{"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/you-only-look-once-unified-real-time-object","title":"You Only Look Once: Unified, Real-Time Object Detection","arxiv_id":"1506.02640","date":"2015-06-08","proceeding":"CVPR 2016 6","authors":["Joseph Redmon","Santosh Divvala","Ross Girshick","Ali Farhadi"],"abstract":"We present YOLO, a new approach to object detection. Prior work on object\ndetection repurposes classifiers to perform detection. Instead, we frame object\ndetection as a regression problem to spatially separated bounding boxes and\nassociated class probabilities. A single neural network predicts bounding boxes\nand class probabilities directly from full images in one evaluation. Since the\nwhole detection pipeline is a single network, it can be optimized end-to-end\ndirectly on detection performance.\n  Our unified architecture is extremely fast. Our base YOLO model processes\nimages in real-time at 45 frames per second. A smaller version of the network,\nFast YOLO, processes an astounding 155 frames per second while still achieving\ndouble the mAP of other real-time detectors. Compared to state-of-the-art\ndetection systems, YOLO makes more localization errors but is far less likely\nto predict false detections where nothing exists. Finally, YOLO learns very\ngeneral representations of objects. It outperforms all other detection methods,\nincluding DPM and R-CNN, by a wide margin when generalizing from natural images\nto artwork on both the Picasso Dataset and the People-Art Dataset.","url_abs":"http://arxiv.org/abs/1506.02640v5","url_pdf":"http://arxiv.org/pdf/1506.02640v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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