Generative Adversarial Networks (GANs) are machine learning frameworks designed to generate realistic images. CycleGAN, in particular, enables image-to-image translation without the need for paired datasets. This project specifically focuses on translating seasonal images, such as from summer to winter and vice versa, using CycleGAN.
- Source: UC Berkeley
- Images: 1279 photos of Yosemite in summer and winter
- Type: Unpaired
- Format: JPEG
- Size: 256 x 256 pixels
- Classes: 2 (Summer, Winter)
- Split: 80% training, 20% testing
The CycleGAN architecture consists of:
- Two Generators (G and F): For translating images from summer to winter and winter to summer.
- Two Discriminators (D_X and D_Y): For evaluating the authenticity of the generated images.
- Generators (G and F): Transform images between domains.
- Discriminators (D_X and D_Y): Distinguish real images from generated ones.
- Cycle Consistency Loss: Ensures that an image can be translated to the target domain and back to the original domain.
To run the Streamlit application:
streamlit run app.py- checkpoints/: Stores model checkpoints.
- data/: Contains dataset-related files.
- models/: Includes model architecture and utilities.
- options/: Configuration files for training and testing.
- summer/: Contains test summer images.
- summer2winter_yosemite/: Contains training data for summer to winter translation.
- util/: Utility scripts.
- winter/: Contains test winter images.
- app.py: Streamlit application for interactive UI.
- style_converter.py: Core script for image style conversion using CycleGAN.
- video.py: work in progress!
The results of the season translation demonstrate effective and realistic transformations between summer and winter scenes. Both qualitative and quantitative evaluations (SSIM, PSNR) show high performance and user satisfaction.
- Implement the video translation functionality
- Goodfellow, I., et al. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems.
- Isola, P., et al. (2017). Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
- Zhu, J. Y., et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision.
This project was created as a part of the Machine Learning (Lab) course.
- Manav Ukani (20BCP167)
- Jenis Gundaraniya (20BCP157)