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HuangYG123 / CurricularFace

CurricularFace(CVPR2020)
MIT License
526 stars 72 forks source link

can not perform same as your pretrained res101 #5

Open ruiming46zrm opened 4 years ago

ruiming46zrm commented 4 years ago

hello

Evaluation: LFW Acc: 0.9968333333333332, CFP_FP Acc: 0.8959999999999999, AgeDB Acc: 0.9726666666666667

it's my training result as 20 epoch , hyper param as you set .. acc on cfp-fp is quite bad...

HuangYG123 commented 4 years ago

train on which dataset? I use refined ms1mv2 provided by the authors of arcface, which has 85742 ids.

ruiming46zrm commented 4 years ago

@HuangYG123 in deepglint(trillion pairs), 18w id there. batchsize300. arcface train with deepglint is better than ms1mv2.

ainnn commented 4 years ago

@HuangYG123 in deepglint(trillion pairs), 18w id there. batchsize300. arcface train with deepglint is better than ms1mv2.

Hey, what are the results on ms1mv2 and deepglint with arcface? thx.

HeshamAMH commented 4 years ago

please ruiming46zrm, I test the model provided by the author on LFW but the result is 0.661 not 99.+. So, please I need a link to LFW and its pair file. I think the problem for me is LFW itself.

Thanks in advance

vietth-bka commented 3 years ago

@HuangYG123 I got the results here when using file pretrained model CurricularFace_Backbone.pth. These are far more different from the pubic results. Please check your file CurricularFace_Backbone.pth Evaluation: LFW Acc: 0.661, CPLFW Acc: 0.5271666666666667

Thanks

luhairong11 commented 3 years ago

@HuangYG123 I got the results here when using file pretrained model CurricularFace_Backbone.pth. These are far more different from the pubic results. Please check your file CurricularFace_Backbone.pth Evaluation: LFW Acc: 0.661, CPLFW Acc: 0.5271666666666667

Thanks

I have the same problem, are you sloved?

marcohuber commented 2 years ago

@luhairong11 I downloaded the (preprocessed) LFW data including the pairs file from here and achieved at least on LFW = 99.783 using the provided CurricularFace model. Still a bit worse, but I guess it's the minor differences in preprocessing. So the provided pre-trained model shouldn't be the problem, maybe there is something wrong with your used preprocessing/pairs?

luhairong11 commented 2 years ago

@luhairong11 I downloaded the (preprocessed) LFW data including the pairs file from here and achieved at least on LFW = 99.783 using the provided CurricularFace model. Still a bit worse, but I guess it's the minor differences in preprocessing. So the provided pre-trained model shouldn't be the problem, maybe there is something wrong with your used preprocessing/pairs?

I downloaded the preprocessed LFW data is https://github.com/ZhaoJ9014/face.evoLVe,it is according to author provide https://github.com/HuangYG123/CurricularFace in Usage,is it wrong?

michellerybak commented 2 years ago

@HuangYG123 I got the results here when using file pretrained model CurricularFace_Backbone.pth. These are far more different from the pubic results. Please check your file CurricularFace_Backbone.pth Evaluation: LFW Acc: 0.661, CPLFW Acc: 0.5271666666666667 Thanks

I have the same problem, are you sloved?

This happens when you don't load the model checkpoint properly!

billpsomas commented 2 years ago

@michellerybak what do u mean by loading the model checkpoint correctly?

michellerybak commented 2 years ago

Sorry, should've specified!

In config.py make sure you specify the correct path for MODEL_ROOT = , otherwise it just loads the model without the pretrained weights and it does not throw an error.

billpsomas commented 2 years ago

Sorry, should've specified!

In config.py make sure you specify the correct path for MODEL_ROOT = , otherwise it just loads the model without the pretrained weights and it does not throw an error.

@michellerybak perfect, you were right. Actually, I also added the path to BACKBONE_RESUME_ROOT = ... and it worked fine. I think this was the problem. I am able to reproduce the numbers now! Thank you!

michellerybak commented 2 years ago

Sorry, should've specified! In config.py make sure you specify the correct path for MODEL_ROOT = , otherwise it just loads the model without the pretrained weights and it does not throw an error.

@michellerybak perfect, you were right. Actually, I also added the path to BACKBONE_RESUME_ROOT = ... and it worked fine. I think this was the problem. I am able to reproduce the numbers now! Thank you!

ah, I think that's probably what I meant and just misspoke. I'm glad you got it working!