《Distance-IoU Loss：Faster and Better Learning for Bounding Box Regression》論文筆記
代碼地址：DIoU 1. 概述 導讀：這篇文章主要的貢獻在邊界框損失函數的優化上面，傳統上使用 L n L_n Ln範數的損失，如smooth-L1或是其對應的改進balanced-L1，但是這些損失函數並不直接與檢測框的質量相關。將檢測框的質量作爲迴歸的度量就引入IoU損失函數，爲了彌補IoU損失函數在不相交的時候不可導的問題引入GIoU，但是GIoU也是存在收斂速度慢且迴歸不準確（GIoU在
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相關文章
- 1. Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression 論文
- 2. 論文：Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression 閱讀筆記
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