olmoearth-paper-embeddings

OlmoEarth — Foundation-Model Embeddings for Paper Table 2 This dataset contains pre-extracted embeddings from **26 Earth-observation foundation models** evaluated on the **24 downstream tasks** that make up Table 2 of the OlmoEarth paper: *OlmoEarth: Stable Latent Image Modeling for Multimodal Earth Observation* AI2, 2025. arXiv:2511.13655. For every supported `(model, task)` pair we ran the model's encoder over the task's train / validation / test splits with the **paper-best hyperparameters** (val-best from the original sweep, captured in `eval_settings/`). Each split is saved to disk as a single `.pt` file so downstream KNN / linear-probe / fine-tuning experiments can skip re-extraction. Total size on disk is ~1.1 TB. Layout Each `.pt` file is a Python dict produced by `torch.save`: **Classification tasks**: `embeddings` is `(N, D)`. `labels` is `(N,)` for single-label tasks, `(N, num_classes)` for the multilabel `m_bigearthnet`. **Segmentation tasks**: `embeddings` is `(N, H, W, D)` at the model's patch grid (e.g. `(N, 16, 16, 768)` for a 64×64 input with stride 4). `labels` is at *full* image resolution, e.g. `(N, 64, 64)` or `(N, 256, 256)` — the LP head upsamples patch logits to label resolution. `D` varies by model (128 for the smallest models, up to 4096 for DINOv3-7B). Loading A more complete example that runs KNN and matches paper Table 2 numbers is in `load_example.py`. Row ordering Within any single `.pt` file, row *i* of `embeddings` corresponds to row i* of `labels` — the pair is always consistent. All three splits are saved in **canonical dataset order** (the dataset's native sample enumeration, identical across models). Concretely: For a given task, `train.pt` row *i* is the **same source sample** in every model's dump, so you can compare / concatenate / ensemble embeddings across models by row index. The same holds for `valid.pt` and `test.pt`. The order also matches the underlying eval dataset, so you can join row i* back to that dataset's *i*-th