datasets links https://www.kaggle.com/datasets/robgonsalves/impressionistlandscapespaintings https://www.kaggle.com/datasets/ashwingupta3012/human-faces
This project uses a Generative Adversarial Network (GAN) — specifically CycleGAN — to transform real landscape photographs into artistic landscape paintings. The model learns to mimic the style of paintings while preserving the structure of real-world photos, making it ideal for creative AI applications.
CycleGAN is used for unpaired image-to-image translation. It learns the mapping between two domains without requiring corresponding image pairs.
- Domain A: Real-world landscape images (e.g., photos)
- Domain B: Artistic landscape paintings
This project demonstrates how CycleGAN can convert a real image into an artwork-style image, such as turning a photo into a painting inspired by famous styles (like Monet, Van Gogh, etc.).
CycleGAN consists of:
- Two Generators (A → B and B → A)
- Two Discriminators (for domain A and B)
- Uses cycle-consistency loss and adversarial loss
CycleGAN Architecture:
Domain A (Real) Domain B (Painting)
| |
| Generator G |
|------------------------->|
| |
| Discriminator DB |
|<-------------------------|
| |
| Generator F |
|<-------------------------|
| |
| Discriminator DA |
|------------------------->|