togheppi / Cyclegan
PyTorch implementation of CycleGAN
Stars: ✭ 38
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CycleGAN
PyTorch implementation of CycleGAN
- Dataset can be downloaded from here.
- Loss values are plotted using Tensorboard in PyTorch.
horse2zebra dataset
- Image size: 256x256
- Number of training images: 1,334 for horse images, 1,067 for zebra images
- Number of test images: 120 for horse images, 140 for zebra images
Results
- Adam optimizer is used. Learning rate = 0.0002, batch size = 1, # of epochs = 200:
- 6 resnet blocks used for Generator.
| GAN losses ( |
Generated images (Input / Generated / Reconstructed) |
|---|---|
 |
 |
-
Generated images using test data
Horse to Zebra
1st column: Input / 2nd column: Generated / 3rd column: Reconstructed
Zebra to Horse
1st column: Input / 2nd column: Generated / 3rd column: Reconstructed
apple2orange dataset
- Image size: 256x256
- Number of training images: 1,019 for apple images, 995 for orange images
- Number of test images: 266 for apple images, 248 for orange images
Results
- Adam optimizer is used. Learning rate = 0.0002, batch size = 1, # of epochs = 200:
- 9 resnet blocks used for Generator.
| GAN losses ( |
Generated images (Input / Generated / Reconstructed) |
|---|---|
 |
 |
-
Generated images using test data
Apple to Orange
1st column: Input / 2nd column: Generated / 3rd column: Reconstructed
Orange to Apple
1st column: Input / 2nd column: Generated / 3rd column: Reconstructed
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