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Introduction
Để sau
- YOLOv8 (You Only Look Once): This deep learning model plays a crucial role in accurately detecting and localizing faces within images and video frames.
It's important to note that this project is intended primarily for educational purposes. While it demonstrates the potential of facial recognition systems for attendance tracking, it is a work in progress and has not undergone extensive testing. As such, it may contain errors, bugs, or limitations.
We encourage users to actively participate in the improvement of this project. If you encounter any issues, errors, or have suggestions for enhancements, please don't hesitate to create an issue report in the repository. Your feedback is valuable in helping us refine and enhance the project.
Feel free to explore the project's code, documentation, and accompanying materials to gain insights into the implementation of real-time facial recognition systems. By contributing to this project or adapting it for your own educational purposes, you can further advance your understanding of computer vision and machine learning.
Please refer to the sections below for instructions on installation, usage, and contributing to the project.
Keep in mind that this project is a work in progress, and your understanding and patience are greatly appreciated as we continue to develop and refine it.
Happy exploring and learning!
Installation
git clone https://github.com/tuan124816/Fire-detection-with-yolov8.git
pip install -r requirements.txt
Usage
To use this project, follow these steps:
- Run the application:
python main.py
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Add a new face, enter the person's name/ID into 'Register Student ID' and press 'Submit' The program will take a few seconds to take the person's picture and train
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To start tracking, press 'Track Face', when you're done, press Q button to escape the tracking screen
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When you have finished your tracking task, press 'Quit' to close the program, the attendance list will be save in the attend list.xlsx file
Contribution
We welcome contributions from the community. To contribute to this project, follow these steps:
- Fork the repository.
- Create a new branch for your feature or bug fix.
- Make your changes and commit them.
- Submit a pull request.
Lisence
This project is licensed under the MIT License - see the MIT License file for details.