{"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/feature-pyramid-networks-for-object-detection","title":"Feature Pyramid Networks for Object Detection","arxiv_id":"1612.03144","date":"2016-12-09","proceeding":"CVPR 2017 7","authors":["Tsung-Yi Lin","Piotr Dollár","Ross Girshick","Kaiming He","Bharath Hariharan","Serge Belongie"],"abstract":"Feature pyramids are a basic component in recognition systems for detecting\nobjects at different scales. But recent deep learning object detectors have\navoided pyramid representations, in part because they are compute and memory\nintensive. In this paper, we exploit the inherent multi-scale, pyramidal\nhierarchy of deep convolutional networks to construct feature pyramids with\nmarginal extra cost. A top-down architecture with lateral connections is\ndeveloped for building high-level semantic feature maps at all scales. This\narchitecture, called a Feature Pyramid Network (FPN), shows significant\nimprovement as a generic feature extractor in several applications. Using FPN\nin a basic Faster R-CNN system, our method achieves state-of-the-art\nsingle-model results on the COCO detection benchmark without bells and\nwhistles, surpassing all existing single-model entries including those from the\nCOCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU\nand thus is a practical and accurate solution to multi-scale object detection.\nCode will be made publicly available.","url_abs":"http://arxiv.org/abs/1612.03144v2","url_pdf":"http://arxiv.org/pdf/1612.03144v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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