Pytorch implementation of Siamese networks that learns to classify its inputs, the neural networks learns to differentiate between two people
The data used is Labeled Faces in the Wild Home (LFW). You can access and download the full dataset here
Please place the data in the PROJECT_FOLDER/lfw/. The resulting folder structure looks like this: 
Splitting for training and testing has been done. You can view the split in test.txt and train.txt with rows containing image files paths of two images and then a 1 if they are the same person and 0 if not.
Feel free to split the dataset however you want, but please be reminded to update the train.txt and test.txt.
A Siamese Network is a CNN that takes two separate image inputs, and both images go through the same exact CNN. Then we use a sort of loss function to compute the similarity between two output. Gupta on Hackernoon has a nice illustration for the network. You can check out his article for more explanation.
The detailed architecture can be found in architecture.md.
In this implementation, two losses function are explored, namely Binary Cross-Entropy(BCE) and Contrastive Loss. Depends on the loss function chosen, the network architecture for the output layer is different.
For BCE, the outputs from two CNNs are concatenated and then put through a sigmoid activation function to return either 1 or 0.
Instead of passing the output to a sigmoid activation function, we take output from two CNNs to compute a contrastive loss. Again, you can read more about it here
To improve the generalization power of the model, data augmentation is implemented. This means, during the training loop, as images are read in, with some probability apply a random transform to the images before running it through the network. The transform can consist of some random combination of: mirror flipping, rotation, translation and scaling.