1 L1Loss(绝对值损失函数) 2 CrossEntropyLoss(交叉熵损失函数) 3 NLLLoss(最大似然损失函数) 4 MSELoss(平方损失函数) 5 DiceLoss(用于计算两个样本点的相似度的距,主要应用,语义分割等) 6 Focal Loss 7 Chamfer Distance(CD、倒角距离) 8 Earth Mover’s Distance (EMD、推土机距离) 9 Density-aware Cha...
将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6): ''' Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions. Assumes the `channels_last` format. # Arguments ...
cross entropy 是普遍使用的loss function,但是做分割的时候很多使用Dice, 二者的区别如下; One compelling reason for using cross-entropy over dice-coefficient or the similar IoU metric is that the gradients are nicer. The gradients of cross-entropy wrt the logits is something like p−t, where p ...
对于每个类别的mask,都计算一个 Dice 损失: 将每个类的 Dice 损失求和取平均,得到最后的 Dice soft loss。 下面是代码实现: def soft_dice_loss(y_true, y_pred, epsilon=1e-6):'''Soft dice loss calculation for arbitrary batch size, number of classes, and number of spatial dimensions.Assumes the...
一、crossentropyloss 用于图像语义分割任务的最常用损失函数是像素级别的交叉熵损失,这种损失会逐个检查每个像素,将对每个像素类别的预测结果(概率分布向量)与我们的独热编码标签向量进行比较。 假设我们需要对每个像素的预测类别有5个,则预测的概率分布向量长度为5: ...
Cross Entropy Loss的局限性 当使用交叉熵损失时,标签的统计分布对训练精度起着很重要的作用。标签分布越不平衡,训练就越困难。虽然加权交叉熵损失可以减轻难度,但改进并不显著,交叉熵损失的内在问题也没有得到解决。在交叉熵损失中,损失按每像素损失的平均值计算,每像素损失按离散值计算,而不知道其相邻像素是否为边...
百度试题 题目元图像分割深度神经网络常用的损失函数( A.cross entropyB.dice coefficientC.focal lossD.以上都是相关知识点: 试题来源: 解析 D.以上都是 反馈 收藏
to tackle unbalanced datasets and improve model convergence, the model achieved 76.67% of Top-1 Accuracy and 85.42% of Top-5 accuracy, which are 3.63%/2.9% higher than symmetric cross-entropy and are significantly higher than the usual fine-tuning method with categorical cross-entropy loss(CCE)...
Before trying dice, I was using sparse categorical crossentropy with very good results. However, because label 0 was being included in the loss calculation, both training and validation accuracy were artificially high (> 0.98). My implementation of dice is based on this: https://github.com/...
sum() + smooth) dice_loss = 1 - dice_coeff return dice_loss 在训练过程中,你可以将 Dice 损失与其他损失函数结合,例如: dice_loss_fn = DiceLoss() cross_entropy_loss_fn = torch.nn.CrossEntropyLoss() # 计算总损失 total_loss = dice_loss_fn(pred, target) + cross_entropy_loss_fn(...