Metadata-Version: 2.1
Name: pytorch-eo
Version: 2023.7.17
Summary: Deep Learning for Earth Observation
Author: EarthPulse
Author-email: it@earthpulse.es
Requires-Python: >=3.8,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Requires-Dist: lightning (>=2.0.1,<3.0.0)
Requires-Dist: pandas (>=1.5.3,<2.0.0)
Requires-Dist: rasterio (>=1.3.5.post1,<2.0.0)
Requires-Dist: scikit-image (>=0.19.3,<0.20.0)
Requires-Dist: scikit-learn (>=1.2.1,<2.0.0)
Requires-Dist: torch (>=2.0.1,<3.0.0)
Requires-Dist: torchmetrics (>=1.0.1,<2.0.0)
Requires-Dist: torchvision (>=0.15.1,<0.16.0)
Description-Content-Type: text/markdown

# 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

```
pip install pytorch-eo
```

## 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).
- [UCMerced](http://weegee.vision.ucmerced.edu/datasets/landuse.html) Land Use Dataset.
- [BigEarthNet](https://mlhub.earth/data/bigearthnet_v1).
<!-- - [LandCoverNet](https://mlhub.earth/10.34911/rdnt.d2ce8i) -->

### Tutorials

Learn how to build with Pytorch EO with our [tutorials](https://github.com/earthpulse/pytorch_eo/tree/main/tutorials).

## Challenges

PytorchEO has been used in the following challenges:

- [EUROAVIA](./challenges/euroavia_hackathon_21) Mission: European Students Space Hackathon, 2021.
- [On Cloud N](./challenges/OnCloudN): Cloud Cover Detection Challenge (DrivenData, 2021).

<!-- ### 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.

