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docs/zh_CN/algorithm_introduction/ImageNet_models.md
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...@@ -62,9 +62,9 @@ ...@@ -62,9 +62,9 @@
### 2.1 服务器端知识蒸馏模型 ### 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------| |---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|
| 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) | | ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.00 | 3.26 | 5.85 | 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.tar)&emsp;&emsp;</span> |
| 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) | | 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) | | 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) | | 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) |
...@@ -78,7 +78,7 @@ ...@@ -78,7 +78,7 @@
### 2.2 移动端知识蒸馏模型 ### 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, 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 | | 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) | <span style="white-space:nowrap;">模型大小(M)</span> | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------| |---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|-----------------------------------|-----------------------------------|-----------------------------------|
| 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) | | 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) | | 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) |
...@@ -91,11 +91,11 @@ ...@@ -91,11 +91,11 @@
### 2.3 Intel CPU 端知识蒸馏模型 ### 2.3 Intel CPU 端知识蒸馏模型
| 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 下载地址 | | 模型 | Top-1 Acc | Reference<br>Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------| |---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------|-----------------------------------|
| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47.28 | 1.89 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) | | PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47.28 | 1.89 | [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 | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) | | PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 160.81 | 2.96 | [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 | 0.808 | 0.766 | 0.042 | 5.39 | 906.49 | 9.04 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) | | PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906.49 | 9.04 | [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) |
...@@ -108,7 +108,7 @@ ...@@ -108,7 +108,7 @@
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)。 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) | 预训练模型下载地址 | inference模型下载地址 | | 模型 | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|:--:|:--:|:--:|:--:|----|----|----|:--:| |:--:|:--:|:--:|:--:|----|----|----|:--:|
| 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_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_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) |
...@@ -125,7 +125,7 @@ PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该 ...@@ -125,7 +125,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------| |---------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------|
| 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 | 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) | | 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) |
...@@ -149,7 +149,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关 ...@@ -149,7 +149,7 @@ 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)。 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, 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 | | 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) | <span style="white-space:nowrap;">模型大小(M)</span> | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------| |----------------------------------|-----------|-----------|------------------------|----------|-----------|---------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------|
| 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_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_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) |
...@@ -174,8 +174,8 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关 ...@@ -174,8 +174,8 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关
| 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_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 | 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>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_<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) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_ssld_infer.tar) |
| 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) | | | 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) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_small_x1_0_ssld_infer.tar) |
| 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 | 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_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_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) |
...@@ -199,7 +199,7 @@ ResNet 及其 Vd 系列模型的精度、速度指标如下表所示，更多关 ...@@ -199,7 +199,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| |---------------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| 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_<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_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) |
...@@ -234,7 +234,7 @@ SEResNeXt 与 Res2Net 系列模型的精度、速度指标如下表所示，更 ...@@ -234,7 +234,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------| |-------------|-----------|-----------|-----------------------|----------------------|----------|-----------|--------------------------------------------------------------------------------------|-------------|-------------|
| 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) | | 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) | | 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) |
...@@ -256,7 +256,7 @@ DPN 与 DenseNet 系列模型的精度、速度指标如下表所示，更多关 ...@@ -256,7 +256,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------| |-------------|-----------|-----------|------------------|------------------|----------|-----------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|--------------------------------------------------------------------------------------|
| 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 | 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_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) |
...@@ -275,7 +275,7 @@ HRNet 系列模型的精度、速度指标如下表所示，更多关于该系 ...@@ -275,7 +275,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------| |--------------------|-----------|-----------|-----------------------|----------------------|----------|-----------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------|
| 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) | | 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 | 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) |
...@@ -293,7 +293,7 @@ Inception 系列模型的精度、速度指标如下表所示，更多关于该 ...@@ -293,7 +293,7 @@ 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)。 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 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预训练模型下载地址 | inference模型 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------| |---------------------------|-----------|-----------|------------------|------------------|----------|-----------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|----------------------------------------------------------------------------------------------------|
| 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>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>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) |
...@@ -317,7 +317,7 @@ EfficientNet 与 ResNeXt101_wsl 系列模型的精度、速度指标如下表所 ...@@ -317,7 +317,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
| 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_<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) | | 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) |
...@@ -379,7 +379,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -379,7 +379,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
| -------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | | -------- | --------- | --------- | ---------------- | ---------------- | ----------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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_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_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) |
...@@ -391,7 +391,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -391,7 +391,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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_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_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) |
...@@ -457,7 +457,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -457,7 +457,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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) | | 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_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) |
...@@ -470,7 +470,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -470,7 +470,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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) | | 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) | | 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) |
...@@ -488,7 +488,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -488,7 +488,7 @@ 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)。 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 | 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 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ | | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
| 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) | | 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) | | 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) |
...@@ -515,7 +515,7 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模 ...@@ -515,7 +515,7 @@ 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)。 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 | time(ms)<br/>bs=8 | FLOPs(G) | Params(M) | 预备训练下载地址 | inference模型下载地址 | | 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) | <span style="white-space:nowrap;">预训练模型<br/>下载地址&emsp;</span> | <span style="white-space:nowrap;">inference模型<br/>下载地址&emsp;</span> |
|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------| |------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
| 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) | | 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_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) |
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