focal loss

Focal Loss is a loss function used in deep learning for classification tasks, where the dataset is imbalanced, meaning the number of samples in each class is not equal. The focal loss was introduced by Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollár in their 2017 paper "Focal Loss for Dense Object Detection."

The focal loss is designed to address the problem of class imbalance by down-weighting the contribution of well-classified examples. It does this by introducing a tuning parameter called the focusing parameter, which allows the loss function to focus more on hard-to-classify examples.

The focal loss function can be written as:

FL(p_t) = -(1 - p_t)^gamma * log(p_t)

where p_t is the predicted probability of the true class, and gamma is the focusing parameter. The focal loss function increases the loss for well-classified examples, which reduces their contribution to the overall loss, and thus, the model focuses more on hard-to-classify examples.

In summary, the focal loss function is an effective way to handle imbalanced datasets, and it is widely used in various deep learning applications like object detection, semantic segmentation, and image classification.