# google-research/scenic

Scenic: A Jax Library for Computer Vision Research and Beyond

Repository: https://github.com/google-research/scenic
Canonical: https://ross.abutalabs.com/products/scenic
Language: Python
License: Apache-2.0
License Family: permissive
Topics: jax, computer-vision, deep-learning, research, attention, transformers, vision-transformer
Last push: 2026-08-10T09:53:31+00:00

## Health v2 (maintenance only)
Score: 76/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 97, release rhythm 35, longevity 100
- inputs: {"age_days": 1878, "days_push": 23, "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 3821, forks 480 (observed 2026-08-28T04:08:21.174595+00:00)

## What it is
Scenic is a JAX-based library from Google Research focused on attention-based models for computer vision, providing shared lightweight libraries for training large-scale multi-device vision models. It includes optimized training/evaluation loops, input pipelines for popular vision datasets, and baseline implementations of SOTA models like ViViT and TokenLearner across image, video, audio, and multimodal modalities.

## Use cases
- train vision transformers on multiple GPUs or TPUs
- reproduce state-of-the-art video classification models like ViViT
- build image segmentation and detection models in JAX
- run multimodal experiments combining images video and audio
- get boilerplate training loops and input pipelines for vision research
- benchmark attention-based model architectures

## When to choose
- doing computer vision research with attention or transformer models
- you need multi-device multi-host training for large vision models
- you want baseline implementations of recent vision transformer papers
- your team already works in the JAX and Flax ecosystem

## When to avoid
- you need a production-ready inference serving system rather than research code
- you prefer PyTorch or TensorFlow over JAX
- you want a simple off-the-shelf pretrained model API with minimal setup
- you need broad model zoo coverage outside attention-based vision research

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing, video-processing, audio-processing, benchmarking, boilerplate
- domain: computer-vision, deep-learning, machine-learning, artificial-intelligence
- platform: python, cross-platform
- tags: jax, flax, vision-transformer, attention, transformers, research-code, training-loops, input-pipelines, multimodal, google-research, research, video, audio, gpu, linux

## Member repositories
- google-research/scenic (main) score 76

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:21.174595+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 2026-08-29T18:26:27.669043+00:00, confidence not recorded.
  - readme: https://github.com/google-research/scenic (fetched 2026-08-28T04:08:21.174595+00:00, sha 9bd97ff497a4)
- Data as of 2026-08-30T08:39:29.467469+00:00.