# xinyu1205/recognize-anything

Open-source and strong foundation image recognition models.

Repository: https://github.com/xinyu1205/recognize-anything
Canonical: https://ross.abutalabs.com/products/recognize-anything
Homepage: https://recognize-anything.github.io/
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: tag2text-iclr2024, recognize-anything
Last push: 2025-02-18T02:35:59+00:00

## Health v2 (maintenance only)
Score: 33/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 7, release rhythm 35, longevity 90
- inputs: {"age_days": 1272, "days_push": 562, "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 3708, forks 327 (observed 2026-08-28T04:08:15.112442+00:00)

## What it is
Recognize Anything is a collection of open-source image recognition foundation models, including RAM, RAM++, and Tag2Text, that perform image tagging and captioning with high accuracy. It provides pretrained models and inference code for recognizing thousands of common and open-set categories.

## Use cases
- tag images with descriptive labels automatically
- recognize any common category in a photo without training
- generate captions for images with a tagging-guided model
- build a visual semantic analysis pipeline with Grounded-SAM
- classify images into open-set categories zero-shot

## When to choose
- you need strong zero-shot image tagging or recognition
- you want open-source alternatives to proprietary tagging APIs like Google's
- you need tagging combined with captioning in one model
- you want to integrate recognition with segmentation models like SAM

## When to avoid
- you need fine-grained classification on a small custom label set where a simple classifier suffices
- you have no GPU and need lightweight real-time inference
- you need object detection or localization alone rather than recognition

## Facets
- artifact type: library
- maturity: active
- function: image-processing, computer-vision, machine-learning, deep-learning
- domain: computer-vision, image-processing, artificial-intelligence, machine-learning
- platform: python, cross-platform
- tags: image-tagging, zero-shot-recognition, vision-language-model, image-captioning, ram, tag2text, foundation-model, gpu

## Member repositories
- xinyu1205/recognize-anything (main) score 33

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:15.112442+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 2026-08-29T18:30:58.195119+00:00, confidence not recorded.
  - readme: https://github.com/xinyu1205/recognize-anything (fetched 2026-08-28T04:08:15.112442+00:00, sha c32c23d21c30)
  - homepage: https://recognize-anything.github.io/ (fetched 2026-08-29T09:24:28.887233+00:00, sha 1e10beccb59b)
- Data as of 2026-08-30T08:39:29.467469+00:00.