# hrnet/higherhrnet-human-pose-estimation

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1,456 stars · 284 forks · Python · MIT

## Links

- GitHub: https://github.com/HRNet/HigherHRNet-Human-Pose-Estimation
- awesome-repositories: https://awesome-repositories.com/repository/hrnet-higherhrnet-human-pose-estimation.md

## Description

HigherHRNet is a deep learning framework designed for bottom-up human pose estimation. It functions as a computer vision keypoint detector that identifies and tracks human body joints by utilizing high-resolution feature pyramids and scale-aware representation learning.

The project distinguishes itself through a bottom-up approach, which identifies individual body parts across an entire image before clustering them into distinct human skeletons. This methodology is supported by multi-resolution feature fusion, which maintains high-resolution representations throughout the network by repeatedly merging parallel branches of varying spatial resolutions. To ensure consistent accuracy for subjects of different sizes, the system employs scale-aware heatmap regression and multi-scale inference, aggregating predictions across multiple image resolutions.

The framework includes comprehensive tools for training neural networks on large datasets. These training pipelines incorporate synchronized batch normalization to stabilize convergence across multiple graphics processing units and mixed-precision tensor computation to manage memory consumption and training speed. The architecture also utilizes deconvolutional upsampling layers to map low-resolution representations back to original input dimensions.

## Tags

### Part of an Awesome List

- [Human Pose Estimation](https://awesome-repositories.com/f/awesome-lists/ai/pose-estimation-frameworks/human-pose-estimation.md) — Provides a deep learning framework for detecting human body keypoints in images using high-resolution feature pyramids. ([source](https://github.com/hrnet/higherhrnet-human-pose-estimation#readme))

### Artificial Intelligence & ML

- [Pose Estimation Training Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/artificial-intelligence-tooling/language-model-integrations/computer-vision-models/pose-estimation-models/pose-estimation-training-pipelines.md) — Provides tools for training neural networks for keypoint detection using large datasets and optimized training techniques. ([source](https://github.com/hrnet/higherhrnet-human-pose-estimation#readme))
- [Batch Normalization](https://awesome-repositories.com/f/artificial-intelligence-ml/batch-normalization.md) — Calculates statistics across multiple graphics processing units during training to stabilize model convergence for large-scale vision tasks.
- [Cross-Resolution Fusion](https://awesome-repositories.com/f/artificial-intelligence-ml/feature-fusion-architectures/cross-resolution-fusion.md) — Maintains high-resolution representations throughout the network by repeatedly merging parallel branches of varying spatial resolutions.
- [Inference Scaling](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-scaling.md) — Aggregates predictions across multiple image resolutions to improve detection accuracy for people of different sizes. ([source](https://github.com/hrnet/higherhrnet-human-pose-estimation#readme))
- [Multi-Scale Feature Pyramids](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-scaling/resolution-scaling/hierarchical-feature-pyramids/multi-scale-feature-pyramids.md) — Maintains high accuracy by preserving spatial details across different visual scales and perspectives.
- [Computer Vision](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision.md) — Analyzes complex images by processing multiple resolutions to ensure accurate detection of objects regardless of their size.
- [Computer Vision Architectures](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/frameworks/computer-vision/computer-vision-architectures.md) — Identifies and tracks human body joints across varying scales and complex visual scenes.
- [Mixed Precision Training](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/machine-learning-training/distributed-and-accelerated-compute/training-acceleration-tools/mixed-precision-training.md) — Employs lower-bit precision formats to accelerate training speeds and reduce memory consumption.
- [Deep Learning Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/deep-learning-optimization.md) — Optimizes neural networks for keypoint detection tasks using large datasets and memory-efficient techniques.
- [Scale-Aware Heatmap Regressors](https://awesome-repositories.com/f/artificial-intelligence-ml/model-predictions/regression-predictions/scale-aware-heatmap-regressors.md) — Predicts keypoint locations at multiple feature levels to ensure consistent detection accuracy for both small and large subjects.
- [Bottom-Up Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/pose-estimation-engines/bottom-up-pipelines.md) — Identifies individual body parts across an entire image before clustering them into distinct human skeletons.
- [Pose Estimation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/pose-estimation-frameworks.md) — Implements models that maintain high accuracy for multi-person pose detection in images.