AI on the Edge: Watch This Before Buying a Raspberry Pi HAILO Accelerator

Why It Matters

Demonstrating functional YOLO inference on a low‑cost Raspberry Pi shows that edge AI can be prototyped without expensive hardware, informing makers and startups about realistic performance expectations and the trade‑offs between software optimization and accelerator accessories.

Summary

The video walks viewers through setting up AI on the edge using a Raspberry Pi 5, focusing on running YOLOv11 for object detection without any external accelerator. Paul McCarter explains why he chooses the 8 GB Pi 5 and a fresh Bookworm 64‑bit image, then details the step‑by‑step installation of OpenCV, MediaPipe, a Python virtual environment, and the YOLO libraries, highlighting version constraints such as pinning NumPy below 2.0.

Key technical insights include switching the display manager to X11, updating the OS, and using a dedicated SD card to isolate the AI workload. He demonstrates how to create a virtual environment that inherits site‑packages so the Pi camera libraries remain accessible, and shows the export of the YOLO model to the NCN format for faster inference on the Pi’s CPU. The demo program captures 1280×720 frames at 60 fps, overlays a live FPS counter, and verifies that the YOLO import succeeds.

Notable moments feature a live check of OpenCV version 4.11, a workaround for MediaPipe’s “externally managed environment” error by adding the –break flag, and a candid admission that the full‑size YOLO model only yields 3‑4 fps on bare metal. McCarter stresses that to achieve usable performance, users should rely on the optimized NCN model or consider a HAILO accelerator hat.

The tutorial underscores that edge AI on inexpensive hardware is feasible but performance‑limited, prompting makers to weigh software optimization against hardware add‑ons. For developers, the guide provides a reproducible, documented workflow that can be adapted as libraries evolve, making the Pi a viable prototyping platform for low‑latency vision tasks.

Original Description

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In this video lesson we present a step-by-step tutorial on how to install YOLO11 on the raspberry pi 5, running on bookworm OS. We show how to convert the standard YOLO models to models optimized for the Raspberry Pi 5. We show you can get 10 FPS on the Pi 5 using the optimized model for videos a full 1280x720 resolution. By the end of this video lesson you will be running full strength object detection on your raspberry pi 5, without buying an accelerator.

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#raspberrypi

#AI

#yolo