This project explores the creative use of the YOLOv5 model, pushing its boundaries beyond object detection to tackle the critical challenge of fire identification.
This project is a user-friendly web application that leverages YoloV5 to identify fire in images or videos accurately. With a deep learning model trained on a dataset of 10,000+ images, this project highlights the practical side of using AI in the real world.
The Fire Detection project offers the following features:
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Fire Detection:
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User-friendly Interface:
To run this Fire Detection project locally, follow these steps:
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Clone the Repository:
git clone https:/Msparihar/Fire-Detection-using-YoloV5.git
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Install the required dependencies:
pip install -r requirements.txt
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Run the App:
python main.py
Contributions to the Fire Detection project are welcome! If you'd like to contribute, please follow these steps:
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Fork the repository on GitHub.
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Create a new branch from the
mainbranch. -
Make your modifications and enhancements.
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Test your changes thoroughly.
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Commit and push your changes to your forked repository.
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Submit a pull request to the main repository, describing your changes in detail.
Please ensure your contributions adhere to the project's coding standards and guidelines.
The Fire Detection project is built upon various open-source libraries and resources. I would like to express my gratitude to the developers and contributors of the following projects:
This project is licensed under the MIT License. Feel free to modify and distribute it according to the terms of the license.
If you have any questions, suggestions, or feedback regarding this project, please contact the project maintainer at [email protected]
I really appreciate your interest in this project and hope you found this project helpful! Keep Exploring!