yolov8学习笔记

根据引用和引用的内容，YOLOv8是一种目标检测模型。与YOLOv5相比，YOLOv8的yaml配置文件改动较小。YOLOv8采用了Anchor-Free的思想，使用VFL Loss、DFL Loss和CIOU Loss作为分类损失函数，并采用了Task-Aligned Assigner匹配方式进行样本匹配。此外，YOLOv8还引入了YOLOv8 Nano（速度最快）和YOLOv8 Extra Large（准确性最高）等不同规模的模型。

如果你想学习YOLOv8的相关知识，可以参考官方文档或者专业的教程。这些资源可以帮助你理解YOLOv8的原理、配置文件和模型性能，并帮助你开始使用和学习YOLOv8。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [YOLOv8模型学习笔记](https://blog.csdn.net/pengxiang1998/article/details/129151514)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
- *2* *3* [YOLOv8学习笔记](https://blog.csdn.net/GoodTime1110/article/details/129767920)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v93^chatsearchT3_1"}}] [.reference_item style="max-width: 50%"]
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