Generalized Focal Loss(GFL)包括了QFL和DFL两部分,如下图所示. 通过实验,证明了GFL提高了mAP.
GFLV2在COCO中达到了53.3AP。 Generalized Focal Loss V1 讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。 Classification-I...
讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。 Classification-IoU Joint Representation 这一块是GFLV1的其中一...
讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。 Classification-IoU Joint Representation 这一块是GFLV1的其中一...
1、QualityFocalLoss 2、DistributionFocalLoss 一、论文解读 论文的名字叫Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection 链接:arxiv.org/pdf/2006.0438 MMDetection官方收录地址: mmdetection/README.md at master · open-mmlab/mmdetection (github.com) 原作者...
Quality Focal Loss Distributed Focal Loss: QFL Pytorch Code Explanation: DLF Pytorch Code Explanation: Varifocal Loss: Problems Addressed by Varifocal Loss: Solutions Introduced: Pytorch Code explanation: Key Takeaways Conclusion References Generalized Focal Loss (GFL): ...
讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。
Generalized Focal Loss V1 讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。
Generalized Focal Loss V1 讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。
Generalized Focal Loss V1 讲GFLV2前先概括地总结一下GFLV1,主要有两点,分别是Classification-IoU Joint Representation以及General Distribution of Bounding Box Representation,具体可以看Generalized Focal Loss:Focal loss魔改以及预测框概率分布,保涨点 | NeurIPS 2020。