Built an interactive deep learning app with Streamlit and PyTorch to apply style transfer.

To OPEN Style Transfer App click here:

The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization . The saved-models for examples shown in the README can be downloaded from here.

The program is written in Python, and uses pytorch, scipy. A GPU is not necessary, but can provide a significant speed up especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

Stylize image

python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --cuda 0

• --content-image: path to content image you want to stylize.
• --model: saved model to be used for stylizing the image (eg: mosaic.pth)
• --output-image: path for saving the output image.
• --content-scale: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
• --cuda: set it to 1 for running on GPU, 0 for CPU.

Train model

python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --save-model-dir </path/to/save-model/folder> --epochs 2 --cuda 1

There are several command line arguments, the important ones are listed below

• --dataset: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] (download).
• --style-image: path to style-image.
• --save-model-dir: path to folder where trained model will be saved.
• --cuda: set it to 1 for running on GPU, 0 for CPU.

Refer to neural_style/neural_style.py for other command line arguments. For training new models you might have to tune the values of --content-weight and --style-weight. The mosaic style model shown above was trained with --content-weight 1e5 and --style-weight 1e10. The remaining 3 models were also trained with similar order of weight parameters with slight variation in the --style-weight (5e10 or 1e11).

Models for the examples shown below can be downloaded from dropbox link or by running the script download_saved_models.py.

Based on this fast neural style code: Fast Neural Style

Streamlit website

It is recommended to use a virtual environment before installing the dependencies

pip install streamlit
pip install torch torchvision

Download the pretrained models

python download_saved_models.py

Move the saved_models folder into the neural_style folder.

Run

streamlit run main.py