https://github.com/mkocabas/pose-residual-network

Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network (ECCV 2018)' paper
https://github.com/mkocabas/pose-residual-network

deep-learning deep-neural-networks eccv eccv-2018 keras pose-estimation python tensorflow

Last synced: 6 days ago
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Code for the Pose Residual Network introduced in 'MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network (ECCV 2018)' paper

• Host: GitHub
• URL: https://github.com/mkocabas/pose-residual-network
• Owner: mkocabas
• Created: 2018-07-11T08:12:02.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-09-14T09:41:29.000Z (over 6 years ago)
• Last Synced: 2025-01-07T23:13:00.296Z (13 days ago)
• Topics: deep-learning, deep-neural-networks, eccv, eccv-2018, keras, pose-estimation, python, tensorflow
• Language: Python
• Homepage: https://arxiv.org/abs/1807.04067
• Size: 21.5 KB
• Stars: 346
• Watchers: 22
• Forks: 92
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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README

        
# Pose Residual Network

This repository contains a Keras 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](https://arxiv.org/abs/1807.04067)

PRN is described in Section 3.2 of the  paper.

## Getting Started

We have tested our method on [COCO Dataset](http://cocodataset.org)

### Prerequisites

```

python

tensorflow

keras

numpy

tqdm

pycocotools

progress

scikit-image

```

### Installing

1. Clone this repository: 

`git clone https://github.com/mkocabas/pose-residual-network.git`

2. Install [Tensorflow](https://www.tensorflow.org/install/).

3. ```pip install -r src/requirements.txt```

4. To download COCO dataset train2017 and val2017 annotations run: `bash data/coco.sh`. (data size: ~240Mb)

## Training

`python main.py`

For more options take a look at `opt.py`

## Results

Results on COCO val2017 Ground Truth data.

```

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.894

 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.971

 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.912

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.875

 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.918

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 20 ] = 0.909

 Average Recall     (AR) @[ IoU=0.50      | area=   all | maxDets= 20 ] = 0.972

 Average Recall     (AR) @[ IoU=0.75      | area=   all | maxDets= 20 ] = 0.928

 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets= 20 ] = 0.896

 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets= 20 ] = 0.947

```

## License

## Other Implementations

[Pytorch Version](https://github.com/salihkaragoz/pose-residual-network-pytorch)

## 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}

}

```