借助Focal Loss的力量,分类分支能够使得少量的正样本和大量的负样本一起成功训练,但是质量估计通常就只针对正样本训练。那么,对于one-stage的检测器而言,在做NMS score排序的时候,所有的样本都会将分类score和质量预测score相乘用于排序,那么必然会存在一部分分数较低的“负样本”的质量预测是没有在训练过程中有监督...
(feature pyramid network) 实验结果 基本内容 论文题目《Focal Loss for Dense Object Detection》 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 分类:类不平衡问题的解决方法 现实...
implus/GFocalofficial 590 open-mmlab/mmdetection 30,510 PaddlePaddle/PaddleDetection 13,176 RangiLyu/nanodet 5,883 Yuxiang1995/ICDAR2021_MFD 130 See all 7implementations Tasks Edit AddRemove Datasets MS COCO Results from the Paper Edit Ranked #106 onObject Detection on COCO test-dev ...
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...
简介:论文阅读笔记 | 目标检测算法——Generalized Focal Lossv1,v2 1. Generalized Focal Loss Abstract One-stage检测器基本上将目标检测定义为密集分类和定位(即边界盒回归)。该分类方法通常采用Focal loss进行优化,回归框位置通常采用狄拉克分布法进行学习。One-stage检测器的一个最新趋势是引入一个独立的预测分支来...
integral set `{0, ..., n}` in paper. label (torch.Tensor): Target distance label for bounding boxes with shape (N,). Return: torch.Tensor: Loss tensor with shape (N,). """# 完全按照论文公式(6)所示,label是真实值(目标框和anchor之间的偏差,参考FCOS)# pred的shape(偏差*分布),如果没...
1. One Paper Hybrid Code Networks: practical and efficientend-to-end dialog control with supervised and reinforcement learning 本文提出了一种特定领域对话系统的端到端训练方案,相比于传统的端到端模型来说,亮点在于用更少量的、更有效的数据进行训练,并且结合 AI科技大本营 2018/04/26 5940 1470篇!CVPR...
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...
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...