diff --git a/PaddleCV/image_classification/README.md b/PaddleCV/image_classification/README.md
index 7b469d1b982cdb6cfc59edfd07f85c4b01641eb3..9712b9cf0f7b791f007fd6f4acc4dffb38e8d11a 100644
--- a/PaddleCV/image_classification/README.md
+++ b/PaddleCV/image_classification/README.md
@@ -156,8 +156,7 @@ python infer.py \
 
 ## Supported models and performances
 
-Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table. Pretrained models can be downloaded by clicking related model names.
-
+Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table. Pretrained models can be downloaded by clicking related model names.Among them, ResNet50_vd_v2 is the distilled version of ResNet50_vd.
 - Released models: specify parameter names
 
 |model | top-1/top-5 accuracy(CV2) |
@@ -174,6 +173,7 @@ Available top-1/top-5 validation accuracy on ImageNet 2012 are listed in table.
 |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% |
 |[ResNet50_vc](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vc_pretrained.tar) |78.35%/94.03% |
 |[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% |
+|[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% |
 |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% |
 |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% |
diff --git a/PaddleCV/image_classification/README_cn.md b/PaddleCV/image_classification/README_cn.md
index 94781e5d6da1aee78fa6b960be5e5c78ade5c48e..25b47260d141339abace63de17258bf705ebcb66 100644
--- a/PaddleCV/image_classification/README_cn.md
+++ b/PaddleCV/image_classification/README_cn.md
@@ -143,7 +143,7 @@ python infer.py \
 
 ## 已有模型及其性能
 表格中列出了在```models```目录下支持的图像分类模型，并且给出了已完成训练的模型在ImageNet-2012验证集合上的top-1/top-5精度，
-可以通过点击相应模型的名称下载相应预训练模型。
+可以通过点击相应模型的名称下载相应预训练模型。其中ResNet50_vd_v2是ResNet50_vd的蒸馏版本。
 
 - Released models: specify parameter names
 
@@ -161,6 +161,7 @@ python infer.py \
 |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% |
 |[ResNet50_vc](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vc_pretrained.tar) |78.35%/94.03% |
 |[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% |
+|[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% |
 |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% |
 |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% |
 |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% |