SVM-CNN
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
- New York University (NYU)
- Organisation type
- Academia
- Country
- United States of America
- Published
- 17 June 2006
- Authors
- Fu Jie Huang, Yann LeCun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Approach
- Supervised
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
- 90.9K
- Training data
- 583,200 tokens
To generate the training set, each image was perturbed with 10 different configurations of the above parameters, which makes up 291,600 image pairs of size 108×108. The testing set has 2 drawings of perturbations, and have
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
- 7.5 × 10¹⁴ FLOP
- How it was established
- Hardware
Training time: 5880+330=6210 minutes (Table 1) “The timing is normalized to hypothetical 1GHz single CPU.” Assuming utilization of 0.5 and AMD Athlon 64 Processor with 4 FLOP/cycle Compute: 6210*60*4*1000000000*0.5=745200000000000 = 7.4e14
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Chips used
- 1
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.
- Frontier model
- Yes
- Why it is tracked
- Historical significance
- Record confidence
- Confident
Sources
Where this record came from and when it was last checked.
- Reference
- Large-scale Learning with SVM and Convolutional for Generic Object Categorization
- Last updated
- 28 November 2025
What the numbers mean
What this model is
SVM-CNN was published by New York University (NYU), in the country recorded as United States of America, during June 2006. The category the publisher falls under is 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.
Training and provenance
Training it took a computation budget of roughly 7.5 × 10¹⁴ FLOP. That figure measures what producing the model cost, and has no bearing on how fast it answers.
Training consumed a corpus of around 583,200 tokens of text.
It is tracked in the underlying dataset for one reason in particular: historical significance.
Answers
SVM-CNN — common questions
SVM-CNN— who created it?
It was published by New York University (NYU), based in United States of America, an organisation categorised as academia.
SVM-CNN— when was it released?
It was published in June 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.
SVM-CNN— 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.
SVM-CNN— how much compute was used to train it?
Training consumed around 7.5 × 10¹⁴ 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.
SVM-CNN— 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.
SVM-CNN— is it open source?
No. Its weights have not been published, so it exists only as a service controlled by its owner.
SVM-CNN— how many parameters does it have?
It has a parameter count of 90.9K. 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.