American-Sign-Language-Detection-With-YOLO

This project demonstrates the use of YOLOv8 for detecting American Sign Language (ASL) gestures. It includes a Flask application that allows you to test the ASL detection model using image uploads or live webcam feeds. You can also retrain the YOLOv8 model with a custom dataset.

Project Structure

Application Directory

The application directory contains all the components needed to run the Flask application for ASL detection.

application/
├── app.py            # The main Flask application file.
├── requirements.txt  # List of dependencies required for the project.
├── templates/        # Directory containing HTML templates.
│   ├── index.html    # Main template for the web interface.
├── static/           # Directory for static files like uploaded images.
│   ├── styles.css    # CSS file for styling the web interface.
│   └── uploads/      # Directory where uploaded images are stored.
├── models/           # Directory containing the trained YOLOv8 model.
│   └── yolov8n.pt    # The YOLOv8 model file used for ASL detection.
├── train/            # Directory containing the training script.
│   └── train.py      # Script to train the YOLOv8 model on a new dataset.
└── README.md         # This documentation file.

Training Directory

The training directory is used for training the YOLOv8 model with your own dataset.

training/
├── dataset/          # All the dataset files including train, valid, test splits, and the data.yaml file.
│   ├── train/        # Training images.
│   ├── valid/        # Validation images.
│   ├── test/         # Test images.
│   └── data.yaml     # Dataset configuration file.
├── runs/             # Directory to store training runs and results.
│   └── exp/          # Example of a training run folder with results.
├── test.jpg          # An example image used for testing.
├── train.ipynb       # Jupyter notebook for training and experimenting with the YOLOv8 model.
└── yolov8n.pt        # Pre-trained YOLOv8 model used for initializing training.

Project Demo

video1.mp4

video2.mp4

Getting Started

Prerequisites

Before you begin, ensure you have the following installed on your system:

• Python 3.8+
• Flask
• PyTorch
• YOLOv8 dependencies

You can install all necessary packages by navigating to the application directory and running:

cd application
python -m virtualenv venv
venv\Scripts\activate
pip install -r requirements.txt

Dataset

The dataset used for training the YOLOv8 model in this project can be downloaded from the following link:

• Roboflow ASL Dataset

Using Your Own Dataset

If you want to use your own dataset for training:

1. Organize Your Dataset:

   • Create subdirectories for train, valid, and test inside the training/dataset directory.
   • Place your training images in the train directory, validation images in the valid directory, and test images in the test directory.

2. Update data.yaml:

   • Modify the data.yaml file in the training/dataset directory to reflect the paths to your dataset.
   • Ensure the train, val, and test fields in data.yaml correctly point to the respective directories.

3. Proceed with Training:

   • Once your dataset is set up and the data.yaml file is configured, you can proceed with training the YOLOv8 model as described in the Training the Model section.

Running the Flask Application

To test ASL detection using the my pre-trained YOLOv8 model:

1. Start the Flask Application: Navigate to the application directory and run the Flask server:

   python app.py

2. Access the Web Interface: Open your web browser and go to http://127.0.0.1:5000/. From here, you can upload an image or use the live webcam feature to detect ASL gestures.

Acknowledgments

Roboflow provided us with a platform for dataset annotation.

Ultralytics, for their pre-trained YOLOv8 weights.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Feel free to contribute to this project by opening issues or submitting pull requests. Happy coding!