RefineNet
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 Adelaide,Australian Centre for Robotic Vision
- Organisation type
- Academia
- Country
- Australia
- Published
- 20 November 2016
- Authors
- Guosheng Lin, Anton Milan, Chunhua Shen, Ian Reid
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Object detection
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
- 3,094
Sources
Where this record came from and when it was last checked.
- Reference
- RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
RefineNet was published by University of Adelaide,Australian Centre for Robotic Vision, in the country recorded as Australia, during November 2016. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of object detection.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Answers
RefineNet — common questions
RefineNet— who created it?
It was published by University of Adelaide,Australian Centre for Robotic Vision, based in Australia, an organisation categorised as academia.
RefineNet— 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.
RefineNet— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of object detection. 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.
RefineNet— 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.
RefineNet— 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.
RefineNet— 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.
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.