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Library for Fast and Flexible Human Pose Estimation
| Date | Stars |
|---|---|
| 2026-07-24 | 1266 |
| 2026-07-25 | 1266 |
| 2026-07-28 | 1266 |
| 2026-07-30 | 1266 |
| 2026-08-10 | 1264 |
| 2026-09-20 | 1264 |
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</a>
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<img src="./docs/markdown/images/logo.png", width="600">
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---
<p align="center">
<a href="#Features">Features</a> •
<a href="#Documentation">Documentation</a> •
<a href="#Quick-Start">Quick Start</a> •
<a href="#Performance">Performance</a> •
<a href="#Accuracy">Accuracy</a> •
<a href="#Cite-Us">Cite Us</a> •
<a href="#License">License</a>
</p>
# HyperPose
HyperPose is a library for building high-performance custom pose estimation applications.
## Features
HyperPose has two key features:
- **High-performance pose estimation with CPUs/GPUs**: HyperPose achieves real-time pose estimation through a high-performance pose estimation engine. This engine implements numerous system optimisations: pipeline parallelism, model inference with TensorRT, CPU/GPU hybrid scheduling, and many others. These optimisations contribute to up to 10x higher FPS compared to OpenPose, TF-Pose and OpenPifPaf.
- **Flexibility for developing custom pose estimation models**: HyperPose provides high-level Python APIs to develop pose estimation models. HyperPose users can:
* Customise training, evaluation, visualisation, pre-processing and post-processing in pose estimation.
* Customise model architectures (e.g., OpenPose, Pifpaf, PoseProposal Network) and training datasets.
* Speed up training with multiple GPUs.
## Demo
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<p align="center">
<img src="./docs/markdown/images/demo-xbd.gif", width="600">
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<p align="center">
新宝岛 with HyperPose (Lightweight OpenPose model)
</p>
## Quick Start
The HyperPose library contains two parts:
* A C++ library for high-performance pose estimation model inference.
* A Python library for developing custom pose estimation models.
### C++ inference library
The easiest way to use the inference library is through a [Docker image](https://hub.docker.com/r/tensorlayer/hyperpose). Pre-requisites for this image:
- [CUDA Driver >= 418.81.07](https://www.tensorflow.org/install/gpu) (For default CUDA 10.0 image)
- [NVIDIA Docker >= 2.0](https://github.com/NVIDIA/nvidia-docker)
- [Docker CE Engine >= 19.03](https://docs.docker.com/engine/install/)
Run this command to check if pre-requisites are ready:
```bash
wget https://raw.githubusercontent.com/tensorlayer/hyperpose/master/scripts/test_docker.py -qO- | python
```
Once pre-requisites are ready, pull the HyperPose docker:
```bash
docker pull tensorlayer/hyperpose
```
We provide 4 examples within this image (The following commands have been tested on Ubuntu 18.04):
```bash
# [Example 1]: Doing inference on given video, copy the output.avi to the local path.
docker run --name quick-start --gpus all tensorlayer/hyperpose --runtime=stream
docExcerpt of 10,777 characters
Read on GitHub
162
Jiawei Liu · University of Illinois Urbana-Champaign
154
Luo Mai · University of Edinburgh · United Kingdom
99
Hao · Peking University · China
78
19
5
zhangrui · China
4
4
3
Jingqing Zhang · Imperial College London, Pangaea Data · United Kingdom
2
watchman-pypi · Future
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Syoyo Fujita · Light Transport Entertainment, Inc. · Japan
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1
1
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:bac82531f6513e19, topic:computer-vision, topic:pose-estimation, desc:pose estimation
matched fp:bac82531f6513e19, topic:tensorflow, topic:distributed-training