Spatially-Sparse 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
- University of Warwick
- Organisation type
- Academia
- Country
- United Kingdom of Great Britain and Northern Ireland
- Published
- 23 September 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.
- Training data
- 901,200 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
- 260
SOTA per https://paperswithcode.com/sota/image-classification-on-cifar-10
Sources
Where this record came from and when it was last checked.
- Reference
- Spatially-sparse convolutional neural networks
- Last updated
- 28 November 2025
What the numbers mean
What this model is
Spatially-Sparse CNN was published by University of Warwick, in United Kingdom of Great Britain and Northern Ireland, in September 2014. It comes out of academia.
It works in Vision, and is recorded as doing image classification.
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
Around 901,200 tokens went into training it.
The reason it appears in this catalogue at all is sOTA improvement.
Answers
Spatially-Sparse CNN — common questions
Is Spatially-Sparse CNN open source?
The licensing for Spatially-Sparse CNN 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 Spatially-Sparse CNN have?
No parameter count has been published for Spatially-Sparse CNN, which is why no memory or speed figure appears on this page.
Who created Spatially-Sparse CNN?
Spatially-Sparse CNN was published by University of Warwick, based in United Kingdom of Great Britain and Northern Ireland, categorised as academia.
When was Spatially-Sparse CNN released?
Spatially-Sparse CNN was published in September 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.
What is Spatially-Sparse CNN used for?
Spatially-Sparse CNN works in Vision, and is recorded as handling image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
What GPU do I need to run Spatially-Sparse CNN?
None. Spatially-Sparse CNN 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.