Realtime_Multi-Person_Pose_Estimation

Overview

Introduction

Multi Person PoseEstimation By PyTorch

Results

License

Require

  1. Pytorch

Installation

  1. git submodule init && git submodule update

Demo

  • Download converted pytorch model.
  • Compile the C++ postprocessing: cd lib/pafprocess; sh make.sh
  • python demo/picture_demo.py to run the picture demo.
  • python demo/web_demo.py to run the web demo.

Evalute

  • python evaluate/evaluation.py to evaluate the model on coco val2017 dataset.
  • It should have mAP 0.653 for the rtpose, previous rtpose have mAP 0.577 because we do left and right flip for heatmap and PAF for the evaluation. c

Main Results

model name mAP Inference Time
[original rtpose] 0.653 -

Download link: rtpose

Development environment

The code is developed using python 3.6 on Ubuntu 18.04. NVIDIA GPUs are needed. The code is developed and tested using 4 1080ti GPU cards. Other platforms or GPU cards are not fully tested.

Quick start

1. Preparation

1.1 Prepare the dataset

  • cd training; bash getData.sh to obtain the COCO 2017 images in /data/root/coco/images/, keypoints annotations in /data/root/coco/annotations/, make them look like this:
${DATA_ROOT}
|-- coco
    |-- annotations
        |-- person_keypoints_train2017.json
        |-- person_keypoints_val2017.json
    |-- images
        |-- train2017
            |-- 000000000009.jpg
            |-- 000000000025.jpg
            |-- 000000000030.jpg
            |-- ... 
        |-- val2017
            |-- 000000000139.jpg
            |-- 000000000285.jpg
            |-- 000000000632.jpg
            |-- ... 
        

2. How to train the model

  • Modify the data directory in train/train_VGG19.py and python train/train_VGG19.py

Related repository

Network Architecture

  • testing architecture Teaser?

  • training architecture Teaser?

Contributions

All contributions are welcomed. If you encounter any issue (including examples of images where it fails) feel free to open an issue.

Citation

Please cite the paper in your publications if it helps your research:

@InProceedings{cao2017realtime,
  title = {Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields},
  author = {Zhe Cao and Tomas Simon and Shih-En Wei and Yaser Sheikh},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2017}
  }
Owner
tensorboy
tensorboy
Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea

J K Terry 32 Nov 09, 2021
U-Time: A Fully Convolutional Network for Time Series Segmentation

U-Time & U-Sleep Official implementation of The U-Time [1] model for general-purpose time-series segmentation. The U-Sleep [2] model for resilient hig

Mathias Perslev 176 Dec 19, 2022
Implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networks, using PyTorch

C-CNN: Contourlet Convolutional Neural Networks This repo implemenets the Contourlet-CNN as described in C-CNN: Contourlet Convolutional Neural Networ

Goh Kun Shun (KHUN) 10 Nov 03, 2022
Balancing Principle for Unsupervised Domain Adaptation

Blancing Principle for Domain Adaptation NeurIPS 2021 Paper Abstract We address the unsolved algorithm design problem of choosing a justified regulari

Marius-Constantin Dinu 4 Dec 15, 2022
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
Extracts data from the database for a graph-node and stores it in parquet files

subgraph-extractor Extracts data from the database for a graph-node and stores it in parquet files Installation For developing, it's recommended to us

Cardstack 0 Jan 10, 2022
This repository contains the implementation of the following paper: Cross-Descriptor Visual Localization and Mapping

Cross-Descriptor Visual Localization and Mapping This repository contains the implementation of the following paper: "Cross-Descriptor Visual Localiza

Mihai Dusmanu 81 Oct 06, 2022
PyTorch Lightning + Hydra. A feature-rich template for rapid, scalable and reproducible ML experimentation with best practices. ⚡🔥⚡

Lightning-Hydra-Template A clean and scalable template to kickstart your deep learning project 🚀 ⚡ 🔥 Click on Use this template to initialize new re

Łukasz Zalewski 2.1k Jan 09, 2023
AirLoop: Lifelong Loop Closure Detection

AirLoop This repo contains the source code for paper: Dasong Gao, Chen Wang, Sebastian Scherer. "AirLoop: Lifelong Loop Closure Detection." arXiv prep

Chen Wang 53 Jan 03, 2023
Gym Threat Defense

Gym Threat Defense The Threat Defense environment is an OpenAI Gym implementation of the environment defined as the toy example in Optimal Defense Pol

Hampus Ramström 5 Dec 08, 2022
🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

🔥 Real-time Super Resolution enhancement (4x) with content loss and relativistic adversarial optimization 🔥

Rishik Mourya 48 Dec 20, 2022
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥

4.8k Jan 07, 2023
Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities

ORB-SLAM2 Authors: Raul Mur-Artal, Juan D. Tardos, J. M. M. Montiel and Dorian Galvez-Lopez (DBoW2) 13 Jan 2017: OpenCV 3 and Eigen 3.3 are now suppor

Raul Mur-Artal 7.8k Dec 30, 2022
Streaming Anomaly Detection Framework in Python (Outlier Detection for Streaming Data)

Python Streaming Anomaly Detection (PySAD) PySAD is an open-source python framework for anomaly detection on streaming multivariate data. Documentatio

Selim Firat Yilmaz 181 Dec 18, 2022
Extracting knowledge graphs from language models as a diagnostic benchmark of model performance.

Interpreting Language Models Through Knowledge Graph Extraction Idea: How do we interpret what a language model learns at various stages of training?

EPFL Machine Learning and Optimization Laboratory 9 Oct 25, 2022
Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space"

Sparse Steerable Convolution (SS-Conv) Code for "Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and

25 Dec 21, 2022
functorch is a prototype of JAX-like composable function transforms for PyTorch.

functorch is a prototype of JAX-like composable function transforms for PyTorch.

Facebook Research 1.2k Jan 09, 2023
Deeper DCGAN with AE stabilization

AEGeAN Deeper DCGAN with AE stabilization Parallel training of generative adversarial network as an autoencoder with dedicated losses for each stage.

Tyler Kvochick 36 Feb 17, 2022
Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow

Mask R-CNN for Object Detection and Segmentation This is an implementation of Mask R-CNN on Python 3, Keras, and TensorFlow. The model generates bound

Matterport, Inc 22.5k Jan 04, 2023
Dogs classification with Deep Metric Learning using some popular losses

Tsinghua Dogs classification with Deep Metric Learning 1. Introduction Tsinghua Dogs dataset Tsinghua Dogs is a fine-grained classification dataset fo

QuocThangNguyen 45 Nov 09, 2022