RP-R-CNN

Official implementation of Renovating Parsing R-CNN for Accurate Multiple Human Parsing (ECCV2020)

In this repository, we release the RP R-CNN code in Pytorch.

• RP R-CNN architecture:

• RP R-CNN output:

Citing RP R-CNN

If you use RP R-CNN, please use the following BibTeX entry.

@inproceedings{yang2020eccv,
title = {Renovating Parsing R-CNN for Accurate Multiple Human Parsing},
author = {Lu Yang and Qing Song and Zhihui Wang and Mengjie Hu and Chun Liu and Xueshi Xin and Wenhe Jia and Songcen Xu},
booktitle = {Proceedings of European Conference on Computer Vision (ECCV)},
year = {2020}
}

Installation

• 8 x TITAN RTX GPU
• pytorch1.4
• python3.6.8

Install RP R-CNN following INSTALL.md.

Results and Models

On CIHP

Backbone	LR	Det AP	mIoU	Parsing (APp50/APvol/PCP50)	DOWNLOAD
baseline	3x	68.3	56.2	64.6/54.3/60.9
R-50-FPN	3x	67.3	58.2	71.6/58.3/62.2
R-50-FPN	6x	68.2	60.2	74.1/59.5/64.9	GoogleDrive
+tta	6x	73.1	61.8	77.2/61.2/70.5

On MHP-v2

Backbone	LR	Det AP	mIoU	Parsing (APp50/APvol/PCP50)	DOWNLOAD
baseline	3x	68.8	35.6	26.6/40.3/37.9
R-50-FPN	3x	68.1	37.3	40.5/45.2/39.2
R-50-FPN	6x	69.1	38.6	45.3/46.8/43.6	GoogleDrive

• 'baseline' denotes our implementation Parsing R-CNN.
• '+tta' denotes using test-time augmentation, including: soft-nms + bbox voting + h-flipping + multi-scale

ImageNet pretrained weight

• R-50
• X-101-32x8d

Training

To train a model with 8 GPUs run:

python -m torch.distributed.launch --nproc_per_node=8 tools/train_net.py --cfg cfgs/CIHP/e2e_rp_rcnn_R-50-FPN_3x_ms.yaml

Evaluation

multi-gpu evaluation,

python tools/test_net.py --cfg ckpts/CIHP/e2e_rp_rcnn_R-50-FPN_6x_ms/e2e_rp_rcnn_R-50-FPN_6x_ms.yaml --gpu_id 0,1,2,3,4,5,6,7

single-gpu evaluation,

python tools/test_net.py --cfg ckpts/CIHP/e2e_rp_rcnn_R-50-FPN_6x_ms/e2e_rp_rcnn_R-50-FPN_6x_ms.yaml --gpu_id 0

License

RP-R-CNN is released under the MIT license.