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:
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}
}
- 8 x TITAN RTX GPU
- pytorch1.4
- python3.6.8
Install RP R-CNN following INSTALL.md.
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
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
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
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
RP-R-CNN is released under the MIT license.