{"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/improving-object-localization-with-fitness","title":"Improving Object Localization with Fitness NMS and Bounded IoU Loss","arxiv_id":"1711.00164","date":"2017-11-01","proceeding":"CVPR 2018 6","authors":["Lachlan Tychsen-Smith","Lars Petersson"],"abstract":"We demonstrate that many detection methods are designed to identify only a\nsufficently accurate bounding box, rather than the best available one. To\naddress this issue we propose a simple and fast modification to the existing\nmethods called Fitness NMS. This method is tested with the DeNet model and\nobtains a significantly improved MAP at greater localization accuracies without\na loss in evaluation rate, and can be used in conjunction with Soft NMS for\nadditional improvements. Next we derive a novel bounding box regression loss\nbased on a set of IoU upper bounds that better matches the goal of IoU\nmaximization while still providing good convergence properties. Following these\nnovelties we investigate RoI clustering schemes for improving evaluation rates\nfor the DeNet wide model variants and provide an analysis of localization\nperformance at various input image dimensions. We obtain a MAP of 33.6%@79Hz\nand 41.8%@5Hz for MSCOCO and a Titan X (Maxwell). Source code available from:\nhttps://github.com/lachlants/denet","url_abs":"http://arxiv.org/abs/1711.00164v3","url_pdf":"http://arxiv.org/pdf/1711.00164v3.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":"improving-object-localization-with-fitness","repo_url":"https://github.com/lachlants/denet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"object-localization","task_name":"Object Localization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1711.00164","atlas_url":"https://app.syntology.ai/?focus=1711.00164","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}