Datasets:
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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/python3.12/site-packages/datasets/builder.py", line 1811, 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 882, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, 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 1832, 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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8 0.724038 0.885546 0.265931 0.150413 |
14 0.772389 0.180937 0.169231 0.155826 |
8 0.245162 0.882153 0.141336 0.062330 |
13 0.753806 0.550177 0.172955 0.189705 |
14 0.299099 0.517655 0.130182 0.089440 |
11 0.260982 0.149771 0.076255 0.090782 |
8 0.519273 0.482469 0.936991 0.512198 |
14 0.663796 0.541871 0.628025 0.841806 |
12 0.531178 0.342912 0.734556 0.456546 |
12 0.299961 0.775670 0.578378 0.448660 |
8 0.209344 0.496564 0.418688 0.993128 |
8 0.501448 0.504821 0.887761 0.988615 |
8 0.517258 0.478742 0.834129 0.916748 |
8 0.840100 0.637264 0.263865 0.725473 |
8 0.477058 0.497010 0.864885 0.994021 |
8 0.822162 0.499823 0.354234 0.999646 |
14 0.681574 0.556677 0.636852 0.834516 |
14 0.396736 0.653086 0.721111 0.647886 |
8 0.577599 0.491194 0.354111 0.921407 |
8 0.429945 0.576995 0.476764 0.606448 |
13 0.548398 0.637758 0.619112 0.435515 |
8 0.738017 0.486844 0.519050 0.863050 |
22 0.518693 0.460172 0.475183 0.706379 |
8 0.548346 0.503054 0.850809 0.909784 |
23 0.656462 0.751477 0.312925 0.348776 |
11 0.425943 0.394810 0.449057 0.258819 |
4 0.565496 0.113270 0.479816 0.219297 |
5 0.402868 0.803027 0.337868 0.393946 |
22 0.189007 0.364270 0.212059 0.377838 |
8 0.659835 0.374757 0.679301 0.304108 |
14 0.648914 0.464870 0.622416 0.759870 |
11 0.527500 0.566314 0.684720 0.446525 |
15 0.536933 0.488365 0.874691 0.919500 |
4 0.429352 0.565102 0.528432 0.723963 |
22 0.252649 0.559338 0.213027 0.757647 |
22 0.777595 0.513860 0.226973 0.795515 |
8 0.450471 0.756373 0.090243 0.336144 |
8 0.850562 0.277843 0.098632 0.398431 |
8 0.505523 0.510051 0.988953 0.528410 |
22 0.743991 0.472389 0.328602 0.796752 |
8 0.313028 0.510924 0.285435 0.971019 |
8 0.467385 0.478490 0.166527 0.303854 |
8 0.520174 0.496959 0.959653 0.895670 |
8 0.464865 0.481108 0.619459 0.388402 |
22 0.558963 0.507411 0.846221 0.985179 |
8 0.519081 0.473160 0.772806 0.916258 |
8 0.525531 0.525991 0.338198 0.377833 |
14 0.517665 0.680486 0.170699 0.262162 |
14 0.605491 0.789399 0.438036 0.412787 |
14 0.574855 0.382186 0.547527 0.278907 |
22 0.077471 0.554948 0.154942 0.320722 |
14 0.524710 0.574485 0.785328 0.705670 |
22 0.497642 0.520211 0.239811 0.485263 |
14 0.494561 0.604010 0.457824 0.686146 |
14 0.472080 0.500807 0.167595 0.312344 |
8 0.479785 0.332072 0.662222 0.546685 |
14 0.499699 0.583034 0.790461 0.833933 |
23 0.439533 0.709422 0.487778 0.418933 |
0 0.517963 0.520116 0.494593 0.440676 |
22 0.859853 0.776000 0.086324 0.322595 |
22 0.420478 0.604730 0.122721 0.133027 |
22 0.110551 0.564973 0.124779 0.252324 |
22 0.642179 0.309674 0.618352 0.371522 |
8 0.595561 0.694198 0.249198 0.564225 |
11 0.252039 0.499218 0.495000 0.954078 |
11 0.298111 0.279000 0.578000 0.498444 |
8 0.239368 0.589552 0.477011 0.702276 |
8 0.501351 0.496621 0.987586 0.976000 |
12 0.496865 0.675258 0.993731 0.618144 |
8 0.506788 0.172552 0.986423 0.345103 |
8 0.505087 0.548639 0.986385 0.902721 |
8 0.478218 0.476359 0.504436 0.501087 |
8 0.502799 0.509051 0.967838 0.981077 |
12 0.506293 0.505793 0.982644 0.988414 |
8 0.584066 0.719765 0.812527 0.419097 |
8 0.501911 0.493655 0.996178 0.958207 |
22 0.513431 0.775747 0.639828 0.448506 |
22 0.595621 0.221810 0.734759 0.443621 |
14 0.336348 0.585330 0.351758 0.319695 |
14 0.232656 0.249746 0.331406 0.192893 |
8 0.506896 0.727950 0.986209 0.515230 |
11 0.511374 0.223285 0.977251 0.435941 |
4 0.268170 0.404586 0.439971 0.661034 |
19 0.665115 0.299966 0.129078 0.219241 |
19 0.915634 0.803172 0.094380 0.209241 |
5 0.588173 0.503486 0.612988 0.496731 |
11 0.466615 0.396462 0.496744 0.488808 |
23 0.606385 0.537468 0.282385 0.281833 |
11 0.223281 0.323587 0.280230 0.451589 |
11 0.724863 0.460426 0.412101 0.309247 |
6 0.516038 0.499981 0.402205 0.607712 |
8 0.405943 0.511000 0.604802 0.140922 |
8 0.510208 0.617811 0.243667 0.446973 |
6 0.521951 0.437262 0.419818 0.354492 |
23 0.441216 0.511021 0.845505 0.594496 |
23 0.954353 0.664397 0.091293 0.256892 |
14 0.474534 0.676166 0.118303 0.156339 |
23 0.870744 0.616237 0.071752 0.166667 |
8 0.606765 0.816651 0.262086 0.327013 |
23 0.697754 0.460299 0.275615 0.256887 |
End of preview.
Poultry Disease Detection Dataset
A custom-annotated dataset of poultry images for detecting 26 disease lesions and clinical signs across 3 major poultry diseases: Newcastle Disease, Coccidiosis, and Fowlpox.
Dataset Details
- ~406 labeled images in YOLO format
- 26 classes (lesions and clinical signs)
- Annotations created with CVAT
- Images from farm and veterinary postmortem contexts
Format
Labels are in YOLO format (.txt per image):
<class_id> <x_center> <y_center> <width> <height>
All coordinates normalized 0–1.
Classes
See dataset.yaml for the full class list.
Associated Model
Fine-tuned YOLOv11n trained on this dataset: Evet-Africa/poultry-disease-detector
License
AGPL-3.0 — must be open-sourced if used in derivative works.
Citation
@misc{poultry-disease-dataset-2025,
title = {Poultry Disease Detection Dataset},
year = {2025},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/Evet-Africa/poultry-disease-dataset}}
}
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