The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
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: Column() changed from object to number in row 0
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 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or valueNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
ClimbInst: Climbing Hold Instance Segmentation Dataset
ClimbInst is an instance segmentation dataset for climbing holds and volumes on indoor climbing walls. It was developed to support research in climbing-hold instance segmentation and related computer vision applications.
The dataset contains 1,509 images with a total of 89,020 annotated instances. Climbing holds and volumes are represented using a single unified class.
Dataset Structure
The dataset is divided into training, validation, and test splits:
ClimbInst/
├── train/
│ ├── images/
│ └── annotations/
├── validation/
│ ├── images/
│ └── annotations/
└── test/
├── images/
└── annotations/
Dataset Statistics
| Split | Images | Annotated Instances |
|---|---|---|
| Train | 1,169 | 71,319 |
| Validation | 290 | 14,268 |
| Test | 50 | 3,433 |
| Total | 1,509 | 89,020 |
The test set is independent of the training and validation data.
Dataset Construction
Images used to construct ClimbInst were collected from multiple sources, including publicly available climbing-wall datasets and separately collected climbing-wall imagery.
Initial instance masks were generated using SAM3 as an annotation-assistance mechanism. The generated masks were subsequently manually inspected and corrected using Labelme. Incorrect masks were refined or removed, and climbing holds missed during the initial annotation process were manually added.
Both climbing holds and climbing volumes are annotated as a single unified class.
Annotations
The dataset provides instance-level segmentation annotations for climbing holds and volumes.
Each training, validation, and test split contains its corresponding images and annotations.
Intended Use
ClimbInst is intended to support research and development involving:
Climbing-hold instance segmentation
Climbing-hold detection
Computer vision for indoor climbing
Digital representation of climbing walls
Climbing route selection and analysis
Related computer vision applications
Dataset Sources
ClimbInst incorporates climbing-wall imagery collected from multiple sources, including publicly available datasets and separately collected images.
The publicly available datasets used during dataset construction include:
Indoor Climbing Gym Hold and Route Segmentation — T. Sláma, Kaggle
https://www.kaggle.com/datasets/tomasslama/indoor-climbing-gym-hold-segmentation
Climbing Holds and Volumes — Roboflow
https://app.roboflow.com/weedlabelling/climbing-holds-and-volumes-zdgn3/browse
License
License information is provided according to the applicable usage and redistribution terms of the constituent image sources.
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