diff --git a/README.md b/README.md
index 1201393..0cb67b4 100644
--- a/README.md
+++ b/README.md
@@ -1,13 +1,13 @@
-# ESRGAN (Enhanced SRGAN) [[Paper]](https://github.com/xinntao/ESRGAN) [[BasicSR]](https://github.com/xinntao/BasicSR)
+# ESRGAN (Enhanced SRGAN) [[Paper]](https://arxiv.org/abs/1809.00219) [[BasicSR]](https://github.com/xinntao/BasicSR)
 ## Enhanced Super-Resolution Generative Adversarial Networks
 By Xintao Wang, [Ke Yu](https://yuke93.github.io/), Shixiang Wu, [Jinjin Gu](http://www.jasongt.com/), Yihao Liu, [Chao Dong](https://scholar.google.com.hk/citations?user=OSDCB0UAAAAJ&hl=en), [Chen Change Loy](http://personal.ie.cuhk.edu.hk/~ccloy/), [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/), [Xiaoou Tang](https://scholar.google.com/citations?user=qpBtpGsAAAAJ&hl=en)
 
-This repo only provides simple testing codes and pretrained models. 
+This repo only provides simple testing codes, pretrained models and the netwrok strategy demo. 
 
 ### :smiley: **For full training and testing codes, please refer to  [BasicSR](https://github.com/xinntao/BasicSR).**
 
 We won the first place in [PIRM2018-SR competition](https://www.pirm2018.org/PIRM-SR.html) (region 3) and got the best perceptual index.
-The paper is accepted to [ECCV'2018 PIRM Workshop](https://pirm2018.org/).
+The paper is accepted to [ECCV2018 PIRM Workshop](https://pirm2018.org/).
 ### BibTeX
 
     @article{wang2018esrgan,
@@ -17,11 +17,41 @@ The paper is accepted to [ECCV'2018 PIRM Workshop](https://pirm2018.org/).
         year={2018}
     }
 
-
 <p align="center">
   <img height="400" src="figures/baboon.png">
 </p>
-                                             
+
+The **RRDB_PSNR** PSNR_oriented model trained with DF2K dataset (a merged dataset with [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) and [Flickr2K](http://cv.snu.ac.kr/research/EDSR/Flickr2K.tar) (proposed in [EDSR](https://github.com/LimBee/NTIRE2017))) is also able to achive high PSNR performance.
+
+| Method | Training dataset | Set5 | Set14 | BSD100 | Urban100 | Manga109 |
+|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [SRCNN](http://mmlab.ie.cuhk.edu.hk/projects/SRCNN.html)| 291| 30.48/0.8628 |27.50/0.7513|26.90/0.7101|24.52/0.7221|27.58/0.8555|
+| [EDSR](https://github.com/thstkdgus35/EDSR-PyTorch) | DIV2K | 32.46/0.8968 | 28.80/0.7876 | 27.71/0.7420 | 26.64/0.8033 | 31.02/0.9148 |
+| [RCAN](https://github.com/yulunzhang/RCAN) |  DIV2K | 32.63/0.9002 | 28.87/0.7889 | 27.77/0.7436 | 26.82/ 0.8087| 31.22/ 0.9173|
+|RRDB(ours)| DF2K| **32.73/0.9011** |**28.99/0.7917** |**27.85/0.7455** |**27.03/0.8153** |**31.66/0.9196**|
+
+
+## Quick Test
+#### Dependencies
+- Python 3
+- PyTorch >= 0.4.0
+- Python package `cv2`, `numpy` 
+#### Test
+1. Clone this github repo. 
+```
+git clone https://github.com/xinntao/ESRGAN
+cd ESRGAN
+```
+2. Place your own **low-resolution images** in `./LR` folder. (There are two sample images - baboon and comic). 
+3. Download pretrained models from [Google Drive](https://drive.google.com/drive/u/0/folders/17VYV_SoZZesU6mbxz2dMAIccSSlqLecY) or [Baidu Drive](https://pan.baidu.com/s/1-Lh6ma-wXzfH8NqeBtPaFQ). Place the models in `./models`. We provide two models with high perceptual quality and high PSNR performance (see [model list](https://github.com/xinntao/ESRGAN/tree/master/models)).
+4. Run test. We provide ESRGAN model and RRDB_PSNR model.
+```
+python test.py models/RRDB_ESRGAN_x4.pth
+python test.py models/RRDB_PSNR_x4.pth
+```
+5. The results are in `./results` folder.
+
+
 ## Introduction 
 We improve the [SRGAN](https://arxiv.org/abs/1609.04802) from three aspects:
 1. adopt a deeper model using Residual-in-Residual Dense Block (RRDB) without batch normalization layers.