HyperPose

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HyperPose is a computer vision tool that provides a library for fast and flexible human pose estimation. It offers high-performance real-time pose estimation on both CPUs and GPUs.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is HyperPose?

HyperPose is a powerful library designed for building high-performance custom human pose estimation applications. It stands out with its real-time capabilities, achieved through a sophisticated pose estimation engine that incorporates numerous system optimizations. These include pipeline parallelism, model inference with TensorRT, and CPU/GPU hybrid scheduling, leading to significantly higher FPS compared to other popular tools like OpenPose, TF-Pose, and OpenPifPaf. Beyond performance, HyperPose offers flexibility for developers, providing high-level Python APIs to customize training, evaluation, visualization, pre-processing, and post-processing. Users can also tailor model architectures and training datasets, and accelerate training with multiple GPUs, making it a versatile solution for advanced computer vision projects.

Best used for

Ideal for developers and data scientists who need to implement high-performance human pose estimation, customize model training, and integrate real-time pose analysis into their applications. Especially valuable for projects requiring significant speed improvements and flexibility in model design and deployment.

Common actions

estimate human pose

develop custom models

optimize computer vision

accelerate training

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Capabilities

Key features

Real-time pose estimation
CPU/GPU optimization
Customizable training
Flexible model architectures
Multi-GPU training
Python APIs
C++ inference library

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the key performance advantages of HyperPose?

HyperPose achieves up to 10x higher FPS compared to OpenPose, TF-Pose, and OpenPifPaf. This is due to system optimizations like pipeline parallelism, TensorRT model inference, and CPU/GPU hybrid scheduling, enabling real-time pose estimation for demanding applications.

Can I customize the pose estimation models with HyperPose?

Yes, HyperPose provides high-level Python APIs that allow users to customize various aspects. This includes training, evaluation, visualization, pre-processing, post-processing, and even the underlying model architectures like OpenPose, Pifpaf, or PoseProposal Network.

What are the prerequisites for using the C++ inference library via Docker?

To use the C++ inference library with Docker, you need CUDA Driver >= 418.81.07, NVIDIA Docker >= 2.0, and Docker CE Engine >= 19.03. A script is provided to check if these prerequisites are met before pulling the HyperPose Docker image.

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