**

• [ ]

Face Mask Detection with YOLOv5

** A deep learning-based solution for detecting face masks in images and live video feeds using the YOLOv5 framework. This project helps ensure safety by identifying people with and without masks in real-time.

Features

• Detects faces with and without masks in images, videos, and live streams.

• High accuracy and fast inference speed using YOLOv5.

• Supports both CPU and GPU for inference.

• Customizable confidence threshold and input sizes.

Installation

Prerequisites

• [ ] Python 3.7 or higher
• [ ] PyTorch installed (with GPU support if available)
• [ ] pip package manager

Steps

• [ ] Clone the Repository

  git clone https://github.com/NinjaIfti/Face-Mask-Detection-YoloV5.git
  

  cd face-mask-detection

Set up a Virtual Environment (Optional but Recommended) A virtual environment helps isolate project dependencies, making it easier to manage and avoid conflicts with other Python projects on your system.

• [ ] Create a Virtual Environment:

  python -m venv yolov5-env
  Activate the Virtual Environment:
  

On Linux/macOS:

source yolov5-env/bin/activate

On Windows:

yolov5-env\Scripts\activate

• [ ] Install Dependencies Once the virtual environment is active, install the required dependencies:

  pip install -r requirements.txt
  

• [ ] Download Pre-trained YOLOv5 Model You can either use a pre-trained YOLOv5 model or train a custom model for face mask detection.

To use the pre-trained model, simply download it:

 wget https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt

• [ ] Running the Detection

On Images

bash
python detect.py --source images --weights yolov5s.pt --conf 0.5

On Video

bash
python detect.py --source video.mp4 --weights yolov5s.pt --conf 0.5

The detected results will be saved in the runs/detect/exp folder.

Live Detection For live webcam detection, simply run:

bash
python detect.py --source 0 --weights yolov5s.pt --conf 0.5

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How It Works

*

Model Training

The model is based on YOLOv5, a state-of-the-art object detection model. It has been fine-tuned to detect faces and classify them as "with mask" or "no mask."

Image and Video Processing

Once the model is loaded, it takes images or video frames as input, performs face detection, and classifies the face as either "mask" or "no mask."

*

Troubleshooting

*

Error: ModuleNotFoundError

If you encounter errors related to missing modules, ensure that all dependencies are installed using:

bash
pip install -r requirements.txt

Low Detection Accuracy

If the model doesn't perform well, consider retraining it with your own dataset for better accuracy.

License

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

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**Acknowledgments**

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YOLOv5

OpenCV

PyTorch