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Error code: DatasetGenerationError
Exception: ArrowIndexError
Message: array slice would exceed array length
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1858, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 781, in finalize
self.write_rows_on_file()
~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
self._write_table(table)
~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 772, in _write_table
pa_table = pa_table.combine_chunks()
File "pyarrow/table.pxi", line 4557, in pyarrow.lib.Table.combine_chunks
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowIndexError: array slice would exceed array length
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.
cls string | custom_metrics null | ytrue list | ypred list | confs list | weights null | ytrue_ids list | ypred_ids list | classes list | missing string | ious list |
|---|---|---|---|---|---|---|---|---|---|---|
fiftyone.utils.eval.detection.DetectionResults | null | ["(none)","(none)","(none)","(none)","(none)","(none)","human","human","human","human","human","huma(...TRUNCATED) | ["human","human","human","human","human","human","human","(none)","(none)","(none)","(none)","(none)(...TRUNCATED) | [0.4833585321903229,0.3566383123397827,0.2692480683326721,0.23572969436645508,0.23567526042461395,0.(...TRUNCATED) | null | ["67b52355d3eb6cedf92c48c2","67b52355d3eb6cedf92c48b6","67b52355d3eb6cedf92c48b7","67b52355d3eb6cedf(...TRUNCATED) | ["67b52371d3eb6cedf92cbd76","67b52371d3eb6cedf92cbd77","67b52371d3eb6cedf92cbd79","67b52371d3eb6cedf(...TRUNCATED) | [
"bouy",
"human",
"kayak",
"sailboat",
"wind/sup-board"
] | (none) | [0.6446907117502083,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,null,(...TRUNCATED) |
Dataset Card for AFO - Aerial Floating Objects
AFO dataset is the first free dataset for training machine learning and deep learning models for maritime Search and Rescue applications. It contains aerial-drone videos with 40,000 hand-annotated persons and objects floating in the water, many of small size, which makes them difficult to detect.
This is a FiftyOne dataset with 1014 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("dgural/AFO-Aerial_Floating_Objects")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details
Dataset Description
The AFO dataset contains images taken from fifty video clips containing objects floating on the water surface, captured by the various drone-mounted cameras (from 1280x720 to 3840x2160 resolutions), which have been used to create AFO. From these videos, we have extracted and manually annotated 3647 images that contain 39991 objects. These have been then split into three parts: the training (67,4% of objects), the test (19,12% of objects), and the validation set (13,48% of objects). In order to prevent overfitting of the model to the given data, the test set contains selected frames from nine videos that were not used in either the training or validation sets. Dataset is prepared in Darknet YOLO format -> https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects
- Funded by [optional]: Polish National Science Center
- Language(s) (NLP): en
- License: Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License
Dataset Sources
- Repository: https://www.kaggle.com/datasets/jangsienicajzkowy/afo-aerial-dataset-of-floating-objects
- Paper: An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance
Uses
The datasets provided on this page are published under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. This means that you must attribute the work in the manner specified by the authors, you may not use this work for commercial purposes and if you alter, transform, or build upon this work, you may distribute the resulting work only under the same license. If you are interested in commercial usage you can contact authors for further options.
Dataset Structure
Ground_truth field plus clip embeddings are included!
Citation
BibTeX:
@article{article, author = {Gąsienica-Józkowy, Jan and Knapik, Mateusz and Cyganek, Boguslaw}, year = {2021}, month = {01}, pages = {1-15}, title = {An ensemble deep learning method with optimized weights for drone-based water rescue and surveillance}, journal = {Integrated Computer-Aided Engineering}, doi = {10.3233/ICA-210649} }
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