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Keras Implementation of Painting outside the box
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checkpoint update outpaint, dataloder, add gitignore Jul 28, 2018
data update outpaint, dataloder, add gitignore Jul 28, 2018
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.gitignore add gitignore Jul 29, 2018 add augment_image Jan 1, 2019
outpaint.ipynb update prepare_data, dataloader Jan 1, 2019 update prepare_data, dataloader Jan 1, 2019

Keras implementation of Image OutPainting

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. Demo

Recursive painting


Install Requirements

sudo pip3 install -r requirements.txt

Get Started

  1. Prepare Data:
    # Downloads the beach data and converts to numpy batch data
    # saves the Numpy batch data to 'data/prepared_data/'
  2. Build Model
    • To build Model from scratch you can directly run 'outpaint.ipynb'
    • You can Download my trained model and move it to 'checkpoint/' and run it.


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