Unsupervised Scale-Invariant Learning
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 Oxford
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 18 June 2003
- Authors
- R Fergus, P Perona, A Zisserman
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.
- Parameters
- 0.5K
- Training data
- 400 tokens
See Table 1
See Table 2 and Figure 1. There are 7 datasets, each with 200-800 of pictures. I pick 500 as the avg number of pictures
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 2,970
Sources
Where this record came from and when it was last checked.
- Reference
- Object Class Recognition by Unsupervised Scale-Invariant Learning
- Last updated
- 28 November 2025
What the numbers mean
About this model
Unsupervised Scale-Invariant Learning was published by University of Oxford, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during June 2003. It comes out of an organisation categorised as 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.
Training and provenance
The training set ran to roughly 400 tokens of text.
Answers
Unsupervised Scale-Invariant Learning — common questions
Unsupervised Scale-Invariant Learning— 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.
Unsupervised Scale-Invariant Learning— 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.
Unsupervised Scale-Invariant Learning— 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.
Unsupervised Scale-Invariant Learning— how many parameters does it have?
It has a parameter count of 0.5K. See Table 1. That figure is the total, and it is what decides how much memory the model needs — roughly half a gigabyte per billion at the compression most people use.
Unsupervised Scale-Invariant Learning— who created it?
It was published by University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia.
Unsupervised Scale-Invariant Learning— when was it released?
It was published in June 2003. 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.