Convolutional Pose Machines
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
- Carnegie Mellon University (CMU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 30 January 2016
- Authors
- Shih-En Wei, Varun Ramakrishna, Takeo Kanade, Yaser Sheikh
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Pose estimation
- 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
- 2,906
Sources
Where this record came from and when it was last checked.
- Reference
- Convolutional Pose Machines
- Last updated
- 25 May 2026
What the numbers mean
What this model is
Convolutional Pose Machines was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during January 2016. The publishing organisation is categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of pose estimation.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
Answers
Convolutional Pose Machines — common questions
Convolutional Pose Machines— when was it released?
It was published in January 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.
Convolutional Pose Machines— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of pose estimation. 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.
Convolutional Pose Machines— 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.
Convolutional Pose Machines— 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.
Convolutional Pose Machines— 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.
Convolutional Pose Machines— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
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.