Raspberry Pi 5 - Unboxing | DigiKey

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Inside the box you’ll find handling instructions and a user guide along with your Raspberry Pi 5. The Raspberry Pi 5 is based on a quad-core 64-bit Arm Cortex-A76 CPU with cryptography extensions, a 4Kp60 HEVC decoder and 2 MIPI camera display transceivers. The Pi 5 also includes a standard 40 pin header, a real time clock, 4 USB ports, dual micro HDMI. Dual-band Wi-Fi®, Bluetooth 5.0, gigabit Ethernet, and a MicroSD slot.
In this demo, we're going to test object recognition using the Pi 5 and a web camera. To get started, you will need a power supply, Micro HDMI cable, mouse, keyboard, and a USB webcam. It’s also recommended to provide active cooling for the raspberry pi or have it enclosed in the official case.
Plug your Pi 5 into a power supply and connect a display monitor with a micro HDMI cable. Connect your mouse, keyboard, and webcam in the USB ports. Next, run the following commands to update your Pi 5 and install the Edge Impulse dependencies in the Terminal:
sudo apt update
curl -sL deb.nodesource.com/setup_18.x | sudo bash -
sudo apt install -y gcc g++ make build-essential nodejs sox gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-base gstreamer1.0-plugins-base-apps
sudo npm install edge-impulse-linux -g --unsafe-perm
Go to your browser to open the Edge Impulse studio and login or create your account if you haven’t already. Create a new project under your account icon, and then enter the following command in the Raspberry Pi 5 terminal to link up your hardware:
edge-impulse-linux -clean
In the Edge Impulse Studio select Data Acquisition. In the collect data panel enter the label name and select the camera for the sensor type. Start sampling multiple images of your object. In this demo, we’re using a chess piece. Note: More samples will give you better accuracy. Select all your samples and move them to the test set. Select all the images you want to include and move them to the training set. Now select "Labeling queue" at the top of the data acquisition window. Draw a box around your object, enter a label name and click "Set label” for each image in your queue. Click create impulse under impulse design and add an image processing block and an object detection learning block then save your impulse.
Under image recognition click generate features. Go to object detection and start training.
Lastly, go to model testing and select Classify All.
Now select live classification to start sampling in the Edge Impulse studio, or enter the on screen command in your terminal to deploy the model locally:
edge-impulse-linux-runner
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00:00 Intro
00:31 Inside the box
00:37 Features
01:09 Demo
03:41 Outro

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