HOW TO CALCULATE RECALL, PRECISION AND IoU test data deep learning
조회 수: 4 (최근 30일)
이전 댓글 표시
Dear all,
I want to calculate precision and recall for my test data. But I gor Error. Because my data is 3D. (as attached)
[precision,recall] = bboxPrecisionRecall(volMask1,tempSeg1)
ERROR
Error using bboxPrecisionRecall
Expected boundingBoxes to be two-dimensional.
Error in bboxPrecisionRecall>validateNonTableInput (line 153)
validateattributes(bbox, {'numeric'},...
Error in bboxPrecisionRecall (line 110)
validateNonTableInput(boundingBoxes, 'boundingBoxes');
댓글 수: 0
채택된 답변
Anusha
2022년 8월 25일
Hi,
I understand that you are trying to calculate the precision, recall and IoU metrics on the deep learning predicted output and groundtruth. I also see from the .mat files attached that your volumetric groundtruth (volMask1) and predicted output (tempSeg1) are of the size 128x128x64.
The bboxPrecisionRecall() function currently supports only 2-D inputs for bboxes and groundTruthBboxes. Therefore, convert the 3-D volumes into 2-D images and you can refer to the following code that does this:
% Access 2-D images from 3-D volume and find the metric average
avgPrecision=0; totPrecision=0;
avgRecall=0;totRecall=0;
for i= 1:size(volmask1,3)
[precision,recall] = bboxPrecisionRecall(volMask1(:,:,i),tempSeg1(:,:,i));
totPrecision=totPrecision+precision
totRecall=totRecall+recall
end
avgPrecision = totalPrecision/size(volmask1,3);
avgRecall = totalRecall/size(volmask1,3);
Please refer to the following documentation for more details regarding precision recall computation on the data:
Thanks,
Anusha
추가 답변 (0개)
참고 항목
카테고리
Help Center 및 File Exchange에서 Get Started with Statistics and Machine Learning Toolbox에 대해 자세히 알아보기
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!