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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1539, in _prepare_split_single
for key, record in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 131, in _generate_examples
for example_idx, example in enumerate(self._get_pipeline_from_tar(tar_path, tar_iterator)):
~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
for filename, f in tar_iterator:
^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/track.py", line 49, in __iter__
for x in self.generator(*self.args):
~~~~~~~~~~~~~~^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 1419, in _iter_from_urlpath
with xopen(urlpath, "rb", download_config=download_config, block_size=0) as f:
~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/file_utils.py", line 976, in xopen
file_obj = fs.open(paths[0], mode)
File "<string>", line 3, in open
File "/usr/local/lib/python3.14/unittest/mock.py", line 1176, in __call__
return self._mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1180, in _mock_call
return self._execute_mock_call(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/unittest/mock.py", line 1247, in _execute_mock_call
result = effect(*args, **kwargs)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 794, in wrapped
tracker.files[urlpath] = {"read": 0, "size": int(f.size)}
~~~^^^^^^^^
TypeError: int() argument must be a string, a bytes-like object or a real number, not 'NoneType'
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 1379, 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 956, 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 1401, 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 1579, 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.
png image | __key__ string | __url__ string |
|---|---|---|
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_21 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_26 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_49 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_56 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_72 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_dreary-rain-and-fog_86 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_12 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_18 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_23 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_30 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_36 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_39 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_66 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_7 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_72 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_90 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_97 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-and-rainy-atmosphere_98 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-rain-scenery_100 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-rain-scenery_13 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-rain-scenery_59 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_foggy-rain-scenery_99 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_heavy-rain-and-mist_1 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
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Not supported with pagination yet | wres_datasets/real_train_dataset/RainReal/google_heavy-rain_10 | hf://datasets/ses7720/DUDA-WRES@e834c18518bb5c84ebb140a3b917c11977cfa0b2/base/real_train_dataset.tar.zst |
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DUDA WRES data
Prepared image folders for Discovering Unseen Degradations to Adapt Semi-Supervised Image Restoration.
- Code: https://github.com/bo-oseng/DUDA
- Pretrained assets: https://huggingface.co/ses7720/DUDA
Choose a download path
The base download contains original images, supervised pairs and full evaluation sets. The training patches can be generated from the base images with the provided code, or downloaded directly as a precomputed archive. Both paths use the same original data and the same patch extraction procedure.
| File | Contents |
|---|---|
base/syn_train_dataset.tar.zst |
Supervised paired data, clean discovery images and existing split manifests |
base/real_train_dataset.tar.zst |
Original unpaired RainReal, SnowReal and UnannotatedHazyImages |
base/real_eval_dataset.tar.zst |
RealRain-2320, RTTS and Snow100K-R full evaluation images |
optional/real_train_patches.tar.zst |
224x224 patches cropped from the 6,772 real training images (183,313 in total), precomputed for adaptation |
SHA256SUMS |
Archive checksums |
archives.json |
Compressed archive sizes and checksums |
Compressed download size: 25.76 GB base, plus 12.91 GB precomputed patches (decimal GB).
| Archive | Compressed GB |
|---|---|
base/syn_train_dataset.tar.zst |
21.99 |
base/real_train_dataset.tar.zst |
1.15 |
base/real_eval_dataset.tar.zst |
2.62 |
optional/real_train_patches.tar.zst |
12.91 |
Model assets are distributed separately.
Option A: generate patches yourself
Run these commands from the DUDA source directory after installing its environment.
The public Docker image in the model repository also contains the preprocessing
commands, so a GitHub checkout is not required when using Docker.
Install the zstd command on the machine used for extraction.
hf download ses7720/DUDA-WRES --repo-type dataset \
--include "base/*" SHA256SUMS archives.json README.md \
--local-dir downloads/duda-wres
mkdir -p datasets
for name in syn_train_dataset real_train_dataset real_eval_dataset; do
tar --zstd -xf "downloads/duda-wres/base/$name.tar.zst" -C datasets
done
python scripts/prepare_datasets.py patches --num-workers 8
python scripts/prepare_datasets.py eval-subset # optional monitoring subset
Patches use a 224-pixel grid stride with a final border crop for full coverage. Border patches can overlap. The code skips images smaller than 224 pixels on either axis and applies no per-image patch cap or quality filter. Image indices restart per class, preserving the released patch filenames.
Option B: use precomputed patches
Download/extract the three base archives as above, then obtain the separate patch archive:
hf download ses7720/DUDA-WRES optional/real_train_patches.tar.zst --repo-type dataset \
--local-dir downloads/duda-wres
tar --zstd -xf downloads/duda-wres/optional/real_train_patches.tar.zst -C datasets
python scripts/prepare_datasets.py eval-subset # optional monitoring subset
Skip the patches command when using this archive. Existing nonempty patch
folders are preserved by the preparation helper.
Archives extract to datasets/wres_datasets/<folder>, the source code's default
layout. Keep the paired filenames, class names and split manifests. The optional
monitoring subset is reconstructed from checked-in filename lists as relative links
into the full evaluation sets (232 RealRain images, 432 RTTS images and 133
Snow100K-R images). Training reports MUSIQ and CLIP-IQA on it and skips this
monitoring when it is absent; final evaluation uses the full sets.
To verify only the three base archives, run:
(cd downloads/duda-wres && grep ' base/' SHA256SUMS | sha256sum -c -)
For a complete download including the precomputed patches, run sha256sum -c SHA256SUMS
from downloads/duda-wres. SHA-256 verification reads the entire archive files.
The collection retains the original benchmark and source-image attributions; its publication does not change their respective terms. See the project README for upstream acknowledgements and dataset folder roles.
Preprocess using the public Docker image
Download and load the image as described in the model repository. After extracting the three base data archives, generate patches without a GitHub checkout:
docker run --rm \
-v "$PWD/datasets/wres_datasets:/app/datasets/wres_datasets" \
duda:neurips2026 python scripts/prepare_datasets.py patches --num-workers 8
docker run --rm \
-v "$PWD/datasets/wres_datasets:/app/datasets/wres_datasets" \
duda:neurips2026 python scripts/prepare_datasets.py eval-subset # optional
Patch preparation does not require a GPU. When using the precomputed patch archive,
skip the patches command.
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