Update README

README

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-# Train
-    just run: python train.py with config_params.json
-    
-    
-# Ground truth format
-    
-    Lables for each pixel is identified by a number . So if you have a binary case n_classes should be set to 2 and 
-    labels should be 0 and 1 for each class and pixel.
-    In the case of multiclass just set n_classes to the number of classes you have and the try to produce the labels
-    by pixels set from 0 , 1 ,2 .., n_classes-1.
-    The labels format should be png. 
-    
-    If you have an image label for binary case it should look like this:
-    
-    Label: [ [[1 0 0 1], [1 0 0 1] ,[1 0 0 1]], [[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ,[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ] 
-    this means that you have an image by 3*4*3 and pixel[0,0] belongs to class 1 and pixel[0,1] to class 0.
-    
-# Training , evaluation and output 
-    train and evaluation folder should have subfolder of images and labels.
-    And output folder should be empty folder which the output model will be written there.
-    
-# Patches
-    
-    if you want to train your model with patches, the height and width of patches should be defined and also number of 
-    batchs (how many patches should be seen by model by each iteration).
-    In the case that model should see the image once, like page extraction, the patches should be set to false.
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