OpenPifPaf plugin to train and evaluate on the MPII pose dataset.
Example prediction for an image of the test set:
To install the openpifpaf_mpii plugin you will need to run the following command:
git clone https://github.com/DuncanZauss/openpifpaf_mpii.git
cd openpifpaf_mpii
pip install -e .If OpenPifPaf is not already installed in your environment, it will be installed as well.
For training the MPII images and the processed annotations will need to be downloaded. The annotations were transformed using this tool. You can download the images and the processed annotations with the following commands:
mkdir MPII
cd MPII
wget https://github.com/DuncanZauss/openpifpaf_mpii/releases/download/v.0.1.0-alpha/annotations.zip
wget https://datasets.d2.mpi-inf.mpg.de/andriluka14cvpr/mpii_human_pose_v1.tar.gz
unzip annotations.zip
tar -xzf mpii_human_pose_v1.tar.gz
rm annotations.zip mpii_human_pose_v1.tar.gzThe resulting folder structure should be like this:
MPII
├── annotations
├── MPII_coco_style_anns_test.json
├── MPII_coco_style_anns_train.json
├── images
├── 000001163.jpg
├── 000003072.jpg
.....
Finally softlink the MPII folder with ln -s to the folder from where you will run the training and evaluation command.
To train an openpifpaf model with the MPII dataset you can run the following command:
CUDA_VISIBLE_DEVICES=0 python -m openpifpaf.train --lr=0.0001 --momentum=0.95 --b-scale=10.0 --clip-grad-value=10 --epochs=350 --lr-decay 320 340 --lr-decay-epochs=10 --batch-size=16 --weight-decay=1e-5 --checkpoint shufflenetv2k16 --dataset mpii --mpii-upsample=2 --mpii-extended-scale --mpii-orientation-invariant=0.1 --head-consolidation=create --lr-warm-up-start-epoch=250To decrease the trainning time, the model in the command above is trained starting from a model that was pretrained on MS COCO. For further training options refer to the OpenPifpaf Guide.
An overview of the MPII evaluation protocol and the matlab code can be found here.