facebookresearch/MetaCLIP

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1854 stars · 80 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Meta's research code and models for Meta CLIP, a reimplementation and scaling recipe for CLIP-style contrastive vision-language models, including Meta CLIP 2 for worldwide multilingual data curation and training. It provides pretrained checkpoints, training/eval code, and metadata curation pipelines released alongside NeurIPS/ICLR/CVPR papers.

Use cases

• train a CLIP model on curated image-text data
• get multilingual CLIP embeddings for images and text
• run zero-shot image classification with a pretrained CLIP model
• curate large-scale non-English training data for vision-language models
• fine-tune or evaluate CLIP models with open_clip or Hugging Face
• reproduce Meta CLIP research results from the papers

When to choose

• you need state-of-the-art multilingual CLIP embeddings
• you want to reproduce or extend Meta's CLIP data curation research
• you need pretrained vision-language backbones for downstream tasks like retrieval or zero-shot classification

When to avoid

• you need a production-ready inference service rather than research code
• you lack GPU resources for large-scale training
• you need a lightweight plug-and-play image classifier without vision-language modeling

Facets

library · maturity active

machine-learning deep-learning image-processing nlp data-science machine-learning deep-learning computer-vision artificial-intelligence python cross-platform clip contrastive-learning multilingual vision-language-models data-curation image-text-retrieval zero-shot-classification research-code facebook-research natural-language-processing gpu linux

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