training process

[jiejie22997]

Hello,
i got questions for the training process.

1. i just want to confirm that the provided baseline model is from the first training stated in the paper, right?
2. can we run start.sh for the second training to reproduce results in the paper?

thanks in advance.

[haibo-qiu]

Hi there,

1. The provided model_p4_baseline_9938_8205_3610.pth.tar model is the Baseline stated in Sec.4.2.
2. Yes, running start.sh with model_p4_baseline_9938_8205_3610.pth.tar as pretrained model (default in this func) is supposed to reproduce our results.

[jiejie22997]

Thanks for the response!
also, im wondering how i can access to LFW-SM and O-LFW. i tried to add mask to LFW but not all images can be masked successfully. For MFR2 and RMF2 stated in the paper, is it referring to the same datasets?

[haibo-qiu]

Hi @jiejie22997

For LFW-SM, if the original paper does not provide the download link, you can use this repo to apply masks. Indeed, it may fail on some images but should work on most face images in my memory. I cannot find this dataset due to moving to a new place :-(

As for O-LFW, I emailed the first author of BFL to get the dataset.

Sorry for the confusing typo of MFR2 and RMF2, they are the same dataset MFR2. I will fix this typo asap in the next version of our paper.

[jiejie22997]

Thanks for the reply!
I feel confused about the AR datasets evaluation. In the image below, why do we need the fc2? Is it a typo? Thanks

[haibo-qiu]

Hi @jiejie22997 ,

This is because the odd number images are the flipped version of even number images, and you can refer to the below code.

FROM/lib/datasets/dataset.py

Lines 727 to 728 in 416c13c

	if index % 2 == 1:
	img = transforms.functional.hflip(img)

So fc = fc1 + fc2 can be regarded as an augmented representation that includes both original and flipped image features.

[jiejie22997]

I see, thank you!

[haibo-qiu]

Feel free to reopen this issue if you have any further questions.