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 United States of America, in November 2014. The organisation is categorised as academia.
It works in Vision, and is recorded as doing 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
What is Fully Convolutional Networks used for?
Fully Convolutional Networks works in Vision, and is recorded as handling image segmentation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Fully Convolutional Networks?
None. Fully Convolutional Networks 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 Fully Convolutional Networks open source?
The licensing for Fully Convolutional Networks 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 Fully Convolutional Networks have?
No parameter count has been published for Fully Convolutional Networks, which is why no memory or speed figure appears on this page.
Who created Fully Convolutional Networks?
Fully Convolutional Networks was published by University of California (UC) Berkeley, based in United States of America, categorised as academia.
When was Fully Convolutional Networks released?
Fully Convolutional Networks 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.