Running AlexNet on Raspberry Pi with Arm Compute Library
Overview Prerequisites Introducing the Graph API Introducing AlexNet Evaluate the example code Download and install the tutorial ZIP file Compile the Arm Compute Library Run the classifier Develop your own network using the Arm Compute Library
This guide follows on from our blog post that introduces the Arm Compute Library.
The instructions given here show you how to develop a Convolutional Neural Network (CNN) called AlexNet using just the Arm Compute Library and a Raspberry Pi. Links are provided to all of the software tools that you need to get up and running.
The guide starts by introducing Arm Compute Library's graph API and AlexNet, two tools that help simplify developing neural networks on a Raspberry Pi. An example of AlexNet using the graph API, in C++, is explained in detail to help you get started running your own and other classifiers.
By following the steps in this guide, you’ll be up and running with AlexNet, one of the first Deep Convolutional Neural Networks (CNN) designed to recognize 1000 different object categories within images. You will use AlexNet to classify an image of a go-kart with the neural network returning some predictions based on the image content. The network can only be used to predict the 1000 object categories that it has been trained with. If you want to use the CNN for a different task, then the network has to be re-trained.