diff --git a/README.md b/README.md
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+++ b/README.md
@@ -1,12 +1,11 @@
-![image](https://github.com/HypoX64/DeepMosaics/blob/master/imgs/hand.gif)
-# DeepMosaics
+![image](./imgs/hand.gif)
+# <img src="./imgs/icon.jpg" width="48">DeepMosaics
 You can use it to automatically remove the mosaics in images and videos, or add mosaics to them.<br>
-This porject based on ‘semantic segmentation’ and ‘Image-to-Image Translation’.
-<br>
+This porject based on ‘semantic segmentation’ and ‘Image-to-Image Translation’.<br>
+* [中文版](./README_CN.md)<br>
 
 ## Notice
-The code do not include the part of training, I will finish it in my free time.
-<br>
+The code do not include the part of training, I will finish it in my free time.<br>
 
 ## Run DeepMosaics
 You can either run DeepMosaics via pre-built binary package or from source.<br>
@@ -15,10 +14,11 @@ You can either run DeepMosaics via pre-built binary package or from source.<br>
 For windows, we bulid a GUI version for easy test.<br>
 Download this version via [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>
 
-![image](https://github.com/HypoX64/DeepMosaics/blob/master/imgs/GUI.png)<br>
+![image](./imgs/GUI.png)<br>
 
 Attentions:<br>
 - Require Windows_x86_64, Windows10 is better.<br>
+- Different pre-trained models are suitable for different effects.<br>
 - Run time depends on computer performance.<br>
 - If output video cannot be played, you can try with [potplayer](https://daumpotplayer.com/download/).
 
@@ -55,4 +55,4 @@ If you want to test other image or video, please refer to this file.
 <br>
 
 ## Acknowledgments
-This code borrows heavily from [[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet).
+This code borrows heavily from [[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet)[[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD).
diff --git a/README_CN.md b/README_CN.md
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@@ -0,0 +1,56 @@
+![image](./imgs/hand.gif)
+# <img src="./imgs/icon.jpg" width="48">DeepMosaics
+这是一个通过深度学习自动的为图片/视频添加马赛克,或消除马赛克的项目.<br>它基于“语义分割”以及“图像翻译”.<br>
+
+## 一些说明
+代码暂不包含训练部分,训练方法我将在空闲时间给出.<br>
+现在,代码已经支持基于[pix2pixHD](https://github.com/NVIDIA/pix2pixHD)训练出的模型,但网络仍在训练中,这将使得输出结果看起来更加清晰,"真实".<br>
+
+## 如何运行
+可以通过我们预编译好的二进制包或源代码运行.<br>
+
+### 预编译的程序包
+对于Windows用户,我们提供了包含GUI界面的免安装软件包.<br>
+可以通过下面两种方式进行下载: [[Google Drive]](https://drive.google.com/open?id=1LTERcN33McoiztYEwBxMuRjjgxh4DEPs)  [[百度云,提取码1x0a]](https://pan.baidu.com/s/10rN3U3zd5TmfGpO_PEShqQ) <br>
+
+![image](./imgs/GUI.png)<br>
+
+注意事项:<br>
+- 程序的运行要求在64位Windows操作系统,我仅在Windows10下运行过,其他版本暂未经过测试<br>
+- 请根据需求选择合适的预训练模型进行测试<br>
+- 运行时间取决于电脑性能,对于视频文件,我们建议可以先使用截图进行测试.<br>
+- 如果输出的视频无法播放,这边建议您可以尝试[potplayer](https://daumpotplayer.com/download/).
+
+### 通过源代码运行
+#### 前提要求
+- Linux, Mac OS, Windows
+- Python 3.6+
+- [ffmpeg 3.4](http://ffmpeg.org/)
+- [Pytorch 1.0+](https://pytorch.org/)  [(Old version codes)](https://github.com/HypoX64/DeepMosaics/tree/Pytorch0.4)
+- CPU or NVIDIA GPU + CUDA CuDNN<br>
+#### Python依赖项
+代码依赖于opencv-python以及 torchvision,可有通过pip install 进行安装.
+#### 克隆源代码:
+```bash
+git clone https://github.com/HypoX64/DeepMosaics
+cd DeepMosaics
+```
+#### 下载测试视频以及预训练模型
+可以通过以下两种方法下载测试视频以及预训练模型,并将他们置于项目文件夹中.<br>
+[[Google Drive]](https://drive.google.com/open?id=10nARsiZoZGcaKw40nQu9fJuRp1oeabPs)   [[百度云,提取码7thu]](https://pan.baidu.com/s/1IG4bdIiIC9PH9-oEyae5Sg) 
+
+#### 简单的例子
+* 为视频添加马赛克,例子中认为手是需要打码的区域 ,可以通过切换预训练模型切换自动打码区域(输出结果将储存到 './result')
+```bash
+python3 deepmosaic.py
+```
+* 将视频中的马赛克移除,对于不同的打码物体需要使用对应的预训练模型进行马赛克消除(输出结果将储存到  './result')
+```bash
+python3 deepmosaic.py --mode clean --model_path ./pretrained_models/clean_hands_unet_128.pth --media_path ./result/hands_test_AddMosaic.mp4
+```
+#### 更多的参数
+如果想要测试其他的图片或视频,请参照以下文件输入参数.
+[[options.py]](https://github.com/HypoX64/DeepMosaics/blob/master/options.py) <br>
+
+## 鸣谢
+代码大量的参考了以下项目:[[pytorch-CycleGAN-and-pix2pix]](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) [[Pytorch-UNet]](https://github.com/milesial/Pytorch-UNet)[[pix2pixHD]](https://github.com/NVIDIA/pix2pixHD)..
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