Datasets:
The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: RuntimeError
Message: File 'ST-Net-dataset/lower2upper/mask/lower_mask_matlab/0010e3db305a315cc2a4d5347cdbc670.jpg' is encrypted, password required for extraction
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 328, in compute
compute_first_rows_from_parquet_response(
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 88, in compute_first_rows_from_parquet_response
rows_index = indexer.get_rows_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 631, in get_rows_index
return RowsIndex(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 512, in __init__
self.parquet_index = self._init_parquet_index(
File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 529, in _init_parquet_index
response = get_previous_step_or_raise(
File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 566, in get_previous_step_or_raise
raise CachedArtifactError(
libcommon.simple_cache.CachedArtifactError: The previous step failed.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 91, in get_rows_or_raise
return get_rows(
File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 68, 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 1392, in __iter__
example = _apply_feature_types_on_example(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1082, 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 1940, in decode_example
return {
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1941, 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 1341, in decode_nested_example
return schema.decode_example(obj, token_per_repo_id=token_per_repo_id)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/image.py", line 180, in decode_example
with xopen(path, "rb", download_config=download_config) as f:
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 512, 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 132, in open
return self.__enter__()
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 100, in __enter__
f = self.fs.open(self.path, mode=mode)
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 1307, in open
f = self._open(
File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 120, in _open
out = self.zip.open(path, mode.strip("b"))
File "/usr/local/lib/python3.9/zipfile.py", line 1563, in open
raise RuntimeError("File %r is encrypted, password "
RuntimeError: File 'ST-Net-dataset/lower2upper/mask/lower_mask_matlab/0010e3db305a315cc2a4d5347cdbc670.jpg' is encrypted, password required for extractionNeed help to make the dataset viewer work? Open a discussion for direct support.
This dataset was constructed for paper Towards Intelligent Design: A Self-Driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures. You need to fill in the form to get password for unzipping files.
Details
This dataset was constructed for self-supervised collocated clothing synthesis (CCS) task. It encompasses 30, 876 images, covering both upper and lower clothing items. We partition the dataset into training and test sets, maintaining a ratio of 4:1, for both 'upper → lower' and 'lower → upper' settings. Within this dataset, there exist instances where certain fashion items recur. To ensure non-overlap between the training and test sets, we employ a graph-based method and similarity comparison with learned perceptual image patch similarity (LPIPS). This ensures that the source domain images in any transformation direction do not intersect. For instance, in the transition direction of 'upper → lower', there is no intersection between the upper clothing images in the training set and those in the test set. All images in the dataset are with a resolution of 256 x 256 pixels.
Citation
If you use this dataset in your work, please cite it as:
@inproceedings{dong2024towards,
title={Towards Intelligent Design: A Self-Driven Framework for Collocated Clothing Synthesis Leveraging Fashion Styles and Textures},
author={Dong, Minglong and Zhou, Dongliang and Ma, Jianghong and Zhang, Haijun},
booktitle={ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages={3725--3729},
year={2024},
organization={IEEE}
}
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