YOLO v4 explained in full detail
For this story, we will take a deep look into the YOLOv4, the original paper is huge and has a ton of things. So, fasten your seat belts as it is going to be an extremely long blog to follow just because of the sheer number of things presented in the YOLOv4 paper. All the YOLO models are object detection models, and with the release of YOLOv4, there is a significant increase in the inference time of the model. It can be trained on a single GPU as that is what the authors initially set out to do.
I’m assuming that you know how YOLO works for those who want to know it in more detail can look here.
YOLOv4 is very complex and that’s why this blog is to understand the advancement made over the original YOLO. So, without further ado let’s unwrap YOLOv4 in different sections.
- Object detector architecture breakdown
- Backbone, neck, head
- Bag of freebies (BoF)
- Bag of specials (BoS)
- YOLOv4 architecture selection
- YOLOv4 BoF and BoS selection
