Super-vector coding
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 Illinois Urbana-Champaign (UIUC),NEC Laboratories,Rutgers University
- Organisation type
- Academia,Industry,Academia
- Country
- United States of America
- Published
- 1 January 2010
- Authors
- Xi Zhou, Kai Yu, Tong Zhang, and Thomas S. Huang
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Object 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.
- Parameters
- 1K
- Training data
- tokens
Somewhat low confidence, but it seems like the number of learnable parameters is the size of the codebook, plus parameters in the SVM used for classification. Since it is a linear SVM, there will be one parameter per input feature, plus a single bias term. So in total, 512 learnable codebook values, plus 513 SVM parameters = 1025 parameters
"PASCAL VOC 2007 consists of 9,963 images which are divided intothree subsets: training data (2501 images), validation data (2510 images), and test data (4952 images). PASCAL VOC 2009 consists of 14,743 images and correspondingly are divided into three subsets: training data(3473 images), validation data(3581 images), and testing data (7689 images)." PASCAL VOC 2009 is the larger experiment; images used in training is 3473 + 3581 = 7,054 For each image, the inputs for the codebook training are…
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
- Speculative
- Citations
- 696
"Our experiments demonstrate that the proposed classification method achieves state-of-the-art accuracy on the well-known PASCAL benchmarks."
Sources
Where this record came from and when it was last checked.
- Reference
- Image Classification using Super-Vector Coding of Local Image Descriptors
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Super-vector coding was published by University of Illinois Urbana-Champaign (UIUC),NEC Laboratories,Rutgers University, in United States of America, in January 2010. It comes out of academia,Industry,Academia.
It works in Vision, and is recorded as doing image classification, Object recognition.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
How it was trained
Its inclusion criterion is sOTA improvement.
Answers
Super-vector coding — common questions
Who created Super-vector coding?
Super-vector coding was published by University of Illinois Urbana-Champaign (UIUC),NEC Laboratories,Rutgers University, based in United States of America, categorised as academia,Industry,Academia.
When was Super-vector coding released?
Super-vector coding was published in January 2010. 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 Super-vector coding used for?
Super-vector coding works in Vision, and is recorded as handling image classification, Object recognition. These are the areas it was designed around; they describe intent rather than a hard boundary.
What GPU do I need to run Super-vector coding?
None. Super-vector coding 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 Super-vector coding open source?
The licensing for Super-vector coding 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 Super-vector coding have?
Super-vector coding has 1K parameters. Somewhat low confidence, but it seems like the number of learnable parameters is the size of the codebook, plus parameters in the SVM used for classification. Since it is a linear SVM, there will be one parameter per input feature, plus a single bias term. So in total, 512 learnable codebook values, plus 513 SVM parameters = 1025 parameters. 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.
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.