pinglmlcv / CVPR2021_VSPW_Implement

A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

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VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild

A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"

Preparation

Download VSPW dataset

The VSPW dataset with extracted frames and masks is available here.

Dependencies

  • Python 3.7
  • Pytorch 1.5
  • Numpy

Download the ImageNet-pretrained models at this link. Put it in the root folder and decompress it.

Train and Test

Resize the frames and masks of the VSPW dataset to 480p.

python change2_480p.py

Edit the .sh files in scripts/ and change the $DATAROOT to your path to VSPW_480p.

Image-based methods

PSPNet

sh scripts/run_psp.sh

OCRNet

sh scripts/run_ocr.sh

Video-based methods

TCB-PSP

sh run_temporal_psp.sh

TCB-OCR

sh run_temporal_ocr.sh

Evaluation on TC and VC

Change dataroot and prediction root in TC_cal.py and VC_perclip.py.

python TC_cal.py
python VC_perclip.py

This implementation utilized this code and RAFT.

Citation

@inproceedings{miao2021vspw,

  title={VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild},

  author={Miao, Jiaxu and Wei, Yunchao and  Wu, Yu and Liang, Chen and Li, Guangrui and Yang, Yi},

  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},

  year={2021}

}

About

A pytorch implementation of the CVPR2021 paper "VSPW: A Large-scale Dataset for Video Scene Parsing in the Wild"


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