Sparse Vision Encoding
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
- Stanford University
- Organisation type
- Academia
- Country
- United States of America
- Published
- 1 November 2006
- Authors
- Honglak Lee, Alexis Battle, Rajat Raina, Andrew Y. Ng
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Approach
- Unsupervised
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
However, these are 1000 20x20 pixel "bases", not images
Training compute
The arithmetic performed to train the model, measured in floating-point operations. It is a measure of what the training run cost, not of how fast the finished model answers you.
- Training compute
- 9.6 × 10¹² FLOP
- How it was established
- Hardware
(Just for natural images) "All experiments were conducted on a Linux machine with AMD Opteron 2GHz CPU and 2GB RAM. ... For example, we were able to learn a set of 1,024 bases (each 14×14 pixels)in about 2 hours and a set of 2,000 bases (each 20×20 pixels) in about 10 hours." I filtered for 2GHz Opteron models that came out in 2005, of which there are five: https://www.techpowerup.com/cpu-specs/?mfgr=AMD&released=2005&generation=AMD%20Opteron&sort=name Found a source which indicates 3 cycles …
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Wall-clock time
- 10 hours
"...in about 2 hours and a set of 2,000 bases (each 20×20 pixels) in about 10 hours."
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 3,512
Sources
Where this record came from and when it was last checked.
- Reference
- Efficient sparse coding algorithms
- Last updated
- 28 November 2025
What the numbers mean
Background
Sparse Vision Encoding was published by Stanford University, in the country recorded as United States of America, during November 2006. The publishing organisation is categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
How it was trained
Producing it required arithmetic totalling around 9.6 × 10¹² FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Answers
Sparse Vision Encoding — common questions
Sparse Vision Encoding— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
Sparse Vision Encoding— 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.
Sparse Vision Encoding— who created it?
It was published by Stanford University, based in United States of America, an organisation categorised as academia.
Sparse Vision Encoding— when was it released?
It was published in November 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.
Sparse Vision Encoding— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. These are the areas it was designed around; they describe intent rather than a hard boundary.
Sparse Vision Encoding— how much compute was used to train it?
Training consumed around 9.6 × 10¹² FLOP. That measures what producing the model cost and says nothing about how quickly it answers once trained — inference speed comes from memory bandwidth, not from the training budget.
Sparse Vision Encoding— 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.
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.