- 1. Model library overview diagram
- 2. SSLD pretrained models
- 3. PP-LCNet series
- 4. ResNet series
- 5. Mobile series
- 6. SEResNeXt and Res2Net series
- 7. DPN and DenseNet series
- 8. HRNet series
- 9. Inception series
- 10. EfficientNet ans ResNeXt101_wsl series
- 11. ResNeSt and RegNet series
- 12. ViT and DeiT series
- 13. RepVGG series
- 14. MixNet series
- 15. ReXNet series
- 16. SwinTransformer series
- 17. LeViT series
- 18. Twins series
- 19. HarDNet series
- 20. DLA series
- 21. RedNet series
- 22. TNT series
- 23. CSWinTransformer series
- 24. PVTV2 series
- 25. MobileViT series
- 26. Other models
- Reference
Based on the ImageNet-1k classification dataset, the 37 classification network structures supported by PaddleClas and the corresponding 217 image classification pretrained models are shown below. Training trick, a brief introduction to each series of network structures, and performance evaluation will be shown in the corresponding chapters. The evaluation environment is as follows.
- Arm CPU evaluation environment is based on Snapdragon 855 (SD855).
- Intel CPU evaluation environment is based on Intel(R) Xeon(R) Gold 6148.
- The GPU evaluation speed is measured by running 2100 times under the FP32+TensorRT configuration (excluding the warmup time of the first 100 times).
- FLOPs and Params are calculated by
paddle.flops()(PaddlePaddle version is 2.2)
Curves of accuracy to the inference time of common server-side models are shown as follows.
Curves of accuracy to the inference time of common mobile-side models are shown as follows.
Curves of accuracy to the inference time of some VisionTransformer models are shown as follows.
Accuracy and inference time of the prtrained models based on SSLD distillation are as follows. More detailed information can be refered to SSLD distillation tutorial.
| Model | Top-1 Acc | Reference Top-1 Acc |
Acc gain | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|---|
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | Download link | Download link |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | Download link | Download link |
| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | Download link | Download link |
| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 3.59 | 6.35 | 9.50 | 4.28 | 25.76 | Download link | Download link |
| Res2Net101_vd_ 26w_4s_ssld |
0.839 | 0.806 | 0.033 | 6.34 | 11.02 | 16.13 | 8.35 | 45.35 | Download link | Download link |
| Res2Net200_vd_ 26w_4s_ssld |
0.851 | 0.812 | 0.049 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | Download link | Download link |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | Download link | Download link |
| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | Download link | Download link |
| SE_HRNet_W64_C_ssld | 0.848 | - | - | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | Download link | Download link |
| Model | Top-1 Acc | Reference Top-1 Acc |
Acc gain | SD855 time(ms) bs=1, thread=1 |
SD855 time(ms) bs=1, thread=2 |
SD855 time(ms) bs=1, thread=4 |
FLOPs(M) | Params(M) | Model大小(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|---|---|
| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | Download link | Download link |
| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | Download link | Download link |
| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | Download link | Download link |
| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | Download link | Download link |
| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | Download link | Download link |
| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | Download link | Download link |
| Model | Top-1 Acc | Reference Top-1 Acc |
Acc gain | Intel-Xeon-Gold-6148 time(ms) bs=1 |
FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|
| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47.28 | 1.89 | Download link | Download link |
| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | Download link | Download link |
| PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906.49 | 9.04 | Download link | Download link |
- Note:
Reference Top-1 Accmeans the accuracy of the pre-trained model obtained by PaddleClas based on ImageNet1k dataset training.
3. PP-LCNet series [28]
The accuracy and speed indicators of the PP-LCNet series models are shown in the following table. For more information about this series of models, please refer to: PP-LCNet series model documents。
| Model | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms) bs=1 |
FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|
| PPLCNet_x0_25 | 0.5186 | 0.7565 | 1.61785 | 18.25 | 1.52 | Download link | Download link |
| PPLCNet_x0_35 | 0.5809 | 0.8083 | 2.11344 | 29.46 | 1.65 | Download link | Download link |
| PPLCNet_x0_5 | 0.6314 | 0.8466 | 2.72974 | 47.28 | 1.89 | Download link | Download link |
| PPLCNet_x0_75 | 0.6818 | 0.8830 | 4.51216 | 98.82 | 2.37 | Download link | Download link |
| PPLCNet_x1_0 | 0.7132 | 0.9003 | 6.49276 | 160.81 | 2.96 | Download link | Download link |
| PPLCNet_x1_5 | 0.7371 | 0.9153 | 12.2601 | 341.86 | 4.52 | Download link | Download link |
| PPLCNet_x2_0 | 0.7518 | 0.9227 | 20.1667 | 590 | 6.54 | Download link | Download link |
| PPLCNet_x2_5 | 0.7660 | 0.9300 | 29.595 | 906 | 9.04 | Download link | Download link |
4. ResNet series [1]
The accuracy and speed indicators of ResNet and ResNet_vd series models are shown in the following table. For more information about this series of models, please refer to: ResNet and ResNet_vd series model documents。
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| ResNet18 | 0.7098 | 0.8992 | 1.22 | 2.19 | 3.63 | 1.83 | 11.70 | Download link | Download link |
| ResNet18_vd | 0.7226 | 0.9080 | 1.26 | 2.28 | 3.89 | 2.07 | 11.72 | Download link | Download link |
| ResNet34 | 0.7457 | 0.9214 | 1.97 | 3.25 | 5.70 | 3.68 | 21.81 | Download link | Download link |
| ResNet34_vd | 0.7598 | 0.9298 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | Download link | Download link |
| ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | Download link | Download link |
| ResNet50 | 0.7650 | 0.9300 | 2.54 | 4.79 | 7.40 | 4.11 | 25.61 | Download link | Download link |
| ResNet50_vc | 0.7835 | 0.9403 | 2.57 | 4.83 | 7.52 | 4.35 | 25.63 | Download link | Download link |
| ResNet50_vd | 0.7912 | 0.9444 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | Download link | Download link |
| ResNet101 | 0.7756 | 0.9364 | 4.37 | 8.18 | 12.38 | 7.83 | 44.65 | Download link | Download link |
| ResNet101_vd | 0.8017 | 0.9497 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | Download link | Download link |
| ResNet152 | 0.7826 | 0.9396 | 6.05 | 11.41 | 17.33 | 11.56 | 60.34 | Download link | Download link |
| ResNet152_vd | 0.8059 | 0.9530 | 6.11 | 11.51 | 17.59 | 11.80 | 60.36 | Download link | Download link |
| ResNet200_vd | 0.8093 | 0.9533 | 7.70 | 14.57 | 22.16 | 15.30 | 74.93 | Download link | Download link |
| ResNet50_vd_ ssld |
0.8300 | 0.9640 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | Download link | Download link |
| ResNet101_vd_ ssld |
0.8373 | 0.9669 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | Download link | Download link |
The accuracy and speed indicators of the mobile series models are shown in the following table. For more information about this series, please refer to: Mobile series model documents。
| Model | Top-1 Acc | Top-5 Acc | SD855 time(ms) bs=1, thread=1 |
SD855 time(ms) bs=1, thread=2 |
SD855 time(ms) bs=1, thread=4 |
FLOPs(M) | Params(M) | Model大小(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|---|
| MobileNetV1_ x0_25 |
0.5143 | 0.7546 | 2.88 | 1.82 | 1.26 | 43.56 | 0.48 | 1.9 | Download link | Download link |
| MobileNetV1_ x0_5 |
0.6352 | 0.8473 | 8.74 | 5.26 | 3.09 | 154.57 | 1.34 | 5.2 | Download link | Download link |
| MobileNetV1_ x0_75 |
0.6881 | 0.8823 | 17.84 | 10.61 | 6.21 | 333.00 | 2.60 | 10 | Download link | Download link |
| MobileNetV1 | 0.7099 | 0.8968 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | Download link | Download link |
| MobileNetV1_ ssld |
0.7789 | 0.9394 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | Download link | Download link |
| MobileNetV2_ x0_25 |
0.5321 | 0.7652 | 3.46 | 2.51 | 2.03 | 34.18 | 1.53 | 6.1 | Download link | Download link |
| MobileNetV2_ x0_5 |
0.6503 | 0.8572 | 7.69 | 4.92 | 3.57 | 99.48 | 1.98 | 7.8 | Download link | Download link |
| MobileNetV2_ x0_75 |
0.6983 | 0.8901 | 13.69 | 8.60 | 5.82 | 197.37 | 2.65 | 10 | Download link | Download link |
| MobileNetV2 | 0.7215 | 0.9065 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | Download link | Download link |
| MobileNetV2_ x1_5 |
0.7412 | 0.9167 | 40.79 | 24.49 | 15.50 | 702.35 | 6.90 | 26 | Download link | Download link |
| MobileNetV2_ x2_0 |
0.7523 | 0.9258 | 67.50 | 40.03 | 25.55 | 1217.25 | 11.33 | 43 | Download link | Download link |
| MobileNetV2_ ssld |
0.7674 | 0.9339 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | Download link | Download link |
| MobileNetV3_ large_x1_25 |
0.7641 | 0.9295 | 24.52 | 14.76 | 9.89 | 362.70 | 7.47 | 29 | Download link | Download link |
| MobileNetV3_ large_x1_0 |
0.7532 | 0.9231 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | Download link | Download link |
| MobileNetV3_ large_x0_75 |
0.7314 | 0.9108 | 11.53 | 7.06 | 4.94 | 151.70 | 3.93 | 16 | Download link | Download link |
| MobileNetV3_ large_x0_5 |
0.6924 | 0.8852 | 6.50 | 4.22 | 3.15 | 71.83 | 2.69 | 11 | Download link | Download link |
| MobileNetV3_ large_x0_35 |
0.6432 | 0.8546 | 4.43 | 3.11 | 2.41 | 40.90 | 2.11 | 8.6 | Download link | Download link |
| MobileNetV3_ small_x1_25 |
0.7067 | 0.8951 | 7.88 | 4.91 | 3.45 | 100.07 | 3.64 | 14 | Download link | Download link |
| MobileNetV3_ small_x1_0 |
0.6824 | 0.8806 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | Download link | Download link |
| MobileNetV3_ small_x0_75 |
0.6602 | 0.8633 | 4.50 | 2.96 | 2.19 | 46.02 | 2.38 | 9.6 | Download link | Download link |
| MobileNetV3_ small_x0_5 |
0.5921 | 0.8152 | 2.89 | 2.04 | 1.62 | 22.60 | 1.91 | 7.8 | Download link | Download link |
| MobileNetV3_ small_x0_35 |
0.5303 | 0.7637 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | Download link | Download link |
| MobileNetV3_ small_x0_35_ssld |
0.5555 | 0.7771 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | Download link | Download link |
| MobileNetV3_ large_x1_0_ssld |
0.7896 | 0.9448 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | Download link | Download link |
| MobileNetV3_small_ x1_0_ssld |
0.7129 | 0.9010 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | Download link | Download link |
| ShuffleNetV2 | 0.6880 | 0.8845 | 9.72 | 5.97 | 4.13 | 148.86 | 2.29 | 9 | Download link | Download link |
| ShuffleNetV2_ x0_25 |
0.4990 | 0.7379 | 1.94 | 1.53 | 1.43 | 18.95 | 0.61 | 2.7 | Download link | Download link |
| ShuffleNetV2_ x0_33 |
0.5373 | 0.7705 | 2.23 | 1.70 | 1.79 | 24.04 | 0.65 | 2.8 | Download link | Download link |
| ShuffleNetV2_ x0_5 |
0.6032 | 0.8226 | 3.67 | 2.63 | 2.06 | 42.58 | 1.37 | 5.6 | Download link | Download link |
| ShuffleNetV2_ x1_5 |
0.7163 | 0.9015 | 17.21 | 10.56 | 6.81 | 301.35 | 3.53 | 14 | Download link | Download link |
| ShuffleNetV2_ x2_0 |
0.7315 | 0.9120 | 31.21 | 18.98 | 11.65 | 571.70 | 7.40 | 28 | Download link | Download link |
| ShuffleNetV2_ swish |
0.7003 | 0.8917 | 31.21 | 9.06 | 5.74 | 148.86 | 2.29 | 9.1 | Download link | Download link |
| GhostNet_ x0_5 |
0.6688 | 0.8695 | 5.28 | 3.95 | 3.29 | 46.15 | 2.60 | 10 | Download link | Download link |
| GhostNet_ x1_0 |
0.7402 | 0.9165 | 12.89 | 8.66 | 6.72 | 148.78 | 5.21 | 20 | Download link | Download link |
| GhostNet_ x1_3 |
0.7579 | 0.9254 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | Download link | Download link |
| GhostNet_ x1_3_ssld |
0.7938 | 0.9449 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | Download link | Download link |
| ESNet_x0_25 | 0.6248 | 0.8346 | 4.12 | 2.97 | 2.51 | 30.85 | 2.83 | 11 | Download link | Download link |
| ESNet_x0_5 | 0.6882 | 0.8804 | 6.45 | 4.42 | 3.35 | 67.31 | 3.25 | 13 | Download link | Download link |
| ESNet_x0_75 | 0.7224 | 0.9045 | 9.59 | 6.28 | 4.52 | 123.74 | 3.87 | 15 | Download link | Download link |
| ESNet_x1_0 | 0.7392 | 0.9140 | 13.67 | 8.71 | 5.97 | 197.33 | 4.64 | 18 | Download link | Download link |
The accuracy and speed indicators of the SEResNeXt and Res2Net series models are shown in the following table. For more information about the models of this series, please refer to: SEResNeXt and Res2Net series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| Res2Net50_ 26w_4s |
0.7933 | 0.9457 | 3.52 | 6.23 | 9.30 | 4.28 | 25.76 | Download link | Download link |
| Res2Net50_vd_ 26w_4s |
0.7975 | 0.9491 | 3.59 | 6.35 | 9.50 | 4.52 | 25.78 | Download link | Download link |
| Res2Net50_ 14w_8s |
0.7946 | 0.9470 | 4.39 | 7.21 | 10.38 | 4.20 | 25.12 | Download link | Download link |
| Res2Net101_vd_ 26w_4s |
0.8064 | 0.9522 | 6.34 | 11.02 | 16.13 | 8.35 | 45.35 | Download link | Download link |
| Res2Net200_vd_ 26w_4s |
0.8121 | 0.9571 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | Download link | Download link |
| Res2Net200_vd_ 26w_4s_ssld |
0.8513 | 0.9742 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | Download link | Download link |
| ResNeXt50_ 32x4d |
0.7775 | 0.9382 | 5.07 | 8.49 | 12.02 | 4.26 | 25.10 | Download link | Download link |
| ResNeXt50_vd_ 32x4d |
0.7956 | 0.9462 | 5.29 | 8.68 | 12.33 | 4.50 | 25.12 | Download link | Download link |
| ResNeXt50_ 64x4d |
0.7843 | 0.9413 | 9.39 | 13.97 | 20.56 | 8.02 | 45.29 | Download link | Download link |
| ResNeXt50_vd_ 64x4d |
0.8012 | 0.9486 | 9.75 | 14.14 | 20.84 | 8.26 | 45.31 | Download link | Download link |
| ResNeXt101_ 32x4d |
0.7865 | 0.9419 | 11.34 | 16.78 | 22.80 | 8.01 | 44.32 | Download link | Download link |
| ResNeXt101_vd_ 32x4d |
0.8033 | 0.9512 | 11.36 | 17.01 | 23.07 | 8.25 | 44.33 | Download link | Download link |
| ResNeXt101_ 64x4d |
0.7835 | 0.9452 | 21.57 | 28.08 | 39.49 | 15.52 | 83.66 | Download link | Download link |
| ResNeXt101_vd_ 64x4d |
0.8078 | 0.9520 | 21.57 | 28.22 | 39.70 | 15.76 | 83.68 | Download link | Download link |
| ResNeXt152_ 32x4d |
0.7898 | 0.9433 | 17.14 | 25.11 | 33.79 | 11.76 | 60.15 | Download link | Download link |
| ResNeXt152_vd_ 32x4d |
0.8072 | 0.9520 | 16.99 | 25.29 | 33.85 | 12.01 | 60.17 | Download link | Download link |
| ResNeXt152_ 64x4d |
0.7951 | 0.9471 | 33.07 | 42.05 | 59.13 | 23.03 | 115.27 | Download link | Download link |
| ResNeXt152_vd_ 64x4d |
0.8108 | 0.9534 | 33.30 | 42.41 | 59.42 | 23.27 | 115.29 | Download link | Download link |
| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.48 | 2.70 | 4.32 | 2.07 | 11.81 | Download link | Download link |
| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.42 | 3.69 | 6.29 | 3.93 | 22.00 | Download link | Download link |
| SE_ResNet50_vd | 0.7952 | 0.9475 | 3.11 | 5.99 | 9.34 | 4.36 | 28.16 | Download link | Download link |
| SE_ResNeXt50_ 32x4d |
0.7844 | 0.9396 | 6.39 | 11.01 | 14.94 | 4.27 | 27.63 | Download link | Download link |
| SE_ResNeXt50_vd_ 32x4d |
0.8024 | 0.9489 | 7.04 | 11.57 | 16.01 | 5.64 | 27.76 | Download link | Download link |
| SE_ResNeXt101_ 32x4d |
0.7939 | 0.9443 | 13.31 | 21.85 | 28.77 | 8.03 | 49.09 | Download link | Download link |
| SENet154_vd | 0.8140 | 0.9548 | 34.83 | 51.22 | 69.74 | 24.45 | 122.03 | Download link | Download link |
The accuracy and speed indicators of the DPN and DenseNet series models are shown in the following table. For more information about the models of this series, please refer to: DPN and DenseNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| DenseNet121 | 0.7566 | 0.9258 | 3.40 | 6.94 | 9.17 | 2.87 | 8.06 | Download link | Download link |
| DenseNet161 | 0.7857 | 0.9414 | 7.06 | 14.37 | 19.55 | 7.79 | 28.90 | Download link | Download link |
| DenseNet169 | 0.7681 | 0.9331 | 5.00 | 10.29 | 12.84 | 3.40 | 14.31 | Download link | Download link |
| DenseNet201 | 0.7763 | 0.9366 | 6.38 | 13.72 | 17.17 | 4.34 | 20.24 | Download link | Download link |
| DenseNet264 | 0.7796 | 0.9385 | 9.34 | 20.95 | 25.41 | 5.82 | 33.74 | Download link | Download link |
| DPN68 | 0.7678 | 0.9343 | 8.18 | 11.40 | 14.82 | 2.35 | 12.68 | Download link | Download link |
| DPN92 | 0.7985 | 0.9480 | 12.48 | 20.04 | 25.10 | 6.54 | 37.79 | Download link | Download link |
| DPN98 | 0.8059 | 0.9510 | 14.70 | 25.55 | 35.12 | 11.728 | 61.74 | Download link | Download link |
| DPN107 | 0.8089 | 0.9532 | 19.46 | 35.62 | 50.22 | 18.38 | 87.13 | Download link | Download link |
| DPN131 | 0.8070 | 0.9514 | 19.64 | 34.60 | 47.42 | 16.09 | 79.48 | Download link | Download link |
8. HRNet series [13]
The accuracy and speed indicators of the HRNet series models are shown in the following table. For more information about the models of this series, please refer to: HRNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| HRNet_W18_C | 0.7692 | 0.9339 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | Download link | Download link |
| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | Download link | Download link |
| HRNet_W30_C | 0.7804 | 0.9402 | 8.61 | 11.40 | 15.23 | 8.15 | 37.78 | Download link | Download link |
| HRNet_W32_C | 0.7828 | 0.9424 | 8.54 | 11.58 | 15.57 | 8.97 | 41.30 | Download link | Download link |
| HRNet_W40_C | 0.7877 | 0.9447 | 9.83 | 15.02 | 20.92 | 12.74 | 57.64 | Download link | Download link |
| HRNet_W44_C | 0.7900 | 0.9451 | 10.62 | 16.18 | 25.92 | 14.94 | 67.16 | Download link | Download link |
| HRNet_W48_C | 0.7895 | 0.9442 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | Download link | Download link |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | Download link | Download link |
| HRNet_W64_C | 0.7930 | 0.9461 | 13.82 | 21.15 | 35.51 | 28.97 | 128.18 | Download link | Download link |
| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | Download link | Download link |
The accuracy and speed indicators of the Inception series models are shown in the following table. For more information about this series of models, please refer to: Inception series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| GoogLeNet | 0.7070 | 0.8966 | 1.41 | 3.25 | 5.00 | 1.44 | 11.54 | Download link | Download link |
| Xception41 | 0.7930 | 0.9453 | 3.58 | 8.76 | 16.61 | 8.57 | 23.02 | Download link | Download link |
| Xception41_deeplab | 0.7955 | 0.9438 | 3.81 | 9.16 | 17.20 | 9.28 | 27.08 | Download link | Download link |
| Xception65 | 0.8100 | 0.9549 | 5.45 | 12.78 | 24.53 | 13.25 | 36.04 | Download link | Download link |
| Xception65_deeplab | 0.8032 | 0.9449 | 5.65 | 13.08 | 24.61 | 13.96 | 40.10 | Download link | Download link |
| Xception71 | 0.8111 | 0.9545 | 6.19 | 15.34 | 29.21 | 16.21 | 37.86 | Download link | Download link |
| InceptionV3 | 0.7914 | 0.9459 | 4.78 | 8.53 | 12.28 | 5.73 | 23.87 | Download link | Download link |
| InceptionV4 | 0.8077 | 0.9526 | 8.93 | 15.17 | 21.56 | 12.29 | 42.74 | Download link | Download link |
The accuracy and speed indicators of the EfficientNet and ResNeXt101_wsl series models are shown in the following table. For more information about this series of models, please refer to: EfficientNet and ResNeXt101_wsl series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| ResNeXt101_ 32x8d_wsl |
0.8255 | 0.9674 | 13.55 | 23.39 | 36.18 | 16.48 | 88.99 | Download link | Download link |
| ResNeXt101_ 32x16d_wsl |
0.8424 | 0.9726 | 21.96 | 38.35 | 63.29 | 36.26 | 194.36 | Download link | Download link |
| ResNeXt101_ 32x32d_wsl |
0.8497 | 0.9759 | 37.28 | 76.50 | 121.56 | 87.28 | 469.12 | Download link | Download link |
| ResNeXt101_ 32x48d_wsl |
0.8537 | 0.9769 | 55.07 | 124.39 | 205.01 | 153.57 | 829.26 | Download link | Download link |
| Fix_ResNeXt101_ 32x48d_wsl |
0.8626 | 0.9797 | 55.01 | 122.63 | 204.66 | 313.41 | 829.26 | Download link | Download link |
| EfficientNetB0 | 0.7738 | 0.9331 | 1.96 | 3.71 | 5.56 | 0.40 | 5.33 | Download link | Download link |
| EfficientNetB1 | 0.7915 | 0.9441 | 2.88 | 5.40 | 7.63 | 0.71 | 7.86 | Download link | Download link |
| EfficientNetB2 | 0.7985 | 0.9474 | 3.26 | 6.20 | 9.17 | 1.02 | 9.18 | Download link | Download link |
| EfficientNetB3 | 0.8115 | 0.9541 | 4.52 | 8.85 | 13.54 | 1.88 | 12.324 | Download link | Download link |
| EfficientNetB4 | 0.8285 | 0.9623 | 6.78 | 15.47 | 24.95 | 4.51 | 19.47 | Download link | Download link |
| EfficientNetB5 | 0.8362 | 0.9672 | 10.97 | 27.24 | 45.93 | 10.51 | 30.56 | Download link | Download link |
| EfficientNetB6 | 0.8400 | 0.9688 | 17.09 | 43.32 | 76.90 | 19.47 | 43.27 | Download link | Download link |
| EfficientNetB7 | 0.8430 | 0.9689 | 25.91 | 71.23 | 128.20 | 38.45 | 66.66 | Download link | Download link |
| EfficientNetB0_ small |
0.7580 | 0.9258 | 1.24 | 2.59 | 3.92 | 0.40 | 4.69 | Download link | Download link |
The accuracy and speed indicators of the ResNeSt and RegNet series models are shown in the following table. For more information about the models of this series, please refer to: ResNeSt and RegNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| ResNeSt50_ fast_1s1x64d |
0.8035 | 0.9528 | 2.73 | 5.33 | 8.24 | 4.36 | 26.27 | Download link | Download link |
| ResNeSt50 | 0.8083 | 0.9542 | 7.36 | 10.23 | 13.84 | 5.40 | 27.54 | Download link | Download link |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46 | 8.48 | 11.45 | 4.00 | 22.23 | Download link | Download link |
The accuracy and speed indicators of ViT (Vision Transformer) and DeiT (Data-efficient Image Transformers) series models are shown in the following table. For more information about this series of models, please refer to: ViT_and_DeiT series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| ViT_small_ patch16_224 |
0.7769 | 0.9342 | 3.71 | 9.05 | 16.72 | 9.41 | 48.60 | Download link | Download link |
| ViT_base_ patch16_224 |
0.8195 | 0.9617 | 6.12 | 14.84 | 28.51 | 16.85 | 86.42 | Download link | Download link |
| ViT_base_ patch16_384 |
0.8414 | 0.9717 | 14.15 | 48.38 | 95.06 | 49.35 | 86.42 | Download link | Download link |
| ViT_base_ patch32_384 |
0.8176 | 0.9613 | 4.94 | 13.43 | 24.08 | 12.66 | 88.19 | Download link | Download link |
| ViT_large_ patch16_224 |
0.8323 | 0.9650 | 15.53 | 49.50 | 94.09 | 59.65 | 304.12 | Download link | Download link |
| ViT_large_ patch16_384 |
0.8513 | 0.9736 | 39.51 | 152.46 | 304.06 | 174.70 | 304.12 | Download link | Download link |
| ViT_large_ patch32_384 |
0.8153 | 0.9608 | 11.44 | 36.09 | 70.63 | 44.24 | 306.48 | Download link | Download link |
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| DeiT_tiny_ patch16_224 |
0.718 | 0.910 | 3.61 | 3.94 | 6.10 | 1.07 | 5.68 | Download link | Download link |
| DeiT_small_ patch16_224 |
0.796 | 0.949 | 3.61 | 6.24 | 10.49 | 4.24 | 21.97 | Download link | Download link |
| DeiT_base_ patch16_224 |
0.817 | 0.957 | 6.13 | 14.87 | 28.50 | 16.85 | 86.42 | Download link | Download link |
| DeiT_base_ patch16_384 |
0.830 | 0.962 | 14.12 | 48.80 | 97.60 | 49.35 | 86.42 | Download link | Download link |
| DeiT_tiny_ distilled_patch16_224 |
0.741 | 0.918 | 3.51 | 4.05 | 6.03 | 1.08 | 5.87 | Download link | Download link |
| DeiT_small_ distilled_patch16_224 |
0.809 | 0.953 | 3.70 | 6.20 | 10.53 | 4.26 | 22.36 | Download link | Download link |
| DeiT_base_ distilled_patch16_224 |
0.831 | 0.964 | 6.17 | 14.94 | 28.58 | 16.93 | 87.18 | Download link | Download link |
| DeiT_base_ distilled_patch16_384 |
0.851 | 0.973 | 14.12 | 48.76 | 97.09 | 49.43 | 87.18 | Download link | Download link |
13. RepVGG series [36]
The accuracy and speed indicators of RepVGG series models are shown in the following table. For more introduction, please refer to: RepVGG series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| RepVGG_A0 | 0.7131 | 0.9016 | 1.36 | 8.31 | Download link | Download link | |||
| RepVGG_A1 | 0.7380 | 0.9146 | 2.37 | 12.79 | Download link | Download link | |||
| RepVGG_A2 | 0.7571 | 0.9264 | 5.12 | 25.50 | Download link | Download link | |||
| RepVGG_B0 | 0.7450 | 0.9213 | 3.06 | 14.34 | Download link | Download link | |||
| RepVGG_B1 | 0.7773 | 0.9385 | 11.82 | 51.83 | Download link | Download link | |||
| RepVGG_B2 | 0.7813 | 0.9410 | 18.38 | 80.32 | Download link | Download link | |||
| RepVGG_B1g2 | 0.7732 | 0.9359 | 8.82 | 41.36 | Download link | Download link | |||
| RepVGG_B1g4 | 0.7675 | 0.9335 | 7.31 | 36.13 | Download link | Download link | |||
| RepVGG_B2g4 | 0.7881 | 0.9448 | 11.34 | 55.78 | Download link | Download link | |||
| RepVGG_B3g4 | 0.7965 | 0.9485 | 16.07 | 75.63 | Download link | Download link |
14. MixNet series [29]
The accuracy and speed indicators of the MixNet series models are shown in the following table. For more introduction, please refer to: MixNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| MixNet_S | 0.7628 | 0.9299 | 2.31 | 3.63 | 5.20 | 252.977 | 4.167 | Download link | Download link |
| MixNet_M | 0.7767 | 0.9364 | 2.84 | 4.60 | 6.62 | 357.119 | 5.065 | Download link | Download link |
| MixNet_L | 0.7860 | 0.9437 | 3.16 | 5.55 | 8.03 | 579.017 | 7.384 | Download link | Download link |
15. ReXNet series [30]
The accuracy and speed indicators of ReXNet series models are shown in the following table. For more introduction, please refer to: ReXNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| ReXNet_1_0 | 0.7746 | 0.9370 | 3.08 | 4.15 | 5.49 | 0.415 | 4.84 | Download link | Download link |
| ReXNet_1_3 | 0.7913 | 0.9464 | 3.54 | 4.87 | 6.54 | 0.68 | 7.61 | Download link | Download link |
| ReXNet_1_5 | 0.8006 | 0.9512 | 3.68 | 5.31 | 7.38 | 0.90 | 9.79 | Download link | Download link |
| ReXNet_2_0 | 0.8122 | 0.9536 | 4.30 | 6.54 | 9.19 | 1.56 | 16.45 | Download link | Download link |
| ReXNet_3_0 | 0.8209 | 0.9612 | 5.74 | 9.49 | 13.62 | 3.44 | 34.83 | Download link | Download link |
16. SwinTransformer series [27]
The accuracy and speed indicators of SwinTransformer series models are shown in the following table. For more introduction, please refer to: SwinTransformer series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| SwinTransformer_tiny_patch4_window7_224 | 0.8069 | 0.9534 | 6.59 | 9.68 | 16.32 | 4.35 | 28.26 | Download link | Download link |
| SwinTransformer_small_patch4_window7_224 | 0.8275 | 0.9613 | 12.54 | 17.07 | 28.08 | 8.51 | 49.56 | Download link | Download link |
| SwinTransformer_base_patch4_window7_224 | 0.8300 | 0.9626 | 13.37 | 23.53 | 39.11 | 15.13 | 87.70 | Download link | Download link |
| SwinTransformer_base_patch4_window12_384 | 0.8439 | 0.9693 | 19.52 | 64.56 | 123.30 | 44.45 | 87.70 | Download link | Download link |
| SwinTransformer_base_patch4_window7_224[1] | 0.8487 | 0.9746 | 13.53 | 23.46 | 39.13 | 15.13 | 87.70 | Download link | Download link |
| SwinTransformer_base_patch4_window12_384[1] | 0.8642 | 0.9807 | 19.65 | 64.72 | 123.42 | 44.45 | 87.70 | Download link | Download link |
| SwinTransformer_large_patch4_window7_224[1] | 0.8596 | 0.9783 | 15.74 | 38.57 | 71.49 | 34.02 | 196.43 | Download link | Download link |
| SwinTransformer_large_patch4_window12_384[1] | 0.8719 | 0.9823 | 32.61 | 116.59 | 223.23 | 99.97 | 196.43 | Download link | Download link |
[1]：It is pre-trained based on the ImageNet22k dataset, and then transferred and learned from the ImageNet1k dataset.
17. LeViT series [33]
The accuracy and speed indicators of LeViT series models are shown in the following table. For more introduction, please refer to: LeViT series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| LeViT_128S | 0.7598 | 0.9269 | 281 | 7.42 | Download link | Download link | |||
| LeViT_128 | 0.7810 | 0.9371 | 365 | 8.87 | Download link | Download link | |||
| LeViT_192 | 0.7934 | 0.9446 | 597 | 10.61 | Download link | Download link | |||
| LeViT_256 | 0.8085 | 0.9497 | 1049 | 18.45 | Download link | Download link | |||
| LeViT_384 | 0.8191 | 0.9551 | 2234 | 38.45 | Download link | Download link |
Note: The accuracy difference with Reference is due to the difference in data preprocessing and the use of no distilled head as output.
18. Twins series [34]
The accuracy and speed indicators of Twins series models are shown in the following table. For more introduction, please refer to: Twins series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| pcpvt_small | 0.8082 | 0.9552 | 7.32 | 10.51 | 15.27 | 3.67 | 24.06 | Download link | Download link |
| pcpvt_base | 0.8242 | 0.9619 | 12.20 | 16.22 | 23.16 | 6.44 | 43.83 | Download link | Download link |
| pcpvt_large | 0.8273 | 0.9650 | 16.47 | 22.90 | 32.73 | 9.50 | 60.99 | Download link | Download link |
| alt_gvt_small | 0.8140 | 0.9546 | 6.94 | 9.01 | 12.27 | 2.81 | 24.06 | Download link | Download link |
| alt_gvt_base | 0.8294 | 0.9621 | 9.37 | 15.02 | 24.54 | 8.34 | 56.07 | Download link | Download link |
| alt_gvt_large | 0.8331 | 0.9642 | 11.76 | 22.08 | 35.12 | 14.81 | 99.27 | Download link | Download link |
Note: The accuracy difference with Reference is due to the difference in data preprocessing.
19. HarDNet series [37]
The accuracy and speed indicators of HarDNet series models are shown in the following table. For more introduction, please refer to: HarDNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| HarDNet39_ds | 0.7133 | 0.8998 | 1.40 | 2.30 | 3.33 | 0.44 | 3.51 | Download link | Download link |
| HarDNet68_ds | 0.7362 | 0.9152 | 2.26 | 3.34 | 5.06 | 0.79 | 4.20 | Download link | Download link |
| HarDNet68 | 0.7546 | 0.9265 | 3.58 | 8.53 | 11.58 | 4.26 | 17.58 | Download link | Download link |
| HarDNet85 | 0.7744 | 0.9355 | 6.24 | 14.85 | 20.57 | 9.09 | 36.69 | Download link | Download link |
20. DLA series [38]
The accuracy and speed indicators of DLA series models are shown in the following table. For more introduction, please refer to: DLA series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| DLA102 | 0.7893 | 0.9452 | 4.95 | 8.08 | 12.40 | 7.19 | 33.34 | Download link | Download link |
| DLA102x2 | 0.7885 | 0.9445 | 19.58 | 23.97 | 31.37 | 9.34 | 41.42 | Download link | Download link |
| DLA102x | 0.781 | 0.9400 | 11.12 | 15.60 | 20.37 | 5.89 | 26.40 | Download link | Download link |
| DLA169 | 0.7809 | 0.9409 | 7.70 | 12.25 | 18.90 | 11.59 | 53.50 | Download link | Download link |
| DLA34 | 0.7603 | 0.9298 | 1.83 | 3.37 | 5.98 | 3.07 | 15.76 | Download link | Download link |
| DLA46_c | 0.6321 | 0.853 | 1.06 | 2.08 | 3.23 | 0.54 | 1.31 | Download link | Download link |
| DLA60 | 0.7610 | 0.9292 | 2.78 | 5.36 | 8.29 | 4.26 | 22.08 | Download link | Download link |
| DLA60x_c | 0.6645 | 0.8754 | 1.79 | 3.68 | 5.19 | 0.59 | 1.33 | Download link | Download link |
| DLA60x | 0.7753 | 0.9378 | 5.98 | 9.24 | 12.52 | 3.54 | 17.41 | Download link | Download link |
21. RedNet series [39]
The accuracy and speed indicators of RedNet series models are shown in the following table. For more introduction, please refer to: RedNet series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| RedNet26 | 0.7595 | 0.9319 | 4.45 | 15.16 | 29.03 | 1.69 | 9.26 | Download link | Download link |
| RedNet38 | 0.7747 | 0.9356 | 6.24 | 21.39 | 41.26 | 2.14 | 12.43 | Download link | Download link |
| RedNet50 | 0.7833 | 0.9417 | 8.04 | 27.71 | 53.73 | 2.61 | 15.60 | Download link | Download link |
| RedNet101 | 0.7894 | 0.9436 | 13.07 | 44.12 | 83.28 | 4.59 | 25.76 | Download link | Download link |
| RedNet152 | 0.7917 | 0.9440 | 18.66 | 63.27 | 119.48 | 6.57 | 34.14 | Download link | Download link |
22. TNT series [35]
The accuracy and speed indicators of TNT series models are shown in the following table. For more introduction, please refer to: TNT series model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|
| TNT_small | 0.8121 | 0.9563 | 4.83 | 23.68 | Download link | Download link |
Note: Both mean and std in the data preprocessing part of the TNT model NormalizeImage are 0.5.
23. CSWinTransformer series [40]
The accuracy and speed indicators of CSWinTransformer series models are shown in the following table. For more introduction, please refer to: CSWinTransformer series model documents。
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| CSWinTransformer_tiny_224 | 0.8281 | 0.9628 | - | - | - | 4.1 | 22 | Download link | Download link |
| CSWinTransformer_small_224 | 0.8358 | 0.9658 | - | - | - | 6.4 | 35 | Download link | Download link |
| CSWinTransformer_base_224 | 0.8420 | 0.9692 | - | - | - | 14.3 | 77 | Download link | Download link |
| CSWinTransformer_large_224 | 0.8643 | 0.9799 | - | - | - | 32.2 | 173.3 | Download link | Download link |
| CSWinTransformer_base_384 | 0.8550 | 0.9749 | - | - | - | 42.2 | 77 | Download link | Download link |
| CSWinTransformer_large_384 | 0.8748 | 0.9833 | - | - | - | 94.7 | 173.3 | Download link | Download link |
24. PVTV2 series [41]
The accuracy and speed indicators of PVTV2 series models are shown in the following table. For more introduction, please refer to: PVTV2 series model documents。
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| PVT_V2_B0 | 0.705 | 0.902 | - | - | - | 0.53 | 3.7 | Download link | Download link |
| PVT_V2_B1 | 0.787 | 0.945 | - | - | - | 2.0 | 14.0 | Download link | Download link |
| PVT_V2_B2 | 0.821 | 0.960 | - | - | - | 3.9 | 25.4 | Download link | Download link |
| PVT_V2_B2_Linear | 0.821 | 0.961 | - | - | - | 3.8 | 22.6 | Download link | Download link |
| PVT_V2_B3 | 0.831 | 0.965 | - | - | - | 6.7 | 45.2 | Download link | Download link |
| PVT_V2_B4 | 0.836 | 0.967 | - | - | - | 9.8 | 62.6 | Download link | Download link |
| PVT_V2_B5 | 0.837 | 0.966 | - | - | - | 11.4 | 82.0 | Download link | Download link |
25. MobileViT series [42]
The accuracy and speed indicators of MobileViT series models are shown in the following table. For more introduction, please refer to:MobileViT series model documents
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 337.24 | 1.28 | Download link | Download link |
| MobileViT_XS | 0.7454 | 0.9227 | - | - | - | 930.75 | 2.33 | Download link | Download link |
| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 1849.35 | 5.59 | Download link | Download link |
The accuracy and speed indicators of AlexNet [18], SqueezeNet series [19], VGG series [20], DarkNet53 [21] and other models are shown in the following table. For more information, please refer to: Other model documents.
| Model | Top-1 Acc | Top-5 Acc | time(ms) bs=1 |
time(ms) bs=4 |
time(ms) bs=8 |
FLOPs(G) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
|---|---|---|---|---|---|---|---|---|---|
| AlexNet | 0.567 | 0.792 | 0.81 | 1.50 | 2.33 | 0.71 | 61.10 | Download link | Download link |
| SqueezeNet1_0 | 0.596 | 0.817 | 0.68 | 1.64 | 2.62 | 0.78 | 1.25 | Download link | Download link |
| SqueezeNet1_1 | 0.601 | 0.819 | 0.62 | 1.30 | 2.09 | 0.35 | 1.24 | Download link | Download link |
| VGG11 | 0.693 | 0.891 | 1.72 | 4.15 | 7.24 | 7.61 | 132.86 | Download link | Download link |
| VGG13 | 0.700 | 0.894 | 2.02 | 5.28 | 9.54 | 11.31 | 133.05 | Download link | Download link |
| VGG16 | 0.720 | 0.907 | 2.48 | 6.79 | 12.33 | 15.470 | 138.35 | Download link | Download link |
| VGG19 | 0.726 | 0.909 | 2.93 | 8.28 | 15.21 | 19.63 | 143.66 | Download link | Download link |
| DarkNet53 | 0.780 | 0.941 | 2.79 | 6.42 | 10.89 | 9.31 | 41.65 | Download link | Download link |
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