Focal Loss

• two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations.
• In contrast, one-stage detectors that are applied over a regular, Dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far.
• Extreme foreground-background class imbalance encountered during training of Dense detectors is the central cause
• modulating term to Cross Entropy in order to focus learning on hard misclassified examples
• scaling factor decays to zero as confidence in the correct class increases
• training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training
• RetinaNet