Comments (2)
Hi there,
It is a good idea. Since the degraded function/branch does not rely on the BBOX angle, I believe it is feasible and easy to extend to the oriented task.
You may just refer to existing oriented methods like Yolov5-OBB for the modifications of label processing part, data augmentation part (e.g., random rotate transformation), rotated loss design (angle loss and rotated IOU loss?). The intermediate model structure can keep fixed.
While I'm not currently available to work on this function at the moment, if you achieve the successful adaption, I would be more than happy to receive a PR from you and see how we can integrate it.
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Thanks for the guidance. I'll try to modify it.
thanks
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Related Issues (20)
- Error when doing inference using augmentation HOT 11
- Adapt for yolov5l6 HOT 6
- performance decrease during training HOT 1
- 运行detect.py出错,您好我把C3ResAtnMHSA加入了yolov7中,出现下面的错误 HOT 2
- DRENet model training on degraded images HOT 8
- Problems in the ConcatFusionFactor of common.py HOT 2
- RuntimeError: result type Float can't be cast to the desired output type long int HOT 2
- 实验 HOT 3
- detect报错 HOT 11
- detect.py HOT 9
- 关于测试标签 HOT 6
- 关于C3ResAtnMHSA模块的问题 HOT 2
- 关于concatFusionFactor的问题 HOT 2
- I cannot run DRENet. Because has a bug: RuntimeError: The size of tensor a (160) must match the size of tensor b (256) at non-singleton dimension 1 HOT 1
- Error Float in Int64 HOT 1
- 看文章中的一些疑问 HOT 16
- 试运行detect.py上遇到的问题 HOT 3
- Cannot run detect.py HOT 2
- 在yolov5-5.0中只使用CRMA模块 HOT 7
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