Face Mask Detection – PyTorch

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

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🧠 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.

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📁 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

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⚙️ 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

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🚀 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

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⚠️ 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.

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💡 Technologies Used

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

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📽 Demo

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