Sparse digit recognition SVM
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 Lubeck
- Organisation type
- Academia
- Country
- Germany
- Published
- 19 November 2008
- Authors
- Kai Labusch, Erhadt Barth, Thomas Martinetz
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
- 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
- SOTA improvement
- Record confidence
- Unknown
- Citations
- 124
"Finally, we train a support vector machine (SVM) on the resulting feature vectors and obtain state-of-the-art classification performance in the digit recognition task defined by the MNIST benchmark"
Sources
Where this record came from and when it was last checked.
- Reference
- Simple method for high-performance digit recognition based on sparse coding
- Last updated
- 28 November 2025
What the numbers mean
Background
Sparse digit recognition SVM was published by University of Lubeck, in Germany, in November 2008. It comes out of academia.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Sparse digit recognition SVM — common questions
Is Sparse digit recognition SVM open source?
The licensing for Sparse digit recognition SVM 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 Sparse digit recognition SVM have?
No parameter count has been published for Sparse digit recognition SVM, which is why no memory or speed figure appears on this page.
Who created Sparse digit recognition SVM?
Sparse digit recognition SVM was published by University of Lubeck, based in Germany, categorised as academia.
When was Sparse digit recognition SVM released?
Sparse digit recognition SVM was published in November 2008. 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 Sparse digit recognition SVM used for?
Sparse digit recognition SVM works in Vision, and is recorded as handling image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Sparse digit recognition SVM?
None. Sparse digit recognition SVM 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.
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.