DeepLabV3+
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 7 February 2018
- Authors
- Liang-Chieh Chen, Yukun Zhu, George Papandreou, Florian Schroff, Hartwig Adam
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Semantic 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
- 8,655,843,074 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
- 16,559
Sources
Where this record came from and when it was last checked.
- Reference
- Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation
- Last updated
- 25 May 2026
What the numbers mean
Background
DeepLabV3+ was published by Google, in the country recorded as United States of America, during February 2018. It comes out of an organisation categorised as industry.
It works in the domain of Vision, and is recorded as performing the task of semantic segmentation.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Training consumed a corpus of around 8,655,843,074 tokens of text.
Its inclusion criterion: highly cited.
Answers
DeepLabV3+ — common questions
DeepLabV3+— when was it released?
It was published in February 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.
DeepLabV3+— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of semantic segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.
DeepLabV3+— 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.
DeepLabV3+— 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.
DeepLabV3+— 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.
DeepLabV3+— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
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.