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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:    ValueError
Message:      Invalid string class label HS-SOD@975d211c87b798c8b76f04f89571e220b83e1120
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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2386, in __iter__
                  example = _apply_feature_types_on_example(
                      example, self.features, token_per_repo_id=self.token_per_repo_id
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2303, in _apply_feature_types_on_example
                  encoded_example = features.encode_example(example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 2178, in encode_example
                  return encode_nested_example(self, example)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1460, in encode_nested_example
                  {k: encode_nested_example(schema[k], obj.get(k), level=level + 1) for k in schema}
                      ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1483, in encode_nested_example
                  return schema.encode_example(obj) if obj is not None else None
                         ~~~~~~~~~~~~~~~~~~~~~^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1158, in encode_example
                  example_data = self.str2int(example_data)
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1095, in str2int
                  output = [self._strval2int(value) for value in values]
                            ~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/features/features.py", line 1116, in _strval2int
                  raise ValueError(f"Invalid string class label {value}")
              ValueError: Invalid string class label HS-SOD@975d211c87b798c8b76f04f89571e220b83e1120

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HS-SOD

HS-SOD: HyperSpectral Salient Object Detection Dataset

HS-SOD is a hyperspectral image dataset for benchmarking salient object detection methods for hyperspectral images.

Dataset Contents

The dataset contains:

  • 60 hyperspectral images
  • 60 corresponding sRGB renderings
  • 60 ground-truth binary masks

Each hyperspectral image has:

  • Spatial resolution: 768 × 1024 pixels
  • Spectral channels: 81
  • Spectral range: 380–780 nm

License and Terms of Use

This dataset is distributed under the original HS-SOD Dataset Terms of Use. It is not distributed under the MIT License.

By downloading or using this dataset, users are considered to have agreed to the terms specified in LICENSE.md.

When publishing results obtained using this dataset, users must cite either the official HS-SOD dataset repository or the reference paper, as specified in the Terms of Use.

Citation

If you use this dataset in your research, please cite the following paper:

@inproceedings{imamoglu2018hyperspectral,
  author    = {Nevrez Imamoglu and
               Yu Oishi and
               Xiaoqiang Zhang and
               Guanqun Ding and
               Yuming Fang and
               Toru Kouyama and
               Ryosuke Nakamura},
  title     = {Hyperspectral Image Dataset for Benchmarking on Salient Object Detection},
  booktitle = {Proceedings of the 10th International Conference on Quality of Multimedia Experience (QoMEX)},
  year      = {2018}
}

Plain-text citation

Nevrez Imamoglu, Yu Oishi, Xiaoqiang Zhang, Guanqun Ding, Yuming Fang, Toru Kouyama, and Ryosuke Nakamura, “Hyperspectral Image Dataset for Benchmarking on Salient Object Detection,” Proceedings of the 10th International Conference on Quality of Multimedia Experience (QoMEX), Sardinia, Italy, May 29–June 1, 2018.

Paper

The paper is available on arXiv:

Acknowledgement

The HS-SOD dataset content is based on results obtained from a project commissioned by the New Energy and Industrial Technology Development Organization (NEDO).

National Institute of Advanced Industrial Science and Technology, Japan
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