  The world is going through one of the most dangerous pandemics of all time
with the rapid spread of the novel coronavirus (COVID-19). According to the
World Health Organisation, the most effective way to thwart the transmission of
coronavirus is to wear medical face masks. Monitoring the use of face masks in
public places has been a challenge because manual monitoring could be unsafe.
This paper proposes an architecture for detecting medical face masks for
deployment on resource-constrained endpoints having extremely low memory
footprints. A small development board with an ARM Cortex-M7 microcontroller
clocked at 480 Mhz and having just 496 KB of framebuffer RAM, has been used for
the deployment of the model. Using the TensorFlow Lite framework, the model is
quantized to further reduce its size. The proposed model is 138 KB post
quantization and runs at the inference speed of 30 FPS.
