I’ve spent half a day and followed through the long unet binary classification thread and several others here on the board plus lots of testing in Jupyter, but I’m still hitting issues…so my question:
What is the current best practice for doing binary unet segmentation? Are we still suppposed to do the subclassing? ala:
def open(self, fn): return open_mask(fn, div=True)
ItemListsuitable for segmentation tasks."
_label_cls,_square_show_res = MySegmentationLabelList,False
src = (MySegmentationItemList(fnames)
.label_from_func(get_y_fn , classes=classes))
or can we now just use SegmentationItemList and push a div=True in there somewhere to ensure its a mask of 1/0?
I’m going to go browse the source code b/c there’s way too many various recommendations on the various threads and some are now obsolete (i.e. using ImageItemList) so I’m unclear what exactly is the current proper way to do the binary segmentation.
Any input would be appreciated!