The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<id: int64, file_name: string, height: int64, width: int64, src_id: int64>
to
{'id': Value('int64'), 'file_name': Value('string'), 'height': Value('int64'), 'width': Value('int64'), 'split': Value('string'), 'src': Value('string'), 'modality': Value('string'), 'sat': Value('float64'), 'bg_median': Value('float64')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 544, 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 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, 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 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, 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 361, 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 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2315, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2121, in cast_array_to_feature
casted_array_values = _c(array.values, feature.feature)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1863, in wrapper
return func(array, *args, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2161, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<id: int64, file_name: string, height: int64, width: int64, src_id: int64>
to
{'id': Value('int64'), 'file_name': Value('string'), 'height': Value('int64'), 'width': Value('int64'), 'split': Value('string'), 'src': Value('string'), 'modality': Value('string'), 'sat': Value('float64'), 'bg_median': Value('float64')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
wsbench: labels and splits
The labels behind What Labels Are Worth Buying? Budget-Matched Weak Supervision for Cell Segmentation (Khalid, Naseem, Dengel, Ahmed; ISBI 2027, submitted).
- Code: https://github.com/nabeelkhalid92/wsbench
- Trained models: https://huggingface.co/nabeelkhalid92/wsbench-models
With these files, any model of the paper can be trained again without running SAM2.1, and the budget comparison
can be repeated on exactly the same labels. No images are included: download LIVECell, the yeast dataset and
BBBC038 from their official sources and convert them with the code (python wsb.py prepare <dataset>).
Layout
The folders mirror the code's workspace, so the files can be downloaded straight into it:
data/coco/yeast/{train,val,test}.json converted yeast splits (296/49/148 images)
data/coco/dsb2018/{train,val,test}.json converted DSB2018 splits (534/67/69 images)
labels/<dataset>/train_box.json boxes of all training cells
labels/<dataset>/train_pseudo_box_all_acc0.json SAM2.1 pseudo-masks for all training boxes, with their verification score
labels/<dataset>/budget/<strategy>_B<seconds>_s<seed>.json what each budget buys (training labels of each model)
labels/livecell/budget_celltype/<strategy>_<celltype>_B<seconds>_s<seed>.json LIVECell cell-type study (SkBr3, BV2, SHSY5Y)
labels/<dataset>/budget/noise/ box-noise study
labels/livecell/*.json further LIVECell label files used by the released models (subsets, SAM2.1-L variants)
LIVECell itself is not included; the code uses the official release and splits unchanged.
<strategy>: mask (full masks), box (boxes + SAM2.1 pseudo-masks), pts1 (box + one point per cell, Box+1pt),
pts1sam (box + one point, both given to SAM2.1), mixed (a share of images fully masked, the rest boxed),
mixednocal (Mixed without calibration). <seconds> is the annotation budget: 46 s per mask, 7 s per box,
+0.9 s per point. B1 = 7 s × all training cells (yeast 7826 s, DSB2018 164605 s, LIVECell 7130032 s). For each seed,
one image order is shared by all strategies. Every file is COCO json; each file's info field records how it was made.
run_info.json of each model at https://huggingface.co/nabeelkhalid92/wsbench-models names the label file it was trained on.
Download
pip install -U huggingface_hub
hf download nabeelkhalid92/wsbench-labels --repo-type dataset --local-dir workspace # everything (~5 GB)
hf download nabeelkhalid92/wsbench-labels --repo-type dataset --local-dir workspace --include "labels/yeast/*" "data/coco/yeast/*"
Licence
See LICENSE.md: labels derived from LIVECell are CC BY-NC 4.0 (non-commercial); yeast-derived files are MIT,
including the original copyright notice of the yeast dataset; DSB2018-derived files are CC0.
Citation
@inproceedings{khalid2027labels,
title = {What Labels Are Worth Buying? Budget-Matched Weak Supervision for Cell Segmentation},
author = {Khalid, Nabeel and Naseem, Khola and Dengel, Andreas and Ahmed, Sheraz},
booktitle = {Proc. IEEE International Symposium on Biomedical Imaging (ISBI)},
year = {2027},
note = {submitted}
}
Please also cite the source datasets: LIVECell (Edlund et al., Nat. Methods 2021), Yeast in microstructures (Reich et al., EMBC 2023) and BBBC038 / DSB2018 (Caicedo et al., Nat. Methods 2019).
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