# 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](Dense.md) [sampling](Sampling.md) 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](Dense.md) detectors is the central cause - modulating term to [Cross Entropy](Cross%20Entropy.md) 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](RetinaNet.md)