Datasets:
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
updated_utc: timestamp[s]
repo_id: string
repo_url: string
total_files: int64
total_bytes: int64
uploaded_files: int64
uploaded_bytes: int64
completed_batches: list<item: null>
child 0, item: null
total_batches: int64
current: struct<phase: string, batch: null, path: null>
child 0, phase: string
child 1, batch: null
child 2, path: null
official_website: string
dataset: string
files: list<item: struct<id: string, name: string, mime: string, bytes: int64, source_path: string, path: s (... 29 chars omitted)
child 0, item: struct<id: string, name: string, mime: string, bytes: int64, source_path: string, path: string, down (... 17 chars omitted)
child 0, id: string
child 1, name: string
child 2, mime: string
child 3, bytes: int64
child 4, source_path: string
child 5, path: string
child 6, download_url: string
batch_limit_bytes: int64
batches: list<item: struct<id: int64, bytes: int64, files: list<item: string>>>
child 0, item: struct<id: int64, bytes: int64, files: list<item: string>>
child 0, id: int64
child 1, bytes: int64
child 2, files: list<item: string>
child 0, item: string
inventory_utc: string
to
{'dataset': Value('string'), 'official_website': Value('string'), 'inventory_utc': Value('string'), 'repo_id': Value('string'), 'batch_limit_bytes': Value('int64'), 'total_files': Value('int64'), 'total_bytes': Value('int64'), 'files': List({'id': Value('string'), 'name': Value('string'), 'mime': Value('string'), 'bytes': Value('int64'), 'source_path': Value('string'), 'path': Value('string'), 'download_url': Value('string')}), 'batches': List({'id': Value('int64'), 'bytes': Value('int64'), 'files': List(Value('string'))})}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
updated_utc: timestamp[s]
repo_id: string
repo_url: string
total_files: int64
total_bytes: int64
uploaded_files: int64
uploaded_bytes: int64
completed_batches: list<item: null>
child 0, item: null
total_batches: int64
current: struct<phase: string, batch: null, path: null>
child 0, phase: string
child 1, batch: null
child 2, path: null
official_website: string
dataset: string
files: list<item: struct<id: string, name: string, mime: string, bytes: int64, source_path: string, path: s (... 29 chars omitted)
child 0, item: struct<id: string, name: string, mime: string, bytes: int64, source_path: string, path: string, down (... 17 chars omitted)
child 0, id: string
child 1, name: string
child 2, mime: string
child 3, bytes: int64
child 4, source_path: string
child 5, path: string
child 6, download_url: string
batch_limit_bytes: int64
batches: list<item: struct<id: int64, bytes: int64, files: list<item: string>>>
child 0, item: struct<id: int64, bytes: int64, files: list<item: string>>
child 0, id: int64
child 1, bytes: int64
child 2, files: list<item: string>
child 0, item: string
inventory_utc: string
to
{'dataset': Value('string'), 'official_website': Value('string'), 'inventory_utc': Value('string'), 'repo_id': Value('string'), 'batch_limit_bytes': Value('int64'), 'total_files': Value('int64'), 'total_bytes': Value('int64'), 'files': List({'id': Value('string'), 'name': Value('string'), 'mime': Value('string'), 'bytes': Value('int64'), 'source_path': Value('string'), 'path': Value('string'), 'download_url': Value('string')}), 'batches': List({'id': Value('int64'), 'bytes': Value('int64'), 'files': List(Value('string'))})}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
DrivingStereo
This is a mirror of the original DrivingStereo download files, preserving the original ZIP archives and camera calibration text files without recompression. The original dataset was introduced at CVPR 2019 by Guorun Yang, Xiao Song, Chaoqing Huang, Zhidong Deng, Jianping Shi, and Bolei Zhou.
- Official website: https://drivingstereo-dataset.github.io/
- Paper: https://openaccess.thecvf.com/content_CVPR_2019/html/Yang_DrivingStereo_A_Large-Scale_Dataset_for_Stereo_Matching_in_Autonomous_Driving_CVPR_2019_paper.html
- Original license: https://github.com/drivingstereo-dataset/drivingstereo-dataset.github.io/blob/master/LICENSE
Contents
| Directory | Contents |
|---|---|
train/ |
All 38 training sequences: half-resolution left/right images, disparity maps, and depth maps |
test/half-size/ |
All four testing sequences at half resolution, with all four modalities |
test/full-size/ |
All four testing sequences at full resolution, with all four modalities |
weather/ |
Sunny, cloudy, foggy, and rainy subsets, with both released resolutions and all four modalities |
calibration/ |
Training and testing camera calibration, for both resolutions |
The inventory contains 300 source files totaling 202,346,258,264 bytes (202.346 GB). The early demonstration package is excluded; the released training, testing, weather, and calibration directories are mirrored in full.
Transfer status
Transfer in progress. As of 2026-10-16T20:56:23+00:00, 0/300 files
(0.000/202.346 GB) are verified on the Hub.
Status is refreshed after each batch. transfer_status.json contains the machine-readable status.
source_manifest.json records source Google Drive file IDs, URLs, original paths,
expected byte sizes, mirror paths, and the approximately 50 GB batch plan.
SHA256SUMS contains hashes for successfully uploaded files. Each ZIP is checked
for CRC errors before upload; uploaded file sizes and content hashes are checked
against Hub metadata before a local batch is removed.
Download
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="kronecker0122/DrivingStereo",
repo_type="dataset",
local_dir="DrivingStereo",
allow_patterns=["train/left-image/*", "train/right-image/*", "train/disparity-map/*", "calibration/train/*"],
)
Download all files by omitting allow_patterns. ZIP archives must be extracted
before using the original images and maps. Follow the official website for
calibration details and the different disparity encodings at each resolution.
License and citation
The official website releases the dataset under the MIT license. The original
copyright notice and license are included in LICENSE. Please cite the authors'
publication when using the dataset:
@inproceedings{yang2019drivingstereo,
title={DrivingStereo: A Large-Scale Dataset for Stereo Matching in Autonomous Driving Scenarios},
author={Yang, Guorun and Song, Xiao and Huang, Chaoqin and Deng, Zhidong and Shi, Jianping and Zhou, Bolei},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019}
}
- Downloads last month
- -