Image & Text Similarity Search | CLIP + FAISS + Milvus

Learn how to build a cross-modal image and text similarity search system using CLIP embeddings, FAISS, and Milvus. This hands-on project demonstrates how modern AI systems understand and match images with natural language queries. This project is ideal for developers building visual search engines, multimodal AI applications, and real-world computer vision systems used in e-commerce, media, and enterprise platforms. 🚀 What You Will Build: • Image & text embedding pipeline using CLIP • Cross-modal similarity search on the COCO dataset • Fast vector search with FAISS • Scalable vector indexing with Milvus 🛠️ TECH STACK & TOOLS USED: • CLIP – Joint image-text embedding model • FAISS – High-speed similarity search engine • Milvus – Scalable vector database for production • OpenCV – Image preprocessing and handling • Python – Core implementation language 👨‍💻 WHO IS THIS FOR? • AI/ML Engineers building multimodal search systems • Developers working on visual & semantic search • Students learning computer vision + NLP integration • Job seekers adding Multimodal AI expertise to their resume 📝 PROJECT CHAPTERS: 0:00 – Intro & Multimodal Search Overview 3:20 – CLIP Architecture & Embeddings 8:40 – Image & Text Indexing with FAISS 15:10 – Similarity Search on COCO Dataset 22:30 – Milvus Indexing & Scalable Inference 33:40 – Final Thoughts & Optimization Tips 🔗 RESOURCES & LINKS: https://www.udemy.com/course/generati... 🔖 Hashtags #GenerativeAI #CLIP #VectorSearch #FAISS #Milvus #MultimodalAI #ComputerVision #AIProjects #AIEngineer #MachineLearning #AIDevelopment #LearnAI #ScratchLearn #BuildInPublic