royguw/grounding-data

grounding_data

The annotation tree behind a grounding/segmentation training stack, plus the images and video frames that live alongside it. Unlike the companion royguw/mm-olmo-images — which is pixels only — this repo carries the annotations: the parquet caches, JSON label files and vocabularies under each dataset's cache/ directory, which is where masks, boxes, referring expressions, captions and category vocabularies actually live.

9,331,994 files / 1.20 TiB, packed as 255 tar shards across 34 groups.

Layout

shards/<group>/<group>-00000.tar   # ~5 GiB each; members are paths relative to the grounding_data root
index/<group>.parquet              # path -> shard, size  (for fetching selectively)

Tar shards rather than loose files because the Hub caps entries at 10k per folder and asks for under 100k files per repo; 9.3M loose files breaks both by two orders of magnitude. The payload is a file tree, not tabular rows, so the dataset viewer does not apply.

Groups

Video groups are named video_data__<dataset>, matching video_data/<dataset>/ in the tree.

Group	Shards	Size	What
video_data__ViCaS	78	388.2 GiB	ViCaS grounded video captions + frames
video_data__SA-FARI	37	242.2 GiB	SA-FARI wildlife video, frames + masklets
Adobe_EntitySeg	38	193.9 GiB	EntitySeg images + annotations
coco	25	143.2 GiB	COCO 2014/2017 images, annotations, caches
video_data__BOSTVG	11	55.0 GiB	BOSTVG / OmniSTVG + SAM3 masks
GoldG	12	53.0 GiB	GoldG (Flickr30k entities + GQA)
rf100	9	43.4 GiB	RF100-VL
coconut	10	41.7 GiB	COCONut panoptic PNGs + caches
video_data__VIPOSeg	4	17.7 GiB	VIPOSeg frames + panomasks
SA-1B	4	17.1 GiB	SA-1B annotation caches (not the images)
pixno_points	3	9.9 GiB	PixMo pointing caches
refclef	2	5.6 GiB	RefCLEF / saiapr_tc-12
22 more	1 each	~14 GiB	reasonseg, lvis, manual_annotation, refcoco/+/g, grefcoco, panoptic-narrative-grounding, PhraseCutDataset, pixmo, VidSTG, and the small video caches

Reconstructing the tree

hf download royguw/grounding-data --repo-type dataset --local-dir ./dl
GD=/your/path/to/grounding_data
for t in ./dl/shards/*/*.tar; do tar -xf "$t" -C "$GD"; done

To pull one dataset only, read its index and fetch just the shards it names:

import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download

idx = pq.read_table(hf_hub_download("royguw/grounding-data", "index/coco.parquet",
                                    repo_type="dataset")).to_pydict()
for shard in dict.fromkeys(idx["shard"]):
    hf_hub_download("royguw/grounding-data", shard, repo_type="dataset")

The index is also how you find a single file without downloading everything: look up its path, and it tells you the one shard to fetch.

A note on paths

The annotation parquets store absolute source paths in columns such as image_path, png_path and frames_dir. After extracting you must rewrite those prefixes to wherever you put the data. Two prefixes appear:

• paths under the grounding_data root -> $GD
• paths under mm-olmo/ -> wherever you extracted royguw/mm-olmo-images

Video frame lists are stored as extension-less stems in frame_basenames_json, resolved against the directory listing; where that column is absent, frames are f"{i:05d}" for i in range(num_frames).

Licensing and attribution

This is a redistribution mirror of third-party datasets assembled for reproducibility. It is not a new dataset, and no new license is claimed over the underlying images, video frames or upstream annotations.

The groups carry different upstream licenses — several are research or non-commercial only (EntitySeg, for one, is Adobe non-commercial), and some require accepting upstream terms before use. Check the license of any group you intend to use and comply with it. If you are an author or rights-holder of an upstream dataset here and want a subset removed, open a discussion on this repo and it will be taken down.