Deconvolutional Network
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
- New York University (NYU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 13 June 2010
- Authors
- Matthew D. Zeiler, Dilip Krishnan, Graham W. Taylor and Rob Fergus
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
- 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
- 1,639
Sources
Where this record came from and when it was last checked.
- Reference
- Deconvolutional Networks
- Last updated
- 1 January 2026
What the numbers mean
Where it came from
Deconvolutional Network was published by New York University (NYU), in United States of America, in June 2010. It comes out of academia.
It works in Vision, and is recorded as doing image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Deconvolutional Network — common questions
When was Deconvolutional Network released?
Deconvolutional Network 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 Deconvolutional Network used for?
Deconvolutional Network works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Deconvolutional Network?
None. Deconvolutional Network 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 Deconvolutional Network open source?
The licensing for Deconvolutional Network 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 Deconvolutional Network have?
No parameter count has been published for Deconvolutional Network, which is why no memory or speed figure appears on this page.
Who created Deconvolutional Network?
Deconvolutional Network was published by New York University (NYU), based in United States of America, categorised as academia.
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.