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https://github.com/ritesh-ojha/crowd-counting-using-pytorch
https://github.com/ritesh-ojha/crowd-counting-using-pytorch
Last synced: 23 days ago
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- Host: GitHub
- URL: https://github.com/ritesh-ojha/crowd-counting-using-pytorch
- Owner: ritesh-ojha
- Created: 2024-04-14T12:34:18.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-05-21T17:20:03.000Z (8 months ago)
- Last Synced: 2024-05-21T18:36:28.479Z (8 months ago)
- Language: Python
- Size: 573 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# CDENet: Crowd Density Estimation Network
CDENet 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.
## Overview
Crowd 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.
## Key Features
- **VGG16 Architecture:** CDENet is built upon the widely used VGG16 architecture, which has shown effectiveness in various computer vision tasks.
- **10-Layer Modification:** To adapt VGG16 for crowd count detection, CDENet modifies the original architecture to have 10 layers, optimizing it for density estimation.
- **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.
- **Deep Learning Framework:** CDENet is implemented using popular deep learning frameworks such as TensorFlow or PyTorch, allowing for easy integration into existing workflows.
- **Pre-Trained Weights:** Pre-trained weights are available, facilitating transfer learning for crowd count detection tasks with limited labeled data.
## Contributing
Contributions to CDENet are welcome! If you have suggestions for improvements, bug fixes, or new features, please open an issue or submit a pull request.
# License
CDENet is licensed under the MIT License. You are free to use, modify, and distribute the code for both commercial and non-commercial purposes.