# TerraTorch
:book: [Documentation](https://IBM.github.io/terratorch/)
## 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:
- Easy access to open source pre-trained Geospatial Foundation Model backbones (e.g., [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M), [SatMAE](https://sustainlab-group.github.io/SatMAE/) and [ScaleMAE](https://github.com/bair-climate-initiative/scale-mae), other backbones available in the [timm](https://github.com/huggingface/pytorch-image-models) (Pytorch image models) or [SMP](https://github.com/qubvel/segmentation_models.pytorch) (Pytorch Segmentation models with pre-training backbones) packages, as well as fine-tuned models such as [granite-geospatial-biomass](https://huggingface.co/ibm-granite/granite-geospatial-biomass)
- Flexible trainers for Image Segmentation, Classification and Pixel Wise Regression fine-tuning tasks
- Launching of fine-tuning tasks through flexible configuration files
## 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`.
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`.
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) clone this repo, install dependencies using `pip install -r requirements/required.txt -r requirements/dev.txt` and run `pip install -e .`
## Quick start
To get started, check out the [quick start guide](https://ibm.github.io/terratorch/quick_start)
## For developers
Check out the [architecture overview](https://ibm.github.io/terratorch/architecture)
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"description": "# TerraTorch\n\n:book: [Documentation](https://IBM.github.io/terratorch/)\n\n## Overview\nTerraTorch is a library based on [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/) and the [TorchGeo](https://github.com/microsoft/torchgeo) domain library\nfor geospatial data. \n\nTerraTorch\u2019s main purpose is to provide a flexible fine-tuning framework for Geospatial Foundation Models, which can be interacted with at different abstraction levels.\n\nThe library provides:\n\n- Easy access to open source pre-trained Geospatial Foundation Model backbones (e.g., [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M), [SatMAE](https://sustainlab-group.github.io/SatMAE/) and [ScaleMAE](https://github.com/bair-climate-initiative/scale-mae), other backbones available in the [timm](https://github.com/huggingface/pytorch-image-models)\u00a0(Pytorch image models) or\u00a0[SMP](https://github.com/qubvel/segmentation_models.pytorch) (Pytorch Segmentation models with pre-training backbones)\u00a0packages, as well as fine-tuned models such as [granite-geospatial-biomass](https://huggingface.co/ibm-granite/granite-geospatial-biomass)\n- Flexible trainers for Image Segmentation, Classification and Pixel Wise Regression fine-tuning tasks\n- Launching of fine-tuning tasks through flexible configuration files\n\n## Install\n### Pip\nIn 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`. \n\nFor a stable point-release, use `pip install terratorch`. \nIf 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`.\n\nAnother 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 \na common CLI tool, with no need of installing dependencies or activating environments. \n\nTerraTorch 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`.\n\nTo install as a developer (e.g. to extend the library) clone this repo, install dependencies using `pip install -r requirements/required.txt -r requirements/dev.txt` and run `pip install -e .`\n\n## Quick start\n\nTo get started, check out the [quick start guide](https://ibm.github.io/terratorch/quick_start)\n\n## For developers\n\nCheck out the [architecture overview](https://ibm.github.io/terratorch/architecture)\n",
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