Update README.md

README.md

@@ -22,30 +22,40 @@ We propose a novel multi-stream architecture and training methodology that explo
 2. Python 2 or 3
 3. Pytorch version 0.4.1
 4. CPU or NVIDIA GPU + CUDA CuDNN (CUDA 8.0)
-
+```
-    - pip install -r requirements.txt
+    pip install -r requirements.txt
-
+```
 
 ## To test UMSN:
 1. Download test datasets provided the authors of Ziyi et al.
-    - https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
+```
+    https://sites.google.com/site/ziyishenmi/cvpr18_face_deblur
+```
 2. run test_data_generation.m
     - It renames the files counting from 1, for example 000001.png
-3. python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth
+3. Run the following command
+```
+python test_face_deblur.py --dataroot ./facades/github/ --valDataroot <path_to_test_data> --netG ./pretrained_models/Deblur_epoch_Best.pth
+```
 
 ## To train UMSN:
 1. Kernels are generated using,
      - [Boracchi and Foi, 2012]	Modeling the Performance of Image Restoration from Motion Blur Giacomo Boracchi and Alessandro Foi, Image Processing, IEEE Transactions on. vol.21, no.8, pp. 3502 - 3517, Aug. 2012,
      - 25000 kernels with size ranging from 13 to 29 are generated and saved as ".mat" file
 2. Clean face images from Helen and CelebA are aligned and used as input to train UMSN 
-3. python train_face_deblur.py --dataroot <path_to_train_data> --valDataroot ./facades/github/ --exp ./face_deblur --batchSize 10
+3. Run the following command,
+```
+python train_face_deblur.py --dataroot <path_to_train_data> --valDataroot ./facades/github/ --exp ./face_deblur --batchSize 10
+```
 - input should be clean image. blurry images for training are generated by the code it self.
 
 ## To train Segmentation Netweork
 Train Segmentation Netweork using the following command
 1. training segmentation network with clean images,
-    
+```
     python seg_train.py --modeclean 1 --dataroot ./facades/github/ --valDataroot ./facades/github/
+```
 2. training segmentation network with blurry images,
-    
+```
     python seg_train.py --modeclean 0 --dataroot ./facades/github/ --valDataroot ./facades/github/
+```