Datasets:

azad-wolf-se
/

MH-FED

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English

ArXiv:

Tags:

Computer Vision

Machine Learning

Deep Learning

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train

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:    RuntimeError
Message:      File 'Images/Amelia/Anger/00000.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 322, 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 640, in get_rows_index
                  return RowsIndex(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 521, in __init__
                  self.parquet_index = self._init_parquet_index(
                File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 538, in _init_parquet_index
                  response = get_previous_step_or_raise(
                File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 591, 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 96, in get_rows_or_raise
                  return get_rows(
                File "/src/libs/libcommon/src/libcommon/utils.py", line 197, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 73, 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 1393, 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 1983, in decode_example
                  return {
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/features.py", line 1984, 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 1349, 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 183, 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 1227, 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 1563, in open
                  raise RuntimeError("File %r is encrypted, password "
              RuntimeError: File 'Images/Amelia/Anger/00000.jpg' is encrypted, password required for extraction

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FExGAN-Meta: Facial Expression Generation with Meta-Humans

This is a facial expression image dataset proposed in the following article:

FExGAN-Meta: Facial Expression Generation with Meta-Humans

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Citation

If you use any part of this dataset for experiments, please cite the following article.

@article{Siddiqui_FExGAN-Meta_2022,
  author = {{Siddiqui}, J. Rafid},
  title = {{FExGAN-Meta: Facial Expression Generation with Meta-Humans}},
  journal = {ArXiv e-prints},
  archivePrefix = "arXiv:2203.05975",
  keywords = {Deep Learning, GAN, Facial Expressions},
  year = {2022}
  url = {https://arxiv.org/abs/2203.05975},
  DOI: 10.31219/osf.io/ygdrt
}

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