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I have used Unet model for image segmentation. I have used RGB images and corresponding image masks and at output i got corresponding region of interest. Now i want to find confusion matrix of this model and from this i want to evaluate other parameters. Can some help? I could not get what are predicted and actual values which are required to build confusion matrix. Thanks

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If your masks have only one channel and in the case of binary segmentation, you can easily compute a confusion matrix from one image thanks to:

  • TP: ground truth and predicted pixel are of class 1 (object)
  • TN: ground truth and predicted pixel are of class 0 (background)
  • FP: ground truth pixel=0 while predicted pixel=1
  • FN: ground truth pixel=1 while predicted pixel=0

On a dataset, you can get the confusion matrix from chosen iou thresholds. For example if your iou threshold is 0.5:

  • TP += 1 for each image where iou(ground_truth, predicted) >= 0.5
  • FP += 1 for each image where iou(ground_truth, predicted) < 0.5 and predicted != empty
  • FN += 1 for each image where iou(ground_truth, predicted) < 0.5 and gt != empty

It is just a suggestion, there is maybe better ways to deal with that.

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