The highest accuracy object detectors to date are based on a two-stageapproach popularized by R-CNN, where a classifier is applied to a sparse set ofcandidate object locations. In contrast, one-stage detectors that are appliedover a regular, dense sampling of possible object locations have the potentialto be faster and simpler, but have trailed the accuracy of two-stage detectorsthus far. In this paper, we investigate why this is the case. We discover thatthe extreme foreground-background class imbalance encountered during trainingof dense detectors is the central cause. We propose to address this classimbalance by reshaping the standard cross entropy loss such that itdown-weights the loss assigned to well-classified examples. Our novel FocalLoss focuses training on a sparse set of hard examples and prevents the vastnumber of easy negatives from overwhelming the detector during training. Toevaluate the effectiveness of our loss, we design and train a simple densedetector we call RetinaNet. Our results show that when trained with the focalloss, RetinaNet is able to match the speed of previous one-stage detectorswhile surpassing the accuracy of all existing state-of-the-art two-stagedetectors. Code is at: https://github.com/facebookresearch/Detectron.