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face_verification

  • Contains an implementation based on Center Loss and based on the repo https://github.com/KaiyangZhou/pytorch-center-loss
  • center loss was chosen because of relative simplicity of implementation over triplet losses etc
  • augmentation was done by random brightness, contrast,saturation, and minor hue jitter, further some random rotation, random up and downscales, as well as random horizontal flips
  • a custom sampler was made to deal with the class imbalance problem

Testing

weights must be in a folder called trained_models ( will be saved during training) you'll need to point to a particular epoch

  • python test_on_two_images.py image1.png image2.png

Training

data must be in a folder called lfw

  • python vgg19.py --dataset lfw --gpu 0

Some things to try

  • The current scheme has a cross entropy loss which pushes apart different identities, and a center loss that compresses clusters. LFW is too small a dataset, with a 0.8 train split we end up with ~4500 classes, and around ~10 k images, not a good ratio at all. Maybe try a simple modification to triplets, where class centers are pulled apart and not class elements

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