Comments (24)
Yeah, recently there are several interesting papers on arXiv tackling transfer in ReID using GAN. The following list is what I have knowledge of.
- Camera Style Adaptation for Person Re-identification
- Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification
- Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
- Pose-Normalized Image Generation for Person Re-identification
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你是在duke上训练然后测的吗?还是直接用market1501训好的模型测试的?
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我是把market1501训练好的模型直接用在duke测试集上。这样出来的效果会差这么多吗?
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是的,不同库之间的差异很大的。所以现在有些文章在研究怎样更好地跨库迁移。
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好的,谢谢。请问您在duke数据集上做个训练吗?如果有的话,能否给我一份。感激不尽啦。
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不客气的。duke上你也可以自己训,运行script/tri_loss/train.py
时,数据集选为duke即可:--dataset duke
。如果需要训好的模型,我后续也上传一个。
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ok 😁
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您好,三个数据库上用triplet loss训练的模型可以下载了,性能有更多提升,参考这个工程.
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你的这么高吗,我的market训练去duke测试,性能是rank-1:33%, mAP:16.5%,我一会用这个代码跑一下,谢谢!
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自己训练的模型效果会更好,你可以试试
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@Simon4john 请问你是用aligned reid的模型测试还是用IDE?
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@liangbh6 我用的是IDE,alignedreid 我直接去杜克数据集合测试性能一选大概是30%,所以很奇怪楼主测出来的怎么这么高。
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@Simon4john 我看你说能到33%这么高,很怀疑自己搞错了什么==。IDE在market1501的baseline我调到83%,拿去在duke上测试只有rank1 27%这样。高手,请问有没有数据集增强或者学习率方面的tricks要用到?
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@liangbh6 你用的是caffe框架吗,我在caffe 上market性能76%,在杜克上测试性能是33%
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@liangbh6 @JpHu2017 楼主的map 能到30.76%,我的mAP才16.5%我也很好奇是不是我的代码搞错了。
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30.76是re-ranking以后得
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@Simon4john 我用的pytorch,在market1501训练,market上测试rank1 83%, mAP 62%, duke上测试rank1 27%, mAP 14%。不同框架差别这么大吗?
@JpHu2017 请问没有re-ranking之前是多少?
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@liangbh6 我用的是caffe的框架,这里https://github.com/zhunzhong07/IDE-baseline-Market-1501。不知道pytorch的情况。
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@Simon4john 好的, 谢谢
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我用了这里提供的triple loss (pytorch),在market训练得到rank-1:86.4%,mAP:72.2%的性能,然后在duke上测试性能是:rank-1:30.1%, mAP:14.0%。
此外,在duke上训练得到rank-1:79.2%,mAP:61.7%的性能,在market上测试,得到了rank-1:39.4%, mAP:16.9%的性能。
另外一方面,我在caffe上用了IDE这个代码训练IDE,在market上得到了76%的rank1,在duke上测试得到了33%的rank-1。
综合上述两种情况,我很好奇不同框架之间的差异这么大吗?还是其他的原因?
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我发现模型在source domain上训得分数越高,不一定在target domain也更高,很可能还会有反作用。这是domain shift不可避免的问题。这也不是说source domain上训得老高的模型就不实用,毕竟有时候训练跟测试的数据分布就是很一致的。要做跨domain的话要专门研究,仅仅在source domain上实现一个表现很好的baseline是不够的。
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@huanghoujing 您说的我很同意,有时候在某一个数据集合学的很好不一定会让学到的特征的泛化能力或者迁移到其他数据集合的能力增强。这里有个文章貌似就说了这个问题:Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (https://arxiv.org/pdf/1711.07027.pdf)
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are same persons appear in training sets? otherwise why is the performance across domain so different?
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Generally, different training sets do not contain the same person. E.g. training sets from Market-1501, DukeMTMC-reID, CUHK03, etc are independent.
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Related Issues (20)
- top-k结果可视化 HOT 2
- CUHK03和DUKE上的识别率 HOT 1
- How to inference my own test set
- Keys not found in source state_dict HOT 1
- Global Feature Extraction HOT 1
- Local feature dimensions
- About performance on market1501 for global learning and mutual learning
- Is it generalised
- TypeError: __init__() got an unexpected keyword argument 'log_dir'
- AssertionError HOT 3
- 请问论文中的Resnet50-Xception结构是不是没有实现? HOT 1
- 论文复现的参数问题
- how to use the test data to draw picture just like roc missrate cmc?
- How to use without GPU? HOT 2
- could you send me a partitions.pkl about market1501 HOT 1
- 为啥用你提供的weight测得market也只有88.78的top1呢 HOT 1
- how to infer some images or videos
- RuntimeError: cannot perform reduction function min on tensor with no elements because the operation does not have an identity
- Poor performnace when reproducing evaluation on market1501 HOT 2
- would you help me to fix this error?
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