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.
loss performs the opposite role of a robust loss: it focuses training on a sparse set of hard examples. 3. Focal Loss The Focal Loss is designed to address the one-stage ob-ject detection scenario in which there is an extreme im-: (: = (). ) = ))) = ), + + ...
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