pred = torch.argmax(input[:, :2], dim=1) about facelib HOT 7 CLOSED

first two columns are logits for (male, female) and the second one is for Age

as you can see I've used last index (aka 3) for age

def l1loss_age(input, targs):
    return F.l1_loss(input[:, -1], targs[:, -1]).mean()

from facelib.

first two columns are logits for (male, female) and the second one is for Age

as you can see I've used last index (aka 3) for age

def l1loss_age(input, targs):
    return F.l1_loss(input[:, -1], targs[:, -1]).mean()

Thanks for your reply!

from facelib.

first two columns are logits for (male, female) and the second one is for Age
as you can see I've used last index (aka 3) for age

def l1loss_age(input, targs):
    return F.l1_loss(input[:, -1], targs[:, -1]).mean()

Thanks for your reply!

In Age & Gender Estimation,did you use the original image of UTKFace Dataset to train ShufflenetFull ?

from facelib.

Although I do not know what you mean by original, I train on UTKFace Dataset with ShufflenetFull as the backbone

from facelib.

Although I do not know what you mean by original, I train on UTKFace Dataset with ShufflenetFull as the backbone

Thanks!I see what you mean.
I have another question to ask：

def multitask_loss(input, target):
input_gender = input[:, :2]
input_age = input[:, -1]
loss_gender = F.cross_entropy(input_gender, target[:, 0].long())
loss_age = F.l1_loss(input_age, target[:, 2])

return loss_gender / (.16) + loss_age * 2

I want to know "loss_gender / (.16) + loss_age * 2"，how is the ratio determined? Is it from the experiment？

from facelib.

Yep, the loss ratio results from some experiments
Depending on how much you care about gender detection or age estimation.

from facelib.

Yep, the loss ratio results from some experiments
Depending on how much you care about gender detection or age estimation.

Thank you for your patience！

from facelib.