ReLU (LFW)
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
- Organisation type
- Academia
- Country
- Canada
- Published
- 15 June 2010
- Authors
- Nair, V., Hinton, G. E.
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
- 233,280 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
- 18,270
Sources
Where this record came from and when it was last checked.
- Reference
- Rectified linear units improve restricted boltzmann machines
- Last updated
- 11 February 2026
What the numbers mean
Where it came from
ReLU (LFW) was published by University of Toronto, in Canada, in June 2010. The organisation is categorised as academia.
It works in Vision, and is recorded as doing face recognition.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It was trained on about 233,280 tokens of text.
Its inclusion criterion is highly cited.
Answers
ReLU (LFW) — common questions
What GPU do I need to run ReLU (LFW)?
None. ReLU (LFW) 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 ReLU (LFW) open source?
The licensing for ReLU (LFW) 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 ReLU (LFW) have?
No parameter count has been published for ReLU (LFW), which is why no memory or speed figure appears on this page.
Who created ReLU (LFW)?
ReLU (LFW) was published by University of Toronto, based in Canada, categorised as academia.
When was ReLU (LFW) released?
ReLU (LFW) was published in June 2010. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is ReLU (LFW) used for?
ReLU (LFW) works in Vision, and is recorded as handling face recognition. A model can carry several of each, so these are the areas it was built for rather than a limit on what it will attempt.
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.