Fractional Max-Pooling
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 Warwick
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 18 December 2014
- Authors
- Benjamin Graham
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
- 27M
- Training data
- 901,200 tokens
- Epochs
- 250
27M weights in largest CIFAR-100 model
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
- 1 × 10¹⁷ FLOP
- How it was established
- Hardware
For the 12M param model, training required "18 hours on a GeForce GTX 780". So would be somewhat larger for 27M. 4 TFLOPS * 18 * 3600 * 0.4 = 1e17
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- NVIDIA GeForce GTX 780
- Wall-clock time
- 18 hours
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
- Likely
- Citations
- 672
"for instance, we improve on the state-of-the art for CIFAR-100 without even using dropout."
Sources
Where this record came from and when it was last checked.
- Reference
- Fractional Max-Pooling
- Last updated
- 28 November 2025
What the numbers mean
Where it came from
Fractional Max-Pooling was published by University of Warwick, in the country recorded as United Kingdom of Great Britain and Northern Ireland, during December 2014. The publishing organisation is categorised as academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
What went into building it
The training run consumed about 1 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX 780. That figure measures what producing the model cost, and has no bearing on how fast it answers.
The training set ran to roughly 901,200 tokens of text.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Fractional Max-Pooling — common questions
Fractional Max-Pooling— is it open source?
The licensing was never recorded in our source data. We treat unstated licensing as closed, because an unrecorded licence is not one to rely on.
Fractional Max-Pooling— how many parameters does it have?
It has a parameter count of 27M. 27M weights in largest CIFAR-100 model. 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.
Fractional Max-Pooling— who created it?
It was published by University of Warwick, based in United Kingdom of Great Britain and Northern Ireland, an organisation categorised as academia.
Fractional Max-Pooling— when was it released?
It was published in December 2014. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
Fractional Max-Pooling— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of 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.
Fractional Max-Pooling— how much compute was used to train it?
Training consumed around 1 × 10¹⁷ FLOP, on hardware recorded as NVIDIA GeForce GTX 780. 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.
Fractional Max-Pooling— 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.