I77/yolo-ferma-vision
Object Detection • Updated
• 1
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1898, 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 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1736, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1919, 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 |
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0 0.150782 0.831251 0.251563 0.234375 |
0 0.231249 0.394531 0.159375 0.392188 |
0 0.342969 0.788282 0.173438 0.273437 |
0 0.302343 0.310938 0.089063 0.150000 |
0 0.344531 0.294532 0.048438 0.067187 |
0 0.678906 0.297656 0.532812 0.354688 |
0 0.106250 0.615000 0.212500 0.766666 |
0 0.330625 0.624166 0.383750 0.748333 |
0 0.188750 0.483333 0.092500 0.130000 |
0 0.702500 0.631667 0.212500 0.733333 |
0 0.818750 0.620833 0.360000 0.755000 |
0 0.269375 0.541745 0.526250 0.914634 |
0 0.131359 0.397500 0.262719 0.566250 |
0 0.346591 0.490385 0.693182 0.980769 |
0 0.608586 0.525962 0.780303 0.471153 |
0 0.738005 0.788462 0.521464 0.419231 |
0 0.555833 0.712812 0.138333 0.203125 |
0 0.243125 0.507042 0.486250 0.984038 |
0 0.419063 0.555868 0.273125 0.878873 |
0 0.609063 0.576995 0.229375 0.844131 |
0 0.799687 0.584977 0.268125 0.828169 |
0 0.873125 0.544601 0.252500 0.908920 |
0 0.042500 0.122328 0.015000 0.056215 |
0 0.065625 0.124703 0.012500 0.051464 |
0 0.635000 0.183294 0.025000 0.089470 |
0 0.648750 0.174980 0.013750 0.079176 |
0 0.840313 0.174188 0.038125 0.101346 |
0 0.334687 0.557157 0.436875 0.883829 |
0 0.221562 0.260688 0.176875 0.512082 |
0 0.673125 0.528346 0.393750 0.941450 |
0 0.915312 0.513475 0.168125 0.792751 |
0 0.351667 0.476667 0.150000 0.540000 |
0 0.094375 0.690477 0.040000 0.028945 |
0 0.204102 0.752604 0.162109 0.492188 |
0 0.434082 0.353516 0.524414 0.389323 |
0 0.319335 0.695312 0.171875 0.606771 |
0 0.301758 0.727213 0.125000 0.542969 |
0 0.361816 0.639974 0.106445 0.532552 |
0 0.454589 0.694661 0.147461 0.608073 |
0 0.513672 0.639974 0.125000 0.545572 |
0 0.607422 0.626954 0.105468 0.545573 |
0 0.655761 0.647135 0.125977 0.505209 |
0 0.730957 0.692058 0.120118 0.613281 |
0 0.827636 0.756510 0.219727 0.484375 |
0 0.626563 0.624297 0.080625 0.205441 |
0 0.302812 0.499531 0.209375 0.999062 |
0 0.470000 0.504221 0.105000 0.424953 |
0 0.566875 0.500938 0.093750 0.405254 |
0 0.783438 0.498124 0.431875 0.478424 |
0 0.103125 0.528750 0.146250 0.649166 |
0 0.351562 0.550000 0.311875 0.688334 |
0 0.467187 0.484584 0.169375 0.660833 |
0 0.654687 0.313333 0.246875 0.215000 |
0 0.843750 0.514166 0.137500 0.671667 |
0 0.961250 0.509584 0.043750 0.225833 |
0 0.239375 0.454844 0.222500 0.493885 |
0 0.377500 0.488241 0.412500 0.553152 |
0 0.730938 0.430856 0.314375 0.534336 |
0 0.922500 0.390874 0.153750 0.516463 |
0 0.253125 0.534583 0.297500 0.620833 |
0 0.545312 0.340000 0.210625 0.346666 |
0 0.618437 0.381667 0.185625 0.305000 |
0 0.053125 0.394737 0.106250 0.672932 |
0 0.161562 0.388158 0.131875 0.701128 |
0 0.193750 0.078947 0.042500 0.152255 |
0 0.248750 0.075658 0.052500 0.151316 |
0 0.268750 0.072839 0.053750 0.145677 |
0 0.288750 0.343985 0.052500 0.088346 |
0 0.373125 0.077067 0.060000 0.152255 |
0 0.495000 0.376410 0.157500 0.562970 |
0 0.453750 0.107143 0.042500 0.080827 |
0 0.599688 0.088815 0.078125 0.106203 |
0 0.592812 0.322838 0.059375 0.100564 |
0 0.647500 0.329417 0.052500 0.087406 |
0 0.662500 0.090226 0.055000 0.097745 |
0 0.821875 0.453947 0.355000 0.907895 |
0 0.721562 0.281015 0.068125 0.174812 |
0 0.835000 0.067200 0.051250 0.128759 |
0 0.861875 0.068609 0.087500 0.137218 |
0 0.911875 0.356204 0.120000 0.556391 |
0 0.662188 0.489681 0.025625 0.090056 |
0 0.705937 0.730769 0.098125 0.234522 |
0 0.202916 0.351503 0.025833 0.078947 |
0 0.349583 0.607143 0.217500 0.783208 |
0 0.552609 0.365625 0.622054 0.613750 |
0 0.197813 0.590377 0.054375 0.511297 |
0 0.299375 0.528870 0.151250 0.575732 |
0 0.352187 0.495816 0.110625 0.543096 |
0 0.453750 0.513808 0.095000 0.436821 |
0 0.537500 0.493724 0.085000 0.356486 |
0 0.604688 0.466109 0.109375 0.297908 |
0 0.690000 0.440586 0.088750 0.380753 |
0 0.790312 0.425104 0.109375 0.376569 |
0 0.844062 0.407950 0.060625 0.384100 |
0 0.107422 0.270834 0.025781 0.052083 |
0 0.638672 0.536979 0.721094 0.923958 |
0 0.208750 0.673077 0.166250 0.539400 |
0 0.459375 0.665572 0.055000 0.293621 |
0 0.566563 0.645404 0.070625 0.322701 |
Цель датасета — обучить YOLO распознавать людей, собак, коров и хищников на камерах в ферме.
Данные собраны из нескольких источников:
images/ — изображения .jpglabels/ — разметка YOLO .txt (same stem)Формат YOLO: class_id x_center y_center width height (нормализованные значения 0..1).
0 — person1 — wolf2 — cow3 — dogApache-2.0