CLIP - Contrastive Language-Image Pre-Training

OpenAI's model that understands images from natural language.

When to use CLIP

Use when:

Zero-shot image classification (no training data needed)
Image-text similarity/matching
Semantic image search
Content moderation (detect NSFW, violence)
Visual question answering
Cross-modal retrieval (image→text, text→image)

Metrics:

25,300+ GitHub stars
Trained on 400M image-text pairs
Matches ResNet-50 on ImageNet (zero-shot)
MIT License

Use alternatives instead:

BLIP-2: Better captioning
LLaVA: Vision-language chat
Segment Anything: Image segmentation

Quick start

Installation

pip install git+https://github.com/openai/CLIP.git pip install torch torchvision ftfy regex tqdm

Zero-shot classification

import torch import clip from PIL import Image

Load model

device = "cuda" if torch.cuda.is_available() else "cpu" model, preprocess = clip.load("ViT-B/32", device=device)

Load image

image = preprocess(Image.open("photo.jpg")).unsqueeze(0).to(device)

Define possible labels

text = clip.tokenize(["a dog", "a cat", "a bird", "a car"]).to(device)

Compute similarity

with torch.no_grad(): image_features = model.encode_image(image) text_features = model.encode_text(text)

# Cosine similarity
logits_per_image, logits_per_text = model(image, text)
probs = logits_per_image.softmax(dim=-1).cpu().numpy()

Print results

labels = ["a dog", "a cat", "a bird", "a car"] for label, prob in zip(labels, probs[0]): print(f"{label}: {prob:.2%}")

Available models

Models (sorted by size)

models = [ "RN50", # ResNet-50 "RN101", # ResNet-101 "ViT-B/32", # Vision Transformer (recommended) "ViT-B/16", # Better quality, slower "ViT-L/14", # Best quality, slowest ]

model, preprocess = clip.load("ViT-B/32")

Model Parameters Speed Quality

RN50 102M Fast Good

ViT-B/32 151M Medium Better

ViT-L/14 428M Slow Best

Image-text similarity

Compute embeddings

image_features = model.encode_image(image) text_features = model.encode_text(text)

Normalize

image_features /= image_features.norm(dim=-1, keepdim=True) text_features /= text_features.norm(dim=-1, keepdim=True)

Cosine similarity

similarity = (image_features @ text_features.T).item() print(f"Similarity: {similarity:.4f}")

Semantic image search

Index images

image_paths = ["img1.jpg", "img2.jpg", "img3.jpg"] image_embeddings = []

for img_path in image_paths: image = preprocess(Image.open(img_path)).unsqueeze(0).to(device) with torch.no_grad(): embedding = model.encode_image(image) embedding /= embedding.norm(dim=-1, keepdim=True) image_embeddings.append(embedding)

image_embeddings = torch.cat(image_embeddings)

Search with text query

query = "a sunset over the ocean" text_input = clip.tokenize([query]).to(device) with torch.no_grad(): text_embedding = model.encode_text(text_input) text_embedding /= text_embedding.norm(dim=-1, keepdim=True)

Find most similar images

similarities = (text_embedding @ image_embeddings.T).squeeze(0) top_k = similarities.topk(3)

for idx, score in zip(top_k.indices, top_k.values): print(f"{image_paths[idx]}: {score:.3f}")

Content moderation

Define categories

categories = [ "safe for work", "not safe for work", "violent content", "graphic content" ]

text = clip.tokenize(categories).to(device)

Check image

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

Get classification

max_idx = probs.argmax().item() max_prob = probs[0, max_idx].item()

print(f"Category: {categories[max_idx]} ({max_prob:.2%})")

Batch processing

Process multiple images

images = [preprocess(Image.open(f"img{i}.jpg")) for i in range(10)] images = torch.stack(images).to(device)

with torch.no_grad(): image_features = model.encode_image(images) image_features /= image_features.norm(dim=-1, keepdim=True)

Batch text

texts = ["a dog", "a cat", "a bird"] text_tokens = clip.tokenize(texts).to(device)

with torch.no_grad(): text_features = model.encode_text(text_tokens) text_features /= text_features.norm(dim=-1, keepdim=True)

Similarity matrix (10 images × 3 texts)

similarities = image_features @ text_features.T print(similarities.shape) # (10, 3)

Integration with vector databases

Store CLIP embeddings in Chroma/FAISS

import chromadb

client = chromadb.Client() collection = client.create_collection("image_embeddings")

Add image embeddings

for img_path, embedding in zip(image_paths, image_embeddings): collection.add( embeddings=[embedding.cpu().numpy().tolist()], metadatas=[{"path": img_path}], ids=[img_path] )

Query with text

query = "a sunset" text_embedding = model.encode_text(clip.tokenize([query])) results = collection.query( query_embeddings=[text_embedding.cpu().numpy().tolist()], n_results=5 )

Best practices

Use ViT-B/32 for most cases - Good balance
Normalize embeddings - Required for cosine similarity
Batch processing - More efficient
Cache embeddings - Expensive to recompute
Use descriptive labels - Better zero-shot performance
GPU recommended - 10-50× faster
Preprocess images - Use provided preprocess function

Performance

Operation CPU GPU (V100)

Image encoding ~200ms ~20ms

Text encoding ~50ms ~5ms

Similarity compute <1ms <1ms

Limitations

Not for fine-grained tasks - Best for broad categories
Requires descriptive text - Vague labels perform poorly
Biased on web data - May have dataset biases
No bounding boxes - Whole image only
Limited spatial understanding - Position/counting weak

Resources

GitHub: https://github.com/openai/CLIP ⭐ 25,300+
Paper: https://arxiv.org/abs/2103.00020
Colab: https://colab.research.google.com/github/openai/clip/
License: MIT

clip

Safety Notice

Copy this and send it to your AI assistant to learn