harsh-awasthi/bluemockdrone
Object Detection β’ Updated β’ 48
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.
text string |
|---|
0 0.187156 0.365237 0.374313 0.730474 |
0 0.384672 0.476475 0.573602 0.561984 |
0 0.840257 0.259814 0.319486 0.519627 |
0 0.249847 0.700730 0.499694 0.598539 |
0 0.812500 0.313365 0.375000 0.626729 |
0 0.278461 0.449489 0.496486 0.374993 |
0 0.882803 0.703178 0.234395 0.593645 |
0 0.744990 0.625705 0.359608 0.479707 |
0 0.478785 0.879680 0.210629 0.240641 |
0 0.811552 0.325424 0.376897 0.523740 |
0 0.606211 0.477039 0.423458 0.651850 |
0 0.743534 0.351934 0.512933 0.695352 |
0 0.500909 0.551500 0.423069 0.549809 |
0 0.743047 0.261266 0.505144 0.522532 |
0 0.588680 0.472071 0.144559 0.369793 |
0 0.393626 0.873163 0.200399 0.253675 |
0 0.724332 0.536941 0.476219 0.378091 |
0 0.727486 0.663505 0.519835 0.672990 |
0 0.839125 0.292753 0.321749 0.585506 |
0 0.608800 0.472531 0.474268 0.605768 |
0 0.430891 0.767667 0.262970 0.380525 |
0 0.412373 0.509240 0.202659 0.390866 |
0 0.506998 0.552524 0.395628 0.547822 |
0 0.627007 0.277488 0.296251 0.437092 |
0 0.422569 0.515625 0.114583 0.337500 |
0 0.592927 0.206457 0.259181 0.412913 |
0 0.587874 0.438388 0.530782 0.623624 |
0 0.644172 0.097147 0.227811 0.194293 |
0 0.281241 0.298443 0.494766 0.596885 |
0 0.408158 0.508840 0.158121 0.379560 |
0 0.680130 0.145332 0.263523 0.290663 |
0 0.218210 0.522263 0.436419 0.631972 |
0 0.690690 0.303504 0.430681 0.607008 |
0 0.274879 0.714566 0.549757 0.570869 |
0 0.422569 0.515625 0.114583 0.337500 |
0 0.496449 0.552160 0.368407 0.535141 |
0 0.196772 0.411147 0.393545 0.822293 |
0 0.296986 0.705745 0.456952 0.588509 |
0 0.221159 0.660181 0.442319 0.679638 |
0 0.798899 0.663084 0.402201 0.673831 |
0 0.134478 0.697741 0.268956 0.522321 |
0 0.269082 0.315439 0.523992 0.615039 |
0 0.562344 0.123262 0.221216 0.246525 |
0 0.288170 0.707908 0.576340 0.584184 |
0 0.206630 0.742587 0.413260 0.514826 |
0 0.308580 0.881464 0.259061 0.237072 |
0 0.388732 0.538237 0.445412 0.605142 |
0 0.782003 0.593932 0.435993 0.522469 |
0 0.637330 0.274044 0.454391 0.548088 |
0 0.574392 0.752406 0.220535 0.334687 |
0 0.585260 0.133647 0.177865 0.267294 |
0 0.295320 0.156107 0.272703 0.312214 |
0 0.187500 0.686635 0.375000 0.626729 |
0 0.421931 0.540159 0.237198 0.423807 |
0 0.381107 0.881266 0.227847 0.237468 |
0 0.186018 0.309800 0.372036 0.619599 |
0 0.669583 0.883592 0.257599 0.232815 |
0 0.481740 0.880995 0.217373 0.238010 |
0 0.475201 0.538201 0.328240 0.545206 |
0 0.500142 0.439150 0.436106 0.642615 |
0 0.407990 0.535934 0.152631 0.428453 |
0 0.387214 0.536390 0.774428 0.904667 |
0 0.401452 0.502961 0.217742 0.376270 |
0 0.518287 0.449803 0.323926 0.571144 |
0 0.395696 0.469672 0.704828 0.784259 |
0 0.741195 0.594581 0.517609 0.810838 |
0 0.706758 0.526306 0.423983 0.308862 |
0 0.394011 0.801837 0.263609 0.396327 |
0 0.382034 0.494925 0.611845 0.852829 |
0 0.524295 0.796653 0.357184 0.406694 |
0 0.422569 0.515625 0.114583 0.337500 |
0 0.401522 0.558882 0.478208 0.576668 |
0 0.235261 0.323520 0.470523 0.647041 |
0 0.293242 0.473694 0.423983 0.308862 |
0 0.718634 0.506627 0.490156 0.380477 |
0 0.399220 0.495304 0.798440 0.915724 |
0 0.630663 0.482138 0.738675 0.964275 |
0 0.320989 0.277810 0.400872 0.555620 |
0 0.209072 0.288817 0.418144 0.577635 |
0 0.524202 0.198125 0.380888 0.396249 |
0 0.298668 0.546805 0.494861 0.382908 |
0 0.518033 0.441271 0.326809 0.600083 |
0 0.768750 0.308333 0.325000 0.458333 |
0 0.209239 0.267252 0.370089 0.534503 |
0 0.391652 0.784487 0.274620 0.386906 |
0 0.377834 0.870739 0.203394 0.258522 |
0 0.600606 0.462635 0.753569 0.916057 |
0 0.610780 0.540103 0.778439 0.919794 |
0 0.329106 0.890862 0.239580 0.218276 |
0 0.645144 0.216571 0.320237 0.433141 |
0 0.591547 0.451948 0.215483 0.400386 |
0 0.428462 0.896494 0.207919 0.207013 |
0 0.281682 0.469449 0.489796 0.348636 |
0 0.791075 0.596177 0.417850 0.523234 |
0 0.187500 0.313365 0.375000 0.626729 |
0 0.198331 0.732650 0.370251 0.510264 |
0 0.592898 0.509860 0.162119 0.371697 |
0 0.577431 0.515625 0.114583 0.337500 |
0 0.493671 0.557813 0.370522 0.606173 |
0 0.373737 0.501414 0.738065 0.955084 |
Clean 500-image object detection dataset derived directly from ground-truth annotated frames (blueObjectDetection.coco 2), keeping authentic blue object coloring and real-world indoor backgrounds with subtle physical augmentations.
train: 400 images + YOLO labelsval: 100 images + YOLO labelsdataset.zip (40MB fast single-file download)0: blue_object