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Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
Hi
Please pardon me once again, quick question in case you know about :
https://github.com/rozentill/pytorch-CycleGAN-and-pix2pix/commits/master
I followed all the install steps successfully on windows 10 x64 using the following steps
https://www.superdatascience.com/pytorch/
then ubuntu for windows with GitHub desktop
https://www.howtogeek.com/249966/how-to-install-and-use-the-linux-bash-shell-on-windows-10/
But im blocked at the latest steps written below for the correct installation , do download the database and start the training... :(
Your help would be be infinitely appreciated , once again i apologize to beg for your assistance...
Very Sincerely, take care and thanks in anycase
Download a pix2pix dataset (e.g.facades):
bash ./datasets/download_pix2pix_dataset.sh facades
Train a model:
#!./scripts/train_pix2pix.sh
python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256 --which_direction BtoA --lambda_A 100 --dataset_mode aligned --no_lsgan --norm batch --pool_size 0
To view training results and loss plots, run python -m visdom.server and click the URL http://localhost:8097. To see more intermediate results, check out ./checkpoints/facades_pix2pix/web/index.html
Test the model (bash ./scripts/test_pix2pix.sh):
#!./scripts/test_pix2pix.sh
python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --which_model_netG unet_256
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