Simple Baselines for Human Pose Estimation and Tracking

News

• Our new work High-Resolution Representations for Labeling Pixels and Regions is available at HRNet. Our HRNet has been applied to a wide range of vision tasks, such as image classification, objection detection, semantic segmentation and facial landmark.
• Our new work Deep High-Resolution Representation Learning for Human Pose Estimation has already been released at https://github.com/leoxiaobin/deep-high-resolution-net.pytorch. The best single HRNet can obtain an AP of 77.0 on COCO test-dev2017 dataset and 92.3% of PCKh@0.5 on MPII test set. The new repositoty also support the SimpleBaseline method, and you are welcomed to try it.
  
• Our entry using this repo has won the winner of PoseTrack2018 Multi-person Pose Tracking Challenge!
  
• Our entry using this repo ranked 2nd place in the keypoint detection task of COCO 2018!

Introduction

This is an official pytorch implementation of Simple Baselines for Human Pose Estimation and Tracking. This work provides baseline methods that are surprisingly simple and effective, thus helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. On COCO keypoints valid dataset, our best single model achieves 74.3 of mAP. You can reproduce our results using this repo. All models are provided for research purpose.

Main Results

Results on MPII val

Arch	Head	Shoulder	Elbow	Wrist	Hip	Knee	Ankle	Mean	Mean@0.1
256x256_pose_resnet_50_d256d256d256	96.351	95.329	88.989	83.176	88.420	83.960	79.594	88.532	33.911
384x384_pose_resnet_50_d256d256d256	96.658	95.754	89.790	84.614	88.523	84.666	79.287	89.066	38.046
256x256_pose_resnet_101_d256d256d256	96.862	95.873	89.518	84.376	88.437	84.486	80.703	89.131	34.020
384x384_pose_resnet_101_d256d256d256	96.965	95.907	90.268	85.780	89.597	85.935	82.098	90.003	38.860
256x256_pose_resnet_152_d256d256d256	97.033	95.941	90.046	84.976	89.164	85.311	81.271	89.620	35.025
384x384_pose_resnet_152_d256d256d256	96.794	95.618	90.080	86.225	89.700	86.862	82.853	90.200	39.433

Note:

• Flip test is used.

Results on COCO val2017 with detector having human AP of 56.4 on COCO val2017 dataset

Arch	AP	Ap .5	AP .75	AP (M)	AP (L)	AR	AR .5	AR .75	AR (M)	AR (L)
256x192_pose_resnet_50_d256d256d256	0.704	0.886	0.783	0.671	0.772	0.763	0.929	0.834	0.721	0.824
384x288_pose_resnet_50_d256d256d256	0.722	0.893	0.789	0.681	0.797	0.776	0.932	0.838	0.728	0.846
256x192_pose_resnet_101_d256d256d256	0.714	0.893	0.793	0.681	0.781	0.771	0.934	0.840	0.730	0.832
384x288_pose_resnet_101_d256d256d256	0.736	0.896	0.803	0.699	0.811	0.791	0.936	0.851	0.745	0.858
256x192_pose_resnet_152_d256d256d256	0.720	0.893	0.798	0.687	0.789	0.778	0.934	0.846	0.736	0.839
384x288_pose_resnet_152_d256d256d256	0.743	0.896	0.811	0.705	0.816	0.797	0.937	0.858	0.751	0.863

Results on Caffe-style ResNet

Arch	AP	Ap .5	AP .75	AP (M)	AP (L)	AR	AR .5	AR .75	AR (M)	AR (L)
256x192_pose_resnet_50_caffe_d256d256d256	0.704	0.914	0.782	0.677	0.744	0.735	0.921	0.805	0.704	0.783
256x192_pose_resnet_101_caffe_d256d256d256	0.720	0.915	0.803	0.693	0.764	0.753	0.928	0.821	0.720	0.802
256x192_pose_resnet_152_caffe_d256d256d256	0.728	0.925	0.804	0.702	0.766	0.760	0.931	0.828	0.729	0.806

Note:

• Flip test is used.
• Person detector has person AP of 56.4 on COCO val2017 dataset.
• Difference between PyTorch-style and Caffe-style ResNet is the position of stride=2 convolution

Environment

The code is developed using python 3.6 on Ubuntu 16.04. NVIDIA GPUs are needed. The code is developed and tested using 4 NVIDIA P100 GPU cards. Other platforms or GPU cards are not fully tested.

Quick start

Installation

1. Install pytorch >= v0.4.0 following official instruction.

2. Disable cudnn for batch_norm:

   # PYTORCH=/path/to/pytorch
   # for pytorch v0.4.0
   sed -i "1194s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   # for pytorch v0.4.1
   sed -i "1254s/torch\.backends\.cudnn\.enabled/False/g" ${PYTORCH}/torch/nn/functional.py
   

   Note that instructions like # PYTORCH=/path/to/pytorch indicate that you should pick a path where you'd like to have pytorch installed and then set an environment variable (PYTORCH in this case) accordingly.

3. Clone this repo, and we'll call the directory that you cloned as ${POSE_ROOT}.

4. Install dependencies:

   pip install -r requirements.txt
   

5. Make libs:

   cd ${POSE_ROOT}/lib
   make
   

6. Install COCOAPI:

   # COCOAPI=/path/to/clone/cocoapi
   git clone https://github.com/cocodataset/cocoapi.git $COCOAPI
   cd $COCOAPI/PythonAPI
   # Install into global site-packages
   make install
   # Alternatively, if you do not have permissions or prefer
   # not to install the COCO API into global site-packages
   python3 setup.py install --user
   

   Note that instructions like # COCOAPI=/path/to/install/cocoapi indicate that you should pick a path where you'd like to have the software cloned and then set an environment variable (COCOAPI in this case) accordingly.

7. Download pytorch imagenet pretrained models from pytorch model zoo and caffe-style pretrained models from GoogleDrive.

8. Download mpii and coco pretrained models from OneDrive or GoogleDrive. Please download them under ${POSE_ROOT}/models/pytorch, and make them look like this:

   ${POSE_ROOT}
    `-- models
        `-- pytorch
            |-- imagenet
            |   |-- resnet50-19c8e357.pth
            |   |-- resnet50-caffe.pth.tar
            |   |-- resnet101-5d3b4d8f.pth
            |   |-- resnet101-caffe.pth.tar
            |   |-- resnet152-b121ed2d.pth
            |   `-- resnet152-caffe.pth.tar
            |-- pose_coco
            |   |-- pose_resnet_101_256x192.pth.tar
            |   |-- pose_resnet_101_384x288.pth.tar
            |   |-- pose_resnet_152_256x192.pth.tar
            |   |-- pose_resnet_152_384x288.pth.tar
            |   |-- pose_resnet_50_256x192.pth.tar
            |   `-- pose_resnet_50_384x288.pth.tar
            `-- pose_mpii
                |-- pose_resnet_101_256x256.pth.tar
                |-- pose_resnet_101_384x384.pth.tar
                |-- pose_resnet_152_256x256.pth.tar
                |-- pose_resnet_152_384x384.pth.tar
                |-- pose_resnet_50_256x256.pth.tar
                `-- pose_resnet_50_384x384.pth.tar
   
   

9. Init output(training model output directory) and log(tensorboard log directory) directory:

   mkdir output 
   mkdir log
   

   Your directory tree should look like this:

   ${POSE_ROOT}
   ├── data
   ├── experiments
   ├── lib
   ├── log
   ├── models
   ├── output
   ├── pose_estimation
   ├── README.md
   └── requirements.txt
   

Data preparation

For MPII data, please download from MPII Human Pose Dataset. The original annotation files are in matlab format. We have converted them into json format, you also need to download them from OneDrive or GoogleDrive. Extract them under {POSE_ROOT}/data, and make them look like this:

${POSE_ROOT}
|-- data
`-- |-- mpii
    `-- |-- annot
        |   |-- gt_valid.mat
        |   |-- test.json
        |   |-- train.json
        |   |-- trainval.json
        |   `-- valid.json
        `-- images
            |-- 000001163.jpg
            |-- 000003072.jpg

For COCO data, please download from COCO download, 2017 Train/Val is needed for COCO keypoints training and validation. We also provide person detection result of COCO val2017 to reproduce our multi-person pose estimation results. Please download from OneDrive or GoogleDrive. Download and extract them under {POSE_ROOT}/data, and make them look like this:

${POSE_ROOT}
|-- data
`-- |-- coco
    `-- |-- annotations
        |   |-- person_keypoints_train2017.json
        |   `-- person_keypoints_val2017.json
        |-- person_detection_results
        |   |-- COCO_val2017_detections_AP_H_56_person.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ... 

Valid on MPII using pretrained models

python pose_estimation/valid.py \
    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3.yaml \
    --flip-test \
    --model-file models/pytorch/pose_mpii/pose_resnet_50_256x256.pth.tar

Training on MPII

python pose_estimation/train.py \
    --cfg experiments/mpii/resnet50/256x256_d256x3_adam_lr1e-3.yaml

Valid on COCO val2017 using pretrained models

python pose_estimation/valid.py \
    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml \
    --flip-test \
    --model-file models/pytorch/pose_coco/pose_resnet_50_256x192.pth.tar

Training on COCO train2017

python pose_estimation/train.py \
    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3.yaml

Other Implementations

• TensorFlow [Version1]
• PaddlePaddle [Version1]
• Gluon [Version1]

Citation

If you use our code or models in your research, please cite with:

@inproceedings{xiao2018simple,
    author={Xiao, Bin and Wu, Haiping and Wei, Yichen},
    title={Simple Baselines for Human Pose Estimation and Tracking},
    booktitle = {European Conference on Computer Vision (ECCV)},
    year = {2018}
}