Focal loss is a loss function designed to address the class imbalance problem in tasks such as object detection. It extends the standard cross-entropy loss by adding a modulating factor that reduces the loss contribution from easy-to-classify examples and focuses more on hard-to-classify examples. This property makes focal loss particularly effective in scenarios where there are significant disparities between the number of instances of different classes.
congrats on reading the definition of focal loss. now let's actually learn it.