Hi, now, some mor data.... After several runs and tagging face recogintion does not recognize any more out of already found faces (~25k images, ~6k faces not recognized ~14k already tagged). But there are very much faces very similar to already recoginzed/tagged ones. Is there a way for me to icrease recognition rate? I've done some "case studies" with my data. Current settings
yolo_v3, with accuracy rate at
As i mentioned, having lots of very similar faces assinged
already, i would assume this a poor recognition rate. (6k pics,
~20% very similar to already tagged ones, but not found any
suggestion.) From the manual, it is suggested to remove tagged "weird" faces (carneval or other not very clear representations), to prevent false suggestions. But this would lead to a processing and suggestion next run again, probably false again, wouldn't it? Cheers, Gerhard |
Have you rebuilt the training database in the maintenance tool (option in the
face detection section) if you have already tagged faces with an older digiKam version? Maik Am Sonntag, 16. Mai 2021, 21:16:21 CEST schrieb KRODER Gerhard: > Hi, > > now, some mor data.... > > After several runs and tagging face recogintion does not recognize any > more out of already found faces (~25k images, ~6k faces not recognized > ~14k already tagged). But there are /very much faces very similar/ to > already recoginzed/tagged ones. > > Is there a way for me to icrease recognition rate? > > I've done some "case studies" with my data. Current settings yolo_v3, > with accuracy rate at > > * 100% does not suggest any recogniton > * 95% finds one downscaled/compressed jpg (rate to 10% size=225kB, no > edits) > * 90% one more suggestion but very inaccurate/wronn > * 85% found 3 more suggestions, one a perfect match, one a fairly > reasonable, one bad > * 80% aims to recoginze/suggest some more, but with a very low > recoginition rate (17/6000), and low match rate (10/71) > > As i mentioned, having lots of very similar faces assinged already, i > would assume this a poor recognition rate. (6k pics, ~20% very similar > to already tagged ones, but not found any suggestion.) > > From the manual, it is suggested to remove tagged "weird" faces > (carneval or other not very clear representations), to prevent false > suggestions. But this would lead to a processing and suggestion next > run again, probably false again, wouldn't it? > > Cheers, Gerhard |
Hi,
this is a new 7.2 version, no previous processing/DB data, so no rebuild others than starting face detecting/recogintion tool. Am 16.05.2021 um 22:15 schrieb Maik Qualmann: > Have you rebuilt the training database in the maintenance tool (option in the > face detection section) if you have already tagged faces with an older digiKam > version? > > Maik > > Am Sonntag, 16. Mai 2021, 21:16:21 CEST schrieb KRODER Gerhard: >> Hi, >> >> now, some mor data.... >> >> After several runs and tagging face recogintion does not recognize any >> more out of already found faces (~25k images, ~6k faces not recognized >> ~14k already tagged). But there are /very much faces very similar/ to >> already recoginzed/tagged ones. >> >> Is there a way for me to icrease recognition rate? >> >> I've done some "case studies" with my data. Current settings yolo_v3, >> with accuracy rate at >> >> * 100% does not suggest any recogniton >> * 95% finds one downscaled/compressed jpg (rate to 10% size=225kB, no >> edits) >> * 90% one more suggestion but very inaccurate/wronn >> * 85% found 3 more suggestions, one a perfect match, one a fairly >> reasonable, one bad >> * 80% aims to recoginze/suggest some more, but with a very low >> recoginition rate (17/6000), and low match rate (10/71) >> >> As i mentioned, having lots of very similar faces assinged already, i >> would assume this a poor recognition rate. (6k pics, ~20% very similar >> to already tagged ones, but not found any suggestion.) >> >> From the manual, it is suggested to remove tagged "weird" faces >> (carneval or other not very clear representations), to prevent false >> suggestions. But this would lead to a processing and suggestion next >> run again, probably false again, wouldn't it? >> >> Cheers, Gerhard > > > |
How many faces per person and in total have you assigned so far to train face
recognition? Maik Am Montag, 17. Mai 2021, 01:11:58 CEST schrieb KRODER Gerhard: > Hi, > > this is a new 7.2 version, no previous processing/DB data, so no rebuild > others than starting face detecting/recogintion tool. > > Am 16.05.2021 um 22:15 schrieb Maik Qualmann: > > Have you rebuilt the training database in the maintenance tool (option in > > the face detection section) if you have already tagged faces with an > > older digiKam version? > > > > Maik > > > > Am Sonntag, 16. Mai 2021, 21:16:21 CEST schrieb KRODER Gerhard: > >> Hi, > >> > >> now, some mor data.... > >> > >> After several runs and tagging face recogintion does not recognize any > >> more out of already found faces (~25k images, ~6k faces not recognized > >> ~14k already tagged). But there are /very much faces very similar/ to > >> already recoginzed/tagged ones. > >> > >> Is there a way for me to icrease recognition rate? > >> > >> I've done some "case studies" with my data. Current settings yolo_v3, > >> with accuracy rate at > >> > >> * 100% does not suggest any recogniton > >> * 95% finds one downscaled/compressed jpg (rate to 10% size=225kB, no > >> > >> edits) > >> > >> * 90% one more suggestion but very inaccurate/wronn > >> * 85% found 3 more suggestions, one a perfect match, one a fairly > >> > >> reasonable, one bad > >> > >> * 80% aims to recoginze/suggest some more, but with a very low > >> > >> recoginition rate (17/6000), and low match rate (10/71) > >> > >> As i mentioned, having lots of very similar faces assinged already, i > >> would assume this a poor recognition rate. (6k pics, ~20% very similar > >> to already tagged ones, but not found any suggestion.) > >> > >> From the manual, it is suggested to remove tagged "weird" faces > >> > >> (carneval or other not very clear representations), to prevent false > >> suggestions. But this would lead to a processing and suggestion next > >> run again, probably false again, wouldn't it? > >> > >> Cheers, Gerhard |
out of about 26000 image items total
out of ~150 persona tags, top faces already assigned /confirmed
Am 17.05.2021 um 07:50 schrieb Maik
Qualmann:
How many faces per person and in total have you assigned so far to train face recognition? Maik Am Montag, 17. Mai 2021, 01:11:58 CEST schrieb KRODER Gerhard:Hi, this is a new 7.2 version, no previous processing/DB data, so no rebuild others than starting face detecting/recogintion tool. Am 16.05.2021 um 22:15 schrieb Maik Qualmann:Have you rebuilt the training database in the maintenance tool (option in the face detection section) if you have already tagged faces with an older digiKam version? Maik Am Sonntag, 16. Mai 2021, 21:16:21 CEST schrieb KRODER Gerhard:Hi, now, some mor data.... After several runs and tagging face recogintion does not recognize any more out of already found faces (~25k images, ~6k faces not recognized ~14k already tagged). But there are /very much faces very similar/ to already recoginzed/tagged ones. Is there a way for me to icrease recognition rate? I've done some "case studies" with my data. Current settings yolo_v3, with accuracy rate at * 100% does not suggest any recogniton * 95% finds one downscaled/compressed jpg (rate to 10% size=225kB, no edits) * 90% one more suggestion but very inaccurate/wronn * 85% found 3 more suggestions, one a perfect match, one a fairly reasonable, one bad * 80% aims to recoginze/suggest some more, but with a very low recoginition rate (17/6000), and low match rate (10/71) As i mentioned, having lots of very similar faces assinged already, i would assume this a poor recognition rate. (6k pics, ~20% very similar to already tagged ones, but not found any suggestion.) From the manual, it is suggested to remove tagged "weird" faces (carneval or other not very clear representations), to prevent false suggestions. But this would lead to a processing and suggestion next run again, probably false again, wouldn't it? Cheers, Gerhard |
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