The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ArrowInvalid
Message: JSON parse error: Invalid value. in row 0
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 324, in _generate_tables
df = pandas_read_json(f)
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 38, in pandas_read_json
return pd.read_json(path_or_buf, **kwargs)
~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 815, in read_json
return json_reader.read()
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1014, in read
obj = self._get_object_parser(self.data)
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1040, in _get_object_parser
obj = FrameParser(json, **kwargs).parse()
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1176, in parse
self._parse()
~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/pandas/io/json/_json.py", line 1392, in _parse
ujson_loads(json, precise_float=self.precise_float), dtype=None
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
During handling of the above exception, another exception occurred:
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 327, in _generate_tables
raise e
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Invalid value. in row 0Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OReNA FOCUS FRAME — HEICO Foreign-Object Annotations
Hugging Face mirror of the v14-heico annotation release, derived from the source snapshot dated 14 September 2026.
Canonical release and citation: 10.5281/zenodo.22751012.
Authors
- Tristan Kirscher — ICube Laboratory, CNRS UMR-7357, University of Strasbourg, Strasbourg, France; CLCC Institut Strauss, Strasbourg, France.
- Lalith Sharan — CAMMA, ICube, UMR7357, University of Strasbourg, CNRS, INSERM, Strasbourg, France; Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France.
- Andrea Naclerio — CAMMA, ICube, UMR7357, University of Strasbourg, CNRS, INSERM, Strasbourg, France; Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France.
- Luc Vedrenne — CAMMA, ICube, UMR7357, University of Strasbourg, CNRS, INSERM, Strasbourg, France; Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France.
- Nicolas Padoy — CAMMA, ICube, UMR7357, University of Strasbourg, CNRS, INSERM, Strasbourg, France; Institute of Image-Guided Surgery, IHU Strasbourg, Strasbourg, France.
Dataset contents
The release contains annotations for 1,274 frames from 20 HEICO training videos, with 2,505 bounding-box instances. Seven of the ten taxonomy categories are represented.
| Category | Bounding boxes |
|---|---|
| Sponge | 291 |
| Clip | 896 |
| Specimen Bag | 74 |
| Silicone Loop | 410 |
| External Drain | 524 |
| Needle | 256 |
| Gallstone | 0 |
| Specimen | 54 |
| Mesh | 0 |
| Absorbable Hemostatic Agent (provisional) | 0 |
Counts refer to instances across frames, not distinct physical objects tracked across videos. Zero examples do not establish clinical absence.
Download
Download and extract the ZIP to access the individual files. The package includes structured annotations in JSONL and COCO formats, frame facts, DINO crop specifications, quality flags, derived question–answer pairs, a source-image mapping manifest, author metadata, and a citation file. PACKAGE_MANIFEST.json records file sizes and SHA-256 hashes.
No source images, videos, model weights or LapChole data are included. DINO records describe crops but do not contain crop pixels.
Using the annotations
image_file_name, image_path, and COCO file_name are unique export aliases, not original dataset paths. Use source_image_manifest.jsonl to map the alias or task ID to source_relative_path, relative to an authorized HEICO root containing frames/. Verify image_sha256 before use. Original filenames can repeat across videos; do not flatten paths to basenames.
Source-image hashes were preserved and checked for consistency against source metadata; they were not recomputed from original image bytes during release preparation. Obtain source images and their access terms from the dataset owners.
There are 4,586 canonical QA rows. The existing frame flag filter removes 86 frames, retaining 1,188 frames and 4,228 canonical QA rows. The optional text-QA union contains 7,013 rows. Use augmented_vqa_clean.jsonl OR its four constituent family files, never both. Names containing balanced describe upstream generation; the HEICO subsets are not exactly balanced.
The full structured annotation, COCO and DINO files retain flagged frames. To apply the same filter to detection training, exclude flagged task IDs and their dependent records. Keep all frames, crops and derived QA from a video in the same downstream split.
Annotation process and limitations
Source records designate 834 frames as label-assisted and 440 as image-only. The source handoff describes human acceptance, correction or drawing of detector/SAM 2 proposals. Workflow completion and exhaustiveness fields are not independent measurements of annotation correctness.
This is selected training material, not an unbiased test set, prevalence sample or clinical reference standard. Structural consistency and package integrity were checked; independent image-level clinical adjudication was not performed during release preparation.
Quality flags identify recorded discrepancies and ambiguity, not all possible annotation errors. A total of 1,012 boxes have uncertain visibility, including 1,010 after frame filtering. Historical Clip undercounts were observed in an earlier subset; residual undercounting in this release has not been quantified. The detailed technical note explains these limitations and the interpretation of default or uncertain attributes.
Annotator identities, free-text notes, annotation timestamps and source question IDs were removed. Structured labels, visibility fields and quality flags were retained. Scientific information loss from removed notes has not been independently assessed. The exact use of this snapshot by the submitted model remains unconfirmed.
The term “foreign object” follows the challenge taxonomy and does not imply that an object was unintentionally retained. Empty-frame and absence answers describe the visible image field, not the entire operation.
Source attribution
Maier-Hein, L., Wagner, M., Ross, T. et al. Heidelberg colorectal data set for surgical data science in the sensor operating room. Scientific Data 8, 101 (2021). 10.1038/s41597-021-00882-2.
License and citation
The additional annotations and accompanying documentation are released under Creative Commons Attribution 4.0 International (CC BY 4.0). Source images and videos retain their own access and reuse terms.
Copyright 2026 Tristan Kirscher, Lalith Sharan, Andrea Naclerio, Luc Vedrenne, Nicolas Padoy.
Please cite the Zenodo record when using these annotations. The archive contains CITATION.cff. Cite the original HEICO publication as appropriate when using the source dataset.
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