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. Now you can directly download VSPW_480P dataset.
Dependencies
- Python 3.7
- Pytorch 1.3.1
- 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}
}
