Multiple face mask wearer detection based on YOLOv3 approach

<div align="left"><a name="_Hlk108683337"></a><span lang="EN-US">The coronavirus disease 2019 (COVID-19) is a highly infectious caused by the SARS-CoV-2 coronavirus. In breaking transmission chain of SARS-CoV-2, government has made it compulsory for people to wear mask in public places prevent COVID-19 transmission. Hence, an automated face detection crucial facilitate monitoring process ensuring public. This project aims develop and multiple applying deep learning-based object algorithm you only look once version 3 (YOLOv3). YOLOv3 was concatenated with different backbones including ResNet-50 Darknet-53 model. Datasets were collected from online resources Kaggle Github images filtered labelled accordingly. The models trained on 4393 evaluated based precision, recall, mean average precision time. conclusion, DarkNet53_YOLOv3 chosen as better model compared ResNet50_YOLOv3 its good performance accuracy mAP 95.94% fast speed time 50 seconds 776 images. </span></div>