OpenAI's model connecting vision and language. Enables zero-shot image classification, image-text matching, and cross-modal retrieval. Trained on 400M image-text pairs. Use for image search, content moderation, or vision-language tasks without fine-tuning. Best for general-purpose image understanding.
npxskills add orchestra-research/ai-research-skills--skill clipLoading…
OpenAI's model that understands images from natural language.
Use when:
Metrics:
Use alternatives instead:
pip install git+https://github.com/openai/CLIP.git
pip install torch torchvision ftfy regex tqdm
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%}")
# 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 |
# 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}")
# 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}")
# 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%})")
# 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)
# 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
)
| Operation | CPU | GPU (V100) |
|---|---|---|
| Image encoding | ~200ms | ~20ms |
| Text encoding | ~50ms | ~5ms |
| Similarity compute | <1ms | <1ms |
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).