Yolov5 Face Mask Detection

Abstract

1. Introduction

Face mask detection is an object detection task that detects whether people are wearing masks or not in videos. This repo includes a demo for building a face mask detector using YOLOv5 model.

2. Objective

Face Mask Detection using YOLOv5

This project demonstrates how Jupyter Notebook, Deep Learning can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/spacewalk01/yolov5-face-mask-detection.git
cd yolov5-face-mask-detection

1. Clone this repo and install YOLOv5:

git clone https://github.com/spacewalk01/face-mask-detection
cd face-mask-detection

# Install yolov5
git clone https://github.com/ultralytics/yolov5
cd yolov5
pip install -r requirements.txt

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by spacewalk01 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.