1. Introduction
2. Features
3. Installation
4. Usage
5. Dataset
6. Demo
7. Target Audience
8. Contributions

This project implements an AI-powered garbage detection and sorting system using the YOLOv8 model for precise waste classification. The system is designed to enhance waste sorting efficiency and promote sustainable waste management practices.

• Real-time garbage detection and classification
• Supports six categories: cardboard, plastic, glass, paper, metal, and biodegradable waste
• User-friendly interface using Streamlit
• Easy integration with camera systems for live detection
• Scalable and adaptable for various environments

1. Clone the repository:

   git clone https://github.com/binguliki/Garbage-Detection.git
   cd Garbage Detection

2. Install the required dependencies:

   pip install -r requirements.txt

1. Start the Streamlit application:

   streamlit run app.py

2. Click the "Start Camera" button to begin the live detection.

3. The system will display detected objects and their classifications in real-time.

4. Click the "Stop Camera" button to stop the live feed.

• Description: The dataset includes images of various waste types, categorized into cardboard, plastic, glass, paper, metal, and biodegradable waste.
• Source: Waste Sorting Dataset (Replace with actual source)
• Preprocessing: Images are resized, normalized, and augmented for better training performance.

• Municipal Waste Authorities: Streamline waste sorting processes, reduce contamination, and improve overall efficiency.
• Recycling Facilities: Automate sorting, enhance recycling rates, and separate materials more effectively.
• Large Businesses: Improve waste management practices, minimize waste generation, and optimize recycling efforts.

We welcome contributions to improve the project! To contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature-branch).
3. Make your changes.
4. Commit your changes (git commit -m 'Add new feature').
5. Push to the branch (git push origin feature-branch).
6. Create a pull request.

Thanks to the following people who have contributed to this project:

• SurajPhani-github