# apple/ml-fastvit

This repository contains the official implementation of the research paper, "FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization" ICCV 2023

Repository: https://github.com/apple/ml-fastvit
Canonical: https://ross.abutalabs.com/products/ml-fastvit
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-11-30T19:22:00+00:00

## Health v2 (maintenance only)
Score: 28/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 79
- inputs: {"age_days": 1115, "days_push": 1007, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2027, forks 127 (observed 2026-08-28T04:06:06.899422+00:00)

## What it is
Official PyTorch implementation of FastViT, a fast hybrid vision transformer architecture using structural reparameterization, published at ICCV 2023. It provides pretrained ImageNet-1K checkpoints and CoreML models optimized for low latency on mobile devices like iPhone.

## Use cases
- run fast image classification on mobile devices
- fine-tune a pretrained vision transformer backbone
- use a fast backbone for detection or segmentation tasks
- export vision models to CoreML for iOS inference
- benchmark image classification latency on iPhone
- train a hybrid CNN-transformer model from scratch

## When to choose
- you need a low-latency image classification model for mobile deployment
- you want a pretrained backbone for downstream vision tasks like detection or segmentation
- you target Apple devices and want CoreML-ready models
- you're researching structural reparameterization in vision transformers

## When to avoid
- you need state-of-the-art accuracy regardless of latency
- you don't use PyTorch or Apple's ecosystem
- you need a general-purpose vision library rather than a specific model implementation
- you require frequent updates or long-term support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, computer-vision
- domain: deep-learning, computer-vision, image-processing, machine-learning
- platform: python, cross-platform
- tags: vision-transformer, structural-reparameterization, image-classification, pytorch, coreml, mobile-inference, iccv-2023, model-zoo, pretrained-models, gpu, macos

## Member repositories
- apple/ml-fastvit (main) score 28

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:06.899422+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 2026-08-30T02:59:41.354398+00:00, confidence not recorded.
  - readme: https://github.com/apple/ml-fastvit (fetched 2026-08-28T04:06:06.899422+00:00, sha 31ad4368331f)
- Data as of 2026-08-30T08:39:29.467469+00:00.