Stacked-GAN-Face-SR

Face Synthesis and Super Resolution Using Stacked Generative Adversarial Network

1. Dataset generate

• a.Using HD CelebA Cropper to generate the dataset raw data.
• b.Use the default 0.7 face_factor.
• c.Copy the raw data to dataset folder.
• c.Run 1.get_celeb_train.py to generate training, validating and testing data

2. Training

• a.Run the training command python 2.train_and_test.py --phase train --dataset_name yourData --origin_size 64 --image_size 256
• b.Check the sample folder during training to make sure the result is correct.

3. Testing

• a.Run the testing command python 2.train_and_test.py --phase test --dataset_name yourData --origin_size 64 --image_size 256
• b.Check the generated test folder for the results

4. Calculate FID

• a.The FID calculating code is from here
• b.Run 3.split_testset.py to seperate blur tesing images and ground truth
• c.Run the FID calculating command python 4.fid_score.py dataset/yourData/gt test

5. FID results

Table 1 FID score compare in different resolution transformation

name	16 to 64	32 to 128	64 to 256
Wavelet SRNet	45.952	54.533	31.929
Cycle GAN	63.837	32.228	42.218
SRN-Deblur	125.576	41.463	12.729
ours	51.646	29.685	13.431

6. Images results