Charting and Navigating Hugging Face's Model Atlas
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.
67d3ba5e4d3a41ed9f8651eb
Eliahu Horwitz
claimed_verified
2025-03-14T08:56:04.270Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/630dd4218df86f1e5beb2ed7/fKvNWyWv6CVBdbXXUlrYv.jpeg
Eliahu Horwitz
user
Eliahu
67d3ba5e4d3a41ed9f8651ec
Nitzan Kurer
admin_assigned
2025-03-14T10:09:18.093Z
674ec6d1ce68874ee4f2d53b
/avatars/4c15c9bdcf51d4bf5e6fceb86195e480.svg
Nitzan Kurer
user
nitzankur
67d3ba5e4d3a41ed9f8651ed
Jonathan Kahana
admin_assigned
2025-03-14T10:09:25.338Z
6465fd33dac127ac80f0b334
/avatars/113f02c1b1f8d33d3487daa867afcd3f.svg
Jonathan Kahana
user
jonkahana
67d3ba5e4d3a41ed9f8651ee
Liel Amar
claimed_verified
2025-03-14T08:56:01.634Z
669ffff5944b597ce2a1aa5b
/avatars/559ca0ad82b1a52208510f09492fafa6.svg
Liel Amar
user
LielAmar
67d3ba5e4d3a41ed9f8651ef
Yedid Hoshen
admin_assigned
2025-03-14T10:09:39.859Z
646cfc3b4220471ca0c56b20
/avatars/19d6ab141ec2cd25c1c3b45fd8f69910.svg
Yedid Hoshen
user
yedid
Charting and Navigating Hugging Face's Model Atlas
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.
An atlas of Hugging Face's model repository provides visualizations and analysis, with methods for mapping undocumented areas based on structural priors.
model landscape model evolution predicting model attributes computer vision models structural priors mapping undocumented areas interactive atlas