Comments (4)
Both GCNet and non-local can be used to add global knowledge for distillation. And we compare them in ablation study. The lighter GCNet is easier to train for distillation.
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@yzd-v
Okay, I have seen Table 6. in your paper.
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You may read the two papers carefully. In table 6, we just use global distillation without focal distillation. While the result in FKD is achieved with all losses. However, Faster RCNN can achieve 42.0 mAP with focal and global distillation together.
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@yzd-v
Ok, it's my fault(feel a bit embarrassed, I will delete above pictures......23333)
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Related Issues (20)
- A question about object detection distill
- 实验数据是否有误?感谢 HOT 2
- Using swin model as teacher
- FCOS实验结果问题 HOT 5
- what does it means HOT 1
- Warnings during the training process for FCOS HOT 1
- if i can use MultiScale training? HOT 3
- the
- Train my self data, the loss is large HOT 6
- FCOS训练及测试结果问题 HOT 4
- Should I change the mmdet/detectors/base.py when I train yolox? HOT 1
- Why the mAP is 0 when train myself data? HOT 1
- The model and loaded state dict do not match exactly HOT 2
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- mAP为0 HOT 17
- Cascadercnn-resnet50蒸馏18,loss_fgd_fpn_0太大(第一张图) HOT 10
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