# Pytorch EO
Deep Learning for Earth Observation applications and research.
> ���� This project is in early development, so bugs and breaking changes are expected until we reach a stable version.
## Installation
Make sure that you have the dependencies installed.
```
pip install pytorch-eo
```
## Dependencies
Pytorch EO is built on top of:
- [Pytorch](https://pytorch.org/)
- [Torchvision](https://pytorch.org/vision/stable/index.html)
- [Pytorch-Lightning](https://www.pytorchlightning.ai/)
- [Rasterio](https://rasterio.readthedocs.io/en/latest/)
Do you need to learn these libraries first ? NO! You can just get started with our [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples) and [tutorials](https://github.com/earthpulse/pytorch_eo/tree/main/tutorials). However, if you plan to use Pytorch EO extensively and want to get the most out of it, you may have to become familiar with them.
## Examples
Learn by doing with our [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples).
### Ready to use Datasets
- [EuroSAT](https://github.com/phelber/EuroSAT)
<!-- ### Build your own Datasets
Using SCAN you can annotate your own data and access it directly through Pytorch EO. -->
## Research
Pytorch EO can be a useful tool for research:
- Flexibility: build and experiment with new models for EO applications.
- Reproducibility: use same data splits and random seeds to compare with others.
See the [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples).
## Production
Pytorch EO was built with production in mind from the beginning:
- Optimize model for production.
- Export models to torchscript.
<!-- - Upload models to our Models Universe
- Use models directly through SPAI -->
See the [examples](https://github.com/earthpulse/pytorch_eo/tree/main/examples).
<!-- ## Documentation
Read our [docs](https://earthpulse.github.io/pytorch_eo/). -->
## Contributing
Read the [CONTRIBUTING](https://github.com/earthpulse/pytorch_eo/blob/main/CONTRIBUTING.md) guide.
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