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 United Kingdom of Great Britain and Northern Ireland, in June 2003. It comes out of academia.
It works in Vision, and is recorded as doing 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.
Answers
Unsupervised Scale-Invariant Learning — common questions
What is Unsupervised Scale-Invariant Learning used for?
Unsupervised Scale-Invariant Learning works in Vision, and is recorded as handling 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.
What GPU do I need to run Unsupervised Scale-Invariant Learning?
None. Unsupervised Scale-Invariant Learning 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 Unsupervised Scale-Invariant Learning open source?
The licensing for Unsupervised Scale-Invariant Learning 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 Unsupervised Scale-Invariant Learning have?
Unsupervised Scale-Invariant Learning has 0.5K parameters. 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.
Who created Unsupervised Scale-Invariant Learning?
Unsupervised Scale-Invariant Learning was published by University of Oxford, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was Unsupervised Scale-Invariant Learning released?
Unsupervised Scale-Invariant Learning 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.