{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/focal-loss-for-dense-object-detection","title":"Focal Loss for Dense Object Detection","arxiv_id":"1708.02002","date":"2017-08-07","proceeding":"ICCV 2017 10","authors":["Tsung-Yi Lin","Priya Goyal","Ross Girshick","Kaiming He","Piotr Dollár"],"abstract":"The highest accuracy object detectors to date are based on a two-stage\napproach popularized by R-CNN, where a classifier is applied to a sparse set of\ncandidate object locations. In contrast, one-stage detectors that are applied\nover a regular, dense sampling of possible object locations have the potential\nto be faster and simpler, but have trailed the accuracy of two-stage detectors\nthus far. In this paper, we investigate why this is the case. We discover that\nthe extreme foreground-background class imbalance encountered during training\nof dense detectors is the central cause. We propose to address this class\nimbalance by reshaping the standard cross entropy loss such that it\ndown-weights the loss assigned to well-classified examples. Our novel Focal\nLoss focuses training on a sparse set of hard examples and prevents the vast\nnumber of easy negatives from overwhelming the detector during training. To\nevaluate the effectiveness of our loss, we design and train a simple dense\ndetector we call RetinaNet. Our results show that when trained with the focal\nloss, RetinaNet is able to match the speed of previous one-stage detectors\nwhile surpassing the accuracy of all existing state-of-the-art two-stage\ndetectors. Code is at: https://github.com/facebookresearch/Detectron.","url_abs":"http://arxiv.org/abs/1708.02002v2","url_pdf":"http://arxiv.org/pdf/1708.02002v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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