delta.data.seg_weights#
- delta.data.seg_weights(mask, classweights=(1, 1), w0=12, sigma=2)[source]#
Compute the weight map as described in the original U-Net paper to force the model to learn borders.
(Slow, best to run this offline before training).
- Parameters
- mask2D array
Training output segmentation mask.
- classweightstuple of 2 int/floats
Weights to apply to background, foreground. The default is (1,1)
- w0int or float, optional
Base weight to apply to smallest distance (1 pixel). The default is 12.
- sigmaint or float, optional
Exponential decay rate to apply to distance weights. The default is 2.
- Returns
- weightmap2D array
Weights map image.