SENet (ImageNet)
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
- Chinese Academy of Sciences,University of Oxford
- Organisation type
- Academia,Academia
- Country
- China, United Kingdom of Great Britain and Northern Ireland
- Published
- 5 September 2017
- Authors
- Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Numerical format
- FP32
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
- 28.1M
- Training data
- 1,280,000 tokens
Table 16
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
- Highly cited
- Citations
- 33,922
Sources
Where this record came from and when it was last checked.
- Reference
- Squeeze-and-Excitation Networks
- Last updated
- 25 May 2026
What the numbers mean
Background
SENet (ImageNet) was published by Chinese Academy of Sciences,University of Oxford, in the country recorded as China, during September 2017. The publishing organisation is categorised as academia,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.
How it was trained
It was trained on a corpus of about 1,280,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
SENet (ImageNet) — common questions
SENet (ImageNet)— how many parameters does it have?
It has a parameter count of 28.1M. Table 16. 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.
SENet (ImageNet)— who created it?
It was published by Chinese Academy of Sciences,University of Oxford, based in China, an organisation categorised as academia,Academia.
SENet (ImageNet)— when was it released?
It was published in September 2017. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SENet (ImageNet)— 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.
SENet (ImageNet)— 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.
SENet (ImageNet)— 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.
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.