| Date | Paper |
|---|---|
| 2020-08-14 | Drink Waste AI Documentation |
This project aims to leverage artificial intelligence (AI) to detect waste materials, with potential applications in recycling plants, as well as the development of waste-collecting devices and robots. The accompanying website serves as a demonstration of how this technology can be implemented effectively. Read More on
- Object Detection Model: We employ YOLOv5, a powerful object detection model, to identify waste materials. If interested in the YOLO v5 model the repo is here.
- Dataset: Our gratitude goes to Kaggle for providing access to the Drinking Waste Classification dataset. This dataset includes contributions from Gary Thung and Mindy Yang, who manually collected portions of it. Their dedication to this task is greatly appreciated. You can find their repository here.
- Drink Waste AI Documentation: For more information about how our system works, its performance, and its intended use and improvements, please refer to our school project paper in this documentation.
This project is licensed under the MIT License. Please review the LICENSE.md file for detailed licensing information.
We welcome contributions from the community. To contribute, follow these guidelines:
- Fork the project repository.
- Create a new branch for your contribution:
git checkout -b feature/your-feature-name. - Make your changes and commit them:
git commit -m "Add your feature". - Push your changes to your fork:
git push origin feature/your-feature-name. - Open a pull request on the main repository with a clear description of your changes.
Kindly adhere to our Code of Conduct when contributing.
By contributing to this project, you agree to license your contributions under the MIT License.