DeepLab
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
- Google,University of California Los Angeles (UCLA)
- Organisation type
- Industry,Academia
- Country
- United States of America
- Published
- 22 December 2014
- Authors
- Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, Alan L. Yuille
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.
- Record confidence
- Unknown
- Citations
- 5,234
Sources
Where this record came from and when it was last checked.
- Reference
- Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs
- Last updated
- 25 May 2026
What the numbers mean
What this model is
DeepLab was published by Google,University of California Los Angeles (UCLA), in United States of America, in December 2014. The organisation is categorised as industry,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.
Answers
DeepLab — common questions
How many parameters does DeepLab have?
No parameter count has been published for DeepLab, which is why no memory or speed figure appears on this page.
Who created DeepLab?
DeepLab was published by Google,University of California Los Angeles (UCLA), based in United States of America, categorised as industry,Academia.
When was DeepLab released?
DeepLab was published in December 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.
What is DeepLab used for?
DeepLab works in Vision, and is recorded as handling image segmentation. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run DeepLab?
None. DeepLab 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 DeepLab open source?
The licensing for DeepLab was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
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.