350 - Efficient Image Retrieval with Vision Transformer (ViT) and FAISS

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...