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Results

CSV files containing an ImageNet-1K and out-of-distribution (OOD) test set validation results for all models with pretrained weights is located in the repository results folder.

Self-trained Weights

The table below includes ImageNet-1k validation results of model weights that I’ve trained myself. It is not updated as frequently as the csv results outputs linked above.

Model Acc@1 (Err) Acc@5 (Err) Param # (M) Interpolation Image Size
efficientnet_b3a 82.242 (17.758) 96.114 (3.886) 12.23 bicubic 320 (1.0 crop)
efficientnet_b3 82.076 (17.924) 96.020 (3.980) 12.23 bicubic 300
regnet_32 82.002 (17.998) 95.906 (4.094) 19.44 bicubic 224
skresnext50d_32x4d 81.278 (18.722) 95.366 (4.634) 27.5 bicubic 288 (1.0 crop)
seresnext50d_32x4d 81.266 (18.734) 95.620 (4.380) 27.6 bicubic 224
efficientnet_b2a 80.608 (19.392) 95.310 (4.690) 9.11 bicubic 288 (1.0 crop)
resnet50d 80.530 (19.470) 95.160 (4.840) 25.6 bicubic 224
mixnet_xl 80.478 (19.522) 94.932 (5.068) 11.90 bicubic 224
efficientnet_b2 80.402 (19.598) 95.076 (4.924) 9.11 bicubic 260
seresnet50 80.274 (19.726) 95.070 (4.930) 28.1 bicubic 224
skresnext50d_32x4d 80.156 (19.844) 94.642 (5.358) 27.5 bicubic 224
cspdarknet53 80.058 (19.942) 95.084 (4.916) 27.6 bicubic 256
cspresnext50 80.040 (19.960) 94.944 (5.056) 20.6 bicubic 224
resnext50_32x4d 79.762 (20.238) 94.600 (5.400) 25 bicubic 224
resnext50d_32x4d 79.674 (20.326) 94.868 (5.132) 25.1 bicubic 224
cspresnet50 79.574 (20.426) 94.712 (5.288) 21.6 bicubic 256
ese_vovnet39b 79.320 (20.680) 94.710 (5.290) 24.6 bicubic 224
resnetblur50 79.290 (20.710) 94.632 (5.368) 25.6 bicubic 224
dpn68b 79.216 (20.784) 94.414 (5.586) 12.6 bicubic 224
resnet50 79.038 (20.962) 94.390 (5.610) 25.6 bicubic 224
mixnet_l 78.976 (21.024 94.184 (5.816) 7.33 bicubic 224
efficientnet_b1 78.692 (21.308) 94.086 (5.914) 7.79 bicubic 240
efficientnet_es 78.066 (21.934) 93.926 (6.074) 5.44 bicubic 224
seresnext26t_32x4d 77.998 (22.002) 93.708 (6.292) 16.8 bicubic 224
seresnext26tn_32x4d 77.986 (22.014) 93.746 (6.254) 16.8 bicubic 224
efficientnet_b0 77.698 (22.302) 93.532 (6.468) 5.29 bicubic 224
seresnext26d_32x4d 77.602 (22.398) 93.608 (6.392) 16.8 bicubic 224
mobilenetv2_120d 77.294 (22.706 93.502 (6.498) 5.8 bicubic 224
mixnet_m 77.256 (22.744) 93.418 (6.582) 5.01 bicubic 224
resnet34d 77.116 (22.884) 93.382 (6.618) 21.8 bicubic 224
seresnext26_32x4d 77.104 (22.896) 93.316 (6.684) 16.8 bicubic 224
skresnet34 76.912 (23.088) 93.322 (6.678) 22.2 bicubic 224
ese_vovnet19b_dw 76.798 (23.202) 93.268 (6.732) 6.5 bicubic 224
resnet26d 76.68 (23.32) 93.166 (6.834) 16 bicubic 224
densenetblur121d 76.576 (23.424) 93.190 (6.810) 8.0 bicubic 224
mobilenetv2_140 76.524 (23.476) 92.990 (7.010) 6.1 bicubic 224
mixnet_s 75.988 (24.012) 92.794 (7.206) 4.13 bicubic 224
mobilenetv3_large_100 75.766 (24.234) 92.542 (7.458) 5.5 bicubic 224
mobilenetv3_rw 75.634 (24.366) 92.708 (7.292) 5.5 bicubic 224
mnasnet_a1 75.448 (24.552) 92.604 (7.396) 3.89 bicubic 224
resnet26 75.292 (24.708) 92.57 (7.43) 16 bicubic 224
fbnetc_100 75.124 (24.876) 92.386 (7.614) 5.6 bilinear 224
resnet34 75.110 (24.890) 92.284 (7.716) 22 bilinear 224
mobilenetv2_110d 75.052 (24.948) 92.180 (7.820) 4.5 bicubic 224
seresnet34 74.808 (25.192) 92.124 (7.876) 22 bilinear 224
mnasnet_b1 74.658 (25.342) 92.114 (7.886) 4.38 bicubic 224
spnasnet_100 74.084 (25.916) 91.818 (8.182) 4.42 bilinear 224
skresnet18 73.038 (26.962) 91.168 (8.832) 11.9 bicubic 224
mobilenetv2_100 72.978 (27.022) 91.016 (8.984) 3.5 bicubic 224
resnet18d 72.260 (27.740) 90.696 (9.304) 11.7 bicubic 224
seresnet18 71.742 (28.258) 90.334 (9.666) 11.8 bicubic 224

Ported and Other Weights

For weights ported from other deep learning frameworks (Tensorflow, MXNet GluonCV) or copied from other PyTorch sources, please see the full results tables for ImageNet and various OOD test sets at in the results tables.

Model code .py files contain links to original sources of models and weights.