Transcription of Focal Loss for Dense Object Detection
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Focal Loss for Dense Object Detection
Focal loss for Dense Object DetectionTsung-Yi LinPriya GoyalRoss GirshickKaiming HePiotr Doll arFacebook AI Research (FAIR) of ground truth class012345loss = 0 = = 1 = 2 = 5well-classi edexampleswell-classi edexamplesCE(pt) = log(pt)FL(pt) = (1 pt) log(pt)Figure 1. We propose a novel loss we term theFocal Lossthatadds a factor(1 pt) to the standard cross entropy >0reduces the relative loss for well-classified examples(pt> .5), putting more focus on hard, misclassified examples. Asour experiments will demonstrate, the proposed Focal loss enablestraining highly accurate Dense Object detectors in the presence ofvast numbers of easy background highest accuracy Object detectors to date are basedon a two-stage approach popularized by R-CNN, where aclassifier is applied to asparseset of candidate Object lo-cations.
Focal Loss for Dense Object Detection Tsung-Yi Lin Priya Goyal Ross Girshick Kaiming He Piotr Doll´ar Facebook AI Research (FAIR) well-classi ed examples CE(p t) = log(p t) FL(p t) = (1 p t) log(p t) Figure 1. We propose a novel loss we term the Focal Loss that adds a factor (1 Enabled by the focal loss, our simple one-stagep
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