Results
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.
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