This is the repository for CVPR2021 Dynamic Metric Learning: Towards a Scalable Metric Space to Accommodate Multiple Semantic Scales

Overview
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Comments
  • Evaluation protocol & Pretrained Models

    Evaluation protocol & Pretrained Models

    Hi !

    I am working with your datasets.

    I have some more questions, do you plan on releasing :

    • Training protocol (optimizer, pretrain or not resnet34, mining code...)
    • Hyperparameters used in your paper (ie. learning rates, hyperparameters for the loss etc.)
    • The evaluation protocol / scripts ?
    • Pre-trained models ?

    Thanks !

    opened by elias-ramzi 0
  • Questions regarding the datasets

    Questions regarding the datasets

    Hi thank you for your work making the datasets public !

    I have those questions regarding the datasets :

    • Are the query sets always included in the gallery sets ?
    • What would be the easiest way to build a test dataset with all the unique images and all their labels ?

    Thank you :)

    opened by elias-ramzi 0
  • 评价指标

    评价指标

    您好,我最近在阅读论文和复现Multi-scale learning baseline时对论文中所采用的评价指标有几点迷惑,还望解答! 首先说一下我的实验设置:

    1. 数据组织按照您所公布的源代码,backbone采用resnet34(imagenet pretrained)。
    2. 我目前只在Multi-Similarity Loss上进行了测试。因为每个batch中fine、middle、coarse的图像各占1/3,在某个scale下进行损失计算时,我只选择当前scale的1/3图像作为anchor来组织样本对。如假设batch-size为1,那么一个batch中有K*3张图片,前K张为fine-scale下采样出来的图片,那么在fine上进行损失函数计算时,我只使用K张图片作为anchor,negative的话则在整个bath上选择。
    3. 训练时batch-size我设置的为5, MS Loss的学习率等参数按照作者源代码中进行设置。
    4. 评价指标目前只使用了Recall@K,与MS Loss中相同。计算方式为3个尺度分别计算Recall@K,求平均。

    下面说一下我遇到的问题:

    1. 目前只在Vechicle上进行测试,无法获得论文中MS Loss下的实验效果(我获得的Recall@1, Recall@10都要高。
    2. 评价指标ASI中需要指定k,论文中公布的ASI指标是在k为多少时得到的。

    感谢您的查看,期待您的指教和解答!

    opened by SuQinghang 2
Owner
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