Textual Imager
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 16 January 2013
- Authors
- R Socher, M Ganjoo, H Sridhar, O Bastani
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object 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
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Unknown
- Citations
- 1,527
Sources
Where this record came from and when it was last checked.
- Reference
- Zero-Shot Learning Through Cross-Modal Transfer
- Last updated
- 25 May 2026
What the numbers mean
Background
Textual Imager was published by Stanford University, in United States of America, in January 2013. The organisation is categorised as academia.
It works in Vision, and is recorded as doing object recognition.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
Textual Imager — common questions
How many parameters does Textual Imager have?
No parameter count has been published for Textual Imager, which is why no memory or speed figure appears on this page.
Who created Textual Imager?
Textual Imager was published by Stanford University, based in United States of America, categorised as academia.
When was Textual Imager released?
Textual Imager was published in January 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 Textual Imager used for?
Textual Imager works in Vision, and is recorded as handling object recognition. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Textual Imager?
None. Textual Imager 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 Textual Imager open source?
The licensing for Textual Imager was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.