Pytorch_Realtime_Multi-Person_Pose_Estimation

Visit Tool

pytorch_Realtime_Multi-Person_Pose_Estimation is an open-source tool for real-time multi-person pose estimation using PyTorch. It provides a robust solution for detecting and estimating human poses in images and videos.

Claim this tool

No Views Yet

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is pytorch_Realtime_Multi-Person_Pose_Estimation?

pytorch_Realtime_Multi-Person_Pose_Estimation is an open-source project that leverages PyTorch for real-time multi-person pose estimation. This tool is designed for researchers and developers in computer vision, offering a robust framework to detect and estimate the poses of multiple individuals within images or video streams. It includes functionalities for running picture and web demos, evaluating models on datasets like COCO val2017, and training custom models. The project emphasizes performance, achieving a mAP of 0.653 for the rtpose model, and provides clear instructions for installation, model download, and C++ post-processing compilation. It's developed using Python 3.6 and requires NVIDIA GPUs for optimal performance.

Best used for

Ideal for professors and researchers who need to implement and evaluate real-time multi-person pose estimation models, develop new computer vision applications, and conduct academic research. Especially valuable for those working with PyTorch and requiring high accuracy in pose detection.

Common actions

estimate human pose

develop computer vision models

evaluate pose estimation

train deep learning models

github copilotface swapping"AI Agents"open-sourcecollaborationautomated workflowworkflowslow-code/no-codedeepfake

Capabilities

Key features

Real-time multi-person pose estimation
PyTorch implementation
Picture and web demos
Model evaluation on COCO
Custom model training
C++ post-processing

Target Audience

professor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running pytorch_Realtime_Multi-Person_Pose_Estimation?

The tool is developed using Python 3.6 on Ubuntu 18.04. It requires NVIDIA GPUs for operation, and has been tested with 4 1080ti GPU cards. Other platforms or GPU cards are not fully tested, so compatibility may vary.

How accurate is the pose estimation model provided?

The rtpose model achieves a mAP (mean Average Precision) of 0.653 on the COCO val2017 dataset. This is an improvement over previous rtpose models, partly due to left and right flip augmentation for heatmap and PAF during evaluation.

Can I train my own models using this framework?

Yes, the repository includes scripts and instructions for training your own models. You will need to prepare the COCO 2017 dataset with images and keypoint annotations, and modify the data directory in the training script.

Trending

Subcategories trending in Research & Education

Study Assistants Knowledge Management Course Creation Scientific Computing Summarization Language Learning

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce