YOLOv7: Making YOLO Great Again
Just a few weeks ago, YOLO v7 came into the limelight by beating all the existing object detection models to date. Anyone who has worked in Object detection has heard about YOLO. It’s been here for a while now, and to date, we have seen a lot of YOLO versions. YOLO is not a single architecture but a flexible research framework written in low-level languages. The framework has three main components: the head, neck, and backbone. Different sets of components and architecture are associated with the above-mentioned three components giving rise to different YOLO versions. For a detailed breakdown of YOLO architecture, read the given blog that discusses each part of YOLO architecture in great detail. In this blog, we are mainly going to focus on what’s new in this version of YOLO.
There are many complex things presented in the YOLO v7 paper, so, without further ado, let’s dive deep into the details of this incredible architecture.
Model re-parameterization
The idea behind model re-parameterization is that it merges multiple computational modules into one at the…
