Our search engine will be powered by “vectors”, or “embeddings”. The final attribute enables you to search a dataset by attributes in an image. The former two attributes can be used to check whether you already have images similar to a specific one in a dataset, and how many. Images that appear in a specific scene, or share attributes with the provided image, and more.We’ll build a search engine using COCO 128, a dataset with a wide range of different objects, to illustrate how CLIP makes it easy to search images using other images as an input. If you upload a photo of a particular object, you can find images with similar objects. What does this mean? If you upload a photo of a scene in a particular environment, you can retrieve results with similar attributes to that scene. The search engine we will build in this article will return results semantically related to an image. How to Build an Image-to-Image Search Engine This tutorial comes with an accompanying Google Colab that you can use to follow along and make your own search engine.
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