Datasets and models for wildfire detection in PyTorch
Project description
Pyrovision: wildfire early 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.
Pyrovision aims at providing the means to create a wildfire early detection system with state-of-the-art performances at minimal deployment costs.
Table of Contents
Getting started
Prerequisites
- Python 3.6 (or more recent)
- pip
Installation
You can install the package using pypi as follows:
pip install pyronear
Usage
datasets
Access all pyrovision datasets just like any torchvision.datasets.VisionDataset
:
from pyrovision.datasets import OpenFire
dataset = OpenFire('./data', download=True)
References
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/OpenFire/fastai/requirements.txt
python references/classification/OpenFire/fastai/train.py --help
You can then run the script with your own arguments:
python references/classification/OpenFire/fastai/train.py --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.
Documentation
The full package documentation is available here for detailed specifications. The documentation was built with Sphinx using a theme provided by Read the Docs.
Contributing
Please refer to CONTRIBUTING
if you wish to contribute to this project.
Credits
This project is developed and maintained by the repo owner and volunteers from Data for Good.
License
Distributed under the AGPLv3 License. See LICENSE
for more information.
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