Stacked hourglass network
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
- University of Michigan
- Organisation type
- Academia
- Country
- United States of America
- Published
- 17 September 2016
- Authors
- Alejandro Newell, Kaiyu Yang, Jia Deng
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
- 802,816,000 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
- 5,316
Sources
Where this record came from and when it was last checked.
- Reference
- Stacked Hourglass Networks for Human Pose Estimation
- Last updated
- 1 January 2026
What the numbers mean
Background
Stacked hourglass network was published by University of Michigan, in United States of America, in September 2016. It comes out of 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.
Training and provenance
The training set ran to roughly 802,816,000 tokens.
Answers
Stacked hourglass network — common questions
How many parameters does Stacked hourglass network have?
No parameter count has been published for Stacked hourglass network, which is why no memory or speed figure appears on this page.
Who created Stacked hourglass network?
Stacked hourglass network was published by University of Michigan, based in United States of America, categorised as academia.
When was Stacked hourglass network released?
Stacked hourglass network was published in September 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 Stacked hourglass network used for?
Stacked hourglass network works in Vision, and is recorded as handling pose estimation. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Stacked hourglass network?
None. Stacked hourglass network 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 Stacked hourglass network open source?
The licensing for Stacked hourglass network 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.