Focal Loss

Focal loss is a powerful loss function designed especially for problems with class imbalance. Standard cross-entropy can be dominated by easy and frequent classes, whereas focal loss reduces that dominance and makes the model focus more on difficult examples. It is particularly useful in object detection, anomaly analysis, and imbalanced datasets. Its value comes from not treating all examples equally. In the real world, some samples are far more informative for learning than others.