docs: fix

docs/zh_CN/models/ImageNet1k/VGG.md

@@ -61,10 +61,10 @@ VGG 由牛津大学计算机视觉组和 DeepMind 公司研究员一起研发的
 
 | Models            | Size | Latency(ms)<br>FP16<br>bs=1 | Latency(ms)<br>FP16<br>bs=4 | Latency(ms)<br>FP16<br>bs=8 | Latency(ms)<br>FP32<br>bs=1 | Latency(ms)<br>FP32<br>bs=4 | Latency(ms)<br>FP32<br>bs=8 |
 |:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
-| VGG11                 | 224       | 256               | 2.24408                      | 4.67794                      | 7.6568                       | 3.90412                      | 9.51147                      | 17.14168                     |
-| VGG13                 | 224       | 256               | 2.58589                      | 5.82708                      | 10.03591                     | 4.64684                      | 12.61558                     | 23.70015                     |
-| VGG16                 | 224       | 256               | 3.13237                      | 7.19257                      | 12.50913                     | 5.61769                      | 16.40064                     | 32.03939                     |
-| VGG19                 | 224       | 256               | 3.69987                      | 8.59168                      | 15.07866                     | 6.65221                      | 20.4334                      | 41.55902                     |
+| VGG11    | 224      | 2.24408      | 4.67794      | 7.6568         | 3.90412      | 9.51147     | 17.14168     |
+| VGG13    | 224      | 2.58589      | 5.82708      | 10.03591       | 4.64684      | 12.61558    | 23.70015     |
+| VGG16    | 224      | 3.13237      | 7.19257      | 12.50913       | 5.61769      | 16.40064    | 32.03939     |
+| VGG19    | 224      | 3.69987      | 8.59168      | 15.07866       | 6.65221      | 20.4334     | 41.55902     |
 
 **备注：** 推理过程使用 TensorRT。
 

docs/zh_CN/models/ImageNet1k/model_list.md

@@ -35,6 +35,7 @@
     - [VAN](#VAN)
     - [PeleeNet](#PeleeNet)
     - [CSPNet](#CSPNet)
+    - [VGG](#VGG)
     - [其他模型](#Others)
   - [3.2 轻量级模型](#CNN_lite)
     - [移动端系列](#Mobile)
@@ -470,21 +471,31 @@ RegNet 系列模型的精度、速度指标如下表所示，更多关于该系
 | ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
 | CSPDarkNet53 | 0.7725 | 0.9355  | - | - | - | 5.041 | 27.678 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSPDarkNet53_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/CSPDarkNet53_infer.tar) |
 
+
+<a name="VGG"></a>
+
+## VGG 系列 <sup>[[20](#ref20)]</sup>
+
+关于 VGG 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[VGG 系列模型文档](VGG.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 |
+|------------------------|-----------|-----------|------------------|------------------|----------|-----------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------|
+| 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) |
+
 <a name="Others"></a>
 
 ## 其他模型
 
-关于 AlexNet <sup>[[18](#ref18)]</sup>、SqueezeNet 系列 <sup>[[19](#ref19)]</sup>、VGG 系列 <sup>[[20](#ref20)]</sup>、DarkNet53 <sup>[[21](#ref21)]</sup> 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](Others.md)。
+关于 AlexNet <sup>[[18](#ref18)]</sup>、SqueezeNet 系列 <sup>[[19](#ref19)]</sup>、DarkNet53 <sup>[[21](#ref21)]</sup> 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](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) | 预训练模型下载地址 | 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) |
 
 <a name="CNN_lite"></a>