Update README.md

README.md

@@ -1,5 +1,5 @@
 # DIDN
-Pytorch Implementation of "Deep Iterative Down-Up CNN for Image Denoising". [[PDF]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Yu_Deep_Iterative_Down-Up_CNN_for_Image_Denoising_CVPRW_2019_paper.pdf)
+Pytorch Implementation of "**Deep Iterative Down-Up CNN for Image Denoising**". [[PDF]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Yu_Deep_Iterative_Down-Up_CNN_for_Image_Denoising_CVPRW_2019_paper.pdf)
 
 Our method took **second** and **third** place at each track in [[NTIRE 2019 Challenge on Real Image Denoising]](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Abdelhamed_NTIRE_2019_Challenge_on_Real_Image_Denoising_Methods_and_Results_CVPRW_2019_paper.pdf)
 
@@ -16,9 +16,54 @@ If you use our work in your research or publication, please cite our work:
   pytorch 1.0.0  
   MATLAB (for pre-processing)  
 
+## Prepare data
+You need to prepare data befor training / testing the model.    
+Please use matlab files in './data' folder.   
+For training, we used [DIV2K](http://www.vision.ee.ethz.ch/~timofter/publications/Agustsson-CVPRW-2017.pdf) dataset, and only sample images are included in this project.  
+Please download the whole dataset for training the model.
 
+## Test
+Download Pre-trained models:[[google drive]](https://drive.google.com/open?id=1dwc6T4Kk5tjwiSFuOiogoWts55GsbMeC)  
+for grayscale images, gray_eval.py  
+for color images, color_eval.py  
+```
+optional arguments:  
+  --cuda : using cuda   
+  --model : model path  
+  --output_path : output image path   
+  --self_ensemble : using self-ensemble  
+  --gpus : gpu ids  
+  
+  You can also change test dataset in the code!
+```
+Example:  
+```
+python gray_eval.py --cuda --self_ensemble
+```
 
+## Training
+```
+optional arguments:  
+  --batchSize : batchsize (default: 16)  
+  --nEpochs : number of epochs (default: 50)  
+  --lr : initial learning rate (default: 0.0001)  
+  --cuda : using cuda   
+  --resume : path to checkpoint (default: none)  
+  --start_epoch : manual starting epoch number (useful for resume, default: 1)  
+  --threads : number of threads for data loader (default: 0)  
+  --gpus : gpu ids  
+  
+  You can also change training data path and validation data path in the code!  
+```
+Example:  
+```
+python gray_train.py --cuda --threads 8
+```
 
-Readme will be updated soon!
+## Weight ensemble
+For weight ensemble, train the model multiple times manually using --resume option,  
+and averaging weights using 'weight_ensemble_2.py'
 
-Download Pre-trained models: https://drive.google.com/open?id=1dwc6T4Kk5tjwiSFuOiogoWts55GsbMeC
+
+## Contact
+If you have any questions about the code or paper, please contact fkdlzmtld@gmail.com