{"id":21059356,"url":"https://github.com/leeyeehoo/csrnet","last_synced_at":"2026-01-02T06:05:50.636Z","repository":{"id":201386716,"uuid":"129959938","full_name":"leeyeehoo/CSRNet","owner":"leeyeehoo","description":"CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes","archived":false,"fork":false,"pushed_at":"2018-07-19T00:52:14.000Z","size":18,"stargazers_count":35,"open_issues_count":3,"forks_count":23,"subscribers_count":7,"default_branch":"master","last_synced_at":"2025-01-20T19:51:54.827Z","etag":null,"topics":["crowdcounting","cvpr2018"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/leeyeehoo.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2018-04-17T20:16:05.000Z","updated_at":"2024-08-26T13:53:57.000Z","dependencies_parsed_at":null,"dependency_job_id":"3f3ce688-0f03-476e-9e36-b24ed5c89378","html_url":"https://github.com/leeyeehoo/CSRNet","commit_stats":null,"previous_names":["leeyeehoo/csrnet"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leeyeehoo%2FCSRNet","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leeyeehoo%2FCSRNet/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leeyeehoo%2FCSRNet/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/leeyeehoo%2FCSRNet/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/leeyeehoo","download_url":"https://codeload.github.com/leeyeehoo/CSRNet/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243505953,"owners_count":20301617,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["crowdcounting","cvpr2018"],"created_at":"2024-11-19T17:10:42.690Z","updated_at":"2026-01-02T06:05:45.617Z","avatar_url":"https://github.com/leeyeehoo.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# CSRNet (Try our [Pytorch Version](https://github.com/leeyeehoo/CSRNet-pytorch/tree/master)!)\nThis is the repo for [CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes](https://arxiv.org/abs/1802.10062) in CVPR 2018, which delivered a state-of-the-art, straightforward and end-to-end architecture for crowd counting tasks.\n## Datasets\nShanghaiTech Dataset: [Google Drive](https://drive.google.com/open?id=16dhJn7k4FWVwByRsQAEpl9lwjuV03jVI)\n\n## Models (Only for tests)\n\nThis is the model for test. The results should be similar to the results shown in the paper(slightly better or worse).\n\n1) ShanghaiTech_Part_A: [Google Drive](https://drive.google.com/open?id=1odZ3B_ZDSepPcVFO_TfGUIrpF2DF7SwY)\n\n2) ShanghaiTech_Part_B: [Google Drive](https://drive.google.com/open?id=1NOpn0ztlye85vrHR2TMwOI2Qu_S8zANj)\n\n## Prerequisites\n\n1) A good CAFFE\n\nWe understand that it's tedious and difficult to config a custom input layer (even installing CAFFE on your own PC), thus we make a pytorch version for the csrnet: [CSRNet Pytorch Version](https://github.com/leeyeehoo/CSRNet-pytorch/tree/master)\n\n## References\n\nIf you find the CSRNet useful, please cite our paper. Thank you!\n\n```\n@inproceedings{li2018csrnet,\n  title={CSRNet: Dilated convolutional neural networks for understanding the highly congested scenes},\n  author={Li, Yuhong and Zhang, Xiaofan and Chen, Deming},\n  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},\n  pages={1091--1100},\n  year={2018}\n}\n```\nPlease cite the Shanghai datasets and other works if you use them.\n\n```\n@inproceedings{zhang2016single,\n  title={Single-image crowd counting via multi-column convolutional neural network},\n  author={Zhang, Yingying and Zhou, Desen and Chen, Siqin and Gao, Shenghua and Ma, Yi},\n  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},\n  pages={589--597},\n  year={2016}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleeyeehoo%2Fcsrnet","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fleeyeehoo%2Fcsrnet","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fleeyeehoo%2Fcsrnet/lists"}