YaroslavPrytula/iaunet-r50-revvity-25
Image Segmentation • Updated • 4 • 3
Datasets:
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
images: list<item: struct<width: int64, height: int64, id: int64, file_name: string>>
child 0, item: struct<width: int64, height: int64, id: int64, file_name: string>
child 0, width: int64
child 1, height: int64
child 2, id: int64
child 3, file_name: string
annotations: list<item: struct<id: int64, image_id: int64, category_id: int64, iscrowd: int64, area: int64, bbox: (... 96 chars omitted)
child 0, item: struct<id: int64, image_id: int64, category_id: int64, iscrowd: int64, area: int64, bbox: list<item: (... 84 chars omitted)
child 0, id: int64
child 1, image_id: int64
child 2, category_id: int64
child 3, iscrowd: int64
child 4, area: int64
child 5, bbox: list<item: double>
child 0, item: double
child 6, segmentation: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 7, width: int64
child 8, height: int64
categories: list<item: struct<id: int64, name: string, supercategory: string>>
child 0, item: struct<id: int64, name: string, supercategory: string>
child 0, id: int64
child 1, name: string
child 2, supercategory: string
info: struct<description: string, url: string, version: string, year: int64, contributor: string, date_cre (... 19 chars omitted)
child 0, description: string
child 1, url: string
child 2, version: string
child 3, year: int64
child 4, contributor: string
child 5, date_created: timestamp[s]
objects: struct<bbox_id: list<item: int64>, category: list<item: int64>, bbox: list<item: fixed_size_list<ite (... 41 chars omitted)
child 0, bbox_id: list<item: int64>
child 0, item: int64
child 1, category: list<item: int64>
child 0, item: int64
child 2, bbox: list<item: fixed_size_list<item: double>[4]>
child 0, item: fixed_size_list<item: double>[4]
child 0, item: double
child 3, area: list<item: double>
child 0, item: double
image: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
image_id: int64
height: int64
width: int64
to
{'image_id': Value('int64'), 'image': Image(mode=None, decode=True), 'width': Value('int64'), 'height': Value('int64'), 'objects': {'bbox_id': List(Value('int64')), 'category': List(ClassLabel(names=['cell'])), 'bbox': List(List(Value('float64'), length=4)), 'area': List(Value('float64'))}}
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
images: list<item: struct<width: int64, height: int64, id: int64, file_name: string>>
child 0, item: struct<width: int64, height: int64, id: int64, file_name: string>
child 0, width: int64
child 1, height: int64
child 2, id: int64
child 3, file_name: string
annotations: list<item: struct<id: int64, image_id: int64, category_id: int64, iscrowd: int64, area: int64, bbox: (... 96 chars omitted)
child 0, item: struct<id: int64, image_id: int64, category_id: int64, iscrowd: int64, area: int64, bbox: list<item: (... 84 chars omitted)
child 0, id: int64
child 1, image_id: int64
child 2, category_id: int64
child 3, iscrowd: int64
child 4, area: int64
child 5, bbox: list<item: double>
child 0, item: double
child 6, segmentation: list<item: list<item: double>>
child 0, item: list<item: double>
child 0, item: double
child 7, width: int64
child 8, height: int64
categories: list<item: struct<id: int64, name: string, supercategory: string>>
child 0, item: struct<id: int64, name: string, supercategory: string>
child 0, id: int64
child 1, name: string
child 2, supercategory: string
info: struct<description: string, url: string, version: string, year: int64, contributor: string, date_cre (... 19 chars omitted)
child 0, description: string
child 1, url: string
child 2, version: string
child 3, year: int64
child 4, contributor: string
child 5, date_created: timestamp[s]
objects: struct<bbox_id: list<item: int64>, category: list<item: int64>, bbox: list<item: fixed_size_list<ite (... 41 chars omitted)
child 0, bbox_id: list<item: int64>
child 0, item: int64
child 1, category: list<item: int64>
child 0, item: int64
child 2, bbox: list<item: fixed_size_list<item: double>[4]>
child 0, item: fixed_size_list<item: double>[4]
child 0, item: double
child 3, area: list<item: double>
child 0, item: double
image: struct<bytes: binary, path: string>
child 0, bytes: binary
child 1, path: string
image_id: int64
height: int64
width: int64
to
{'image_id': Value('int64'), 'image': Image(mode=None, decode=True), 'width': Value('int64'), 'height': Value('int64'), 'objects': {'bbox_id': List(Value('int64')), 'category': List(ClassLabel(names=['cell'])), 'bbox': List(List(Value('float64'), length=4)), 'area': List(Value('float64'))}}
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.
Revvity-25 (CVPRW 2025)
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🔥 Paper: https://arxiv.org/abs/2508.01928
⭐️ Github: https://github.com/SlavkoPrytula/IAUNet
🌐 Project page: https://slavkoprytula.github.io/IAUNet/
We present the Revvity-25 Full Cell Segmentation Dataset, a novel 2025 benchmark designed to advance cell segmentation research. One of our key contributions in the paper IAUNet: Instance-Aware U-Net is a novel cell instance segmentation dataset named Revvity-25. It includes 110 high-resolution 1080 x 1080 brightfield images, each containing, on average, 27 manually labeled and expert-validated cancer cells, totaling 2937 annotated cells. To our knowledge, this is the first dataset with accurate and detailed annotations for cell borders and overlaps, with each cell annotated using an average of 60 polygon points, reaching up to 400 points for more complex structures. Revvity-25 dataset provides a unique resource that opens new possibilities for testing and benchmarking models for modal and amodal semantic and instance segmentation.
- You can also check out and download the dataset from our webpage: Revvity-25
Papers using Revvity-25
- QCell: Recombining and Aligning Cell Queries for Overlapping Instance Segmentation – Accepted at BMVC 2026
Directory structure
Revvity-25/
├── images/
└── annotations/
├── train.json
└── valid.json
Citing Revvity-25
If you use this work in your research, please cite:
@InProceedings{Prytula_2025_CVPR,
author = {Prytula, Yaroslav and Tsiporenko, Illia and Zeynalli, Ali and Fishman, Dmytro},
title = {IAUNet: Instance-Aware U-Net},
booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR) Workshops},
month = {June},
year = {2025},
pages = {4739--4748}
}
License
This project is licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). You are free to share and adapt the work for non-commercial purposes as long as you give appropriate credit. For more details, see the LICENSE file or visit Creative Commons.
Contact
📧 s.prytula@ucu.edu.ua or yaroslav.prytula@ut.ee
Acknowledgements
This work was supported by Revvity and funded by the TEM-TA101 grant “Artificial Intelligence for Smart Automation.” Computational resources were provided by the High-Performance Computing Cluster at the University of Tartu 🇪🇪. We thank the Biomedical Computer Vision Lab for their invaluable support. We express gratitude to the Armed Forces of Ukraine 🇺🇦 and the bravery of the Ukrainian people for enabling a secure working environment, without which this work would not have been possible.
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