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| -rw-r--r-- | README.md | 4 |
1 files changed, 2 insertions, 2 deletions
@@ -87,7 +87,7 @@ bash ./datasets/download_pix2pix_dataset.sh facades - Train a model: ```bash #!./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 --use_dropout --no_lsgan --norm batch +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 ``` - 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`): @@ -103,7 +103,7 @@ More example scripts can be found at `scripts` directory. If you would like to apply a pre-trained model to a collection of input photos (without image pairs), please use `--dataset_mode single` and `--model test` options. Here is a script to apply a pix2pix model to facade label maps (stored in the directory `facades/testB`). ``` bash #!./scripts/test_single.sh -python test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --model test --which_model_netG unet_256 --which_direction BtoA --dataset_mode single --use_dropout +python test.py --dataroot ./datasets/facades/testB/ --name facades_pix2pix --model test --which_model_netG unet_256 --which_direction BtoA --dataset_mode single ``` ## Training/test Details |