The covid-19 outbreak caused a global health crisis, and it still continues to spread rapidly today. Considering that it is very difficult to manually control the use of masks in public places, it is inevitable to turn the process into an automatic detection system. In this study, various Transfer Learning networks in detecting the usage of face masks are analyzed using a large dataset. As a consequence, it can be determined whether the face images recorded in-the-wild and low resolution from various angles are masked / unmasked and whether they are wearing the mask correctly or not. The architecture depths and execution times are also examined to see whether they have a positive effect on the accuracy or not. The results indicate that ResNet50 has a significant success over the rest of the models and the depth of the used model does not guarantee the success nor does it determine the time it takes for training. © 2022 IEEE.
Analysis of Transfer Learning Models for Face Mask Detection