I explain the main object detection metrics and the interpretation behind their abstract notions and percentages. As well as how to knowing if your model has a decent performance and if not what to do to improve it.

I explain how YOLO works and its main features, I also discuss YOLOv2 implementing some significant changes to address YOLO's constraints while improving speed and accuracy, finally presenting YOLO9000 as a new step towards building more comprehensive detection systems.

A thorough explanation of the inner workings of SSD and its key contributions to faster performance than state of the art detectors namely YOLO, while being as accurate as Faster R-CNN.

Pytorch is one of the leading frameworks and one of the fastest growing platforms in the deep learning research community, in this guide we will learn about the building blocks of Pytorch along with a hands-on example.