Datasets and models for wildfire detection in PyTorch
PyroNear: early wildfire detection
The increasing adoption of mobile phones have significantly shortened the time required for firefighting agents to be alerted of a starting wildfire. In less dense areas, limiting and minimizing this duration remains critical to preserve forest areas.
PyroNear aims at offering an wildfire early detection system with state-of-the-art performances at minimal deployment costs.
Table of Contents
- Getting Started
- Python 3.6 (or more recent)
Use pip to install the package from git
pip install git+https://github.com/frgfm/PyroNear@master
Access all PyroNear datasets just like any
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.
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