Face Detection Model

Overview

This project involves developing a robust face detection model utilizing advanced machine learning techniques and tools. The process begins with setting up the environment and installing necessary dependencies like TensorFlow, OpenCV, and matplotlib. Following this, we collect images using the OpenCV library to create a comprehensive dataset.

Table of Contents

• Installation
• Usage
• Contributing
• License

Installation

To get started, clone the repository and install the required dependencies.

git clone https://github.com/HoomKh/Face-Detection-Model.git
cd Face-Detection-Model
pip install -r requirements.txt

Usage

1. Setup the Environment:

   • Install necessary dependencies.
   • Configure the environment for TensorFlow, OpenCV, and matplotlib.

2. Collect Data:

   • Use the OpenCV library to capture images.
   • Create a comprehensive dataset for training and testing the model.

3. Train the Model:

   • Implement and train the face detection model using the collected dataset.

4. Evaluate the Model:

   • Assess the performance of the model using various metrics.

Contributing

Contributions are welcome! Please fork the repository and submit pull requests for any enhancements or bug fixes.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

---