Let’s understand OpenAI’s CLIP and how to use OpenAI’s CLIP to Search and Classify Images with Natural Language
How to Use CLIP in Python to Perform Zero-Shot Image Search and Classification
What Is CLIP?
CLIP (Contrastive Language–Image Pretraining) is a powerful vision-language model developed by OpenAI. It’s trained on 400 million image-text pairs and can understand images in the context of natural language — allowing you to search images with words, classify them with custom labels, or find similar images without needing labeled datasets.
Why CLIP Matters
- No training required — it’s zero-shot
- Works on unlabeled images
- Can compare images and text directly
- Enables semantic image search (e.g., “a cat sitting on a chair”)
What Can You Do With CLIP?
Use Case | Example |
---|---|
Search with text | Find all images of “a dog on a beach” |
Zero-shot classification | Classify images using custom text labels |
Similar image detection | Group or retrieve related images |
Visual search engines | Build search features without tags |
How CLIP Works (In Simple Terms)
CLIP has two neural networks:
- Text encoder: Converts natural language into a vector
- Image encoder: Converts images into a vector
Both outputs are mapped to the same embedding space, so you can compare a caption like “a red car” directly with an image vector. The closer the vectors, the more semantically similar they are.
Getting Started with CLIP in Python
1. Install Dependencies
pip install torch torchvision ftfy regex tqdm
pip install git+https://github.com/openai/CLIP.git
pip install matplotlib pillow
2. Load the Model
import clip
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model, preprocess = clip.load("ViT-B/32", device=device)
3. Encode Text and Images
from PIL import Image
# Encode a sentence
text = clip.tokenize(["a cat sitting on a couch"]).to(device)
with torch.no_grad():
text_features = model.encode_text(text)
# Encode an image
image = preprocess(Image.open("cat.jpg")).unsqueeze(0).to(device)
with torch.no_grad():
image_features = model.encode_image(image)
# Normalize and compare
text_features /= text_features.norm(dim=-1, keepdim=True)
image_features /= image_features.norm(dim=-1, keepdim=True)
similarity = (image_features @ text_features.T).item()
print("Similarity Score:", similarity)
4. Search Images Using Text
Embed all your dataset images into vectors and compare them to the query text:
# Get top 5 similar images
similarities = (image_embeddings @ text_features.T).squeeze(1)
top_k = similarities.topk(5)
You can display the top matches using matplotlib or return file paths in a web app.
Bonus: Zero-Shot Classification Example
Let’s say you want to classify an image using your own custom labels:
labels = ["a cat", "a dog", "a bird", "a car"]
text_tokens = clip.tokenize(labels).to(device)
with torch.no_grad():
text_features = model.encode_text(text_tokens)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
similarities = (image_features @ text_features.T)
probs = similarities.softmax(dim=-1).cpu().numpy()
print("Label Probabilities:", probs)
Real-World Use Cases
- E-commerce: Search products visually using natural language
- Media platforms: Detect or organize similar content
- Content moderation: Flag inappropriate or unsafe imagery
- Dataset curation: Find duplicates or cluster content
- Art generation: Guide AI art models with visual prompts
Next Steps & Add-ons
- Integrate with FAISS for scalable similarity search on 100k+ images
- Use Gradio or Streamlit to build a front-end UI
- Save and reuse embeddings with NumPy or HDF5
- Cluster similar images using KMeans or UMAP
Conclusion
CLIP opens the door to advanced image understanding without the need for labels or fine-tuning. With just a few lines of code, you can build intelligent systems that “see” images the way we describe them — using natural language.
Whether you’re a developer, data scientist, or creative technologist, CLIP makes powerful visual AI accessible and incredibly flexible.
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