torchgeo-bench 0.4.0

Lightweight benchmarking framework for geospatial models

torchgeo-bench

A lightweight benchmarking framework for evaluating frozen geospatial foundation models on the GeoBench V1 and V2 suites. Plug in any backbone, get KNN-5 / linear-probe accuracy on classification datasets and mIoU on segmentation datasets, with bootstrapped 95% confidence intervals — all configured through Hydra.

• Frozen-backbone evaluation — KNN-5, L-BFGS logistic regression, and linear / conv / FPN / DPT segmentation probes.
• GeoBench V1 + V2 built in — classification and segmentation, RGB or full multispectral / multi-modal stacks.
• Hydra-driven — sweep models, datasets, partitions, image sizes, and bands without code changes.
• Resumable — resume=true skips already-computed (dataset, method, model, …) rows. Atomic CSV appends are safe across parallel jobs.
• Bring your own model — copy contrib_template.py, implement _forward_patch_features, and add a one-file Hydra config. See the Stage 1 guide for a full walkthrough, or the Stage 2 guide to contribute the model back upstream.

Installation

pip install torchgeo-bench

For development:

git clone https://github.com/torchgeo/torchgeo-bench
cd torchgeo-bench
uv sync --extra dev

For GPU-accelerated KNN (Linux + CUDA 12 + glibc ≥ 2.28):

pip install 'torchgeo-bench[cuda]'

Requires Python 3.12+. The default (CPU) install runs on Linux, macOS, and Windows; GPU-accelerated KNN (the [cuda] extra) is Linux-only.

Download a dataset

The runner expects datasets under ./data/. To grab GeoBench V1:

torchgeo-bench download geobench_v1

V2 (classification + segmentation) and torchgeo's EuroSAT downloader work the same way (torchgeo-bench download geobench_v2, torchgeo-bench download eurosat). See the documentation for all options.

Run a basic experiment

# Default: random convolutional features (RCF) on every available dataset
torchgeo-bench run

# A single dataset with a pretrained ImageNet ResNet-50
torchgeo-bench run model=timm/resnet50 dataset.names=[m-eurosat]

The default device is cuda:0. On a machine without a working CUDA GPU (or if a GPU run crashes — see troubleshooting), fall back to CPU:

torchgeo-bench run dataset.names=[m-eurosat] device=cpu

Results are appended to results/all_results.csv, which ships pre-populated with reference results — to start from a clean slate, write to your own file with output=results/my_run.csv. Re-run with resume=true to skip already-completed rows.

Learn more

• Documentation — full configuration reference, available models, dataset tables, multi-band experiments, evaluation methodology, output schema, dev / release workflow, and troubleshooting.
• AGENTS.md — contributor guide and house style.

Citation

If you use this framework, please cite it (once the torchgeo-bench paper is available):

@misc{torchgeobench,
  title  = {torchgeo-bench: A lightweight benchmarking framework for geospatial foundation models},
  author = {torchgeo-bench Contributors},
  year   = {TBD},
  note   = {Software},
  url    = {https://github.com/torchgeo/torchgeo-bench}
}

License

MIT.