Image-to-image domain translation using GAN

Getting Started (Training)

Steps:

1. Download all of the files and folders in this repo and prepare the dataset. In my project, in this project we used CelebA dataset and Bitmoji dataset run python create_emojis.py and set the number of bitmoji images on the num_emojis variable.

2. Put the training CelebA dataset inside dataset/CelebA/trainA/ folder, and test CelebA dataset inside dataset/CelebA/test.

3. Put all the Bitmoji dataset inside dataset/Bitmoji folder.

4. Set up the config file inside configs/cifar.json. Generally, You can determine the number of epochs, n_save_steps, and batch_size. I use batch_size=32 for faster converged.

5. Run program using command

python train.py --log log_photo2emoji --project_name photo2emoji  

Testing

Steps:

1. Change the saved_model key in config.json to be ./log_photo2emoji/model_500.pt or whenever number of iteration model you use.

2. run program using command

python testAtoB.py --project_name photo2emoji --log log_photo2emoji