The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
mm-olmo images and video frames
A pixel mirror: the exact set of images and video frames that a grounding/segmentation
training stack reads from outside its own annotation tree. It contains no annotations of its
own — every mask, box, caption and vocabulary lives in the annotation parquets of the
companion tree (grounding_data). This repo exists so that the annotations are actually
usable by someone who does not have the original cluster filesystem.
The file list is not a whole-dataset dump. It is exactly the files referenced by the
training/eval parquets, which is 5–10× smaller than mirroring the upstream datasets whole
(for example the BURST frames are 143,519 keyframes / 15.4 GiB, out of 1,574,936 files /
171 GiB present in the source TAO-Amodal tree).
Contents
| Group | Files | Size | Upstream source |
|---|---|---|---|
Objects365-2020 |
345,442 | 78.6 GiB | Objects365 v2 train images |
SA-1B |
71,077 | 56.8 GiB | SA-1B (high-instance-count subset) |
BURST |
143,519 | 15.4 GiB | TAO-Amodal frames (BURST annotates TAO videos) |
LVOSv1 |
96,395 | 14.0 GiB | LVOS v1 |
OVIS |
53,857 | 12.6 GiB | OVIS |
pixmo_images |
56,831 | 10.0 GiB | PixMo pointing images |
Ref-YT-VOS |
100,074 | 8.5 GiB | Refer-YouTube-VOS |
LV-VIS |
88,577 | 8.4 GiB | LV-VIS |
YT-VIS |
30,452 | 7.4 GiB | YouTube-VIS (JPEG dirs + train .mp4s) |
ADE20k |
55,151 | 6.0 GiB | ADE20K, Supervisely format (img/ + ann/ + parquet caches) |
ReVOS |
35,161 | 5.8 GiB | ReVOS |
Ref-DAVIS17 |
6,208 | 895 MiB | Refer-DAVIS17 |
| Total | 1,082,744 | 224.5 GiB | 52 tar shards |
Layout
shards/<group>/<group>-00000.tar # ~5 GiB each; tar members are paths relative to the mm-olmo root
index/<group>.parquet # path -> shard, size (for fetching selectively)
Packed as tar shards rather than loose files because the Hub asks for fewer than 100k files per repo and at most 10k entries per folder; 1.08M loose images would violate both. Note this means the dataset viewer does not apply — the payload is a file tree, not tabular rows.
Reconstructing the tree
Tar members are relative paths, so extracting every shard into one directory rebuilds the original layout:
hf download royguw/mm-olmo-images --repo-type dataset --local-dir ./dl
MM_ROOT=/your/path/to/mm-olmo
for t in ./dl/shards/*/*.tar; do tar -xf "$t" -C "$MM_ROOT"; done
You then get $MM_ROOT/img_datasets/…, $MM_ROOT/images/ADE20k/…,
$MM_ROOT/torch_datasets/pixmo_images/… and $MM_ROOT/video_datasets/….
To grab one subset only, read its index/<group>.parquet, take the distinct shard
values, and download just those:
import pyarrow.parquet as pq
from huggingface_hub import hf_hub_download
idx = pq.read_table(hf_hub_download("royguw/mm-olmo-images", "index/ADE20k.parquet",
repo_type="dataset")).to_pydict()
for shard in dict.fromkeys(idx["shard"]):
hf_hub_download("royguw/mm-olmo-images", shard, repo_type="dataset")
The annotation parquets that reference these files store absolute paths, so after extracting
you will need to rewrite their stored prefix to $MM_ROOT.
Licensing and attribution
This is a redistribution mirror of third-party datasets, assembled only to make the companion annotations reproducible. It is not a new dataset and no new license is claimed over the pixels.
Each group above remains under the license of its upstream source, and those licenses differ — several are research/non-commercial only, and some require agreeing to upstream terms before use. Before using any group, check that group's upstream license and comply with it. If you are an author or rights-holder of any upstream dataset here and want a subset removed, open a discussion on this repo and it will be taken down.
No attempt has been made to relicense, and the per-group provenance table above is provided precisely so that each source stays traceable.
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