Pose estimation for mobile devices
Top 37.5% on sourcepulse
This repository provides real-time single-person pose estimation models (CPM and Hourglass) optimized for mobile devices using TensorFlow and inverted residuals (MobileNet V2). It targets mobile developers and researchers seeking efficient pose estimation solutions, offering significant speed improvements on mobile hardware.
How It Works
The project implements Convolutional Pose Machines (CPM) and Hourglass network architectures, enhanced with MobileNet V2's inverted residual blocks. This design choice reduces model complexity and computational cost (FLOPs), enabling real-time inference on mobile CPUs and GPUs. The models are trained on the AI Challenger dataset and can be converted to formats like TFLite and CoreML for deployment.
Quick Start & Requirements
Highlighted Details
Maintenance & Community
Licensing & Compatibility
Limitations & Caveats
The repository is presented as a baseline, with significant room for improvement in model architecture and dataset utilization. Mace framework build is not supported on Windows.
2 years ago
Inactive