Description
Dataset
- Created my own dataset
- 360 pairs of distorted-corrected images of architectural photography
- distorted images produced by augmenting a perspective-corrected photo 10 times
- with random augmentations (skew, rotations)
Model Training
- trained pix2pix, a Conditional Generative Adversarial Network
- 200 epochs
- on Generator loss, discriminator loss, and L1 loss
Results
- Model produces decent perspective correction
- But inaccurately transfers pixels, resulting in the 'content' of the buildings being blurred / having new content
Future improvements
- More realistic augmentations (less rotation)
- Highlight building edges for the model to attend to
- Train for more epochs as the losses are still steadily increasing/decreasing
Project Members
Images
