In this tutorial you will walk through the process of storing images into KDB.AI using a pretrained neural network. You will then use nearest neighbor search capability to find and compare MRI Scans based on their Euclidean distance.
![](https://kdb.ai/files/2024/01/img-diagram-1024x412.png)
You will begin by loading the public dataset into your environment and then create embeddings using the ResNet-50 model. From here, you will visualize the results and add the embeddings into KDB.AI.
Finally, you will perform a series of image similarity searches to test your embeddings.
Download the Jupyter Notebook and any accompanying files at the repository on GitHub.