In Africa, effective waste management is a significant challenge due to the increasing population and urbanization. This project explores the potential of AI to offer a solution to this problem by automating the classification of waste, thus improving recycling processes and promoting environmental sustainability.
The model was trained on a dataset comprising 22,500 images of organic and recyclable objects obtained from Kaggle.
The model training was conducted on Google Colab using a T4 GPU, ensuring efficient learning and model performance.
The Streamlit app, hosted here, serves as a user-friendly interface for waste image classification.
- Visit the Streamlit app link.
- Upload an image of the waste item.
- View the classification results.
- Contribute to proper waste management by disposing of items as directed.
Your contributions are welcome to enhance the functionality and performance of the Waste Management Classification App. If you would like to contribute, please follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Push your changes to your forked repository.
- Submit a pull request, describing your changes in detail.
The Waste Management Classification App is licensed under the MIT License. Feel free to use, modify, and distribute the app as per the terms of the license.