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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- computer vision |
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- wildfire |
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--- |
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# Pyronear YOLOv8s for Early Wildfire Detection |
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This repository contains the custom YOLOv8s model trained by Pyronear for early wildfire detection. Our model leverages the powerful capabilities of the Ultralytics YOLOv8 framework to accurately identify potential wildfire hotspots in real-time. |
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## Installation |
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Install the ultralytics package including all requirements in a Python>=3.8 environment with PyTorch>=1.8. |
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```bash |
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pip install ultralytics |
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``` |
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For alternative installation methods including Conda, Docker, and Git, please refer to the [Ultralytics Quickstart Guide](https://docs.ultralytics.com/quickstart). |
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## Usage |
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### Python |
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You can also use the YOLOv8 model in a Python environment: |
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```python |
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from ultralytics import YOLO |
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from PIL import Image |
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# Load the model |
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model = YOLO('pyronear/yolov8s.pt') |
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# Run inference |
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results = model('path/to/your/image.jpg', conf=0.2, iou=0.1) |
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# Display results |
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results.show() |
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``` |
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For more examples and detailed usage, see the [YOLOv8 Python Docs](https://docs.ultralytics.com/usage/python). |
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## Acknowledgements |
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This project utilizes the [Ultralytics YOLOv8 framework](https://ultralytics.com/yolov8). Special thanks to the Ultralytics team for their support and contributions to the open-source community. |
