The problem ArtiGAN solves

Develop a Generative Adversarial Network (GAN) architecture to generate Monet-style images.
Train the GAN using a high-quality dataset of Monet paintings to produce 7,000 to 10,000 generated images.
Evaluate the quality of the generated images using both quantitative and qualitative methods.
Compare the generated images with real Monet paintings to assess the realism and artistic merit of the GAN-generated images.
Experiment with different hyperparameters and training settings to improve the quality of the generated images.
Explore the potential applications of the GAN-generated images in art, design, and other fields.
Contribute to the research on GANs and generative art by proposing novel techniques and insights.
Provide a well-documented and reusable codebase for other researchers and practitioners to build upon.

Challenges we ran into

1. how to import correct dataset in the code
2. receiving maximum accuracy
3. getting the hardware that is GPU
4. getting the pictures generated more than 2000