Prototypical networks
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
- University of Toronto,Twitter
- Organisation type
- Academia,Industry
- Country
- Canada, United States of America
- Published
- 15 March 2017
- Authors
- Jake Snell, Kevin Swersky, Richard S. Zemel
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.
- Training data
- 38,400 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 9,877
Sources
Where this record came from and when it was last checked.
- Reference
- Prototypical Networks for Few-shot Learning
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Prototypical networks was published by University of Toronto,Twitter, in the country recorded as Canada, during March 2017. The publishing organisation is categorised as academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of 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 a corpus of about 38,400 tokens of text.
Its inclusion criterion: highly cited.
Answers
Prototypical networks — common questions
Prototypical networks— when was it released?
It was published in March 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.
Prototypical networks— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Prototypical networks— what GPU do I need to run it?
None. This 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.
Prototypical networks— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Prototypical networks— how many parameters does it have?
No parameter count has been published for it, which is why no memory or speed figure appears on this page.
Prototypical networks— who created it?
It was published by University of Toronto,Twitter, based in Canada, an organisation categorised as academia,Industry.
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.