Metadata-Version: 2.2
Name: terratorch
Version: 0.99.9
Summary: TerraTorch - A model training toolkit for geospatial tasks
Author: Carlos Gomes, Romeo Kienzler, Benedikt Blumenstiel, Francesc Martí Escofet, Paolo Fraccaro, Pedro Henrique Conrado, Jaione Tirapu Azpiroz, Daniela Szwarcman, Bianca Zadrozny, Þorsteinn Elí Gíslason, Raunak Bhansali, Takao Moriyama, Þorsteinn Elí Gíslason
Author-email: Joao Lucas de Sousa Almeida <joao.lucas.sousa.almeida@ibm.com>
License: Apache License, Version 2.0
Project-URL: Documentation, https://ibm.github.io/terratorch/
Project-URL: Issues, https://github.com/IBM/terratorch/issues
Project-URL: Source, https://github.com/IBM/terratorch
Keywords: fine-tuning,geospatial foundation models,artificial intelligence
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
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<img src="https://github.com/user-attachments/assets/f8c9586f-6220-4a53-9669-2aee3300b492" alt="TerraTorch"  width="400"/>

## Overview
TerraTorch is a library based on [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/) and the [TorchGeo](https://github.com/microsoft/torchgeo) domain library
for geospatial data. 

TerraTorch’s main purpose is to provide a flexible fine-tuning framework for Geospatial Foundation Models, which can be interacted with at different abstraction levels. The library provides:

- Convenient modelling tools:
    - Flexible trainers for Image Segmentation, Classification and Pixel Wise Regression fine-tuning tasks
    - Model factories that allow to easily combine backbones and decoders for different tasks
    - Ready-to-go datasets and datamodules that require only to point to your data with no need of creating new custom classes
    - Launching of fine-tuning tasks through CLI and flexible configuration files, or via jupyter notebooks
- Easy access to:
    - Open source pre-trained Geospatial Foundation Model backbones:
      * [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M)
      * [SatMAE](https://sustainlab-group.github.io/SatMAE/)
      * [ScaleMAE](https://github.com/bair-climate-initiative/scale-mae)
      * Satlas (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
      * DOFA (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
      * SSL4EO-L and SSL4EO-S12 models (as implemented in [TorchGeo](https://github.com/microsoft/torchgeo))
      * [Clay](https://github.com/Clay-foundation/model)
    - Backbones available in the [timm](https://github.com/huggingface/pytorch-image-models) (Pytorch image models)
    - Decoders available in [SMP](https://github.com/qubvel/segmentation_models.pytorch) (Pytorch Segmentation models with pre-training backbones) and [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) packages
    - Fine-tuned models such as [granite-geospatial-biomass](https://huggingface.co/ibm-granite/granite-geospatial-biomass)
    - All GEO-Bench datasets and datamodules
    - All [TorchGeo](https://github.com/microsoft/torchgeo) datasets and datamodules 

## Install
### Pip
In order to use th file `pyproject.toml` it is necessary to guarantee `pip>=21.8`. If necessary upgrade `pip` using `python -m pip install --upgrade pip`. 

For a stable point-release, use `pip install terratorch==<version>`.

[comment]: <If you prefer to get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.>
To get the most recent version of the main branch, install the library with `pip install git+https://github.com/IBM/terratorch.git`.

[comment]: <Another alternative is to install using [pipx](https://github.com/pypa/pipx) via `pipx install terratorch`, which creates an isolated environment and allows the user to run the application as a common CLI tool, with no need of installing dependencies or activating environments.>

TerraTorch requires gdal to be installed, which can be quite a complex process. If you don't have GDAL set up on your system, we reccomend using a conda environment and installing it with `conda install -c conda-forge gdal`.

To install as a developer (e.g. to extend the library):
```
git clone https://github.com/IBM/terratorch.git
cd terratorch
pip install -r requirements_test.txt
conda install -c conda-forge gdal
pip install -e .
```

To install terratorch with partial (work in development) support for Weather Foundation Models, `pip install -e .[wxc]`, which currently works just for `Python >= 3.11`. 

## Documentation

To get started, check out the [quick start guide](https://ibm.github.io/terratorch/quick_start).

Developers, check out the [architecture overview](https://ibm.github.io/terratorch/architecture).

## Contributing

This project welcomes contributions and suggestions.

A simple hint for any contributor. If you want to meet the GitHub DCO checks, just do your commits as below:
```
git commit -s -m <message>
```
It will sign the commit with your ID and the check will be met. 
