YOLOv7 is the latest iteration of the YOLO object detection model, offering significant improvements over previous versions due to enhancements like model re-parameterization, E-ELAN techniques, and compound scaling. The tutorial covers the theoretical background, practical steps for training a custom YOLOv7 model, and a
Table of contents
IntroductionWhat is YOLO?How does YOLO work?What changes were made in YOLOv7Extended efficient layer aggregation networksModel scaling for concatenation-based modelsTrainable bag of freebiesCoarse for the auxiliary heads, and fine for the lead loss headSetting up your custom datasetsCode demoHelpersTrainDetectTestClosing thoughtsSort: