OmniCount: Multi-label Object Counting with Semantic-Geometric Priors
Paper • 2403.05435 • Published • 1
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 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 61, in get_rows
ds = load_dataset(
^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1705, in load_dataset
return builder_instance.as_streaming_dataset(split=split)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1196, in as_streaming_dataset
raise ValueError(f"Bad split: {split}. Available splits: {list(splits_generators)}")
ValueError: Bad split: test. Available splits: ['train']Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
A comprehensive benchmark for multi-label object counting, introduced in OmniCount: Multi-label Object Counting with Semantic-Geometric Priors (AAAI 2025).
OmniCount-191 consists of 30,230 images with multi-label object counts, including point, bounding box, and VQA annotations across 191 object categories.
@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}
}
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.