Update README

JDAI-CV · May 2, 2019 · 0c18fdc · 0c18fdc
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diff --git a/README.md b/README.md
@@ -5,7 +5,7 @@
 
 *Enjoy binary neural networks on mobile!*
 
-[中文](README_CN.md)
+[English](README.md) [中文](README_CN.md)
 
 QQ Group (Chinese): 1021964010, answer: nndab
 
@@ -37,7 +37,7 @@ BM_bireal18_imagenet        61809506 ns   61056865 ns         10       <--- Bi-r
 BM_bireal18_imagenet_stem   43279353 ns   41533009 ns         14       <--- Bi-real Net 18 with stem module (The network structure will be described in detail in the coming paper), 56.4% top-1 on ImageNet
 ```
 
-Comparison on Google Pixel 1 with [Caffe](http://caffe.berkeleyvision.org) (full precision), [TensorFlow Lite](https://www.tensorflow.org/lite) (full precision) and [BMXNet](https://github.com/hpi-xnor/BMXNet) (binary). We surprisingly observe that BMXNet is even slower the full precision TensorFlow Lite. It suggests that the potential of binary neural networks is far from exploited before dabnn.
+The following is the comparison between our dabnn and [Caffe](http://caffe.berkeleyvision.org) (full precision), [TensorFlow Lite](https://www.tensorflow.org/lite) (full precision) and [BMXNet](https://github.com/hpi-xnor/BMXNet) (binary). We surprisingly observe that BMXNet is even slower the full precision TensorFlow Lite. It suggests that the potential of binary neural networks is far from exploited until our dabnn is published.
 
 ![Comparison](images/comparison.png)
 

diff --git a/README_CN.md b/README_CN.md
@@ -3,6 +3,8 @@
 [![License](https://img.shields.io/badge/license-BSD--3--Clause-blue.svg)](LICENSE) 
 [![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/JDAI-CV/dabnn/pulls)
 
+[English](README.md) [中文](README_CN.md)
+
 QQ 群：1021964010, 入群答案: nndab
 
 ## 简介
@@ -35,7 +37,7 @@ BM_bireal18_imagenet        61809506 ns   61056865 ns         10       <--- Bi-r
 BM_bireal18_imagenet_stem   43279353 ns   41533009 ns         14       <--- 带有 stem 模块的 Bi-real Net 18 (将在论文中描述), ImageNet top-1 为 56.4%
 ```
 
-在 Google Pixel 1 上与 [Caffe](http://caffe.berkeleyvision.org)（全精度）, [TensorFlow Lite](https://www.tensorflow.org/lite)（全精度）和 [BMXNet](https://github.com/hpi-xnor/BMXNet)（二值）的对比如下。我们很惊讶的发现现有的二值 inference 框架 BMXNet 甚至比全精度的 TensorFlow Lite 还要慢，这表明在 dabnn 之前，二值网络的潜力远远没有被挖掘出来。
+在 Google Pixel 1 上与 [Caffe](http://caffe.berkeleyvision.org)（全精度）, [TensorFlow Lite](https://www.tensorflow.org/lite)（全精度）和 [BMXNet](https://github.com/hpi-xnor/BMXNet)（二值）的对比如下。我们很惊讶的发现现有的二值 inference 框架 BMXNet 甚至比全精度的 TensorFlow Lite 还要慢，这表明，直到 dabnn 推出之前，二值网络的潜力都远远没有被挖掘出来。
 
 ![Comparison](images/comparison.png)