# hustvl/Vim

[ICML 2024] Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model

Repository: https://github.com/hustvl/Vim
Canonical: https://ross.abutalabs.com/products/hustvl-vim
Language: Python
License: Apache-2.0
License Family: permissive
Last push: 2025-02-13T02:19:19+00:00

## Health v2 (maintenance only)
Score: 29/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 6, release rhythm 35, longevity 68
- inputs: {"age_days": 961, "days_push": 567, "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 3899, forks 287 (observed 2026-08-28T04:08:28.557239+00:00)

## What it is
Vision Mamba (Vim) is a PyTorch implementation of a generic vision backbone built on bidirectional Mamba state space models, published at ICML 2024. It replaces self-attention with efficient SSMs for visual representation learning, achieving better speed and memory efficiency than vision transformers like DeiT on high-resolution images.

## Use cases
- train a vision backbone without self-attention
- classify images on ImageNet with Mamba blocks
- extract features from high-resolution images efficiently
- use a Mamba backbone for object detection on COCO
- semantic segmentation on ADE20k with state space models
- research alternatives to vision transformers

## When to choose
- you need memory-efficient feature extraction on high-resolution images
- you are researching state space models for computer vision
- you want a DeiT alternative with lower GPU memory usage

## When to avoid
- you need a battle-tested production backbone with broad ecosystem support
- you require pretrained weights for many downstream tasks out of the box
- your project depends on standard transformer tooling

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, image-processing
- domain: computer-vision, deep-learning, machine-learning
- platform: python
- tags: mamba, state-space-models, vision-backbone, image-classification, object-detection, semantic-segmentation, research, gpu

## Member repositories
- hustvl/Vim (main) score 29

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:28.557239+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 2026-08-29T18:25:33.805561+00:00, confidence not recorded.
  - readme: https://github.com/hustvl/Vim (fetched 2026-08-28T04:08:28.557239+00:00, sha 84b6ea7f46ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.