Best qaulity result without speed / performance concern

[MyraBaba]

What is the best for detection (both accuracy and good landmark )and recognition model if there is no speed concern ? It could be run slower but produce highest quality results.

Such as RetFace-50 very good but old one. little bit slower . Is SCRFD superior than the retinaFace ? Specially feeding the recognition models ?

Is there any past experience or theoretical information ?

Best

[SthPhoenix]

From my observations scrfd_10g_bnkps is better in term of lower false positive detections rate and better recall, though it produces worse landmarks for faces rotated at 90 and more degrees. Retinaface gives inaccurate landmarks in those conditions too, but they are mostly closer to expected.
Secondly bnkps model doesn't work well for large faces, it mostly misses them. gnkps model was retrained to fix this issue, but it has a bit lower recall and landmarks quality.
Thought I must admit faces missed by gnkps model are mostly useless for recognition, since they are of very low quality.

If you are not concerned about false detections and speed - Retinaface is still most universal detector IMO.

As for recognition I am mostly using glintr100 as most accurate from published models.
BUT, there is also Webface based model w600k_r50 from official package, which is almost twice faster then glintr100 and have better accuracy on some benchmarks as reported by authors. I have tested it for clustering and it seem to produce clusters almost identical as glintr100 model, but I can't tell you if it's really better or not.

[MyraBaba]

@SthPhoenix Thanks for the reply. So best for accurate recognition is retinaface landmarks (R50).

have you tried yolo5l ?

Best

[SthPhoenix]

@SthPhoenix Thanks for the reply. So best for accurate recognition is retinaface landmarks (R50).

It'll work better in extreme cases, but in general scrfd models should work pretty the same.

have you tried yolo5l ?

I have cloned the repo weeks ago, but still can't get some time to test it. It seems promising though.

[MyraBaba]

@SthPhoenix What is your preferred metrics for glintr100 threshold according to your experience ?

and the paper said yolo5 landmarks better than the retinaFace so is it means producing better accuracy ? paper

finally what is your practical preference for arcface or cosface ? or distance make you satisfied

[SthPhoenix]

I'm using (1. + np.dot(A, B)) / 2. as similarity metric, which gives score in 0.0-1.0 range.
From my experience scores above 0.8 are exact matches.
0.78-0.8 might contain false positive, though for really similar people and quite rarely.
0.75-0.78 contains more false positives though still rarely.
Everything below 0.75 could contain many false positives.

Also take note that true positives might be anywhere in range 0.6-1.0, poor image quality or side images may greatly decrease score, as well as comparing lot of poor images might give you lots of false positives with high scores.

[MyraBaba]

@SthPhoenix so you are using similarity metric instead of distance ?

[SthPhoenix]

Yes, I found it more human readable and convenient for display.

[MyraBaba]

@SthPhoenix
I am testing lfw for glintr100.onx. as you know many small images.
Is there any way to index as batches to increase speed. ? GPU utilization not more than %15 .

[SthPhoenix]

You can tweak batch sizes in deploy_trt.sh.
Also you can set multiple workers, though you should tweak your lfw code to support multithreading.

[MyraBaba]

@SthPhoenix

Funny thing that genderModel says Queen_Elizabeth_II is Male :)

have you tested ?

[SthPhoenix]

I always knew she has some secrets )
I'm not using gender age detection anywhere, this model was added just for compatibility with original lib. In my tests it shown bad accuracy too.

[MyraBaba]

Have you tested IR-152 ? Looks better than the IR-50 FYI

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On 12 Mar 2022, at 09:53, SthPhoenix ***@***.***> wrote: From my observations scrfd_10g_bnkps is better in term of lower false positive detections rate and better recall, though it produces worse landmarks for faces rotated at 90 and more degrees. Retinaface gives inaccurate landmarks in those conditions too, but they are mostly closer to expected. Secondly bnkps model doesn't work well for large faces, it mostly misses them. gnkps model was retrained to fix this issue, but it has a bit lower recall and landmarks quality. Thought I must admit faces missed by gnkps model are mostly useless for recognition, since they are of very low quality. If you are not concerned about false detections and speed - Retinaface is still most universal detector IMO. As for recognition I am mostly using glintr100 as most accurate from published models. BUT, there is also Webface based model w600k_r50 from official package, which is almost twice faster then glintr100 and have better accuracy on some benchmarks as reported by authors. I have tested it for clustering and it seem to produce clusters almost identical as glintr100 model, but I can't tell you if it's really better or not. — Reply to this email directly, view it on GitHub <#74 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEFRZHZ2IBXLOLP4GCTVG7DU7Q5PTANCNFSM5QQVUKHQ>. Triage notifications on the go with GitHub Mobile for iOS <https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675> or Android <https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub>. You are receiving this because you authored the thread.

[SthPhoenix]

Have you tested IR-152 ? Looks better than the IR-50

Could you please provide link to the model you are talking about?

[MyraBaba]

https://drive.google.com/file/d/142hTmfmTiw1MkUdzn5M7dWIMyIBOwFO7/view?usp=sharing Let me know when you download.

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On 25 Mar 2022, at 19:56, SthPhoenix ***@***.***> wrote: Have you tested IR-152 ? Looks better than the IR-50 Could you please provide link to the model you are talking about? — Reply to this email directly, view it on GitHub <#74 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AEFRZH6XUCHTKU5PCH2S2DDVBXV5PANCNFSM5QQVUKHQ>. You are receiving this because you authored the thread.

[SthPhoenix]

I've downloaded it, but still some more info is needed ) What dataset was used for training? Are there any performance metrics available?
I haven't seen any info about ir-152 based models in deepinsight repo.