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wymanCV avatar wymanCV commented on September 24, 2024

您好,谢谢对我们工作的关注。

我之前使用这个工具可视化预测框的,只需要收集inference产生的bbox.json 文件(包含每张图片的类别以及box结果),配合这个工具就可以可视化。

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SATURN2021 avatar SATURN2021 commented on September 24, 2024

好的,感谢作者的回复!

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SATURN2021 avatar SATURN2021 commented on September 24, 2024

作者您好!通过研究您的代码,看到vis_tools.py里面有和t-SNE有关的内容,但不太清楚应该如何使用?期待您的回复!

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wymanCV avatar wymanCV commented on September 24, 2024

作者您好!通过研究您的代码,看到vis_tools.py里面有和t-SNE有关的内容,但不太清楚应该如何使用?期待您的回复!

您好,抱歉那个文件不是我们最终使用的工具。我们是通过以下几个步骤操作的:

1、首先在源域和目标域的gt框内按类别采样特征点(可以直接用我们的node 采样的接口,尝试不同位置的node特征),并且存下来
2、用sklearn中的TSNE对采样的所有node特征一起降维到2维
3、用不同颜色绘制不同类别的点,观察不同类别特征分布情况

另外,我们提供了更复杂的可视化过程在 issue

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SATURN2021 avatar SATURN2021 commented on September 24, 2024

好的,十分感谢作者回复!!

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