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Face Mask Detection – PyTorch

Watch the Demo

🎥 Click the image above to watch a live demo of this project on YouTube.

🧠 Project Overview

This project detects whether a person is wearing a face mask in real time using deep learning and computer vision. Built with PyTorch, OpenCV, and MTCNN, it classifies faces as either:

  • 😷 With Mask
  • 😶 Without Mask

It promotes public health by enabling real-time monitoring using a webcam, and is trained on labeled image data.

📁 Dataset

The dataset used is provided in mask_images_ready.zip, containing labeled images split into:

  • with_mask
  • without_mask

To use the dataset:

  1. Download mask_images_ready.zip
  2. Extract it into your project directory
  3. Use it for training/testing the model

⚙️ Installation

Ensure you have the following dependencies installed:

pip install -r requirements.txt

Required Versions:

  • Python ≥ 3.6
  • torch==2.0.1
  • torchvision==0.9.1
  • MTCNN==0.1.12
  • opencv-python==4.5.3.56
  • numpy==1.21.5
  • Pillow==8.3.2
  • matplotlib==3.7.1

🚀 How to Use

1. Train the Model

Open and run the following notebook in Google Colab or locally (with GPU support):

model.ipynb
  • Follow in-notebook instructions
  • Ensure your dataset is organized as with_mask/ and without_mask/
  • Train and save your model

2. Real-Time Inference

After training:

Open this notebook in Jupyter Notebook or JupyterLab (⚠️ Not Google Colab):

realTimeDetection.ipynb
  • Connect a webcam
  • Run the notebook to begin real-time face mask detection

⚠️ Notes

  • Colab users: Google Colab cannot access your local webcam, so use it only for training.
  • Webcam access: For real-time detection, ensure your device has a functional camera and use Jupyter locally.

💡 Technologies Used

  • PyTorch
  • OpenCV
  • MTCNN (Face Detection)
  • Python
  • Jupyter Notebook
  • Google Colab

📽 Demo

▶️ Watch the live demo here: https://youtu.be/xf0f9s3kzEg