Image classification on high-performance MCU.
We provide two different networks, which offer a specific trade-off between inference time and accuracy.
Model: Standard Convolutional Neural Network quantized
Input size: 224x224x3
Memory footprint:
132 KB Flash for weights
148 KBRAM for activations
Accuracy: 72.8%
Performance on STM32H747 (High-Perf) @ 400 MHz
Inference time: 79 ms
Frame rate: 11.8 fps
Model: Optimized Convolutional Neural Network quantized
Input size: 224x224x3
Memory footprint:
148 KB Flash for weights
199 KBRAM for activations
Accuracy: 77,5%
Performance on STM32H747 (High-Perf) @ 400 MHz
Inference time: 145 ms
Frame rate: 6.6 fps
On-board validation summary of information for a food recognition example
A free STM32Cube expansion package, X-CUBE-AI allows developers to convert pretrained AI algorithms automatically, such as neural network and machine learning models, into optimized C code for STM32.
The STM32 family of 32-bit microcontrollers based on the Arm Cortex®-M processor is designed to offer new degrees of freedom to MCU users. It offers products combining very high performance, real-time capabilities, digital signal processing, low-power / low-voltage operation, and connectivity, while maintaining full integration and ease of development.