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 United States of America, in January 2016. The organisation is categorised as academia.
It works in Vision, and is recorded as doing 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
When was Convolutional Pose Machines released?
Convolutional Pose Machines 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.
What is Convolutional Pose Machines used for?
Convolutional Pose Machines works in Vision, and is recorded as handling 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.
What GPU do I need to run Convolutional Pose Machines?
None. Convolutional Pose Machines 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 Convolutional Pose Machines open source?
The licensing for Convolutional Pose Machines 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 Convolutional Pose Machines have?
No parameter count has been published for Convolutional Pose Machines, which is why no memory or speed figure appears on this page.
Who created Convolutional Pose Machines?
Convolutional Pose Machines was published by Carnegie Mellon University (CMU), based in United States of America, 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.