# KMnP/vpt

❄️🔥 Visual Prompt Tuning [ECCV 2022] https://arxiv.org/abs/2203.12119

Repository: https://github.com/KMnP/vpt
Canonical: https://ross.abutalabs.com/products/vpt
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-09-02T18:32:58+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 1515, "days_push": 1096, "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 1244, forks 105 (observed 2026-08-28T04:04:06.796843+00:00)

## What it is
Official PyTorch implementation of Visual Prompt Tuning (VPT), an ECCV 2022 method for parameter-efficient fine-tuning of vision transformers by learning input prompts. It provides training and hyperparameter tuning scripts for ViT and Swin backbones on FGVC and VTAB datasets.

## Use cases
- reproduce visual prompt tuning experiments from the ECCV 2022 paper
- fine-tune pretrained vision transformers without updating backbone weights
- evaluate prompt-based transfer learning on VTAB benchmarks
- compare shallow vs deep prompting on fine-grained classification tasks
- adapt ViT or Swin models to new image classification datasets efficiently

## When to choose
- you need parameter-efficient adaptation of vision transformers
- you want the official reference implementation of VPT for research
- you are benchmarking prompt tuning against full fine-tuning on VTAB or FGVC datasets

## When to avoid
- you need a production-ready training framework with broad model support
- you require non-ViT/Swin architectures or the latest PEFT methods
- you want a maintained library with frequent updates and long-term support

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, llm-training
- domain: machine-learning, computer-vision, deep-learning
- platform: python
- tags: visual-prompt-tuning, parameter-efficient-fine-tuning, vision-transformer, pytorch, eccv-2022, transfer-learning, research-code, linux, gpu

## Member repositories
- KMnP/vpt (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:06.796843+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 2026-08-30T05:08:12.968328+00:00, confidence not recorded.
  - readme: https://github.com/KMnP/vpt (fetched 2026-08-28T04:04:06.796843+00:00, sha 18742b240872)
- Data as of 2026-08-30T08:39:29.467469+00:00.