{"url":"/method/yolov2","slug":"yolov2","name":"YOLOv2","full_name":"YOLOv2","full_name_withheld":false,"description_markdown":"**YOLOv2**, or [**YOLO9000**](https://www.youtube.com/watch?v=QsDDXSmGJZA), is a single-stage real-time object detection model. It improves upon [YOLOv1](https://paperswithcode.com/method/yolov1) in several ways, including the use of [Darknet-19](https://paperswithcode.com/method/darknet-19) as a backbone, [batch normalization](https://paperswithcode.com/method/batch-normalization), use of a high-resolution classifier, and the use of anchor boxes to predict bounding boxes, and more.","description_state":"present","introduced_year":null,"introduced_by":{"title":"YOLO9000: Better, Faster, Stronger","paper":"/paper/yolo9000-better-faster-stronger","first_author":"Joseph Redmon","n_authors":2,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/yolo9000-better-faster-stronger"},"source":{"url":"http://arxiv.org/abs/1612.08242v1","title":"YOLO9000: Better, Faster, Stronger","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/pjreddie/darknet","code_snippet_url_on_a_code_host":true,"categories":[{"area":"Computer Vision","area_id":"computer-vision","collection":"One-Stage Object 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