In this lesson we’ll talk about a few different types of CNN architectures that can do more than single image classification. First we’ll talk about classifying one object in an image and localizing it, which means finding its location in the image. This is typically done by placing a bounding box around the object. Finding the location of an object in an image and more generally being able to analyze an image by breaking it up into smaller bounded regions is the key to creating a model that can classify multiple objects in an image. We’ll build up to learning about region-based CNN’s like the faster R-CNN model which analyzes different cropped areas of a single input image, decides which regions correspond to objects and then performs classification as usual. This is the kind of architecture that’s used in cutting edge applications, from medical diagnostic tools to autonomous vehicles. I’m really excited to show you how they work.