Eco-Bin is a cutting-edge project developed by CMU students in 2023. It's an innovative solution that combines environmental sustainability, automation for humanity, and space exploration. The primary goal of Eco-Bin is to create an autosorting trash bin that leverages machine learning to identify and sort items into different categories such as trash, recyclables, glass, and plastic. By doing so, Eco-Bin aims to reduce waste and promote recycling, contributing to a more sustainable future on Earth and potentially in space.
- Machine Learning Powered: Eco-Bin utilizes state-of-the-art machine learning algorithms to accurately classify items placed in the bin.
- Auto-Sorting: Items are automatically sorted into appropriate categories, reducing the need for manual sorting and promoting recycling.
- Real-time Feedback: Eco-Bin provides real-time feedback on the types and quantities of items it sorts, helping users become more environmentally conscious.
- Web Interface: An intuitive web interface allows users to monitor Eco-Bin's performance and track their environmental impact.
- Extensible: The project is designed to be easily expandable, allowing for future improvements and additions.
To use Eco-Bin, follow these steps:
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Clone the Repository:
git clone https://github.com/mehulgoel873/Eco-Bin.git
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Install Dependencies:
pip install joblib pip install flask pip install pygame pip install Pillow pip install scikit-image pip install numpy
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Run the Application:
python ecoapp.py
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Access the Web Interface: Open your web browser and navigate to
http://localhost:5000to interact with Eco-Bin. -->
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Place Items in Eco-Bin: Simply deposit items into Eco-Bin as you would with a regular trash can. Eco-Bin will automatically classify and sort the items.
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Monitor Progress: Access the web interface to track your recycling efforts, and gain insights into the environmental impact of your recycling habits.
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Contribute: You can contribute to the project by reporting issues, submitting pull requests, or by improving the machine learning model for better classification accuracy.
We welcome contributions from the community. If you'd like to contribute to Eco-Bin, please contact Mehul Goel (mehulg@andrew.cmu.edu) or Anirudh Mani (anirudhm@andrew.cmu.edu).
The main goal of this project is to make the Carnegie Mellon campus more eco-friendly by enabling proper disposal of waste. Currently, many recyclable materials are put in the wrong bins, leading to less reusing overall. This machine-learning platform alleviates much of the confusion surrounding waste classification by accurately classifying between recyclables and other pieces of trash.
We built this project using Python, HTML, and CSS. The main challenge for our team was the large amount of time it took to train the machine learning model, which delayed the implementation of our project on the back end. However, building this project was an excellent learning experience for our team, since we gained the expertise on how to create a real-world application in a short space of time.
This project is licensed under the MIT License - see the LICENSE file for details.