Some insights into the YOLOv5 algorithm (framework + directory)

Recently I have been studying drone vision and came into contact with the YOLOv5 algorithm. As a visual novice, some details will always take me a lot of time, which can easily dampen my enthusiasm for learning. Therefore, I want to write some blogs to introduce the use of YOLOv5 and record the problems and solutions I encountered, so as to help everyone save some time and facilitate their own review. Of course, my level is limited and mistakes are inevitable. Everyone is welcome to criticize and correct me.

The content of the next update is roughly as follows (the block diagram I drew is a bit ugly, please forgive me). Some key steps have been given in the diagram for your convenience. This framework is the basic framework. As the study progresses, I will update some newly learned content into the framework in time, hoping to help everyone. This is the first time I write a blog. There is a lot of nonsense and not much practical information. I will actively correct it in the future. I hope everyone will support me.

Table of contents:

Environment configuration related

• Some details in YOLOv5 environment configuration

• Some details of YOLOv7 environment configuration

• Some common problems in YOLO environment configuration

Introduction to YOLO algorithm

• Use YOLOv5 to achieve target detection in pictures and videos

• Using YOLOv5 to achieve single-camera real-time target detection

• Real-time multi-camera target detection using YOLOv5

• Real-time coordinate printing of YOLOv5 recognized targets

• YOLOv5 training deployment

• YOLOv5 self-made data set training and deployment

YOLO expanded functions

• YOLOv5 unpopular knowledge

• YOLOv5 calls IP camera

• YOLOv5 implements goal counting

• YOLOv5+DeepSort realizes target tracking

• Use YOLOv5 to save real-time target detection results

On the way to study, you and I encourage each other (๑•̀ㅂ•́)و✧