README.md

metadata

dataset_info:
  - config_name: Full
    features:
      - name: id
        dtype: string
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: image_source
        dtype: string
      - name: image
        dtype: image
      - name: category
        dtype: string
    splits:
      - name: adversarial
        num_bytes: 490408158
        num_examples: 3000
      - name: popular
        num_bytes: 490397000
        num_examples: 3000
      - name: random
        num_bytes: 490394976
        num_examples: 3000
    download_size: 255022914
    dataset_size: 1471200134
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: question_id
        dtype: string
      - name: question
        dtype: string
      - name: answer
        dtype: string
      - name: image_source
        dtype: string
      - name: image
        dtype: image
      - name: category
        dtype: string
    splits:
      - name: test
        num_bytes: 1471200135
        num_examples: 9000
    download_size: 255022914
    dataset_size: 1471200135
configs:
  - config_name: Full
    data_files:
      - split: adversarial
        path: Full/adversarial-*
      - split: popular
        path: Full/popular-*
      - split: random
        path: Full/random-*
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

Large-scale Multi-modality Models Evaluation Suite

Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval

🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets

This Dataset

This is a formatted version of POPE. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.

@article{li2023evaluating,
  title={Evaluating object hallucination in large vision-language models},
  author={Li, Yifan and Du, Yifan and Zhou, Kun and Wang, Jinpeng and Zhao, Wayne Xin and Wen, Ji-Rong},
  journal={arXiv preprint arXiv:2305.10355},
  year={2023}
}