Big Transfer (BiT-L)
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
- Google Brain
- Organisation type
- Industry
- Country
- United States of America
- Published
- 24 December 2019
- Authors
- Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, Neil Houlsby
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
- 928M
- Training data
- 300,000,000 tokens
- Epochs
- 40
"For BiT-L, we train for 40 epochs"
The training run
What it physically took to train: which chips, how many, for how long, and what that drew from the wall.
- Training hardware
- Google TPU v3
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Closed — provider access only
- Model access
- Unreleased
- Training code
- Unreleased
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
- Citations
- 1,361
"We transfer BiT to many diverse tasks... These tasks include ImageNet’s ILSVRC-2012 [10], CIFAR-10/100 [27], Oxford-IIIT Pet [41], Oxford Flowers-102 [39] (including few-shot variants), and the 1000-sample VTAB-1k benchmark [66], which consists of 19 diverse datasets. BiT-L attains state-ofthe-art performance on many of these tasks
Sources
Where this record came from and when it was last checked.
- Reference
- Big Transfer (BiT): General Visual Representation Learning
- Last updated
- 25 May 2026
What the numbers mean
Where it came from
Big Transfer (BiT-L) was published by Google Brain, in United States of America, in December 2019. It comes out of industry.
It works in Vision, and is recorded as doing image classification.
Its weights were never published, so it can only be reached through its provider. No graphics card changes that.
Training and provenance
Around 300,000,000 tokens went into training it.
It is tracked in the underlying dataset for one reason in particular: sOTA improvement.
Answers
Big Transfer (BiT-L) — common questions
When was Big Transfer (BiT-L) released?
Big Transfer (BiT-L) was published in December 2019. 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 Big Transfer (BiT-L) used for?
Big Transfer (BiT-L) 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 Big Transfer (BiT-L)?
None. Big Transfer (BiT-L) 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.
Is Big Transfer (BiT-L) open source?
No. Big Transfer (BiT-L) has not had its weights published, so it exists only as a service controlled by its owner.
How many parameters does Big Transfer (BiT-L) have?
Big Transfer (BiT-L) has 928M 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.
Who created Big Transfer (BiT-L)?
Big Transfer (BiT-L) was published by Google Brain, based in United States of America, categorised as industry.
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.