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… See the full description on the dataset page: https://huggingface.co/datasets/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.
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"; doneTo 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.
