Bug 472031

Summary: Faces detection gets worse with lots of faces to train on
Product: [Applications] digikam Reporter: iain <iain+kde>
Component: Faces-RecognitionAssignee: Digikam Developers <digikam-bugs-null>
Status: RESOLVED DUPLICATE    
Severity: normal CC: metzpinguin
Priority: NOR    
Version: 8.0.0   
Target Milestone: ---   
Platform: macOS (DMG)   
OS: macOS   
Latest Commit: Version Fixed In:

Description iain 2023-07-07 06:56:06 UTC
I believe that using the "Clear and re-build all training data" for face detection makes the detection worse with the more faces you have tagged.

Initially I manually tagged 4-5 faces of a few people and detected faces (I have around 10000). The initial scan went well and I'd say it was 80% accurate.
As I tagged more people manually and rebuilt the training data the new Recognise Faces runs got worse.
I now have 2000+ faces tagged over 50+ people and the accuracy is almost 0.

I believe that it now has too much training data and the faces are too different across all the images to train a model properly. I'm not sure how other engines cope with this problem. (e.g. I am now tagged over 1000 times in my albums).

What might be a simple workaround is to allow a user to choose 4-5 good images for each person and use those as the training set rather than trying to use every example.
Comment 1 Maik Qualmann 2023-07-07 08:32:36 UTC

*** This bug has been marked as a duplicate of bug 431797 ***