Keras functions that you've always wanted!
- BatchNormalization that supports float16!
- Random cropping for augmentation
- Foreground sparse accuracy metric (if backgroud is set to '0')
- Background spare accuracy (if backgroud is set to '0')
- Sparse accuracy ignoring void (last) label
- Mean IOU (support for multi class labels)
- Segmentation generator: yields images, masks and sample weights
- Cyclic learning rate
Note: it works only when the output of the net is flattened. e.g., if the output is a mask of (M, N, #Classes) then you must add a reshape layer to make it (M*N, #Classes) and your labels are one hot encoded (and not sparse)