{"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/csrnet-dilated-convolutional-neural-networks","title":"CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes","arxiv_id":"1802.10062","date":"2018-02-27","proceeding":"CVPR 2018 6","authors":["Yuhong Li","Xiaofan Zhang","Deming Chen"],"abstract":"We propose a network for Congested Scene Recognition called CSRNet to provide\na data-driven and deep learning method that can understand highly congested\nscenes and perform accurate count estimation as well as present high-quality\ndensity maps. The proposed CSRNet is composed of two major components: a\nconvolutional neural network (CNN) as the front-end for 2D feature extraction\nand a dilated CNN for the back-end, which uses dilated kernels to deliver\nlarger reception fields and to replace pooling operations. CSRNet is an\neasy-trained model because of its pure convolutional structure. We demonstrate\nCSRNet on four datasets (ShanghaiTech dataset, the UCF_CC_50 dataset, the\nWorldEXPO'10 dataset, and the UCSD dataset) and we deliver the state-of-the-art\nperformance. In the ShanghaiTech Part_B dataset, CSRNet achieves 47.3% lower\nMean Absolute Error (MAE) than the previous state-of-the-art method. We extend\nthe targeted applications for counting other objects, such as the vehicle in\nTRANCOS dataset. Results show that CSRNet significantly improves the output\nquality with 15.4% lower MAE than the previous state-of-the-art approach.","url_abs":"http://arxiv.org/abs/1802.10062v4","url_pdf":"http://arxiv.org/pdf/1802.10062v4.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":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/Bazingaliu/learning_CSRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/CommissarMa/CSRNet-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/DiaoXY/CSRnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/Neerajj9/CSRNet-keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/RTalha/CROWD-COUNTING-USING-CSRNET","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/Saritus/Crowd-Counter","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/dattatrayshinde/oc_sd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/karanjsingh/Improved-CSRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/krutikabapat/Crowd_Counting","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/leeyeehoo/CSRNet-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"csrnet-dilated-convolutional-neural-networks","repo_url":"https://github.com/xr0927/chapter5-learning_CSRNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"crowd-counting","task_name":"Crowd Counting"},{"task_slug":"scene-recognition","task_name":"Scene Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/crowd-counting-on-shanghaitech-a","task":"Crowd Counting","dataset":"ShanghaiTech A","model":"CSRNet","rank_in_archive_order":24,"of":35,"metrics":{"MAE":"68.2"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-shanghaitech-b","task":"Crowd Counting","dataset":"ShanghaiTech B","model":"CSRNet","rank_in_archive_order":22,"of":32,"metrics":{"MAE":"10.6"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-trancos","task":"Crowd Counting","dataset":"TRANCOS","model":"CSRNet","rank_in_archive_order":4,"of":4,"metrics":{"MAE":"3.56"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-ucf-cc-50","task":"Crowd Counting","dataset":"UCF CC 50","model":"CSRNet","rank_in_archive_order":12,"of":22,"metrics":{"MAE":"266.1"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-venice","task":"Crowd Counting","dataset":"Venice","model":"CSRNet","rank_in_archive_order":3,"of":5,"metrics":{"MAE":"35.8"},"uses_additional_data":false},{"leaderboard":"/sota/crowd-counting-on-worldexpo10","task":"Crowd Counting","dataset":"WorldExpo’10","model":"CSRNet","rank_in_archive_order":7,"of":15,"metrics":{"Average MAE":"8.6"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.10062","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.10062"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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