{"id":22751650,"url":"https://github.com/ritesh-ojha/crowd-counting-using-pytorch","last_synced_at":"2025-03-30T06:42:39.944Z","repository":{"id":233279787,"uuid":"786426253","full_name":"ritesh-ojha/Crowd-Counting-Using-PyTorch","owner":"ritesh-ojha","description":null,"archived":false,"fork":false,"pushed_at":"2024-05-21T17:20:03.000Z","size":587,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T08:51:11.401Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","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/ritesh-ojha.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,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-04-14T12:34:18.000Z","updated_at":"2024-05-21T17:20:07.000Z","dependencies_parsed_at":"2024-05-05T18:51:39.609Z","dependency_job_id":null,"html_url":"https://github.com/ritesh-ojha/Crowd-Counting-Using-PyTorch","commit_stats":null,"previous_names":["ritesh-ojha/crowd-counting-using-pytorch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritesh-ojha%2FCrowd-Counting-Using-PyTorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritesh-ojha%2FCrowd-Counting-Using-PyTorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritesh-ojha%2FCrowd-Counting-Using-PyTorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ritesh-ojha%2FCrowd-Counting-Using-PyTorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ritesh-ojha","download_url":"https://codeload.github.com/ritesh-ojha/Crowd-Counting-Using-PyTorch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246285666,"owners_count":20752953,"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":[],"created_at":"2024-12-11T05:06:39.130Z","updated_at":"2025-03-30T06:42:39.925Z","avatar_url":"https://github.com/ritesh-ojha.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# CDENet: Crowd Density Estimation Network\n\nCDENet is a deep learning model designed for crowd count detection tasks. It utilizes a modified VGG16 architecture with 10 layers and incorporates dilated convolutions in the backend to improve accuracy in dense crowd scenarios.\n\n## Overview\nCrowd count detection is a crucial task in various domains such as urban planning, security surveillance, and event management. CDENet offers a robust solution by accurately estimating crowd density in images or videos, enabling better crowd management and analysis.\n\n## Key Features\n- **VGG16 Architecture:** CDENet is built upon the widely used VGG16 architecture, which has shown effectiveness in various computer vision tasks.\n- **10-Layer Modification:** To adapt VGG16 for crowd count detection, CDENet modifies the original architecture to have 10 layers, optimizing it for density estimation.\n- **Dilated Convolutions:** In the backend layers, CDENet incorporates dilated convolutions to capture contextual information over larger receptive fields, improving accuracy, especially in densely packed crowd scenarios.\n- **Deep Learning Framework:** CDENet is implemented using popular deep learning frameworks such as TensorFlow or PyTorch, allowing for easy integration into existing workflows.\n- **Pre-Trained Weights:** Pre-trained weights are available, facilitating transfer learning for crowd count detection tasks with limited labeled data.\n\n## Contributing\nContributions to CDENet are welcome! If you have suggestions for improvements, bug fixes, or new features, please open an issue or submit a pull request.\n\n# License\nCDENet is licensed under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritesh-ojha%2Fcrowd-counting-using-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fritesh-ojha%2Fcrowd-counting-using-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fritesh-ojha%2Fcrowd-counting-using-pytorch/lists"}