OpenCLIP for Image Search and Automatic Captioning

I have been using and writing about OpenAI's CLIP system since it came out in 2021 [1]. It consists of image and text encoding models that can be used for various forms of cross-modal comparison, like using a text query to find the best matching image in a library quickly. In December 2022, an independent group of researchers known as LAION released a paper called "Reproducible scaling laws for contrastive language-image learning" [2] that describes how they first reimplemented and trained a model similar to CLIP and then experimented with improving the system by training with a larger dataset and using new ML techniques. They call their new model OpenCLIP. In this article, I will provide some background info on the original CLIP, describe how LAION improved the model, and show some results from my experiments with the two systems using images from the Library of Congress's Flickr photostream.