Comments (1)
在net.py和resnet.py中,请问forward函数的最后为什么要加入x = torch.sigmoid(x) ?,这里之后return x应该和标签值求损失函数然后反向传播,sigmoid之后相当于归一化,明显与标签值不匹配吧
这是为了将网络的输出约束到0-1范围内 softmax函数才是归一化
from pytorch-yolo-v1.
Related Issues (20)
- eval error HOT 6
- about grid_num=14 HOT 2
- 您能提供一下预训练好的模型文件吗,谢谢! HOT 2
- 你的resnet50作为backbone时,输入(3,448,448)的图片,输出维度不是(7,7,30)! HOT 7
- yoloLoss的组成部分中contain_loss的可疑之处 HOT 1
- predict.py中的nms是对所有类别一起做nms吗?
- 执行eval_voc,Expected 4-dimensional input for 4-dimensional weight 64 3 7 7, but got 3-dimensional input of size [3, 448, 448] instead
- 这里操作是不是有问题? HOT 6
- predict.py error HOT 2
- Can't find the listfile.txt HOT 4
- About BatchNormalization HOT 1
- 损失函数的参数好像有点问题 HOT 4
- ImportError: cannot import name 'queue' from 'torch._six' (/home/liqi/.local/lib/python3.8/site-packages/torch/_six.py) HOT 5
- loss变化图片
- ValueError: Input must be >= 2-d. HOT 3
- 训练了 5 个epoch , pred bbox 的x2 竟然小于 x1 HOT 6
- best.pth
- IndexError: invalid index of a 0-dim tensor
- some pictures in annotation txt(voc2007.txt+ voc2012.txt) are not in the image folder(2007trainval + 2012trainval)
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from pytorch-yolo-v1.