Datasets:

ckyrkou
/

csd

Error code:   StreamingRowsError
Exception:    ValueError
Message:      Invalid string class label csd@5edc65e72c57496fa4c63bcbc96470cc540480f5
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 csd@5edc65e72c57496fa4c63bcbc96470cc540480f5

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CamSDD Dataset (Camera Scene Detection Dataset)

Overview

CamSDD is a large-scale image classification dataset designed for camera scene detection on smartphones and mobile devices. The dataset was introduced to support the development of efficient deep learning models capable of automatically recognizing common photography scenes in real time. It contains more than 11,000 images spanning 30 scene categories commonly encountered in mobile photography.

Associated Publication

This dataset was used in:

Ignatov, A., Malivenko, G., Timofte, R., Chen, S., Xia, X., Liu, Z., Zhang, Y., Zhu, F., Li, J., Xiao, X., Tian, Y., Wu, X., Kyrkou, C., et al. Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2021, pp. 2558-2568.

Statistics

Property	Value
Task	Camera Scene Classification
Classes	30
Images	11,000+
Data Type	RGB Images
Application Domain	Mobile Photography & Smartphone AI

Applications

Camera Scene Detection
Smartphone Photography Enhancement
Mobile AI
Edge AI
Image Classification
Efficient Deep Learning

Dataset Characteristics

Manually collected and curated image dataset
Represents common scenes encountered in smartphone photography
Designed for deployment on resource-constrained devices
Suitable for benchmarking lightweight CNN and Transformer models
Used in the Mobile AI 2021 Camera Scene Detection Challenge【1-9aa7ee】【3-8df4a0】

Source

Project related publication:

https://openaccess.thecvf.com/content/CVPR2021W/MAI/html/Ignatov_Fast_and_Accurate_Quantized_Camera_Scene_Detection_on_Smartphones_Mobile_CVPRW_2021_paper.html

Original camera scene detection paper:

https://openaccess.thecvf.com/content/CVPR2021W/MAI/html/Pouget_Fast_and_Accurate_Camera_Scene_Detection_on_Smartphones_CVPRW_2021_paper.html

Citation

@inproceedings{ignatov2021camsddchallenge,
  title={Fast and Accurate Quantized Camera Scene Detection on Smartphones, Mobile AI 2021 Challenge: Report},
  author={Ignatov, Andrey and Malivenko, Grigory and Timofte, Radu and Chen, Sheng and Xia, Xin and Liu, Zhaoyan and Zhang, Yuwei and Zhu, Feng and Li, Jiashi and Xiao, Xuefeng and Tian, Yuan and Wu, Xinglong and Kyrkou, Christos and others},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  pages={2558--2568},
  year={2021}
}

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