{"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/pvanet-lightweight-deep-neural-networks-for","title":"PVANet: Lightweight Deep Neural Networks for Real-time Object Detection","arxiv_id":"1611.08588","date":"2016-11-23","proceeding":null,"authors":["Sanghoon Hong","Byungseok Roh","Kye-Hyeon Kim","Yeongjae Cheon","Minje Park"],"abstract":"In object detection, reducing computational cost is as important as improving\naccuracy for most practical usages. This paper proposes a novel network\nstructure, which is an order of magnitude lighter than other state-of-the-art\nnetworks while maintaining the accuracy. Based on the basic principle of more\nlayers with less channels, this new deep neural network minimizes its\nredundancy by adopting recent innovations including C.ReLU and Inception\nstructure. We also show that this network can be trained efficiently to achieve\nsolid results on well-known object detection benchmarks: 84.9% and 84.2% mAP on\nVOC2007 and VOC2012 while the required compute is less than 10% of the recent\nResNet-101.","url_abs":"http://arxiv.org/abs/1611.08588v2","url_pdf":"http://arxiv.org/pdf/1611.08588v2.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":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/sanghoon/pva-faster-rcnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/busyboxs/Some-resources-useful-for-me","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/jeffshih/autoTrain","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/wuyx/pva-faster-rcnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/2023-MindSpore-1/ms-code-216/tree/main/PVAnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/2023-MindSpore-4/Code6/tree/main/PVAnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/Mind23-2/MindCode-5/tree/main/PVAnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/MindSpore-paper-code-2/code2/tree/main/PVAnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null},{"paper_slug":"pvanet-lightweight-deep-neural-networks-for","repo_url":"https://github.com/code-implementation1/Code6/tree/main/PVAnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"real-time-object-detection","task_name":"Real-Time Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}