facenet-pytorch-glint360k
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tamerthamoqa
A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.
Hi @tamerthamoqa , Thanks a lot for your great repo. According to FaceNet paper the best dimension for embedded vector is 128. I am curious to know is there any...
@tamerthamoqa Hello again! Your pre-trained model is trained on unaligned VGG2 dataset, so it performs well with variances over pose. But many projects pre-process the images to obtain aligned faces...
the speed of training is slower, and the cpu usage rate is very high,about 800%, is there anything to solve it?
usually: Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) insightface: Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]) the reason why your set? __
I tried to run a test that trains only 20 of VGGFace2's train data. And I tried to use inceptionresnet v2 as the network architecture, but it does not run...
Hi @tamerthamoqa , I'm curious about L2 Normalization, which would constrain the embedding into an euclidean feature space and , so the maximum distance of two features in feature space...
Hello, I'm daniel, While running your project, one question arose. In dataloader/triplet_loss_dataloader, It is a system that generates (pos, neg) class randomly as the number of triplets allocated for each...
I have a puzzle，when generate triplet, I think the distance between pos and anc should less than the distance between anc and neg.
hello, thank you for your contribution. i download your weight file and load it to check the performance on LFW , while load the pt file according to your description,...
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A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the glint360k dataset. A pre-trained model using Triplet Loss is available for download.