How to Build an Image-to-Image Search Engine with CLIP and Faiss

An image-to-image search engine can efficiently locate semantically related images by using CLIP, an open-source text-to-image vision model developed by OpenAI, and faiss, a local vector database. This search method leverages the semantic richness of images over traditional text-based search queries, allowing for more precise and intuitive results. The guide outlines a step-by-step process for building an engine that uses CLIP to calculate image embeddings, which are stored in a vector database, enabling users to perform similarity searches. By employing embeddings, which encode different features of an image, the search engine can retrieve results ranging from exact duplicates to images with shared attributes. This approach is particularly useful for auditing datasets or serving as a search tool for media archives. The tutorial provides practical instructions on setting up the necessary dependencies, calculating embeddings, and executing search queries, with examples using the COCO 128 dataset to demonstrate the engine's capabilities.