This project utilizes a YOLOv8l model trained for detecting face masks in images. The model classifies images into three categories: wearing mask correctly, not wearing mask, and not wearing mask properly. It aims to provide a solution for monitoring adherence to face mask guidelines in public spaces.
The dataset used for training and validation is sourced from Kaggle's Face Mask Detection dataset. The dataset consists of images annotated with bounding boxes specifying regions of interest (ROI) around faces with different mask categories.
- XML Parsing: XML annotations are parsed to extract image names and instance details.
- Dataset Structuring: Images are organized into YOLOv8-compatible folder structures (
dataset/images/train, dataset/images/validation) along with corresponding label files (dataset/labels/train, dataset/labels/validation).
The YOLOv8l model from Ultralytics is utilized for its efficient object detection capabilities. Instead of training from scratch, a pretrained model from Kaggle's model repository is employed, which has been fine-tuned for face mask detection.
To know more about fine tuning process you can look at fine-tuning process
Ensure the following dependencies are installed:
- Python 3.x
- Required Python packages (listed in requirements.txt)
- Clone the repository:
git clone https://github.com/Shivam-21-11/mask-detection-yolov8L.git
cd mask-detection-yolov8L- Install dependencies:
pip install -r requirements.txtYou can run the notebook and all the necessary files will be created/downloaded.
output.webM
- The dataset is sourced from Kaggle's Face Mask Detection dataset.
- The YOLOv8 model is provided by Ultralytics.
Contributions are welcome! Please fork the repository and create a pull request with your improvements.
MIT License
Copyright (c) 2024 Shivam Singh
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
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