Comments (1)
abeardear
commented on September 26, 2024
我想了一下，box_target的中心是编码成相对于cell的左上角，box_pred预测的中心坐标也是相对于cell左上角，因为他的target就是这样设置，自然也学习成这样。也就是说，二者编码相同，所以这样计算IOU问题应该不大。
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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）
- 请问为什么要代码中加入了sigmoid？ HOT 1
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from pytorch-yolo-v1.