What is focal loss in machine learning?

What is focal loss in machine learning?

Focal loss is a loss function designed to address class imbalance in tasks like object detection. It reduces the relative loss for well-classified examples, focusing more on hard-to-classify examples.

How does focal loss differ from regular cross-entropy loss?

Unlike cross-entropy loss, which treats all misclassifications equally, focal loss adds a modulating factor to down-weight easy examples and focus on hard ones, making it more effective for imbalanced datasets.

Can you give me an example of when to use focal loss?

Focal loss is particularly useful in scenarios like detecting rare objects in images, where the number of background examples far exceeds the number of target objects.

What are the parameters of focal loss?

Focal loss has two main parameters: gamma, which adjusts the rate at which easy examples are down-weighted, and alpha, which balances the importance of positive/negative examples.