Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
archives: list<item: struct<archive: string, size: int64, sha256: string, files: list<item: struct<path: strin (... 92 chars omitted)
child 0, item: struct<archive: string, size: int64, sha256: string, files: list<item: struct<path: string, size: in (... 80 chars omitted)
child 0, archive: string
child 1, size: int64
child 2, sha256: string
child 3, files: list<item: struct<path: string, size: int64, sha256: string>>
child 0, item: struct<path: string, size: int64, sha256: string>
child 0, path: string
child 1, size: int64
child 2, sha256: string
child 4, created_at: string
child 5, compression_integrity_verified: bool
completed_at: string
image_resolution: list<item: int64>
child 0, item: int64
upstream: string
tasks: list<item: struct<task_id: string, archive: string, lerobot_path: string, annotations_path: string, (... 453 chars omitted)
child 0, item: struct<task_id: string, archive: string, lerobot_path: string, annotations_path: string, data_accept (... 441 chars omitted)
child 0, task_id: string
child 1, archive: string
child 2, lerobot_path: string
child 3, annotations_path: string
child 4, data_acceptance: struct<passed: bool, source_demos: int64, unique_frames: int64, expanded_frames: int64, subtask_epis (... 334 chars omitted)
child 0, passed: bool
child 1, source_demos: int64
child 2, unique_frames: int64
child 3, expanded_frames: int64
child 4, subtask_episodes: int64
child 5, boundary_masks_checked: int64
child 6, normalization_recomputed_from_unique_frames: bool
child 7, all_fallback_batches_have_exact_epoch_coverage: bool
child 8, padding_has_no_supervision: bool
child 9, norm_sha256: string
child 10, arrow_path: string
child 11, decoded_unique_image_pairs: int64
child 12, all_images_rgb_224x224: bool
child 13, validated_on: string
child 14, training_started: bool
to
{'tasks': List({'task_id': Value('string'), 'archive': Value('string'), 'lerobot_path': Value('string'), 'annotations_path': Value('string'), 'data_acceptance': {'passed': Value('bool'), 'source_demos': Value('int64'), 'unique_frames': Value('int64'), 'expanded_frames': Value('int64'), 'subtask_episodes': Value('int64'), 'boundary_masks_checked': Value('int64'), 'normalization_recomputed_from_unique_frames': Value('bool'), 'all_fallback_batches_have_exact_epoch_coverage': Value('bool'), 'padding_has_no_supervision': Value('bool'), 'norm_sha256': Value('string'), 'arrow_path': Value('string'), 'decoded_unique_image_pairs': Value('int64'), 'all_images_rgb_224x224': Value('bool'), 'validated_on': Value('string'), 'training_started': Value('bool')}}), 'image_resolution': List(Value('int64')), 'upstream': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
archives: list<item: struct<archive: string, size: int64, sha256: string, files: list<item: struct<path: strin (... 92 chars omitted)
child 0, item: struct<archive: string, size: int64, sha256: string, files: list<item: struct<path: string, size: in (... 80 chars omitted)
child 0, archive: string
child 1, size: int64
child 2, sha256: string
child 3, files: list<item: struct<path: string, size: int64, sha256: string>>
child 0, item: struct<path: string, size: int64, sha256: string>
child 0, path: string
child 1, size: int64
child 2, sha256: string
child 4, created_at: string
child 5, compression_integrity_verified: bool
completed_at: string
image_resolution: list<item: int64>
child 0, item: int64
upstream: string
tasks: list<item: struct<task_id: string, archive: string, lerobot_path: string, annotations_path: string, (... 453 chars omitted)
child 0, item: struct<task_id: string, archive: string, lerobot_path: string, annotations_path: string, data_accept (... 441 chars omitted)
child 0, task_id: string
child 1, archive: string
child 2, lerobot_path: string
child 3, annotations_path: string
child 4, data_acceptance: struct<passed: bool, source_demos: int64, unique_frames: int64, expanded_frames: int64, subtask_epis (... 334 chars omitted)
child 0, passed: bool
child 1, source_demos: int64
child 2, unique_frames: int64
child 3, expanded_frames: int64
child 4, subtask_episodes: int64
child 5, boundary_masks_checked: int64
child 6, normalization_recomputed_from_unique_frames: bool
child 7, all_fallback_batches_have_exact_epoch_coverage: bool
child 8, padding_has_no_supervision: bool
child 9, norm_sha256: string
child 10, arrow_path: string
child 11, decoded_unique_image_pairs: int64
child 12, all_images_rgb_224x224: bool
child 13, validated_on: string
child 14, training_started: bool
to
{'tasks': List({'task_id': Value('string'), 'archive': Value('string'), 'lerobot_path': Value('string'), 'annotations_path': Value('string'), 'data_acceptance': {'passed': Value('bool'), 'source_demos': Value('int64'), 'unique_frames': Value('int64'), 'expanded_frames': Value('int64'), 'subtask_episodes': Value('int64'), 'boundary_masks_checked': Value('int64'), 'normalization_recomputed_from_unique_frames': Value('bool'), 'all_fallback_batches_have_exact_epoch_coverage': Value('bool'), 'padding_has_no_supervision': Value('bool'), 'norm_sha256': Value('string'), 'arrow_path': Value('string'), 'decoded_unique_image_pairs': Value('int64'), 'all_images_rgb_224x224': Value('bool'), 'validated_on': Value('string'), 'training_started': Value('bool')}}), 'image_resolution': List(Value('int64')), 'upstream': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
INOVO Subtask Dataset
100 demonstrations, 448 annotated subtask episodes; complete 224x224 dual-camera LeRobot training data.
完整文件格式、字段、边界与解压说明见 FILE_FORMAT.txt。
Data are stored in independent losslessly compressed .tar.zst archives. Annotations and metadata are also available directly in this repository. See INDEX.json, archive_manifest.json, and SHA256SUMS for paths and integrity.
Upstream data retain their original terms; no new license is assigned by this export.
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