如何从分割蒙版中找到IoU?

问题描述

我正在执行图像分割任务，并且我使用的数据集仅具有基本事实，而没有边界框或多边形.

I am doing an image segmentation task and I am using a dataset that only has ground truths but no bounding boxes or polygons.

我有2个类(忽略背景的0)，输出和地面真相标签位于类似

I have 2 classes( ignoring 0 for background) and the outputs and ground truth labels are in an array like

预测-/---标签

0|0|0|1|2 0|0|0|1|2 0|2|1|0|0 0|2|1|0|0 0|0|1|1|1 0|0|1|1|1 0|0|0|0|1 0|0|0|0|1

0|0|0|1|2 0|0|0|1|2 0|2|1|0|0 0|2|1|0|0 0|0|1|1|1 0|0|1|1|1 0|0|0|0|1 0|0|0|0|1

如何从这些计算IoU?

How do I calculate IoU from these ?

PS:我在pytorch api中使用python3

PS: I am using python3 with pytorch api

推荐答案

所以我刚刚发现jaccard_similarity_score被视为IoU.

So I just found out that jaccard_similarity_score is regarded as IoU.

所以解决方案非常简单，

So the solution is very simple,

from sklearn.metrics import jaccard_similarity_score jac = jaccard_similarity_score(predictions, label, Normalize = True/False)

from sklearn.metrics import jaccard_similarity_score jac = jaccard_similarity_score(predictions, label, Normalize = True/False)

源链接: https://scikit-learn.org/stable/modules/generation/sklearn.metrics.jaccard_score.html#sklearn.metrics.jaccard_score

Source link: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.jaccard_score.html#sklearn.metrics.jaccard_score

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