This project provides the baseline model and evaluation code for track1 and track2 for CVPR 2023 NTIRE workshop Video Colorization Challenge.
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
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)))
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
First modify the res_dir in user_result_evaluation.py, and then run:
python ntire23_scripts/user_result_evaluation.py
ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation.
In particular, with rich layers of API-abstraction, the ModelScope library offers unified experience to explore state-of-the-art models spanning across domains such as CV, NLP, Speech, Multi-Modality, and Scientific-computation. Model contributors of different areas can integrate models into the ModelScope ecosystem through the layered-APIs, allowing easy and unified access to their models. Once integrated, model inference, fine-tuning, and evaluations can be done with only a few lines of codes. In the meantime, flexibilities are also provided so that different components in the model applications can be customized wherever necessary.
Apart from harboring implementations of a wide range of different models, ModelScope library also enables the necessary interactions with ModelScope backend services, particularly with the Model-Hub and Dataset-Hub. Such interactions facilitate management of various entities (models and datasets) to be performed seamlessly under-the-hood, including entity lookup, version control, cache management, and many others.
This project is licensed under the Apache License (Version 2.0).