# google-research/big_vision

Official codebase used to develop Vision Transformer, SigLIP, MLP-Mixer, LiT and more.

Repository: https://github.com/google-research/big_vision
Canonical: https://ross.abutalabs.com/products/big_vision
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2025-05-19T13:55:52+00:00

## Health v2 (maintenance only)
Score: 42/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 22, release rhythm 35, longevity 100
- inputs: {"age_days": 1612, "days_push": 471, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3528, forks 226 (observed 2026-08-28T04:08:08.821615+00:00)

## What it is
Google Research's official Jax/Flax codebase for training large-scale vision models such as Vision Transformer, SigLIP, MLP-Mixer, and LiT on Cloud TPUs or GPUs. It provides reproducible training pipelines that scale from a single TPU core to distributed setups with up to 2048 TPU cores.

## Use cases
- train a vision transformer from scratch on ImageNet
- reproduce SigLIP or LiT training results
- run large-scale vision experiments on Cloud TPUs
- fine-tune pretrained ViT models for image classification
- implement and benchmark new vision model architectures
- scale vision model training across many TPU cores

## When to choose
- you need a battle-tested Jax codebase for large-scale vision model training
- you want to reproduce or build on Google's vision transformer research
- you have TPU or multi-GPU resources and need seamless scaling
- you want official implementations of ViT, SigLIP, MLP-Mixer, or LiT

## When to avoid
- you need a production inference or deployment library
- you expect community-driven feature development or quick external contributions
- you work outside the Jax/Flax ecosystem, e.g. prefer PyTorch
- you need a simple transfer-only fine-tuning tool (the vision_transformer repo is a better fit)

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, llm-training
- domain: deep-learning, computer-vision, machine-learning
- platform: python, cloud
- tags: jax, flax, vision-transformer, tpu, research-codebase, siglip, image-classification, research, gpu

## Member repositories
- google-research/big_vision (main) score 42

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:08.821615+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:35:08.720106+00:00, confidence not recorded.
  - readme: https://github.com/google-research/big_vision (fetched 2026-08-28T04:08:08.821615+00:00, sha b779545fdec9)
- Data as of 2026-08-30T08:39:29.467469+00:00.