The main goal of this project is to implement GANs to generate images of frogs and toads despite to variations of colors and shapes.
Current implementation is based on DCGAN from this article
Additionally, added support for processing 128 x 128 images, Dropout layers for Generator network and noise input for real image samples.
Real frog images are from jonshamir repository, additional input augmentations (Rotation, Mirror, HSV shift) are defined in transform.py file.
Pretrained models are listed in models README.
Notebooks are located in notebooks folder.
Train script is located in the root directory and suppots following list of arguments:
Generation script is located in the root directory and suppots following list of arguments:
- train.py & generate.py scripts
- Research for best combination of hyperparameters for Generator & Discriminator
- Figure out with generation for 128 x 128 images
- Augmentations using Pytorch random rotation & mirror
Try to use STYLEGAN (Currently unavailable due unavailability of GPU)- Keep frog wet
Just take model from models folder and generate with stylegan2-ada/generate.py
Generate with:
python generate.py --network network-snapshot-001048.pkl --outdir generated --seeds 1-1000