Spatial Pyramid Matching
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
- INRIA,University of Illinois Urbana-Champaign (UIUC),Ecole Normale Supèrieure
- Organisation type
- Academia,Academia,Academia
- Country
- France, United States of America
- Published
- 17 June 2006
- Authors
- S Lazebnik, C Schmid, J Ponce
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
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
- 3,030 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Why it is tracked
- Highly cited
- Record confidence
- Unknown
- Citations
- 9,807
Sources
Where this record came from and when it was last checked.
- Reference
- Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
- Last updated
- 28 November 2025
What the numbers mean
Background
Spatial Pyramid Matching was published by INRIA,University of Illinois Urbana-Champaign (UIUC),Ecole Normale Supèrieure, in the country recorded as France, during June 2006. The publishing organisation is categorised as academia,Academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
Training consumed a corpus of around 3,030 tokens of text.
It is tracked in the underlying dataset for one reason in particular: highly cited.
Answers
Spatial Pyramid Matching — common questions
Spatial Pyramid Matching— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. 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.
Spatial Pyramid Matching— 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.
Spatial Pyramid Matching— 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.
Spatial Pyramid Matching— 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.
Spatial Pyramid Matching— who created it?
It was published by INRIA,University of Illinois Urbana-Champaign (UIUC),Ecole Normale Supèrieure, based in France, an organisation categorised as academia,Academia,Academia.
Spatial Pyramid Matching— when was it released?
It was published in June 2006. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.