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@ -23,14 +23,16 @@ 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.
Our lables are 3 channel png images but only information of first channel is used.
If you have an image label with height and width of 10, for a binary case the first channel should look like this:
If you have an image label for a binary case it should look like this:
Label: [ [1, 0, 0, 1, 1, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
...,
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 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
This means that you have an image by `10*10*3` and `pixel[0,0]` belongs
to class `1` and `pixel[0,1]` belongs to class `0`.
### Training , evaluation and output