深度学习论文: Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection及其PyTorch实现 PDF: https://arxiv.org/pdf/2011.12885.pdf PyTorch: https://github.com/shanglianlm0525/PyTorch-Networks PyTorch: https://...
代码地址:github.com/implus/GFoca 前言:GFLv1文章名:Generalized Focal Loss Learning Qualified and Distributed Bounding Boxes for Dense Object Detection。GFLv1已经很漂亮了,没想到GFLv2能利用分布的统计特征来进行定位质量估计,而且还取得优于卷积特征的效果,感觉作者太强了。 (二)Summary 目前Localization Qualit...
参考代码:https://github.com/RangiLyu/nanodet import torchimport torch.nn as nnimport torch.nn.functional as Ffrom .utils import weighted_loss@weighted_lossdef quality_focal_loss(pred, target, beta=2.0):r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: LearningQualified and Distr...
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021 - xthzhjwzyc/GFocalV2
Generalized Focal Loss V2 论文地址:https://arxiv.org/pdf/2011.12885.pdf 源码和预训练模型地址:https://github.com/implus/GFoca 本文应该是检测领域首次引入用边界框的不确定性的统计量来高效地指导定位质量估计,从而基本无cost(包括在训练和测试阶段)地提升one-stage的检测器性能,涨幅在1~2个点AP。
Generalized Focal Loss V2 这篇文章提出了利用边界框分布(在Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection中提出的一般分布) 本文观察到一般的统计分布与其真正的定位质量有很强的相关性,边界框分布的形状(平整度)可以清楚地反... ...
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021 - implus/GFocalV2
源码和预训练模型地址:https://github.com/implus/GFocalV2 老规矩,还是一句话总结: 本文应该是检测领域首次引入用边界框的不确定性的统计量来高效地指导定位质量估计,从而基本无cost(包括在训练和测试阶段)地提升one-stage的检测器性能,涨幅在1~2个点AP。又是一个超超超良心技术,欢迎各位看官试用~ ...
Generalized Focal Loss V2 而GFL2就比较好理解了。通过统计‘边界框的不确定性’来高效地指导定位质量估计。即分类标签中不仅包含IOU,还包含‘边界框的不确定性’的统计结果。 ‘边界框的不确定性’的统计是什么呢?如下解释: 预测的边界框分布非常尖锐的话,分布的Topk这几个数通常就会很大(定位质量好);反之Topk...
GFLV2是一个轻量化的即插即用的方法,可以方便地改造其他网络而获得性能提升,而不带来额外的训练推断开销。 Reference 【1】Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection 【2】Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for...