diff --git a/example/mobilenetv2/Readme.md b/example/mobilenetv2/Readme.md
index 420ce03279d61977fbdaf6708914e02bf6908a16..d3dedb3cb846d9eabb3a8e5ca468f62e89f9acf7 100644
--- a/example/mobilenetv2/Readme.md
+++ b/example/mobilenetv2/Readme.md
@@ -1,16 +1,11 @@
 # MobileNetV2 Description
 
+MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state of the art for mobile visual recognition including classification, object detection and semantic segmentation.
 
-MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
+MobileNetV2 builds upon the ideas from MobileNetV1, using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks1.
 
 [Paper](https://arxiv.org/pdf/1801.04381) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
 
-# Model architecture
-
-The overall network architecture of MobileNetV2 is show below:
-
-[Link](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html)
-
 # Dataset
 
 Dataset used: imagenet
@@ -21,10 +16,6 @@ Dataset used: imagenet
 - Data format: RGB images.
 	- Note: Data will be processed in src/dataset.py 
 
-
-# Features
-
-
 # Environment Requirements
 
 - Hardware（Ascend/GPU）
diff --git a/example/mobilenetv2_quant/Readme.md b/example/mobilenetv2_quant/Readme.md
index 351fc80d761ea3696eb9ed4e0ab331e105f4d032..01cc7e360f6ed12f6adadada24f1b4e9f3b88689 100644
--- a/example/mobilenetv2_quant/Readme.md
+++ b/example/mobilenetv2_quant/Readme.md
@@ -1,15 +1,10 @@
 # MobileNetV2 Description
 
+MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state of the art for mobile visual recognition including classification, object detection and semantic segmentation.
 
-MobileNetV2 is tuned to mobile phone CPUs through a combination of hardware- aware network architecture search (NAS) complemented by the NetAdapt algorithm and then subsequently improved through novel architecture advances.Nov 20, 2019.
+MobileNetV2 builds upon the ideas from MobileNetV1, using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks1.
 
-[Paper](https://arxiv.org/pdf/1905.02244) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
-
-# Model architecture
-
-The overall network architecture of MobileNetV2 is show below:
-
-[Link](https://ai.googleblog.com/2018/04/mobilenetv2-next-generation-of-on.html)
+[Paper](https://arxiv.org/pdf/1801.04381) Howard, Andrew, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang et al. "Searching for MobileNetV2." In Proceedings of the IEEE International Conference on Computer Vision, pp. 1314-1324. 2019.
 
 # Dataset
 
@@ -22,9 +17,6 @@ Dataset used: imagenet
 	- Note: Data will be processed in src/dataset.py 
 
 
-# Features
-
-
 # Environment Requirements
 
 - Hardware（Ascend)