Semantic Image Search with OpenAI CLIP and Meta FAISS
Introduction
This guide walks you through building a semantic image search engine using OpenAI CLIP, Meta FAISS, and Flask. By combining CLIP's powerful visual-language embeddings with FAISS's efficient nearest-neighbor search, you can create a fully functional web interface where you can retrieve relevant images using natural language queries.
Semantic Image Search Visual Preview
How It Works
- CLIP uses a vision encoder (e.g., ResNet or ViT) for images and a text encoder (Transformer-based) for language to project both into the same multimodal embedding space. This allows for direct comparison between text and images using cosine similarity.
- FAISS (Facebook AI Similarity Search) builds an index of the image embeddings and enables fast, scalable retrieval of the closest vectors to a given query.
- Flask provides a simple web interface to submit natural language queries and display semantically matched images from the index.
This architecture supports zero-shot search, meaning you don't need labels or categories, just image data and a good prompt.
Semantic Image Search using Ultralytics Python package
Image Path Warning
If you're using your own images, make sure to provide an absolute path to the image directory. Otherwise, the images may not appear on the webpage due to Flask's file serving limitations.
from ultralytics import solutions
app = solutions.SearchApp(
# data = "path/to/img/directory" # Optional, build search engine with your own images
device="cpu" # configure the device for processing i.e "cpu" or "cuda"
)
app.run(debug=False) # You can also use `debug=True` argument for testing
VisualAISearch class
This class performs all the backend operations:
- Loads or builds a FAISS index from local images.
- Extracts image and text embeddings using CLIP.
- Performs similarity search using cosine similarity.
Similar Images Search
Image Path Warning
If you're using your own images, make sure to provide an absolute path to the image directory. Otherwise, the images may not appear on the webpage due to Flask's file serving limitations.
from ultralytics import solutions
searcher = solutions.VisualAISearch(
# data = "path/to/img/directory" # Optional, build search engine with your own images
device="cuda" # configure the device for processing i.e "cpu" or "cuda"
)
results = searcher("a dog sitting on a bench")
# Ranked Results:
# - 000000546829.jpg | Similarity: 0.3269
# - 000000549220.jpg | Similarity: 0.2899
# - 000000517069.jpg | Similarity: 0.2761
# - 000000029393.jpg | Similarity: 0.2742
# - 000000534270.jpg | Similarity: 0.2680
VisualAISearch Parameters
The table below outlines the available parameters for VisualAISearch:
| Argument | Type | Default | Description |
|---|---|---|---|
data |
str |
images |
Path to image directory used for similarity search. |
| Argument | Type | Default | Description |
|---|---|---|---|
device |
str |
None |
Specifies the device for inference (e.g., cpu, cuda:0 or 0). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. |
Advantages of Semantic Image Search with CLIP and FAISS
Building your own semantic image search system with CLIP and FAISS provides several compelling advantages:
-
Zero-Shot Capabilities: You don't need to train the model on your specific dataset. CLIP's zero-shot learning lets you perform search queries on any image dataset using free-form natural language, saving both time and resources.
-
Human-Like Understanding: Unlike keyword-based search engines, CLIP understands semantic context. It can retrieve images based on abstract, emotional, or relational queries like "a happy child in nature" or "a futuristic city skyline at night".
-
No Need for Labels or Metadata: Traditional image search systems require carefully labeled data. This approach only needs raw images. CLIP generates embeddings without needing any manual annotation.
-
Flexible and Scalable Search: FAISS enables fast nearest-neighbor search even with large-scale datasets. It's optimized for speed and memory, allowing real-time response even with thousands (or millions) of embeddings.
-
Cross-Domain Applications: Whether you're building a personal photo archive, a creative inspiration tool, a product search engine, or even an art recommendation system, this stack adapts to diverse domains with minimal tweaking.
FAQ
How does CLIP understand both images and text?
CLIP (Contrastive Language Image Pretraining) is a model developed by OpenAI that learns to connect visual and linguistic information. It's trained on a massive dataset of images paired with natural language captions. This training allows it to map both images and text into a shared embedding space, so you can compare them directly using vector similarity.
Why is CLIP considered so powerful for AI tasks?
What makes CLIP stand out is its ability to generalize. Instead of being trained just for specific labels or tasks, it learns from natural language itself. This allows it to handle flexible queries like “a man riding a jet ski” or “a surreal dreamscape,” making it useful for everything from classification to creative semantic search, without retraining.
What exactly does FAISS do in this project (Semantic Search)?
FAISS (Facebook AI Similarity Search) is a toolkit that helps you search through high-dimensional vectors very efficiently. Once CLIP turns your images into embeddings, FAISS makes it fast and easy to find the closest matches to a text query, perfect for real-time image retrieval.
Why use the Ultralytics Python package if CLIP and FAISS are from OpenAI and Meta?
While CLIP and FAISS are developed by OpenAI and Meta respectively, the Ultralytics Python package simplifies their integration into a complete semantic image search pipeline in a 2-lines workflow that just works:
Similar Images Search
from ultralytics import solutions
searcher = solutions.VisualAISearch(
# data = "path/to/img/directory" # Optional, build search engine with your own images
device="cuda" # configure the device for processing i.e "cpu" or "cuda"
)
results = searcher("a dog sitting on a bench")
# Ranked Results:
# - 000000546829.jpg | Similarity: 0.3269
# - 000000549220.jpg | Similarity: 0.2899
# - 000000517069.jpg | Similarity: 0.2761
# - 000000029393.jpg | Similarity: 0.2742
# - 000000534270.jpg | Similarity: 0.2680
This high-level implementation handles:
- CLIP-based image and text embedding generation.
- FAISS index creation and management.
- Efficient semantic search with cosine similarity.
- Directory-based image loading and visualization.
Can I customize the frontend of this app?
Yes, you absolutely can. The current setup uses Flask with a basic HTML frontend, but you're free to swap in your own HTML or even build something more dynamic with React, Vue, or another frontend framework. Flask can easily serve as the backend API for your custom interface.
Is it possible to search through videos instead of static images?
Not directly—but there's a simple workaround. You can extract individual frames from your videos (e.g., one every second), treat them as standalone images, and feed those into the system. This way, the search engine can semantically index visual moments from your videos.
