PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence) and pre-trained model on ImageNet dataset

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Deep Learning Reference-Based-Sketch-Image-Colorization-ImageNet
Overview

Reference-Based-Sketch-Image-Colorization-ImageNet

This is a PyTorch implementation of CVPR 2020 paper (Reference-Based Sketch Image Colorization using Augmented-Self Reference and Dense Semantic Correspondence)

We will provide pre-trained model on ImageNet dataset shortly

1 Training

  • Prepare the ImageNet dataset (i.e., upload ILSVRC2012_train_256 folder to your server)

  • Download the PyTorch official pre-trained VGG-16 model, and then rename it to 'vgg16_pretrained.pth'

(torchvision webpage: https://github.com/pytorch/vision/blob/main/torchvision/models/vgg.py)

(download webpage: https://download.pytorch.org/models/vgg16-397923af.pth) (this is good)

  • Change the parameter in yaml file and run

(--vgg_name -> your VGG-16 model path)

(--baseroot_train -> your ImageNet dataset path, i.e., ILSVRC2012_train_256 path)

sh sbatch_run.sh or sh local_run.sh

By the way, I use 8 Titan GPUs to train the network with batch size of 32, epoch of 40. It takes approximately 16 days!

The forward of GAN discriminator and VGG-16 take a lot of time, which are used to compute GAN loss and perceptual loss, etc.

2 Validation

  • Prepare the references with same names to ImageNet test10k

  • Change the parameter in yaml file and run

sh val_run.sh or sh validation.sh
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