pose-residual-network-pytorch
                                
                                
                                
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                        Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network' paper https://arxiv.org/abs/1807.04067
Pose Residual Network
This repository contains a PyTorch implementation of the Pose Residual Network (PRN) presented in our ECCV 2018 paper:
Muhammed Kocabas, Salih Karagoz, Emre Akbas. MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network. In ECCV, 2018. arxiv
PRN is described in Section 3.2 of the paper.
Getting Started
We have tested our method on Coco Dataset
Prerequisites
python
pytorch
numpy
tqdm
pycocotools
progress
scikit-image
Installing
- 
Clone this repository
git clone https://github.com/salihkaragoz/pose-residual-network-pytorch.git - 
Install Pytorch
 - 
pip install -r src/requirements.txt - 
To download COCO dataset train2017 and val2017 annotations run:
bash data/coco.sh. (data size: ~240Mb) 
Training
python train.py
For more options look at opt.py
Testing
- 
Download pre-train model
 - 
python test.py --test_cp=PathToPreTrainModel/PRN.pth.tar 
Results
Results on COCO val2017 Ground Truth data.
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.892
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.978
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.921
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.883
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.912
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.917
 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.982
 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.937
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.902
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.944
License
Citation
If you find this code useful for your research, please consider citing our paper:
@Inproceedings{kocabas18prn,
  Title          = {Multi{P}ose{N}et: Fast Multi-Person Pose Estimation using Pose Residual Network},
  Author         = {Kocabas, Muhammed and Karagoz, Salih and Akbas, Emre},
  Booktitle      = {European Conference on Computer Vision (ECCV)},
  Year           = {2018}
}