Update README

README.md

@@ -4,16 +4,21 @@
     
 # 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 
+    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
+    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.
+    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.
@@ -21,6 +26,11 @@
     
 # Patches
     
-    if you want to train your model with patches, the height and width of patches should be defined and also number of 
+    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.
+    In the case that model should see the image once, like page extraction,
+    the patches should be set to false.
+# Pretrained encoder
+Download weights from this limk and add it to pretrained_model folder.
+https://file.spk-berlin.de:8443/pretrained_encoder/