clip

name	clip
description	OpenAI CLIP — contrastive language-image pre-training. Zero-shot image classification, image-text similarity, concept search, and cross-modal retrieval. Embed images and text into shared space.
tags	["clip","multimodal","image-text","zero-shot","embeddings","openai","zorai"]

Overview

OpenAI CLIP (Contrastive Language-Image Pre-training) learns joint text-image representations. Enables zero-shot image classification, image-text similarity, cross-modal search, and image captioning without task-specific training.

Installation

uv pip install openai-clip

Zero-Shot Classification

import clip
import torch

model, preprocess = clip.load("ViT-B/32")
image = preprocess(load_image("photo.jpg")).unsqueeze(0)
text = clip.tokenize(["a dog", "a cat", "a bird"])

with torch.no_grad():
    logits, _ = model(image, text)
    probs = logits.softmax(dim=-1)

print(f"Predicted: class {probs.argmax().item()} with {probs.max():.2%} confidence")

Text-Image Similarity

images = torch.stack([preprocess(img) for img in [load_image("a.jpg"), load_image("b.jpg")]])
texts = clip.tokenize(["sunset", "ocean", "mountain"])

with torch.no_grad():
    similarity = model(images, texts)[0].softmax(dim=-1)

References

• CLIP GitHub
• CLIP paper