Grove - Vision AI Module

Grove Vision AI Module is a thumb-sized board based on Himax HX6537-A processor and equips with an OV2640 camera. It's fully supported by Edge Impulse which allows you seamlessly sample raw data, build models and deploy the trained ML models to the module without any programming. It is compatible with the ecology of XIAO series and Arduino, ideal for a wide range of object detection applications.

The module features a compact AI-powered OV 2640 camera sensor embedded with Edge Machine Learning algorithms and can be easily deployed with a few simple clicks. It is a small and great assistant for various object detection projects and can be customized based on different needs. Just imagine endowing devices with vision through a tiny sensor.

Key Features

• Fully supported by Edge Impulse: Seamlessly sample raw data from the camera, build models and deploy trained ML models to the module from the studio without any programming
• Compact AI camera sensor: Equipped with OV2640 SOC sensor that supports auto-exposure and auto-white balance to offer good images
• Customizable model: Support the import of up to 3 customized models to realize mainstream ML functions like object detection
• Simple development & deployment: Support Arduino IDE programming and drag-and-drop model implementation
• High compatibility: Perfectly match with XIAO series interface, supports Arduino ecosystem through Grove connector

Use YOLOv5 to Build Detect Models of Your Own

YOLO is an abbreviation for the term 'You Only Look Once'. It is an algorithm that detects and recognizes various objects in an image in real-time. Ultralytics YOLOv5 is the version of YOLO based on the PyTorch framework. Based on its repository, Seeed Studio has published a more suitable version that fits in Seeed Studio AIoT hardware/devices. You can use this repository to build detection models of your own. They can not only be implemented on the Grove vision AI module but also on the industrial-level SenseCAP A1101 - LoRaWAN Vision AI Sensor.

Meanwhile, Seeed Studio have provided a specific tutorial to show you how to train the AI model for specific applications and then deploy it onto the devices.