This tutorial teaches you how to train a custom machine learning model with Edge Impulse® and to do image classification on the Arduino Nicla Vision. The Machine Learning (ML) model will use the TensorFlow Lite format and the classification example will run on OpenMV.

Oct 5, 2023 · By the end of this tutorial, you'll have a working prototype capable of classifying images in real-time, all running on a low-power embedded system based on the Arduino Nicla Vision. The Arduino Nicla Vision (or NiclaV) is a development board that includes two processors that can run tasks in parallel.

Feb 21, 2025 · With image classification, Arduino-powered robots can identify objects in their environment, helping them navigate and interact with the world around them. For example, a robot can be trained to recognize and avoid obstacles.

Use a TinyML neural network to recognize images taken by a OV7670 camera attached to a Arduino Nano 33 BLE. Inferencing and recognition runs on the Nano and gives predictions of which object is placed in front of the camera. The network is trained and …

In this ESP32-CAM tutorial, we will use machine learning techniques to build an image classification project using ESP32 CAM. The ESP32-CAM will be used to capture an image which will then be identified using a trained Machine learning model.

In this tutorial, you'll use machine learning to build a system that can recognize objects in your house through a camera - a task known as image classification - connected to a microcontroller.

By the end of this tutorial, you’ll have a working prototype capable of classifying images in real-time, all running on a low-power embedded system based on the Arduino Nicla Vision board.

Image classification on Arduino Nano 33 BLE Sense with 8-bit quantization. Uses TensorFlow Lite to optimize model size for real-time inference, balancing performance with memory efficiency on embedded hardware.

In this example, we will be using Ameba Pro2 development board to identify images and perform classification. Open image classification example in “File” -> “Examples” -> “AmebaNN” -> “RTSPImageClassification”.

In this article I will teach you how to create your own custom image classifier with transfer learning in Keras, convert the trained model to .kmodel format and run it on Sipeed board (can be any board, Bit/Dock or Go) using Micropython or Arduino IDE.