niteshbharadwaj/ignoringhumanpose

Learning By Ignoring for Human Pose Estimation

We build upon Simple Baselines for Human Pose Estimation and Tracking and apply learning by ignoring framework

Introduction

Learning by ignoring is a framework to address domain shifts and noisy annotations where the network learns to ignore datapoints. In this work, associate an ignoring variable with each keypoint of human pose.

1. Install dependencies:

   pip install -r requirements.txt
   

2. Make libs:

   cd ${POSE_ROOT}/lib
   make
   

3. 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.

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

5. 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
   
   

6. 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
                |-- ... 

Train LBI on COCO and test on MPII

python pose_estimation/main.py \
    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3_lbi.yaml \
    --ours2

Train Baseline on COCO and test on MPII

python pose_estimation/main.py \
    --cfg experiments/coco/resnet50/256x192_d256x3_adam_lr1e-3_lbi.yaml \
    --baseline2