This is an implementation of Painting Outside the Box: Image Outpainting paper from Standford University. Some changes have been made to work with 256*256 image:

• Added Identity loss i.e from generated image to the original image
• Removed patches from training data. (training pipeline)
• Replaced masking with cropping. (training pipeline)
• Added convolution layers.

The model was train with 3500 scrapped beach data with agumentation totalling upto 10500 images for 25 epochs.

sudo pip3 install -r requirements.txt

1. Prepare Data:

   # Downloads the beach data and converts to numpy batch data
   # saves the Numpy batch data to 'data/prepared_data/'
   sh prepare_data.sh

2. Build Model
   • To build Model from scratch you can directly run 'outpaint.ipynb'
     OR
     
   • You can Download my trained model and move it to 'checkpoint/' and run it.

• Painting Outside the Box: Image Outpainting