Probabilistic modeling for object recognition
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
- 23 June 1998
- Authors
- H Schneiderman, T Kanade
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Face recognition
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
Section 5.1: "We formed training sets from 991 faces images and 1,552 non-face images." "For each face image we generated 120 synthetic variations" 991*120+1552 = 120472
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 602
Sources
Where this record came from and when it was last checked.
- Reference
- Probabilistic modeling of local appearance and spatial relationships for object recognition
- Last updated
- 28 November 2025
What the numbers mean
Background
Probabilistic modeling for object recognition was published by Carnegie Mellon University (CMU), in the country recorded as United States of America, during June 1998. The category the publisher falls under is academia.
It works in the domain of Vision, and is recorded as performing the task of face recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Answers
Probabilistic modeling for object recognition — common questions
Probabilistic modeling for object recognition— who created it?
It was published by Carnegie Mellon University (CMU), based in United States of America, an organisation categorised as academia.
Probabilistic modeling for object recognition— when was it released?
It was published in June 1998. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Probabilistic modeling for object recognition— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of face recognition. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Probabilistic modeling for object recognition— 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.
Probabilistic modeling for object recognition— 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.
Probabilistic modeling for object recognition— 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.
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.