{"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/varifocalnet-an-iou-aware-dense-object","title":"VarifocalNet: An IoU-aware Dense Object Detector","arxiv_id":"2008.13367","date":"2020-08-31","proceeding":"CVPR 2021 1","authors":["Haoyang Zhang","Ying Wang","Feras Dayoub","Niko Sünderhauf"],"abstract":"Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by $\\sim$2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors.Code is available at https://github.com/hyz-xmaster/VarifocalNet .","url_abs":"https://arxiv.org/abs/2008.13367v2","url_pdf":"https://arxiv.org/pdf/2008.13367v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"varifocalnet-an-iou-aware-dense-object","repo_url":"https://github.com/hyz-xmaster/VarifocalNet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"varifocalnet-an-iou-aware-dense-object","repo_url":"https://github.com/fcakyon/sahi-benchmark","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"varifocalnet-an-iou-aware-dense-object","repo_url":"https://github.com/n-friederich/airport_fence_inspection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"varifocalnet-an-iou-aware-dense-object","repo_url":"https://github.com/open-mmlab/mmdetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcos","method_name":"FCOS"},{"method_slug":"fpn","method_name":"FPN"},{"method_slug":"non-maximum-suppression","method_name":"Non Maximum Suppression"},{"method_slug":"varifocalnet","method_name":"VFNet"},{"method_slug":"varifocal-loss","method_name":"Varifocal Loss"}],"datasets_introduced":[],"methods_introduced":[{"slug":"varifocalnet","name":"VFNet","full_name":"VarifocalNet"},{"slug":"varifocal-loss","name":"Varifocal Loss","full_name":"Varifocal Loss"}],"results":[{"leaderboard":"/sota/object-detection-on-coco-o","task":"Object Detection","dataset":"COCO-O","model":"VFNet\n(RX-101-64x4d)","rank_in_archive_order":24,"of":45,"metrics":{"Average mAP":"28.0","Effective Robustness":"5.27"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2008.13367","atlas_url":"https://app.syntology.ai/?focus=2008.13367","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2008.13367"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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