1. Introduction to this article
This article introduces the major improvements of YOLOv5, especially the innovations in the loss function. It not only includes a variety of improvements and variants of IoU loss functions, such as SIoU, WIoU, GIoU, DIoU, EIOU, CIoU, but also incorporates the "Focus" idea to create a series of new loss functions. There are more than two dozen combinations of these loss functions, each optimized for a specific object detection challenge. The article will discuss in detail how these loss functions improve the performance of YOLOv5 in various detection tasks , including improving accuracy, accelerating convergence, and enhancing the model's adaptability to complex scenes. This article is mainly to publish the various EIoU article services that have recently been improved by Inner ideas . Through experiments, in most cases, the effects are better than the various loss effects mentioned in this article.
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