harveyslash / Facial-Similarity-with-Siamese-Networks-in-Pytorch

Implementing Siamese networks with a contrastive loss for similarity learning

https://hackernoon.com/one-shot-learning-with-siamese-networks-in-pytorch-8ddaab10340e

MIT License

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deep-learning face-recognition neural-network pytorch pytorch-tutorial siamese-network

readme

Facial Similarity with Siamese Networks in Pytorch

You can read the accompanying article at https://hackernoon.com/one-shot-learning-with-siamese-networks-in-pytorch-8ddaab10340e

The goal is to teach a siamese network to be able to distinguish pairs of images. This project uses pytorch.

Any dataset can be used. Each class must be in its own folder. This is the same structure that PyTorch's own image folder dataset uses.

Update: Looking for contributor (July 2020)

If you would like to be a part of this projec, please head to https://github.com/harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch/issues/35

Converting pgm files (if you decide to use the AT&T dataset) to png

Install imagemagick
Go to root directory of the images
Run find -name "*pgm" | xargs -I {} convert {} {}.png

Installing the right version of PyTorch

This project is updated to be compatible with pytorch 0.4.0

This project requires python3.6