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line drawing colorization using pytorch

Home Page: https://omnissiah.ys2lee.com/

License: MIT License

Python 100.00%
machine-learning deep-learning pytorch generative-adversarial-network gans paintschainer onnx

sketchcolorization's Introduction

SketchColorization (Web)

web

01

Model Structure

02

Samples

03

04

GUI


5

Requirements

  • torch==1.7.1
  • torchvision==0.82
  • numpy==1.19.1
  • tensorboard==2.3.0
  • tqdm==4.28.1
  • opencv_python==4.4.0.46
  • scipy==1.5.2
  • Pillow==7.2.0
  • scikit-learn==0.23.2
  • fbs==0.9.0
  • onnx==1.7.0
  • onnxruntime==1.5.1
  • PyQt5==5.15.1
  • QDarkStyle==2.8.1

Dataset

  • We crawled over 700,000 illustrations from shuushuu-image-board and used them for learning.

  • We have filtered out noise such as extreme aspect ratio, black and white image, low / high key images and etc.

Training

  • The learning sequence is 1. autoencoder, 2. draft, 3. colorization.

  • set hyperparameters.yml, e.g. paths (image_path and line_path, logdir)

  • Start learning after adjusting hyperparameters for each learning step

    • run 'python main.py -M {autoencoder | draft | colorization}'

Run APP with source code

  • download pretrained onnx model SketchColorizationModel.onnx
  • Copy model to "app/src/main/resources/base/SketchColorizationModel.onnx"
  • cd app
  • fbs run

sketchcolorization's People

Contributors

rapidrabbit76 avatar

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Watchers

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Forkers

brandonbian

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