Restricted Boltzmann machine for Face Recognition
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,University College London (UCL)
- Organisation type
- Academia,Academia
- Country
- Canada, United Kingdom of Great Britain and Northern Ireland
- Published
- 1 April 2001
- Authors
- Yee Whye Teh, Geoffrey E. Hinton
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face recognition
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
- tokens
"Our version of the FERET database contained 1002 frontal face images of 429 individuals taken over a period of a few years under varying lighting conditions. Of these images, 818 are used as both the gallery and the training set"
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Rate-coded Restricted Boltzmann Machines for Face Recognition
- Last updated
- 28 November 2025
What the numbers mean
About this model
Restricted Boltzmann machine for Face Recognition was published by University of Toronto,University College London (UCL), in the country recorded as Canada, during April 2001. It comes out of an organisation categorised as academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of face recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Restricted Boltzmann machine for Face Recognition — common questions
Restricted Boltzmann machine for Face Recognition— who created it?
It was published by University of Toronto,University College London (UCL), based in Canada, an organisation categorised as academia,Academia.
Restricted Boltzmann machine for Face Recognition— when was it released?
It was published in April 2001. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Restricted Boltzmann machine for Face Recognition— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of face recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
Restricted Boltzmann machine for Face Recognition— 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.
Restricted Boltzmann machine for Face Recognition— 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.
Restricted Boltzmann machine for Face Recognition— 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.
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.