Person Classification 256x448 (SR100 Series)

Model Overview

The Person Classification 256x448 model, developed by Synaptics, is a lightweight quantized tflite model developed for the SR100 processor in the Synaptics Astra™ SR MCU Series.

It efficiently classifies input images as either person or non-person, enabling reliable human presence detection. This model can fit in Flash and designed to fit in SRAM as well.

Model Features

• Model Type: Binary classification
• Input Size: 256x448
• Classes:
  • 0: Non-person
  • 1: Person

Deployment on Astra

You can optimize this model for Synaptics Astra SR100 MCU using our our hosted SR100 Model Compiler HF Space.

• Processor: Astra™ SR100 MCU
• Platform: Astra™ Machina Micro Dev Kit
• Quantization: INT8 (fully quantized)
• Compiler: SR100 Model Compiler
• Preprocessing: Input images must be resized and quantized to match model requirements

Intended Applications

Designed for real-time person classification on embedded, resource-constrained edge devices. Typical use cases include:

• Smart home and office presence detection
• Wake-on-person activation
• Security and surveillance systems

Evaluate Model

You can evaluate and test this model directly in our hosted Hugging Face Space, optimized for Synaptics SR110 MCU. This space provides a seamless sandbox for model evaluation using hardware-specific quantization and runtime settings.

For a detailed walkthrough on how to optimize and evaluate a model, please see our Evaluate Model Guide page.

To get started quickly with Astra SR Series, visit our SR Quick Start page.

License

Distributed under the Apache License 2.0, allowing flexible use, modification, and distribution.

Learn More

• Synaptics AI Developer Zone: Get started with documentation, tutorials and resources for your Edge AI journey.
• Astra Support Portal: Connect with our engineering team and community.