BRPose: Enhancing Human Pose Estimation through Bi-Level Routing Attention and Multi-Level Weight Fusion

Quick start

Installation

1. Install pytorch == 1.8.0.

2. Install dependencies:

   pip install -r requirements.txt
   

3. Install COCOAPI if you want to test in COCO: 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.

   Your directory tree should look like this:

   ${POSE_ROOT}
   ├── experiments
   ├── models
   ├── tools 
   ├── README.md
   └── requirements.txt
   

Data preparation

For MPII data, please download from MPII Human Pose Dataset. The original annotation files are in matlab format. 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 test-dev2017 to reproduce our multi-person pose estimation results. Please download from 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
        |   |-- COCO_test-dev2017_detections_AP_H_609_person.json
        `-- images
            |-- train2017
            |   |-- 000000000009.jpg
            |   |-- 000000000025.jpg
            |   |-- 000000000030.jpg
            |   |-- ... 
            `-- val2017
                |-- 000000000139.jpg
                |-- 000000000285.jpg
                |-- 000000000632.jpg
                |-- ... 

Training

Training on MPII dataset

python tools/train.py

Testing on MPII val dataset

python tools/test.py