Build Face-mask-detector-using-RetinaNet-model

We are grappling with a pandemic that’s operating at a never-before-seen scale. Researchers all over the globe are frantically trying to develop a vaccine or a cure for COVID-19 while doctors are just about keeping the pandemic from overwhelming the entire world. On the other hand, many countries have found social distancing, using masks & gloves a way to curb the situation a little.

I recently had an idea to apply my deep learning knowledge to help the current situation a little. In this project, I’ll introduce you to the implementation of RetinaNet with little background & working on it.

Directory Structure

• Data : This directory has input files that you'll need to important orginal Data. You can create your own dataset too. You can follow my this article to create your own dataset for your deep learning tasks. *maskDetectorJPEGImages: This directory contains original data.

  • maskDetectorXMLfiles: This directory contained the xml files produced after annotation of images. Each xml file contains the coordinates of the bounding box.

• keras-retinanet: This directory is produced after installing keras-retinanet. You can check retinaNet-maskDetector.ipynb notebook to get more details on installation.

  • snapshots: This directory under keras-retinanet directory will be used to save model's weights at different checkpoints.

• maskDetectorClasses: This file has been exporrted from the experiment . It contains the coordinates of the bounding boxes along with their classes. Please check notebook for more details.

• maskDetectorClasses: This file contains the information about the classes involved, mask and noMask in our case study.

• retinaNet-maskDetector.ipynb: This is the python notebook that you can run to implement face mask detector on your own.

Note: Kindly note that I've tested it on a very small Dataset with very minimal epochs. You'll need much larger dataset and epochs to get better results and accuracy.

Instructions for Installation

Dependencies: : You'll need to install below dependencies to run this project.

• numpy: 1.18.1
• pandas: 1.0.1
• matplotlib: 3.1.3
• requests: 2.22.0
• PIL: 7.0.0
• keras_retinanet
• labelImg
• shutil
• urllib
• glob
• xml

Article published on Analytics Vidhya:

• I've published a comprehensive case study on implementation of Face Mask Detector using RetinaNet Model. You can refer this link to get more details.

Important learnings from implementation of Face mask detector using RetinaNet model

• How to clone & install the keras-retinanet repository
• How to gather a large amount of Data for Deep learning tasks
• Create Dataset for your model training.
• Model Training
• Model Testing
• Final Notes

Important learnings from the article:

• What is RetinaNet Model
• Need for RetinaNet Model
• The Architecture of RetinaNet
  • Backbone Network
  • Subnetwork for object Classification
  • Subnetwork for object Regression
• What is Focal Loss, and why it's important in object detection algorithms?

License:

This project is open-source and distributed under the MIT License. Feel free to use and modify the code as needed.

Issues:

If you encounter any issues or have suggestions for improvement, please open an issue in the Issues section of this repository.

Contact:

The code has been tested on Windows system. It should work well on other distributions but has not yet been tested. In case of any issue with installation or otherwise, please contact me on Linkedin

About Me:

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.