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.
Getting Started
We have tested our method on Coco Dataset
Prerequisites
python
pytorch
numpy
tqdm
pycocotools
progress
scikit-image
Installing
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Clone this repository
git clone https://github.com/salihkaragoz/pose-residual-network-pytorch.git
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Install Pytorch
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pip install -r src/requirements.txt
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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
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Download pre-train model
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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