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🏖 Keras Implementation of Painting outside the box
| Date | Stars |
|---|---|
| 2026-07-24 | 1148 |
| 2026-07-25 | 1148 |
| 2026-07-28 | 1148 |
| 2026-07-30 | 1148 |
| 2026-08-06 | 1148 |
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# 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)
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matched fp:15c65c0beca6e412, topic:deep-learning, topic:tensorflow
matched fp:15c65c0beca6e412, topic:computer-vision