24
cait_xxs24_384,70.600,29.400,89.720,10.280,12.03,384,1.000,bicubic,-10.366,-5.926,+27
convit_small,70.590,29.410,89.580,10.420,27.78,224,0.875,bicubic,-10.830,-6.160,-3
twins_pcpvt_small,70.560,29.440,89.080,10.920,24.11,224,0.900,bicubic,-10.530,-6.560,+18
ssl_resnext101_32x4d,70.530,29.470,89.780,10.220,44.18,224,0.875,bilinear,-10.394,-5.946,+27
deit_small_distilled_patch16_224,70.530,29.470,89.470,10.530,22.44,224,0.900,bicubic,-10.678,-5.904,+8
legacy_senet154,70.520,29.480,89.000,11.000,115.09,224,0.875,bilinear,-10.790,-6.496,+2
Update results csvs for latest release
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vit_small_r26_s32_224,70.510,29.490,90.110,9.890,36.43,224,0.900,bicubic,-11.346,-5.910,-26
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
halonet50ts,70.490,29.510,89.330,10.670,22.73,256,0.940,bicubic,-11.170,-6.282,-21
regnetz_b16,70.450,29.550,89.530,10.470,9.72,288,0.940,bicubic,-10.264,-5.948,+32
gluon_seresnext101_64x4d,70.440,29.560,89.350,10.650,88.23,224,0.875,bicubic,-10.438,-5.948,+23
twins_svt_small,70.440,29.560,89.350,10.650,24.06,224,0.900,bicubic,-11.240,-6.320,-26
crossvit_15_240,70.440,29.560,89.540,10.460,27.53,240,0.875,bicubic,-11.102,-6.150,-18
tf_efficientnet_lite4,70.430,29.570,89.110,10.890,13.01,380,0.920,bilinear,-11.106,-6.558,-20
resnest50d,70.420,29.580,88.760,11.240,27.48,224,0.875,bilinear,-10.562,-6.620,+13
seresnext50_32x4d,70.410,29.590,89.110,10.890,27.56,224,0.875,bicubic,-10.848,-6.520,-5
resnest50d_1s4x24d,70.390,29.610,89.230,10.770,25.68,224,0.875,bicubic,-10.600,-6.094,+10
gernet_l,70.360,29.640,88.980,11.020,31.08,256,0.875,bilinear,-10.984,-6.552,-11
gluon_resnet152_v1s,70.300,29.700,88.850,11.150,60.32,224,0.875,bicubic,-10.716,-6.562,+7
repvgg_b3,70.250,29.750,88.740,11.260,123.09,224,0.875,bilinear,-10.246,-6.524,+29
xception41p,70.210,29.790,89.100,10.900,26.91,299,0.940,bicubic,-11.750,-6.694,-43
coat_mini,70.210,29.790,89.450,10.550,10.34,224,0.900,bicubic,-11.056,-5.944,-11
sebotnet33ts_256,70.150,29.850,88.800,11.200,13.70,256,0.940,bicubic,-11.006,-6.370,-7
inception_resnet_v2,70.120,29.880,88.690,11.310,55.84,299,0.897,bicubic,-10.340,-6.618,+29
ecaresnet101d_pruned,70.110,29.890,89.590,10.410,24.88,224,0.875,bicubic,-10.704,-6.040,+12
efficientnet_el,70.110,29.890,89.290,10.710,10.59,300,0.904,bicubic,-11.200,-6.240,-18
haloregnetz_b,70.100,29.900,88.860,11.140,11.68,224,0.940,bicubic,-10.950,-6.336,-4
resmlp_36_distilled_224,70.100,29.900,89.100,10.900,44.69,224,0.875,bicubic,-11.054,-6.388,-11
gluon_seresnext101_32x4d,70.040,29.960,88.910,11.090,48.96,224,0.875,bicubic,-10.866,-6.384,+5
poolformer_s36,70.030,29.970,89.200,10.800,30.86,224,0.900,bicubic,-11.388,-6.250,-28
sehalonet33ts,70.020,29.980,88.700,11.300,13.69,256,0.940,bicubic,-10.944,-6.572,-1
regnety_320,69.990,30.010,88.880,11.120,145.05,224,0.875,bicubic,-10.820,-6.364,+6
gluon_resnet152_v1d,69.970,30.030,88.490,11.510,60.21,224,0.875,bicubic,-10.506,-6.714,+18
levit_256,69.950,30.050,89.240,10.760,18.89,224,0.900,bicubic,-11.556,-6.252,-38
pit_s_224,69.890,30.110,88.930,11.070,23.46,224,0.900,bicubic,-11.210,-6.400,-14
ecaresnet50d,69.840,30.160,89.390,10.610,25.58,224,0.875,bicubic,-10.760,-5.930,+10
Update results csvs for latest release
3 years ago
ssl_resnext50_32x4d,69.720,30.280,89.440,10.560,25.03,224,0.875,bilinear,-10.596,-5.970,+26
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
gluon_resnext101_64x4d,69.700,30.300,88.270,11.730,83.46,224,0.875,bicubic,-10.904,-6.722,+7
xcit_tiny_24_p16_224_dist,69.700,30.300,88.720,11.280,12.12,224,1.000,bicubic,-10.746,-6.496,+16
xcit_tiny_12_p16_384_dist,69.690,30.310,89.020,10.980,6.72,384,1.000,bicubic,-11.254,-6.392,-10
lambda_resnet50ts,69.690,30.310,88.830,11.170,21.54,256,0.950,bicubic,-11.456,-6.272,-22
Update results csvs for latest release
3 years ago
resmlp_24_distilled_224,69.680,30.320,89.050,10.950,30.02,224,0.875,bicubic,-11.084,-6.174,-3
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
resnext50_32x4d,69.670,30.330,88.650,11.350,25.03,224,0.950,bicubic,-11.438,-6.676,-23
efficientnet_b3_pruned,69.580,30.420,88.980,11.020,9.86,300,0.904,bicubic,-11.278,-6.264,-8
gernet_m,69.550,30.450,88.690,11.310,21.14,224,0.875,bilinear,-11.194,-6.494,-5
nf_resnet50,69.540,30.460,88.730,11.270,25.56,288,0.940,bicubic,-11.114,-6.604,-3
gcresnext50ts,69.530,30.470,88.840,11.160,15.67,256,0.900,bicubic,-11.048,-6.330,0
repvgg_b3g4,69.520,30.480,88.450,11.550,83.83,224,0.875,bilinear,-10.692,-6.656,+26
efficientnet_el_pruned,69.510,30.490,88.930,11.070,10.59,300,0.904,bicubic,-10.792,-6.098,+18
ens_adv_inception_resnet_v2,69.510,30.490,88.510,11.490,55.84,299,0.897,bicubic,-10.468,-6.428,+37
efficientnet_b2,69.500,30.500,88.670,11.330,9.11,288,1.000,bicubic,-11.114,-6.646,-7
gcresnet50t,69.500,30.500,89.060,10.940,25.90,256,0.900,bicubic,-11.442,-6.394,-20
rexnet_150,69.470,30.530,88.980,11.020,9.73,224,0.875,bicubic,-10.840,-6.186,+12
swin_tiny_patch4_window7_224,69.450,30.550,89.020,10.980,28.29,224,0.900,bicubic,-11.924,-6.524,-49
regnetx_320,69.440,30.560,88.280,11.720,107.81,224,0.875,bicubic,-10.800,-6.742,+17
vit_base_patch32_224,69.410,30.590,89.440,10.560,88.22,224,0.900,bicubic,-11.312,-6.126,-15
Update results csvs for latest release
3 years ago
convmixer_768_32,69.400,30.600,88.870,11.130,21.11,224,0.960,bicubic,-10.764,-6.202,+20
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
inception_v4,69.360,30.640,88.780,11.220,42.68,299,0.875,bicubic,-10.802,-6.186,+20
legacy_seresnext101_32x4d,69.360,30.640,88.060,11.940,48.96,224,0.875,bilinear,-10.864,-6.950,+14
ecaresnetlight,69.340,30.660,89.230,10.770,30.16,224,0.875,bicubic,-11.112,-6.020,-6
resnet50d,69.340,30.660,88.230,11.770,25.58,224,0.875,bicubic,-11.182,-6.932,-13
xception71,69.320,30.680,88.270,11.730,42.34,299,0.903,bicubic,-10.556,-6.652,+30
vit_small_patch32_384,69.280,30.720,89.820,10.180,22.92,384,1.000,bicubic,-11.204,-5.780,-13
gluon_xception65,69.160,30.840,88.080,11.920,39.92,299,0.903,bicubic,-10.556,-6.780,+37
gluon_resnet152_v1c,69.150,30.850,87.860,12.140,60.21,224,0.875,bicubic,-10.758,-6.988,+25
mixnet_xl,69.120,30.880,88.310,11.690,11.90,224,0.875,bicubic,-11.354,-6.624,-14
seresnet33ts,69.100,30.900,88.490,11.510,19.78,256,0.900,bicubic,-11.250,-6.616,-7
tf_efficientnetv2_b2,69.090,30.910,88.220,11.780,10.10,260,0.890,bicubic,-11.116,-6.822,+7
resnetv2_50,69.070,30.930,88.440,11.560,25.55,224,0.950,bicubic,-11.350,-6.576,-13
gluon_resnet101_v1d,69.010,30.990,88.100,11.900,44.57,224,0.875,bicubic,-11.410,-6.974,-13
repvgg_b2g4,69.000,31.000,88.350,11.650,61.76,224,0.875,bilinear,-10.370,-6.338,+48
gcresnet33ts,68.990,31.010,88.470,11.530,19.88,256,0.900,bicubic,-11.090,-6.530,+9
gluon_resnext101_32x4d,68.950,31.050,88.360,11.640,44.18,224,0.875,bicubic,-11.394,-6.566,-12
seresnet50,68.950,31.050,88.700,11.300,28.09,224,0.875,bicubic,-11.314,-6.372,-5
tf_efficientnet_b2_ap,68.950,31.050,88.350,11.650,9.11,260,0.890,bicubic,-11.352,-6.866,-9
cspdarknet53,68.920,31.080,88.600,11.400,27.64,256,0.887,bilinear,-11.142,-6.484,+7
regnety_120,68.870,31.130,88.330,11.670,51.82,224,0.875,bicubic,-11.506,-6.286,-18
resnet50_gn,68.850,31.150,88.420,11.580,25.56,224,0.940,bicubic,-11.204,-6.528,+6
gluon_resnet152_v1b,68.830,31.170,87.710,12.290,60.19,224,0.875,bicubic,-10.850,-7.028,+26
eca_resnet33ts,68.810,31.190,88.580,11.420,19.68,256,0.900,bicubic,-11.270,-6.390,0
cspresnext50,68.790,31.210,87.940,12.060,20.57,224,0.875,bilinear,-11.260,-7.006,+4
dpn131,68.760,31.240,87.440,12.560,79.25,224,0.875,bicubic,-11.064,-7.268,+15
gmlp_s16_224,68.760,31.240,88.080,11.920,19.42,224,0.875,bicubic,-10.880,-6.544,+25
poolformer_s24,68.760,31.240,88.210,11.790,21.39,224,0.900,bicubic,-11.554,-6.836,-20
tf_efficientnet_b2,68.740,31.260,87.980,12.020,9.11,260,0.890,bicubic,-11.340,-6.928,-3
resnext50d_32x4d,68.730,31.270,88.300,11.700,25.05,224,0.875,bicubic,-10.940,-6.564,+20
resnet50,68.720,31.280,87.660,12.340,25.56,224,0.950,bicubic,-11.656,-7.466,-29
gluon_resnet101_v1s,68.710,31.290,87.910,12.090,44.67,224,0.875,bicubic,-11.588,-7.254,-20
deit_small_patch16_224,68.700,31.300,88.210,11.790,22.05,224,0.900,bicubic,-11.160,-6.836,+4
dpn107,68.700,31.300,88.140,11.860,86.92,224,0.875,bicubic,-11.472,-6.766,-14
gluon_seresnext50_32x4d,68.670,31.330,88.320,11.680,27.56,224,0.875,bicubic,-11.244,-6.512,-2
hrnet_w64,68.650,31.350,88.050,11.950,128.06,224,0.875,bilinear,-10.822,-6.602,+22
dpn98,68.610,31.390,87.660,12.340,61.57,224,0.875,bicubic,-11.036,-6.936,+14
xcit_tiny_12_p8_224,68.580,31.420,88.690,11.310,6.71,224,1.000,bicubic,-11.110,-6.364,+9
regnetx_160,68.530,31.470,88.450,11.550,54.28,224,0.875,bicubic,-11.320,-6.380,-1
rexnet_130,68.460,31.540,88.040,11.960,7.56,224,0.875,bicubic,-11.040,-6.644,+16
cspresnet50,68.440,31.560,87.970,12.030,21.62,256,0.887,bilinear,-11.142,-6.734,+13
tf_efficientnet_el,68.430,31.570,88.210,11.790,10.59,300,0.904,bicubic,-11.820,-6.912,-28
xcit_tiny_24_p16_224,68.430,31.570,88.290,11.710,12.12,224,1.000,bicubic,-11.014,-6.594,+18
ecaresnet50d_pruned,68.410,31.590,88.370,11.630,19.94,224,0.875,bicubic,-11.298,-6.510,+2
cait_xxs36_224,68.400,31.600,88.630,11.370,17.30,224,1.000,bicubic,-11.348,-6.236,-1
skresnext50_32x4d,68.370,31.630,87.560,12.440,27.48,224,0.875,bicubic,-11.782,-7.084,-23
ssl_resnet50,68.360,31.640,88.530,11.470,25.56,224,0.875,bilinear,-10.866,-6.306,+30
fbnetv3_d,68.340,31.660,88.450,11.550,10.31,256,0.950,bilinear,-11.342,-6.498,-1
dla102x2,68.340,31.660,87.890,12.110,41.28,224,0.875,bilinear,-11.106,-6.742,+12
efficientnet_b2_pruned,68.320,31.680,88.100,11.900,8.31,260,0.890,bicubic,-11.596,-6.754,-18
resmlp_big_24_224,68.320,31.680,87.530,12.470,129.14,224,0.875,bicubic,-12.712,-7.490,-83
gluon_resnext50_32x4d,68.310,31.690,87.300,12.700,25.03,224,0.875,bicubic,-11.054,-7.126,+12
vit_base_patch16_224_sam,68.260,31.740,87.730,12.270,86.57,224,0.900,bicubic,-11.982,-7.024,-38
ecaresnet26t,68.230,31.770,88.790,11.210,16.01,320,0.950,bicubic,-11.618,-6.296,-15
tf_efficientnet_lite3,68.230,31.770,87.740,12.260,8.20,300,0.904,bilinear,-11.590,-7.172,-13
ese_vovnet39b,68.200,31.800,88.260,11.740,24.57,224,0.875,bicubic,-11.112,-6.454,+10
fbnetv3_b,68.180,31.820,87.930,12.070,8.60,256,0.950,bilinear,-10.968,-6.816,+25
Update results csvs for latest release
3 years ago
regnetx_120,68.170,31.830,87.660,12.340,46.11,224,0.875,bicubic,-11.422,-7.074,-5
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3 years ago
resmlp_36_224,68.060,31.940,88.190,11.810,44.69,224,0.875,bicubic,-11.708,-6.696,-16
resnetrs50,68.030,31.970,87.710,12.290,35.69,224,0.910,bicubic,-11.856,-7.256,-25
pit_xs_distilled_224,68.000,32.000,87.720,12.280,11.00,224,0.900,bicubic,-11.306,-6.644,+6
dpn92,67.960,32.040,87.560,12.440,37.67,224,0.875,bicubic,-12.056,-7.264,-32
nf_regnet_b1,67.960,32.040,88.200,11.800,10.22,288,0.900,bicubic,-11.328,-6.548,+9
gluon_resnet50_v1d,67.950,32.050,87.130,12.870,25.58,224,0.875,bicubic,-11.126,-7.342,+22
resnetv2_50x1_bitm,67.920,32.080,89.290,10.710,25.55,448,1.000,bilinear,-12.422,-6.390,-59
levit_192,67.900,32.100,87.890,12.110,10.95,224,0.900,bicubic,-11.932,-6.896,-26
tf_efficientnetv2_b1,67.890,32.110,87.790,12.210,8.14,240,0.882,bicubic,-11.574,-6.934,-8
regnetx_080,67.880,32.120,86.990,13.010,39.57,224,0.875,bicubic,-11.322,-7.564,+12
resnext101_32x8d,67.860,32.140,87.490,12.510,88.79,224,0.875,bilinear,-11.456,-7.028,-4
legacy_seresnext50_32x4d,67.850,32.150,87.620,12.380,27.56,224,0.875,bilinear,-11.218,-6.814,+17
efficientnet_em,67.830,32.170,88.120,11.880,6.90,240,0.882,bicubic,-11.420,-6.674,+5
lambda_resnet26t,67.820,32.180,87.770,12.230,10.96,256,0.940,bicubic,-11.278,-6.818,+12
Update results csvs for latest release
3 years ago
resmlp_24_224,67.800,32.200,87.620,12.380,30.02,224,0.875,bicubic,-11.582,-6.926,-11
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
hrnet_w48,67.770,32.230,87.410,12.590,77.47,224,0.875,bilinear,-11.532,-7.102,-3
hrnet_w44,67.740,32.260,87.560,12.440,67.06,224,0.875,bilinear,-11.160,-6.814,+20
coat_lite_mini,67.710,32.290,87.680,12.320,11.01,224,0.900,bicubic,-11.386,-6.924,+9
tf_efficientnet_b0_ns,67.700,32.300,88.070,11.930,5.29,224,0.875,bicubic,-10.958,-6.308,+27
regnetx_064,67.680,32.320,87.530,12.470,26.21,224,0.875,bicubic,-11.386,-6.928,+10
eca_botnext26ts_256,67.670,32.330,87.050,12.950,10.59,256,0.950,bicubic,-11.604,-7.566,-5
xception,67.670,32.330,87.580,12.420,22.86,299,0.897,bicubic,-11.380,-6.812,+9
dla169,67.630,32.370,87.590,12.410,53.39,224,0.875,bilinear,-11.062,-6.750,+22
dpn68b,67.630,32.370,87.670,12.330,12.61,224,0.875,bicubic,-11.590,-6.748,-4
halonet26t,67.630,32.370,87.240,12.760,12.48,256,0.950,bicubic,-11.486,-7.070,0
gluon_inception_v3,67.590,32.410,87.470,12.530,23.83,299,0.875,bicubic,-11.214,-6.900,+14
Update results csvs for latest release
3 years ago
hrnet_w40,67.580,32.420,87.130,12.870,57.56,224,0.875,bilinear,-11.336,-7.344,+8
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
gluon_resnet101_v1c,67.580,32.420,87.180,12.820,44.57,224,0.875,bicubic,-11.954,-7.400,-30
legacy_seresnet152,67.530,32.470,87.400,12.600,66.82,224,0.875,bilinear,-11.122,-6.970,+18
tf_efficientnet_b1_ap,67.520,32.480,87.760,12.240,7.79,240,0.882,bicubic,-11.760,-6.544,-15
eca_halonext26ts,67.470,32.530,87.230,12.770,10.76,256,0.940,bicubic,-12.020,-7.368,-32
gluon_resnet101_v1b,67.470,32.530,87.240,12.760,44.55,224,0.875,bicubic,-11.832,-7.280,-20
efficientnet_b1,67.460,32.540,87.510,12.490,7.79,256,1.000,bicubic,-11.336,-6.832,+8
res2net50_26w_8s,67.460,32.540,87.280,12.720,48.40,224,0.875,bilinear,-11.520,-7.014,-1
resnetblur50,67.460,32.540,87.440,12.560,25.56,224,0.875,bicubic,-11.844,-7.194,-24
tf_efficientnet_cc_b1_8e,67.450,32.550,87.320,12.680,39.72,240,0.882,bicubic,-11.856,-7.052,-27
res2net101_26w_4s,67.450,32.550,87.010,12.990,45.21,224,0.875,bilinear,-11.746,-7.426,-13
resnet33ts,67.370,32.630,87.580,12.420,19.68,256,0.900,bicubic,-11.840,-6.992,-17
cait_xxs24_224,67.340,32.660,87.510,12.490,11.96,224,1.000,bicubic,-11.044,-6.800,+23
regnetx_032,67.290,32.710,86.990,13.010,15.30,224,0.875,bicubic,-10.882,-7.098,+33
coat_tiny,67.260,32.740,87.290,12.710,5.50,224,0.900,bicubic,-11.170,-6.750,+18
xception41,67.250,32.750,87.210,12.790,26.97,299,0.903,bicubic,-11.260,-7.068,+9
resnest26d,67.190,32.810,87.170,12.830,17.07,224,0.875,bilinear,-11.286,-7.122,+11
legacy_seresnet101,67.160,32.840,87.050,12.950,49.33,224,0.875,bilinear,-11.228,-7.214,+17
repvgg_b2,67.160,32.840,87.330,12.670,89.02,224,0.875,bilinear,-11.632,-7.088,-3
botnet26t_256,67.130,32.870,87.530,12.470,12.49,256,0.950,bicubic,-12.122,-6.998,-29
dla60x,67.100,32.900,87.180,12.820,17.35,224,0.875,bilinear,-11.144,-6.838,+22
gluon_resnet50_v1s,67.070,32.930,86.860,13.140,25.68,224,0.875,bicubic,-11.642,-7.380,-4
tv_resnet152,67.040,32.960,87.550,12.450,60.19,224,0.875,bilinear,-11.276,-6.484,+16
dla102x,67.030,32.970,86.790,13.210,26.31,224,0.875,bilinear,-11.486,-7.436,0
dla60_res2net,67.020,32.980,87.160,12.840,20.85,224,0.875,bilinear,-11.442,-7.046,+4
xcit_tiny_12_p16_224_dist,67.010,32.990,87.420,12.580,6.72,224,1.000,bicubic,-11.566,-6.776,-4
Update results csvs for latest release
3 years ago
lambda_resnet26rpt_256,66.970,33.030,87.130,12.870,10.99,256,0.940,bicubic,-11.998,-7.298,-18
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
mixnet_l,66.940,33.060,86.920,13.080,7.33,224,0.875,bicubic,-12.036,-7.258,-20
res2net50_26w_6s,66.920,33.080,86.860,13.140,37.05,224,0.875,bilinear,-11.646,-7.274,-6
pit_xs_224,66.910,33.090,87.290,12.710,10.62,224,0.900,bicubic,-11.276,-6.874,+16
Update results csvs for latest release
3 years ago
repvgg_b1,66.910,33.090,86.790,13.210,57.42,224,0.875,bilinear,-11.458,-7.306,+6
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
tf_efficientnet_b1,66.890,33.110,87.010,12.990,7.79,240,0.882,bicubic,-11.938,-7.188,-19
xcit_nano_12_p8_384_dist,66.870,33.130,87.110,12.890,3.05,384,1.000,bicubic,-10.948,-6.934,+34
efficientnet_es,66.860,33.140,86.710,13.290,5.44,224,0.875,bicubic,-11.196,-7.226,+17
mobilevit_s,66.860,33.140,87.080,12.920,5.58,256,0.900,bicubic,-11.452,-7.072,+5
regnetx_040,66.840,33.160,86.730,13.270,22.12,224,0.875,bicubic,-11.642,-7.514,-9
resnet32ts,66.830,33.170,87.260,12.740,17.96,256,0.900,bicubic,-12.182,-7.098,-31
tf_mixnet_l,66.790,33.210,86.460,13.540,7.33,224,0.875,bicubic,-11.984,-7.536,-21
Update results csvs for latest release
3 years ago
hrnet_w30,66.780,33.220,86.780,13.220,37.71,224,0.875,bilinear,-11.418,-7.444,+6
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
hrnet_w32,66.750,33.250,87.310,12.690,41.23,224,0.875,bilinear,-11.698,-6.884,-10
selecsls60b,66.730,33.270,86.520,13.480,32.77,224,0.875,bicubic,-11.682,-7.654,-7
wide_resnet101_2,66.710,33.290,87.030,12.970,126.89,224,0.875,bilinear,-12.144,-7.260,-30
tf_efficientnetv2_b0,66.690,33.310,86.710,13.290,7.14,224,0.875,bicubic,-11.670,-7.310,-5
adv_inception_v3,66.660,33.340,86.540,13.460,23.83,299,0.875,bicubic,-10.922,-7.196,+31
dla60_res2next,66.640,33.360,87.030,12.970,17.03,224,0.875,bilinear,-11.800,-7.120,-14
vit_tiny_patch16_384,66.580,33.420,87.270,12.730,5.79,384,1.000,bicubic,-11.854,-7.272,-14
levit_128,66.570,33.430,86.750,13.250,9.21,224,0.900,bicubic,-11.922,-7.256,-21
gluon_resnet50_v1c,66.550,33.450,86.180,13.820,25.58,224,0.875,bicubic,-11.462,-7.810,+6
dla102,66.530,33.470,86.910,13.090,33.27,224,0.875,bilinear,-11.500,-7.038,+4
gmixer_24_224,66.430,33.570,86.160,13.840,24.72,224,0.875,bicubic,-11.606,-7.510,+2
tf_inception_v3,66.410,33.590,86.660,13.340,23.83,299,0.875,bicubic,-11.446,-6.980,+13
bat_resnext26ts,66.390,33.610,86.830,13.170,10.73,256,0.900,bicubic,-11.860,-7.268,-10
hardcorenas_f,66.380,33.620,86.200,13.800,8.20,224,0.875,bilinear,-11.718,-7.602,-3
coat_lite_tiny,66.300,33.700,86.980,13.020,5.72,224,0.900,bicubic,-11.214,-6.936,+24
efficientnet_b0,66.300,33.700,85.980,14.020,5.29,224,0.875,bicubic,-11.390,-7.550,+14
legacy_seresnet50,66.260,33.740,86.340,13.660,28.09,224,0.875,bilinear,-11.370,-7.410,+15
selecsls60,66.200,33.800,86.330,13.670,30.67,224,0.875,bicubic,-11.776,-7.416,-1
tf_efficientnet_em,66.180,33.820,86.360,13.640,6.90,240,0.882,bicubic,-11.952,-7.684,-9
tv_resnext50_32x4d,66.170,33.830,86.040,13.960,25.03,224,0.875,bilinear,-11.446,-7.660,+13
tf_efficientnet_cc_b0_8e,66.160,33.840,86.240,13.760,24.01,224,0.875,bicubic,-11.746,-7.416,0
res2net50_26w_4s,66.140,33.860,86.590,13.410,25.70,224,0.875,bilinear,-11.820,-7.262,-4
resmlp_12_distilled_224,66.140,33.860,86.620,13.380,15.35,224,0.875,bicubic,-11.802,-6.938,-4
inception_v3,66.130,33.870,86.320,13.680,23.83,299,0.875,bicubic,-11.308,-7.154,+18
efficientnet_b1_pruned,66.090,33.910,86.570,13.430,6.33,240,0.882,bicubic,-12.150,-7.264,-20
rexnet_100,66.080,33.920,86.500,13.500,4.80,224,0.875,bicubic,-11.778,-7.370,-3
Update results csvs for latest release
3 years ago
regnety_016,66.080,33.920,86.370,13.630,11.20,224,0.875,bicubic,-11.780,-7.352,-3
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
gluon_resnet50_v1b,66.070,33.930,86.260,13.740,25.56,224,0.875,bicubic,-11.510,-7.462,+9
res2net50_14w_8s,66.010,33.990,86.250,13.750,25.06,224,0.875,bilinear,-12.134,-7.598,-20
tinynet_a,66.010,33.990,85.790,14.210,6.19,192,0.875,bicubic,-11.640,-7.746,+1
gcresnext26ts,65.950,34.050,85.920,14.080,10.48,256,0.900,bicubic,-11.870,-7.910,-4
seresnext26t_32x4d,65.880,34.120,85.670,14.330,16.81,224,0.875,bicubic,-12.096,-8.160,-16
res2next50,65.860,34.140,85.840,14.160,24.67,224,0.875,bilinear,-12.392,-8.046,-31
densenet161,65.850,34.150,86.450,13.550,28.68,224,0.875,bicubic,-11.504,-7.186,+11
hardcorenas_e,65.840,34.160,85.980,14.020,8.07,224,0.875,bilinear,-11.954,-7.716,-6
repvgg_b1g4,65.840,34.160,86.120,13.880,39.97,224,0.875,bilinear,-11.746,-7.710,-1
resnet34d,65.790,34.210,86.720,13.280,21.82,224,0.875,bicubic,-11.324,-6.660,+16
eca_resnext26ts,65.780,34.220,85.840,14.160,10.30,256,0.900,bicubic,-11.674,-7.726,+3
xcit_tiny_12_p16_224,65.770,34.230,86.220,13.780,6.72,224,1.000,bicubic,-11.356,-7.496,+13
mobilenetv3_large_100_miil,65.760,34.240,85.200,14.800,5.48,224,0.875,bilinear,-12.156,-7.706,-20
skresnet34,65.740,34.260,85.970,14.030,22.28,224,0.875,bicubic,-11.164,-7.350,+22
Update results csvs for latest release
3 years ago
tv_resnet101,65.690,34.310,85.980,14.020,44.55,224,0.875,bilinear,-11.688,-7.562,+2
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
seresnext26ts,65.650,34.350,86.160,13.840,10.39,256,0.900,bicubic,-12.202,-7.630,-17
hardcorenas_d,65.630,34.370,85.460,14.540,7.50,224,0.875,bilinear,-11.800,-8.022,-1
selecsls42b,65.620,34.380,85.810,14.190,32.46,224,0.875,bicubic,-11.554,-7.582,+6
poolformer_s12,65.590,34.410,86.130,13.870,11.92,224,0.900,bicubic,-11.646,-7.374,+4
tf_efficientnet_b0_ap,65.490,34.510,85.590,14.410,5.29,224,0.875,bicubic,-11.604,-7.666,+8
seresnext26d_32x4d,65.420,34.580,85.970,14.030,16.81,224,0.875,bicubic,-12.184,-7.638,-14
convmixer_1024_20_ks9_p14,65.410,34.590,85.590,14.410,24.38,224,0.960,bicubic,-11.532,-7.766,+11
resnet26t,65.390,34.610,86.110,13.890,16.01,256,0.940,bicubic,-12.472,-7.734,-28
res2net50_48w_2s,65.380,34.620,85.960,14.040,25.29,224,0.875,bilinear,-12.140,-7.592,-13
tf_efficientnet_lite2,65.380,34.620,86.000,14.000,6.09,260,0.890,bicubic,-12.088,-7.756,-12
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3 years ago
densenet201,65.290,34.710,85.680,14.320,20.01,224,0.875,bicubic,-12.000,-7.798,-5
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3 years ago
densenetblur121d,65.290,34.710,85.700,14.300,8.00,224,0.875,bicubic,-11.294,-7.492,+19
dla60,65.200,34.800,85.760,14.240,22.04,224,0.875,bilinear,-11.830,-7.560,+1
crossvit_9_dagger_240,65.200,34.800,86.600,13.400,8.78,240,0.875,bicubic,-11.782,-7.010,+3
ese_vovnet19b_dw,65.190,34.810,85.460,14.540,6.54,224,0.875,bicubic,-11.612,-7.812,+8
tf_efficientnet_cc_b0_4e,65.150,34.850,85.140,14.860,13.31,224,0.875,bicubic,-12.152,-8.194,-12
gernet_s,65.130,34.870,85.520,14.480,8.17,224,0.875,bilinear,-11.778,-7.612,+3
legacy_seresnext26_32x4d,65.060,34.940,85.640,14.360,16.79,224,0.875,bicubic,-12.046,-7.678,-6
mobilenetv2_120d,65.010,34.990,85.950,14.050,5.83,224,0.875,bicubic,-12.284,-7.546,-14
hrnet_w18,64.930,35.070,85.740,14.260,21.30,224,0.875,bilinear,-11.824,-7.700,+5
hardcorenas_c,64.850,35.150,85.240,14.760,5.52,224,0.875,bilinear,-12.200,-7.918,-7
densenet169,64.760,35.240,85.240,14.760,14.15,224,0.875,bicubic,-11.138,-7.790,+22
mixnet_m,64.700,35.300,85.460,14.540,5.01,224,0.875,bicubic,-12.564,-7.964,-16
resnet26d,64.680,35.320,85.110,14.890,16.01,224,0.875,bicubic,-12.024,-8.040,+2
levit_128s,64.590,35.410,84.750,15.250,7.78,224,0.900,bicubic,-11.930,-8.122,+9
resnext26ts,64.590,35.410,85.110,14.890,10.30,256,0.900,bicubic,-12.190,-8.018,-2
xcit_nano_12_p8_224_dist,64.560,35.440,85.980,14.020,3.05,224,1.000,bicubic,-11.760,-7.108,+9
repvgg_a2,64.440,35.560,85.140,14.860,28.21,224,0.875,bilinear,-12.018,-7.870,+7
xcit_nano_12_p16_384_dist,64.430,35.570,85.310,14.690,3.05,384,1.000,bicubic,-11.026,-7.380,+24
hardcorenas_b,64.410,35.590,84.860,15.140,5.18,224,0.875,bilinear,-12.126,-7.894,+2
tf_efficientnet_lite1,64.390,35.610,85.480,14.520,5.42,240,0.882,bicubic,-12.250,-7.740,-3
Update results csvs for latest release
3 years ago
regnetx_016,64.370,35.630,85.460,14.540,9.19,224,0.875,bicubic,-12.580,-7.962,-15
resmlp_12_224,64.350,35.650,85.590,14.410,15.35,224,0.875,bicubic,-12.306,-7.590,-6
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3 years ago
tf_efficientnet_b0,64.300,35.700,85.280,14.720,5.29,224,0.875,bicubic,-12.544,-7.948,-12
tf_mixnet_m,64.270,35.730,85.090,14.910,5.01,224,0.875,bicubic,-12.672,-8.064,-16
dpn68,64.240,35.760,85.180,14.820,12.61,224,0.875,bicubic,-12.066,-7.794,+2
tf_efficientnet_es,64.230,35.770,84.740,15.260,5.44,224,0.875,bicubic,-12.366,-8.464,-7
regnety_008,64.150,35.850,85.280,14.720,6.26,224,0.875,bicubic,-12.160,-7.790,-1
vit_small_patch32_224,64.060,35.940,85.560,14.440,22.88,224,0.900,bicubic,-11.926,-7.710,+2
mobilenetv2_140,64.050,35.950,85.030,14.970,6.11,224,0.875,bicubic,-12.472,-7.966,-7
densenet121,63.740,36.260,84.600,15.400,7.98,224,0.875,bicubic,-11.844,-8.052,+7
hardcorenas_a,63.710,36.290,84.400,15.600,5.26,224,0.875,bilinear,-12.210,-8.120,+1
resnest14d,63.610,36.390,84.260,15.740,10.61,224,0.875,bilinear,-11.894,-8.260,+7
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3 years ago
tf_mixnet_s,63.560,36.440,84.270,15.730,4.13,224,0.875,bicubic,-12.090,-8.358,+2
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3 years ago
resnet26,63.450,36.550,84.260,15.740,16.00,224,0.875,bicubic,-11.850,-8.318,+10
mixnet_s,63.380,36.620,84.740,15.260,4.13,224,0.875,bicubic,-12.612,-8.058,-6
mobilenetv3_large_100,63.350,36.650,84.080,15.920,5.48,224,0.875,bicubic,-12.416,-8.464,-2
vit_tiny_r_s16_p8_384,63.340,36.660,85.270,14.730,6.36,384,1.000,bicubic,-12.614,-7.994,-7
tv_resnet50,63.340,36.660,84.660,15.340,25.56,224,0.875,bilinear,-12.794,-8.208,-9
efficientnet_es_pruned,63.310,36.690,84.950,15.050,5.44,224,0.875,bicubic,-11.686,-7.490,+13
mixer_b16_224,63.280,36.720,83.320,16.680,59.88,224,0.875,bicubic,-13.332,-8.908,-22
efficientnet_lite0,63.240,36.760,84.440,15.560,4.65,224,0.875,bicubic,-12.236,-8.072,-1
mobilenetv3_rw,63.230,36.770,84.510,15.490,5.48,224,0.875,bicubic,-12.402,-8.198,-6
semnasnet_100,63.160,36.840,84.530,15.470,3.89,224,0.875,bicubic,-12.290,-8.070,0
pit_ti_distilled_224,63.140,36.860,83.960,16.040,5.10,224,0.900,bicubic,-11.392,-8.136,+17
vit_tiny_patch16_224,63.110,36.890,84.850,15.150,5.72,224,0.900,bicubic,-12.352,-7.994,-4
regnety_006,63.100,36.900,84.260,15.740,6.06,224,0.875,bicubic,-12.150,-8.274,-1
tv_densenet121,62.950,37.050,84.250,15.750,7.98,224,0.875,bicubic,-11.794,-7.902,+9
mobilevit_xs,62.940,37.060,84.830,15.170,2.32,256,0.900,bicubic,-11.704,-7.526,+10
resnet34,62.870,37.130,84.130,15.870,21.80,224,0.875,bilinear,-12.244,-8.154,0
legacy_seresnet34,62.840,37.160,84.220,15.780,21.96,224,0.875,bilinear,-11.968,-7.906,+5
mobilenetv2_110d,62.820,37.180,84.490,15.510,4.52,224,0.875,bicubic,-12.218,-7.694,-1
deit_tiny_distilled_patch16_224,62.800,37.200,83.920,16.080,5.91,224,0.900,bicubic,-11.712,-7.966,+10
hrnet_w18_small_v2,62.790,37.210,83.990,16.010,15.60,224,0.875,bilinear,-12.328,-8.426,-5
swsl_resnet18,62.770,37.230,84.300,15.700,11.69,224,0.875,bilinear,-10.506,-7.436,+21
repvgg_b0,62.730,37.270,83.860,16.140,15.82,224,0.875,bilinear,-12.430,-8.558,-9
tinynet_b,62.730,37.270,84.250,15.750,3.73,188,0.875,bicubic,-12.246,-7.934,-3
gluon_resnet34_v1b,62.570,37.430,83.990,16.010,21.80,224,0.875,bicubic,-12.018,-7.998,+3
xcit_nano_12_p8_224,62.550,37.450,84.200,15.800,3.05,224,1.000,bicubic,-11.360,-7.968,+10
tf_efficientnet_lite0,62.540,37.460,84.220,15.780,4.65,224,0.875,bicubic,-12.292,-7.954,-5
regnetx_008,62.490,37.510,84.020,15.980,7.26,224,0.875,bicubic,-12.544,-8.320,-9
Update results csvs for latest release
3 years ago
dla34,62.480,37.520,83.910,16.090,15.74,224,0.875,bilinear,-12.140,-8.162,-2
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3 years ago
fbnetc_100,62.470,37.530,83.380,16.620,5.57,224,0.875,bilinear,-12.660,-9.006,-15
tf_mobilenetv3_large_100,62.460,37.540,83.970,16.030,5.48,224,0.875,bilinear,-13.058,-8.634,-25
crossvit_9_240,62.260,37.740,84.240,15.760,8.55,240,0.875,bicubic,-11.700,-7.728,+3
crossvit_tiny_240,62.070,37.930,83.610,16.390,7.01,240,0.875,bicubic,-11.262,-8.304,+8
mnasnet_100,61.890,38.110,83.710,16.290,4.38,224,0.875,bicubic,-12.768,-8.402,-9
Update results csvs for latest release
3 years ago
regnety_004,61.860,38.140,83.420,16.580,4.34,224,0.875,bicubic,-12.164,-8.334,-2
vgg19_bn,61.860,38.140,83.450,16.550,143.68,224,0.875,bilinear,-12.354,-8.398,-5
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
convit_tiny,61.570,38.430,84.130,15.870,5.71,224,0.875,bicubic,-11.544,-7.584,+7
ssl_resnet18,61.470,38.530,83.310,16.690,11.69,224,0.875,bilinear,-11.138,-8.114,+11
regnetx_006,61.370,38.630,83.450,16.550,6.20,224,0.875,bicubic,-12.490,-8.222,-2
spnasnet_100,61.210,38.790,82.790,17.210,4.42,224,0.875,bilinear,-12.874,-9.030,-8
tv_resnet34,61.190,38.810,82.720,17.280,21.80,224,0.875,bilinear,-12.116,-8.704,+1
pit_ti_224,60.960,39.040,83.860,16.140,4.85,224,0.900,bicubic,-11.952,-7.546,+6
skresnet18,60.850,39.150,82.880,17.120,11.96,224,0.875,bicubic,-12.186,-8.288,+2
ghostnet_100,60.830,39.170,82.360,17.640,5.18,224,0.875,bilinear,-13.144,-9.100,-10
Update results csvs for latest release
3 years ago
vgg16_bn,60.760,39.240,82.950,17.050,138.37,224,0.875,bilinear,-12.590,-8.554,-5
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3 years ago
semnasnet_075,60.710,39.290,82.510,17.490,2.91,224,0.875,bicubic,-12.262,-8.626,0
tf_mobilenetv3_large_075,60.390,39.610,81.950,18.050,3.99,224,0.875,bilinear,-13.046,-9.394,-8
xcit_nano_12_p16_224_dist,60.250,39.750,82.490,17.510,3.05,224,1.000,bicubic,-12.052,-8.368,+5
mobilenetv2_100,60.190,39.810,82.240,17.760,3.50,224,0.875,bicubic,-12.780,-8.780,-2
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3 years ago
resnet18d,60.180,39.820,82.310,17.690,11.71,224,0.875,bicubic,-12.070,-8.378,+4
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3 years ago
vit_base_patch32_224_sam,60.000,40.000,81.230,18.770,88.22,224,0.900,bicubic,-13.694,-9.780,-13
deit_tiny_patch16_224,59.860,40.140,82.660,17.340,5.72,224,0.900,bicubic,-12.312,-8.454,+4
Update results csvs for latest release
3 years ago
legacy_seresnet18,59.810,40.190,81.690,18.310,11.78,224,0.875,bicubic,-11.932,-8.642,+7
vgg19,59.710,40.290,81.450,18.550,143.67,224,0.875,bilinear,-12.656,-9.420,-3
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
regnetx_004,59.420,40.580,81.690,18.310,5.16,224,0.875,bicubic,-12.972,-9.142,-5
Update results csvs for latest release
3 years ago
tf_mobilenetv3_large_minimal_100,59.080,40.920,81.150,18.850,3.92,224,0.875,bilinear,-13.170,-9.480,-1
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3 years ago
vit_tiny_r_s16_p8_224,59.070,40.930,81.760,18.240,6.34,224,0.900,bicubic,-12.722,-9.062,+2
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3 years ago
vgg13_bn,59.000,41.000,81.080,18.920,133.05,224,0.875,bilinear,-12.594,-9.296,+3
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3 years ago
hrnet_w18_small,58.970,41.030,81.350,18.650,13.19,224,0.875,bilinear,-13.368,-9.330,-7
lcnet_100,58.860,41.140,81.190,18.810,2.95,224,0.875,bicubic,-13.244,-9.186,-3
Update results csvs for latest release
3 years ago
vgg16,58.840,41.160,81.660,18.340,138.36,224,0.875,bilinear,-12.750,-8.722,+1
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
xcit_nano_12_p16_224,58.350,41.650,80.890,19.110,3.05,224,1.000,bicubic,-11.604,-8.864,+5
gluon_resnet18_v1b,58.320,41.680,80.970,19.030,11.69,224,0.875,bicubic,-12.514,-8.792,+1
Update results csvs for latest release
3 years ago
tinynet_c,58.170,41.830,80.280,19.720,2.46,184,0.875,bicubic,-13.058,-9.470,-1
vgg11_bn,57.410,42.590,80.020,19.980,132.87,224,0.875,bilinear,-12.950,-9.782,0
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3 years ago
resnet18,57.170,42.830,80.200,19.800,11.69,224,0.875,bilinear,-12.574,-8.882,+3
mobilevit_xxs,57.150,42.850,79.730,20.270,1.27,256,0.900,bicubic,-11.770,-9.214,+4
vgg13,57.140,42.860,79.550,20.450,133.05,224,0.875,bilinear,-12.786,-9.696,0
regnety_002,56.990,43.010,79.860,20.140,3.16,224,0.875,bicubic,-13.264,-9.672,-3
mixer_l16_224,56.680,43.320,75.980,24.020,208.20,224,0.875,bicubic,-15.374,-11.682,-12
regnetx_002,56.060,43.940,79.220,20.780,2.68,224,0.875,bicubic,-12.696,-9.336,+2
Update results csvs for latest release
3 years ago
dla60x_c,56.000,44.000,78.930,21.070,1.32,224,0.875,bilinear,-11.892,-9.496,+3
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3 years ago
vgg11,55.800,44.200,78.840,21.160,132.86,224,0.875,bilinear,-13.228,-9.786,-3
lcnet_075,55.420,44.580,78.300,21.700,2.36,224,0.875,bicubic,-13.396,-10.070,-2
mobilenetv3_small_100,54.700,45.300,77.770,22.230,2.54,224,0.875,bicubic,-12.956,-9.864,+1
tf_mobilenetv3_small_100,54.530,45.470,77.060,22.940,2.54,224,0.875,bilinear,-13.394,-10.604,-2
Update results csvs for latest release
3 years ago
tinynet_d,53.420,46.580,76.350,23.650,2.34,152,0.875,bicubic,-13.538,-10.714,0
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3 years ago
mnasnet_small,53.270,46.730,75.890,24.110,2.03,224,0.875,bicubic,-12.936,-10.618,0
Update results csvs for latest release
3 years ago
dla46x_c,53.050,46.950,76.870,23.130,1.07,224,0.875,bilinear,-12.920,-10.110,0
mobilenetv2_050,52.850,47.150,75.430,24.570,1.97,224,0.875,bicubic,-13.092,-10.652,0
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
tf_mobilenetv3_small_075,52.150,47.850,75.480,24.520,2.04,224,0.875,bilinear,-13.564,-10.654,0
dla46_c,52.130,47.870,75.690,24.310,1.30,224,0.875,bilinear,-12.736,-10.604,+1
mobilenetv3_small_075,51.920,48.080,74.730,25.270,2.04,224,0.875,bicubic,-13.322,-10.708,-1
Update results csvs for latest release
3 years ago
lcnet_050,49.990,50.010,73.460,26.540,1.88,224,0.875,bicubic,-13.110,-10.922,0
tf_mobilenetv3_small_minimal_100,49.500,50.500,73.050,26.950,2.04,224,0.875,bilinear,-13.408,-11.184,0
Update eval results and add latest PyTorch 1.11 inference benchmarks
3 years ago
tinynet_e,46.700,53.300,70.360,29.640,2.04,106,0.875,bicubic,-13.156,-11.404,0
mobilenetv3_small_050,44.890,55.110,67.670,32.330,1.59,224,0.875,bicubic,-13.000,-12.524,0