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
stems: list<item: string>
child 0, item: string
size_name: string
files_layout: struct<Training: struct<volumes: string, labels: string>, Validation: struct<volumes: string, labels (... 57 chars omitted)
child 0, Training: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
child 1, Validation: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
child 2, Test: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
splits: struct<Training: struct<built_n: int64>, Validation: struct<built_n: int64>, Test: struct<built_n: i (... 6 chars omitted)
child 0, Training: struct<built_n: int64>
child 0, built_n: int64
child 1, Validation: struct<built_n: int64>
child 0, built_n: int64
child 2, Test: struct<built_n: int64>
child 0, built_n: int64
source_shape: list<item: int64>
child 0, item: int64
source_datasets: list<item: string>
child 0, item: string
store_shape: list<item: int64>
child 0, item: int64
antialias: bool
to
{'size_name': Value('string'), 'store_shape': List(Value('int64')), 'source_shape': List(Value('int64')), 'antialias': Value('bool'), 'source_datasets': List(Value('string')), 'splits': {'Training': {'built_n': Value('int64')}, 'Validation': {'built_n': Value('int64')}, 'Test': {'built_n': Value('int64')}}, 'files_layout': {'Training': {'volumes': Value('string'), 'labels': Value('string')}, 'Validation': {'volumes': Value('string'), 'labels': Value('string')}, 'Test': {'volumes': Value('string'), 'labels': Value('string')}}}
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
stems: list<item: string>
child 0, item: string
size_name: string
files_layout: struct<Training: struct<volumes: string, labels: string>, Validation: struct<volumes: string, labels (... 57 chars omitted)
child 0, Training: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
child 1, Validation: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
child 2, Test: struct<volumes: string, labels: string>
child 0, volumes: string
child 1, labels: string
splits: struct<Training: struct<built_n: int64>, Validation: struct<built_n: int64>, Test: struct<built_n: i (... 6 chars omitted)
child 0, Training: struct<built_n: int64>
child 0, built_n: int64
child 1, Validation: struct<built_n: int64>
child 0, built_n: int64
child 2, Test: struct<built_n: int64>
child 0, built_n: int64
source_shape: list<item: int64>
child 0, item: int64
source_datasets: list<item: string>
child 0, item: string
store_shape: list<item: int64>
child 0, item: int64
antialias: bool
to
{'size_name': Value('string'), 'store_shape': List(Value('int64')), 'source_shape': List(Value('int64')), 'antialias': Value('bool'), 'source_datasets': List(Value('string')), 'splits': {'Training': {'built_n': Value('int64')}, 'Validation': {'built_n': Value('int64')}, 'Test': {'built_n': Value('int64')}}, 'files_layout': {'Training': {'volumes': Value('string'), 'labels': Value('string')}, 'Validation': {'volumes': Value('string'), 'labels': Value('string')}, 'Test': {'volumes': Value('string'), 'labels': 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.
Harvard-GF 200^3 consolidated dataset
Canonical raw 200^3 Harvard-GF OCT volumes as consolidated .npy (uint8),
split-matched with the 96^3/128^3 convenience copies.
- Source:
harvardairobotics/Harvard-GF(per-scan.npz[oct_bscans]), IEEE TMI 2024 (Luo et al.) - store_shape:
[1, 200, 200, 200]uint8 | source_shape:[200, 200, 200]| antialias: n/a (raw) - Splits (volumes, pos=glaucoma):
- Training: 2100 (pos 1083 / neg 1017)
- Validation: 300 (pos 176 / neg 124)
- Test: 900 (pos 489 / neg 411)
- Layout:
{Training,Validation,Test}_{volumes,labels}.npy(volumes(N,1,200,200,200)uint8, labels(N,)int64) - Downstream normalization:
x / 255.0.
This is the canonical resolution; 96^3 and 128^3 are resized copies for fast sweeps.
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