Simple-HRNet

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simple-HRNet is an Open Source & Models tool that provides multi-person human pose estimation using HRNet in PyTorch. It offers a simplified implementation compatible with official pre-trained weights for ease of use.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is simple-HRNet?

simple-HRNet is an unofficial yet fully compatible implementation of the Deep High-Resolution Representation Learning for Human Pose Estimation paper, built with PyTorch. This tool simplifies the process of human pose estimation, offering compatibility with official pre-trained weights and delivering results consistent with the original implementation. It supports both Windows and Linux environments and includes features like multi-GPU inference, options for retrieving YOLO bounding boxes and HRNet heatmaps, and multi-person support with YOLOv3, YOLOv3-tiny, or YOLOv5. The repository also provides a live demo, scripts for training and testing on datasets like COCO, and support for TensorRT, making it a versatile solution for developers and researchers in computer vision.

Best used for

Ideal for developers and researchers who need to implement robust multi-person human pose estimation, train custom HRNet models, and extract keypoints from video or live camera feeds. Especially valuable for those requiring compatibility with official pre-trained weights and multi-GPU inference capabilities.

Common actions

estimate human pose

implement computer vision

train HRNet models

detect keypoints

analyze human movement

open-sourcecollaborationworkflowsautomated workflowgithub copilotface swapping"AI Agents"deepfakelow-code/no-code

Capabilities

Key features

Multi-person pose estimation
HRNet implementation PyTorch
YOLOv3/YOLOv5 support
Multi-GPU inference
TensorRT conversion
Training scripts included
Pre-trained weights compatible

Target Audience

professordeveloperresearcher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for running simple-HRNet?

simple-HRNet requires Python (>=1.0) and its dependencies, which can be installed via `pip install -r requirements.txt`. For multi-person support, YOLOv3/YOLOv5 needs to be cloned and its dependencies installed. Training scripts on COCO require a Linux machine with CUDA.

Can simple-HRNet be used for real-time human pose estimation?

Yes, simple-HRNet provides a reference code for running a live demo, reading frames from a webcam or a video file. This allows for real-time or near real-time human pose estimation, especially when leveraging GPU acceleration and TensorRT conversion.

How do I integrate YOLO for multi-person detection with simple-HRNet?

To enable multi-person support, you need to clone YOLOv3 into the `./models/detectors` folder or update git submodules. After cloning, install YOLOv3's specific requirements and download its pre-trained weights. This setup allows simple-HRNet to utilize YOLO for bounding box detection.

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Content & Design › 3D & Animation Research & Education › Academic Research AI Agents & Automation › AI Frameworks & Infra

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