Swift Mask Detection

Abstract

1. Introduction

SwiftMaskDetection is a face mask detection library with a Swift interface.

It is a port of AIZOO's FaceMaskDetection model to CoreML. The model runs at over 30fps on recent iPhones and iPads. For more information on the model and training data, please see https://github.com/AIZOOTech/FaceMaskDetection (AIZOO did all the hard work).

the "Signing & Capabilities" tab, and select an option from the "Team" menu.

2. Objective

CoreML face mask detector for iOS apps

This project demonstrates how Swift, Machine Learning, Computer Vision 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.

• Xcode on macOS
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/keithito/SwiftMaskDetection.git
cd SwiftMaskDetection

pod 'SwiftMaskDetection'

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 keithito 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.