Trash Detect

Trash Detect is a comprehensive project for detecting trash using machine learning and providing a web-based reward system. It consists of a React web application, model training scripts, and database integration.

Project Structure

• bin-detect-reward-main/
  Main web application for trash detection and reward system.

  • src/ — React frontend source code, including UI components, pages, and logic for interacting with the backend and Supabase.
  • public/ — Static assets such as images, icons, and the main HTML file.
  • supabase/ — Supabase integration files for authentication, database, and storage.
  • Configuration files:
    • .env — Environment variables for API keys and configuration.
    • package.json — Project dependencies and scripts.
    • vite.config.ts — Vite configuration for building and serving the app.

• model_train/Trash_Detect/
  Model training scripts and resources for trash detection.

  • data/ — Datasets for training and validation (images and annotations).
  • models/ — Saved model checkpoints and exported models.
  • notebooks/ — Jupyter notebooks for data analysis and model prototyping.
  • scripts/ — Python scripts for preprocessing, training, evaluation, and inference.
  • requirements.txt — Python dependencies for model training.

• supabase/
  Database migrations, configuration files, and temporary files for Supabase integration.

Getting Started

1. Clone the Repository

git clone https://github.com/Dhruv-D-Bhrasadiya/Trash_Detect.git
cd Trash_Detect

2. Set Up the Web Application

Navigate to the web app folder and install dependencies:

cd bin-detect-reward-main
npm install

Start the development server:

npm run dev

3. Set Up Model Training

Navigate to the model training folder:

cd model_train/Trash_Detect

Create and activate a Python virtual environment (recommended):

python -m venv venv
venv\Scripts\activate

Install Python dependencies:

pip install -r requirements.txt

Prepare your dataset by placing images and annotation files in the data/ directory.

4. Train the Model

Run the training script (adjust config and paths as needed):

python scripts/train.py --config configs/config.yaml

5. Evaluate and Use the Model

Evaluate the trained model:

python scripts/evaluate.py --model models/best_model.pth --data data/validation/

Run inference on new images:

python scripts/predict.py --model models/best_model.pth --input data/test/image1.jpg

Supabase Integration

• Configure Supabase credentials in .env files.
• Use the supabase/ folder for database migrations and setup.

License

See individual folders for license details.

Contributing

Feel free to open issues or submit pull requests for improvements or bug fixes.

Contact

For questions or support, please open an issue in the repository.