pytorch 二分类focal loss 文心快码BaiduComate 1. Focal Loss的定义和应用场景 Focal Loss是由何恺明等人在2017年的论文《Focal Loss for Dense Object Detection》中提出的,旨在解决目标检测任务中的类别不平衡和难易样本不平衡问题。Focal Loss通过对易分类样本的损失进行下加权,使模型更加关注难分类样本,从而提高...
alpha = alpha[idx] loss = -1* alpha * torch.pow((1- pt), gamma) * logptifself.size_average: loss = loss.mean()else: loss = loss.sum()returnlossclassBCEFocalLoss(torch.nn.Module):""" 二分类的Focalloss alpha 固定 """def__init__(self, gamma=2, alpha=0.25, reduction='elementw...
Focal_Loss= -1*alpha*(1-pt)^gamma*log(pt) :param num_class: :param alpha: (tensor) 3D or 4D the scalar factor for this criterion :param gamma: (float,double) gamma > 0 reduces the relative loss for well-classified examples (p>0.5) putting more focus on hard misclassified example :...