Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Dollár Facebook AI Research (FAIR) 0 0.2 0.4 0.6 0.8 1 probability of ground truth class 0 1 2 3 4 5 lo s s = 0 = 0.5 = 1 = 2 = 5 well-classi ed examples CE(pt) = − log(pt) FL(pt) = −(1− pt)γ log(pt) Figure 1. We propose a novel loss we term the Focal Loss that adds a factor (1 − pt)γ to the standard cross entropy criterion. Setting γ > 0 reduces the relative loss for well-classified examples (pt > .5), putting more focus on hard, misclassified examples. As our experiments will demonstrate, the proposed focal loss enables training highly accurate dense object detectors in the presence of vast numbers of easy background examples. Abstract The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object lo- cations. In contrast, one-stage detectors that a
