{"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/an-implementation-of-faster-rcnn-with-study","title":"An Implementation of Faster RCNN with Study for Region Sampling","arxiv_id":"1702.02138","date":"2017-02-07","proceeding":null,"authors":["Xinlei Chen","Abhinav Gupta"],"abstract":"We adapted the join-training scheme of Faster RCNN framework from Caffe to\nTensorFlow as a baseline implementation for object detection. Our code is made\npublicly available. This report documents the simplifications made to the\noriginal pipeline, with justifications from ablation analysis on both PASCAL\nVOC 2007 and COCO 2014. We further investigated the role of non-maximal\nsuppression (NMS) in selecting regions-of-interest (RoIs) for region\nclassification, and found that a biased sampling toward small regions helps\nperformance and can achieve on-par mAP to NMS-based sampling when converged\nsufficiently.","url_abs":"http://arxiv.org/abs/1702.02138v2","url_pdf":"http://arxiv.org/pdf/1702.02138v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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