Image Segmentation Evaluation

Related tags

Deep Learning py_img_seg_eval
Overview

Image Segmentation Evaluation

Martin Keršner, [email protected]

Evaluation metrics for image segmentation inspired by paper Fully Convolutional Networks for Semantic Segmentation.

Pixel accuracy

Mean accuracy

Mean IU

Frequency Weighted IU

Explanatory notes

  • n_cl : number of classes included in ground truth segmentation
  • n_ij : number of pixels of class i predicted to belong to class j
  • t_i : total number of pixels of class i in ground truth segmentation
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Comments
  • Maybe the mIoU is not standard calculation form

    Maybe the mIoU is not standard calculation form

    Hi, martinkersner: In your code, it seems that you only consider one image pair's evaluation,since I can see your evaluation demo in your another repo : train-deeplab.

    however, after I compared your implemention details with other codes, such as deeplabv2,cityscapesScripts,PSPNet evaluation. I found that you didn't involve in confusion matrix calculation, which considers all evaluation images totally. we can take a look at cityscapes benchmark. all of them accumulate all pixels of all images statistically to calculate a final confusion matrix.

    Therefore, I think the standard way is: 1).calculate confusion matrix based on all evaluation images. 2). calculate IoU of each class based on the confusion matrix from step (1). 3). calculate the final mIoU from step (2).

    I found this problem, since I have different evaluation result between your scripts(56.1) and others.(52) in my task. maybe this is the problem.

    hope this issue can help someone else cares about it.

    opened by dongzhuoyao 1
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