DeViSE
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 5 December 2013
- Authors
- Andrea Frome, G. Corrado, Jonathon Shlens, Samy Bengio, J. Dean, Marc'Aurelio Ranzato, Tomas Mikolov
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Semantic embedding
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
- 5,401,200,000 tokens
"We trained a skip-gram text model on a corpus of 5.7 million documents (5.4 billion words) " Additionally, an image model component is trained on ImageNet (1.2M images)
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,SOTA improvement
- Record confidence
- Confident
" We demonstrate that this model matches state-of-the-art performance on the 1000-class ImageNet object recognition challenge while making more semantically reasonable errors, and also show that the semantic information can be exploited to make predictions about tens of thousands of image labels not observed during training." not absolute SOTA
Sources
Where this record came from and when it was last checked.
- Reference
- DeViSE: A Deep Visual-Semantic Embedding Model
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
DeViSE was published by Google, in United States of America, in December 2013. The organisation is categorised as industry.
It works in Vision, and is recorded as doing semantic embedding.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
The training set ran to roughly 5,401,200,000 tokens.
It is tracked in the underlying dataset for one reason in particular: highly cited,SOTA improvement.
Answers
DeViSE — common questions
Who created DeViSE?
DeViSE was published by Google, based in United States of America, categorised as industry.
When was DeViSE released?
DeViSE was published in December 2013. 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 DeViSE used for?
DeViSE works in Vision, and is recorded as handling semantic embedding. 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.
What GPU do I need to run DeViSE?
None. DeViSE 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 DeViSE open source?
The licensing for DeViSE 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 DeViSE have?
No parameter count has been published for DeViSE, 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.