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chenxi-wang avatar chenxi-wang commented on June 2, 2024

您好,我没有尝试过多卡训练,看起来可能是模型上一部分参数的device不对,可以检查一下

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xiaozheng-liu avatar xiaozheng-liu commented on June 2, 2024

感谢您的回答,我会在尝试修改代码;如果修改产生效果,我会向您回复的。

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xiaozheng-liu avatar xiaozheng-liu commented on June 2, 2024

您好,在阅读论文的过程中,产生一些疑问:
关于评价精度AP,以 u = 0.8 为例 ,对应的抓取分数应该是 s = 1.1 -u = 0.3,假设排名前50的抓取位姿分数都是0.3,总的分数是15,为什么您论文中报告的是 33.43/36.19,我对此不太理解?AP是如何计算的?期待您的回复

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xiaozheng-liu avatar xiaozheng-liu commented on June 2, 2024

对于u=0.8时,夹爪预测的分数是 s = 0.3或者s = 0吗?在您的论文中,我看到Operation Network的损失函数是:
image
对于分数Sij的预测,我理解可以设计为分类损失函数,您为何选择回归预测呢?

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Fang-Haoshu avatar Fang-Haoshu commented on June 2, 2024

你好,抓取分数不是50*0.3这么算的,具体可以看我们论文中的阐述,或者网页的解释:https://graspnet.net/evaluation.html
分数的预测也可以使用分类损失,回归是为了兼容未来可能出现的其他度量。

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xiaozheng-liu avatar xiaozheng-liu commented on June 2, 2024

感谢您及时的回复,根据您的建议,我在论文中看到: the percentage of true positive is more important.,是否可以这样理解:在 u = 0.8 是,AP代表的是排名前50个抓取位姿中,真阳性的比例?比如说真阳性的比例是33.43%?

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Fang-Haoshu avatar Fang-Haoshu commented on June 2, 2024

您好,是这样的

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xiaozheng-liu avatar xiaozheng-liu commented on June 2, 2024

十分感谢您的回复,解答了疑惑,再次感谢您出色的工作!

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