anime-sketch-colorizer's Introduction

Anime-Sketch-Colorizer

Automatic Sketch Colorization with reference image

Prerequisites

pytorch

torchvision

numpy

openCV2

matplotlib

Dataset

Taebum Kim, "Anime Sketch Colorization Pair", https://www.kaggle.com/ktaebum/anime-sketch-colorization-pair

Train

Please refer train.ipynb

Test

Please refer test.ipynb

You can download pretrained checkpoint on https://drive.google.com/open?id=1pIZCjubtyOUr7AXtGQMvzcbKczJ9CtQG (449MB)

Training details

Parameter	Value
Learning rate	2e-4
Batch size	2
Epoch	25
Optimizer	Adam
(beta1, beta2)	(0.5, 0.999)
(lambda1, lambda2, lambda3)	(100, 1e-4, 1e-2)
Data Augmentation	RandomResizedCrop(256) RandomHorizontalFlip()
HW	CPU : Intel i5-8400 RAM : 16G GPU : NVIDIA GTX1060 6G
Training Time	About 0.93s per iteration (About 45 hours for 25 epoch)

Model

For more details, please refer Model_details.pdf

Results

Reference / Sketch / Colorization Result / Ground Truth

Reference

[1] Taebum Kim, "Anime Sketch Colorization Pair", https://www.kaggle.com/ktaebum/anime-sketch-colorization-pair, 2019., 2020.1.13.

[2] Jim Bohnslav,"opencv_transforms", https://github.com/jbohnslav/opencv_transforms, 2020.1.13.

[3] Takeru Miyato et al., "Spectral Normalization for Generative Adversarial Networks", ICLR 2018, 2018.2.18.

[4] Ozan Oktay et al., "Attention U-Net: Learning Where to Look for the Pancreas", MIDL 2018, 2018.5.20.

[5] Siyuan Qiao et al., "Weight Standardization", https://arxiv.org/abs/1903.10520, 2019. 3. 25., 2020.1.19.

[6] Tero Karras, Samuli Laine, Timo Aila, "A Style-Based Generator Architecture for Generative Adversarial Networks", https://arxiv.org/abs/1812.04948, 2019.3.29., 2020.1.22.

Recommend Projects