Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: KeyError
Message: "There is no item named 'drop/bdd-1000_hazeraindrop.png' in the archive"
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 271, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 2266, in __iter__
for key, example in ex_iterable:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1869, in __iter__
example = _apply_feature_types_on_example(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1781, in _apply_feature_types_on_example
decoded_example = features.decode_example(encoded_example, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2092, in decode_example
return {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 2093, in <dictcomp>
column_name: decode_nested_example(feature, value, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1407, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id) if obj is not None else None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 182, in decode_example
with xopen(path, "rb", download_config=download_config) as f:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/utils/file_utils.py", line 949, in xopen
file_obj = fsspec.open(file, mode=mode, *args, **kwargs).open()
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 135, in open
return self.__enter__()
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 103, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1293, in open
f = self._open(
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 129, in _open
out = self.zip.open(path, mode.strip("b"), force_zip64=self.force_zip_64)
File "/usr/local/lib/python3.9/zipfile.py", line 1511, in open
zinfo = self.getinfo(name)
File "/usr/local/lib/python3.9/zipfile.py", line 1438, in getinfo
raise KeyError(
KeyError: "There is no item named 'drop/bdd-1000_hazeraindrop.png' in the archive"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.
Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration
Paper | Github | Page | Dataset
AllWeatherNight
We observe that uneven lighting conditions in real-world nighttime scenes often interact with weather degradations. To synthesize more realistic nighttime images with adverse weather conditions, we introduce an illumination-aware degradation generation approach. We show four different synthetic image variants of nighttime scenes. Weather Only and Flare Only denote synthesis with illumination-aware weather degradation and flare, respectively. Ours involves synthesis with both types of degradations.
Dataset Statistics
We synthesize 8,000 nighttime images for model training, encompassing both multi-degradation and single-degradation scenarios with various degradation scales, directions, patterns and intensities. The test dataset consists of two parts: a synthetic subset and a real subset, each containing 1,000 images. The synthetic subset evaluates models across 7 dimensions, covering synthetic images with both multiple and single degradations. The 1,000 collected real-world images are categorized into 4 different degradation types and serve as the real subset for assessing models in real-world scenarios.
Intended Use
Our AllWeatherNight dataset is released under the BSD 3-Clause License, a permissive open-source license that grants users the freedom to use, copy, modify, and distribute the dataset, whether in its original form or as part of derivative works. The license is employed on generated degraded images and labels. The ground-truths from BDD100K and Exdark adhere to the BSD 3-Clause License.
Citation
If you find our work is helpful to your research, please cite the papers as follows:
@article{liu2025clearnight,
title={Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration},
author={Liu, Yuetong and Xu, Yunqiu and Wei, Yang and Bi, Xiuli and Xiao, Bin},
year={2025},
journal={arXiv preprint arXiv:2505.16479},
year={2025}
}
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