SqueezeNet
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
- DeepScale,University of California (UC) Berkeley,Stanford University
- Organisation type
- Academia,Academia
- Country
- United States of America
- Published
- 24 February 2016
- Authors
- Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
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
- 1.2M
- Training data
- 1,280,000 tokens
The paper says "SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters." AlexNet has 60 million parameters.
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
- 8,366
Sources
Where this record came from and when it was last checked.
- Reference
- SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- Last updated
- 25 May 2026
What the numbers mean
What this model is
SqueezeNet was published by DeepScale,University of California (UC) Berkeley,Stanford University, in the country recorded as United States of America, during February 2016. The category the publisher falls under is academia,Academia.
It works in the domain of Vision, and is recorded as performing the task of image classification.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
Training and provenance
Training consumed a corpus of around 1,280,000 tokens of text.
The reason it appears in this catalogue at all: highly cited.
Answers
SqueezeNet — common questions
SqueezeNet— 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.
SqueezeNet— 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.
SqueezeNet— how many parameters does it have?
It has a parameter count of 1.2M. The paper says "SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters." AlexNet has 60 million parameters. 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.
SqueezeNet— who created it?
It was published by DeepScale,University of California (UC) Berkeley,Stanford University, based in United States of America, an organisation categorised as academia,Academia.
SqueezeNet— when was it released?
It was published in February 2016. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
SqueezeNet— 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.
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.