{
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    "@base": "https://mlcommons.org/croissant/",
    "ml": "http://mlcommons.org/schema#",
    "@language": "en"
  },
  "@type": "Dataset",
  "name": "Panel-Understanding-and-Operation",
  "description": "Panel Understanding and Operation (PUO) is a benchmark for evaluating vision-language models on panel understanding and operation tasks.",
  "url": "https://huggingface.co/datasets/Tele-AI-MAIL/Panel-Understanding-and-Operation",
  "version": "0.0.0",
  "keywords": ["vision-language models", "panel understanding", "instruction following", "privacy-preserving framework", "benchmark"],
  "license": "https://creativecommons.org/licenses/by/4.0/",
  "datePublished": "2025-05-14",
  "creator": {
    "@type": "Organization",
    "name": "Tele-AI-MAIL"
  },
  "includedInDataCatalog": {
    "@type": "DataCatalog",
    "name": "Hugging Face Datasets"
  },
  "distribution": [
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      "@type": "https://schema.org/FileObject",
      "name": "image.zip",
      "description": "Contains all panel images.",
      "contentUrl": "https://huggingface.co/datasets/Tele-AI-MAIL/Panel-Understanding-and-Operation/resolve/main/image.zip ",
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      "@type": "https://schema.org/FileObject",
      "name": "label.zip",
      "description": "Corresponding label information, see Fig.5 in the paper.",
      "contentUrl": "https://huggingface.co/datasets/Tele-AI-MAIL/Panel-Understanding-and-Operation/resolve/main/label.zip ",
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      "name": "instruction.zip",
      "description": "All QA pairs, see Fig.7 in the paper.",
      "contentUrl": "https://huggingface.co/datasets/Tele-AI-MAIL/Panel-Understanding-and-Operation/resolve/main/instruction.zip ",
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      "@type": "https://schema.org/FileObject",
      "name": "split.json",
      "description": "Shows how training and test set are split.",
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  "isAccessibleForFree": true,
  "about": "A large-scale image-based dataset containing over 19k annotated panel images and 430k instruction-following QA pairs. Using this dataset. It can be used to fine-tune VLMs to perform key PUO tasks: panel description, element grounding, function estimation, and goal-based planning."
}