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The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1859, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
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 1369, 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 948, 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 1694, 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 1880, 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.
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2.0 0.7546879947185516 0.8465030193328857 0.06062501668930054 0.07084500789642334 |
End of preview.
Blade Defect Detection Dataset (notch / crack / ablation)
A YOLO-format object detection dataset for blade surface defect detection, covering
three defect types: notch, crack, and ablation.
Dataset Structure
├── anomaly.yaml # Ultralytics YOLO dataset config (relative paths)
├── train/ # images + labels
├── val/ # images + labels
└── test/ # images + labels
anomaly.yamluses relative paths — training can be launched directly from this folder.- Labels follow the standard Ultralytics YOLO format:
class cx cy w h(normalized). - The test split also contains defect-free (negative) images with intentionally empty label files, so the false-positive rate on normal blades can be evaluated.
Classes
| id | name |
|---|---|
| 0 | notch |
| 1 | crack |
| 2 | ablation |
Usage
# Ultralytics YOLO
from ultralytics import YOLO
model = YOLO("yolo11n.pt")
model.train(data="anomaly.yaml", epochs=100, imgsz=1024)
Notes
- Images are of varying resolution; no size normalization was applied.
- The dataset is small-scale; consider data augmentation during training and expect some variance in metrics across runs.
- Val and test metrics are not directly comparable, since the test split includes negative samples.
License / Usage Terms
This dataset is released for non-commercial academic research and educational purposes only. By downloading or using it you agree to the following:
- Academic research / personal study: free to use, please cite or credit the author.
- Any commercial use, redistribution, or incorporation into other datasets or products: requires prior written consent from the author.
For permission requests, please contact the dataset author via the HuggingFace repository page (Community tab) or the contact information on the author's profile.
All rights not explicitly granted here are reserved by the author.
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