Unet_binary segmentation

Part 1 (2019)
42

For a hopefully helpful reference, I’ve updated my binary segmentation notebook (for mapping buildings from aerial/drone imagery) to work on fastai v1.0.33:

https://nbviewer.jupyter.org/github/daveluo/zanzibar-aerial-mapping/blob/master/znz-segment-buildingfootprint-20181205-comboloss-rn34.ipynb

For those having issues changing SegmentationLabelList to open binary masks with div=True by default, this worked for me based on @sgugger’s suggestion:

class SegLabelListCustom(SegmentationLabelList):
    def open(self, fn): return open_mask(fn, div=True)
    
class SegItemListCustom(ImageItemList):
    _label_cls = SegLabelListCustom

src = (SegItemListCustom.from_folder(path_img)
        .split_by_idx(valid_idx)
        .label_from_func(get_y_fn, classes=codes))
...

In the notebook, I also add a custom loss function (combo of BCE and soft dice loss…not sure that my dice loss function is working entirely correctly yet so please let me know if you spot any bugs!) and make use of a slightly modified SaveModelCallback to auto-save and load weights from the best-resulting epoch.

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