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OmniCount-191

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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    ValueError
Message:      Bad split: test. Available splits: ['train']
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 116, in get_rows
                  ds = safe_load_dataset(
                      dataset,
                  ...<4 lines>...
                      download_config=download_config,
                  )
                File "/src/services/worker/src/worker/utils.py", line 465, in safe_load_dataset
                  return load_dataset(
                      path,
                  ...<5 lines>...
                      token=token,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/load.py", line 1715, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                         ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1154, in as_streaming_dataset
                  raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
              ValueError: Bad split: test. Available splits: ['train']

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OmniCount-191

A comprehensive benchmark for multi-label object counting, introduced in OmniCount: Multi-label Object Counting with Semantic-Geometric Priors (AAAI 2025).

Dataset Description

OmniCount-191 consists of 30,230 images with multi-label object counts, including point, bounding box, and VQA annotations across 191 object categories.

Citation 

@inproceedings{mondal2025omnicount,
  title={OmniCount: Multi-label Object Counting with Semantic-Geometric Priors},
  author={Mondal, Anindya and Nag, Sauradip and Zhu, Xiatian and Dutta, Anjan},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2025}
}

License

This dataset is released under the Open RAIL++-S License. Usage that enables monitoring of individuals without proper legal safeguards and ethical constraints is prohibited.
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