Edge AI Object Detection: YOLO Mobile Optimization Guide

Most Mobile YOLO Deployments Are Over-Engineered

I see the same pattern everywhere: teams grab YOLOv8 or v11, export to ONNX, realize it's too slow, then spend weeks chasing quantization frameworks and custom operators. They burn through their MVP timeline before discovering that the model architecture itself was the problem.

The uncomfortable truth? For most mobile MVPs, you don't need the latest YOLO. You need the smallest one that hits your accuracy floor, running on the simplest inference stack that doesn't crash. Everything else is premature optimization.

Here's what actually matters when you're trying to ship object detection on a phone with two weeks and limited ML experience.

Photo by Patrick Gamelkoorn on Pexels

Pick Your Poison: Model Size vs Inference Latency

YOLOv5n (nano) is 1.9MB. YOLOv8n is 3.2MB. YOLOv11n is 2.6MB. All three can detect 80 COCO classes at ~30 FPS on a mid-range Android phone (tested on Pixel 6, TFLite with XNNPACK delegate, 320×320 input).

But the ecosystem complexity escalates fast:

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