We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
首先非常感谢您的工作!在您的论文中提到在RCS-YOLO、RepVGG-CSP的消融实验,想请问一下是否是在相同的位置采用BottleneckCSPC、RCS-OSA两个模块进行实验对比?如果是我认为的这样,BottleneckCSPC仅比C3模块多一个conv1x1,那在YOLOv5中,采用RCS-OSA替换C3模块应该也可以提升模型的检测性能。请问您是否做过RCS-OSA有效性的验证实验?该模块是否能提升检测速度?
The text was updated successfully, but these errors were encountered:
当我采用RCSOSA模块替换C3进行训练一轮后验证时报如下错误:RuntimeError: Given input size: (512x12x21). Calculated output size: (512x0x1). Output size is too small,请问您知道解决方法嘛?
Sorry, something went wrong.
That error demonstrated your network structure was not reasonable. Please see repvgg-csp.yaml. FYI.
No branches or pull requests
首先非常感谢您的工作!在您的论文中提到在RCS-YOLO、RepVGG-CSP的消融实验,想请问一下是否是在相同的位置采用BottleneckCSPC、RCS-OSA两个模块进行实验对比?如果是我认为的这样,BottleneckCSPC仅比C3模块多一个conv1x1,那在YOLOv5中,采用RCS-OSA替换C3模块应该也可以提升模型的检测性能。请问您是否做过RCS-OSA有效性的验证实验?该模块是否能提升检测速度?
The text was updated successfully, but these errors were encountered: