philippslang/RSN

"Learning Delicate Local Representations for Multi-Person Pose Estimation" (ECCV 2020 Spotlight) & (COCO 2019 Keypoint Challenge Winner and Best Paper Award)

Learning Delicate Local Representations for Multi-Person Pose Estimation (ECCV 2020 Spotlight)

*This is a pytorch realization of Residual Steps Network which won 2019 COCO Keypoint Challenge and ranks 1st place on both COCO test-dev and test-challenge datasets as shown in COCO leaderboard.

News

• 2020.07 : Our paper has been accepted as Spotlight by ECCV 2020 🚀
• 2019.09 : Our work won the First place and Best Paper Award in COCO 2019 Keypoint Challenge 🏆

---

Abstract: In this paper, we propose a novel method called Residual Steps Network (RSN). RSN aggregates features with the same spatialsize (Intra-level features) efficiently to obtain delicate local representations, which retain rich low-level spatial information and result in precise keypoint localization. In addition, we propose an efficient attention mechanism - Pose Refine Machine (PRM) to further refine the keypoint locations. Our approach won the 1st place of COCO Keypoint Challenge 2019 and achieves state-of-the-art results on both COCO and MPII benchmarks, without using extra training data and pretrained model. Our single model achieves 78.6 on COCO test-dev, 93.0 on MPII test dataset. Ensembled models achieve 79.2 on COCO test-dev, 77.1 on COCO test-challenge dataset.

---

Pipieline of Residual Steps Network

Architecture of Pose Refine Machine

Some prediction resullts of our method on COCO and MPII valid datasets

Results(Original Version)

Results on COCO val dataset

Model	Input Size	GFLOPs	AP	AP50	AP75	APM	APL	AR
Res-18	256x192	2.3	70.7	89.5	77.5	66.8	75.9	75.8
RSN-18	256x192	2.5	73.6	90.5	80.9	67.8	79.1	78.8
RSN-50	256x192	6.4	74.7	91.4	81.5	71.0	80.2	80.0
RSN-101	256x192	11.5	75.8	92.4	83.0	72.1	81.2	81.1
2×RSN-50	256x192	13.9	77.2	92.3	84.0	73.8	82.5	82.2
3×RSN-50	256x192	20.7	78.2	92.3	85.1	74.7	83.7	83.1
4×RSN-50	256x192	29.3	79.0	92.5	85.7	75.2	84.5	83.7
4×RSN-50	384x288	65.9	79.6	92.5	85.8	75.5	85.2	84.2

Results on COCO test-dev dataset

Model	Input Size	GFLOPs	AP	AP50	AP75	APM	APL	AR
RSN-18	256x192	2.5	71.6	92.6	80.3	68.8	75.8	77.7
RSN-50	256x192	6.4	72.5	93.0	81.3	69.9	76.5	78.8
2×RSN-50	256x192	13.9	75.5	93.6	84.0	73.0	79.6	81.3
4×RSN-50	256x192	29.3	78.0	94.2	86.5	75.3	82.2	83.4
4×RSN-50	384x288	65.9	78.6	94.3	86.6	75.5	83.3	83.8
4×RSN-50+	-	-	79.2	94.4	87.1	76.1	83.8	84.1

Results on COCO test-challenge dataset

Model	Input Size	GFLOPs	AP	AP50	AP75	APM	APL	AR
4×RSN-50+	-	-	77.1	93.3	83.6	72.2	83.6	82.6

Results on MPII dataset

Model	Split	Input Size	Head	Shoulder	Elbow	Wrist	Hip	Knee	Ankle	Mean
4×RSN-50	val	256x256	96.7	96.7	92.3	88.2	90.3	89.0	85.3	91.6
4×RSN-50	test	256x256	98.5	97.3	93.9	89.9	92.0	90.6	86.8	93.0

Results(Pytorch Version)

Results on COCO val dataset

Model	Input Size	GFLOPs	AP	AP50	AP75	APM	APL	AR
Res-18	256x192	2.3	65.2	87.3	71.5	61.2	72.2	71.3
RSN-18	256x192	2.5	70.4	88.8	77.7	67.2	76.7	76.5

Note

• + means using ensemble models.
• All models are trained on 8 V100 GPUs
• We done all the experiments using our orginal DL-Platform, all results in our paper are reported on this DL-Platform. There are some differences between it and Pytorch.

Repo Structure

This repo is organized as following:

$RSN_HOME
|-- cvpack
|
|-- dataset
|   |-- COCO
|   |   |-- det_json
|   |   |-- gt_json
|   |   |-- images
|   |       |-- train2014
|   |       |-- val2014
|   |
|   |-- MPII
|       |-- det_json
|       |-- gt_json
|       |-- images
|   
|-- lib
|   |-- models
|   |-- utils
|
|-- exps
|   |-- exp1
|   |-- exp2
|   |-- ...
|
|-- model_logs
|
|-- README.md
|-- requirements.txt

Quick Start

Installation

1. Install Pytorch referring to Pytorch website.

2. Clone this repo, and config RSN_HOME in /etc/profile or ~/.bashrc, e.g.

export RSN_HOME='/path/of/your/cloned/repo'
export PYTHONPATH=$PYTHONPATH:$RSN_HOME

3. Install requirements:

pip3 install -r requirements.txt

4. Install COCOAPI referring to cocoapi website, or:

git clone https://github.com/cocodataset/cocoapi.git $RSN_HOME/lib/COCOAPI
cd $RSN_HOME/lib/COCOAPI/PythonAPI
make install

Dataset

COCO

1. Download images from COCO website, and put train2014/val2014 splits into $RSN_HOME/dataset/COCO/images/ respectively.

2. Download ground truth from Google Drive or Baidu Drive (code: fc51), and put it into $RSN_HOME/dataset/COCO/gt_json/.

3. Download detection result from Google Drive or Baidu Drive (code: fc51), and put it into $RSN_HOME/dataset/COCO/det_json/.

MPII

1. Download images from MPII website, and put images into $RSN_HOME/dataset/MPII/images/.

2. Download ground truth from Google Drive or Baidu Drive (code: fc51), and put it into $RSN_HOME/dataset/MPII/gt_json/.

3. Download detection result from Google Drive or Baidu Drive (code: fc51), and put it into $RSN_HOME/dataset/MPII/det_json/.

Log

Create a directory to save logs and models:

mkdir $RSN_HOME/model_logs

Train

Go to specified experiment repository, e.g.

cd $RSN_HOME/exps/RSN50.coco

and run:

python config.py -log
python -m torch.distributed.launch --nproc_per_node=gpu_num train.py

the gpu_num is the number of gpus.

Test

python -m torch.distributed.launch --nproc_per_node=gpu_num test.py -i iter_num

the gpu_num is the number of gpus, and iter_num is the iteration number you want to test.

Citation

Please considering citing our projects in your publications if they help your research.

@inproceedings{cai2020learning,
  title={Learning Delicate Local Representations for Multi-Person Pose Estimation},
  author={Yuanhao Cai and Zhicheng Wang and Zhengxiong Luo and Binyi Yin and Angang Du and Haoqian Wang and Xinyu Zhou and Erjin Zhou and Xiangyu Zhang and Jian Sun},
  booktitle={ECCV},
  year={2020}
}

@inproceedings{cai2019res,
  title={Res-steps-net for multi-person pose estimation},
  author={Cai, Yuanhao and Wang, Zhicheng and Yin, Binyi and Yin, Ruihao and Du, Angang and Luo, Zhengxiong and Li, Zeming and Zhou, Xinyu and Yu, Gang and Zhou, Erjin and others},
  booktitle={Joint COCO and Mapillary Workshop at ICCV},
  year={2019}
}