Comments (4)
一共分为三阶段训练,并非端到端的训练,具体的训练时间我也没有统计过,但是每一个阶段的训练时间不会很长,不到一天就可以训练好,使用的是8卡的1080ti训练,后面也在3090上面训练过,因为使用的是单卡,3090单卡速度确实快一些,但是算法本身是MDNet的限制,所以速度不快
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谢谢您的回答,我还有一个疑问,因为mdnet是一个在线跟踪的模型,所以随机种子对于最终结果会很大,那怎么确定自己设计的模型是有效的呢?(可能换一个随机种子效果就不好了)非常感谢您
from apfnet.
是的,随机数种子有影响的,目前使用控制变量法,代码中的随机数种子是写死的,不修改这个随机数种子设置,其他实验设置进行改变,以此对比实验结果,证明自己模块的有效性;
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谢谢您
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Related Issues (18)
- 下载的GTOT数据集里没有challenge tag HOT 4
- 如何选择最终的模型权重呢 HOT 1
- 数据集的问题 HOT 3
- imagenet权重
- log文件问题 HOT 1
- model_stage3代码问题 HOT 1
- 关于预训练权重mdnet_imagenet_vid.pth的问题
- 属性标签
- illum_change.tag
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- Pretrained Model Link HOT 2
- 模型测试问题(pytorch版本)
- 复现结果
- Challenge Tag HOT 2
- about CMPP results HOT 1
- About generating the LasHeR dataset five attribute branches HOT 1
- 关于评测工具的问题 HOT 8
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