Hello,
I have a question that may seem trivial but which confused me because I found different approaches to doing it in different papers.
So let’s take the recall as an example : to calculate it on the test set I found two different methods in the codes available for published papers. Some calculate the recall for every image then calculate the mean on all images, other simply use the formula as if each pixel was an element in it’s own. What is the correct way to doing so ?
Thanks in advance !
As long as the specific method they use is documented (and, ideally, their ground truth and results) I would say it doesn’t really matter. There aren’t really any single correct methods for scoring most ML models, AFAIK
If all the images have the same size, it does not matter because in this case the sum of the means equals the mean of the sum.
How is that ? You devide by the either the pred positive pixel or the ground truth positive pixel depending of either you are calculating the recall or the precision
Wrote https://arxiv.org/abs/2207.12120 but never published. I think the “metric definition” is a pretty nice explanation of how it works. Process is the same between bounding boxes & segmentation maps aside from the IoU computation.