Image-OutPainting
# Keras implementation of Image OutPainting This is an implementation of [Painting Outside the Box: Image Outpainting](https://cs230.stanford.edu/projects_spring_2018/posters/8265861.pdf) 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. ## Results The model was train with [3500 scrapped beach data](https://drive.google.com/open?id=1hKIn-Z8Uf3voESbJZVsapLHESPabjjrb) with agumentation totalling upto 10500 images for 25 epochs.  #### Recursive painting  ### Install Requirements ``` sudo apt-get install curl sudo pip3 install -r requirements.txt ``` ## Get Started 1. Prepare Data: ```sh # 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' <br/>OR<br/> * You can [Download](https://drive.google.com/open?id=1MfXsRwjx5CTRGBoLx154S0h-Q3rIUNH0) my trained model and move it to 'checkpoint/' and run it. ## References * [Painting Outside the Box: Image Outpainting](https://cs230.stanford.edu/projects_spring_2018/posters/8265861.pdf)