This post describes the implementation of text-to-image search and image-to-image search using a pre-trained model called uform, which is inspired by Contrastive Language Image Pre-Training (CLIP). The post provides code snippets for implementing these search functions and explains how cosine similarity is used to calculate similarity between text and images. The results of the searches are displayed with the top five most similar images. Basic coding experience is required to implement these search functions.
Cutting-edge Image Search Made Simple and Quick
Who is this useful for?
This article is for developers who want to implement image search, data scientists interested in practical applications, and non-technical readers who want to learn about AI in practice.
How advanced is this post?
This post provides a step-by-step guide on implementing image search quickly and simply.
What We’re Doing, and How We’re Doing it
We will be implementing text-to-image search and image-to-image search using a lightweight pre-trained model called uform, which is conceptually similar to CLIP (Contrastive Language Image Pre-Training). The model uses encoders to create vector embeddings of images and text, allowing us to calculate similarities using cosine similarity.
Implementation
To implement image search, we need to download the uform model and define a database of images to search through. We can then use the model to encode text and images, and calculate cosine similarity to find similar images.
Text-to-Image Search
To perform text-to-image search, we define a search phrase and embed the text. We then compare the text embedding to the embeddings of all images in the database using cosine similarity. The top five images with the highest similarity to the search text are displayed.
Image-to-Image Search
Image-to-image search works similarly to text-to-image search. We embed the search image and compare its embedding to the embeddings of all other images in the database using cosine similarity. The top five images with the highest similarity to the search image are displayed.
Conclusion
By using the uform model and cosine similarity, we successfully implemented text-to-image and image-to-image search. This allows us to quickly find similar images based on text or reference images. To learn more about CLIP and AI, refer to the companion article.
Discover AI Solutions for Your Company
If you want to evolve your company with AI, stay competitive, and use image search to your advantage, consider using AI solutions from itinai.com. AI can redefine your way of work by automating customer engagement, identifying automation opportunities, and providing measurable impacts on business outcomes. Start with a pilot, gather data, and expand AI usage judiciously. For more information and AI sales bot solutions, visit itinai.com.
