mlfoundations/open_clip

Health v2 · maintenance only

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14095 stars · 1303 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

OpenCLIP is an open-source PyTorch implementation of CLIP and related multimodal contrastive models, with many pretrained image/text checkpoints. It supports both inference with pretrained models and large-scale contrastive training including newer architectures like CoCa, MaMMUT, and NaFlex variants.

Use cases

• compute image and text embeddings with pretrained CLIP models
• zero-shot image classification from natural language prompts
• train a custom CLIP model on my own image-text dataset
• build image search by comparing text queries to image embeddings
• fine-tune contrastive image-text models
• extract features from images for downstream tasks

When to choose

• you need CLIP-style multimodal embeddings in PyTorch
• you want zero-shot classification without training a custom classifier
• you need to pretrain or fine-tune contrastive image-text models at scale
• you want access to many open pretrained checkpoints (OpenAI, LAION, DataComp)

When to avoid

• you need a non-PyTorch framework like TensorFlow or JAX
• you only need a hosted inference API without local GPU resources
• your task is single-modality (pure vision or pure NLP) with no cross-modal needs

Facets

library · maturity active

machine-learning deep-learning computer-vision nlp llm-training deep-learning computer-vision machine-learning python clip contrastive-learning pytorch multimodal zero-shot-classification pretrained-models image-text natural-language-processing gpu

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