PyTorch implementation of the Pose Residual Network (PRN)

Last update: Nov 28, 2022

Overview

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}
}

PyTorch implementation of the Pose Residual Network (PRN)

Related tags

Overview

Pose Residual Network

Getting Started

Prerequisites

Installing

Training

Testing

Results

License

Citation

Owner

Salih Karagoz

🎃 Core identification module of AI powerful point reading system platform.

Implementation of "DeepOrder: Deep Learning for Test Case Prioritization in Continuous Integration Testing".

A library for uncertainty representation and training in neural networks.

Official Pytorch Implementation for Splicing ViT Features for Semantic Appearance Transfer presenting Splice

A small library for creating and manipulating custom JAX Pytree classes

A library for optimization on Riemannian manifolds

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Simple Baselines for Human Pose Estimation and Tracking

Diffgram - Supervised Learning Data Platform

PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

Hardware-accelerated DNN model inference ROS2 packages using NVIDIA Triton/TensorRT for both Jetson and x86_64 with CUDA-capable GPU

Time Series Cross-Validation -- an extension for scikit-learn

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

Differentiable Wavetable Synthesis

The code for our paper submitted to RAL/IROS 2022: OverlapTransformer: An Efficient and Rotation-Invariant Transformer Network for LiDAR-Based Place Recognition.

Learning Continuous Signed Distance Functions for Shape Representation

Pytorch cuda extension of grid_sample1d

Hippocampal segmentation using the UNet network for each axis

Code for the paper "Asymptotics of ℓ2 Regularized Network Embeddings"