Comments (1)
Maycbj
commented on July 20, 2024
I'm sorry that I reply to you so late.
We have tried to use 9 anchors to train our method based on RetinaNet, which is similar to the anchor-free detectors with BorderAlign. But it is computationally intensive. So we tried different strategies to reduce the amount of calculation.
- choose the highest score to train
- choose the highest IoU to train
- randomly choose an anchor to train
Strategy 3) yields the best performance in our experiments, owing to we supervise them equally to overcome overfitting.
Besides, I suggest that BorderDet based on Anchor-Free detector may be a better choice for you to get better performance.
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Related Issues (20)
- example for instance segmentation. HOT 2
- AssertionError: Requires PyTorch >= 1.3 HOT 1
- How to trun on evaluation during the training. HOT 2
- Details of enhacing the single point feature with region features? HOT 2
- 一个不成熟的小建议 HOT 2
- > 1. They are the same.
- I wonder how you implement border align? HOT 1
- How do you realize border feature extracting in Table 1? HOT 2
- Border Feature Extraction HOT 2
- code issue HOT 1
- Can not reproduce the results. HOT 2
- where is the implementation details of border_align_forward HOT 2
- How to solve the error "cuda runtime error (98) : invalid device function" when run the borderDet? HOT 4
- Error at inference
- HTTP Error HOT 5
- no kernel image is available for execution on the device: border_align_kernel.cu:202 HOT 3
- BorderDet can not run on RTX 3090 GPU? HOT 3
- 论文图2中的实验也是在coarse的分支基础上，再添加新的分支来实现的吗
- 多尺度训练
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