Papers with Code - Focal Loss Explained

Focal Loss

Introduced by Lin et al. in Focal Loss for Dense Object Detection

A Focal Loss function addresses class imbalance during training in tasks like object detection. Focal loss applies a modulating term to the cross entropy loss in order to focus learning on hard misclassified examples. It is a dynamically scaled cross entropy loss, where the scaling factor decays to zero as confidence in the correct class increases. Intuitively, this scaling factor can automatically down-weight the contribution of easy examples during training and rapidly focus the model on hard examples.

Formally, the Focal Loss adds a factor $(1-pt)\gamma$ to the standard cross entropy criterion. Setting $\gamma >0$ reduces the relative loss for well-classified examples ( $pt>.5$ ), putting more focus on hard, misclassified examples. Here there is tunable focusing parameter $\gamma \ge 0$ .

$$FL(pt)=-(1-pt)\gamma log(pt)$$

Source: Focal Loss for Dense Object Detection

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