ShuffleNet v2
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
- Tsinghua University,Megvii Inc
- Organisation type
- Academia,Industry
- Country
- China
- Published
- 30 June 2018
- Authors
- Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification, Object detection
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
- 2.3M
- Training data
- 1,280,000 tokens
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Citations
- 6,289
Sources
Where this record came from and when it was last checked.
- Reference
- ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
- Last updated
- 25 May 2026
What the numbers mean
Background
ShuffleNet v2 was published by Tsinghua University,Megvii Inc, in the country recorded as China, during June 2018. It comes out of an organisation categorised as academia,Industry.
It works in the domain of Vision, and is recorded as performing the task of image classification, Object detection.
Because the weights are not available, none of the hardware figures elsewhere on this site apply to it.
How it was trained
The training set ran to roughly 1,280,000 tokens of text.
Answers
ShuffleNet v2 — common questions
ShuffleNet v2— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification, Object detection. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
ShuffleNet v2— 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.
ShuffleNet v2— 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.
ShuffleNet v2— how many parameters does it have?
It has a parameter count of 2.3M. 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.
ShuffleNet v2— who created it?
It was published by Tsinghua University,Megvii Inc, based in China, an organisation categorised as academia,Industry.
ShuffleNet v2— when was it released?
It was published in June 2018. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
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.