zdyshine / NTIRE23-VIDEO-COLORIZATION

NTIRE 2023 Video Colorization Challenge @ CVPR 2023

Track 1: Fréchet Inception Distance (FID) Optimization

Please visit test_NTIRE23_Track_1_FID.py to evaluate our model.

We provide the colorized images HERE, and the reference images used to obtain the results HERE.

💼 Dependencies and Installation

• PyTorch >= 1.8.0

• CUDA >= 10.2

• Other required packages

  # git clone this repository
  git clone https://github.com/yyang181/NTIRE23-VIDEO-COLORIZATION.git
  cd NTIRE23-VIDEO-COLORIZATION
  

Environment configuration:

cd BiSTNet-NTIRE2023

# create a new anaconda env
conda create -n bistnet python=3.6
conda activate bistnet

# install pytortch
conda install pytorch==1.10.1 torchvision==0.11.2 torchaudio==0.10.1 cudatoolkit=11.3 -c pytorch -c conda-forge

# mmcv install 
pip install -U openmim
mim install mmcv-full

# install mmediting 
git clone https://github.com/open-mmlab/mmediting.git
cd mmediting
pip3 install -e .

# install other pip pkgs 
cd .. && pip install -r pip_requirements.txt

🎁 Checkpoints

Name	URL	Script	FID	CDC
BiSTNet	model	test_NTIRE23_Track_1_FID.py	21.5372	0.001717

⚡ Quick Inference

• Download Pre-trained Models: download a pretrained colorization model from the tabulated links, and put it into the folder ./BiSTNet-NTIRE2023/, like ./BiSTNet-NTIRE2023/checkpoints , ./BiSTNet-NTIRE2023/data and ./BiSTNet-NTIRE2023/models/protoseg_core/checkpoints .
• Prepare Testing Data: You can put the testing images in a folder, like ./demo_dataset.
  • demo_dataset/input: the directory of input grayscale images.
  • demo_dataset/ref: the directory of reference images (only f001.png, f050.png and f100.png are colorful images).
  • demo_dataset/output: the directory to save the colorization results.
• Test on Images:

conda activate bistnet && cd BiSTNet-NTIRE2023
CUDA_VISIBLE_DEVICES=0 python test_NTIRE23_Track_1_FID.py

For more details please refer to test_NTIRE23_Track_1_FID.py.

Acknowledgement

Part of our codes are taken from DeepExemplar, RAFT, HED and ProtoSeg. Thanks for their awesome works.