Arbitrary-Style-Transfer

Arbitrary-Style-Per-Model Fast Neural Style Transfer Method

Description

Using an Encoder-AdaIN-Decoder architecture Deep Convolutional Neural Network as the Style Transfer Network which can take two arbitrary images as input (one as content, the orther one as style) and output a generated image that holds the content and structure from the former and the style from the latter without re-training the network. The network is trained over Microsoft COCO dataset and WikiArt dataset.

This code is based on Huang et al. Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization (ICCV 2017)

Prerequisites

• Pre-trained VGG19 network (MD5 8ee3263992981a1d26e73b3ca028a123)
  I have provided a convertor in the tool folder. It can convert the matlab file into a npz file which is much smaller and easier to process via NumPy.
  Or simply download my pre-processed Pre-trained VGG19 network npz format (MD5 c7ddd13b12e40033b5031ff43e467065) The npz format is about 80MB while the mat format is about 550MB.
• Microsoft COCO dataset
• WikiArt dataset

My Running Environment

Hardware

• CPU: Intel® Core™ i9-7900X (3.30GHz x 10 cores, 20 threads)
• GPU: NVIDIA® Titan Xp (Architecture: Pascal, Frame buffer: 12GB)
• Memory: 32GB DDR4

Operating System

• ubuntu 16.04.03 LTS

Software

• Python 3.6.2
• NumPy 1.13.1
• TensorFlow 1.3.0
• SciPy 0.19.1
• CUDA 8.0.61
• cuDNN 6.0.21

References

• The Encoder which is implemented with first few layers(up to relu4_1) of a pre-trained VGG-19 is based on Anish Athalye's vgg.py

Citation

  @misc{ye2017arbitrarystyletransfer,
    author = {Wengao Ye},
    title = {Arbitrary Style Transfer},
    year = {2017},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/elleryqueenhomels/arbitrary_style_transfer}}
  }