YOLOv3 can be performed in real-time end-to-target detection, to fast reflexes.
Course "YOLOv3 target detection combat: training their own data set" the hand and teach people to use YOLOv3 training their own data sets. Course is divided into three small projects: Football target detection, target detection Messi, Messi football and at the same time target detection.
YOLOv3 this course use Darknet, made presentations on the project Ubuntu. Including: installation Darknet, to their own data sets to tag, organize their own data set, modify configuration files, training their own data set, testing the trained network model, performance statistics (mAP calculate and draw the PR curve) and a priori box cluster.
Darknet is the use of lightweight open source deep learning framework implemented in C, less dependent, portability is good, worthy of further exploration.
I introduced about YOLOv3 series of courses include:
(1) YOLOv3 target detection combat: training their own data sets (ie, this course)
Course Link: https://edu.51cto.com/course/18271.html
(2) YOLOv3 target detection combat: traffic sign recognition
(3) YOLOv3 target detection: Principles and analytical source
(4) YOLOv3 target detection: Improvement Network Model
Stay tuned and choose to study!
The figure is a test result while the football object detection and Massey:
