IOU mapping

IOU mapping 是指通过计算目标检测中预测框和真实框之间的交并比(IOU)来对框进行映射或匹配的过程。引用中提到，IOU loss是一种衡量框回归质量的方法，它通过最小化预测框和真实框之间的IOU来训练模型。在实际应用中，通常使用1-IoU来定义IOU loss。通过使用IOU loss，可以提高对IOU的衡量，从而改善目标检测任务中的框定位问题。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
- *1* *2* *3* [【目标检测】28、Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://blog.csdn.net/jiaoyangwm/article/details/126174971)[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: 100%"]
[ .reference_list ]