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
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: ) (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.
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