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 is a sequel to our blog post on how to apply a cartoon effect with the Compute Library. The blog post introduces the Arm Compute Library and provides a simple example of how to use Raspberry with SSH. The post explains how to compile or cross-compile the Arm Compute Library for Raspberry Pi, but this is also covered here.
In addition to some basic knowledge of the Arm Compute Library, this guide assumes some knowledge of a CNN. You don’t need to be an expert, just have an idea of the main functions.
Further ahead in this guide, you will download and unzip the tutorial .zip file on the Raspberry Pi. This file contains:
- The AlexNet model. This is the same one as in the Caffe Model Zoo.
- A text file, containing the ImageNet labels that are required to map the predicted objects to the name of the classes.
- Several images in the ppm file format that are ready to be used with the network.
Raspberry Pi and host machine requirements
- Raspberry Pi 2 or 3 with Ubuntu Mate 16.04.02.
- A blank Micro SD card. We recommend an 8 GB (minimum 6GB) Class 6 or Class 10 microSDHC card for installing the Raspberry Pi OS and storing the CNN model.
- Router and ethernet cable. This is to connect to the Raspberry Pi using SSH.