The increasing adoption of mobile phones have significantly shortened the duration between the beginning of a wildfire and the firefighting agents being alerted. In less dense areas, limiting and minimizing this amount of time is critical to preserve forest areas.

PyroNear aims at offering an wildfire early detection system with state-of-the-art performances at minimal deployment costs.

• Getting Started
  • Prerequisites
  • Installation
• Usage
• References
• Documentation
• Contributing
• Credits
• License

• Python 3.6 (or more recent)
• pip

Use pip to install the package from git

pip install git+https://github.com/frgfm/PyroNear@master

Access all PyroNear datasets just like any torchvision.datasets.VisionDataset:

from pyronear.datasets import OpenFire
dataset = OpenFire('./data', download=True)

You are free to use any training script, but some are already provided for reference. In order to use them, install the specific requirements and check script options as follows:

pip install -r references/classification/fastai/requirements.txt
python references/classification/fastai/train.py --help

You can then run the script with your own arguments:

python references/classification/fastai/train.py --data-path ./data --lr 3e-3 --epochs 4 --pretrained --deterministic

Please note that most tasks are provided with two training scripts (and their requirements.txt): one using fastai and the other without it.

The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.

Please refer to CONTRIBUTING if you wish to contribute to this project.

This project is developed and maintained by the repo owner and volunteers from Data for Good.

Distributed under the MIT License. See LICENSE for more information.