base on A Python toolkit for fine-tuning Geospatial Foundation Models (GFMs). <!--- <img src="https://github.com/user-attachments/assets/f7c9586f-6220-4a53-9669-2aee3300b492#light-only" alt="TerraTorch" width="400"/> <img src="assets/logo_white.png#dark-only" alt="TerraTorch" width="400"/> --> <picture> <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/f8c9586f-6220-4a53-9669-2aee3300b492"> <source media="(prefers-color-scheme: dark)" srcset="assets/logo_white.png"> <center><img style="display: block; margin-left: auto; margin-right: auto"; src="https://github.com/user-attachments/assets/f7c9586f-6220-4a53-9669-2aee3300b492" alt="TerraTorch" width="400"/></center> </picture> <!-- <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/figs/logo_inv.png"> <source media="(prefers-color-scheme: light)" srcset="docs/figs/logo.png"> </picture> --> [](https://huggingface.co/ibm-nasa-geospatial) [](https://pypi.org/project/terratorch) [](https://github.com/ibm/terratorch/actions/workflows/test.yaml) [](https://ibm.github.io/terratorch/)  [](https://pypi.org/project/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. <hr> <a href="https://www.youtube.com/watch?v=CB3FKtmuPI8"> <img src="https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png" alt="YouTube" width="20"> Watch the latest recording on YouTube: Earth observation foundation models with Prithvi-EO-2.0 and TerraTorch <img src="https://upload.wikimedia.org/wikipedia/commons/4/42/YouTube_icon_%282013-2017%29.png" alt="YouTube" width="20"> </a> <hr> 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) * [TerraMind](https://research.ibm.com/blog/terramind-esa-earth-observation-model) * [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 recommend 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. ## License This project is primarily licensed under the **Apache License 2.0**. However, some files contain code licensed under the **MIT License**. These files are explicitly listed in [`MIT_FILES.txt`](./MIT_FILES.txt). By contributing to this repository, you agree that your contributions will be licensed under the Apache 2.0 License unless otherwise stated. For more details, see the [LICENSE](./LICENSE) file. ", Assign "at most 3 tags" to the expected json: {"id":"13488","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"