Plastic pollution is a big global problem, and sorting waste correctly is key to solving it. This project uses a CNN model to sort plastic waste into different types, making automated waste management easier.
The dataset used for this project is the Waste Classification Data by Sashaank Sekar. It contains a total of 25,077 labeled images, divided into two categories: Organic and Recyclable. This dataset is designed to facilitate waste classification tasks using machine learning techniques.
- Total Images: 25,077
- Training Data: 22,564 images (85%)
- Test Data: 2,513 images (15%)
- Classes: Organic and Recyclable
- Purpose: To aid in automating waste management and reducing the environmental impact of improper waste disposal
- Reviewed various waste management strategies and relevant white papers.
- Examined the composition of household waste.
- Divided the waste into two categories: Organic and Recyclable.
- Leveraged IoT and machine learning to automate the waste classification process.
You can access the dataset here: Waste Classification Data
Note: Ensure appropriate dataset licensing and usage guidelines are followed.
This section will be updated weekly with progress details and corresponding Jupyter Notebooks.
Date: 20th January 2025 – 27th January 2025
- Imported the required libraries and frameworks.
- Set up the project environment.
- Explored the dataset structure.
Note: If the file takes too long to load, you can view the Kaggle notebook directly: Kaggle Notebook.
- Python
- TensorFlow/Keras
- OpenCV
- NumPy
- Pandas
- Matplotlib
- Expanding the dataset to include more plastic waste categories.
- Deploying the model as a web or mobile application for real-time use.
- Integration with IoT-enabled waste management systems.
Contributions are welcome! If you would like to contribute, please open an issue or submit a pull request.
Made with ❤️ by Neha Maurya
📧 mauryaneha2006@gmail.com |
🔗 LinkedIn