How to install HyperPose on Windows?

HyperPose is primarily tested on Ubuntu; for Windows, use Docker Desktop with WSL2 or build from source with CUDA support, but setup is more complex and less documented.

HyperPose vs OpenPose: which should I use?

Choose HyperPose for speed-critical real-time applications (up to 10x faster FPS), but prefer OpenPose if you need higher accuracy out-of-the-box or a larger community for support.

How to train a custom pose estimation model with HyperPose?

Use the Python library: set model name, type, backbone, and dataset in Config, then call Model.get_train() with your data—example code in the README shows training on MSCOCO.

Does HyperPose support real-time camera input?

Yes, via Docker with the --source=camera flag and X11 server for visualization, but it requires specific GPU drivers and Linux setup for optimal performance.

What are the system requirements for HyperPose?

Requires NVIDIA GPU with CUDA Driver >=418.81.07, Docker CE >=19.03, and specific CUDA toolkits (10.0-10.2) as listed in the README for compatibility.

Can I use HyperPose without a GPU?

No, the inference engine relies on GPU acceleration via TensorRT; CPU-only inference is not supported, limiting use to systems with NVIDIA hardware.

hyperpose

Pythonv2.2.0

A library for building high-performance custom human pose estimation applications with real-time inference and flexible model development.

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What is hyperpose?

HyperPose is a library for building high-performance custom human pose estimation applications. It provides a fast C++ inference engine for real-time pose detection and a flexible Python library for developing and training custom models. It solves the need for both speed and adaptability in pose estimation tasks, outperforming existing solutions like OpenPose in frames per second.

Target Audience

Computer vision researchers and developers working on real-time human pose estimation applications, such as motion analysis, sports analytics, or interactive systems, who need both high performance and model customization.

Value Proposition

Developers choose HyperPose for its combination of real-time inference speeds (up to 10x faster than OpenPose) and the flexibility to customize model architectures and training pipelines, all within an open-source framework.

Overview

Library for Fast and Flexible Human Pose Estimation

Use Cases

Best For

Real-time human pose estimation in video streams

Related Projects

Community-curated · Updated weekly · 100% open source

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Developing custom pose estimation models with specific architectures

High-performance inference on CPU/GPU hybrid systems

Multi-GPU accelerated training of pose estimation models

Deploying pose estimation applications via Docker containers

Research and experimentation with pose estimation algorithms

Not Ideal For

Projects requiring out-of-the-box pose estimation with minimal setup and no Docker dependency
Teams without access to NVIDIA GPUs or CUDA-enabled hardware
Applications where maximum accuracy is paramount and standard models like OpenPose VGG19 are sufficient
Environments mandating pure Python workflows to avoid C++ integration complexities

Pros & Cons

Pros

Blazing Fast Inference

Achieves real-time performance with up to 10x higher FPS than OpenPose, leveraging TensorRT integration and pipeline parallelism for CPU/GPU hybrid scheduling.

Flexible Model Customization

High-level Python APIs enable easy customization of model architectures (e.g., OpenPose, PifPaf), training pipelines, and datasets for tailored pose estimation.

Multi-GPU Training Support

Accelerates model development with distributed training on multiple GPUs, reducing training time for large datasets.

Docker Deployment Ease

Pre-built Docker images simplify inference deployment for video, camera, and real-time visualization, streamlining production setups.

Cons

Steep Setup Complexity

Requires specific NVIDIA drivers, CUDA toolkits, Docker, and Anaconda environments, making initial installation and configuration cumbersome.

Accuracy Trade-offs

Some custom models, like LightweightOpenPose variants, show lower accuracy (e.g., 44.2 map vs. 28.06 map) compared to original implementations, as noted in the README's accuracy table.

Limited Ecosystem

As a newer library, it has fewer pre-trained models and community resources than established alternatives like OpenPose, potentially increasing development time.

Open Source Alternative To

hyperpose is an open-source alternative to the following products:

O

OpenPose

OpenPose is a real-time multi-person keypoint detection library for body, face, hand, and foot estimation, widely used in computer vision research and applications.

O

OpenPifPaf

OpenPifPaf is a deep learning-based pose estimation framework that detects human poses in images using composite field predictions and part association fields.

TF-Pose

TF-Pose is a real-time human pose estimation library using TensorFlow that detects and tracks human body keypoints from images or video streams.

Frequently Asked Questions

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