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
rows: list<item: struct<dias_multisequence_mean_cldice: double, dias_multisequence_mean_dice: double, dias (... 238 chars omitted)
child 0, item: struct<dias_multisequence_mean_cldice: double, dias_multisequence_mean_dice: double, dias_multiseque (... 226 chars omitted)
child 0, dias_multisequence_mean_cldice: double
child 1, dias_multisequence_mean_dice: double
child 2, dias_multisequence_median_dice: double
child 3, evaluated_frame_count: int64
child 4, evaluated_sequence_count: int64
child 5, model_id: string
child 6, primary_metric: string
child 7, rank: int64
child 8, sequence_mean_dice_max: double
child 9, sequence_mean_dice_min: double
child 10, surface_id: string
prediction_count: int64
sequence_count: int64
surface_id: string
source_dir: string
surface_role: string
run_id: string
primary_outputs: list<item: string>
child 0, item: string
model_count: int64
frame_count_per_model: int64
benchmark_id: string
source_run_id: string
validation_passed: bool
claim_boundary: string
to
{'benchmark_id': Value('string'), 'claim_boundary': Value('string'), 'frame_count_per_model': Value('int64'), 'model_count': Value('int64'), 'prediction_count': Value('int64'), 'primary_outputs': List(Value('string')), 'run_id': Value('string'), 'sequence_count': Value('int64'), 'source_dir': Value('string'), 'source_run_id': Value('string'), 'surface_id': Value('string'), 'surface_role': Value('string'), 'validation_passed': 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(
^^^^^^^^^
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 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2815, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2352, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2377, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 310, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 130, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
rows: list<item: struct<dias_multisequence_mean_cldice: double, dias_multisequence_mean_dice: double, dias (... 238 chars omitted)
child 0, item: struct<dias_multisequence_mean_cldice: double, dias_multisequence_mean_dice: double, dias_multiseque (... 226 chars omitted)
child 0, dias_multisequence_mean_cldice: double
child 1, dias_multisequence_mean_dice: double
child 2, dias_multisequence_median_dice: double
child 3, evaluated_frame_count: int64
child 4, evaluated_sequence_count: int64
child 5, model_id: string
child 6, primary_metric: string
child 7, rank: int64
child 8, sequence_mean_dice_max: double
child 9, sequence_mean_dice_min: double
child 10, surface_id: string
prediction_count: int64
sequence_count: int64
surface_id: string
source_dir: string
surface_role: string
run_id: string
primary_outputs: list<item: string>
child 0, item: string
model_count: int64
frame_count_per_model: int64
benchmark_id: string
source_run_id: string
validation_passed: bool
claim_boundary: string
to
{'benchmark_id': Value('string'), 'claim_boundary': Value('string'), 'frame_count_per_model': Value('int64'), 'model_count': Value('int64'), 'prediction_count': Value('int64'), 'primary_outputs': List(Value('string')), 'run_id': Value('string'), 'sequence_count': Value('int64'), 'source_dir': Value('string'), 'source_run_id': Value('string'), 'surface_id': Value('string'), 'surface_role': Value('string'), 'validation_passed': 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.
AngioStress v0.1 Real-Data Benchmark Artifacts
This dataset repository contains derived benchmark artifacts for AngioStress v0.1. The core benchmark surfaces are DIAS and CathAction real angiography surfaces; the TopCoW-derived synthetic projection is an auxiliary regression fixture only.
Public Links
- GitHub code repository: https://github.com/txmed82/angiostress-benchmark
- Hugging Face dataset artifact: https://huggingface.co/datasets/txmedai/angiostress-benchmark
Release Boundary
Publish benchmark code, contracts, manifests, metrics, derived predictions/overlays, and provenance only. Do not publish manuscript LaTeX, paper PDFs, comments, or private review material.
DIAS and CathAction source datasets must be obtained from their original sources and licenses; this package records benchmark outputs and provenance rather than redistributing raw source data.
Benchmark artifact and measurement package; no clinical validation, no model improvement claim, and no positive synthetic-to-real transfer claim.
Audit Summary
- Real core surfaces: 2
- Real prediction rows: 16020
- CathAction full-tier pairs: 5225
- Derived prediction files: 31695
- Derived overlay files: 15675
- Private manifest hits: 0
Large Derived Artifacts
Large prediction and overlay directories are stored as tar archives so the benchmark remains practical to download from the Hub without expanding tens of thousands of small files in the repository tree.
archives/experiments__main__run-dias-contract-full-test-v0__outputs__predictions.tar: 345 files, sha256f541216ecb0663e43b9fe326bdaffcb74c98ebcf5b6206ae965afd73a1b0d123archives/cathaction_full_nonempty_5225_predictions.tar: 31350 files, sha25676a0a25338b107e549016320c3403595f19a7892e864d619f54bed8ea46f3055archives/cathaction_full_nonempty_5225_overlays.tar: 15675 files, sha2563d323079264de6faf5e1e87b70f5f6a734a9ade851f464a3887b4b216afb4309
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