YOLOV7损失函数组成

YOLOV7并不是一个官方的版本，通常我们所提到的YOLO版本是从YOLOv1到YOLOv4。从引用中可以看到，损失函数的计算包括了正样本与target的IOU loss、objectness loss和分类loss这三部分。其中，IOU loss用于度量预测框与真实框之间的重叠程度，objectness loss用于衡量预测框是否包含物体，分类loss用于对物体进行分类。这些损失函数的具体计算可以在相关的代码文件中找到。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* [[YOLOv7/YOLOv5系列算法改进NO.7]损失函数改进](https://blog.csdn.net/m0_70388905/article/details/125419887)[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_2"}}] [.reference_item style="max-width: 50%"]
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