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. levearaged IoT and machine learning to automate the waste classification process.
You can access the dataset here: [Waste Classification Data]https://www.kaggle.com/datasets/techsash/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.
Week 1: Libraries, Data Import, and Setup 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.