{"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/ssd-single-shot-multibox-detector","title":"SSD: Single Shot MultiBox Detector","arxiv_id":"1512.02325","date":"2015-12-08","proceeding":null,"authors":["Wei Liu","Dragomir Anguelov","Dumitru Erhan","Christian Szegedy","Scott Reed","Cheng-Yang Fu","Alexander C. Berg"],"abstract":"We present a method for detecting objects in images using a single deep\nneural network. Our approach, named SSD, discretizes the output space of\nbounding boxes into a set of default boxes over different aspect ratios and\nscales per feature map location. At prediction time, the network generates\nscores for the presence of each object category in each default box and\nproduces adjustments to the box to better match the object shape. Additionally,\nthe network combines predictions from multiple feature maps with different\nresolutions to naturally handle objects of various sizes. Our SSD model is\nsimple relative to methods that require object proposals because it completely\neliminates proposal generation and subsequent pixel or feature resampling stage\nand encapsulates all computation in a single network. This makes SSD easy to\ntrain and straightforward to integrate into systems that require a detection\ncomponent. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets\nconfirm that SSD has comparable accuracy to methods that utilize an additional\nobject proposal step and is much faster, while providing a unified framework\nfor both training and inference. Compared to other single stage methods, SSD\nhas much better accuracy, even with a smaller input image size. For $300\\times\n300$ input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan\nX and for $500\\times 500$ input, SSD achieves 75.1% mAP, outperforming a\ncomparable state of the art Faster R-CNN model. Code is available at\nhttps://github.com/weiliu89/caffe/tree/ssd .","url_abs":"http://arxiv.org/abs/1512.02325v5","url_pdf":"http://arxiv.org/pdf/1512.02325v5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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