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你好,我看了论文,好像没做YOLOv5s的剪枝测试,是因为s模型已经Scaling后冗余没大模型多,然后剪枝性能提升不大吗?
The text was updated successfully, but these errors were encountered:
您好,感谢您对工作的关注。 文中没做YOLOv5s的压缩的原因主要是:我们在考虑压缩YOLOv5时选择性能更接近sota的模型,从而在高性能模型上也验证算法的有效性,同时考虑到训练成本的问题,我们折中选择了YOLOv5m模型进行测试,并在之后补充了更高性能和规模的模型上的压缩结果。至于YOLOv5s的冗余是否没大模型多这个问题,我个人觉得这主要和训练策略有关,可能冗余比例是差不多的,我们没有在s模型上做测试,如果您感兴趣的话可以自行测试一下。 希望上述回复能解答您的相关疑问。若仍有疑惑,欢迎进一步交流。
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你好,我看了论文,好像没做YOLOv5s的剪枝测试,是因为s模型已经Scaling后冗余没大模型多,然后剪枝性能提升不大吗?
The text was updated successfully, but these errors were encountered: