Custom object detection is the trending landscape these days in computer vision mainly due to the thousands of emerging use cases with a huge number of real-world applications. A neural network with some level of complexity, usually at least two layers, qualifies as a deep neural network (DNN), or deep net for short. Deep nets process data in complex ways by employing sophisticated math modeling. YOLO, Also Known as You Only Look Once is one of the most powerful real-time object detector algorithms. It is called that way because unlike previous object detector algorithms, like R-CNN or its upgrade Faster R-CNN it only needs the image (or video) to pass one time through its network. 

In this course, you’ll understand the mathematical intuition behind YOLO (You Only Look Once) & how to deal with the right data while building custom models with respect to object detection. You will explore different YOLO models & will discuss with real time implementation which to choose & which to not. You are going to build real time production-based solution using YOLO, build customized production ready object detection models using YOLO.

By the end of this course, you’ll master architecture and mathematics behind YOLO, you’ll know about object detection models, you’ll know how to label your data and train your custom object detection model using YOLO.