The focal loss was proposed for dense object detection task early this year. It enablestraining highly accurate dense object detectors with an imbalance between foreground and background classes at1:1000scale.
Multi-class classification with focal loss for imbalanced datasets作者| Chengwei Zhang翻译| 汪鹏 校对| 斯蒂芬·二狗子审核| Pita 整理 | 立鱼王原文链接:medium.com/swlh/multi-c 焦点损失函数 Focal Loss(2017年何凯明大佬的论文)被提出用于密集物体检测任务。它可以训练高精度的密集物体探测器,哪怕前景和背景...
focal_for_multiclass Introduction Focal loss is proposed in the paperFocal Loss for Dense Object Detection. This paper was facing a task for binary classification, however there are other tasks need multiple class classification. There were few implementation about this task, so I implemented it wi...
https://medium.com/swlh/multi-class-classification-with-focal-loss-for-imbalanced-datasets-c478700e65f5 焦点损失函数 Focal Loss(2017年何凯明大佬的论文)被提出用于密集物体检测任务。它可以训练高精度的密集物体探测器,哪怕前景和背景之间比例为1:1000(译者注:facal loss 就是为了解决目标检测中类别样本比例严...
论文链接:Focal loss for dense object detection 总体上讲,Focal Loss是一个缓解分类问题中类别不平衡、难易样本不均衡的损失函数。首先看一下论文中的这张图: 解释: 横轴是ground truth类别对应的概率(经过sigmoid/softmax处理过的logits),纵轴是对应的loss值; ...
https://medium.com/swlh/multi-class-classification-with-focal-loss-for-imbalanced-datasets-c478700e65f5 焦点损失函数 Focal Loss(2017年何凯明大佬的论文)被提出用于密集物体检测任务。它可以训练高精度的密集物体探测器,哪怕前景和背景之间比例为1:1000(译者注:facal loss 就是为了解决目标检测中类别样本比例严...
defcategorical_focal_loss(gamma=2.0,alpha=0.25):""" ImplementationofFocal Loss from the paperinmulticlass classificationFormula:loss=-alpha*((1-p)^gamma)*log(p)Parameters:alpha--the sameaswighting factorinbalanced cross entropy gamma--focusing parameterformodulatingfactor(1-p)Default value:gamma-...
A standard way for dealing with the data imbalance is by using a loss function that favors the minority class. A constant weight is considered for each class in the loss function, which is generally inversely related to the number of instances in the class. In this paper, for multi-class ...
在多分类场景中,Focal Loss通常通过对每个类别的交叉熵损失进行加权来实现。 python class FocalLoss(nn.Module): def __init__(self, gamma=2.0, alpha=None, reduction='mean'): super(FocalLoss, self).__init__() self.gamma = gamma self.alpha = alpha self.reduction = reduction def forward(self...
Focal IoU Loss 的设计原理主要基于以下两个方面: 平衡高质量样本和低质量样本对损失的贡献:通过引入一个调节因子,使得高质量样本(即 IoU 值较大的样本)在损失计算中占据更大的权重,而低质量样本(即 IoU 值较小的样本)的权重则相对较小。这样可以在一定程度上避免低质量样本对模型训练的负面影响。