arubique/waterbirds
Waterbirds (OCCAM layout) This repository hosts the Waterbirds image files used in the OCCAM codebase (arXiv), laid out for experiments on robust classification evaluation. Original data and credit The images come from the Waterbirds benchmark introduced with the group distributionally robust optimization in: Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang, Distributionally Robust Neural Networks for Group Shifts: On the Importance of… See the full description on the dataset page: https://huggingface.co/datasets/arubique/waterbirds.
Waterbirds (OCCAM layout)
This repository hosts the Waterbirds image files used in the OCCAM codebase (arXiv), laid out for experiments on robust classification evaluation.
Original data and credit
The images come from the Waterbirds benchmark introduced with the group distributionally robust optimization in:
Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, Percy Liang, Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization, arXiv:1911.08731.
Please cite that work when using the original benchmark. Licensing and redistribution terms of the underlying images follow the original dataset / WILDS release; refer to the paper and official sources for details.
Folder layout (twelve subscenarios)
On the Hugging Face Files tab you should see twelve top-level folders (three per historical `group_0 … group_3): the original scene, *_fg_only (foreground crop), and *_bg_only` (background image). Each triplet shares the same spurious-cue group:
Background-only images are paired with fg+bg composites via `metadata.csv` at the dataset root (copied from the original Waterbirds dataset). The upload script copies pixels from Places 256 dataset.
Class labels inside `0/ and 1/`
Each subscenario folder contains subfolders `0` and `1`, which are the binary coarse bird-type labels:
- `1` → landbird
- `0` → waterbird
Background-only crops use the same grouping as the original Waterbirds benchmark; they are distributed alongside the other subscenarios for analysis (e.g. background shift without the bird).
Example usage
Example:
from datasets import load_dataset
ds = load_dataset("arubique/waterbirds", "landbird_on_land_fg_only", split="train")`metadata.csv`
The repository includes `metadata.csv` at the root (WILDS-style columns: `img_id, img_filename, y, split, place, place_filename). Use it to recover the original bird image path and background Places path for each composite. Under each Hub subscenario, image files are named from img_filename; the matching *_bg_only` file uses the same basename so fg+bg and bg-only subsets stay aligned.
OCCAM codebase
Download scripts, configs, and full experiment documentation live in the OCCAM repo:
The download script in the codebase is scripts/download_datasets_and_checkpoints.py.
Citation (OCCAM)
If you use this exact packaging together with OCCAM, please also cite the OCCAM paper (HF paper page, arXiv).