CRF-RNN
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 Oxford,Stanford University,Baidu
- Organisation type
- Academia,Academia,Industry
- Country
- United Kingdom of Great Britain and Northern Ireland, United States of America, China
- Published
- 11 February 2015
- Authors
- Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes, Vibhav Vineet, Zhizhong Su, Dalong Du, Chang Huang, Philip H. S. Torr
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image segmentation
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
- 2,661
Sources
Where this record came from and when it was last checked.
- Reference
- Conditional Random Fields as Recurrent Neural Networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
CRF-RNN was published by University of Oxford,Stanford University,Baidu, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during February 2015. The category the publisher falls under is academia,Academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of image segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
CRF-RNN — common questions
CRF-RNN— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.
CRF-RNN— 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.
CRF-RNN— 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.
CRF-RNN— 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.
CRF-RNN— who created it?
It was published by University of Oxford,Stanford University,Baidu, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia,Academia,Industry.
CRF-RNN— when was it released?
It was published in February 2015. 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.