RCAN
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
- Northeastern University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 8 July 2018
- Authors
- Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Image generation, Vision
- Task
- Image super-resolution
- Numerical format
- FP32
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.
- Parameters
- 16M
- Training data
- tokens
"EDSR has much larger number of parameters (43 M) than ours (16 M), but our RCAN obtains much better performance."
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
- 4,954
Sources
Where this record came from and when it was last checked.
- Reference
- Image Super-Resolution Using Very Deep Residual Channel Attention Networks
- Last updated
- 1 January 2026
What the numbers mean
Background
RCAN was published by Northeastern University, in the country recorded as United States of America, during July 2018. The publishing organisation is categorised as academia.
It works in the domain of Image generation, Vision, and is recorded as performing the task of image super-resolution.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
RCAN — common questions
RCAN— what is it used for?
It works in the domain of Image generation, Vision, and is recorded as handling the task of image super-resolution. 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.
RCAN— 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.
RCAN— 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.
RCAN— how many parameters does it have?
It has a parameter count of 16M. "EDSR has much larger number of parameters (43 M) than ours (16 M), but our RCAN obtains much better performance.". That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
RCAN— who created it?
It was published by Northeastern University, based in United States of America, an organisation categorised as academia.
RCAN— when was it released?
It was published in July 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.