Posenet-Mobilenet-Quantized
| Property | Value |
|---|---|
| Model Size | 3.47 MB |
| Parameters | 3.31M |
| Input Resolution | 513x257 |
| License | Apache-2.0 |
| Paper | PersonLab Paper |
What is Posenet-Mobilenet-Quantized?
Posenet-Mobilenet-Quantized is a highly optimized human pose estimation model designed specifically for mobile deployment. Built on MobileNet architecture and quantized to INT8 precision, it delivers efficient pose detection while maintaining performance across various Qualcomm devices.
Implementation Details
The model represents a quantized version of the original PoseNet architecture, optimized for mobile deployment. It achieves impressive inference times as low as 0.393ms on modern devices like the Samsung Galaxy S24, while maintaining minimal memory footprint ranging from 0-49MB depending on the device and runtime.
- Utilizes MobileNet V1 checkpoint for efficient feature extraction
- Supports both TFLite and QNN runtime environments
- Operates with INT8 precision for optimal mobile performance
- Primary compute unit is NPU across supported devices
Core Capabilities
- Real-time human pose estimation on mobile devices
- Efficient keypoint detection with minimal latency
- Cross-platform support across various Qualcomm chipsets
- Optimized performance on NPU hardware
Frequently Asked Questions
Q: What makes this model unique?
This model stands out for its optimization for mobile deployment, featuring INT8 quantization and impressive inference times below 1ms on modern devices, while maintaining reasonable memory usage.
Q: What are the recommended use cases?
The model is ideal for mobile applications requiring real-time human pose estimation, such as fitness apps, motion tracking, and augmented reality applications where efficiency and low latency are crucial.
