A machine learning model for a CS 229 final project
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Across the globe, there have been 55.6M million reported coronavirus cases (up from 33.8M last month). Covid-19 has plunged countless nations into chaos and recession as they scramble to keep the virus contained. Due to the highly contagious nature of the virus, every individual must do their part in preventing the spread by taking precautions such as wearing a face mask. Yet there are still many individuals who refuse to do so - this careless behavior puts many lives at risk, so it is imperative that we hold these individuals responsible.
In light of this issue, our project aims to create a machine learning model that can accurately detect, given an image, whether a person is properly wearing a face mask or not. This project will especially be important in the global return to work effort as businesses continue to search for ways to keep their employees and customers safe. Automating the process of face mask detection will reduce human labor while creating a system of accountability.
To get a local copy up and running follow these simple steps.
- Clone the repo
git clone https://github.com/dastratakos/Face-Mask-Detection.git- Install packages
pip install -r requirements.txt- Run the pipeline and pass in the model to run. Note that this pipeline will run the data preprocessing if it has not been done yet. For example:
python run_pipeline.py -m SVM- For ResNet models, it is recommended to use a VM through a platform such as GCP (Google Cloud Platform).
Distributed under the Apache 2.0 License. See LICENSE for more information.
Charles Pan, Gilbert Rosal, and Dean Stratakos - {cpan22, rosalg, dstratak}@stanford.edu
Project Link: https://github.com/dastratakos/Face-Mask-Detection