@文心快码epoch gpu_mem box_loss obj_loss cls_loss instances size 文心快码 作为Comate,我很乐意为你解答这些问题。以下是对你提出的问题的详细解答: epoch在深度学习训练中的含义: 解释:在深度学习模型的训练过程中,epoch是指整个数据集被前向传播和反向传播算法遍历一次的完整过程。简单来说,epoch表示训练...
这时候思路就很明显了,要想“软化”这个 loss,就得“软化”θ(x),而软化它就再容易不过,它就是 sigmoid 函数。我们有: 所以很显然,我们将θ(x)替换为σ(Kx)即可: 这就是我昨晚思考得到的 loss 了,显然实现上也是很容易的。 现在跟 Focal Loss Focal Loss Kaiming 大神的 Focal Loss 如果落实到ŷ =...
可以使用常见的损失函数,如均方差损失函数nn.MSELoss、交叉熵损失函数nn.CrossEntropyLoss等。 输入数据不匹配:检查损失函数计算的输入数据,确保输入数据的形状与标签数据相匹配。可以
Box regression loss is also updated for stability and complexity improvements. Author karl-gardner commented Oct 6, 2021 @glenn-jocher so the cls loss is the same as the original yolo papers: while the obj_loss is updated slightly to the CIOU loss which can be found in this review on ob...
我用YOLOX训练自己的数据集的时候一直显示loss_cls: 0.0000, loss_bbox: 0.0000,然后测试的时候会有ERROR The testing results of the whole dataset is empty 请问可能是什么原因?
for obj_idx in range(downsampled_masks.size(0)): segment_t[cur_class_t[obj_idx]] = torch.max(segment_t[cur_class_t[obj_idx]], downsampled_masks[obj_idx]) loss_s += F.binary_cross_entropy_with_logits(cur_segment, segment_t, reduction='sum') return...
目标检测模型三大损失cls_loss box_loss model_loss 目标检测 sota,步骤:(1)训练的时候有3D点云和3D框,将3D框投影到图像上,获得2D框(前景点),然后使用GTSamples对点云进行数据增强,在3D点云场景中插入一些3Dobject,这些插入的3Dobject也要投影到图像上,但是不是
As the image shows below, there are disorders on the Y-axis of val/box_loss, val/obj_loss and val/cls_loss. And matplotlib output the following warning: Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as ...
iter: 10 / 80000, total loss: 0.8165, rpn_loss_cls: 0.4789, rpn_loss_box: 0.3374, loss_cls: 0.0002, loss_box: 0.0000, lr: 0.001000 speed: 0.446s / iter iter: 20 / 80000, total loss: 0.3478, rpn_loss_cls: 0.2360, rpn_loss_box: 0.1119, los...