DLCV 2018 Team 2
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README.md
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README.md

2018-dlcv-team2

DLCV 2018 Team 2

In this project we used the architecture of Pix2Pix, which is a Conditional GAN, to colourise facades of buildings. Then we tried to transfer this learning to be able to colourise cats instead.

A link to the paper describing Pix2Pix can be found here: https://arxiv.org/pdf/1611.07004v1.pdf

You can also find the repository in which we based the project in the folowing link: https://github.com/mrzhu-cool/pix2pix-pytorch

If you want to run the code yourself this is what you need installed:

Linux
Python with numpy
NVIDIA GPU + CUDA 8.0 + CuDNNv5.1
pytorch
torchvision

Then you should clone the repo and extract the datasets. If you want to train the network you should run:

python train.py --dataset facades --nEpochs 200 --cuda

And for training through transfer of the colourising of facades to cats:

python train_transfer.py --dataset cat_dataset --nEpochs 50 --cuda

If you want to see the results the commands are

python test.py --dataset facades --model checkpoint/facades/netG_model_epoch_200.pth --cuda
python test.py --dataset cat_dataset --model checkpoint/facades/netG_transfer_epoch_50.pth --cuda

A result is shown in the Jupyter notebook