---
title: CVPR2023_NTIRE_Video_Colorization
canonical_url: "https://www.modelscope.cn/models/damo/CVPR2023_NTIRE_Video_Colorization"
md_url: "https://www.modelscope.cn/models/damo/CVPR2023_NTIRE_Video_Colorization.md"
repository: damo/CVPR2023_NTIRE_Video_Colorization
chinese_name: Video_Colorization_CodeBase_CVPR23_NTIRE
last_updated: 2023-02-22
license: "Apache License 2.0"
pipeline_tag: video-colorization
tasks:
  - video-colorization
library_name:
  - pytorch
frameworks:
  - pytorch
domain:
  - cv
downloads: 3493
stars: 4
tags:
  - "CVPR2023 NTIRE Workshop"
  - "Video Colorization"
  - "Baseline Model"
---

# CVPR2023_NTIRE_Video_Colorization

> CVPR2023_NTIRE_Video_Colorization - damo 在 ModelScope 开源的模型。NTIRE 2023 Video Colorization Model and Dataset

damo/CVPR2023_NTIRE_Video_Colorization 是 ModelScope 魔搭社区上的video-colorization模型，采用 Apache License 2.0 许可。

- **Repository**: damo/CVPR2023_NTIRE_Video_Colorization
- **License**: Apache License 2.0
- **Tasks**: video-colorization
- **Tags**: CVPR2023 NTIRE Workshop, Video Colorization, Baseline Model
- **Downloads**: 3493
- **Stars**: 4
- **Last updated**: 2023-02-22

Source: https://www.modelscope.cn/models/damo/CVPR2023_NTIRE_Video_Colorization

---

# NTIRE 2023 Video Colorization Model and Dataset

This project provides the baseline model and evaluation code for track1 and track2 for CVPR 2023 NTIRE workshop Video Colorization Challenge.

## Installation

```
conda create -n video_colorization python=3.7
conda activate video_colorization

pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116

git clone https://github.com/piddnad/CVPR2023_NTIRE_Video_Colorization.git

cd CVPR2023_NTIRE_Video_Colorization
pip install -r requirements/tests.txt
pip install -r requirements/framework.txt
pip install -r requirements/cv.txt

```


## Download Dataset (Optional)

You can Run the code below to download the validation set:

```
from modelscope.msdatasets import MsDataset
from modelscope.utils.constant import DownloadMode


# Set dataset download path
cache_dir = './datasets' 

# Download validation set
val_set = MsDataset.load('ntire23_video_colorization', namespace='damo', subset_name='val_frames', split='validation', cache_dir=cache_dir, download_mode=DownloadMode.FORCE_REDOWNLOAD)
print(next(iter(val_set)))
```


## Baseline Evaluation on Validation Set

This step will automatically download the validation set.

```
cd CVPR2023_NTIRE_Video_Colorization
CUDA_VISIBLE_DEVICES=0  PYTHONPATH=. python ntire23_scripts/baseline_evaluation.py

# Then you might get output similar to:
# FID evaluation time: xxxx
# CDC evaluation time: xxxx
# Total evaluation time: xxxx
# FID: 47.15574537543114, CDC: 0.003475072230336491

```


## Evaluation on Your Results

First modify the `res_dir` in user_result_evaluation.py, and then run:

```
python ntire23_scripts/user_result_evaluation.py
```


#### Clone with HTTP
```bash
 git clone https://www.modelscope.cn/damo/CVPR2023_NTIRE_Video_Colorization.git
```