clip

OpenAI's CLIP scores how well a picture and a piece of text go together — enough to sort photos into any categories you name, search a library by description, or flag unwanted images, with no training of your own.

WHEN TO USE

SCOPE

USE FOR

Classify an image into arbitrary categories by comparing it against text labels.
Build a semantic image search engine driven by a plain-text query.
Flag NSFW or violent images by scoring them against moderation category labels.
Rank a batch of images by similarity to a text description for retrieval.

NOT FOR

For better image captioning, use BLIP-2 instead.
For vision-language chat, use LLaVA instead.
For image segmentation, use Segment Anything instead.
It performs best on broad categories — fine-grained classification is a weak spot.

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In this document (13)

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

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