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How to calculate TP , TN , FP , FN ? #2408
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If you take only into account mask (and not class prediction) you can use the compute_matches function in utils.py : This function return vectors gt_match and pred_match : There is not TN in this context. |
@awalshz, Thanks for the reply. In some cases, I am getting gt_match = [0. 1.] and pred_match = [ 0. 1. -1. -1. -1.] Is this prediction considered as TP or FP? |
In one image you have TP, FP and FN masks. In this case you have a image with 2 object (two masks) and you get 5 predicted masks. The two first are TP and the other are FP. You don't have FN |
The whole image can not be classified as TP or TN or anything |
Hi, i write it's in Object detection terms for bouding boxes. It's works for only one class detection for one image. Only what you need - extract GT boxes from coco and predicted boxes from model results
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For binary segmentation, you can get the predicted mask for each image and compare it with the true mask using this code https://gist.github.com/aunsid/b28c87f98983f00163f6e588e3da1191 |
You can get implementation for mask based calculution TP, FP, FN from here |
@konstantin-frolov thanks for the code. |
Hello.Plz answer my question. |
No. You need rewrite this code for checking class of bounding boxes and recalculate TP, FP, FN if the classes don't match. |
thanks. but I find compute_recall in utils.py. |
is there any way to calculate the TP, FP, FN for faster R-CNN with multi classes? |
@konstantin-frolov can u help me to rewrite your code that will work for multiple classification as u u said your code onnlyy works for single one |
Hi guys,
I would like know how to calculate tp,tn,fp,tn on prediction mask rcnn?
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