PNAS-net
No estimate
No hardware requirements for this model
The weights for this model have not been published, so it cannot be downloaded or run on your own hardware at any size. It is reachable only through its provider, and no graphics card changes that.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- Johns Hopkins University,Google AI,Stanford University
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America
- Published
- 2 December 2017
- Authors
- C Liu, B Zoph, M Neumann, J Shlens
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Parameters
- 86M
- Training data
- 45,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 2,140
Sources
Where this record came from and when it was last checked.
- Reference
- Progressive Neural Architecture Search
- Last updated
- 25 May 2026
What the numbers mean
Background
PNAS-net was published by Johns Hopkins University,Google AI,Stanford University, in United States of America, in December 2017. It comes out of academia,Industry,Academia.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
It was trained on about 45,000 tokens of text.
Answers
PNAS-net — common questions
What is PNAS-net used for?
PNAS-net works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run PNAS-net?
None. PNAS-net is a closed model — its weights were never published, so it cannot be downloaded or run on your own hardware at any price. It is reachable only through its provider.
Is PNAS-net open source?
The licensing for PNAS-net was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
How many parameters does PNAS-net have?
PNAS-net has 86M parameters. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Who created PNAS-net?
PNAS-net was published by Johns Hopkins University,Google AI,Stanford University, based in United States of America, categorised as academia,Industry,Academia.
When was PNAS-net released?
PNAS-net was published in December 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.