https://bugs.kde.org/show_bug.cgi?id=472031
Bug ID: 472031
Summary: Faces detection gets worse with lots of faces to train
on
Classification: Applications
Product: digikam
Version: 8.0.0
Platform: macOS (DMG)
OS: macOS
Status: REPORTED
Severity: normal
Priority: NOR
Component: Faces-Detection
Assignee: digikam-bugs-n...@kde.org
Reporter: iain+...@cardnell.co.uk
Target Milestone: ---
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.
--
You are receiving this mail because:
You are watching all bug changes.
