Optimizing YOLOv8 & Scaling Object Detection on Small Devices 0 like January 6, 2025Share this postAuthor(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.This member-only story is on us. Upgrade to access all of Medium.Photo by Museums Victoria on UnsplashImagine 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 its the future of object detection, powered by YOLOv8.YOLOv8 is one of the fastest and most accurate models for identifying objects, but its also hefty. When deploying this model on smaller, resource-limited devices like phones, sensors, or embedded systems, you can quickly run into performance issues. Thats where optimization techniques come in.In this article, well 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 youre 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 imageYOLOv8 is the latest release in the well-known You Only Look Once (YOLO) series, and its a game-changer when it comes to speed and accuracy. Its widely used in areas like self-driving cars, surveillance systems, and robotics. But while YOLOv8 is extremely powerful, its also quite resource-intensive. Thats where optimization comes Read the full blog for free on Medium.Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming asponsor. Published via Towards AITowards AI - Medium Share this post