This project implements a Fire Detection model using YOLOv8, optimized for detecting fire in various environments. The model can process real-time video feeds or images to provide timely fire alerts.
- YOLOv8 Architecture: Leveraging the powerful YOLOv8 architecture for fast and accurate fire detection.
- Real-Time Detection: Optimized for quick processing, allowing the detection of fire as it occurs.
- Custom Trained Weights: Pre-trained on a comprehensive fire dataset and stored in a zip file. The model is fine-tuned for detecting fire across various conditions and angles.
- Classes: The model is designed to detect a single class - fire.
- Integration Ready: This model can be integrated into various real-world applications like surveillance systems, fire alarms, or industrial monitoring systems.
The pre-trained model weights are stored in the provided ZIP file. You can load and use these weights for inference and further fine-tuning.
To start using the fire detection model, clone the repository:
!git clone https://github.com/abuzarkhaaan/fire-detection-yolov8.git