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您好,我最近想用这个方法做一个输电线路数据集的弱监督分割,分割目标是输电线,只有一类,并且想要的到的评价指标为语义分割常用的mIOU和像素级的Precision,但boxlevelset中的评价指标为mAP值,我一步步查看了cocoapi的源码,发现cocoapi中的computeIou计算得到的是一个矩阵,我有尝试过将网络预测结果保存为json文件,在自己写一个代码计算像素级的mIOU和Precision,但保存的json文件为rle格式的,并且只能是这个格式。 请问我怎么才能计算预测结果和标签的mIOU和Precision值?谢谢。
The text was updated successfully, but these errors were encountered:
@TaoshuaiZ 你好,mIoU的计算可以参考此处的代码。
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您好,我阅读了point2mask中计算iou的代码,并加在boxlevelset中,但在debug的过程中,发现了两个问题: 1.boxlevelset中模型预测结果results于point2mask中的results键值对并不相同 在后续的iou_compute函数中就发生了错误,找不到模型预测的输出图像。 2.数据集也不相同,point2mask中val的标注是以图像的形式呈现的,但boxlevelset中val的标注是一个json文件
iou_compute
val
请问怎么解决第一个问题,该怎么修改代码实现语义分割iou指标的计算?
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您好,我最近想用这个方法做一个输电线路数据集的弱监督分割,分割目标是输电线,只有一类,并且想要的到的评价指标为语义分割常用的mIOU和像素级的Precision,但boxlevelset中的评价指标为mAP值,我一步步查看了cocoapi的源码,发现cocoapi中的computeIou计算得到的是一个矩阵,我有尝试过将网络预测结果保存为json文件,在自己写一个代码计算像素级的mIOU和Precision,但保存的json文件为rle格式的,并且只能是这个格式。
请问我怎么才能计算预测结果和标签的mIOU和Precision值?谢谢。
The text was updated successfully, but these errors were encountered: