BatchNorm
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
- Organisation type
- Industry
- Country
- United States of America
- Published
- 15 June 2015
- Authors
- Sergey Ioffe, Christian Szegedy
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Vision
- Task
- Image classification
- Base model
- GoogLeNet / InceptionV1
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
- 13.6M
- Training data
- 12,441,600,000 tokens
- Epochs
- 72
"The network contains 13.6 · 106 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
- Record confidence
- Confident
- Citations
- 46,718
Sources
Where this record came from and when it was last checked.
- Reference
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Last updated
- 25 May 2026
What the numbers mean
What this model is
BatchNorm was published by Google, in the country recorded as United States of America, during June 2015. The category the publisher falls under is industry.
It works in the domain of Vision, and is recorded as performing the task of image classification.
Its starting point was an existing base model, GoogLeNet / InceptionV1. That is why it shares the base model's general shape and size.
This is a closed model: the trained values stayed with whoever produced them, and there is no local version to run.
What went into building it
It was trained on a corpus of about 12,441,600,000 tokens of text.
Its inclusion criterion: highly cited.
Answers
BatchNorm — common questions
BatchNorm— who created it?
It was published by Google, based in United States of America, an organisation categorised as industry.
BatchNorm— when was it released?
It was published in June 2015. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
BatchNorm— what is it used for?
It works in the domain of Vision, and is recorded as handling the task of image classification. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
BatchNorm— 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.
BatchNorm— 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.
BatchNorm— how many parameters does it have?
It has a parameter count of 13.6M. "The network contains 13.6 · 106 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.
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.