Getting Started with Raspberry Pi AI Kit

The Raspberry Pi 5 is a small, single-board computer designed for edge development. It features a quad-core 64-bit Arm Cortex-A76 CPU with a clock rate of 2.4 GHz and 4GB or 8GB of low-power LPDDR4X-4267 SDRAM memory. However, even with these specifications, it may not be sufficient to run high-load AI/ML applications. This is where the Raspberry Pi AI Kit comes in. It is powered by a Hailo-8L AI acceleration module capable of 13 tera-operations per second.

To begin developing AI and machine learning projects using the Raspberry Pi AI Kit, Raspberry Pi provides various resources, including official documentation and sample codes. Also, the official tutorial can be found on Hailo's GitHub repository. I recommend that you go through these steps slowly and carefully.

Raspberry Pi AI Kit

The Raspberry Pi AI Kit is a powerful HAT for a Raspberry Pi 5 board designed for AI and machine learning applications. With its small form factor and impressive performance, it is an ideal platform for developers, hobbyists, and educators to explore and create AI-powered projects.

Combine that with its low $70 USD price tag, and AI/ML enthusiasts see a cheap device that opens up exciting possibilities for AI applications in robotics, autonomous vehicles, surveillance systems, and more.

It stands out with its specialized AI chip developed by Hailo Technologies, optimized for efficient AI processing, despite its small size, it can handle complex AI models.

Running the Inference demos

Hailo's repository includes scripts that allow you to perform object detection, pose estimation and semantic segmentation on both video and a live video stream.

Below I'll go through how to run object detection, pose estimation and semantic segmentation.

Run object detection example using below command:

python basic_pipelines/detection.py --input /path/to/video/ --show-fps --disable-sync

A new window will open displaying the video image.

Ctrl + C to terminate the application and close the window

Now, it's time to run the instance segmentation example

python basic_pipelines/instance_segmentation.py --input /path/to/video/ --show-fps --disable-sync

Here's an example video demonstrating instance segmentation with multiple objects using the Ultralytics YOLOv5 model, achieving an average inference speed of 30fps.

Run pose estimation example:

python basic_pipelines/pose_estimation.py --input /path/to/video/ --show-fps --disable-sync

In this link, you can find a clear guidance of how to create custom deep leaning models and convert the model to Hailo format. Check it out.

From here, you're ready to take advantage of the Raspberry Pi AI Kit's powerful processing capabilities. Depending on your project's needs and your own creativity, your options are nearly endless. Happy developing!

I hope you found this guide useful and thanks for reading. If you have any questions or feedback? Leave a comment below. If you like this post, please support me by subscribing to my blog.

References

• Raspberry Pi AI kit documentation