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This is a walkthrough python tutorial to build an Image Retrieval System using Vision Transformer (ViT) and FAISS. Here, we implement a system for finding similar images using feature-based similarity search. It extracts visual features from images using a neural network and enables fast similarity search through the following main components: 1. Feature Extraction: Converts images into numerical feature vectors that capture their visual characteristics (handled by a separate ImageFeatureExtractor class) 2. Indexing: Processes a directory of images and extracts their features Stores these features in a FAISS index (Facebook AI Similarity Search) Maintains metadata about each indexed image (path, filename, indexing date) 3. Search: Takes a query image and finds the k most similar images from the indexed collection Uses IndexIVFFlat to measure similarity between images Returns matched images sorted by similarity score Note about IndexIVFFlat: Uses a "divide and conquer" approach First divides vectors into clusters/regions When searching: First finds which clusters are most relevant Only searches within those chosen clusters Requires two extra steps: Training: Learning how to divide vectors into clusters nprobe: Choosing how many clusters to check (tradeoff between speed and accuracy) Usually much faster for large datasets Might miss some matches (approximate search) but usually good enough Python code link: https://github.com/bnsreenu/python_fo...