# yzhuoning/Awesome-CLIP

Awesome list for research on CLIP (Contrastive Language-Image Pre-Training).

Repository: https://github.com/yzhuoning/Awesome-CLIP
Canonical: https://ross.abutalabs.com/products/awesome-clip
License Family: other
Topics: clip, contrastive-learning, pre-training
Last push: 2024-06-28T09:40:56+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": 1824, "days_push": 796, "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 1227, forks 58 (observed 2026-08-28T04:04:03.274806+00:00)

## What it is
A curated awesome list of research papers, code implementations, and applications built around CLIP (Contrastive Language-Image Pre-Training). It organizes resources covering the original model, training frameworks, and downstream applications like GANs, object detection, and image retrieval.

## Use cases
- find papers on CLIP research
- learn about contrastive language-image pre-training
- find open-source CLIP implementations
- discover CLIP applications like zero-shot detection
- research vision-language models
- find CLIP training frameworks like OpenCLIP
- explore text-to-image generation with CLIP

## When to choose
- you are researching CLIP or vision-language models
- you want a curated index of CLIP papers and code
- you need to survey applications built on CLIP

## When to avoid
- you need a runnable CLIP model or library itself
- you want maintained software rather than a paper list
- you need tutorials for beginners rather than research references

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, nlp, computer-vision
- domain: machine-learning, computer-vision, awesome-lists
- platform: cross-platform
- tags: awesome-list, clip, contrastive-learning, vision-language, research-papers, multimodal, natural-language-processing

## Member repositories
- yzhuoning/Awesome-CLIP (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:03.274806+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 2026-08-30T06:15:17.165397+00:00, confidence not recorded.
  - readme: https://github.com/yzhuoning/Awesome-CLIP (fetched 2026-08-28T04:04:03.274806+00:00, sha 2291a7b5a546)
- Data as of 2026-08-30T08:39:29.467469+00:00.