Arduino TinyML Kit AKX00028 Nano 33 BLE Sense OV7675 Camera
The Official Arduino Tiny Machine Learning Kit AKX00028 provides a comprehensive platform for TinyML projects, featuring the powerful Arduino Nano 33 BLE Sense LITE board with an nRF52840 (ARM Cortex-M4) microcontroller, an OV7675 camera module, and a custom shield. This kit empowers developers to implement edge AI and machine learning solutions with integrated multi-sensor capabilities.
Features
- Integrates the Arduino Nano 33 BLE Sense LITE with nRF52840 (ARM Cortex-M4) MCU and ECC608 crypto chip.
- Features an OV7675 camera module for vision-based TinyML applications.
- Equipped with a wide array of onboard sensors including IMU, temperature, humidity, pressure, sound, and gesture.
- Includes a custom Arduino shield for easy component attachment and prototyping.
- Designed for hands-on TinyML projects, supporting TensorFlow Lite Micro and classical ML algorithms.
Out of stock
The Official Arduino Tiny Machine Learning Kit AKX00028 is a complete solution designed to introduce and advance users in the field of TinyML. Central to the kit is the powerful Arduino Nano 33 BLE Sense LITE board, which incorporates an nRF52840 microcontroller featuring a 32-bit ARM Cortex-M4 CPU running at 64 MHz, along with an ECC608 crypto chip for enhanced security. This board is distinguished by its extensive integrated sensor suite, capable of detecting movement, acceleration, rotation, temperature, humidity, barometric pressure, sounds, gestures, proximity, color, and light intensity.
Complementing the Nano 33 BLE Sense LITE, the kit includes a high-quality OV7675 camera module, enabling vision-based machine learning applications on the edge. A custom Arduino shield is also part of the bundle, simplifying the connection of additional components and facilitating the rapid development of unique TinyML projects. This setup is ideal for exploring practical ML use cases, from keyword spotting to gesture recognition and even facial recognition, utilizing both classical algorithms and deep neural networks powered by TensorFlow Lite Micro. The kit is particularly effective when combined with TinyML specialization courses, providing a robust platform for bringing intelligent, small-scale devices to life.
Additional Information
| Weight | 0.794 kg |
|---|---|
| Dimensions | 23 × 18 × 3 cm |
