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Rethinking on Multi-Stage Networks for Human Pose Estimation

Rethinking on Multi-Stage Networks for Human Pose Estimation

Multi-Stage Pose Network

This is a pytorch realization of MSPN proposed in Rethinking on Multi-Stage Networks for Human Pose Estimation . which wins 2018 COCO Keypoints Challenge. The original repo is based on the inner deep learning framework (MegBrain) in Megvii Inc.

In this work, we design an effective network MSPN to fulfill human pose estimation task.

Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods. While multistage methods are seemingly more suited for the task, their performance in current practice is not as good as singlestage methods. This work studies this issue. We argue that the current multi-stage methods’ unsatisfactory performance comes from the insufficiency in various design choices. We propose several improvements, including the single-stage module design, cross stage feature aggregation, and coarse-tofine supervision.

The resulting method establishes the new state-of-the-art on both MS COCO and MPII Human Pose dataset, justifying the effectiveness of a multi-stage architecture.

Results

Results on COCO val dataset

Model Input Size AP AP50 AP75 APM APL AR AR50 AR75 ARM ARL
1-stg MSPN 256x192 71.5 90.1 78.4 67.4 77.5 77.0 93.2 83.1 72.6 83.1
2-stg MSPN 256x192 74.5 91.2 81.2 70.5 80.4 79.7 94.2 85.6 75.4 85.7
3-stg MSPN 256x192 75.2 91.5 82.2 71.1 81.1 80.3 94.3 86.4 76.0 86.4
4-stg MSPN 256x192 75.9 91.8 82.9 72.0 81.6 81.1 94.9 87.1 76.9 87.0
4-stg MSPN 384x288 76.9 91.8 83.2 72.7 83.1 81.8 94.8 87.3 77.4 87.8

Results on COCO test-dev dataset

Model Input Size AP AP50 AP75 APM APL AR AR50 AR75 ARM ARL
4-stg MSPN 384x288 76.1 93.4 83.8 72.3 81.5 81.6 96.3 88.1 77.5 87.1
4-stg MSPN+ 384x288 78.1 94.1 85.9 74.5 83.3 83.1 96.7 89.8 79.3 88.2

Results on MPII dataset

Model Split Input Size Head Shoulder Elbow Wrist Hip Knee Ankle Mean
4-stg MSPN val 256x256 96.8 96.5 92.0 87.0 89.9 88.0 84.0 91.1
4-stg MSPN test 256x256 98.4 97.1 93.2 89.2 92.0 90.1 85.5 92.6

Note

  • + means using model ensemble.

Repo Structure

This repo is organized as following:

$MSPN_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].

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

export MSPN_HOME='/path/of/your/cloned/repo'
export PYTHONPATH=$PYTHONPATH:$MSPN_HOME
  1. Install requirements:
pip3 install -r requirements.txt
  1. Install COCOAPI referring to [cocoapi website][3], or:
git clone https://github.com/cocodataset/cocoapi.git $MSPN_HOME/lib/COCOAPI
cd $MSPN_HOME/lib/COCOAPI/PythonAPI
make install

Dataset

COCO

  1. Download images from [COCO website][4], and put train2014/val2014 splits into $MSPN_HOME/dataset/COCO/images/ respectively.

  2. Download ground truth from [Google Drive][6], and put it into $MSPN_HOME/dataset/COCO/gt_json/.

  3. Download detection result from [Google Drive][6], and put it into $MSPN_HOME/dataset/COCO/det_json/.

MPII

  1. Download images from [MPII website][5], and put images into $MSPN_HOME/dataset/MPII/images/.

  2. Download ground truth from [Google Drive][6], and put it into $MSPN_HOME/dataset/MPII/gt_json/.

  3. Download detection result from [Google Drive][6], and put it into $MSPN_HOME/dataset/MPII/det_json/.

Model

Download ImageNet pretained ResNet-50 model from [Google Drive][6], and put it into $MSPN_HOME/lib/models/. For your convenience, We also provide a well-trained 2-stage MSPN model for COCO.

Log

Create a directory to save logs and models:

mkdir $MSPN_HOME/model_logs

Train

Go to specified experiment repository, e.g.

cd $MSPN_HOME/exps/mspn.2xstg.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.

@article{li2019rethinking,
  title={Rethinking on Multi-Stage Networks for Human Pose Estimation},
  author={Li, Wenbo and Wang, Zhicheng and Yin, Binyi and Peng, Qixiang and Du, Yuming and Xiao, Tianzi and Yu, Gang and Lu, Hongtao and Wei, Yichen and Sun, Jian},
  journal={arXiv preprint arXiv:1901.00148},
  year={2019}
}

@inproceedings{chen2018cascaded,
  title={Cascaded pyramid network for multi-person pose estimation},
  author={Chen, Yilun and Wang, Zhicheng and Peng, Yuxiang and Zhang, Zhiqiang and Yu, Gang and Sun, Jian},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={7103--7112},
  year={2018}
}

GitHub