/M2FP

Mask2Former for Parsing (M2FP)

Primary LanguagePythonOtherNOASSERTION

M2FP: Mask2Former for Parsing

Deep Learning Technique for Human Parsing: A Survey and Outlook
paper

If you find this repository helpful, please consider citing:

@article{yang2023humanparsing,
  title={Deep Learning Technique for Human Parsing: A Survey and Outlook},
  author={Lu Yang and Wenhe Jia and Shan Li and Qing Song},
  journal={arXiv preprint arXiv:2301.00394},
  year={2023}
}

Features

  • A single architecture for single human parsing, and multiple (instance-level) human parsing.
  • Support several parsing datasets: LIP, PASCAL-Person-Part, CIHP, MHP-v2.

Updates

[2023/1/19] models in GoogleDrive are released.

[2023/1/3] paper and code released.

[2022/6/19] code initialization.

Installation

See installation instructions.

Getting Started

See Preparing Datasets for M2FP.

See Getting Started with M2FP.

Results and Models

Datasets mIoU APr APp DOWNLOAD
LIP 59.86 BaiduCloud (passwd: 36ec), GoogleDrive
PASCAL-Person-Part 72.54 56.46
CIHP 69.15 60.47 BaiduCloud (passwd: jzrn), GoogleDrive
MHP-v2 47.64 53.36 BaiduCloud (passwd: seel), GoogleDrive

License

Shield: CC BY-NC 4.0

The majority of M2FP is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

CC BY-NC 4.0

Citing MaskFormer and Mask2Former

If you find the code useful, please also consider the following MaskFormer and Mask2Former BibTeX entry.

@inproceedings{cheng2021mask2former,
  title={Masked-attention Mask Transformer for Universal Image Segmentation},
  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
  journal={CVPR},
  year={2021}
}
@inproceedings{cheng2021maskformer,
  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
  journal={NeurIPS},
  year={2021}
}

Acknowledgement

Code is largely based on Mask2Former (https://github.com/facebookresearch/Mask2Former).