Focal loss Focal Loss for Dense Object Detection focal loss的设计很巧妙,就是在cross entropy的基础上加上权重,让模型注重学习难以学习的样本,训练数据不均衡中占比较少的样本,相对放大对难分类样本的梯度,相对降低对易分类样本的梯度,并在一定程度上解决类别不均衡问题。 如果将cross loss定义为: 那focal...
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 ...
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...
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...
Therefore, we show the effectiveness of the proposed theoretical framework by analyzing some particular cases that are represented in literature: classical cost-sensitive learning approaches in Section 5, weighted cross entropy loss functions in Section 6 and value-weighted skill scores in Section 7. ...
In this way, the better-learned class weighed less, and the less-learned class weighed more. Additionally, the application of focus loss reduced the importance of simple samples while increasing the importance of hard ones compared to cross-entropy. The experiments on several imbalanced datasets ...
Recognition accuracy has been increased by 6.67% and 13.33% for underwater targets, 25% and 2.5% for underwater simulation communication signals, and 12.5% for underwater experimental communication signals, compared with the cross-entropy loss and the focal loss. Results on simulation data and ...
Results on simulation data and experimental data show that the proposed approach can obtain higher recognition accuracy than the cross-entropy loss and the focal loss, which provides evidence for its effectiveness. 展开 关键词: Imbalanced underwater acoustic dataset Recognition Convolutional neural network...
3.3. Heavily Weighted Cross-Entropy Focal Loss Function The loss function is an operation function that measures the degree of difference between the predicted and the true value. The common loss functions such as CE loss perform well when trained on balanced datasets, such as CIFAR-10/100 datas...
Nonetheless, these methods do not take into account the loss of information during the layer-by-layer feature extraction and spatial transformation of data, which is crucial for the detection of steel defects. In order to further improve the detection accuracy while ensuring the lightweight of the...