With the onset of the COVID-19 pandemic, the whole world is in turmoil and is thinking of innovative solutions to prevent the spread of the virus. As a precautionary measure, people around the world are wearing masks to avoid contracting this virus. While some are following and taking this measure, some are not still not following despite official advice from the government and public health agencies. In this project, a face mask detection model that can accurately detect whether a person is wearing a mask or not is implemented. The model architecture uses MobileNetV2 which is a lightweight convolutional neural network therefore requires less computational power and can be easily embedded in computer vision systems and mobile. As a result, it can create a low-cost mask detector system which can help to identify whether a person is wearing a mask or not and act as a surveillance system. The model has two-stage detector framework and employs transfer learning using MobileNetV2.The face detector model achieved high accuracy of 99.98% on training data, 99.56% on validation data and 99.75% on testing data.
https://www.kaggle.com/ashishjangra27/face-mask-12k-images-dataset
