You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
68 lines
4.7 KiB
68 lines
4.7 KiB
2 years ago
|
# 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](https://github.com/rwightman/pytorch-image-models/tree/master/results).
|
||
|
|
|
||
|
|
## 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](https://github.com/rwightman/pytorch-image-models/tree/master/results).
|
||
|
|
|
||
|
|
Model code .py files contain links to original sources of models and weights.
|