The purpose of this project is to create a Deep Learning model that identifies whether or not someone is wearing a mask. My model is based on YOLO's object detection algorithm, and I'm using the dataset from Roboflow website.

https://public.roboflow.ai/object-detection/mask-wearing

The dataset consists of two classes:

• with_mask
• without_mask

• Total 105 images for training and 29 images for validation present in 2 classes.
• Create a bounding boxes with the help of label-img And makesense.ai website according to YoloV5.
• Prepare folder structure that can be accept by YoloV5.

• Cloning the YoloV5 file from official repository.
• Changing the directory of yolov5
• Installing the dependencies
• Download all versions pre-trained weights.

• Go to yolov5/data/.
• Open data.yaml
• Edit the following inside it:

1. Training and Validation file path
2. Number of classes and Class names.

• Set images size 640 with batch of 8.
• Train model around 600 epochs .
• Visualise the training metrics with the help of tensorboard.

Face mask detection is an object detection task that detects whether people are wearing masks or not in videos. This repo includes a demo for building a face mask detector using YOLOv5 model.

[Mr.Mukesh DPawar].