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center loss for face recognition
can not tar -xvf the pretrained model.
Is it necessary to normalize features?
Hi, thanks for your work, i want to ask you a question, how to do the test? I tested the resnet50 and epoch_100.pth.tar on lfw, with crop face, but the accuracy is 0.66. How can I reproduce this result? thank you.
Could you please let me know if you are generating landmarks for lfw? I am currently using a dataset with low resolution images for which I am unable to generate landmarks (using MTCNN), so I'm looking for an implementation that isn't using landmarks..
thanks for your codes!
i have a question that what is the protocol do you use in your training,
there are
PAIRS_TRAIN = "http://vis-www.cs.umass.edu/lfw/pairsDevTrain.txt"
PAIRS_VAL = "http://vis-www.cs.umass.edu/lfw/pairsDevTest.txt"
in the codes,but doesnot use.
Hello, I am a beginner. So maybe my question is simple. Please forgive me.
In the main.py. The code:
model = model_class(num_classes).to(device)
trainables_wo_bn = [param for name, param in model.named_parameters() if
param.requires_grad and 'bn' not in name]
trainables_only_bn = [param for name, param in model.named_parameters() if
param.requires_grad and 'bn' in name]
optimizer = torch.optim.SGD([
{'params': trainables_wo_bn, 'weight_decay': 0.0001},
{'params': trainables_only_bn}
], lr=args.lr, momentum=0.9)
I want to know why you separate the parameters to two parts: trainables_wo_bn and trainables_only_bn,
In the optimizer, I always write:
optimizer = torch.optim.SGD(model.parameters(), lr = args.lr, momentum=0.9)
Did I do something wrong? Please help me, thank you so much!
Hi, thanks for your pytorch implementation of the center face .
I played with your code for a while, and I may found some problem.
by this way, too much background are envoled.
for klass, name in enumerate(names):
def add_class(image):
image_path = os.path.join(images_root, name, image)
return (image_path, klass, name)
images_of_person = os.listdir(os.path.join(images_root, name))
total = len(images_of_person)
training_set += map(
add_class,
images_of_person[:ceil(total * train_val_split)])
validation_set += map(
add_class,
images_of_person[floor(total * train_val_split):])
I think you should use samples listed in the pairsDevTrain.txt to train and pairsDevTest.txt to test by the lfw paper
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