| { |
| "@context": { |
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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": [ |
| { |
| "@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 ", |
| "encodingFormat": "application/zip", |
| "sha256": "62436497a8ae518d8322cbdd1574be6fed3c8e12ed0019d6995c8d28d684e336" |
| }, |
| { |
| "@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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| { |
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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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| "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." |
| } |
