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# Train
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just run: python train.py with config_params.json
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# Ground truth format
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Lables for each pixel is identified by a number . So if you have a
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binary case n_classes should be set to 2 and
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labels should be 0 and 1 for each class and pixel.
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In the case of multiclass just set n_classes to the number of classes
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you have and the try to produce the labels
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by pixels set from 0 , 1 ,2 .., n_classes-1.
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The labels format should be png.
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If you have an image label for binary case it should look like this:
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Label: [ [[1 0 0 1], [1 0 0 1] ,[1 0 0 1]],
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[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ,
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[[1 0 0 1], [1 0 0 1] ,[1 0 0 1]] ]
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This means that you have an image by 3*4*3 and pixel[0,0] belongs
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to class 1 and pixel[0,1] to class 0.
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# Training , evaluation and output
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train and evaluation folder should have subfolder of images and labels.
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And output folder should be empty folder which the output model will be written there.
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# Patches
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if you want to train your model with patches, the height and width of
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patches should be defined and also number of
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batchs (how many patches should be seen by model by each iteration).
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In the case that model should see the image once, like page extraction,
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the patches should be set to false.
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# Pretrained encoder
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Download weights from this limk and add it to pretrained_model folder.
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https://file.spk-berlin.de:8443/pretrained_encoder/
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