title: Charting and Navigating Hugging Face's Model Atlas
summary: As there are now millions of publicly available neural networks, searching
  and analyzing large model repositories becomes increasingly important.
  Navigating so many models requires an atlas, but as most models are poorly
  documented charting such an atlas is challenging. To explore the hidden
  potential of model repositories, we chart a preliminary atlas representing the
  documented fraction of Hugging Face. It provides stunning visualizations of the
  model landscape and evolution. We demonstrate several applications of this
  atlas including predicting model attributes (e.g., accuracy), and analyzing
  trends in computer vision models. However, as the current atlas remains
  incomplete, we propose a method for charting undocumented regions.
  Specifically, we identify high-confidence structural priors based on dominant
  real-world model training practices. Leveraging these priors, our approach
  enables accurate mapping of previously undocumented areas of the atlas. We
  publicly release our datasets, code, and interactive atlas.
paper_authors._id: 67d3ba5e4d3a41ed9f8651eb
paper_authors.name: Eliahu Horwitz
paper_authors.status: claimed_verified
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paper_authors._id: 630dd4218df86f1e5beb2ed7
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/630dd4218df86f1e5beb2ed7/fKvNWyWv6CVBdbXXUlrYv.jpeg
paper_authors.fullname: Eliahu Horwitz
paper_authors.type: user
paper_authors.user: Eliahu
paper_authors._id: 67d3ba5e4d3a41ed9f8651ec
paper_authors.name: Nitzan Kurer
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paper_authors.fullname: Nitzan Kurer
paper_authors.type: user
paper_authors.user: nitzankur
paper_authors._id: 67d3ba5e4d3a41ed9f8651ed
paper_authors.name: Jonathan Kahana
paper_authors.status: admin_assigned
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paper_authors._id: 6465fd33dac127ac80f0b334
paper_authors.avatarUrl: /avatars/113f02c1b1f8d33d3487daa867afcd3f.svg
paper_authors.fullname: Jonathan Kahana
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paper_authors.user: jonkahana
paper_authors._id: 67d3ba5e4d3a41ed9f8651ee
paper_authors.name: Liel Amar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:56:01.634Z
paper_authors._id: 669ffff5944b597ce2a1aa5b
paper_authors.avatarUrl: /avatars/559ca0ad82b1a52208510f09492fafa6.svg
paper_authors.fullname: Liel Amar
paper_authors.type: user
paper_authors.user: LielAmar
paper_authors._id: 67d3ba5e4d3a41ed9f8651ef
paper_authors.name: Yedid Hoshen
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-14T10:09:39.859Z
paper_authors._id: 646cfc3b4220471ca0c56b20
paper_authors.avatarUrl: /avatars/19d6ab141ec2cd25c1c3b45fd8f69910.svg
paper_authors.fullname: Yedid Hoshen
paper_authors.type: user
paper_authors.user: yedid
paper_title: Charting and Navigating Hugging Face's Model Atlas
paper_summary: As there are now millions of publicly available neural networks, searching
  and analyzing large model repositories becomes increasingly important.
  Navigating so many models requires an atlas, but as most models are poorly
  documented charting such an atlas is challenging. To explore the hidden
  potential of model repositories, we chart a preliminary atlas representing the
  documented fraction of Hugging Face. It provides stunning visualizations of the
  model landscape and evolution. We demonstrate several applications of this
  atlas including predicting model attributes (e.g., accuracy), and analyzing
  trends in computer vision models. However, as the current atlas remains
  incomplete, we propose a method for charting undocumented regions.
  Specifically, we identify high-confidence structural priors based on dominant
  real-world model training practices. Leveraging these priors, our approach
  enables accurate mapping of previously undocumented areas of the atlas. We
  publicly release our datasets, code, and interactive atlas.
paper_ai_summary: An atlas of Hugging Face's model repository provides visualizations and analysis, with methods for mapping undocumented areas based on structural priors.
paper_ai_keywords: model landscape model evolution predicting model attributes computer vision models structural priors mapping undocumented areas interactive atlas