为了实现以上两种方案,文中提出了提出了两个新的损失函数,QFL(Quanlity Focal Loss)和DFL(Distribution Focal Loss),两者结合统称为本文所说的 Generalized Focal Loss。以下分别来看这两种loss. QFL: 将localization quality和Focal loss结合,采用localization quality(例如使用IoU)来作为正样本的label表示。但是传统的Fo...
解读Generalized Focal Loss Mario 自动驾驶37 人赞同了该文章 paper: https://arxiv.org/pdf/2006.04388.pdfarxiv.org/pdf/2006.04388.pdf code:(作者源码) https://github.com/implus/GFocalgithub.com/implus/GFocal mmdetection: https://github.com/open-mmlab/mmdetection/blob/master/configs/...
我们成功地解决了这个问题,将FL从{1,0}离散版本扩展到其连续变体,称为Generalized Focal Loss广义焦点损耗(GFL)。 Quality Focal Loss (QFL)专注于一组稀疏的难例子,同时对相应类别产生连续的0 ~ 1质量估计;Distribution Focal Loss(DFL)使得网络在任意、灵活分布的情况下,快速地专注于学习目标包围盒连续位置周...
论文魔改一下Focal-Loss、center-ness统一为一个Loss 此部分比较简单,基本和FCOS类似 # 代码出自mmdetection@weighted_lossdefquality_focal_loss(pred, target, beta=2.0):"""Quality Focal Loss (QFL) is from Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection...
(feature pyramid network) 实验结果 基本内容论文题目 《FocalLossforDenseObjectDetection》论文地址 http://openaccess.thecvf.com/content_ICCV_2017/papers/Lin_Focal_Loss_for_ICCV_2017_paper.pdf论文简介 2016年提出的目标检测框架,结合 分类:类不平衡问题的解决方法 ...
This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2...
1、Focal loss 2、QFL 完整的保留了focal loss的结构,为了支持连续值监督,将 变成 ,全局最小解即是 时。实际场景下测试 时,效果最好。 以下参考mmdetection中的实现,先将所有样本都视作negative samples计算loss,再计算positive samples的loss。 多类别分类使用sigmoid ...
盏茶论文CV篇(五) Focal Loss for Dense Object Detection (feature pyramid network) 实验结果 基本内容 论文题目 《FocalLossforDenseObjectDetection》 论文地址 http://openaccess.thecvf.com/content_ICCV_2017/papers/Lin_Focal_Loss_for_ICCV_2017_paper.pdf 论文简介 2016年提出的目标检测框架,结合 ...
Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection, NeurIPS2020 - implus/GFocal
In this paper, we explore a completely novel and different perspective to perform LQE -- based on the learned distributions of the four parameters of the bounding box. The bounding box distributions are inspired and introduced as ''General Distribution'' in GFLV1, which describes the uncertainty...