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Given a simple anime line-art sketch the model outputs a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs) concept.

License: MIT License

Python 0.01% Jupyter Notebook 99.99%
deep-learning generative-adversarial-network image-synthesis image-processing-python implementation-of-research-paper implementation-from-scratch hacktoberfest hacktoberfest2020 hacktoberfest-2020

sketch2color-anime-translation's Introduction

Sketch2Color-anime-translation:

Given a simple line-art anime sketch the model should output a decent colored anime image using Conditional-Generative Adversarial Networks (C-GANs).

Implemented the anime sketch colorization using the reference(s) paper-1 below.

References:

[1] https://arxiv.org/abs/1705.01908

[2] https://machinelearningmastery.com

[3] https://github.com/soumith/ganhacks

Prerequisites:

  • Miniconda
  • Tensorflow 1.15
  • NVIDIA GPU (8G memory) + CUDA cuDNN

Installation:

  • Clone this repo:
> git clone https://github.com/sanjay235/Sketch2Color-anime-translation.git
> cd Sketch2Color-anime-translation
> conda env create -f environment.yml
  • Then activate the environment created and launch the jupyter:
> conda activate Sketch2ColorAnimeTranslation
> jupyter notebook

Dataset:

  • Download and extract the dataset to current directory.
  • Open an another anaconda prompt with same environment activated and run the pre-processing script as below,
> python DataPreprocessing.py

Training and testing:

  • For the training process refer the Sketch2Color_Anime.ipynb notebook.
  • For the evaluation on test data refer the final.ipynb notebook.

C-GAN Architecture:

C-GAN

Tensorboard logs:

Tensorboard

In progress training results:

At epoch 30,

result_epoch_30

At epoch 40,

result_epoch_40

At epoch 43,

result_epoch_43

Prediction on test sketches:

Sample_1

Sample_2

Contribute:

Correction & Contribution are always welcome!! ๐Ÿ˜ƒ

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