Fully Convolutional Networks
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
- 14 November 2014
- Authors
- J Long, E Shelhamer, T Darrell
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image segmentation
- 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.
- Training data
- 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
- 41,855
Sources
Where this record came from and when it was last checked.
- Reference
- Fully Convolutional Networks for Semantic Segmentation
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Fully Convolutional Networks was published by University of California (UC) Berkeley, in the country recorded as United States of America, during November 2014. The publishing organisation is categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of image segmentation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Training and provenance
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Fully Convolutional Networks — common questions
Fully Convolutional Networks— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Fully Convolutional Networks— 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.
Fully Convolutional Networks— 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.
Fully Convolutional Networks— 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.
Fully Convolutional Networks— who created it?
It was published by University of California (UC) Berkeley, based in United States of America, an organisation categorised as academia.
Fully Convolutional Networks— when was it released?
It was published in November 2014. 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.