An (unofficial) implementation of Focal Loss, as described in the RetinaNet paper, generalized to the multi-class case. - AdeelH/pytorch-multi-class-focal-loss
In this PyTorch file, we provide implementations of our new loss function, ASL, that can serve as a drop-in replacement for standard loss functions (Cross-Entropy and Focal-Loss)For the multi-label case (sigmoids), the two implementations are:class AsymmetricLoss(nn.Module) class Asymmetric...
They recommend using one that’s pretrained on the ImageNet Large Scale Visual Recognition Competition (ILSVRC) classification task. Luckily, there’s one already available in PyTorch, as are other popular architectures. If you wish, you could opt for something larger like the ResNet. Just be m...
再回顾PyTorch的CrossEntropyLoss(),官方文档中提到时将nn.LogSoftmax()和 nn.NLLLoss()进行了结合,nn.LogSoftmax() 相当于激活函数 , nn.NLLLoss()是损失函数,将其结合,完整的是否可以叫做softmax+交叉熵损失函数呢? 计算公式: 参数: weight(Tensor)- 为每个类别的loss设置权值,常用于类别不均衡问题。weight...
使用PyTorch实现Inner SIoU计算的源代码 import torch def calculate_inner_siou(box1, box2): # box1: [x1, y1, x2, y2] # box2: [x3, y3, x4, y4] # 计算交集的左上角和右下角坐标 inter_left_top = torch.stack((torch.maximum(box1[0], box2[0]), torch.maximum(box1[1], box2...
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--2.0asmentionedinthe paper ...
In other words, to apply cross-entropy to a multi-class classification task, the loss for each class is calculated separately and then summed to determine the total loss. Implementing Cross-Entropy Loss in PyTorch and TensorFlow In this part of the tutorial, we will learn how to use the cro...
(3) Prototype Enhancement: By averaging the sentence representations for each relation class in the query set, basic prototypes are obtained. These prototypes are then enhanced using a multi-head self-attention mechanism to generate more expressive prototype representations. Finally, an adaptive ...
We evaluate the performance of our model by assessing the results of the classification network and the overall weakly supervised segmentation. All experiments were done on Ubuntu 18.04, using the PyTorch deep learning framework, with an NVIDIA RTX 3090 GPU as the hardware device. 4.3. Forest ...
Let's explore cross-entropy functions in detail and discuss their applications in machine learning, particularly for classification issues.