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
dataset: string
identity: string
row_order: string
image_ids: list<item: string>
child 0, item: string
repo_id: string
upload_single_file_limit_bytes: int64
schema_version: int64
payload_files: int64
license_status: string
human_feedback_data: bool
package: string
payload_bytes: int64
repo_type: string
cohort: string
raw_images: bool
reassemble: list<item: struct<path: string, bytes: int64, sha256: string, parts: list<item: struct<path: string, (... 53 chars omitted)
child 0, item: struct<path: string, bytes: int64, sha256: string, parts: list<item: struct<path: string, byte_offse (... 41 chars omitted)
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
child 3, parts: list<item: struct<path: string, byte_offset: int64, bytes: int64, sha256: string>>
child 0, item: struct<path: string, byte_offset: int64, bytes: int64, sha256: string>
child 0, path: string
child 1, byte_offset: int64
child 2, bytes: int64
child 3, sha256: string
files: list<item: struct<path: string, bytes: int64, sha256: string, git_blob_sha1: string, artifact_type: (... 226 chars omitted)
child 0, item: struct<path: string, bytes: int64, sha256: string, git_blob_sha1: string, artifact_type: string, dat (... 214 chars omitted)
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
child 3, git_blob_sha1: string
child 4, artifact_type: string
child 5, dataset_id: string
child 6, shape: list<item: int64>
child 0, item: int64
child 7, dtype: string
child 8, records_path: string
child 9, image_ids_path: string
child 10, source_bytes: int64
child 11, byte_offset: int64
child 12, original_path: string
child 13, original_sha256: string
child 14, shard_format: string
source_arrays_preserved: bool
initial_version: string
to
{'schema_version': Value('int64'), 'package': Value('string'), 'repo_type': Value('string'), 'repo_id': Value('string'), 'initial_version': Value('string'), 'cohort': Value('string'), 'human_feedback_data': Value('bool'), 'raw_images': Value('bool'), 'license_status': Value('string'), 'payload_files': Value('int64'), 'payload_bytes': Value('int64'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'git_blob_sha1': Value('string'), 'artifact_type': Value('string'), 'dataset_id': Value('string'), 'shape': List(Value('int64')), 'dtype': Value('string'), 'records_path': Value('string'), 'image_ids_path': Value('string'), 'source_bytes': Value('int64'), 'byte_offset': Value('int64'), 'original_path': Value('string'), 'original_sha256': Value('string'), 'shard_format': Value('string')}), 'reassemble': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'parts': List({'path': Value('string'), 'byte_offset': Value('int64'), 'bytes': Value('int64'), 'sha256': Value('string')})}), 'upload_single_file_limit_bytes': Value('int64'), 'source_arrays_preserved': Value('bool')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
dataset: string
identity: string
row_order: string
image_ids: list<item: string>
child 0, item: string
repo_id: string
upload_single_file_limit_bytes: int64
schema_version: int64
payload_files: int64
license_status: string
human_feedback_data: bool
package: string
payload_bytes: int64
repo_type: string
cohort: string
raw_images: bool
reassemble: list<item: struct<path: string, bytes: int64, sha256: string, parts: list<item: struct<path: string, (... 53 chars omitted)
child 0, item: struct<path: string, bytes: int64, sha256: string, parts: list<item: struct<path: string, byte_offse (... 41 chars omitted)
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
child 3, parts: list<item: struct<path: string, byte_offset: int64, bytes: int64, sha256: string>>
child 0, item: struct<path: string, byte_offset: int64, bytes: int64, sha256: string>
child 0, path: string
child 1, byte_offset: int64
child 2, bytes: int64
child 3, sha256: string
files: list<item: struct<path: string, bytes: int64, sha256: string, git_blob_sha1: string, artifact_type: (... 226 chars omitted)
child 0, item: struct<path: string, bytes: int64, sha256: string, git_blob_sha1: string, artifact_type: string, dat (... 214 chars omitted)
child 0, path: string
child 1, bytes: int64
child 2, sha256: string
child 3, git_blob_sha1: string
child 4, artifact_type: string
child 5, dataset_id: string
child 6, shape: list<item: int64>
child 0, item: int64
child 7, dtype: string
child 8, records_path: string
child 9, image_ids_path: string
child 10, source_bytes: int64
child 11, byte_offset: int64
child 12, original_path: string
child 13, original_sha256: string
child 14, shard_format: string
source_arrays_preserved: bool
initial_version: string
to
{'schema_version': Value('int64'), 'package': Value('string'), 'repo_type': Value('string'), 'repo_id': Value('string'), 'initial_version': Value('string'), 'cohort': Value('string'), 'human_feedback_data': Value('bool'), 'raw_images': Value('bool'), 'license_status': Value('string'), 'payload_files': Value('int64'), 'payload_bytes': Value('int64'), 'files': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'git_blob_sha1': Value('string'), 'artifact_type': Value('string'), 'dataset_id': Value('string'), 'shape': List(Value('int64')), 'dtype': Value('string'), 'records_path': Value('string'), 'image_ids_path': Value('string'), 'source_bytes': Value('int64'), 'byte_offset': Value('int64'), 'original_path': Value('string'), 'original_sha256': Value('string'), 'shard_format': Value('string')}), 'reassemble': List({'path': Value('string'), 'bytes': Value('int64'), 'sha256': Value('string'), 'parts': List({'path': Value('string'), 'byte_offset': Value('int64'), 'bytes': Value('int64'), 'sha256': Value('string')})}), 'upload_single_file_limit_bytes': Value('int64'), 'source_arrays_preserved': Value('bool')}
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.
ProbeScout features
Frozen SigLIP features for Stanford Cars, HICO-DET and CelebA:
float32 global embeddings [N, 768], float16 patch tokens [N, 196, 768],
and records that map each row to an image ID. Total size: 81.10 GB.
Use the ProbeScout setup guide
and dataset-specific download commands.
Download only the dataset you need. Place its dataset/ contents under the code
repository's dataset/ directory, preserving paths and row order.
These features skip extraction for training and probe updates. Original images are still required for photograph previews. Browsing saved tasks and using the paper's Weight Tune / Staged feedback only require images and the task package.
CelebA patch tokens
The 60.99 GB CelebA patch NPY is stored as 29 byte-range parts. From the download
directory containing restore_patch_features.py, run:
python restore_patch_features.py --root .
Restoration verifies file hashes and needs an additional 60.99 GB of disk space. It keeps the parts. Cars and HICO patch tokens are ordinary NPY files.
The code pins payload revision 3ef237a82d0c738a159aa49bb86764732d331265.
Checksums are in asset_manifest.json. Original dataset terms apply;
photographs and historical human feedback are excluded.
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