UDP++ (ECCVW 2020 Oral), (Winner of COCO 2020 Keypoint Challenge).

Overview

UDP-Pose

This is the pytorch implementation for UDP++, which won the Fisrt place in COCO Keypoint Challenge at ECCV 2020 Workshop. Illustrating the performance of the proposed UDP

Top-Down

Results on MPII val dataset

Method--- Head Sho. Elb. Wri. Hip Kne. Ank. Mean Mean 0.1
HRNet32 97.1 95.9 90.3 86.5 89.1 87.1 83.3 90.3 37.7
+Dark 97.2 95.9 91.2 86.7 89.7 86.7 84.0 90.6 42.0
+UDP 97.4 96.0 91.0 86.5 89.1 86.6 83.3 90.4 42.1

Results on COCO val2017 with detector having human AP of 65.1 on COCO val2017 dataset

Arch Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR
pose_resnet_50 256x192 34.0M 8.90 71.3 89.9 78.9 68.3 77.4 76.9
+UDP 256x192 34.2M 8.96 72.9 90.0 80.2 69.7 79.3 78.2
pose_resnet_50 384x288 34.0M 20.0 73.2 90.7 79.9 69.4 80.1 78.2
+UDP 384x288 34.2M 20.1 74.0 90.3 80.0 70.2 81.0 79.0
pose_resnet_152 256x192 68.6M 15.7 72.9 90.6 80.8 69.9 79.0 78.3
+UDP 256x192 68.8M 15.8 74.3 90.9 81.6 71.2 80.6 79.6
pose_resnet_152 384x288 68.6M 35.6 75.3 91.0 82.3 71.9 82.0 80.4
+UDP 384x288 68.8M 35.7 76.2 90.8 83.0 72.8 82.9 81.2
pose_hrnet_w32 256x192 28.5M 7.10 75.6 91.9 83.0 72.2 81.6 80.5
+UDP 256x192 28.7M 7.16 76.8 91.9 83.7 73.1 83.3 81.6
+UDPv1 256x192 28.7M 7.16 77.2 91.6 84.2 73.7 83.7 82.5
+UDPv1+AID 256x192 28.7M 7.16 77.9 92.1 84.5 74.1 84.1 82.8
RSN18+UDP 256x192 - 2.5 74.7 - - - - -
pose_hrnet_w32 384x288 28.5M 16.0 76.7 91.9 83.6 73.2 83.2 81.6
+UDP 384x288 28.7M 16.1 77.8 91.7 84.5 74.2 84.3 82.4
pose_hrnet_w48 256x192 63.6M 14.6 75.9 91.9 83.5 72.6 82.1 80.9
+UDP 256x192 63.8M 14.7 77.2 91.8 83.7 73.8 83.7 82.0
pose_hrnet_w48 384x288 63.6M 32.9 77.1 91.8 83.8 73.5 83.5 81.8
+UDP 384x288 63.8M 33.0 77.8 92.0 84.3 74.2 84.5 82.5

Note:

  • Flip test is used.
  • Person detector has person AP of 65.1 on COCO val2017 dataset.
  • GFLOPs is for convolution and linear layers only.
  • UDPv1: v0:LOSS.KPD=4.0, v1:LOSS.KPD=3.5

Results on COCO test-dev with detector having human AP of 65.1 on COCO val2017 dataset

Arch Input size #Params GFLOPs AP Ap .5 AP .75 AP (M) AP (L) AR
pose_resnet_50 256x192 34.0M 8.90 70.2 90.9 78.3 67.1 75.9 75.8
+UDP 256x192 34.2M 8.96 71.7 91.1 79.6 68.6 77.5 77.2
pose_resnet_50 384x288 34.0M 20.0 71.3 91.0 78.5 67.3 77.9 76.6
+UDP 384x288 34.2M 20.1 72.5 91.1 79.7 68.8 79.1 77.9
pose_resnet_152 256x192 68.6M 15.7 71.9 91.4 80.1 68.9 77.4 77.5
+UDP 256x192 68.8M 15.8 72.9 91.6 80.9 70.0 78.5 78.4
pose_resnet_152 384x288 68.6M 35.6 73.8 91.7 81.2 70.3 80.0 79.1
+UDP 384x288 68.8M 35.7 74.7 91.8 82.1 71.5 80.8 80.0
pose_hrnet_w32 256x192 28.5M 7.10 73.5 92.2 82.0 70.4 79.0 79.0
+UDP 256x192 28.7M 7.16 75.2 92.4 82.9 72.0 80.8 80.4
pose_hrnet_w32 384x288 28.5M 16.0 74.9 92.5 82.8 71.3 80.9 80.1
+UDP 384x288 28.7M 16.1 76.1 92.5 83.5 72.8 82.0 81.3
pose_hrnet_w48 256x192 63.6M 14.6 74.3 92.4 82.6 71.2 79.6 79.7
+UDP 256x192 63.8M 14.7 75.7 92.4 83.3 72.5 81.4 80.9
pose_hrnet_w48 384x288 63.6M 32.9 75.5 92.5 83.3 71.9 81.5 80.5
+UDP 384x288 63.8M 33.0 76.5 92.7 84.0 73.0 82.4 81.6

Note:

  • Flip test is used.
  • Person detector has person AP of 65.1 on COCO val2017 dataset.
  • GFLOPs is for convolution and linear layers only.

Bottom-Up

HRNet

Arch P2I Input size Speed(task/s) AP Ap .5 AP .75 AP (M) AP (L) AR
HRNet(ori) T 512x512 - 64.4 - - 57.1 75.6 -
HRNet(mmpose) F 512x512 39.5 65.8 86.3 71.8 59.2 76.0 70.7
HRNet(mmpose) T 512x512 6.8 65.3 86.2 71.5 58.6 75.7 70.9
HRNet+UDP T 512x512 5.8 65.9 86.2 71.8 59.4 76.0 71.4
HRNet+UDP F 512x512 37.2 67.0 86.2 72.0 60.7 76.7 71.6
HRNet+UDP+AID F 512x512 37.2 68.4 88.1 74.9 62.7 77.1 73.0

HigherHRNet

Arch P2I Input size Speed(task/s) AP Ap .5 AP .75 AP (M) AP (L) AR
HigherHRNet(ori) T 512x512 - 67.1 - - 61.5 76.1 -
HigherHRNet T 512x512 9.4 67.2 86.1 72.9 61.8 76.1 72.2
HigherHRNet+UDP T 512x512 9.0 67.6 86.1 73.7 62.2 76.2 72.4
HigherHRNet F 512x512 24.1 67.1 86.1 73.6 61.7 75.9 72.0
HigherHRNet+UDP F 512x512 23.0 67.6 86.2 73.8 62.2 76.2 72.4
HigherHRNet+UDP+AID F 512x512 23.0 69.0 88.0 74.9 64.0 76.9 73.8

Note:

  • ori : Result from original HigherHrnet
  • mmpose : Pretrained models from mmpose
  • P2I : PROJECT2IMAGE
  • we use mmpose for codebase
  • the configurations of the baseline are HRNet-W32-512x512-batch16-lr0.001
  • Speed is tested with dist_test in mmpose codebase and 8 Gpus + 16 batchsize

Quick Start

(Recommend) For mmpose, please refer to MMPose

For hrnet, please refer to Hrnet

For RSN, please refer to RSN

Data preparation For coco, we provide the human detection result and pretrained model at BaiduDisk(dsa9)

Citation

If you use our code or models in your research, please cite with:

@inproceedings{cai2020learning,
  title={Learning Delicate Local Representations for Multi-Person Pose Estimation},
  author={Yuanhao Cai and Zhicheng Wang and Zhengxiong Luo and Binyi Yin and Angang Du and Haoqian Wang and Xinyu Zhou and Erjin Zhou and Xiangyu Zhang and Jian Sun},
  booktitle={ECCV},
  year={2020}
}
@article{huang2020joint,
  title={Joint coco and lvis workshop at eccv 2020: Coco keypoint challenge track technical report: Udp+},
  author={Huang, Junjie and Shan, Zengguang and Cai, Yuanhao and Guo, Feng and Ye, Yun and Chen, Xinze and Zhu, Zheng and Huang, Guan and Lu, Jiwen and Du, Dalong},
  year={2020}
}
Owner
Tsinghua University, Megvii Inc [email protected]
Joint Detection and Identification Feature Learning for Person Search

Person Search Project This repository hosts the code for our paper Joint Detection and Identification Feature Learning for Person Search. The code is

712 Dec 17, 2022
This project is for a Twitter bot that monitors a bird feeder in my backyard. Any detected birds are identified and posted to Twitter.

Backyard Birdbot Introduction This is a silly hobby project to use existing ML models to: Detect any birds sighted by a webcam Identify whic

Chi Young Moon 71 Dec 25, 2022
Match SafeGraph POIs with Data collected through a cultural resource survey in Washington DC.

Match SafeGraph POI data with Cultural Resource Places in Washington DC Match SafeGraph POIs with Data collected through a cultural resource survey in

Changjie Chen 1 Jan 05, 2022
Explore extreme compression for pre-trained language models

Code for paper "Exploring extreme parameter compression for pre-trained language models ICLR2022"

twinkle 16 Nov 14, 2022
A small library for doing fluid simulation with neural networks.

Neural Fluid Fields This is a small library for doing fluid simulation with neural fields. Check out our review paper, Neural Fields in Visual Computi

Towaki 23 Jun 23, 2022
🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Image Super-Resolution (ISR) The goal of this project is to upscale and improve the quality of low resolution images. This project contains Keras impl

idealo 4k Jan 08, 2023
An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

Shen Zhuoran 194 Dec 15, 2022
A decent AI that solves daily Wordle puzzles. Works with different websites with similar wordlists,.

Wordle-AI A decent AI that solves daily "Wordle" puzzles. Works with different websites with similar wordlists. When prompted with "Word:" enter the w

Ethan 1 Feb 10, 2022
Code for reproducing our analysis in the paper titled: Image Cropping on Twitter: Fairness Metrics, their Limitations, and the Importance of Representation, Design, and Agency

Image Crop Analysis This is a repo for the code used for reproducing our Image Crop Analysis paper as shared on our blog post. If you plan to use this

Twitter Research 239 Jan 02, 2023
Invasive Plant Species Identification

Invasive_Plant_Species_Identification Used LiDAR Odometry and Mapping (LOAM) to create a 3D point cloud map which can be used to identify invasive pla

2 May 12, 2022
Pacman-AI - AI project designed by UC Berkeley. Designed reflex and minimax agents for the game Pacman.

Pacman AI Jussi Doherty CAP 4601 - Introduction to Artificial Intelligence - Fall 2020 Python version 3.0+ Source of this project This repo contains a

Jussi Doherty 1 Jan 03, 2022
Code for C2-Matching (CVPR2021). Paper: Robust Reference-based Super-Resolution via C2-Matching.

C2-Matching (CVPR2021) This repository contains the implementation of the following paper: Robust Reference-based Super-Resolution via C2-Matching Yum

Yuming Jiang 151 Dec 26, 2022
PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment

logit-adj-pytorch PyTorch implementation of the paper: Long-tail Learning via Logit Adjustment This code implements the paper: Long-tail Learning via

Chamuditha Jayanga 53 Dec 23, 2022
Worktory is a python library created with the single purpose of simplifying the inventory management of network automation scripts.

Worktory is a python library created with the single purpose of simplifying the inventory management of network automation scripts.

Renato Almeida de Oliveira 18 Aug 31, 2022
Resources for the "Evaluating the Factual Consistency of Abstractive Text Summarization" paper

Evaluating the Factual Consistency of Abstractive Text Summarization Authors: Wojciech Kryściński, Bryan McCann, Caiming Xiong, and Richard Socher Int

Salesforce 165 Dec 21, 2022
(Personalized) Page-Rank computation using PyTorch

torch-ppr This package allows calculating page-rank and personalized page-rank via power iteration with PyTorch, which also supports calculation on GP

Max Berrendorf 69 Dec 03, 2022
Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space

extrinsic2pyramid Visualize Camera's Pose Using Extrinsic Parameter by Plotting Pyramid Model on 3D Space Intro A very simple and straightforward modu

JEONG HYEONJIN 106 Dec 28, 2022
Randomized Correspondence Algorithm for Structural Image Editing

===================================== README: Inpainting based PatchMatch ===================================== @Author: Younesse ANDAM @Conta

Younesse 116 Dec 24, 2022
Reporting and Visualization for Hazardous Events

Reporting and Visualization for Hazardous Events

Jv Kyle Eclarin 2 Oct 03, 2021
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022