Datasets:

Voxel51
/

AFO-Aerial_Floating_Objects

Error code:   DatasetGenerationError
Exception:    ArrowIndexError
Message:      array slice would exceed array length
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 621, in write_table
                  pa_table = pa_table.combine_chunks()
                File "pyarrow/table.pxi", line 3638, in pyarrow.lib.Table.combine_chunks
                File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_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 1420, 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 1052, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1897, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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cls string	custom_metrics null	ytrue sequence	ypred sequence	confs sequence	weights null	ytrue_ids sequence	ypred_ids sequence	classes sequence	missing string	ious sequence
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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