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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1570, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 835, in finalize
self._build_writer(self.schema)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 861, in _build_writer
self.pa_writer = pq.ParquetWriter(
~~~~~~~~~~~~~~~~^
self.stream,
^^^^^^^^^^^^
...<3 lines>...
**get_parquet_column_options(self._features, self._schema),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/pyarrow/parquet/core.py", line 1115, in __init__
self.writer = _parquet.ParquetWriter(
~~~~~~~~~~~~~~~~~~~~~~^
sink, schema,
^^^^^^^^^^^^^
...<20 lines>...
max_rows_per_page=max_rows_per_page,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
**options)
^^^^^^^^^^
File "pyarrow/_parquet.pyx", line 2430, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'flags' with no child field to Parquet. Consider adding a dummy child field.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1401, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1579, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
txt string | jpg image | json dict | __key__ string | __url__ string |
|---|---|---|---|---|
"In the oil and gas chemical scenario, there is smoke and accumulated liquid, and personnel are not (...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Valve Gate","score":null,"points":[[790.6639954002(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002880/oil_chemical-Level01-W(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
"In the oil and gas chemical scenario, the potential hazards in the image are personnel smoking and (...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Cigarette","score":null,"points":[[1273.3958724202(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002708/oil_chemical-Level01-W(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
In the power scenario, there is a foreign object on the ground. The safety level is Grade 2. | {"version":"2.4.0","flags":{},"shapes":[{"label":"Cardboard Box","score":null,"points":[[633.3144475(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/power-Level02-SuspendedRail-003020/power-Level02-Suspended(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
In the tunnel scene, there is water accumulation on the walls. The safety level is grade three. | {"version":"2.4.0","flags":{},"shapes":[{"label":"Liquid","score":null,"points":[[465.0,21.0],[384.0(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level03-SuspendedRail-003232/tunnel-Level03-Suspend(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
In the oil and gas chemical scene, there is smoke. The safety level is grade one. | {"version":"2.4.0","flags":{},"shapes":[{"label":"Smoke","score":null,"points":[[760.8510638297872,6(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/oil_chemical-Level01-Wheeled-002840/oil_chemical-Level01-W(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
"In the tunnel scenario, personnel are not wearing gloves and not wearing safety helmets. The safety(...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Fire Hydrant","score":null,"points":[[591.0,99.0],(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level02-SuspendedRail-003149/tunnel-Level02-Suspend(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
"In the tunnel scenario, personnel are not wearing masks, gloves, or safety helmets. The safety leve(...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Person","score":null,"points":[[886.887417218543,2(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/tunnel-Level02-SuspendedRail-003194/tunnel-Level02-Suspend(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
"In the coal conveying trestle scenario, personnel are using mobile phones, not wearing masks, and n(...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Mobile Phone","score":null,"points":[[926.58399098(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/coal_conveyor-Level02-SuspendedRail-002645/coal_conveyor-L(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
"In the power scenario, the personnel are not wearing gloves, and there is an open flame in the imag(...TRUNCATED) | {"version":"2.4.0","flags":{},"shapes":[{"label":"Lighter","score":null,"points":[[1179.299721448467(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/power-Level01-SuspendedRail-002965/power-Level01-Suspended(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz | |
In the power scenario, there is an open flame. Therefore, the safety level is Grade One. | {"version":"2.4.0","flags":{},"shapes":[{"label":"Open Flame","score":null,"points":[[982.2841225626(...TRUNCATED) | "DATA_PATH/train/Annotations/Anomaly_data/power-Level01-SuspendedRail-002963/power-Level01-Suspended(...TRUNCATED) | hf://datasets/aodomayu/InspecSafe-V1@cc291d4ec892a03fd7fc0cec365cfc04fb7a3d43/train.tar.gz |
End of preview.
InspecSafe-V1
Overview
InspecSafe-V1 is a high-quality, multimodal annotated dataset designed for world model construction and analysis in industrial environments. The data was collected from real-world inspection robots deployed across industrial sites and has been carefully cleaned and standardized for research and applications in predictive world modeling for industrial scenarios.
The dataset covers five representative industrial settings: tunnels, power facilities, sintering equipment, oil/gas/chemical plants, and coal conveyor galleries. It was constructed using data from 41 wheeled or rail-mounted inspection robots operating at 2,239 valid inspection waypoints. Across the dataset, multimodal records may include visible-light video, infrared video, audio, depth or LiDAR point clouds, gas concentration readings, temperature, and humidity. Depending on the inspection robot, sensing configuration, and waypoint conditions, each inspection waypoint is associated with the available subset of these modalities rather than necessarily containing all modality types. The available modality types include:
- Visible-light video
- Infrared video
- Audio
- Depth or LiDAR point clouds
- Gas concentration readings
- Temperature
- Humidity
Additionally, pixel-level polygonal segmentation annotations are provided for industrial objects in visible-light images. To support downstream tasks, each sample is also accompanied by a semantic scene description and a corresponding safety-level label based on real inspection protocols.
Dataset Format
The dataset is divided into a training set and a test set, both of which are organized in a structured directory layout with aligned multimodal streams and annotations. An overview of the data structure is shown below:
DATA_PATH
├── train
│ ├── Annotations
│ │ ├── Normal_data
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560
│ │ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-001.jpg
│ │ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-001.json
│ │ │ │ └── coal_conveyor-Level04-SuspendedRail-000560-001.txt
│ │ │ └── ...
│ │ └── Anomaly_data
│ │ ├── coal_conveyor-Level01-SuspendedRail-002486
│ │ │ ├── coal_conveyor-Level01-SuspendedRail-002486-001.jpg
│ │ │ ├── coal_conveyor-Level01-SuspendedRail-002486-001.json
│ │ │ └── coal_conveyor-Level01-SuspendedRail-002486-001.txt
│ │ └── ...
│ ├── Other_modalities
│ │ ├── coal_conveyor-Level04-SuspendedRail-000560
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-visible.mp4
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-infrared.mp4
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-sensor.txt
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000560-point_cloud.bag
│ │ │ └── coal_conveyor-Level04-SuspendedRail-000560-audio.wav
│ │ └── ...
│ └── Parameters
│ ├── Hardware
│ ├── Device_A.json
│ ├── Device_B.json
│ └── ...
└── test
├── Annotations
│ ├── Normal_data
│ │ ├── coal_conveyor-Level04-SuspendedRail-000001
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000001-001.jpg
│ │ │ ├── coal_conveyor-Level04-SuspendedRail-000001-001.json
│ │ │ └── coal_conveyor-Level04-SuspendedRail-000001-001.txt
│ │ └── ...
│ └── Anomaly_data
│ ├── coal_conveyor-Level01-SuspendedRail-002235
│ │ ├── coal_conveyor-Level01-SuspendedRail-002235-001.jpg
│ │ ├── coal_conveyor-Level01-SuspendedRail-002235-001.json
│ │ └── coal_conveyor-Level01-SuspendedRail-002235-001.txt
│ └── ...
├── Other_modalities
│ ├── coal_conveyor-Level04-SuspendedRail-000001
│ │ ├── coal_conveyor-Level04-SuspendedRail-000001-visible.mp4
│ │ ├── coal_conveyor-Level04-SuspendedRail-000001-infrared.mp4
│ │ ├── coal_conveyor-Level04-SuspendedRail-000001-sensor.txt
│ │ ├── coal_conveyor-Level04-SuspendedRail-000001-point_cloud.bag
│ │ └── coal_conveyor-Level04-SuspendedRail-000001-audio.wav
│ └── ...
└── Parameters
├── Hardware
├── Device_A.json
├── Device_B.json
└── ...
Notes:
- Inspection point identifier: Each folder name represents an inspection point, such as
coal_conveyor-Level04-SuspendedRail-000560. The same identifier is used in both theAnnotationsandOther_modalitiesfolders to enable cross-modal correspondence. - Inspection instances: A single inspection point may contain multiple inspection instances. In the
Annotationsfolder, these instances are distinguished by numerical suffixes appended to the filenames, such as-001,-002, and-003. - Annotations:
.jpg: Visible-light image frame for the corresponding inspection instance..json: Pixel-level polygonal segmentation annotations and related metadata..txt: Human-readable semantic description of the scene.
- Other modalities:
.mp4: Visible-light and infrared videos..txt: Sensor logs, including gas concentration, temperature, and humidity..bag: Point-cloud data in ROS bag format..wav: Audio recordings.
- Parameters: The
Parametersfolder contains hardware specifications, software settings, and calibration-related files used to support multimodal interpretation and fusion.This structure ensures synchronized access across all modalities and supports both supervised learning and world modeling tasks. Each sample metadata (e.g., robot ID, location, timestamp, safety label) is stored in JSON format. Segmentation masks are provided as PNG images with instance IDs matching the annotation JSON.
The shared inspection point identifier allows the multimodal and sensory records in Other_modalities to be linked to one or more annotated inspection instances in Annotations.
Data Splits
| Split | Number of Samples |
|---|---|
| train | 3,763 |
| test | 1,250 |
Note: The dataset does not include a separate validation split; users are encouraged to create one from the training set as needed.
License
This dataset is released under the CC-BY-4.0 License.
Acknowledgements
We thank the multimodal recognition algorithm team who contributed to data collection and annotation. This work was supported by TetraBOT.
For questions or contributions, please open an issue in the repository.
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