{"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/yolact-real-time-instance-segmentation","title":"YOLACT: Real-time Instance Segmentation","arxiv_id":"1904.02689","date":"2019-04-04","proceeding":"ICCV 2019 10","authors":["Daniel Bolya","Chong Zhou","Fanyi Xiao","Yong Jae Lee"],"abstract":"We present a simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Moreover, we obtain this result after training on only one GPU. 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