Building an Image Search Engine with Hugging Face Datasets and Faiss

The Hugging Face datasets library, traditionally used for text data, now offers robust support for images. This blog post demonstrates how to combine datasets with Faiss and sentence-transformers to create a powerful image search application.

We start with the "Digitised Books - Images identified as Embellishments" dataset from the British Library, which contains historical images extracted from OCR output. Using datasets, we load and process these images efficiently, then compute embeddings with a sentence-transformers model (e.g., CLIP). These embeddings are indexed with Faiss for fast similarity search.

The workflow includes:

• Installing required libraries: datasets, pillow, sentence-transformers, faiss, and rich.
• Creating a dataset with the Image feature, which accepts file paths, byte data, or PIL images.
• Adding a Faiss index to the dataset for efficient retrieval.
• Running text queries (e.g., "a cat sitting on a chair") to find visually similar images from the collection.

The final result is a functional image search demo that can be deployed as a Hugging Face Space. This approach showcases the versatility of datasets beyond text and opens up possibilities for other multimedia search applications.