Optimizing YOLOv8 & Scaling Object Detection on Small Devices

Author(s): Supriya Rani

Originally published on Towards AI.

From Quantization Aware Training to Pruning, discover how to make YOLOv8 faster and more efficient for edge devices.

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Photo by Museums Victoria on Unsplash

Imagine running an AI model that can detect objects in real-time on your smartphone or a drone without freezing or draining the battery. Sounds like magic, right? But it’s the future of object detection, powered by YOLOv8.

YOLOv8 is one of the fastest and most accurate models for identifying objects, but it’s also hefty. When deploying this model on smaller, resource-limited devices like phones, sensors, or embedded systems, you can quickly run into performance issues. That’s where optimization techniques come in.

In this article, we’ll explore how tools like Quantization Aware Training (QAT), pruning, and others can transform YOLOv8 into a lean, mean detection machine that works seamlessly on low-resource devices. Whether you’re building the next generation of smart cameras or mobile apps, optimizing YOLOv8 is the key to delivering high-performance, real-time object detection.

AI generated image

YOLOv8 is the latest release in the well-known “You Only Look Once” (YOLO) series, and it’s a game-changer when it comes to speed and accuracy. It’s widely used in areas like self-driving cars, surveillance systems, and robotics. But while YOLOv8 is extremely powerful, it’s also quite resource-intensive. That’s where optimization comes… Read the full blog for free on Medium.

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Published via Towards AI