Building an Image Similarity Search Engine with FAISS and CLIP

🌈 Abstract

The article discusses how to build an image similarity search engine using the CLIP (Contrastive Language-Image Pre-training) model and the FAISS (Facebook AI Similarity Search) library. The goal is to enable efficient similarity searches for images using either text queries or reference images.

🙋 Q&A

[01] Introduction

1. What is the purpose of building an image similarity search engine? The purpose is to easily find images within a large dataset using either a text query or a reference image. The article mentions that searching for images in a never-ending dataset can be tedious, so building an image similarity search engine can help solve this problem.

2. How does the image similarity search engine work? The engine works by:

• Using the CLIP model to generate numerical embedding vectors that represent the semantic meaning of each image in the dataset
• Storing these embedding vectors in a FAISS index
• When a text query or reference image is provided, generating its embedding vector and comparing it against the indexed embeddings to retrieve the most similar images

3. What are the key components used in this implementation? The key components are:

• The CLIP model, which maps images and text to the same latent space
• The FAISS library, which enables efficient similarity search and clustering of the dense embedding vectors

[02] Generating CLIP Embeddings

1. How are the CLIP embeddings generated for the image dataset? The article provides a function called generate_clip_embeddings that:

• Iterates through the image dataset directory
• Opens each image using PIL
• Generates an embedding vector for each image using the CLIP model
• Returns a list of the embedding vectors and a list of the image paths

2. What is the purpose of creating the FAISS index from the embedding vectors? The FAISS index is created to enable efficient similarity search. It stores the embedding vectors and associates them with the corresponding image paths. This allows the search engine to quickly compare a query embedding against the indexed embeddings to retrieve the most similar images.

[03] Retrieving Similar Images

1. How are similar images retrieved using a text query? To retrieve similar images using a text query:

• The query text is passed to the retrieve_similar_images function
• The function generates an embedding vector for the text query using the CLIP model
• It then uses the FAISS index to search for the top_k most similar embedding vectors, and returns the corresponding image paths

2. How are similar images retrieved using a reference image? To retrieve similar images using a reference image:

• The reference image path is passed to the retrieve_similar_images function
• The function opens the image and generates an embedding vector for it using the CLIP model
• It then uses the FAISS index to search for the top_k most similar embedding vectors, and returns the corresponding image paths

3. What are the key steps in the retrieve_similar_images function? The key steps are:

1. If the query is an image path, open the image and generate its embedding vector using the CLIP model
2. Use the FAISS index's search method to find the top_k most similar embedding vectors to the query
3. Retrieve the image paths corresponding to the top similar embedding vectors
4. Return the query and the list of retrieved image paths

[04] Conclusion

1. What are the limitations of the CLIP model mentioned in the article? The article mentions that the CLIP model, while showing nice results as a zero-shot model, may exhibit low performance on out-of-distribution data, fine-grained tasks, and may inherit the natural bias of the data it was trained on.

2. What are the suggested ways to overcome the limitations of the CLIP model? The article suggests two ways to overcome the limitations of the CLIP model:

1. Try other CLIP-like pre-trained models, such as those available in the OpenClip library
2. Fine-tune the CLIP model on a custom dataset to improve its performance on specific tasks or data