mirrors
/
PaddleClas
mirror of https://github.com/PaddlePaddle/PaddleClas.git
1
0
Fork 0
Code Issues Projects Releases Wiki Activity

modify latency

pull/2610/head
zhangyubo0722 2023-01-10 12:30:37 +00:00 committed by cuicheng01
parent 1b0658e6a5
commit 24168fb25e
9 changed files with 48 additions and 45 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
docs/zh_CN/models/ImageNet1k/GhostNet.md
View File

@ -73,7 +73,7 @@ GhostNet 是华为于 2020 年提出的一种全新的轻量化网络结构，
| GhostNet_x0_5 | 224 | 256 | 1.10 | 1.42 | 1.47 |
| GhostNet_x1_0 | 224 | 256 | 1.07 | 1.71 | 2.25 |
| GhostNet_x1_3 | 224 | 256 | 1.28 | 2.04 | 2.66 |
| GhostNet_x1_3_ssld | 224 | 256 | 1.85 | 3.17 | 4.29 |
| GhostNet_x1_3_ssld | 224 | 256 | 1.28 | 2.04 | 2.66 |
**备注：** 精度类型为 FP32，推理过程使用 TensorRT。

4
docs/zh_CN/models/ImageNet1k/HRNet.md
View File

@ -67,13 +67,13 @@ HRNet 是 2019 年由微软亚洲研究院提出的一种全新的神经网络
| Models | Size | Latency(ms)<br>bs=1 | Latency(ms)<br>bs=4 | Latency(ms)<br>bs=8 |
|-------------|-----------|-------------------|-------------------|-------------------|-------------------|
| HRNet_W18_C | 224 | 6.33 | 8.12 | 10.91 |
| HRNet_W18_C_ssld | 224 | 6.66 | 8.92 | 11.93 |
| HRNet_W18_C_ssld | 224 | 6.33 | 8.12 | 10.91 |
| HRNet_W30_C | 224 | 8.34 | 10.65 | 13.95 |
| HRNet_W32_C | 224 | 8.03 | 10.46 | 14.11 |
| HRNet_W40_C | 224 | 9.64 | 14.27 | 19.54 |
| HRNet_W44_C | 224 | 10.54 | 15.41 | 24.50 |
| HRNet_W48_C | 224 | 10.81 | 15.67 | 15.53 |
| HRNet_W48_C_ssld | 224 | 11.09 | 17.04 | 27.28 |
| HRNet_W48_C_ssld | 224 | 10.81 | 15.67 | 15.53 |
| HRNet_W64_C | 224 | 13.12 | 19.49 | 33.80 |
**备注：** 精度类型为 FP32，推理过程使用 TensorRT。

3
docs/zh_CN/models/ImageNet1k/NextViT.md
View File

@ -61,6 +61,9 @@ NextViT 是一种新的视觉 Transformer 网络，可以用作计算机视觉
| NextViT_small_224 | 224 | 7.76 | 10.86 | 14.20 |
| NextViT_base_224 | 224 | 12.02 | 16.21 | 20.63 |
| NextViT_large_224 | 224 | 16.51 | 21.91 | 27.25 |
| NextViT_small_224_ssld | 224 | 7.76 | 10.86 | 14.20 |
| NextViT_base_224_ssld | 224 | 12.02 | 16.21 | 20.63 |
| NextViT_large_224_ssld | 224 | 16.51 | 21.91 | 27.25 |
**备注：** 精度类型为 FP32，推理过程使用 TensorRT。

24
docs/zh_CN/models/ImageNet1k/PP-HGNet.md
View File

@ -75,20 +75,20 @@ PP-HGNet 与其他模型的比较如下，其中测试机器为 NVIDIA® Tesla®
| Model | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
|:--: |:--: |:--: |:--: |
| ResNet34 | 74.57 | 92.14 | 1.97 |
| ResNet34_vd | 75.98 | 92.98 | 2.00 |
| EfficientNetB0 | 77.38 | 93.31 | 1.96 |
| <b>PPHGNet_tiny<b> | <b>79.83<b> | <b>95.04<b> | <b>1.77<b> |
| <b>PPHGNet_tiny_ssld<b> | <b>81.95<b> | <b>96.12<b> | <b>1.77<b> |
| ResNet50 | 76.50 | 93.00 | 2.54 |
| ResNet50_vd | 79.12 | 94.44 | 2.60 |
| ResNet34 | 74.57 | 92.14 | 1.83 |
| ResNet34_vd | 75.98 | 92.98 | 1.87 |
| EfficientNetB0 | 77.38 | 93.31 | 1.58 |
| <b>PPHGNet_tiny<b> | <b>79.83<b> | <b>95.04<b> | <b>1.72<b> |
| <b>PPHGNet_tiny_ssld<b> | <b>81.95<b> | <b>96.12<b> | <b>1.72<b> |
| ResNet50 | 76.50 | 93.00 | 2.19 |
| ResNet50_vd | 79.12 | 94.44 | 2.23 |
| ResNet50_rsb | 80.40 | | 2.54 |
| EfficientNetB1 | 79.15 | 94.41 | 2.88 |
| EfficientNetB1 | 79.15 | 94.41 | 2.29 |
| SwinTransformer_tiny | 81.2 | 95.5 | 6.59 |
| <b>PPHGNet_small<b> | <b>81.51<b>| <b>95.82<b> | <b>2.52<b> |
| <b>PPHGNet_small_ssld<b> | <b>83.82<b>| <b>96.81<b> | <b>2.52<b> |
| Res2Net200_vd_26w_4s_ssld| 85.13 | 97.42 | 11.45 |
| ResNeXt101_32x48d_wsl | 85.37 | 97.69 | 55.07 |
| <b>PPHGNet_small<b> | <b>81.51<b>| <b>95.82<b> | <b>2.46<b> |
| <b>PPHGNet_small_ssld<b> | <b>83.82<b>| <b>96.81<b> | <b>2.46<b> |
| Res2Net200_vd_26w_4s_ssld| 85.13 | 97.42 | 10.80 |
| ResNeXt101_32x48d_wsl | 85.37 | 97.69 | 69.81 |
| SwinTransformer_base | 85.2 | 97.5 | 13.53 |
| <b>PPHGNet_base_ssld<b> | <b>85.00<b>| <b>97.35<b> | <b>5.97<b> |

6
docs/zh_CN/models/ImageNet1k/PP-LCNet.md
View File

@ -129,9 +129,9 @@ BaseNet 经过以上四个方面的改进，得到了 PP-LCNet。下表进一步
| PPLCNet_x1_5 | 4.5 | 342 | 73.71 | 91.53 | 0.54 | [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 | 6.5 | 590 | 75.18 | 92.27 | 0.64 | [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 | 9.0 | 906 | 76.60 | 93.00 | 0.71 | [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) |
| PPLCNet_x0_5_ssld | 1.9 | 47 | 66.10 | 86.46 | 2.05 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_ssld_infer.tar) |
| PPLCNet_x1_0_ssld | 3.0 | 161 | 74.39 | 92.09 | 2.46 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_ssld_infer.tar) |
| PPLCNet_x2_5_ssld | 9.0 | 906 | 80.82 | 95.33 | 5.39 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_ssld_infer.tar) |
| PPLCNet_x0_5_ssld | 1.9 | 47 | 66.10 | 86.46 | 0.44 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_ssld_infer.tar) |
| PPLCNet_x1_0_ssld | 3.0 | 161 | 74.39 | 92.09 | 0.47 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_ssld_infer.tar) |
| PPLCNet_x2_5_ssld | 9.0 | 906 | 80.82 | 95.33 | 0.71 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_ssld_infer.tar) |
其中 `_ssld` 表示使用 `SSLD 蒸馏`后的模型。关于 `SSLD蒸馏` 的内容，详情 [SSLD 蒸馏](../../training/advanced/knowledge_distillation.md)。

6
docs/zh_CN/models/ImageNet1k/PP-LCNetV2.md
View File

@ -98,8 +98,8 @@ PPLCNetV2 目前提供的模型的精度、速度指标及预训练权重链接
| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) | 预训练模型下载地址 | inference模型下载地址 |
|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
| <b>PPLCNetV2_base<b> | <b>6.6<b> | <b>604<b> | <b>77.04<b> | <b>93.27<b> | <b>4.32<b> | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |
| <b>PPLCNetV2_base_ssld<b> | <b>6.6<b> | <b>604<b> | <b>80.07<b> | <b>94.87<b> | <b>4.32<b> | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_ssld_infer.tar) |
| <b>PPLCNetV2_base<b> | <b>6.6<b> | <b>604<b> | <b>77.04<b> | <b>93.27<b> | <b>0.68<b> | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar) |
| <b>PPLCNetV2_base_ssld<b> | <b>6.6<b> | <b>604<b> | <b>80.07<b> | <b>94.87<b> | <b>0.68<b> | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_ssld_infer.tar) |
**备注：**
@ -113,7 +113,7 @@ PPLCNetV2 目前提供的模型的精度、速度指标及预训练权重链接
| MobileNetV3_Large_x1_25 | 7.4 | 714 | 76.4 | 93.00 | 5.19 |
| PPLCNetV1_x2_5 | 9 | 906 | 76.60 | 93.00 | 7.25 |
| <b>PPLCNetV2_base<b> | <b>6.6<b> | <b>604<b> | <b>77.04<b> | <b>93.27<b> | <b>0.68<b> |
| <b>PPLCNetV2_base_ssld<b> | <b>6.6<b> | <b>604<b> | <b>80.07<b> | <b>94.87<b> | <b>4.32<b> |
| <b>PPLCNetV2_base_ssld<b> | <b>6.6<b> | <b>604<b> | <b>80.07<b> | <b>94.87<b> | <b>0.68<b> |
<a name="2"></a>

38
docs/zh_CN/models/ImageNet1k/README.md
View File

@ -89,17 +89,17 @@
| 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 | 3.28 | 5.84 | 3.93 | 21.84 | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar)&emsp;&emsp;</span> |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 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_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 | 12.60 | 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) |
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 1.87 | 2.49 | 4.41 | 3.93 | 21.84 | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams)&emsp;&emsp;</span> | <span style="white-space:nowrap;">[下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet34_vd_ssld_infer.tar)&emsp;&emsp;</span> |
| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 2.23 | 3.92 | 6.46 | 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 | 4.04 | 6.84 | 11.44 | 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 | 3.59 | 6.35 | 9.50 | 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 | 5.96 | 10.56 | 15.20 | 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 | 10.79 | 19.48 | 27.95 | 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 | 8.94 | 11.95 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_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 | 11.07 | 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_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 6.33 | 8.12 | 10.91 | 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 | 10.81 | 15.67 | 25.53 | 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) |
| PPHGNet_tiny_ssld | 0.8195 | 0.7983 | 0.021 | 1.77 | - | - | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
| PPHGNet_small_ssld | 0.8382 | 0.8151 | 0.023 | 2.52 | - | - | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
| PPHGNet_tiny_ssld | 0.8195 | 0.7983 | 0.021 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
| PPHGNet_small_ssld | 0.8382 | 0.8151 | 0.023 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
<a name="SSLD_mobile"></a>
@ -143,9 +143,9 @@ PP-HGNet 系列模型的精度、速度指标如下表所示，更多关于该
| 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 |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| PPHGNet_tiny | 0.7983 | 0.9504 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar) |
| PPHGNet_tiny_ssld | 0.8195 | 0.9612 | 1.77 | - | - | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
| PPHGNet_tiny_ssld | 0.8195 | 0.9612 | 1.72 | 3.40 | 5.29 | 4.54 | 14.75 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_ssld_infer.tar) |
| PPHGNet_small | 0.8151 | 0.9582 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar) |
| PPHGNet_small_ssld | 0.8382 | 0.9681 | 2.52 | - | - | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
| PPHGNet_small_ssld | 0.8382 | 0.9681 | 2.46 | 5.12 | 8.77 | 8.53 | 24.38 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_ssld_infer.tar) |
| PPHGNet_base_ssld | 0.8500 | 0.9735 | 5.97 | - | - | 25.14 | 71.62 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_base_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_base_ssld_infer.tar) |
<a name="ResNet"></a>
@ -160,7 +160,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
| ResNet18_vd | 0.7226 | 0.9080 | 1.11 | 1.52 | 2.60 | 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 | 1.83 | 2.41 | 4.23 | 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 | 1.87 | 2.49 | 4.41 | 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 | 3.28 | 5.84 | 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) |
| ResNet34_vd_ssld | 0.7972 | 0.9490 | 1.87 | 2.49 | 4.41 | 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 | 2.19 | 3.77 | 6.22 | 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 | 2.23 | 3.92 | 6.46 | 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) |
@ -169,8 +169,8 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
| ResNet152 | 0.7826 | 0.9396 | 5.71 | 9.58 | 16.16 | 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 | 5.76 | 9.75 | 16.40 | 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 | 7.32 | 12.45 | 21.10 | 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 | 2.60 | 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_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 | 12.60 | 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) |
| ResNet50_vd_<br>ssld | 0.8300 | 0.9640 | 2.23 | 3.92 | 6.46 | 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 | 4.04 | 6.84 | 11.44 | 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="ResNeXt"></a>
@ -207,7 +207,7 @@ Res2Net 系列模型的精度、速度指标如下表所示，更多关于该系
| Res2Net50_<br>14w_8s | 0.7946 | 0.9470 | 4.13 | 6.56 | 9.45 | 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 | 5.96 | 10.56 | 15.20 | 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 | 10.79 | 19.48 | 27.95 | 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 | 11.45 | 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_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/Res2Net200_vd_26w_4s_ssld_infer.tar) |
| Res2Net200_vd_<br>26w_4s_ssld | 0.8513 | 0.9742 | 10.79 | 19.48 | 27.95 | 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) |
<a name="SENet"></a>
@ -262,13 +262,13 @@ HRNet 系列模型的精度、速度指标如下表所示，更多关于该系
| 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 | 6.33 | 8.12 | 10.91 | 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 | 8.94 | 11.95 | 4.32 | 21.35 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W18_C_ssld_infer.tar) |
| HRNet_W18_C_ssld | 0.81162 | 0.95804 | 6.33 | 8.12 | 10.91 | 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 | 8.34 | 10.65 | 13.95 | 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 | 8.03 | 10.46 | 14.11 | 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 | 9.64 | 14.27 | 19.54 | 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 | 10.54 | 15.41 | 24.50 | 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 | 10.81 | 15.67 | 25.53 | 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 | 11.07 | 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_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/HRNet_W48_C_ssld_infer.tar) |
| HRNet_W48_C_ssld | 0.8363 | 0.9682 | 10.81 | 15.67 |25.53 | 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 | 13.12 | 19.49 | 33.80 | 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) |
@ -427,7 +427,7 @@ RegNet 系列模型的精度、速度指标如下表所示，更多关于该系
| 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 | 4.15 | 6.81 | 11.60 | 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 | 6.40 | 16.8 | 33.51 | 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) |
| DLA102x2 |0.7885 | 0.9445 | 6.40 | 16.80 | 33.51 | 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 | 4.68 | 16.44 | 20.98 | 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 | 6.45 | 10.79 | 18.31 | 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 | 1.67 | 2.49 | 4.31 | 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) |
@ -743,9 +743,9 @@ DeiT（Data-efficient Image Transformers）系列模型的精度、速度指标
| NextViT_small_384 | 0.8401 | 0.9698 | - | - | - | 17.00 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_384_infer.tar) |
| NextViT_base_384 | 0.8465 | 0.9723 | - | - | - | 24.27 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_384_infer.tar) |
| NextViT_large_384 | 0.8492 | 0.9728 | - | - | - | 31.53 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_384_infer.tar) |
| NextViT_small_224_ssld | 0.8472 | 0.9734 | - | - | - | 5.79 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_224_ssld_infer.tar) |
| NextViT_base_224_ssld | 0.8500 | 0.9753 | - | - | - | 8.26 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_224_ssld_infer.tar) |
| NextViT_large_224_ssld | 0.8536 | 0.9762 | - | - | - | 10.73 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_224_ssld_infer.tar) |
| NextViT_small_224_ssld | 0.8472 | 0.9734 | 7.76 | 10.86 | 14.20 | 5.79 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_224_ssld_infer.tar) |
| NextViT_base_224_ssld | 0.8500 | 0.9753 | 12.01 | 16.21 | 20.63 | 8.26 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_224_ssld_infer.tar) |
| NextViT_large_224_ssld | 0.8536 | 0.9762 | 16.51 | 21.91 | 27.25 | 10.73 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_224_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_224_ssld_infer.tar) |
| NextViT_small_384_ssld | 0.8597 | 0.9790 | - | - | - | 17.00 | 31.80 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_small_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_small_384_ssld_infer.tar) |
| NextViT_base_384_ssld | 0.8634 | 0.9806 | - | - | - | 24.27 | 44.88 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_base_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_base_384_ssld_infer.tar) |
| NextViT_large_384_ssld | 0.8654 | 0.9814 | - | - | - | 31.53 | 57.95 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/NextViT_large_384_ssld_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/NextViT_large_384_ssld_infer.tar) |

4
docs/zh_CN/models/ImageNet1k/Res2Net.md
View File

@ -69,8 +69,8 @@ Res2Net 是 2019 年提出的一种全新的对 ResNet 的改进方案，该方
| Res2Net50_14w_8s | 224 | 4.13 | 6.56 | 9.45 |
| Res2Net101_vd_26w_4s | 224 | 5.96 | 10.56 | 15.20 |
| Res2Net200_vd_26w_4s | 224 | 10.80 | 19.48 | 27.95 |
| Res2Net50_vd_26w_4s_ssld | 224 | 3.58 | 6.35 | 9.52 |
| Res2Net101_vd_26w_4s_ssld | 224 | 9.56 | 10.56 | 15.20 |
| Res2Net50_vd_26w_4s_ssld | 224 | 3.35 | 5.79 | 8.63 |
| Res2Net101_vd_26w_4s_ssld | 224 | 5.96 | 10.56 | 15.20 |
| Res2Net200_vd_26w_4s_ssld | 224 | 10.80 | 19.48 | 27.95 |
**备注：** 精度类型为 FP32，推理过程使用 TensorRT。

6
docs/zh_CN/models/ImageNet1k/ResNet.md
View File

@ -87,7 +87,7 @@ PaddleClas 提供的 ResNet 系列的模型包括 ResNet50，ResNet50_vd，ResNe
| ResNet18_vd | 224 | 1.11 | 1.52 | 2.60 |
| ResNet34 | 224 | 1.83 | 2.41 | 4.23 |
| ResNet34_vd | 224 | 1.87 | 2.49 | 4.41 |
| ResNet34_vd_ssld | 224 | 2.00 | 3.26 | 5.85 |
| ResNet34_vd_ssld | 224 | 1.87 | 2.49 | 4.41 |
| ResNet50 | 224 | 2.19 | 3.77 | 6.22 |
| ResNet50_vc | 224 | 2.57 | 4.83 | 7.52 |
| ResNet50_vd | 224 | 2.23 | 3.92 | 6.46 |
@ -99,8 +99,8 @@ PaddleClas 提供的 ResNet 系列的模型包括 ResNet50，ResNet50_vd，ResNe
| SE_ResNet18_vd | 224 | 1.31 | 1.77 | 2.92 |
| SE_ResNet34_vd | 224 | 2.20 | 2.99 | 5.09 |
| SE_ResNet50_vd | 224 | 2.72 | 5.07 | 8.12 |
| ResNet50_vd_ssld | 224 | 2.59 | 4.87 | 7.62 |
| ResNet101_vd_ssld | 224 | 4.43 | 8.25 | 12.58 |
| ResNet50_vd_ssld | 224 | 2.23 | 3.92 | 6.46 |
| ResNet101_vd_ssld | 224 | 4.04 | 6.84 | 11.44 |
**备注：** 精度类型为 FP32，推理过程使用 TensorRT。