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 United States of America, in February 2018. It comes out of industry.
It works in Vision, and is recorded as doing 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
Around 8,655,843,074 tokens went into training it.
Its inclusion criterion is highly cited.
Answers
DeepLabV3+ — common questions
When was DeepLabV3+ released?
DeepLabV3+ 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.
What is DeepLabV3+ used for?
DeepLabV3+ works in Vision, and is recorded as handling semantic segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DeepLabV3+?
None. DeepLabV3+ 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 DeepLabV3+ open source?
The licensing for DeepLabV3+ 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 DeepLabV3+ have?
No parameter count has been published for DeepLabV3+, which is why no memory or speed figure appears on this page.
Who created DeepLabV3+?
DeepLabV3+ was published by Google, based in United States of America, 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.