diff --git a/model_zoo/official/cv/vgg16/README.md b/model_zoo/official/cv/vgg16/README.md
index d6c492865b9..27f534e01a0 100644
--- a/model_zoo/official/cv/vgg16/README.md
+++ b/model_zoo/official/cv/vgg16/README.md
@@ -79,6 +79,7 @@ here basic modules mainly include basic operation like:  **3×3 conv** and **2×
 ## Mixed Precision
 
 The [mixed precision](https://www.mindspore.cn/tutorial/zh-CN/master/advanced_use/mixed_precision.html) training method accelerates the deep learning neural network training process by using both the single-precision and half-precision data formats, and maintains the network precision achieved by the single-precision training at the same time. Mixed precision training can accelerate the computation process, reduce memory usage, and enable a larger model or batch size to be trained on specific hardware. 
+
 For FP16 operators, if the input data type is FP32, the backend of MindSpore will automatically handle it with reduced precision. Users could check the reduced-precision operators by enabling INFO log and then searching ‘reduce precision’.
 
 
@@ -370,4 +371,4 @@ after allreduce eval: top5_correct=45582, tot=50000, acc=91.16%
 In dataset.py, we set the seed inside “create_dataset" function. We also use random seed in train.py.
 
 # [ModelZoo Homepage](#contents)  
- Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).  
\ No newline at end of file
+ Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).