perceptual-loss-style-transfer

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perceptual-loss-style-transfer

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Tensorflow Implementation: Johnson, J., Alahi, A. and Fei-Fei, L., 2016, October. Perceptual losses for real-time style transfer and super-resolution. In European Conference on Computer Vision (pp. 694-711). Springer, Cham.

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# Perceptual losses for real-time style transfer and super-resolution

Implementing: 
Johnson, J., Alahi, A. and Fei-Fei, L., 2016, October. 
Perceptual losses for real-time style transfer and super-resolution. 
In European Conference on Computer Vision (pp. 694-711). Springer, Cham.

## Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

### Prerequisites

This project was built on Python 3.5.2.

The following packages should be installed:
* ```matplotlib (1.5.3)```
* ```numpy (1.14.0)```
* ```opencv-python (3.4.3.18)```
* ```Pillow (3.3.1)```
* ```tensorflow-gpu (1.4.0)```

The following packages which are part of the Python standard library are used in this project:
* ```os```
* ```shutil```

Additionally, I utilise a pretrained VGG16 model checkpoint. You can download the compressed folder from [here](http://download.tensorflow.org/models/vgg_16_2016_08_28.tar.gz). After extraction, place the vgg_16.ckpt file in this folder or within the scripts you are using, change the variable ```checkpoint_path``` to point to the relevant location.

### Installing
You may install these packages yourself or use the [requirements.txt](requirements.txt) file like so: 
```
pip install -r requirements.txt
```

### Docker
Alternatively, I've supplied a [Dockerfile](Dockerfile) which you can use to create a container for running all this code:
```
nvidia-docker build .
```

Note that I've used ```nvidia-docker``` instead of ```docker```. This is for better GPU support. 
To learn more, please refer to these links: 
* [Nvidia Runtime Container for Docker](https://github.com/NVIDIA/nvidia-docker)
* [Enabling GPUs in the Container Runtime Ecosystem](https://devblogs.nvidia.com/gpu-containers-runtime/)

### Data
For training and validation, I utilized the [COCO 2014](http://cocodataset.org/#download) datasets, as used by the authors in the paper. 
You can obtain these datasets here: 
* [Training data](http://images.cocodataset.org/zips/train2014.zip)
* [Validation data](http://images.cocodataset.org/zips/val2014.zip)

Alternatively, you may also use your own training and validation set. 
Whichever you choose, simply change the appropriate paths in the [config.py](config.py) file.

## Usage

### Scripts
If you prefer, you can run Python scripts directly. 
Ensure you have downloaded a content image and a style image and set the paths appropriately in [config.py](config.py).
Other parameters can also be tweaked in [config.py](config.py).

Once done, please run:

```python train.py```
<!---## Deployment

Add additional notes about how to deploy this on a live system

## Built With

* [Dropwizard](http://www.dropwizard.io/1.0.2/docs/) - The web framework used
* [Maven](https://maven.apache.org/) - Dependency Management
* [ROME](https://rometools.github.io/rome/) - Used to generate RSS Feeds

## Contributing

Please read [CONTRIBUTING.md](https://gist.github.com/PurpleBooth/b24679402957c63ec426) for details on our code of conduct, and the process for submitting pull requests to us.
--->
## Versioning

I use [SemVer](http://semver.org/) for versioning. For the versions available, see the [tags on this repository](https://github.com/ashwindcruz/perceptual-loss-style-transfer/tags).

## Authors

* **Ashwin D'Cruz** - [ashwindcruz](https://github.com/ashwindcruz)

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details
<!---
## Acknowledgments

* Hat tip to anyone whose code was used
* Inspiration
* etc
--->

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