Metadata-Version: 2.1
Name: pyrovision
Version: 0.1.1
Summary: Datasets and models for wildfire detection in PyTorch
Home-page: https://github.com/pyronear/pyro-vision
Author: PyroNear Contributors
Author-email: pyronear.d4g@gmail.com
Maintainer: Pyronear
License: CeCILL-2.1 or AGPLv3
Download-URL: https://github.com/pyronear/pyro-vision/tags
Keywords: pytorch,deep learning,vision,models,wildfire,object detection
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Classifier: Intended Audience :: Science/Research
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Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
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Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
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Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Requires-Dist: pylocron (>=0.1.3)

![PyroNear Logo](docs/source/_static/img/pyronear-logo-dark.png)

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# 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](#getting-started)
  * [Prerequisites](#prerequisites)
  * [Installation](#installation)
* [Usage](#usage)
* [References](#references)
* [Documentation](#documentation)
* [Contributing](#contributing)
* [Credits](#credits)
* [License](#license)



## Getting started

### Prerequisites

- Python 3.6 (or more recent)
- [pip](https://pip.pypa.io/en/stable/)

### Installation

You can install the package using [pypi](https://pypi.org/project/pyronear/) as follows:

```shell
pip install pyronear
```



## Usage

### datasets

Access all pyrovision datasets just like any `torchvision.datasets.VisionDataset`:

```python
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:

```shell
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:

```shell
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](https://github.com/fastai/fastai) and the other without it.*



## Documentation

The full package documentation is available [here](https://pyronear.github.io/pyro-vision/) for detailed specifications. The documentation was built with [Sphinx](https://www.sphinx-doc.org) using a [theme](https://github.com/readthedocs/sphinx_rtd_theme) provided by [Read the Docs](https://readthedocs.org).



## 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](https://dataforgood.fr/).



## License

Distributed under the AGPLv3 License. See `LICENSE` for more information.


