在Quality Focal Loss中,通过这种方式,模型能够在训练过程中考虑到每个样本的定位质量,使得损失函数能够更加关注那些定位或分类困难的样本。 2.3 连续标签支持 连续标签支持是指在质量焦点损失(Quality Focal Loss, QFL)中,分类的输出标签不再是传统的0或1(如在one-hot编码中),而是可以取任意在0到1之间的连续值。
--MSTK 8. Re:运行Keras版本的Faster R-CNN(1) 博主我运行Train文件报这个错误Exception: Error when checking target: expected rpn_out_class to have shape (None, ... --九块九毛九 9. Re:运行Keras版本的Faster R-CNN(1) 楼主还有之前的代码吗?现在报404错误 --初末月光 10. Re:Java时间和时间戳...
Of course, the ultimate goal is to make the quality factor play a role in the training process. We propose an advanced classification loss function, Soft Focal Loss (SFL), which makes the samples with large q get more attention. Actually, the quality factor has another important role, which...
If you find GFocal useful in your research, please consider citing: @article{li2020gflv2, title={Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection}, author={Li, Xiang and Wang, Wenhai and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang...
Localization Quality Estimation (LQE) is crucial and popular in the recent advancement of dense object detectors since it can provide accurate ranking scores that benefit the Non-Maximum Suppression processing and improve detection performance. As a common practice, most existing methods predict LQE scor...
Quality Focal Loss (QFL) 是一种用于目标检测的改进损失函数。它的主要创新是将目标的定位质量(如边界框与真实对象的重叠度量,例如IoU得分)直接融合到分类损失中,形成一个联合表示。这种方法能够解决传统目标检测中分类与定位任务之间存在的不一致性问题。QFL通过为每个类别的得分赋予根据定位质量调整的权重,使得检测...
Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021 - xthzhjwzyc/GFocalV2