What are the different loss functions used in image segmentation?
In image segmentation, common loss functions include Cross-Entropy Loss, Dice Loss, and Jaccard Loss. Each has its strengths.
Can you explain how Dice Loss works?
Dice Loss measures the overlap between the predicted segmentation and the ground truth. It ranges from 0 to 1, where 1 indicates perfect overlap.
When should I use Jaccard Loss instead of Dice Loss?
Jaccard Loss is useful when you want to penalize false positives more heavily. It's often preferred in cases with imbalanced classes.
Are there any other loss functions I should consider?
Yes, you might also consider Focal Loss for handling class imbalance or Boundary Loss for improving edge detection in segmentation.
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