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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
schema: string
created_utc: string
status: string
factor: string
changed_variable: string
controlled_variable: string
arm: string
epochs: int64
seed: int64
scales: list<item: int64>
child 0, item: int64
lr_drop: int64
data: string
data_manifest_sha256: string
checkpoint: string
checkpoint_sha256: string
model: struct<family: string, anchor: int64, parameter_count: int64>
child 0, family: string
child 1, anchor: int64
child 2, parameter_count: int64
geometry: struct<dynamic_scales: list<item: int64>, long_side_cap: int64, raw_rasterizations_per_sample: int64 (... 64 chars omitted)
child 0, dynamic_scales: list<item: int64>
child 0, item: int64
child 1, long_side_cap: int64
child 2, raw_rasterizations_per_sample: int64
child 3, stretch: bool
child 4, crop: bool
child 5, post_transform_interpolation: bool
photometric: struct<version: string, applies_to: string, position: string, extra_random_draws: int64, brightness_ (... 631 chars omitted)
child 0, version: string
child 1, applies_to: string
child 2, position: string
child 3, extra_random_draws: int64
child 4, brightness_probability: double
child 5, brightness_factor_range: list<item: double>
child 0, item: double
child 6, contrast_probability: double
child 7, contrast_factor_range: list<item: double>
child 0, item: double
child 8, gamma_probability: double
child 9, gamma_range: list<item: double>
child 0, item: double
child 10, grayscale_probability: double
ch
...
_allocated_b (... 121 chars omitted)
child 0, wall_seconds: double
child 1, gpu_seconds: double
child 2, images_per_second: double
child 3, peak_memory_allocated_bytes: int64
child 4, peak_memory_reserved_bytes: int64
child 5, max_output_long_side: int64
child 6, long_side_cap: int64
child 7, cap_hit_images: int64
totals: struct<images: int64, detections: int64, detections_at_tau: int64, tau: double>
child 0, images: int64
child 1, detections: int64
child 2, detections_at_tau: int64
child 3, tau: double
images: list<item: struct<image_id: int64, file_name: string, abs_path: string, sha256: string, width: int64 (... 150 chars omitted)
child 0, item: struct<image_id: int64, file_name: string, abs_path: string, sha256: string, width: int64, height: i (... 138 chars omitted)
child 0, image_id: int64
child 1, file_name: string
child 2, abs_path: string
child 3, sha256: string
child 4, width: int64
child 5, height: int64
child 6, n_det: int64
child 7, boxes_xyxy: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 8, scores: list<item: double>
child 0, item: double
child 9, keep_tau: list<item: int64>
child 0, item: int64
child 10, n_det_tau: int64
source: struct<mode: string, coco_json: string, coco_json_sha256: string>
child 0, mode: string
child 1, coco_json: string
child 2, coco_json_sha256: string
to
{'schema': Value('string'), 'created_utc': Value('string'), 'finished_utc': Value('string'), 'script': {'path': Value('string'), 'sha256': Value('string')}, 'frozen_reference': {'path': Value('string'), 'sha256': Value('string'), 'expected_sha256': Value('string'), 'reused': List(Value('string'))}, 'detector': {'checkpoint': Value('string'), 'checkpoint_sha256': Value('string'), 'arm': Value('string'), 'arm_key': Value('string'), 'requested_side': Value('int64'), 'model_build_resolution': Value('int64'), 'long_side_cap': Value('int64'), 'num_classes': Value('int64'), 'num_select_max_det': Value('int64'), 'block_size': Value('int64'), 'autocast_dtype': Value('string'), 'tau': Value('float64'), 'boxes_frame': Value('string'), 'filtered': Value('bool')}, 'source': {'mode': Value('string'), 'coco_json': Value('string'), 'coco_json_sha256': Value('string')}, 'environment': {'python': Value('string'), 'platform': Value('string'), 'torch': Value('string'), 'gpu': Value('string'), 'cuda_visible_devices': Value('string')}, 'runtime': {'wall_seconds': Value('float64'), 'gpu_seconds': Value('float64'), 'images_per_second': Value('float64'), 'peak_memory_allocated_bytes': Value('int64'), 'peak_memory_reserved_bytes': Value('int64'), 'max_output_long_side': Value('int64'), 'long_side_cap': Value('int64'), 'cap_hit_images': Value('int64')}, 'totals': {'images': Value('int64'), 'detections': Value('int64'), 'detections_at_tau': Value('int64'), 'tau': Value('float64')}, 'regression': Value('null'), 'images': List({'image_id': Value('int64'), 'file_name': Value('string'), 'abs_path': Value('string'), 'sha256': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'n_det': Value('int64'), 'boxes_xyxy': List(List(Value('float64'))), 'scores': List(Value('float64')), 'keep_tau': List(Value('int64')), 'n_det_tau': Value('int64')})}
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
schema: string
created_utc: string
status: string
factor: string
changed_variable: string
controlled_variable: string
arm: string
epochs: int64
seed: int64
scales: list<item: int64>
child 0, item: int64
lr_drop: int64
data: string
data_manifest_sha256: string
checkpoint: string
checkpoint_sha256: string
model: struct<family: string, anchor: int64, parameter_count: int64>
child 0, family: string
child 1, anchor: int64
child 2, parameter_count: int64
geometry: struct<dynamic_scales: list<item: int64>, long_side_cap: int64, raw_rasterizations_per_sample: int64 (... 64 chars omitted)
child 0, dynamic_scales: list<item: int64>
child 0, item: int64
child 1, long_side_cap: int64
child 2, raw_rasterizations_per_sample: int64
child 3, stretch: bool
child 4, crop: bool
child 5, post_transform_interpolation: bool
photometric: struct<version: string, applies_to: string, position: string, extra_random_draws: int64, brightness_ (... 631 chars omitted)
child 0, version: string
child 1, applies_to: string
child 2, position: string
child 3, extra_random_draws: int64
child 4, brightness_probability: double
child 5, brightness_factor_range: list<item: double>
child 0, item: double
child 6, contrast_probability: double
child 7, contrast_factor_range: list<item: double>
child 0, item: double
child 8, gamma_probability: double
child 9, gamma_range: list<item: double>
child 0, item: double
child 10, grayscale_probability: double
ch
...
_allocated_b (... 121 chars omitted)
child 0, wall_seconds: double
child 1, gpu_seconds: double
child 2, images_per_second: double
child 3, peak_memory_allocated_bytes: int64
child 4, peak_memory_reserved_bytes: int64
child 5, max_output_long_side: int64
child 6, long_side_cap: int64
child 7, cap_hit_images: int64
totals: struct<images: int64, detections: int64, detections_at_tau: int64, tau: double>
child 0, images: int64
child 1, detections: int64
child 2, detections_at_tau: int64
child 3, tau: double
images: list<item: struct<image_id: int64, file_name: string, abs_path: string, sha256: string, width: int64 (... 150 chars omitted)
child 0, item: struct<image_id: int64, file_name: string, abs_path: string, sha256: string, width: int64, height: i (... 138 chars omitted)
child 0, image_id: int64
child 1, file_name: string
child 2, abs_path: string
child 3, sha256: string
child 4, width: int64
child 5, height: int64
child 6, n_det: int64
child 7, boxes_xyxy: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 8, scores: list<item: double>
child 0, item: double
child 9, keep_tau: list<item: int64>
child 0, item: int64
child 10, n_det_tau: int64
source: struct<mode: string, coco_json: string, coco_json_sha256: string>
child 0, mode: string
child 1, coco_json: string
child 2, coco_json_sha256: string
to
{'schema': Value('string'), 'created_utc': Value('string'), 'finished_utc': Value('string'), 'script': {'path': Value('string'), 'sha256': Value('string')}, 'frozen_reference': {'path': Value('string'), 'sha256': Value('string'), 'expected_sha256': Value('string'), 'reused': List(Value('string'))}, 'detector': {'checkpoint': Value('string'), 'checkpoint_sha256': Value('string'), 'arm': Value('string'), 'arm_key': Value('string'), 'requested_side': Value('int64'), 'model_build_resolution': Value('int64'), 'long_side_cap': Value('int64'), 'num_classes': Value('int64'), 'num_select_max_det': Value('int64'), 'block_size': Value('int64'), 'autocast_dtype': Value('string'), 'tau': Value('float64'), 'boxes_frame': Value('string'), 'filtered': Value('bool')}, 'source': {'mode': Value('string'), 'coco_json': Value('string'), 'coco_json_sha256': Value('string')}, 'environment': {'python': Value('string'), 'platform': Value('string'), 'torch': Value('string'), 'gpu': Value('string'), 'cuda_visible_devices': Value('string')}, 'runtime': {'wall_seconds': Value('float64'), 'gpu_seconds': Value('float64'), 'images_per_second': Value('float64'), 'peak_memory_allocated_bytes': Value('int64'), 'peak_memory_reserved_bytes': Value('int64'), 'max_output_long_side': Value('int64'), 'long_side_cap': Value('int64'), 'cap_hit_images': Value('int64')}, 'totals': {'images': Value('int64'), 'detections': Value('int64'), 'detections_at_tau': Value('int64'), 'tau': Value('float64')}, 'regression': Value('null'), 'images': List({'image_id': Value('int64'), 'file_name': Value('string'), 'abs_path': Value('string'), 'sha256': Value('string'), 'width': Value('int64'), 'height': Value('int64'), 'n_det': Value('int64'), 'boxes_xyxy': List(List(Value('float64'))), 'scores': List(Value('float64')), 'keep_tau': List(Value('int64')), 'n_det_tau': Value('int64')})}
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.
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