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
