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docs/zh_CN/algorithm_introduction/ImageNet_models.md
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......@@ -62,30 +62,30 @@
### 2.1 服务器端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.434 | 6.222 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net101_vd_<br>26w_4s_ssld | 0.839 | 0.806 | 0.033 | 8.087 | 17.312 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.851 | 0.812 | 0.049 | 14.678 | 32.350 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 7.406 | 13.297 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 13.707 | 17.34 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.848 | - | - | 31.697 | 94.995 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.00 | 3.26 | 5.85 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld.tar) |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 2.59 | 4.87 | 7.62 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 4.43 | 8.25 | 12.58 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 3.58 | 6.35 | 9.52 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_ssld_infer.tar) |
| Res2Net101_vd_<br>26w_4s_ssld | 0.839 | 0.806 | 0.033 | 6.33 | 11.02 | 16.11 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_ssld_infer.tar) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.851 | 0.812 | 0.049 | 11.47 | 19.75 | 28.83 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 6.66 | 8.92 | 11.93 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) |
| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 11.09 | 17.04 | 27.28 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
| SE_HRNet_W64_C_ssld | 0.848 | - | - | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) |
<a name="2.2"></a>
### 2.2 移动端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | SD855 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 模型大小(M) | 下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 32.523 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 23.318 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 | FLOPs(M) | Params(M) | 模型大小(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 30.19 | 17.85 | 10.24 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) |
| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 20.71 | 12.70 | 8.06 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) |
| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | | | | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | |
| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 16.56 | 10.10 | 6.86 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 5.64 | 3.67 | 2.61 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) |
| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.16 | 12.26 | 10.18 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) |
<a name="2.3"></a>
......@@ -108,16 +108,16 @@
PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PP-LCNet 系列模型文档](../models/PP-LCNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 下载地址 |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.74 | 18.25 | 1.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) |
| PPLCNet_x0_35 |0.5809 | 0.8083 | 1.92 | 29.46 | 1.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) |
| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.05 | 47.28 | 1.89 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) |
| PPLCNet_x0_75 |0.6818 | 0.8830 | 2.29 | 98.82 | 2.37 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) |
| PPLCNet_x1_0 |0.7132 | 0.9003 | 2.46 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) |
| PPLCNet_x1_5 |0.7371 | 0.9153 | 3.19 | 341.86 | 4.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) |
| PPLCNet_x2_0 |0.7518 | 0.9227 | 4.27 | 590 | 6.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) |
| PPLCNet_x2_5 |0.7660 | 0.9300 | 5.39 | 906 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|:--:|:--:|:--:|:--:|----|----|----|:--:|
| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.61785 | 18.25 | 1.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar) |
| PPLCNet_x0_35 |0.5809 | 0.8083 | 2.11344 | 29.46 | 1.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar) |
| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.72974 | 47.28 | 1.89 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar) |
| PPLCNet_x0_75 |0.6818 | 0.8830 | 4.51216 | 98.82 | 2.37 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar) |
| PPLCNet_x1_0 |0.7132 | 0.9003 | 6.49276 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar) |
| PPLCNet_x1_5 |0.7371 | 0.9153 | 12.2601 | 341.86 | 4.52 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar) |
| PPLCNet_x2_0 |0.7518 | 0.9227 | 20.1667 | 590 | 6.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar) |
| PPLCNet_x2_5 |0.7660 | 0.9300 | 29.595 | 906 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar) |
<a name="4"></a>
......@@ -125,23 +125,23 @@ PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该
ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNet 及其 Vd 系列模型文档](../models/ResNet_and_vd.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|
| ResNet18 | 0.7098 | 0.8992 | 1.45606 | 3.56305 | 1.83 | 11.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) |
| ResNet18_vd | 0.7226 | 0.9080 | 1.54557 | 3.85363 | 2.07 | 11.72 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams) |
| ResNet34 | 0.7457 | 0.9214 | 2.34957 | 5.89821 | 3.68 | 21.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) |
| ResNet34_vd | 0.7598 | 0.9298 | 2.43427 | 6.22257 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) |
| ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.43427 | 6.22257 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) |
| ResNet50 | 0.7650 | 0.9300 | 3.47712 | 7.84421 | 4.11 | 25.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) |
| ResNet50_vc | 0.7835 | 0.9403 | 3.52346 | 8.10725 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams) |
| ResNet50_vd | 0.7912 | 0.9444 | 3.53131 | 8.09057 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) |
| ResNet101 | 0.7756 | 0.9364 | 6.07125 | 13.40573 | 7.83 | 44.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) |
| ResNet101_vd | 0.8017 | 0.9497 | 6.11704 | 13.76222 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) |
| ResNet152 | 0.7826 | 0.9396 | 8.50198 | 19.17073 | 11.56 | 60.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) |
| ResNet152_vd | 0.8059 | 0.9530 | 8.54376 | 19.52157 | 11.80 | 60.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) |
| ResNet200_vd | 0.8093 | 0.9533 | 10.80619 | 25.01731 | 15.30 | 74.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) |
| ResNet50_vd_<br>ssld | 0.8300 | 0.9640 | 3.53131 | 8.09057 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
| ResNet101_vd_<br>ssld | 0.8373 | 0.9669 | 6.11704 | 13.76222 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
| ResNet18 | 0.7098 | 0.8992 | 1.22 | 2.19 | 3.63 | 1.83 | 11.70 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_infer.tar) |
| ResNet18_vd | 0.7226 | 0.9080 | 1.26 | 2.28 | 3.89 | 2.07 | 11.72 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet18_vd_infer.tar) |
| ResNet34 | 0.7457 | 0.9214 | 1.97 | 3.25 | 5.70 | 3.68 | 21.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_infer.tar) |
| ResNet34_vd | 0.7598 | 0.9298 | 2.00 | 3.28 | 5.84 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_infer.tar) |
| ResNet34_vd_ssld | 0.7972 | 0.9490 | 2.00 | 3.26 | 5.85 | 3.93 | 21.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar) |
| ResNet50 | 0.7650 | 0.9300 | 2.54 | 4.79 | 7.40 | 4.11 | 25.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar) |
| ResNet50_vc | 0.7835 | 0.9403 | 2.57 | 4.83 | 7.52 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vc_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vc_infer.tar) |
| ResNet50_vd | 0.7912 | 0.9444 | 2.60 | 4.86 | 7.63 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_infer.tar) |
| ResNet101 | 0.7756 | 0.9364 | 4.37 | 8.18 | 12.38 | 7.83 | 44.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_infer.tar) |
| ResNet101_vd | 0.8017 | 0.9497 | 4.43 | 8.25 | 12.60 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_infer.tar) |
| ResNet152 | 0.7826 | 0.9396 | 6.05 | 11.41 | 17.33 | 11.56 | 60.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_infer.tar) |
| ResNet152_vd | 0.8059 | 0.9530 | 6.11 | 11.51 | 17.59 | 11.80 | 60.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet152_vd_infer.tar) |
| ResNet200_vd | 0.8093 | 0.9533 | 7.70 | 14.57 | 22.16 | 15.30 | 74.93 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet200_vd_infer.tar) |
| ResNet50_vd_<br>ssld | 0.8300 | 0.9640 | 2.59 | 4.87 | 7.62 | 4.35 | 25.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_vd_ssld_infer.tar) |
| ResNet101_vd_<br>ssld | 0.8373 | 0.9669 | 4.43 | 8.25 | 12.58 | 8.08 | 44.67 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet101_vd_ssld_infer.tar) |
<a name="5"></a>
......@@ -149,48 +149,48 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
移动端系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[移动端系列模型文档](../models/Mobile.md)。
| 模型 | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 模型大小(M) | 下载地址 |
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|
| MobileNetV1_<br>x0_25 | 0.5143 | 0.7546 | 3.21985 | 43.56 | 0.48 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) |
| MobileNetV1_<br>x0_5 | 0.6352 | 0.8473 | 9.579599 | 154.57 | 1.34 | 5.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) |
| MobileNetV1_<br>x0_75 | 0.6881 | 0.8823 | 19.436399 | 333.00 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) |
| MobileNetV1 | 0.7099 | 0.8968 | 32.523048 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) |
| MobileNetV1_<br>ssld | 0.7789 | 0.9394 | 32.523048 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
| MobileNetV2_<br>x0_25 | 0.5321 | 0.7652 | 3.79925 | 34.18 | 1.53 | 6.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams) |
| MobileNetV2_<br>x0_5 | 0.6503 | 0.8572 | 8.7021 | 99.48 | 1.98 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams) |
| MobileNetV2_<br>x0_75 | 0.6983 | 0.8901 | 15.531351 | 197.37 | 2.65 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams) |
| MobileNetV2 | 0.7215 | 0.9065 | 23.317699 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams) |
| MobileNetV2_<br>x1_5 | 0.7412 | 0.9167 | 45.623848 | 702.35 | 6.90 | 26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams) |
| MobileNetV2_<br>x2_0 | 0.7523 | 0.9258 | 74.291649 | 1217.25 | 11.33 | 43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams) |
| MobileNetV2_<br>ssld | 0.7674 | 0.9339 | 23.317699 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
| MobileNetV3_<br>large_x1_25 | 0.7641 | 0.9295 | 28.217701 | 362.70 | 7.47 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) |
| MobileNetV3_<br>large_x1_0 | 0.7532 | 0.9231 | 19.30835 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) |
| MobileNetV3_<br>large_x0_75 | 0.7314 | 0.9108 | 13.5646 | 151.70 | 3.93 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) |
| MobileNetV3_<br>large_x0_5 | 0.6924 | 0.8852 | 7.49315 | 71.83 | 2.69 | 11 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) |
| MobileNetV3_<br>large_x0_35 | 0.6432 | 0.8546 | 5.13695 | 40.90 | 2.11 | 8.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) |
| MobileNetV3_<br>small_x1_25 | 0.7067 | 0.8951 | 9.2745 | 100.07 | 3.64 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) |
| MobileNetV3_<br>small_x1_0 | 0.6824 | 0.8806 | 6.5463 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) |
| MobileNetV3_<br>small_x0_75 | 0.6602 | 0.8633 | 5.28435 | 46.02 | 2.38 | 9.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) |
| MobileNetV3_<br>small_x0_5 | 0.5921 | 0.8152 | 3.35165 | 22.60 | 1.91 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) |
| MobileNetV3_<br>small_x0_35 | 0.5303 | 0.7637 | 2.6352 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) |
| MobileNetV3_<br>small_x0_35_ssld | 0.5555 | 0.7771 | 2.6352 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
| MobileNetV3_<br>large_x1_0_ssld | 0.7896 | 0.9448 | 19.30835 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
| MobileNetV3_small_<br>x1_0_ssld | 0.7129 | 0.9010 | 6.5463 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
| ShuffleNetV2 | 0.6880 | 0.8845 | 10.941 | 148.86 | 2.29 | 9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams) |
| ShuffleNetV2_<br>x0_25 | 0.4990 | 0.7379 | 2.329 | 18.95 | 0.61 | 2.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams) |
| ShuffleNetV2_<br>x0_33 | 0.5373 | 0.7705 | 2.64335 | 24.04 | 0.65 | 2.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams) |
| ShuffleNetV2_<br>x0_5 | 0.6032 | 0.8226 | 4.2613 | 42.58 | 1.37 | 5.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams) |
| ShuffleNetV2_<br>x1_5 | 0.7163 | 0.9015 | 19.3522 | 301.35 | 3.53 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams) |
| ShuffleNetV2_<br>x2_0 | 0.7315 | 0.9120 | 34.770149 | 571.70 | 7.40 | 28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams) |
| ShuffleNetV2_<br>swish | 0.7003 | 0.8917 | 16.023151 | 148.86 | 2.29 | 9.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams) |
| GhostNet_<br>x0_5 | 0.6688 | 0.8695 | 5.7143 | 46.15 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams) |
| GhostNet_<br>x1_0 | 0.7402 | 0.9165 | 13.5587 | 148.78 | 5.21 | 20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) |
| GhostNet_<br>x1_3 | 0.7579 | 0.9254 | 19.9825 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) |
| GhostNet_<br>x1_3_ssld | 0.7938 | 0.9449 | 19.9825 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
| ESNet_x0_25 | 62.48 | 83.46 || 30.85 | 2.83 | 11 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams) |
| ESNet_x0_5 | 68.82 | 88.04 || 67.31 | 3.25 | 13 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams) |
| ESNet_x0_75 | 72.24 | 90.45 || 123.74 | 3.87 | 15 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams) |
| ESNet_x1_0 | 73.92 | 91.40 || 197.33 | 4.64 | 18 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 | FLOPs(M) | Params(M) | 模型大小(M) | 预训练下载地址 | inference模型下载地址 |
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| MobileNetV1_<br>x0_25 | 0.5143 | 0.7546 | 2.88 | 1.82 | 1.26 | 43.56 | 0.48 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_25_infer.tar) |
| MobileNetV1_<br>x0_5 | 0.6352 | 0.8473 | 8.74 | 5.26 | 3.09 | 154.57 | 1.34 | 5.2 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_5_infer.tar) |
| MobileNetV1_<br>x0_75 | 0.6881 | 0.8823 | 17.84 | 10.61 | 6.21 | 333.00 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_x0_75_infer.tar) |
| MobileNetV1 | 0.7099 | 0.8968 | 30.24 | 17.86 | 10.30 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_infer.tar) |
| MobileNetV1_<br>ssld | 0.7789 | 0.9394 | 30.19 | 17.85 | 10.23 | 578.88 | 4.25 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV1_ssld_infer.tar) |
| MobileNetV2_<br>x0_25 | 0.5321 | 0.7652 | 3.46 | 2.51 | 2.03 | 34.18 | 1.53 | 6.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_25_infer.tar) |
| MobileNetV2_<br>x0_5 | 0.6503 | 0.8572 | 7.69 | 4.92 | 3.57 | 99.48 | 1.98 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_5_infer.tar) |
| MobileNetV2_<br>x0_75 | 0.6983 | 0.8901 | 13.69 | 8.60 | 5.82 | 197.37 | 2.65 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x0_75_infer.tar) |
| MobileNetV2 | 0.7215 | 0.9065 | 20.74 | 12.71 | 8.10 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_infer.tar) |
| MobileNetV2_<br>x1_5 | 0.7412 | 0.9167 | 40.79 | 24.49 | 15.50 | 702.35 | 6.90 | 26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x1_5_infer.tar) |
| MobileNetV2_<br>x2_0 | 0.7523 | 0.9258 | 67.50 | 40.03 | 25.55 | 1217.25 | 11.33 | 43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_x2_0_infer.tar) |
| MobileNetV2_<br>ssld | 0.7674 | 0.9339 | 20.71 | 12.70 | 8.06 | 327.84 | 3.54 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV2_ssld_infer.tar) |
| MobileNetV3_<br>large_x1_25 | 0.7641 | 0.9295 | 24.52 | 14.76 | 9.89 | 362.70 | 7.47 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_25_infer.tar) |
| MobileNetV3_<br>large_x1_0 | 0.7532 | 0.9231 | 16.55 | 10.09 | 6.84 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar) |
| MobileNetV3_<br>large_x0_75 | 0.7314 | 0.9108 | 11.53 | 7.06 | 4.94 | 151.70 | 3.93 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_75_infer.tar) |
| MobileNetV3_<br>large_x0_5 | 0.6924 | 0.8852 | 6.50 | 4.22 | 3.15 | 71.83 | 2.69 | 11 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_5_infer.tar) |
| MobileNetV3_<br>large_x0_35 | 0.6432 | 0.8546 | 4.43 | 3.11 | 2.41 | 40.90 | 2.11 | 8.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x0_35_infer.tar) |
| MobileNetV3_<br>small_x1_25 | 0.7067 | 0.8951 | 7.88 | 4.91 | 3.45 | 100.07 | 3.64 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_25_infer.tar) |
| MobileNetV3_<br>small_x1_0 | 0.6824 | 0.8806 | 5.63 | 3.65 | 2.60 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_infer.tar) |
| MobileNetV3_<br>small_x0_75 | 0.6602 | 0.8633 | 4.50 | 2.96 | 2.19 | 46.02 | 2.38 | 9.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_75_infer.tar) |
| MobileNetV3_<br>small_x0_5 | 0.5921 | 0.8152 | 2.89 | 2.04 | 1.62 | 22.60 | 1.91 | 7.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_5_infer.tar) |
| MobileNetV3_<br>small_x0_35 | 0.5303 | 0.7637 | 2.23 | 1.66 | 1.43 | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x0_35_infer.tar) |
| MobileNetV3_<br>small_x0_35_ssld | 0.5555 | 0.7771 | | | | 14.56 | 1.67 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) | |
| MobileNetV3_<br>large_x1_0_ssld | 0.7896 | 0.9448 | 16.56 | 10.10 | 6.86 | 229.66 | 5.50 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) | |
| MobileNetV3_small_<br>x1_0_ssld | 0.7129 | 0.9010 | 5.64 | 3.67 | 2.61 | 63.67 | 2.95 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) | |
| ShuffleNetV2 | 0.6880 | 0.8845 | 9.72 | 5.97 | 4.13 | 148.86 | 2.29 | 9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_0_infer.tar) |
| ShuffleNetV2_<br>x0_25 | 0.4990 | 0.7379 | 1.94 | 1.53 | 1.43 | 18.95 | 0.61 | 2.7 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_25_infer.tar) |
| ShuffleNetV2_<br>x0_33 | 0.5373 | 0.7705 | 2.23 | 1.70 | 1.79 | 24.04 | 0.65 | 2.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_33_infer.tar) |
| ShuffleNetV2_<br>x0_5 | 0.6032 | 0.8226 | 3.67 | 2.63 | 2.06 | 42.58 | 1.37 | 5.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x0_5_infer.tar) |
| ShuffleNetV2_<br>x1_5 | 0.7163 | 0.9015 | 17.21 | 10.56 | 6.81 | 301.35 | 3.53 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x1_5_infer.tar) |
| ShuffleNetV2_<br>x2_0 | 0.7315 | 0.9120 | 31.21 | 18.98 | 11.65 | 571.70 | 7.40 | 28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_x2_0_infer.tar) |
| ShuffleNetV2_<br>swish | 0.7003 | 0.8917 | 31.21 | 9.06 | 5.74 | 148.86 | 2.29 | 9.1 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ShuffleNetV2_swish_infer.tar) |
| GhostNet_<br>x0_5 | 0.6688 | 0.8695 | 5.28 | 3.95 | 3.29 | 46.15 | 2.60 | 10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x0_5_infer.tar) |
| GhostNet_<br>x1_0 | 0.7402 | 0.9165 | 12.89 | 8.66 | 6.72 | 148.78 | 5.21 | 20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_0_infer.tar) |
| GhostNet_<br>x1_3 | 0.7579 | 0.9254 | 19.16 | 12.25 | 9.40 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_infer.tar) |
| GhostNet_<br>x1_3_ssld | 0.7938 | 0.9449 | 19.16 | 17.85 | 10.18 | 236.89 | 7.38 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GhostNet_x1_3_ssld_infer.tar) |
| ESNet_x0_25 | 0.6248 | 0.8346 |4.12|2.97|2.51| 30.85 | 2.83 | 11 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_25_infer.tar) |
| ESNet_x0_5 | 0.6882 | 0.8804 |6.45|4.42|3.35| 67.31 | 3.25 | 13 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_5_infer.tar) |
| ESNet_x0_75 | 0.7224 | 0.9045 |9.59|6.28|4.52| 123.74 | 3.87 | 15 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x0_75_infer.tar) |
| ESNet_x1_0 | 0.7392 | 0.9140 |13.67|8.71|5.97| 197.33 | 4.64 | 18 |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams) |[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ESNet_x1_0_infer.tar) |
<a name="6"></a>
......@@ -199,33 +199,33 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
SEResNeXt 与 Res2Net 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[SEResNeXt 与 Res2Net 系列模型文档](../models/SEResNext_and_Res2Net.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
| Res2Net50_<br>26w_4s | 0.7933 | 0.9457 | 4.47188 | 9.65722 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams) |
| Res2Net50_vd_<br>26w_4s | 0.7975 | 0.9491 | 4.52712 | 9.93247 | 4.52 | 25.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams) |
| Res2Net50_<br>14w_8s | 0.7946 | 0.9470 | 5.4026 | 10.60273 | 4.20 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams) |
| Res2Net101_vd_<br>26w_4s | 0.8064 | 0.9522 | 8.08729 | 17.31208 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams) |
| Res2Net200_vd_<br>26w_4s | 0.8121 | 0.9571 | 14.67806 | 32.35032 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.8513 | 0.9742 | 14.67806 | 32.35032 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| ResNeXt50_<br>32x4d | 0.7775 | 0.9382 | 7.56327 | 10.6134 | 4.26 | 25.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams) |
| ResNeXt50_vd_<br>32x4d | 0.7956 | 0.9462 | 7.62044 | 11.03385 | 4.50 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams) |
| ResNeXt50_<br>64x4d | 0.7843 | 0.9413 | 13.80962 | 18.4712 | 8.02 | 45.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams) |
| ResNeXt50_vd_<br>64x4d | 0.8012 | 0.9486 | 13.94449 | 18.88759 | 8.26 | 45.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams) |
| ResNeXt101_<br>32x4d | 0.7865 | 0.9419 | 16.21503 | 19.96568 | 8.01 | 44.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams) |
| ResNeXt101_vd_<br>32x4d | 0.8033 | 0.9512 | 16.28103 | 20.25611 | 8.25 | 44.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams) |
| ResNeXt101_<br>64x4d | 0.7835 | 0.9452 | 30.4788 | 36.29801 | 15.52 | 83.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams) |
| ResNeXt101_vd_<br>64x4d | 0.8078 | 0.9520 | 30.40456 | 36.77324 | 15.76 | 83.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams) |
| ResNeXt152_<br>32x4d | 0.7898 | 0.9433 | 24.86299 | 29.36764 | 11.76 | 60.15 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams) |
| ResNeXt152_vd_<br>32x4d | 0.8072 | 0.9520 | 25.03258 | 30.08987 | 12.01 | 60.17 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams) |
| ResNeXt152_<br>64x4d | 0.7951 | 0.9471 | 46.7564 | 56.34108 | 23.03 | 115.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams) |
| ResNeXt152_vd_<br>64x4d | 0.8108 | 0.9534 | 47.18638 | 57.16257 | 23.27 | 115.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams) |
| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.7691 | 4.19877 | 2.07 | 11.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams) |
| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.88559 | 7.03291 | 3.93 | 22.00 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams) |
| SE_ResNet50_vd | 0.7952 | 0.9475 | 4.28393 | 10.38846 | 4.36 | 28.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams) |
| SE_ResNeXt50_<br>32x4d | 0.7844 | 0.9396 | 8.74121 | 13.563 | 4.27 | 27.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams) |
| SE_ResNeXt50_vd_<br>32x4d | 0.8024 | 0.9489 | 9.17134 | 14.76192 | 5.64 | 27.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams) |
| SE_ResNeXt101_<br>32x4d | 0.7939 | 0.9443 | 18.82604 | 25.31814 | 8.03 | 49.09 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams) |
| SENet154_vd | 0.8140 | 0.9548 | 53.79794 | 66.31684 | 24.45 | 122.03 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| Res2Net50_<br>26w_4s | 0.7933 | 0.9457 | 3.52 | 6.23 | 9.30 | 4.28 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_26w_4s_infer.tar) |
| Res2Net50_vd_<br>26w_4s | 0.7975 | 0.9491 | 3.59 | 6.35 | 9.50 | 4.52 | 25.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_vd_26w_4s_infer.tar) |
| Res2Net50_<br>14w_8s | 0.7946 | 0.9470 | 4.39 | 7.21 | 10.38 | 4.20 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net50_14w_8s_infer.tar) |
| Res2Net101_vd_<br>26w_4s | 0.8064 | 0.9522 | 6.34 | 11.02 | 16.13 | 8.35 | 45.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net101_vd_26w_4s_infer.tar) |
| Res2Net200_vd_<br>26w_4s | 0.8121 | 0.9571 | 11.45 | 19.77 | 28.81 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_infer.tar) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.8513 | 0.9742 | 11.47 | 19.75 | 28.83 | 15.77 | 76.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) |
| ResNeXt50_<br>32x4d | 0.7775 | 0.9382 | 5.07 | 8.49 | 12.02 | 4.26 | 25.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_32x4d_infer.tar) |
| ResNeXt50_vd_<br>32x4d | 0.7956 | 0.9462 | 5.29 | 8.68 | 12.33 | 4.50 | 25.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_32x4d_infer.tar) |
| ResNeXt50_<br>64x4d | 0.7843 | 0.9413 | 9.39 | 13.97 | 20.56 | 8.02 | 45.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_64x4d_infer.tar) |
| ResNeXt50_vd_<br>64x4d | 0.8012 | 0.9486 | 9.75 | 14.14 | 20.84 | 8.26 | 45.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt50_vd_64x4d_infer.tar) |
| ResNeXt101_<br>32x4d | 0.7865 | 0.9419 | 11.34 | 16.78 | 22.80 | 8.01 | 44.32 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x4d_infer.tar) |
| ResNeXt101_vd_<br>32x4d | 0.8033 | 0.9512 | 11.36 | 17.01 | 23.07 | 8.25 | 44.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_32x4d_infer.tar) |
| ResNeXt101_<br>64x4d | 0.7835 | 0.9452 | 21.57 | 28.08 | 39.49 | 15.52 | 83.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_64x4d_infer.tar) |
| ResNeXt101_vd_<br>64x4d | 0.8078 | 0.9520 | 21.57 | 28.22 | 39.70 | 15.76 | 83.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_vd_64x4d_infer.tar) |
| ResNeXt152_<br>32x4d | 0.7898 | 0.9433 | 17.14 | 25.11 | 33.79 | 11.76 | 60.15 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_32x4d_infer.tar) |
| ResNeXt152_vd_<br>32x4d | 0.8072 | 0.9520 | 16.99 | 25.29 | 33.85 | 12.01 | 60.17 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_32x4d_infer.tar) |
| ResNeXt152_<br>64x4d | 0.7951 | 0.9471 | 33.07 | 42.05 | 59.13 | 23.03 | 115.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_64x4d_infer.tar) |
| ResNeXt152_vd_<br>64x4d | 0.8108 | 0.9534 | 33.30 | 42.41 | 59.42 | 23.27 | 115.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt152_vd_64x4d_infer.tar) |
| SE_ResNet18_vd | 0.7333 | 0.9138 | 1.48 | 2.70 | 4.32 | 2.07 | 11.81 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet18_vd_infer.tar) |
| SE_ResNet34_vd | 0.7651 | 0.9320 | 2.42 | 3.69 | 6.29 | 3.93 | 22.00 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet34_vd_infer.tar) |
| SE_ResNet50_vd | 0.7952 | 0.9475 | 3.11 | 5.99 | 9.34 | 4.36 | 28.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNet50_vd_infer.tar) |
| SE_ResNeXt50_<br>32x4d | 0.7844 | 0.9396 | 6.39 | 11.01 | 14.94 | 4.27 | 27.63 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_32x4d_infer.tar) |
| SE_ResNeXt50_vd_<br>32x4d | 0.8024 | 0.9489 | 7.04 | 11.57 | 16.01 | 5.64 | 27.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt50_vd_32x4d_infer.tar) |
| SE_ResNeXt101_<br>32x4d | 0.7939 | 0.9443 | 13.31 | 21.85 | 28.77 | 8.03 | 49.09 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_ResNeXt101_32x4d_infer.tar) |
| SENet154_vd | 0.8140 | 0.9548 | 34.83 | 51.22 | 69.74 | 24.45 | 122.03 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SENet154_vd_infer.tar) |
<a name="7"></a>
......@@ -234,18 +234,18 @@ SEResNeXt 与 Res2Net 系列模型的精度、速度指标如下表所示，更
DPN 与 DenseNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[DPN 与 DenseNet 系列模型文档](../models/DPN_DenseNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|
| DenseNet121 | 0.7566 | 0.9258 | 4.40447 | 9.32623 | 2.87 | 8.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) |
| DenseNet161 | 0.7857 | 0.9414 | 10.39152 | 22.15555 | 7.79 | 28.90 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) |
| DenseNet169 | 0.7681 | 0.9331 | 6.43598 | 12.98832 | 3.40 | 14.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) |
| DenseNet201 | 0.7763 | 0.9366 | 8.20652 | 17.45838 | 4.34 | 20.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) |
| DenseNet264 | 0.7796 | 0.9385 | 12.14722 | 26.27707 | 5.82 | 33.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) |
| DPN68 | 0.7678 | 0.9343 | 11.64915 | 12.82807 | 2.35 | 12.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams) |
| DPN92 | 0.7985 | 0.9480 | 18.15746 | 23.87545 | 6.54 | 37.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams) |
| DPN98 | 0.8059 | 0.9510 | 21.18196 | 33.23925 | 11.728 | 61.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams) |
| DPN107 | 0.8089 | 0.9532 | 27.62046 | 52.65353 | 18.38 | 87.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) |
| DPN131 | 0.8070 | 0.9514 | 28.33119 | 46.19439 | 16.09 | 79.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------|
| DenseNet121 | 0.7566 | 0.9258 | 3.40 | 6.94 | 9.17 | 2.87 | 8.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet121_infer.tar) |
| DenseNet161 | 0.7857 | 0.9414 | 7.06 | 14.37 | 19.55 | 7.79 | 28.90 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet161_infer.tar) |
| DenseNet169 | 0.7681 | 0.9331 | 5.00 | 10.29 | 12.84 | 3.40 | 14.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet169_infer.tar) |
| DenseNet201 | 0.7763 | 0.9366 | 6.38 | 13.72 | 17.17 | 4.34 | 20.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet201_infer.tar) |
| DenseNet264 | 0.7796 | 0.9385 | 9.34 | 20.95 | 25.41 | 5.82 | 33.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DenseNet264_infer.tar) |
| DPN68 | 0.7678 | 0.9343 | 8.18 | 11.40 | 14.82 | 2.35 | 12.68 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN68_infer.tar) |
| DPN92 | 0.7985 | 0.9480 | 12.48 | 20.04 | 25.10 | 6.54 | 37.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN92_infer.tar) |
| DPN98 | 0.8059 | 0.9510 | 14.70 | 25.55 | 35.12 | 11.728 | 61.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN98_infer.tar) |
| DPN107 | 0.8089 | 0.9532 | 19.46 | 35.62 | 50.22 | 18.38 | 87.13 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN107_infer.tar) |
| DPN131 | 0.8070 | 0.9514 | 19.64 | 34.60 | 47.42 | 16.09 | 79.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DPN131_infer.tar) |
......@@ -256,18 +256,18 @@ DPN 与 DenseNet 系列模型的精度、速度指标如下表所示，更多关
HRNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[HRNet 系列模型文档](../models/HRNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|
| HRNet_W18_C | 0.7692 | 0.9339 | 7.40636 | 13.29752 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) |
| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 7.40636 | 13.29752 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) |
| HRNet_W30_C | 0.7804 | 0.9402 | 9.57594 | 17.35485 | 8.15 | 37.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) |
| HRNet_W32_C | 0.7828 | 0.9424 | 9.49807 | 17.72921 | 8.97 | 41.30 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) |
| HRNet_W40_C | 0.7877 | 0.9447 | 12.12202 | 25.68184 | 12.74 | 57.64 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) |
| HRNet_W44_C | 0.7900 | 0.9451 | 13.19858 | 32.25202 | 14.94 | 67.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) |
| HRNet_W48_C | 0.7895 | 0.9442 | 13.70761 | 34.43572 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 13.70761 | 34.43572 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) |
| HRNet_W64_C | 0.7930 | 0.9461 | 17.57527 | 47.9533 | 28.97 | 128.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 31.69770 | 94.99546 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
| HRNet_W18_C | 0.7692 | 0.9339 | 6.66 | 8.94 | 11.95 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_infer.tar) |
| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 6.66 | 8.92 | 11.93 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) |
| HRNet_W30_C | 0.7804 | 0.9402 | 8.61 | 11.40 | 15.23 | 8.15 | 37.78 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W30_C_infer.tar) |
| HRNet_W32_C | 0.7828 | 0.9424 | 8.54 | 11.58 | 15.57 | 8.97 | 41.30 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W32_C_infer.tar) |
| HRNet_W40_C | 0.7877 | 0.9447 | 9.83 | 15.02 | 20.92 | 12.74 | 57.64 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W40_C_infer.tar) |
| HRNet_W44_C | 0.7900 | 0.9451 | 10.62 | 16.18 | 25.92 | 14.94 | 67.16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W44_C_infer.tar) |
| HRNet_W48_C | 0.7895 | 0.9442 | 11.07 | 17.06 | 27.28 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_infer.tar) |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 11.09 | 17.04 | 27.28 | 17.34 | 77.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
| HRNet_W64_C | 0.7930 | 0.9461 | 13.82 | 21.15 | 35.51 | 28.97 | 128.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W64_C_infer.tar) |
| SE_HRNet_W64_C_ssld | 0.8475 | 0.9726 | 17.11 | 26.87 | 43.24 | 29.00 | 129.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SE_HRNet_W64_C_ssld_infer.tar) |
<a name="9"></a>
......@@ -275,16 +275,16 @@ HRNet 系列模型的精度、速度指标如下表所示，更多关于该系
Inception 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[Inception 系列模型文档](../models/Inception.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|
| GoogLeNet | 0.7070 | 0.8966 | 1.88038 | 4.48882 | 1.44 | 11.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) |
| Xception41 | 0.7930 | 0.9453 | 4.96939 | 17.01361 | 8.57 | 23.02 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams) |
| Xception41_deeplab | 0.7955 | 0.9438 | 5.33541 | 17.55938 | 9.28 | 27.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) |
| Xception65 | 0.8100 | 0.9549 | 7.26158 | 25.88778 | 13.25 | 36.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams) |
| Xception65_deeplab | 0.8032 | 0.9449 | 7.60208 | 26.03699 | 13.96 | 40.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) |
| Xception71 | 0.8111 | 0.9545 | 8.72457 | 31.55549 | 16.21 | 37.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams) |
| InceptionV3 | 0.7914 | 0.9459 | 6.64054 | 13.53630 | 5.73 | 23.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) |
| InceptionV4 | 0.8077 | 0.9526 | 12.99342 | 25.23416 | 12.29 | 42.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| GoogLeNet | 0.7070 | 0.8966 | 1.41 | 3.25 | 5.00 | 1.44 | 11.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/GoogLeNet_infer.tar) |
| Xception41 | 0.7930 | 0.9453 | 3.58 | 8.76 | 16.61 | 8.57 | 23.02 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_infer.tar) |
| Xception41_deeplab | 0.7955 | 0.9438 | 3.81 | 9.16 | 17.20 | 9.28 | 27.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception41_deeplab_infer.tar) |
| Xception65 | 0.8100 | 0.9549 | 5.45 | 12.78 | 24.53 | 13.25 | 36.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_infer.tar) |
| Xception65_deeplab | 0.8032 | 0.9449 | 5.65 | 13.08 | 24.61 | 13.96 | 40.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception65_deeplab_infer.tar) |
| Xception71 | 0.8111 | 0.9545 | 6.19 | 15.34 | 29.21 | 16.21 | 37.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Xception71_infer.tar) |
| InceptionV3 | 0.7914 | 0.9459 | 4.78 | 8.53 | 12.28 | 5.73 | 23.87 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/InceptionV3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV3_infer.tar) |
| InceptionV4 | 0.8077 | 0.9526 | 8.93 | 15.17 | 21.56 | 12.29 | 42.74 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/InceptionV4_infer.tar) |
<a name="10"></a>
......@@ -293,22 +293,22 @@ Inception 系列模型的精度、速度指标如下表所示，更多关于该
EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[EfficientNet 与 ResNeXt101_wsl 系列模型文档](../models/EfficientNet_and_ResNeXt101_wsl.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|
| ResNeXt101_<br>32x8d_wsl | 0.8255 | 0.9674 | 18.52528 | 34.25319 | 16.48 | 88.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) |
| ResNeXt101_<br>32x16d_wsl | 0.8424 | 0.9726 | 25.60395 | 71.88384 | 36.26 | 194.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams) |
| ResNeXt101_<br>32x32d_wsl | 0.8497 | 0.9759 | 54.87396 | 160.04337 | 87.28 | 469.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams) |
| ResNeXt101_<br>32x48d_wsl | 0.8537 | 0.9769 | 99.01698256 | 315.91261 | 153.57 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams) |
| Fix_ResNeXt101_<br>32x48d_wsl | 0.8626 | 0.9797 | 160.0838242 | 595.99296 | 313.41 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) |
| EfficientNetB0 | 0.7738 | 0.9331 | 3.442 | 6.11476 | 0.40 | 5.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams) |
| EfficientNetB1 | 0.7915 | 0.9441 | 5.3322 | 9.41795 | 0.71 | 7.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams) |
| EfficientNetB2 | 0.7985 | 0.9474 | 6.29351 | 10.95702 | 1.02 | 9.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams) |
| EfficientNetB3 | 0.8115 | 0.9541 | 7.67749 | 16.53288 | 1.88 | 12.324 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams) |
| EfficientNetB4 | 0.8285 | 0.9623 | 12.15894 | 30.94567 | 4.51 | 19.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams) |
| EfficientNetB5 | 0.8362 | 0.9672 | 20.48571 | 61.60252 | 10.51 | 30.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams) |
| EfficientNetB6 | 0.8400 | 0.9688 | 32.62402 | - | 19.47 | 43.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams) |
| EfficientNetB7 | 0.8430 | 0.9689 | 53.93823 | - | 38.45 | 66.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams) |
| EfficientNetB0_<br>small | 0.7580 | 0.9258 | 2.3076 | 4.71886 | 0.40 | 4.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型 |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| ResNeXt101_<br>32x8d_wsl | 0.8255 | 0.9674 | 13.55 | 23.39 | 36.18 | 16.48 | 88.99 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x8d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x8d_wsl_infer.tar) |
| ResNeXt101_<br>32x16d_wsl | 0.8424 | 0.9726 | 21.96 | 38.35 | 63.29 | 36.26 | 194.36 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x16d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x16d_wsl_infer.tar) |
| ResNeXt101_<br>32x32d_wsl | 0.8497 | 0.9759 | 37.28 | 76.50 | 121.56 | 87.28 | 469.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x32d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x32d_wsl_infer.tar) |
| ResNeXt101_<br>32x48d_wsl | 0.8537 | 0.9769 | 55.07 | 124.39 | 205.01 | 153.57 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x48d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeXt101_32x48d_wsl_infer.tar) |
| Fix_ResNeXt101_<br>32x48d_wsl | 0.8626 | 0.9797 | 55.01 | 122.63 | 204.66 | 313.41 | 829.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Fix_ResNeXt101_32x48d_wsl_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Fix_ResNeXt101_32x48d_wsl_infer.tar) |
| EfficientNetB0 | 0.7738 | 0.9331 | 1.96 | 3.71 | 5.56 | 0.40 | 5.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_infer.tar) |
| EfficientNetB1 | 0.7915 | 0.9441 | 2.88 | 5.40 | 7.63 | 0.71 | 7.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB1_infer.tar) |
| EfficientNetB2 | 0.7985 | 0.9474 | 3.26 | 6.20 | 9.17 | 1.02 | 9.18 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB2_infer.tar) |
| EfficientNetB3 | 0.8115 | 0.9541 | 4.52 | 8.85 | 13.54 | 1.88 | 12.324 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB3_infer.tar) |
| EfficientNetB4 | 0.8285 | 0.9623 | 6.78 | 15.47 | 24.95 | 4.51 | 19.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB4_infer.tar) |
| EfficientNetB5 | 0.8362 | 0.9672 | 10.97 | 27.24 | 45.93 | 10.51 | 30.56 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB5_infer.tar) |
| EfficientNetB6 | 0.8400 | 0.9688 | 17.09 | 43.32 | 76.90 | 19.47 | 43.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB6_infer.tar) |
| EfficientNetB7 | 0.8430 | 0.9689 | 25.91 | 71.23 | 128.20 | 38.45 | 66.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB7_infer.tar) |
| EfficientNetB0_<br>small | 0.7580 | 0.9258 | 1.24 | 2.59 | 3.92 | 0.40 | 4.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/EfficientNetB0_small_infer.tar) |
<a name="11"></a>
......@@ -317,11 +317,11 @@ EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所
ResNeSt 与 RegNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[ResNeSt 与 RegNet 系列模型文档](../models/ResNeSt_RegNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| ResNeSt50_<br>fast_1s1x64d | 0.8035 | 0.9528 | 3.45405 | 8.72680 | 4.36 | 26.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) |
| ResNeSt50 | 0.8083 | 0.9542 | 6.69042 | 8.01664 | 5.40 | 27.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams) |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46478 | 11.19862 | 4.00 | 22.23 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
| ResNeSt50_<br>fast_1s1x64d | 0.8035 | 0.9528 | 2.73 | 5.33 | 8.24 | 4.36 | 26.27 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_fast_1s1x64d_infer.tar) |
| ResNeSt50 | 0.8083 | 0.9542 | 7.36 | 10.23 | 13.84 | 5.40 | 27.54 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNeSt50_infer.tar) |
| RegNetX_4GF | 0.785 | 0.9416 | 6.46 | 8.48 | 11.45 | 4.00 | 22.23 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RegNetX_4GF_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RegNetX_4GF_infer.tar) |
<a name="12"></a>
......@@ -337,10 +337,8 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
| ViT_base_<br/>patch16_384 | 0.8414 | 0.9717 | - | - | 49.35 | 86.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams) |
| ViT_base_<br/>patch32_384 | 0.8176 | 0.9613 | - | - | 12.66 | 88.19 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams) |
| ViT_large_<br/>patch16_224 | 0.8323 | 0.9650 | - | - | 59.65 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams) |
| ViT_large_<br/>patch16_384 | 0.8513 | 0.9736 | - | - | 174.70 | 304.12
| [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) |
| ViT_large_<br/>patch32_384 | 0.8153 | 0.9608 | - | - | 44.24 | 306.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) |
|ViT_large_<br/>patch16_384| 0.8513 | 0.9736 | - | - | 174.70 | 304.12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams) |
|ViT_large_<br/>patch32_384| 0.8153 | 0.9608 | - | - | 44.24 | 306.48 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams) |
......@@ -381,11 +379,11 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 MixNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MixNet 系列模型文档](../models/MixNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(M) | Params(M) | 下载地址 |
| -------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| MixNet_S | 0.7628 | 0.9299 | | | 252.977 | 4.167 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) |
| MixNet_M | 0.7767 | 0.9364 | | | 357.119 | 5.065 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) |
| MixNet_L | 0.7860 | 0.9437 | | | 579.017 | 7.384 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| -------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| MixNet_S | 0.7628 | 0.9299 | 2.31 | 3.63 | 5.20 | 252.977 | 4.167 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_S_infer.tar) |
| MixNet_M | 0.7767 | 0.9364 | 2.84 | 4.60 | 6.62 | 357.119 | 5.065 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_M_infer.tar) |
| MixNet_L | 0.7860 | 0.9437 | 3.16 | 5.55 | 8.03 | 579.017 | 7.384 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MixNet_L_infer.tar) |
<a name="15"></a>
......@@ -393,13 +391,13 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 ReXNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[ReXNet 系列模型文档](../models/ReXNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| ReXNet_1_0 | 0.7746 | 0.9370 | | | 0.415 | 4.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) |
| ReXNet_1_3 | 0.7913 | 0.9464 | | | 0.68 | 7.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) |
| ReXNet_1_5 | 0.8006 | 0.9512 | | | 0.90 | 9.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) |
| ReXNet_2_0 | 0.8122 | 0.9536 | | | 1.56 | 16.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) |
| ReXNet_3_0 | 0.8209 | 0.9612 | | | 3.44 | 34.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| ReXNet_1_0 | 0.7746 | 0.9370 | 3.08 | 4.15 | 5.49 | 0.415 | 4.84 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_0_infer.tar) |
| ReXNet_1_3 | 0.7913 | 0.9464 | 3.54 | 4.87 | 6.54 | 0.68 | 7.61 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_3_infer.tar) |
| ReXNet_1_5 | 0.8006 | 0.9512 | 3.68 | 5.31 | 7.38 | 0.90 | 9.79 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_1_5_infer.tar) |
| ReXNet_2_0 | 0.8122 | 0.9536 | 4.30 | 6.54 | 9.19 | 1.56 | 16.45 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_2_0_infer.tar) |
| ReXNet_3_0 | 0.8209 | 0.9612 | 5.74 | 9.49 | 13.62 | 3.44 | 34.83 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ReXNet_3_0_infer.tar) |
<a name="16"></a>
......@@ -459,12 +457,12 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 HarDNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[HarDNet 系列模型文档](../models/HarDNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| HarDNet39_ds | 0.7133 |0.8998 | | | 0.44 | 3.51 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams) |
| HarDNet68_ds |0.7362 | 0.9152 | | | 0.79 | 4.20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams) |
| HarDNet68| 0.7546 | 0.9265 | | | 4.26 | 17.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams) |
| HarDNet85 | 0.7744 | 0.9355 | | | 9.09 | 36.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| HarDNet39_ds | 0.7133 |0.8998 | 1.40 | 2.30 | 3.33 | 0.44 | 3.51 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet39_ds_infer.tar) |
| HarDNet68_ds |0.7362 | 0.9152 | 2.26 | 3.34 | 5.06 | 0.79 | 4.20 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_ds_infer.tar) |
| HarDNet68| 0.7546 | 0.9265 | 3.58 | 8.53 | 11.58 | 4.26 | 17.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet68_infer.tar) |
| HarDNet85 | 0.7744 | 0.9355 | 6.24 | 14.85 | 20.57 | 9.09 | 36.69 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HarDNet85_infer.tar) |
<a name="20"></a>
......@@ -472,17 +470,17 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 DLA 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[DLA 系列模型文档](../models/DLA.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| DLA102 | 0.7893 |0.9452 | | | 7.19 | 33.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams) |
| DLA102x2 |0.7885 | 0.9445 | | | 9.34 | 41.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams) |
| DLA102x| 0.781 | 0.9400 | | | 5.89 | 26.40 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams) |
| DLA169 | 0.7809 | 0.9409 | | | 11.59 | 53.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams) |
| DLA34 | 0.7603 | 0.9298 | | | 3.07 | 15.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams) |
| DLA46_c |0.6321 | 0.853 | | | 0.54 | 1.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams) |
| DLA60 | 0.7610 | 0.9292 | | | 4.26 | 22.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams) |
| DLA60x_c | 0.6645 | 0.8754 | | | 0.59 | 1.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams) |
| DLA60x | 0.7753 | 0.9378 | | | 3.54 | 17.41 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| DLA102 | 0.7893 |0.9452 | 4.95 | 8.08 | 12.40 | 7.19 | 33.34 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102_infer.tar) |
| DLA102x2 |0.7885 | 0.9445 | 19.58 | 23.97 | 31.37 | 9.34 | 41.42 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x2_infer.tar) |
| DLA102x| 0.781 | 0.9400 | 11.12 | 15.60 | 20.37 | 5.89 | 26.40 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA102x_infer.tar) |
| DLA169 | 0.7809 | 0.9409 | 7.70 | 12.25 | 18.90 | 11.59 | 53.50 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA169_infer.tar) |
| DLA34 | 0.7603 | 0.9298 | 1.83 | 3.37 | 5.98 | 3.07 | 15.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA34_infer.tar) |
| DLA46_c |0.6321 | 0.853 | 1.06 | 2.08 | 3.23 | 0.54 | 1.31 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA46_c_infer.tar) |
| DLA60 | 0.7610 | 0.9292 | 2.78 | 5.36 | 8.29 | 4.26 | 22.08 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60_infer.tar) |
| DLA60x_c | 0.6645 | 0.8754 | 1.79 | 3.68 | 5.19 | 0.59 | 1.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_c_infer.tar) |
| DLA60x | 0.7753 | 0.9378 | 5.98 | 9.24 | 12.52 | 3.54 | 17.41 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DLA60x_infer.tar) |
<a name="21"></a>
......@@ -490,13 +488,13 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 RedNet 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[RedNet 系列模型文档](../models/RedNet.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
| RedNet26 | 0.7595 |0.9319 | | | 1.69 | 9.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams) |
| RedNet38 |0.7747 | 0.9356 | | | 2.14 | 12.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams) |
| RedNet50| 0.7833 | 0.9417 | | | 2.61 | 15.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams) |
| RedNet101 | 0.7894 | 0.9436 | | | 4.59 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams) |
| RedNet152 | 0.7917 | 0.9440 | | | 6.57 | 34.14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| RedNet26 | 0.7595 |0.9319 | 4.45 | 15.16 | 29.03 | 1.69 | 9.26 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet26_infer.tar) |
| RedNet38 |0.7747 | 0.9356 | 6.24 | 21.39 | 41.26 | 2.14 | 12.43 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet38_infer.tar) |
| RedNet50| 0.7833 | 0.9417 | 8.04 | 27.71 | 53.73 | 2.61 | 15.60 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet50_infer.tar) |
| RedNet101 | 0.7894 | 0.9436 | 13.07 | 44.12 | 83.28 | 4.59 | 25.76 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet101_infer.tar) |
| RedNet152 | 0.7917 | 0.9440 | 18.66 | 63.27 | 119.48 | 6.57 | 34.14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/RedNet152_infer.tar) |
<a name="22"></a>
......@@ -517,13 +515,13 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
关于 AlexNet、SqueezeNet 系列、VGG 系列、DarkNet53 等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](../models/Others.md)。
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | FLOPs(G) | Params(M) | 下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|
| AlexNet | 0.567 | 0.792 | 1.44993 | 2.46696 | 0.71 | 61.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) |
| SqueezeNet1_0 | 0.596 | 0.817 | 0.96736 | 2.53221 | 0.78 | 1.25 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) |
| SqueezeNet1_1 | 0.601 | 0.819 | 0.76032 | 1.877 | 0.35 | 1.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) |
| VGG11 | 0.693 | 0.891 | 3.90412 | 9.51147 | 7.61 | 132.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) |
| VGG13 | 0.700 | 0.894 | 4.64684 | 12.61558 | 11.31 | 133.05 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) |
| VGG16 | 0.720 | 0.907 | 5.61769 | 16.40064 | 15.470 | 138.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) |
| VGG19 | 0.726 | 0.909 | 6.65221 | 20.4334 | 19.63 | 143.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) |
| DarkNet53 | 0.780 | 0.941 | 4.10829 | 12.1714 | 9.31 | 41.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) |
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预备训练下载地址 | inference模型下载地址 |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
| AlexNet | 0.567 | 0.792 | 0.81 | 1.50 | 2.33 | 0.71 | 61.10 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/AlexNet_infer.tar) |
| SqueezeNet1_0 | 0.596 | 0.817 | 0.68 | 1.64 | 2.62 | 0.78 | 1.25 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_0_infer.tar) |
| SqueezeNet1_1 | 0.601 | 0.819 | 0.62 | 1.30 | 2.09 | 0.35 | 1.24 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/SqueezeNet1_1_infer.tar) |
| VGG11 | 0.693 | 0.891 | 1.72 | 4.15 | 7.24 | 7.61 | 132.86 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG11_infer.tar) |
| VGG13 | 0.700 | 0.894 | 2.02 | 5.28 | 9.54 | 11.31 | 133.05 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG13_infer.tar) |
| VGG16 | 0.720 | 0.907 | 2.48 | 6.79 | 12.33 | 15.470 | 138.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG16_infer.tar) |
| VGG19 | 0.726 | 0.909 | 2.93 | 8.28 | 15.21 | 19.63 | 143.66 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/VGG19_infer.tar) |
| DarkNet53 | 0.780 | 0.941 | 2.79 | 6.42 | 10.89 | 9.31 | 41.65 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/DarkNet53_infer.tar) |
docs/zh_CN/models/DLA.md
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......@@ -3,6 +3,7 @@
## 目录
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 V100 GPU 的预测速度](#3)
<a name='1'></a>
......@@ -25,4 +26,20 @@ DLA(Deep Layer Aggregation)。 视觉识别需要丰富的表示形式，其范
| DLA102 | 33.3 | 7.2 | 78.93 | 94.52 |
| DLA102x | 26.4 | 5.9 | 78.10 | 94.00 |
| DLA102x2 | 41.4 | 9.3 | 78.85 | 94.45 |
| DLA169 | 53.5 | 11.6 | 78.09 | 94.09 |
\ No newline at end of file
| DLA169 | 53.5 | 11.6 | 78.09 | 94.09 |
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| 模型 | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| -------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| DLA102 | 224 | 256 | 4.95 | 8.08 | 12.40 |
| DLA102x2 | 224 | 256 | 19.58 | 23.97 | 31.37 |
| DLA102x | 224 | 256 | 11.12 | 15.60 | 20.37 |
| DLA169 | 224 | 256 | 7.70 | 12.25 | 18.90 |
| DLA34 | 224 | 256 | 1.83 | 3.37 | 5.98 |
| DLA46_c | 224 | 256 | 1.06 | 2.08 | 3.23 |
| DLA60 | 224 | 256 | 2.78 | 5.36 | 8.29 |
| DLA60x_c | 224 | 256 | 1.79 | 3.68 | 5.19 |
| DLA60x | 224 | 256 | 5.98 | 9.24 | 12.52 |
\ No newline at end of file
docs/zh_CN/models/DPN_DenseNet.md
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......@@ -12,7 +12,7 @@
## 1. 概述
DenseNet 是 2017 年 CVPR best paper 提出的一种新的网络结构，该网络设计了一种新的跨层连接的 block，即 dense-block。相比 ResNet 中的 bottleneck，dense-block 设计了一个更激进的密集连接机制，即互相连接所有的层，每个层都会接受其前面所有层作为其额外的输入。DenseNet 将所有的 dense-block 堆叠，组合成了一个密集连接型网络。密集的连接方式使得 DenseNe 更容易进行梯度的反向传播，使得网络更容易训练。
DPN 的全称是 Dual Path Networks，即双通道网络。该网络是由 DenseNet 和 ResNeXt 结合的一个网络，其证明了 DenseNet 能从靠前的层级中提取到新的特征，而 ResNeXt 本质上是对之前层级中已提取特征的复用。作者进一步分析发现，ResNeXt 对特征有高复用率，但冗余度低，DenseNet 能创造新特征，但冗余度高。结合二者结构的优势，作者设计了 DPN 网络。最终 DPN 网络在同样 FLOPS 和参数量下，取得了比 ResNeXt 与 DenseNet 更好的结果。
该系列模型的 FLOPS、参数量以及 T4 GPU 上的预测耗时如下图所示。
![](../../images/models/T4_benchmark/t4.fp32.bs4.DPN.flops.png)
......@@ -48,18 +48,18 @@ DPN 的全称是 Dual Path Networks，即双通道网络。该网络是由 Dense
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|-------------|-----------|-------------------|--------------------------|
| DenseNet121 | 224 | 256 | 4.371 |
| DenseNet161 | 224 | 256 | 8.863 |
| DenseNet169 | 224 | 256 | 6.391 |
| DenseNet201 | 224 | 256 | 8.173 |
| DenseNet264 | 224 | 256 | 11.942 |
| DPN68 | 224 | 256 | 11.805 |
| DPN92 | 224 | 256 | 17.840 |
| DPN98 | 224 | 256 | 21.057 |
| DPN107 | 224 | 256 | 28.685 |
| DPN131 | 224 | 256 | 28.083 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|-------------|-----------|-------------------|-------------------|-------------------|-------------------|
| DenseNet121 | 224 | 256 | 3.40 | 6.94 | 9.17 |
| DenseNet161 | 224 | 256 | 7.06 | 14.37 | 19.55 |
| DenseNet169 | 224 | 256 | 5.00 | 10.29 | 12.84 |
| DenseNet201 | 224 | 256 | 6.38 | 13.72 | 17.17 |
| DenseNet264 | 224 | 256 | 9.34 | 20.95 | 25.41 |
| DPN68 | 224 | 256 | 8.18 | 11.40 | 14.82 |
| DPN92 | 224 | 256 | 12.48 | 20.04 | 25.10 |
| DPN98 | 224 | 256 | 14.70 | 25.55 | 35.12 |
| DPN107 | 224 | 256 | 19.46 | 35.62 | 50.22 |
| DPN131 | 224 | 256 | 19.64 | 34.60 | 47.42 |
<a name='4'></a>
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docs/zh_CN/models/EfficientNet_and_ResNeXt101_wsl.md
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......@@ -50,22 +50,22 @@ ResNeXt 是 facebook 于 2016 年提出的一种对 ResNet 的改进版网络。
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|-------------------------------|-----------|-------------------|--------------------------|
| ResNeXt101_<br>32x8d_wsl | 224 | 256 | 19.127 |
| ResNeXt101_<br>32x16d_wsl | 224 | 256 | 23.629 |
| ResNeXt101_<br>32x32d_wsl | 224 | 256 | 40.214 |
| ResNeXt101_<br>32x48d_wsl | 224 | 256 | 59.714 |
| Fix_ResNeXt101_<br>32x48d_wsl | 320 | 320 | 82.431 |
| EfficientNetB0 | 224 | 256 | 2.449 |
| EfficientNetB1 | 240 | 272 | 3.547 |
| EfficientNetB2 | 260 | 292 | 3.908 |
| EfficientNetB3 | 300 | 332 | 5.145 |
| EfficientNetB4 | 380 | 412 | 7.609 |
| EfficientNetB5 | 456 | 488 | 12.078 |
| EfficientNetB6 | 528 | 560 | 18.381 |
| EfficientNetB7 | 600 | 632 | 27.817 |
| EfficientNetB0_<br>small | 224 | 256 | 1.692 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|-------------------------------|-----------|-------------------|-------------------------------|-------------------------------|-------------------------------|
| ResNeXt101_<br>32x8d_wsl | 224 | 256 | 13.55 | 23.39 | 36.18 |
| ResNeXt101_<br>32x16d_wsl | 224 | 256 | 21.96 | 38.35 | 63.29 |
| ResNeXt101_<br>32x32d_wsl | 224 | 256 | 37.28 | 76.50 | 121.56 |
| ResNeXt101_<br>32x48d_wsl | 224 | 256 | 55.07 | 124.39 | 205.01 |
| Fix_ResNeXt101_<br>32x48d_wsl | 320 | 320 | 55.01 | 122.63 | 204.66 |
| EfficientNetB0 | 224 | 256 | 1.96 | 3.71 | 5.56 |
| EfficientNetB1 | 240 | 272 | 2.88 | 5.40 | 7.63 |
| EfficientNetB2 | 260 | 292 | 3.26 | 6.20 | 9.17 |
| EfficientNetB3 | 300 | 332 | 4.52 | 8.85 | 13.54 |
| EfficientNetB4 | 380 | 412 | 6.78 | 15.47 | 24.95 |
| EfficientNetB5 | 456 | 488 | 10.97 | 27.24 | 45.93 |
| EfficientNetB6 | 528 | 560 | 17.09 | 43.32 | 76.90 |
| EfficientNetB7 | 600 | 632 | 25.91 | 71.23 | 128.20 |
| EfficientNetB0_<br>small | 224 | 256 | 1.24 | 2.59 | 3.92 |
<a name='4'></a>
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docs/zh_CN/models/HRNet.md
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......@@ -43,17 +43,17 @@ HRNet 是 2019 年由微软亚洲研究院提出的一种全新的神经网络
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|-------------|-----------|-------------------|--------------------------|
| HRNet_W18_C | 224 | 256 | 7.368 |
| HRNet_W18_C_ssld | 224 | 256 | 7.368 |
| HRNet_W30_C | 224 | 256 | 9.402 |
| HRNet_W32_C | 224 | 256 | 9.467 |
| HRNet_W40_C | 224 | 256 | 10.739 |
| HRNet_W44_C | 224 | 256 | 11.497 |
| HRNet_W48_C | 224 | 256 | 12.165 |
| HRNet_W48_C_ssld | 224 | 256 | 12.165 |
| HRNet_W64_C | 224 | 256 | 15.003 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|-------------|-----------|-------------------|-------------------|-------------------|-------------------|
| HRNet_W18_C | 224 | 256 | 6.66 | 8.94 | 11.95 |
| HRNet_W18_C_ssld | 224 | 256 | 6.66 | 8.92 | 11.93 |
| HRNet_W30_C | 224 | 256 | 8.61 | 11.40 | 15.23 |
| HRNet_W32_C | 224 | 256 | 8.54 | 11.58 | 15.57 |
| HRNet_W40_C | 224 | 256 | 9.83 | 15.02 | 20.92 |
| HRNet_W44_C | 224 | 256 | 10.62 | 16.18 | 25.92 |
| HRNet_W48_C | 224 | 256 | 11.07 | 17.06 | 27.28 |
| HRNet_W48_C_ssld | 224 | 256 | 11.09 | 17.04 | 27.28 |
| HRNet_W64_C | 224 | 256 | 13.82 | 21.15 | 35.51 |
<a name='4'></a>
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docs/zh_CN/models/HarDNet.md
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......@@ -4,6 +4,7 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 V100 GPU 的预测速度](#3)
<a name='1'></a>
## 1. 概述
......@@ -20,3 +21,15 @@ HarDNet（Harmonic DenseNet）是 2019 年由国立清华大学提出的一种
| HarDNet85 | 36.7 | 9.1 | 77.44 | 93.55 |
| HarDNet39_ds | 3.5 | 0.4 | 71.33 | 89.98 |
| HarDNet68_ds | 4.2 | 0.8 | 73.62 | 91.52 |
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| ------------ | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| HarDNet68 | 224 | 256 | 3.58 | 8.53 | 11.58 |
| HarDNet85 | 224 | 256 | 6.24 | 14.85 | 20.57 |
| HarDNet39_ds | 224 | 256 | 1.40 | 2.30 | 3.33 |
| HarDNet68_ds | 224 | 256 | 2.26 | 3.34 | 5.06 |
docs/zh_CN/models/Inception.md
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......@@ -53,15 +53,15 @@ InceptionV4 是 2016 年由 Google 设计的新的神经网络，当时残差结
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|------------------------|-----------|-------------------|--------------------------|
| GoogLeNet | 224 | 256 | 1.807 |
| Xception41 | 299 | 320 | 3.972 |
| Xception41_<br>deeplab | 299 | 320 | 4.408 |
| Xception65 | 299 | 320 | 6.174 |
| Xception65_<br>deeplab | 299 | 320 | 6.464 |
| Xception71 | 299 | 320 | 6.782 |
| InceptionV4 | 299 | 320 | 11.141 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|------------------------|-----------|-------------------|------------------------|------------------------|------------------------|
| GoogLeNet | 224 | 256 | 1.41 | 3.25 | 5.00 |
| Xception41 | 299 | 320 | 3.58 | 8.76 | 16.61 |
| Xception41_<br>deeplab | 299 | 320 | 3.81 | 9.16 | 17.20 |
| Xception65 | 299 | 320 | 5.45 | 12.78 | 24.53 |
| Xception65_<br>deeplab | 299 | 320 | 5.65 | 13.08 | 24.61 |
| Xception71 | 299 | 320 | 6.19 | 15.34 | 29.21 |
| InceptionV4 | 299 | 320 | 8.93 | 15.17 | 21.56 |
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docs/zh_CN/models/MixNet.md
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......@@ -4,6 +4,7 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 V100 GPU 的预测速度](#3)
<a name='1'></a>
......@@ -26,4 +27,14 @@ MixNet 是谷歌出的一篇关于轻量级网络的文章，主要工作就在
| MixNet_M | 77.67 | 93.64 | 77.0 | 357.119 | 5.065 |
| MixNet_L | 78.60 | 94.37 | 78.9 | 579.017 | 7.384 |
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| -------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| MixNet_S | 224 | 256 | 2.31 | 3.63 | 5.20 |
| MixNet_M | 224 | 256 | 2.84 | 4.60 | 6.62 |
| MixNet_L | 224 | 256 | 3.16 | 5.55 | 8.03 |
关于 Inference speed 等信息，敬请期待。
docs/zh_CN/models/Mobile.md
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......@@ -5,7 +5,8 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 SD855 的预测速度和存储大小](#3)
* [4. 基于 T4 GPU 的预测速度](#4)
* [4. 基于 V100 GPU 的预测速度](#4)
* [5. 基于 T4 GPU 的预测速度](#5)
<a name='1'></a>
......@@ -79,84 +80,128 @@ GhostNet 是华为于 2020 年提出的一种全新的轻量化网络结构，
## 3. 基于 SD855 的预测速度和存储大小
| Models | Batch Size=1(ms) | Storage Size(M) |
|:--:|:--:|:--:|
| MobileNetV1_x0_25 | 3.220 | 1.900 |
| MobileNetV1_x0_5 | 9.580 | 5.200 |
| MobileNetV1_x0_75 | 19.436 | 10.000 |
| MobileNetV1 | 32.523 | 16.000 |
| MobileNetV1_ssld | 32.523 | 16.000 |
| MobileNetV2_x0_25 | 3.799 | 6.100 |
| MobileNetV2_x0_5 | 8.702 | 7.800 |
| MobileNetV2_x0_75 | 15.531 | 10.000 |
| MobileNetV2 | 23.318 | 14.000 |
| MobileNetV2_x1_5 | 45.624 | 26.000 |
| MobileNetV2_x2_0 | 74.292 | 43.000 |
| MobileNetV2_ssld | 23.318 | 14.000 |
| MobileNetV3_large_x1_25 | 28.218 | 29.000 |
| MobileNetV3_large_x1_0 | 19.308 | 21.000 |
| MobileNetV3_large_x0_75 | 13.565 | 16.000 |
| MobileNetV3_large_x0_5 | 7.493 | 11.000 |
| MobileNetV3_large_x0_35 | 5.137 | 8.600 |
| MobileNetV3_small_x1_25 | 9.275 | 14.000 |
| MobileNetV3_small_x1_0 | 6.546 | 12.000 |
| MobileNetV3_small_x0_75 | 5.284 | 9.600 |
| MobileNetV3_small_x0_5 | 3.352 | 7.800 |
| MobileNetV3_small_x0_35 | 2.635 | 6.900 |
| MobileNetV3_small_x0_35_ssld | 2.635 | 6.900 |
| MobileNetV3_large_x1_0_ssld | 19.308 | 21.000 |
| MobileNetV3_large_x1_0_ssld_int8 | 14.395 | 10.000 |
| MobileNetV3_small_x1_0_ssld | 6.546 | 12.000 |
| ShuffleNetV2 | 10.941 | 9.000 |
| ShuffleNetV2_x0_25 | 2.329 | 2.700 |
| ShuffleNetV2_x0_33 | 2.643 | 2.800 |
| ShuffleNetV2_x0_5 | 4.261 | 5.600 |
| ShuffleNetV2_x1_5 | 19.352 | 14.000 |
| ShuffleNetV2_x2_0 | 34.770 | 28.000 |
| ShuffleNetV2_swish | 16.023 | 9.100 |
| GhostNet_x0_5 | 5.714 | 10.000 |
| GhostNet_x1_0 | 13.558 | 20.000 |
| GhostNet_x1_3 | 19.982 | 29.000 |
| GhostNet_x1_3_ssld | 19.982 | 29.000 |
| Models | SD855 time(ms)<br>bs=1, thread=1 | SD855 time(ms)<br/>bs=1, thread=2 | SD855 time(ms)<br/>bs=1, thread=4 | Storage Size(M) |
|:--:|----|----|----|----|
| MobileNetV1_x0_25 | 2.88 | 1.82 | 1.26 | 1.900 |
| MobileNetV1_x0_5 | 8.74 | 5.26 | 3.09 | 5.200 |
| MobileNetV1_x0_75 | 17.84 | 10.61 | 6.21 | 10.000 |
| MobileNetV1 | 30.24 | 17.86 | 10.30 | 16.000 |
| MobileNetV1_ssld | 30.19 | 17.85 | 10.23 | 16.000 |
| MobileNetV2_x0_25 | 3.46 | 2.51 | 2.03 | 6.100 |
| MobileNetV2_x0_5 | 7.69 | 4.92 | 3.57 | 7.800 |
| MobileNetV2_x0_75 | 13.69 | 8.60 | 5.82 | 10.000 |
| MobileNetV2 | 20.74 | 12.71 | 8.10 | 14.000 |
| MobileNetV2_x1_5 | 40.79 | 24.49 | 15.50 | 26.000 |
| MobileNetV2_x2_0 | 67.50 | 40.03 | 25.55 | 43.000 |
| MobileNetV2_ssld | 20.71 | 12.70 | 8.06 | 14.000 |
| MobileNetV3_large_x1_25 | 24.52 | 14.76 | 9.89 | 29.000 |
| MobileNetV3_large_x1_0 | 16.55 | 10.09 | 6.84 | 21.000 |
| MobileNetV3_large_x0_75 | 11.53 | 7.06 | 4.94 | 16.000 |
| MobileNetV3_large_x0_5 | 6.50 | 4.22 | 3.15 | 11.000 |
| MobileNetV3_large_x0_35 | 4.43 | 3.11 | 2.41 | 8.600 |
| MobileNetV3_small_x1_25 | 7.88 | 4.91 | 3.45 | 14.000 |
| MobileNetV3_small_x1_0 | 5.63 | 3.65 | 2.60 | 12.000 |
| MobileNetV3_small_x0_75 | 4.50 | 2.96 | 2.19 | 9.600 |
| MobileNetV3_small_x0_5 | 2.89 | 2.04 | 1.62 | 7.800 |
| MobileNetV3_small_x0_35 | 2.23 | 1.66 | 1.43 | 6.900 |
| MobileNetV3_small_x0_35_ssld | | | | 6.900 |
| MobileNetV3_large_x1_0_ssld | 16.56 | 10.10 | 6.86 | 21.000 |
| MobileNetV3_large_x1_0_ssld_int8 | | | | 10.000 |
| MobileNetV3_small_x1_0_ssld | 5.64 | 3.67 | 2.61 | 12.000 |
| ShuffleNetV2 | 9.72 | 5.97 | 4.13 | 9.000 |
| ShuffleNetV2_x0_25 | 1.94 | 1.53 | 1.43 | 2.700 |
| ShuffleNetV2_x0_33 | 2.23 | 1.70 | 1.79 | 2.800 |
| ShuffleNetV2_x0_5 | 3.67 | 2.63 | 2.06 | 5.600 |
| ShuffleNetV2_x1_5 | 17.21 | 10.56 | 6.81 | 14.000 |
| ShuffleNetV2_x2_0 | 31.21 | 18.98 | 11.65 | 28.000 |
| ShuffleNetV2_swish | 31.21 | 9.06 | 5.74 | 9.100 |
| GhostNet_x0_5 | 5.28 | 3.95 | 3.29 | 10.000 |
| GhostNet_x1_0 | 12.89 | 8.66 | 6.72 | 20.000 |
| GhostNet_x1_3 | 19.16 | 12.25 | 9.40 | 29.000 |
| GhostNet_x1_3_ssld | 19.16 | 17.85 | 10.18 | 29.000 |
<a name='4'></a>
## 4. 基于 T4 GPU 的预测速度
| Models | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
|-----------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| MobileNetV1_x0_25 | 0.68422 | 1.13021 | 1.72095 | 0.67274 | 1.226 | 1.84096 |
| MobileNetV1_x0_5 | 0.69326 | 1.09027 | 1.84746 | 0.69947 | 1.43045 | 2.39353 |
| MobileNetV1_x0_75 | 0.6793 | 1.29524 | 2.15495 | 0.79844 | 1.86205 | 3.064 |
| MobileNetV1 | 0.71942 | 1.45018 | 2.47953 | 0.91164 | 2.26871 | 3.90797 |
| MobileNetV1_ssld | 0.71942 | 1.45018 | 2.47953 | 0.91164 | 2.26871 | 3.90797 |
| MobileNetV2_x0_25 | 2.85399 | 3.62405 | 4.29952 | 2.81989 | 3.52695 | 4.2432 |
| MobileNetV2_x0_5 | 2.84258 | 3.1511 | 4.10267 | 2.80264 | 3.65284 | 4.31737 |
| MobileNetV2_x0_75 | 2.82183 | 3.27622 | 4.98161 | 2.86538 | 3.55198 | 5.10678 |
| MobileNetV2 | 2.78603 | 3.71982 | 6.27879 | 2.62398 | 3.54429 | 6.41178 |
| MobileNetV2_x1_5 | 2.81852 | 4.87434 | 8.97934 | 2.79398 | 5.30149 | 9.30899 |
| MobileNetV2_x2_0 | 3.65197 | 6.32329 | 11.644 | 3.29788 | 7.08644 | 12.45375 |
| MobileNetV2_ssld | 2.78603 | 3.71982 | 6.27879 | 2.62398 | 3.54429 | 6.41178 |
| MobileNetV3_large_x1_25 | 2.34387 | 3.16103 | 4.79742 | 2.35117 | 3.44903 | 5.45658 |
| MobileNetV3_large_x1_0 | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 |
| MobileNetV3_large_x0_75 | 2.1058 | 2.61426 | 3.61021 | 2.0006 | 2.56987 | 3.78005 |
| MobileNetV3_large_x0_5 | 2.06934 | 2.77341 | 3.35313 | 2.11199 | 2.88172 | 3.19029 |
| MobileNetV3_large_x0_35 | 2.14965 | 2.7868 | 3.36145 | 1.9041 | 2.62951 | 3.26036 |
| MobileNetV3_small_x1_25 | 2.06817 | 2.90193 | 3.5245 | 2.02916 | 2.91866 | 3.34528 |
| MobileNetV3_small_x1_0 | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 |
| MobileNetV3_small_x0_75 | 1.80617 | 2.64646 | 3.24513 | 1.93697 | 2.64285 | 3.32797 |
| MobileNetV3_small_x0_5 | 1.95001 | 2.74014 | 3.39485 | 1.88406 | 2.99601 | 3.3908 |
| MobileNetV3_small_x0_35 | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 |
| MobileNetV3_small_x0_35_ssld | 2.10683 | 2.94267 | 3.44254 | 1.94427 | 2.94116 | 3.41082 |
| MobileNetV3_large_x1_0_ssld | 2.20149 | 3.08423 | 4.07779 | 2.04296 | 2.9322 | 4.53184 |
| MobileNetV3_small_x1_0_ssld | 1.73933 | 2.59478 | 3.40276 | 1.74527 | 2.63565 | 3.28124 |
| ShuffleNetV2 | 1.95064 | 2.15928 | 2.97169 | 1.89436 | 2.26339 | 3.17615 |
| ShuffleNetV2_x0_25 | 1.43242 | 2.38172 | 2.96768 | 1.48698 | 2.29085 | 2.90284 |
| ShuffleNetV2_x0_33 | 1.69008 | 2.65706 | 2.97373 | 1.75526 | 2.85557 | 3.09688 |
| ShuffleNetV2_x0_5 | 1.48073 | 2.28174 | 2.85436 | 1.59055 | 2.18708 | 3.09141 |
| ShuffleNetV2_x1_5 | 1.51054 | 2.4565 | 3.41738 | 1.45389 | 2.5203 | 3.99872 |
| ShuffleNetV2_x2_0 | 1.95616 | 2.44751 | 4.19173 | 2.15654 | 3.18247 | 5.46893 |
| ShuffleNetV2_swish | 2.50213 | 2.92881 | 3.474 | 2.5129 | 2.97422 | 3.69357 |
| GhostNet_x0_5 | 2.64492 | 3.48473 | 4.48844 | 2.36115 | 3.52802 | 3.89444 |
| GhostNet_x1_0 | 2.63120 | 3.92065 | 4.48296 | 2.57042 | 3.56296 | 4.85524 |
| GhostNet_x1_3 | 2.89715 | 3.80329 | 4.81661 | 2.81810 | 3.72071 | 5.92269 |
## 4. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| -------------------------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| MobileNetV1_x0_25 | 224 | 256 | 0.47 | 0.93 | 1.39 |
| MobileNetV1_x0_5 | 224 | 256 | 0.48 | 1.09 | 1.69 |
| MobileNetV1_x0_75 | 224 | 256 | 0.55 | 1.34 | 2.03 |
| MobileNetV1 | 224 | 256 | 0.64 | 1.57 | 2.48 |
| MobileNetV1_ssld | 224 | 256 | 0.66 | 1.59 | 2.58 |
| MobileNetV2_x0_25 | 224 | 256 | 0.83 | 1.17 | 1.78 |
| MobileNetV2_x0_5 | 224 | 256 | 0.84 | 1.45 | 2.04 |
| MobileNetV2_x0_75 | 224 | 256 | 0.96 | 1.62 | 2.53 |
| MobileNetV2 | 224 | 256 | 1.02 | 1.93 | 2.89 |
| MobileNetV2_x1_5 | 224 | 256 | 1.32 | 2.58 | 4.14 |
| MobileNetV2_x2_0 | 224 | 256 | 1.57 | 3.13 | 4.76 |
| MobileNetV2_ssld | 224 | 256 | 1.01 | 1.97 | 2.84 |
| MobileNetV3_large_x1_25 | 224 | 256 | 1.75 | 2.87 | 4.23 |
| MobileNetV3_large_x1_0 | 224 | 256 | 1.37 | 2.67 | 3.46 |
| MobileNetV3_large_x0_75 | 224 | 256 | 1.37 | 2.23 | 3.17 |
| MobileNetV3_large_x0_5 | 224 | 256 | 1.10 | 1.85 | 2.69 |
| MobileNetV3_large_x0_35 | 224 | 256 | 1.01 | 1.44 | 1.92 |
| MobileNetV3_small_x1_25 | 224 | 256 | 1.20 | 2.04 | 2.64 |
| MobileNetV3_small_x1_0 | 224 | 256 | 1.03 | 1.76 | 2.50 |
| MobileNetV3_small_x0_75 | 224 | 256 | 1.04 | 1.71 | 2.37 |
| MobileNetV3_small_x0_5 | 224 | 256 | 1.01 | 1.49 | 2.01 |
| MobileNetV3_small_x0_35 | 224 | 256 | 1.01 | 1.44 | 1.92 |
| MobileNetV3_small_x0_35_ssld | 224 | 256 | | | |
| MobileNetV3_large_x1_0_ssld | 224 | 256 | 1.35 | 2.47 | 3.72 |
| MobileNetV3_large_x1_0_ssld_int8 | 224 | 256 | | | |
| MobileNetV3_small_x1_0_ssld | 224 | 256 | 1.06 | 1.89 | 2.48 |
| ShuffleNetV2 | 224 | 256 | 1.05 | 1.76 | 2.37 |
| ShuffleNetV2_x0_25 | 224 | 256 | 0.92 | 1.27 | 1.73 |
| ShuffleNetV2_x0_33 | 224 | 256 | 0.91 | 1.29 | 1.81 |
| ShuffleNetV2_x0_5 | 224 | 256 | 0.89 | 1.43 | 1.94 |
| ShuffleNetV2_x1_5 | 224 | 256 | 0.93 | 1.99 | 2.85 |
| ShuffleNetV2_x2_0 | 224 | 256 | 1.45 | 2.70 | 3.35 |
| ShuffleNetV2_swish | 224 | 256 | 1.43 | 1.93 | 2.69 |
| GhostNet_x0_5 | 224 | 256 | 1.66 | 2.24 | 2.73 |
| GhostNet_x1_0 | 224 | 256 | 1.69 | 2.73 | 3.81 |
| GhostNet_x1_3 | 224 | 256 | 1.84 | 2.88 | 3.94 |
| GhostNet_x1_3_ssld | 224 | 256 | 1.85 | 3.17 | 4.29 |
<a name='5'></a>
## 5. 基于 T4 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
|-----------------------------|-----------------------|-----------------------|-----------------------|-----------------------|-----------------------|
| MobileNetV1_x0_25 | 224 | 256 | 0.47 | 0.93 | 1.39 |
| MobileNetV1_x0_5 | 224 | 256 | 0.48 | 1.09 | 1.69 |
| MobileNetV1_x0_75 | 224 | 256 | 0.55 | 1.34 | 2.03 |
| MobileNetV1 | 224 | 256 | 0.64 | 1.57 | 2.48 |
| MobileNetV1_ssld | 224 | 256 | 0.66 | 1.59 | 2.58 |
| MobileNetV2_x0_25 | 224 | 256 | 0.83 | 1.17 | 1.78 |
| MobileNetV2_x0_5 | 224 | 256 | 0.84 | 1.45 | 2.04 |
| MobileNetV2_x0_75 | 224 | 256 | 0.96 | 1.62 | 2.53 |
| MobileNetV2 | 224 | 256 | 1.02 | 1.93 | 2.89 |
| MobileNetV2_x1_5 | 224 | 256 | 1.32 | 2.58 | 4.14 |
| MobileNetV2_x2_0 | 224 | 256 | 1.57 | 3.13 | 4.76 |
| MobileNetV2_ssld | 224 | 256 | 1.01 | 1.97 | 2.84 |
| MobileNetV3_small_x0_35 | 224 | 256 | 1.01 | 1.44 | 1.92 |
| MobileNetV3_small_x0_5 | 224 | 256 | 1.01 | 1.49 | 2.01 |
| MobileNetV3_small_x0_75 | 224 | 256 | 1.04 | 1.71 | 2.37 |
| MobileNetV3_small_x1_0 | 224 | 256 | 1.03 | 1.76 | 2.50 |
| MobileNetV3_small_x1_25 | 224 | 256 | 1.20 | 2.04 | 2.64 |
| MobileNetV3_large_x0_35 | 224 | 256 | 1.10 | 1.74 | 2.34 |
| MobileNetV3_large_x0_5 | 224 | 256 | 1.10 | 1.85 | 2.69 |
| MobileNetV3_large_x0_75 | 224 | 256 | 1.37 | 2.23 | 3.17 |
| MobileNetV3_large_x1_0 | 224 | 256 | 1.37 | 2.67 | 3.46 |
| MobileNetV3_large_x1_25 | 224 | 256 | 1.75 | 2.87 | 4.23 |
| MobileNetV3_small_x1_0_ssld | 224 | 256 | 1.06 | 1.89 | 2.48 |
| MobileNetV3_large_x1_0_ssld | 224 | 256 | 1.35 | 2.47 | 3.72 |
| ShuffleNetV2_swish | 224 | 256 | 1.43 | 1.93 | 2.69 |
| ShuffleNetV2_x0_25 | 224 | 256 | 0.92 | 1.27 | 1.73 |
| ShuffleNetV2_x0_33 | 224 | 256 | 0.91 | 1.29 | 1.81 |
| ShuffleNetV2_x0_5 | 224 | 256 | 0.89 | 1.43 | 1.94 |
| ShuffleNetV2_x1_0 | 224 | 256 | 1.05 | 1.76 | 2.37 |
| ShuffleNetV2_x1_5 | 224 | 256 | 0.93 | 1.99 | 2.85 |
| ShuffleNetV2_x2_0 | 224 | 256 | 1.45 | 2.70 | 3.35 |
| GhostNet_x0_5 | 224 | 256 | 1.66 | 2.24 | 2.73 |
| GhostNet_x1_0 | 224 | 256 | 1.69 | 2.73 | 3.81 |
| GhostNet_x1_3 | 224 | 256 | 1.84 | 2.88 | 3.94 |
| GhostNet_x1_3_ssld | 224 | 256 | 1.85 | 3.17 | 4.29 |
docs/zh_CN/models/Others.md
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......@@ -37,16 +37,16 @@ DarkNet53 是 YOLO 作者在论文设计的用于目标检测的 backbone，该
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|---------------------------|-----------|-------------------|----------------------|
| AlexNet | 224 | 256 | 1.176 |
| SqueezeNet1_0 | 224 | 256 | 0.860 |
| SqueezeNet1_1 | 224 | 256 | 0.763 |
| VGG11 | 224 | 256 | 1.867 |
| VGG13 | 224 | 256 | 2.148 |
| VGG16 | 224 | 256 | 2.616 |
| VGG19 | 224 | 256 | 3.076 |
| DarkNet53 | 256 | 256 | 3.139 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|---------------------------|-----------|-------------------|-------------------|-------------------|-------------------|
| AlexNet | 224 | 256 | 0.81 | 1.50 | 2.33 |
| SqueezeNet1_0 | 224 | 256 | 0.68 | 1.64 | 2.62 |
| SqueezeNet1_1 | 224 | 256 | 0.62 | 1.30 | 2.09 |
| VGG11 | 224 | 256 | 1.72 | 4.15 | 7.24 |
| VGG13 | 224 | 256 | 2.02 | 5.28 | 9.54 |
| VGG16 | 224 | 256 | 2.48 | 6.79 | 12.33 |
| VGG19 | 224 | 256 | 2.93 | 8.28 | 15.21 |
| DarkNet53 | 256 | 256 | 2.79 | 6.42 | 10.89 |
<a name='4'></a>
......
docs/zh_CN/models/PP-LCNet.md
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......@@ -14,8 +14,9 @@
- [4.1 图像分类](#4.1)
- [4.2 目标检测](#4.2)
- [4.3 语义分割](#4.3)
- [5. 总结](#5)
- [6. 引用](#6)
- [5. 基于 V100 GPU 的预测速度](#5)
- [6. 总结](#6)
- [7. 引用](#7)
<a name="1"></a>
## 1. 摘要
......@@ -54,7 +55,7 @@ SE 模块是 SENet 提出的一种通道注意力机制，可以有效提升模
| <b>0000000000011<b> | <b>63.14<b> | <b>2.05<b> |
| 1111111111111 | 64.27 | 3.80 |
最终，PP-LCNet 中的 SE 模块的位置选用了表格中第三行的方案。
<a name="3.3"></a>
......@@ -106,7 +107,7 @@ BaseNet 经过以上四个方面的改进，得到了 PP-LCNet。下表进一步
| PP-LCNet-0.5x\* | 1.9 | 47 | 66.10 | 86.46 | 2.05 |
| PP-LCNet-1.0x\* | 3.0 | 161 | 74.39 | 92.09 | 2.46 |
| PP-LCNet-2.5x\* | 9.0 | 906 | 80.82 | 95.33 | 5.39 |
其中\*表示使用 SSLD 蒸馏后的模型。
与其他轻量级网络的性能对比：
......@@ -145,18 +146,34 @@ MobileNetV3-large-0.75x | 25.8 | 11.1 |
| Backbone | mIoU(%) | Latency(ms) |
|-------|-----------|----------|
MobileNetV3-large-0.5x | 55.42 | 135 |
<b>PP-LCNet-0.5x<b> | <b>58.36<b> | <b>82<b> |
MobileNetV3-large-0.75x | 64.53 | 151 |
<b>PP-LCNet-1x<b> | <b>66.03<b> | <b>96<b> |
|MobileNetV3-large-0.5x | 55.42 | 135 |
|<b>PP-LCNet-0.5x<b> | <b>58.36<b> | <b>82<b> |
|MobileNetV3-large-0.75x | 64.53 | 151 |
|<b>PP-LCNet-1x<b> | <b>66.03<b> | <b>96<b> |
<a name="5"></a>
## 5. 总结
PP-LCNet 没有像学术界那样死扣极致的 FLOPs 与 Params，而是着眼于分析如何添加对 Intel CPU 友好的模块来提升模型的性能，这样可以更好的平衡准确率和推理时间，其中的实验结论也很适合其他网络结构设计的研究者，同时也为 NAS 搜索研究者提供了更小的搜索空间和一般结论。最终的 PP-LCNet 在产业界也可以更好的落地和应用。
## 5. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) | FP32<br/>Batch Size=1\4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| ------------- | --------- | ----------------- | ---------------------------- | -------------------------------- | ------------------------------ |
| PPLCNet_x0_25 | 224 | 256 | 0.72 | 1.17 | 1.71 |
| PPLCNet_x0_35 | 224 | 256 | 0.69 | 1.21 | 1.82 |
| PPLCNet_x0_5 | 224 | 256 | 0.70 | 1.32 | 1.94 |
| PPLCNet_x0_75 | 224 | 256 | 0.71 | 1.49 | 2.19 |
| PPLCNet_x1_0 | 224 | 256 | 0.73 | 1.64 | 2.53 |
| PPLCNet_x1_5 | 224 | 256 | 0.82 | 2.06 | 3.12 |
| PPLCNet_x2_0 | 224 | 256 | 0.94 | 2.58 | 4.08 |
<a name="6"></a>
## 6. 引用
## 6. 总结
PP-LCNet 没有像学术界那样死扣极致的 FLOPs 与 Params，而是着眼于分析如何添加对 Intel CPU 友好的模块来提升模型的性能，这样可以更好的平衡准确率和推理时间，其中的实验结论也很适合其他网络结构设计的研究者，同时也为 NAS 搜索研究者提供了更小的搜索空间和一般结论。最终的 PP-LCNet 在产业界也可以更好的落地和应用。
<a name="7"></a>
## 7. 引用
如果你的论文用到了 PP-LCNet 的方法，请添加如下 cite：
```
......
docs/zh_CN/models/ReXNet.md
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......@@ -4,6 +4,7 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 V100 GPU 的预测速度](#3)
<a name='1'></a>
......@@ -24,4 +25,16 @@ ReXNet 是 NAVER 集团 ClovaAI 研发中心基于一种网络架构设计新范
| ReXNet_2_0 | 81.22 | 95.36 | 81.6 | 1.561 | 16.449 |
| ReXNet_3_0 | 82.09 | 96.12 | 82.8 | 3.445 | 34.833 |
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| ---------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| ReXNet_1_0 | 224 | 256 | 3.08 | 4.15 | 5.49 |
| ReXNet_1_3 | 224 | 256 | 3.54 | 4.87 | 6.54 |
| ReXNet_1_5 | 224 | 256 | 3.68 | 5.31 | 7.38 |
| ReXNet_2_0 | 224 | 256 | 4.30 | 6.54 | 9.19 |
| ReXNet_3_0 | 224 | 256 | 5.74 | 9.49 | 13.62 |
关于 Inference speed 等信息，敬请期待。
docs/zh_CN/models/RedNet.md
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......@@ -4,6 +4,7 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 V100 GPU 的预测速度](#3)
<a name='1'></a>
## 1. 概述
......@@ -19,4 +20,16 @@
| RedNet38 | 12.4 | 2.2 | 77.47 | 93.56 |
| RedNet50 | 15.5 | 2.7 | 78.33 | 94.17 |
| RedNet101 | 25.7 | 4.7 | 78.94 | 94.36 |
| RedNet152 | 34.0 | 6.8 | 79.17 | 94.40 |
\ No newline at end of file
| RedNet152 | 34.0 | 6.8 | 79.17 | 94.40 |
<a name='3'></a>
## 3. 基于 V100 GPU 的预测速度
| 模型 | Crop Size | Resize Short Size | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 |
| --------- | --------- | ----------------- | ---------------- | ---------------- | ----------------- |
| RedNet26 | 224 | 256 | 4.45 | 15.16 | 29.03 |
| RedNet38 | 224 | 256 | 6.24 | 21.39 | 41.26 |
| RedNet50 | 224 | 256 | 8.04 | 27.71 | 53.73 |
| RedNet101 | 224 | 256 | 13.07 | 44.12 | 83.28 |
| RedNet152 | 224 | 256 | 18.66 | 63.27 | 119.48 |
\ No newline at end of file
docs/zh_CN/models/ResNeSt_RegNet.md
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......@@ -4,7 +4,8 @@
* [1. 概述](#1)
* [2. 精度、FLOPS 和参数量](#2)
* [3. 基于 T4 GPU 的预测速度](#3)
* [3. 基于 V100 GPU 的预测速度](#3)
* [4. 基于 T4 GPU 的预测速度](#4)
<a name='1'></a>
......@@ -26,7 +27,17 @@ RegNet 是由 facebook 于 2020 年提出，旨在深化设计空间理念的概
<a name='3'></a>
## 3. 基于 T4 GPU 的预测速度
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
| ---------------------- | --------- | ----------------- | ------------------------------ | ------------------------------ | ------------------------------ |
| ResNeSt50_fast_1s1x64d | 224 | 256 | 2.73 | 5.33 | 8.24 |
| ResNeSt50 | 224 | 256 | 7.36 | 10.23 | 13.84 |
| RegNetX_4GF | 224 | 256 | 6.46 | 8.48 | 11.45 |
<a name='4'></a>
## 4. 基于 T4 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP16<br>Batch Size=1<br>(ms) | FP16<br>Batch Size=4<br>(ms) | FP16<br>Batch Size=8<br>(ms) | FP32<br>Batch Size=1<br>(ms) | FP32<br>Batch Size=4<br>(ms) | FP32<br>Batch Size=8<br>(ms) |
|--------------------|-----------|-------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|------------------------------|
......
docs/zh_CN/models/ResNet_and_vd.md
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......@@ -63,24 +63,24 @@ ResNet 系列模型是在 2015 年提出的，一举在 ILSVRC2015 比赛中取
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|------------------|-----------|-------------------|--------------------------|
| ResNet18 | 224 | 256 | 1.499 |
| ResNet18_vd | 224 | 256 | 1.603 |
| ResNet34 | 224 | 256 | 2.272 |
| ResNet34_vd | 224 | 256 | 2.343 |
| ResNet34_vd_ssld | 224 | 256 | 2.343 |
| ResNet50 | 224 | 256 | 2.939 |
| ResNet50_vc | 224 | 256 | 3.041 |
| ResNet50_vd | 224 | 256 | 3.165 |
| ResNet50_vd_v2 | 224 | 256 | 3.165 |
| ResNet101 | 224 | 256 | 5.314 |
| ResNet101_vd | 224 | 256 | 5.252 |
| ResNet152 | 224 | 256 | 7.205 |
| ResNet152_vd | 224 | 256 | 7.200 |
| ResNet200_vd | 224 | 256 | 8.885 |
| ResNet50_vd_ssld | 224 | 256 | 3.165 |
| ResNet101_vd_ssld | 224 | 256 | 5.252 |
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) | FP32<br/>Batch Size=1\4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|------------------|-----------|-------------------|--------------------------|--------------------------|--------------------------|
| ResNet18 | 224 | 256 | 1.22 | 2.19 | 3.63 |
| ResNet18_vd | 224 | 256 | 1.26 | 2.28 | 3.89 |
| ResNet34 | 224 | 256 | 1.97 | 3.25 | 5.70 |
| ResNet34_vd | 224 | 256 | 2.00 | 3.28 | 5.84 |
| ResNet34_vd_ssld | 224 | 256 | 2.00 | 3.26 | 5.85 |
| ResNet50 | 224 | 256 | 2.54 | 4.79 | 7.40 |
| ResNet50_vc | 224 | 256 | 2.57 | 4.83 | 7.52 |
| ResNet50_vd | 224 | 256 | 2.60 | 4.86 | 7.63 |
| ResNet50_vd_v2 | 224 | 256 | 2.59 | 4.86 | 7.59 |
| ResNet101 | 224 | 256 | 4.37 | 8.18 | 12.38 |
| ResNet101_vd | 224 | 256 | 4.43 | 8.25 | 12.60 |
| ResNet152 | 224 | 256 | 6.05 | 11.41 | 17.33 |
| ResNet152_vd | 224 | 256 | 6.11 | 11.51 | 17.59 |
| ResNet200_vd | 224 | 256 | 7.70 | 14.57 | 22.16 |
| ResNet50_vd_ssld | 224 | 256 | 2.59 | 4.87 | 7.62 |
| ResNet101_vd_ssld | 224 | 256 | 4.43 | 8.25 | 12.58 |
<a name='4'></a>
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docs/zh_CN/models/SEResNext_and_Res2Net.md
浏览文件 @ a1ad2c89
......@@ -71,32 +71,35 @@ Res2Net 是 2019 年提出的一种全新的对 ResNet 的改进方案，该方
## 3. 基于 V100 GPU 的预测速度
| Models | Crop Size | Resize Short Size | FP32<br>Batch Size=1<br>(ms) |
|-----------------------|-----------|-------------------|--------------------------|
| Res2Net50_26w_4s | 224 | 256 | 4.148 |
| Res2Net50_vd_26w_4s | 224 | 256 | 4.172 |
| Res2Net50_14w_8s | 224 | 256 | 5.113 |
| Res2Net101_vd_26w_4s | 224 | 256 | 7.327 |
| Res2Net200_vd_26w_4s | 224 | 256 | 12.806 |
| ResNeXt50_32x4d | 224 | 256 | 10.964 |
| ResNeXt50_vd_32x4d | 224 | 256 | 7.566 |
| ResNeXt50_64x4d | 224 | 256 | 13.905 |
| ResNeXt50_vd_64x4d | 224 | 256 | 14.321 |
| ResNeXt101_32x4d | 224 | 256 | 14.915 |
| ResNeXt101_vd_32x4d | 224 | 256 | 14.885 |
| ResNeXt101_64x4d | 224 | 256 | 28.716 |
| ResNeXt101_vd_64x4d | 224 | 256 | 28.398 |
| ResNeXt152_32x4d | 224 | 256 | 22.996 |
| ResNeXt152_vd_32x4d | 224 | 256 | 22.729 |
| ResNeXt152_64x4d | 224 | 256 | 46.705 |
| ResNeXt152_vd_64x4d | 224 | 256 | 46.395 |
| SE_ResNet18_vd | 224 | 256 | 1.694 |
| SE_ResNet34_vd | 224 | 256 | 2.786 |
| SE_ResNet50_vd | 224 | 256 | 3.749 |
| SE_ResNeXt50_32x4d | 224 | 256 | 8.924 |
| SE_ResNeXt50_vd_32x4d | 224 | 256 | 9.011 |
| SE_ResNeXt101_32x4d | 224 | 256 | 19.204 |
| SENet154_vd | 224 | 256 | 50.406 |
| Models | Crop Size | Resize Short Size | FP32<br/>Batch Size=1<br/>(ms) | FP32<br/>Batch Size=4<br/>(ms) | FP32<br/>Batch Size=8<br/>(ms) |
|-----------------------|-----------|-------------------|-----------------------|-----------------------|-----------------------|
| Res2Net50_26w_4s | 224 | 256 | 3.52 | 6.23 | 9.30 |
| Res2Net50_vd_26w_4s | 224 | 256 | 3.59 | 6.35 | 9.50 |
| Res2Net50_14w_8s | 224 | 256 | 4.39 | 7.21 | 10.38 |
| Res2Net101_vd_26w_4s | 224 | 256 | 6.34 | 11.02 | 16.13 |
| Res2Net200_vd_26w_4s | 224 | 256 | 11.45 | 19.77 | 28.81 |
| ResNeXt50_32x4d | 224 | 256 | 5.07 | 8.49 | 12.02 |
| ResNeXt50_vd_32x4d | 224 | 256 | 5.29 | 8.68 | 12.33 |
| ResNeXt50_64x4d | 224 | 256 | 9.39 | 13.97 | 20.56 |
| ResNeXt50_vd_64x4d | 224 | 256 | 9.75 | 14.14 | 20.84 |
| ResNeXt101_32x4d | 224 | 256 | 11.34 | 16.78 | 22.80 |
| ResNeXt101_vd_32x4d | 224 | 256 | 11.36 | 17.01 | 23.07 |
| ResNeXt101_64x4d | 224 | 256 | 21.57 | 28.08 | 39.49 |
| ResNeXt101_vd_64x4d | 224 | 256 | 21.57 | 28.22 | 39.70 |
| ResNeXt152_32x4d | 224 | 256 | 17.14 | 25.11 | 33.79 |
| ResNeXt152_vd_32x4d | 224 | 256 | 16.99 | 25.29 | 33.85 |
| ResNeXt152_64x4d | 224 | 256 | 33.07 | 42.05 | 59.13 |
| ResNeXt152_vd_64x4d | 224 | 256 | 33.30 | 42.41 | 59.42 |
| SE_ResNet18_vd | 224 | 256 | 1.48 | 2.70 | 4.32 |
| SE_ResNet34_vd | 224 | 256 | 2.42 | 3.69 | 6.29 |
| SE_ResNet50_vd | 224 | 256 | 3.11 | 5.99 | 9.34 |
| SE_ResNeXt50_32x4d | 224 | 256 | 6.39 | 11.01 | 14.94 |
| SE_ResNeXt50_vd_32x4d | 224 | 256 | 7.04 | 11.57 | 16.01 |
| SE_ResNeXt101_32x4d | 224 | 256 | 13.31 | 21.85 | 28.77 |
| SENet154_vd | 224 | 256 | 34.83 | 51.22 | 69.74 |
| Res2Net50_vd_26w_4s_ssld | 224 | 256 | 3.58 | 6.35 | 9.52 |
| Res2Net101_vd_26w_4s_ssld | 224 | 256 | 6.33 | 11.02 | 16.11 |
| Res2Net200_vd_26w_4s_ssld | 224 | 256 | 11.47 | 19.75 | 28.83 |
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