Comments (3)
We follow the cspdarknet53 architecture to build yolov4, and darknet53 block is used to reproduce yolov3 in our repo.
Suffering from the lack of gpu device, the difference or the improvement might not be clear in ablation study.
I think CIoU Loss is a good choice.
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OK,Thanks for reply!
you have used binary cross entropy to calculate cls_loss, do you notice any benefit using this? I am using categorical cross entropy. And using focal loss would give any benefit?
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Yes, most of the researchers regard the classification task in detection as multi-label learning.
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Related Issues (19)
- Getting Error while executing yolo_utils.non_max_suppression(predictions) HOT 1
- AP is not improved!!! HOT 1
- yolox initial weight HOT 3
- conv2d_17_loss 和conv2d_20_loss HOT 1
- convert weights to .h5 HOT 2
- IndexError: index 3 is out of bounds for axis 0 with size 3 HOT 5
- Reason for calling fit thrice in train.py HOT 4
- GPU usage HOT 1
- Handling non-square images HOT 2
- Does your code support all the functions of yolov4 and can it train normally ? HOT 3
- where do I find ./ckpts/yolov4.weights
- yolov4-tiny pretrained model? HOT 1
- train.py not working HOT 4
- 利用单gpu训练,速度很慢,GPU利用率很低,CPU经常跑满 HOT 3
- train error!!!!! HOT 2
- 通过关闭cfg中的数据增强手段和减小epch大小 确实加快了训练速度 但map值却一直没有上涨 HOT 3
- Transform Model to Real_Time: Serialize Model HOT 1
- Weights file used in the implementation HOT 7
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