add_results_link

Author: daitao

README.md

@@ -1,5 +1,5 @@
 # Second-order Attention Network for Single Image Super-resolution (CVPR-2019)
-
+[]()
 "[Second-order Attention Network for Single Image Super-resolution](http://openaccess.thecvf.com/content_CVPR_2019/html/Dai_Second-Order_Attention_Network_for_Single_Image_Super-Resolution_CVPR_2019_paper.html)" is published on CVPR-2019.
 The code is built on [RCAN(pytorch)](https://github.com/yulunzhang/RCAN) and tested on Ubuntu 16.04 (Pytorch 0.4.0)
 
@@ -34,18 +34,18 @@ Recently, deep convolutional neural networks (CNNs) have been widely explored in
 >
 
 ### 3. Test code
--  1. You can Download the pretrained model first
+-  1. You can [Download the pretrained model first](https://pan.baidu.com/s/1aTYG4Wy72MI-gCRGnJgkvQ), password: eq1v
 -  2. CD to 'TestCode/code', run the following scripts
 
 >  
 >  ## BI degradation, scale 2, 3, 4,8
 >  ## SAN_2x
 > 
->  python main.py  --model san  --data_test MyImage  --save `save_name`  --scale 2 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX2.pt
+>  python main.py  --model san  --data_test MyImage  --save `save_name`  --scale 2 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX2.pt  
 > 
 >  # SAN_3x   
 > 
->  python main.py  --model san --data_test MyImage  --save `save_name`  --scale 3 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX3.pt
+>  python main.py  --model san --data_test MyImage  --save `save_name`  --scale 3 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX3.pt  
 > 
 >  # SAN_4x
 >  python main.py  --model san --data_test MyImage  --save `save_name`  --scale 4 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX4.pt
@@ -55,7 +55,7 @@ Recently, deep convolutional neural networks (CNNs) have been widely explored in
 >  python main.py  --model san --data_test MyImage  --save `save_name`  --scale 8 --n_resgroups 20 --n_resblocks 10 --n_feats 64 --reset --chop --save_results --test_only --testpath 'your path' --testset Set5  --pre_train ../model/SAN_BIX8.pt
 > 
 ### 4. Results
-- Some of the test results can be downloaded.
+- Some of [the test results can be downloaded.](https://pan.baidu.com/s/1j0ZgfbGKyYZqsSCLOb3nUg)  Password:w3da
 
 ### 5. Citation
 If the the work or the code is helpful, please cite the following papers