Developed a machine learning model to detect the presence of face masks in images and real-time video streams. This system was designed to contribute to public health efforts by identifying mask usage compliance. Key Features: Trained the model on a dataset of over 5,000 images, achieving a 90% detection accuracy. Optimized the system for real-time processing at 30 frames per second. Contributed to public health initiatives by deploying the model in community spaces for monitoring mask compliance.
Sep 01, 2021 - Nov 30, 2021