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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 1848, 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 1683, 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 1869, 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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1 0.8893805309734514 0.5494505494505495 0.09292035398230089 0.10989010989010989 |
1 0.8893805309734514 0.5494505494505495 0.09292035398230089 0.10989010989010989 |
3 0.2920353982300885 0.5164835164835165 0.048672566371681415 0.9560439560439561 |
3 0.5619469026548672 0.5384615384615384 0.07079646017699115 0.9120879120879121 |
3 0.2920353982300885 0.5164835164835165 0.048672566371681415 0.9560439560439561 |
3 0.5619469026548672 0.5384615384615384 0.07079646017699115 0.9120879120879121 |
3 0.2920353982300885 0.5164835164835165 0.048672566371681415 0.9560439560439561 |
3 0.5619469026548672 0.5384615384615384 0.07079646017699115 0.9120879120879121 |
1 0.7345132743362832 0.3076923076923077 0.34513274336283184 0.6153846153846154 |
1 0.7345132743362832 0.3076923076923077 0.34513274336283184 0.6153846153846154 |
1 0.7345132743362832 0.3076923076923077 0.34513274336283184 0.6153846153846154 |
3 0.084070796460177 0.5164835164835165 0.04424778761061947 0.967032967032967 |
3 0.336283185840708 0.5934065934065934 0.03982300884955752 0.8131868131868132 |
3 0.084070796460177 0.5164835164835165 0.04424778761061947 0.967032967032967 |
3 0.336283185840708 0.5934065934065934 0.03982300884955752 0.8131868131868132 |
3 0.084070796460177 0.5164835164835165 0.04424778761061947 0.967032967032967 |
3 0.336283185840708 0.5934065934065934 0.03982300884955752 0.8131868131868132 |
2 0.6326530612244898 0.8958333333333334 0.336734693877551 0.14583333333333334 |
2 0.6326530612244898 0.8958333333333334 0.336734693877551 0.14583333333333334 |
2 0.6326530612244898 0.8958333333333334 0.336734693877551 0.14583333333333334 |
3 0.35398230088495575 0.5217391304347826 0.035398230088495575 0.9565217391304348 |
3 0.7964601769911505 0.5652173913043478 0.022123893805309734 0.8695652173913043 |
3 0.6769911504424779 0.5217391304347826 0.017699115044247787 0.9130434782608695 |
3 0.35398230088495575 0.5217391304347826 0.035398230088495575 0.9565217391304348 |
3 0.7964601769911505 0.5652173913043478 0.022123893805309734 0.8695652173913043 |
3 0.6769911504424779 0.5217391304347826 0.017699115044247787 0.9130434782608695 |
3 0.35398230088495575 0.5217391304347826 0.035398230088495575 0.9565217391304348 |
3 0.7964601769911505 0.5652173913043478 0.022123893805309734 0.8695652173913043 |
3 0.6769911504424779 0.5217391304347826 0.017699115044247787 0.9130434782608695 |
1 0.4296875 0.5902777777777778 0.23828125 0.4166666666666667 |
1 0.4296875 0.5902777777777778 0.23828125 0.4166666666666667 |
1 0.4296875 0.5902777777777778 0.23828125 0.4166666666666667 |
1 0.1085972850678733 0.18811881188118812 0.04524886877828054 0.1188118811881188 |
1 0.1085972850678733 0.18811881188118812 0.04524886877828054 0.1188118811881188 |
1 0.1085972850678733 0.18811881188118812 0.04524886877828054 0.1188118811881188 |
3 0.415929203539823 0.5604395604395604 0.048672566371681415 0.8681318681318682 |
3 0.7035398230088495 0.5934065934065934 0.04424778761061947 0.8131868131868132 |
3 0.415929203539823 0.5604395604395604 0.048672566371681415 0.8681318681318682 |
3 0.7035398230088495 0.5934065934065934 0.04424778761061947 0.8131868131868132 |
3 0.415929203539823 0.5604395604395604 0.048672566371681415 0.8681318681318682 |
3 0.7035398230088495 0.5934065934065934 0.04424778761061947 0.8131868131868132 |
2 0.23046875 0.8402777777777778 0.16796875 0.20833333333333334 |
2 0.5 0.8541666666666666 0.1640625 0.19444444444444445 |
2 0.23046875 0.8402777777777778 0.16796875 0.20833333333333334 |
2 0.5 0.8541666666666666 0.1640625 0.19444444444444445 |
2 0.23046875 0.8402777777777778 0.16796875 0.20833333333333334 |
2 0.5 0.8541666666666666 0.1640625 0.19444444444444445 |
3 0.2890625 0.5625 0.04296875 0.875 |
3 0.2890625 0.5625 0.04296875 0.875 |
3 0.2890625 0.5625 0.04296875 0.875 |
2 0.19921875 0.8333333333333334 0.21875 0.2222222222222222 |
2 0.51171875 0.7986111111111112 0.23046875 0.2916666666666667 |
2 0.19921875 0.8333333333333334 0.21875 0.2222222222222222 |
2 0.51171875 0.7986111111111112 0.23046875 0.2916666666666667 |
2 0.19921875 0.8333333333333334 0.21875 0.2222222222222222 |
2 0.51171875 0.7986111111111112 0.23046875 0.2916666666666667 |
0 0.41796875 0.3125 0.0234375 0.1875 |
0 0.41796875 0.3125 0.0234375 0.1875 |
0 0.41796875 0.3125 0.0234375 0.1875 |
3 0.6194690265486725 0.38461538461538464 0.035398230088495575 0.7142857142857143 |
3 0.672566371681416 0.3626373626373626 0.030973451327433628 0.6483516483516484 |
3 0.7212389380530974 0.5054945054945055 0.048672566371681415 0.978021978021978 |
3 0.6194690265486725 0.38461538461538464 0.035398230088495575 0.7142857142857143 |
3 0.672566371681416 0.3626373626373626 0.030973451327433628 0.6483516483516484 |
3 0.7212389380530974 0.5054945054945055 0.048672566371681415 0.978021978021978 |
3 0.6194690265486725 0.38461538461538464 0.035398230088495575 0.7142857142857143 |
3 0.672566371681416 0.3626373626373626 0.030973451327433628 0.6483516483516484 |
3 0.7212389380530974 0.5054945054945055 0.048672566371681415 0.978021978021978 |
3 0.8628318584070797 0.5274725274725275 0.07079646017699115 0.945054945054945 |
3 0.8628318584070797 0.5274725274725275 0.07079646017699115 0.945054945054945 |
3 0.8628318584070797 0.5274725274725275 0.07079646017699115 0.945054945054945 |
3 0.47123893805309736 0.5 0.048672566371681415 1 |
3 0.5353982300884956 0.5274725274725275 0.061946902654867256 0.9230769230769231 |
3 0.47123893805309736 0.5 0.048672566371681415 1 |
3 0.5353982300884956 0.5274725274725275 0.061946902654867256 0.9230769230769231 |
3 0.47123893805309736 0.5 0.048672566371681415 1 |
3 0.5353982300884956 0.5274725274725275 0.061946902654867256 0.9230769230769231 |
2 0.2109375 0.7986111111111112 0.203125 0.25 |
2 0.5 0.7777777777777778 0.2421875 0.3333333333333333 |
2 0.2109375 0.7986111111111112 0.203125 0.25 |
2 0.5 0.7777777777777778 0.2421875 0.3333333333333333 |
2 0.2109375 0.7986111111111112 0.203125 0.25 |
2 0.5 0.7777777777777778 0.2421875 0.3333333333333333 |
2 0.21484375 0.8263888888888888 0.1953125 0.22916666666666666 |
2 0.51171875 0.7777777777777778 0.23046875 0.3333333333333333 |
2 0.21484375 0.8263888888888888 0.1953125 0.22916666666666666 |
2 0.51171875 0.7777777777777778 0.23046875 0.3333333333333333 |
2 0.21484375 0.8263888888888888 0.1953125 0.22916666666666666 |
2 0.51171875 0.7777777777777778 0.23046875 0.3333333333333333 |
0 0.41796875 0.296875 0.0234375 0.171875 |
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0 0.41796875 0.296875 0.0234375 0.171875 |
1 0.665158371040724 0.1485148514851485 0.04524886877828054 0.09900990099009901 |
1 0.665158371040724 0.1485148514851485 0.04524886877828054 0.09900990099009901 |
1 0.665158371040724 0.1485148514851485 0.04524886877828054 0.09900990099009901 |
3 0.08849557522123894 0.5274725274725275 0.09734513274336283 0.9340659340659341 |
3 0.5221238938053098 0.5384615384615384 0.05309734513274336 0.9230769230769231 |
3 0.08849557522123894 0.5274725274725275 0.09734513274336283 0.9340659340659341 |
3 0.5221238938053098 0.5384615384615384 0.05309734513274336 0.9230769230769231 |
Dataset Description
The dataset contains shadowgraph images of different high-speed flows taken by the high-speed camera. The dataset is prepared for the YOLO model. There are 4 classes of objects: shock waves, bow shocks, plumes, particles in the flow and background.
Languages
english
Citation Information
If you use the dataset, please provide a reference to the paper:
Doroshchenko I.A. Analysis of the Experimental Flow Shadowgraph Images by Computer Vision Methods // Numerical Methods and Programming (Vychislitel’nye Metody i Programmirovanie). 2023. 24. 231-242. doi 10.26089/NumMet.v24r217
Acknowledgements
This study was supported by the Russian Science Foundation (Grant No. 22-79-00054)
Licensing Information
The dataset is released under Apache 2.0.
Citation
If you use this model, dataset, or accompanying software in your research, please cite one or more of the following publications, selecting those most relevant to your work:
- https://doi.org/10.1016/j.actaastro.2025.09.091
- https://doi.org/10.26089/NumMet.v24r217
- https://doi.org/10.1615/JFlowVisImageProc.2025058234
- https://doi.org/10.1016/j.actaastro.2023.11.021
- https://doi.org/10.26583/sv.15.4.01
license: apache-2.0
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