This project implements a visual semantic search system that allows users to search for products using natural language queries. The system retrieves visually relevant images based on text descriptions, bridging the gap between language and visual content.
How It Works
The system leverages OpenAI’s CLIP (Contrastive Language-Image Pre-training) model to create a unified embedding space for both images and text. This enables powerful cross-modal search capabilities:
- Image Embedding: Product images are processed through CLIP’s vision encoder to create visual embeddings
- Query Embedding: User text queries are processed through CLIP’s text encoder
- Similarity Matching: The system finds images whose embeddings are closest to the query embedding
- Ranked Results: Results are presented in order of semantic similarity to the query
Key Features
- Text-to-Image Search: Find relevant product images using natural language descriptions
- Semantic Understanding: The system understands concepts beyond simple keywords
- Interactive Interface: User-friendly Streamlit application for search and exploration
- Scalable Architecture: Designed to handle large product catalogs efficiently
- Pre-computed Embeddings: Fast search through vectorized product database
Technologies
- Python
- OpenAI CLIP
- Streamlit
- PyTorch
- Vector Search
- Deep Learning
- Computer Vision
- Natural Language Processing
Applications
This technology has numerous practical applications:
- E-commerce product search
- Visual inventory management
- Content discovery systems
- Design inspiration tools
- Fashion and style recommendation
The project demonstrates how multimodal AI models can create intuitive and powerful search experiences that understand both visual and textual information.