https://bugs.kde.org/show_bug.cgi?id=444160
Hans Lauter <lavaw...@getnada.com> changed:
What |Removed |Added
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CC| |lavaw...@getnada.com
--- Comment #8 from Hans Lauter <lavaw...@getnada.com> ---
I have the same problem.
The suggestions of face recognition output many wrong results. Around 20-40% of
all suggestions are right. This makes the manual rework quite tedious (the
benefit of using a face recognition evaporates). Digikam suggests faces, which
are completely different from each other. For example a young boy gets assigned
to the same person like the ones from a 80 yo lady. It makes no sense at all.
On the other side images which are completely similar to already labelled ones,
are not suggested, but are put in the group "unknown". So yeah, I can confirm,
this recognition is guess work. Face detection works quite good (it sometimes
detects objects as people, but the failure rate is here is acceptable), but
face recognition malperforms.
Some additions:
- Deleting all databases and rebuilding them did not help
- Retraining the model (maintenance/...) did not help
- Changing the accuracy slider from the default 70% to 90% did not help (maybe
it changed the quantity, but not quality).
I use digikam 8.1.0.
Sidenote: I noted that if I did labeled just a few pictures (~10) for a person,
then the suggestions are way better but also much less. So if I label 10
pictures to a person X, the algortihm returns lets sa 5-20 pictures, which are
actually that person. But for a person Y, if I label > 100 more pictures, the
suggestion become really bad. Isn't it the opposite, that AI get better, the
more you give?
You might say: Then I gave many bad labelled images to person Y. But no, the
pictures for person Y are in a good quality, similar facial expression, so the
variance is not too high.
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