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.

Pretrained encoder

Download weights from this link and add it to pretrained_model folder. https://file.spk-berlin.de:8443/pretrained_encoder/