Comments (1)
感谢关注~
- 我们是用4*2080TI的环境实验的,也是按照默认配置训练的结果,并没有使用额外的技巧。
- 环境差异以及随机性可能对结果有些许影响,这个任务4卡训练和较大的batchsize对结果影响较大。
- 对于额外的技巧,主页里提到的gem等方都是能够提升效果的,另外有时适当增加训练几个epoch会对mAP有一些收益。
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Related Issues (19)
- example文件夹缺失;readme中的Training部分,代码名称还是cluster_contrast开头。。。 HOT 2
- model下载链接指向SPCL;库中文件缺失 HOT 2
- 关于CMhybrid和CMhybrid_v2两种Memory Bank的理解疑惑 HOT 2
- 所提供的训练模型在duke上表现mAP:73.2% HOT 2
- 关于总loss公式 HOT 8
- 可以汇报一下在MSMT17上的效果吗 HOT 4
- 单卡训练降低10+ pp. 性能 HOT 2
- 精度偏低的原因 HOT 2
- 难正负样本公式问题 HOT 2
- 关于outlier的弃用 HOT 1
- hard sample HOT 1
- ModuleNotFoundError: No module named 'hhcl.datasets.veri' HOT 1
- 为什么loss cls和loss ins值是一样的? HOT 2
- GPU显存 HOT 2
- 运行问题
- 可视化
- 你好,请问我在本地运行了market的实验,为什么跑了两次达不到那个最高的84mAP呢,是不是得跑很多次才有。 HOT 1
- GPU numbers
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