Update README.md

README.md

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 To Run the Network on Test Data:
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-Step 1: Running the Network to Detect the ON and OFF surfaces 
+-Step 1: Running the Network to Detect the ON and OFF surfaces 
 + First, put all the images to be processed (STD Tiff stacks of chAT Bands) into the ImagesHere folder.
 
 + Then, open Matlab, make sure Matlab has included all the folders and subfolders inside of VNet, and run the script RunMe.m
@@ -11,7 +11,7 @@ Step 1: Running the Network to Detect the ON and OFF surfaces
 + Manually verify that the overlays are correct by looking at the Tiff files mentioned above. If yes, import the 2 chAT surfaces, stored as filename_ON.mat and filename_OFF.mat into Sumbul RGC code to continue with the Warping step. These two .mat files contain the 2 surfaces that would have been returned by Sumbul's firSurfaceToSACAnnotation function.
 
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-Step 2: Clean Up
+-Step 2: Clean Up
 + Make sure to delete or remove the images in ImageHere so that you can use the network again with other images.
 
 + Delete all the Detected Surfaces in DetectedSurfaces that are no longer needed also.
@@ -22,18 +22,18 @@ Step 2: Clean Up
 Notes: In the case where the overlays are not correct, manually annotated the data, and store the Annotated files (xls or txt format) in the VNet/Dataset/Annotations folder so that the network can train itself with the new data.
 
 To Train the Network with new Data:
-Step 1: Install Dependencies and Libraries 
+-Step 1: Install Dependencies and Libraries 
 + Run CaffeInstallation.sh and PythonLibraryInstallation.sh. To run the 2 files, either double-click on the file name and choose 'Run On Terminal', or manually open up a new Terminal, change directory (cd command) to locate the file, and type:
             ./filename.sh 
 in order to execute the command that download and install dependencies necessary to run pycaffe.
 
-Step 2: Prepare training data
+-Step 2: Prepare training data
 + The ON and OFF band should be trained on separated network for best results. 
 + Thus, create a 
 + All the images has to be resized to 128x128x64. 
 + Put the resized images and its corresponding mask/groundtruth in the Dataset folder, either under ON or OFF depending on the type of groundtruths 
 
-Step 3: Run the training script
+-Step 3: Run the training script
 + Open a Terminal, locate the VNet Directory, and type: python main.py -train to run and train the network