Computer vision

Image recognition on Cortex-M

Using an STM32F7 development board, learn how to use the CMSIS-NN library to improve the performance and energy efficiency of your Cortex-M based real-time image recognition application.

Deploy a Caffe model using CMSIS-NN

Run a smile detection program on the Cortex-M7 based OpenMV Cam, using a Caffe model.

Image recognition with TensorFlow

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

Add intelligent vision

Understand the concepts of adding intelligent vision to your next embedded device.

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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.

ML solutions on embedded devices

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

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.

Run AlexNet on Raspberry Pi

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

Accelerate ML using Compute Library on HiKey 960

Run ML applications on a high development platform using the HiKey 960, a mobile development platform built on Arm Cortex processors and Mali GPUs.