Image-to-image cGAN
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 California (UC) Berkeley
- Organisation type
- Academia
- Country
- United States of America
- Published
- 21 November 2016
- Authors
- Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision, Image generation
- Task
- Image generation, Image-to-image
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
- 2,400,000 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
- 22,297
Sources
Where this record came from and when it was last checked.
- Reference
- Image-to-Image Translation with Conditional Adversarial Networks
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Image-to-image cGAN was published by University of California (UC) Berkeley, in the country recorded as United States of America, during November 2016. The category the publisher falls under is academia.
It works in the domain of Vision, Image generation, and is recorded as performing the task of image generation, Image-to-image.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
It was trained on a corpus of about 2,400,000 tokens of text.
The reason it appears in this catalogue at all: highly cited.
Answers
Image-to-image cGAN — common questions
Image-to-image cGAN— 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.
Image-to-image cGAN— 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.
Image-to-image cGAN— who created it?
It was published by University of California (UC) Berkeley, based in United States of America, an organisation categorised as academia.
Image-to-image cGAN— when was it released?
It was published in November 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Image-to-image cGAN— what is it used for?
It works in the domain of Vision, Image generation, and is recorded as handling the task of image generation, Image-to-image. These are the areas it was designed around; they describe intent rather than a hard boundary.
Image-to-image cGAN— 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.
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.