If you are using computer vision techniques like image classification and object detection, here are some resources from Arm and partners that can help. Advancements in deep learning, neural networks and embedded compute capabilities enables machine vision on low-power Cortex-M processors, as well as even more efficiency for Cortex-A based devices. Go to section:

Computer vision on Cortex-M | Computer vision on Cortex-A | Get Support

Computer vision on Cortex-M

Person detection with CMSIS-NN and TensorFlow Lite for Microcontrollers

Detect a person on the Arduino Nano BLE Sense with CMSIS-NN optimizations in Tensorflow Lite for Microcontrollers.

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Image classification using OpenMV Cam, Edge Impulse, and Arduino

Learn simplified deep learning for image classification on an OpenMV Cam using Edge Impulse and an Arduino.

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Low code approach to AIoT application development

Build an end-to-end tinyML IoT application on an Arduino Portenta board, running on Mbed OS and CMSIS and  communicating with the Arduino IoT Cloud.

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Image classification to detect production line defects

 Learn how an optimized image classification model can be trained to detect defects on a production line using the OpenMV H7 board.

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Image recognition with TensorFlow

Learn how to use Tensorflow Lite for Microcontrollers to run a neural network to recognize people in images.

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Image recognition on the Cortex-M1 processor

Create a low-cost image solution using a Xilinx Spartan S7 FPGA, the Xilinx Vivado Design Suite and Keil MDK.

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Adding sight to your sensors

Use ML to build a system that can recognize objects in a house using the OpenMV Cam H7 Plus and Edge Impulse.

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MNIST handwriting recognition

Build a MNIST handwriting recognition app using TensorFlow Lite for Microcontrollers on a Cortex M7-based processor.

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Smile detection with OpenMV IDE and Edge Impulse

Train powerful Convolutional Neural Networks in the cloud for any application, and have them run on the OpenMV Cam in just 15 minutes.

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Computer vision on Cortex-A

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Train your Raspberry Pi to detect what gesture you are performing, through transfer learning.

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More robust models

Build and train a more robust model, to have your Raspberry Pi detect multiple gestures.

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Image analysis on Raspberry Pi

Build an Arm NN-based application for an IoT device that performs automatic trash sorting using image analysis.

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ML solutions on embedded devices

Use the Au-Zone DeepView ML toolkit for on-target runtime performance on vision applications.

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Profile AlexNet on Raspberry Pi and HiKey 960

Use Streamline to profile the AlexNet example application from the Arm Compute Library for Raspberry Pi and the HiKey 960 board.

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Run AlexNet on Raspberry Pi

Develop a Convolutional Neural Network (CNN) called AlexNet using the Arm Compute Library and a Raspberry Pi.

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Using Arm NN to develop edge AI in the smart city

Learn in this tech session how to apply Arm NN to vision applications as a neural network inference engine for Arm CPUs, GPUs and NPUs.

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