Face Mask Detection

Abstract

1. Introduction

Face mask detection system using Deep learning.

2. Objective

Face mask detection system using Deep learning.

This project demonstrates how Jupyter Notebook, OpenCV 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/Spidy20/face_mask_detection.git
cd face_mask_detection

1. Install Anaconda(Python 3.8) & Tensorflow GPU (I used 1.15 TF)
2. First Download Object Detecion official code from here
3. Then Download this Repository via GIT or zip.
4. Download Training.zip
5. In my Repository extract Training.zip.
6. Now copy all the files & folders from my repository.
7. Go to the master/research/object_detection and paste all files & folders of my repository to there.
8. Open face_mask_detection.ipynb in Jupyter and give the image in which you want to detect Mask(Mentioned in code).

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 Spidy20 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.