harveyslash / Facial Similarity With Siamese Networks In Pytorch
Licence: mit
Implementing Siamese networks with a contrastive loss for similarity learning
Stars: ✭ 719
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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
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