• This is an MCU-based vision AI module powered by Arm Cortex-M55 and Ethos-U55, supporting TensorFlow and PyTorch frameworks.
• It has a standard CSI interface, onboard digital microphone, and SD card slot.
• Compatible with XIAO series, Arduino, Raspberry Pi, and ESP dev board.
• Supports off-the-shelf and custom AI models from SenseCraft AI, including Mobilenet V1, V2, Efficientnet-lite, Yolo v5 & v8.
• Can be used for industrial automation, smart cities, transportation, smart agriculture, and mobile IoT devices.
• It has a standard CSI interface, onboard digital microphone, and SD card slot.
• Compatible with XIAO series, Arduino, Raspberry Pi, and ESP dev board.
• Supports off-the-shelf and custom AI models from SenseCraft AI, including Mobilenet V1, V2, Efficientnet-lite, Yolo v5 & v8.
• Can be used for industrial automation, smart cities, transportation, smart agriculture, and mobile IoT devices.
A simple and fast data pipeline foundation with sophisticated functionality.
emlearn is an open-source machine learning inference engine designed for microcontrollers and embedded devices. It supports various machine learning models for classification, regression, unsupervised learning, and feature extraction. The engine is portable, with a single header file include, and uses C99 code and static memory allocation. Users can train models in Python and convert them to C code for inference.
This article explains permutation feature importance (PFI), a popular method for understanding feature importance in explainable AI. The author walks through calculating PFI from scratch using Python and XGBoost, discussing the rationale behind the method and its limitations.
There’s a reason you’re confused
