MIMO-VRN

KO | EN

gitlite — search

#python #react #typescript #llm #rust #docker #nodejs #awesome #kubernetes #awesome-list #security #ios #openai #open-source

(★ 47)

Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)

MIMO-VRN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)
Pytorch Implementation of the paper "Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling (CVPR 2021)".

Project Page: [Link](https://ding3820.github.io/MIMO-VRN/)

Paper (arXiv): [Link](https://arxiv.org/abs/2103.14858)

## Prerequisite
- Python 3 via Anaconda (recommended)
- PyTorch >= 1.4.0
- NVIDIA GPU + CUDA
- Python Package: `pip install numpy opencv-python lmdb pyyaml`

## Dataset Preparation
Training and testing dataset can be found [here](http://toflow.csail.mit.edu/). 
We adopt the LMDB format and also provide the script in `codes/data_scripts`. 
For more detail, please refer to [BasicSR](https://github.com/xinntao/BasicSR).

## Usage
Pretrained weight can be downloaded from [Google Drive](https://drive.google.com/drive/folders/1hlQ8nHSJysqZ6h5vyPz-HApyTD_an7Xb?usp=sharing).

All the implementation is in `/codes`. To run the code, 
select the corresponding configuration file in `/codes/options/` and run as following command (MIMO-VRN for example):
#### Training
```
python train.py -opt options/train/train_MIMO-VRN.yml
```
#### Testing
```
python test.py -opt options/test/test_MIMO-VRN.yml
```

## Quantitative Results 
#### HR Reconstruction on Vid4
![table1](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/table1.png)

## Qualitative Results
#### HR Reconstruction on Vid4
![calendar](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/vid4_calendar.png)
![city](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/vid4_city.png)
![walk](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/vid4_walk.png)
![foliage](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/vid4_foilage.png)

#### LR Reconstruction on Vid4
![LR Vid4](https://raw.githubusercontent.com/ding3820/MIMO-VRN/main/examples/lr.png)

## Citation
```
@InProceedings{Huang_2021_CVPR,
    author    = {Huang, Yan-Cheng and Chen, Yi-Hsin and Lu, Cheng-You and Wang, Hui-Po and Peng, Wen-Hsiao and Huang, Ching-Chun},
    title     = {Video Rescaling Networks With Joint Optimization Strategies for Downscaling and Upscaling},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2021},
    pages     = {3527-3536}
}
```
## Acknowledgement
Our project is heavily based on [Invertible-Image-Rescaling](https://github.com/pkuxmq/Invertible-Image-Rescaling) and they adopt [BasicSR](https://github.com/xinntao/BasicSR) as basic framework.

MIMO-VRN

MIMO-VRN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

MIMO-VRN

MIMO-VRN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README