Comments (1)
khdlr
commented on July 20, 2024
- They are in fact two independent loss terms. The
auto_weight_bceloss function calculates both in parallel for efficiency. - Beta is equivalent to the term
$|Y_+| / |Y_+ \cup Y_-|$ from eq. 1 in the paper. The purpose of this term is to adaptively weight the loss contributions of both classes to be equal. This is done because the classes are very imbalanced, especially for the edge detection task.
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Related Issues (18)
- How could I get the complete images on result HOT 1
- Training steps HOT 4
- Is the image input during the training process the original image and the binary image? HOT 17
- Testing data HOT 7
- Whether this program can handle .png files. If you can’t handle it, can you convert the .tif file to a .png file? HOT 1
- AttributeError: 'Array' object has no attribute 'numpy' HOT 1
- RuntimeError: Given groups=1, weight of size [16, 3, 1, 1], expected input[8, 4, 256, 256] to have 3 channels, but got 4 channels instead HOT 10
- Hello, my training is successful, then I would like to ask how to test my own data? HOT 1
- About training HOT 21
- About Loss HOT 5
- About mutil classes HOT 19
- Hed HOT 7
- Weight Map HOT 2
- I have some questions about testing. HOT 6
- About the differences between predictions and queries. HOT 3
- Can you please share the pre-trained weights for interence HOT 1
- Can the author provide code for prediction
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