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.# Argumentsy_true:...
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.# Argumentsy_true:...
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 ...
.losses.py—Line27 (该py文件下还有其他的损失函数,感兴趣可以自行查阅) 功能:通过看最后一行,可以发现就是(binary_cross_entropy)交叉熵损失函数,只不过多了一个weight权重,用于平衡计算最终loss。 代码解析(注释): class BalancedLoss(nn.Module): def __init__(self, neg_weight=1.0): super(BalancedLoss,...
在比赛过程中,作者要兼顾LVIS数据集的两大特征:1.数据的长尾分布问题;2.高质量的实例分割...了dice loss和二值交叉熵损失。特别的,掩码损失函数的权重是根据面积比例(掩码的面积与框面积的比值)进行动态变化的。 3. 方法汇总1.表示学习阶段 EQL:Equalization...
My implementation of label-smooth, amsoftmax, focal-loss, dual-focal-loss, triplet-loss, giou-loss, affinity-loss, pc_softmax_cross_entropy, and dice-loss(both generalized soft dice loss and batch soft dice loss). Maybe this is useful in my future work. Also tried to implement swish and...
Cross-entropySoft DiceVolumeSegmentation is a fundamental task in medical image analysis. The clinical interest is often to measure the volume of a structure. To evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measured using metrics ...
My implementation of label-smooth, amsoftmax, focal-loss, dual-focal-loss, triplet-loss, giou-loss, affinity-loss, pc_softmax_cross_entropy, ohem-loss(softmax based on line hard mining loss), large-margin-softmax(bmvc2019), and dice-loss(both generalized soft dice loss and batch soft di...
def __init__(self, weights=None, num_class=3): super(MultiLabelSoftDiceLoss, self).__init__() if num_class>1: self.sm = nn.Softmax2d() else: self.sm = nn.Sigmoid() self.weights = nn.Parameter(torch.from_numpy(np.array(weights) or np.array([1 for i in range(num_class)]...
Decoder cross-attention(来源:作者分享的材料) 罪魁祸首:Learnable Queries 我们已经为 Decoder 的 query “赋予”了角色:图像内容 + 物体位置,但目前为止,我们还未搞清楚 DETR 慢收敛以及效果不佳(vs CNN-based detector)的原因何在,接下来,大家要和 CW 握紧拳头,好好发力,一起攻克那个难关(别!我开玩笑的,看...