The dataset viewer is not available for this split.
Error code: FeaturesError
Exception: FileNotFoundError
Message: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7fed85d566f0>'
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
iterable_dataset = iterable_dataset._resolve_features()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4408, in _resolve_features
features = _infer_features_from_batch(self.with_format(None)._head())
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2679, in _head
return next(iter(self.iter(batch_size=n)))
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2861, in iter
for key, pa_table in ex_iterable.iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2395, in _iter_arrow
yield from self.ex_iterable._iter_arrow()
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/xml/xml.py", line 67, in _generate_tables
with open(file, encoding=self.config.encoding, errors=self.config.encoding_errors) as f:
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/streaming.py", line 73, in wrapper
return function(*args, download_config=download_config, **kwargs)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 967, in xopen
return open(main_hop, mode, *args, **kwargs)
FileNotFoundError: [Errno 2] No such file or directory: '<datasets.utils.file_utils.FilesIterable object at 0x7fed85d566f0>'Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
VEDAI Dataset
Overview
VEDAI (Vehicle Detection in Aerial Imagery) is a benchmark dataset for vehicle detection and classification in aerial imagery. The dataset was created to evaluate automatic target recognition and object detection algorithms under challenging real-world conditions, including varying vehicle orientations, shadows, occlusions, and lighting conditions.
Statistics
| Property | Value |
|---|---|
| Images | 2,059 |
| Resolution | 256 × 256 pixels |
| Modalities | RGB |
| Vehicle Classes | 8 |
Classes
- Car
- Truck
- Tractor
- Camping Car
- Van
- Pickup
- Boat
- Other Vehicles
Dataset Characteristics
- High-resolution aerial imagery
- RGB and infrared image modalities
- Small object detection benchmark
- Oriented bounding box annotations
- Diverse rural and urban environments
- Multiple viewing angles and vehicle orientations
Applications
- Vehicle Detection
- Vehicle Classification
- Aerial Object Detection
- UAV Vision
- Remote Sensing
- Traffic Monitoring
- Small Object Detection Research
Source
Official dataset page:
https://downloads.greyc.fr/vedai/
Citation
@article{razakarivony2015vedai,
title={Vehicle Detection in Aerial Imagery: A Small Target Detection Benchmark},
author={Razakarivony, Sebastien and Jurie, Frederic},
journal={Journal of Visual Communication and Image Representation},
volume={34},
pages={187--203},
year={2015}
}
Acknowledgements
If you use this dataset, please cite the original VEDAI publication and acknowledge the dataset creators.
The VEDAI dataset was introduced by:
Sébastien Razakarivony and Frédéric Jurie,
Vehicle Detection in Aerial Imagery: A Small Target Detection Benchmark, Journal of Visual Communication and Image Representation, 2015.
- Downloads last month
- 32