Datasets:

arubique
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waterbirds

Tasks:

Image Classification

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Image

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imagefolder

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10K - 100K

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Subset (12)

landbird_on_land · 2.26k rows

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train · 2.26k rows

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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:

Folder	Description
`landbird_on_land`	Original image (foreground + background); same spurious-cue group as historical `group_0`
`landbird_on_water`	Original image (foreground + background); same spurious-cue group as historical `group_1`
`waterbird_on_land`	Original image (foreground + background); same spurious-cue group as historical `group_2`
`waterbird_on_water`	Original image (foreground + background); same spurious-cue group as historical `group_3`
`landbird_on_land_fg_only`	Foreground-only crop for the same group as `landbird_on_land`
`landbird_on_water_fg_only`	Foreground-only crop for the same group as `landbird_on_water`
`waterbird_on_land_fg_only`	Foreground-only crop for the same group as `waterbird_on_land`
`waterbird_on_water_fg_only`	Foreground-only crop for the same group as `waterbird_on_water`
`landbird_on_land_bg_only`	Background-only crop for the same group as `landbird_on_land`
`landbird_on_water_bg_only`	Background-only crop for the same group as `landbird_on_water`
`waterbird_on_land_bg_only`	Background-only crop for the same group as `waterbird_on_land`
`waterbird_on_water_bg_only`	Background-only crop for the same group as `waterbird_on_water`

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.