Object detectors count objects in a scene and determine their precise locations, while also labeling them. Object detection plays a crucial role in many vision applications, from autonomous driving to smart appliances. In many of these applications, it’s necessary or desirable to implement object detection at the edge. In this presentation, we will explore the evolution of object detection algorithms, from traditional approaches to deep learning-based methods and transformer-based architectures. We will delve into widely used approaches for object detection, such as two-stage R-CNNs and one-stage YOLO algorithms, and examine their strengths and weaknesses. And we will provide guidance on how to evaluate and select an object detector for an edge application.