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
- 17 June 2017
- 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
- 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
- 8,354,563,074 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
- 9,873
Sources
Where this record came from and when it was last checked.
- Reference
- Rethinking Atrous Convolution for Semantic Image Segmentation
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
DeepLabV3 was published by Google, in United States of America, in June 2017. It comes out of industry.
It works in Vision, and is recorded as doing semantic segmentation.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
What went into building it
Around 8,354,563,074 tokens went into training it.
The reason it appears in this catalogue at all is highly cited.
Answers
DeepLabV3 — common questions
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.
When was DeepLabV3 released?
DeepLabV3 was published in June 2017. 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.