Face Mask Detection Tf2

A face mask detection using ssd with simplified Mobilenet and RFB or Pelee in Tensorflow 2.1. Training on your own dataset. Can be converted to kmodel and run on the edge device of k210

AI & Machine LearningPythonMIT

Abstract

Face Mask Detection Tf2 is an open-source AI & Machine Learning project. A face mask detection using ssd with simplified Mobilenet and RFB or Pelee in Tensorflow 2.1. Training on your own dataset. Can be converted to kmodel and run on the edge device of k210. This model is a lightweight face mask detection model. Based on ssd,the backbone is Mobilenet and RFB. It is built using Python. Key capabilities include: Tensorflow 2.1; Trainging and Inference; Precision with mAP. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building an AI & Machine Learning mini project or final-year project.

1. Introduction

This model is a lightweight face mask detection model. Based on ssd,the backbone is Mobilenet and RFB.

2. Objective

A face mask detection using ssd with simplified Mobilenet and RFB or Pelee in Tensorflow 2.1. Training on your own dataset. Can be converted to kmodel and run on the edge device of k210

This project demonstrates how Python can be applied to a real-world AI & Machine Learning problem.

3. Key Features / Modules

  • Tensorflow 2.1
  • Trainging and Inference
  • Precision with mAP
  • Eager mode training with tf.GradientTape
  • Network function with tf.keras
  • Dataset prepocessing with tf.data.TFRecordDataset

4. Technology Stack

Python

5. System Requirements

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

  • Python 3.8 or later
  • pip / virtualenv for dependencies
  • VS Code, PyCharm or Jupyter Notebook
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/PureHing/face-mask-detection-tf2.git
cd face-mask-detection-tf2

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 PureHing 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.

View Source on GitHubDownload ZIP

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