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.
LIBERO for detection
A small multi-class object detection set rendered from LIBERO, for an undergraduate robot perception module at the National University of Singapore.
Nothing here is a new benchmark. LIBERO is a lifelong-robot-learning benchmark by Liu, Zhu, Zhu, Stone and colleagues; the scenes, objects, task descriptions and initial states are theirs, at https://libero-project.github.io, and that is the thing to cite.
What it is
LIBERO's scenes are built from description files and rendered, so the simulator
also produces an instance segmentation in which every mask carries the name of
the model that made it — akita_black_bowl_2 is a labelled instance of
akita_black_bowl. Thirty-one classes come out of that, and nobody drew a box.
Each frame also carries its task's language instruction and the instance that
instruction refers to, which LIBERO states in the task file as
(:obj_of_interest ...). Some scenes hold three identical bowls and the
instruction names one of them, so the referring expression is labelled rather
than guessed.
How it was rendered
The scenes are LIBERO's. Four decisions in the rendering are worth stating:
- The scene settles before the shutter. Objects are dropped a few centimetres and gravity is allowed to finish, about six simulator steps. Rendering at once boxes a bowl where it is not going to be.
- The layouts are LIBERO's own fifty validated initial states per task, not re-sampled placements — its sampler can leave an object falling off a shelf.
- The camera moves slightly in every frame, by up to 8 cm, 4° and 3° of
field of view, around LIBERO's one fixed
agentviewpose. - Half the frames have the arm over the table. It is driven there between the two renders of a layout, and the frame is dropped if anything it passed over shifted more than 2 cm. Occlusion is wanted; rearranging the scene is not.
Rendered at 512×512 and resampled to 256×256.
The split
By scene. Six of the twenty-two rooms are held out whole: KITCHEN_SCENE1,
KITCHEN_SCENE4, KITCHEN_SCENE6, KITCHEN_SCENE9, LIVING_ROOM_SCENE3,
STUDY_SCENE2. An earlier version split by layout within a task instead, which
leaves near-duplicates on both sides: LIBERO's initial states move objects by
only a few centimetres.
Twenty-two of the thirty-one classes appear in the held-out rooms; the other nine are trained on and not scored. A split in which all thirty-one appear on both sides does not exist on this object set — several classes live in only four or five tasks.
So a number from this set measures unseen rooms and unseen arrangements, with objects the detector has met elsewhere. It does not measure transfer to new object categories, and it does not measure transfer to a new viewpoint: every frame on both sides is that same camera, with the jitter above.
Contents
images/train/<task>__<layout>_<pose>.jpg 2154 frames, 256x256
images/test/ … 718 frames
train.json / test.json
Each record:
| field | |
|---|---|
file |
the image |
suite, task |
which LIBERO task it came from |
language |
the instruction that task was recorded for |
target |
the instance(s) the instruction is about |
objects[] |
instance, cls, label (1–31), box as x1 y1 x2 y2, area |
| train | test | |
|---|---|---|
| frames | 2154 | 718 |
| boxes | 10935 | 2820 |
| scenes | 16 | 6 |
| classes | 31 | 22 in the held-out rooms |
| objects per frame, median | 5 | 4 |
| box side, median | 39 px | 43 px |
| frames whose referred object is visible | 98% | 99% |
| download | 34 MB |
Licence
CC BY 4.0, following LIBERO. If you use it, cite LIBERO.
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