README.md

metadata

dataset_info:
  features:
    - name: file_name
      dtype: string
    - name: image
      dtype: image
    - name: refs
      sequence: string
    - name: mt
      dtype: string
    - name: human_score
      dtype: float64
  splits:
    - name: train
      num_bytes: 4783117925.25
      num_examples: 26382
    - name: valid
      num_bytes: 593047697.75
      num_examples: 3298
    - name: test
      num_bytes: 595467629.75
      num_examples: 3298
  download_size: 5956104723
  dataset_size: 5971633252.75
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: valid
        path: data/valid-*
      - split: test
        path: data/test-*

Nebula Dataset

[ACCV 2024] DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning

Usage

>>> from datasets import load_dataset
>>> nebula = load_dataset("Ka2ukiMatsuda/Nebula")
>>> print(nebula)
DatasetDict({
    train: Dataset({
        features: ['file_name', 'image', 'refs', 'mt', 'human_score'],
        num_rows: 26382
    })
    valid: Dataset({
        features: ['file_name', 'image', 'refs', 'mt', 'human_score'],
        num_rows: 3298
    })
    test: Dataset({
        features: ['file_name', 'image', 'refs', 'mt', 'human_score'],
        num_rows: 3298
    })
})

Citation

@inproceedings{matsuda2024deneb,
  title={DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning},
  author={Kazuki Matsuda and Yuiga Wada and Komei Sugiura},
  booktitle={Proceedings of the Asian Conference on Computer Vision (ACCV)},
  year={2024},
  pages={3570--3586}
}