MobileNet v3 vs EfficientNet-Lite: Pi 4 Latency Benchmarks

The 47ms Gap That Changed My Model Choice

MobileNet v3 Large runs at 89ms per frame on a Raspberry Pi 4. EfficientNet-Lite0 hits 136ms. That 47ms difference sounds small until you realize it's the gap between 11 FPS and 7 FPS — between "usably smooth" and "noticeably laggy" for a live camera feed.

I expected these models to perform similarly. Both target mobile deployment. Both claim sub-100ms inference on "edge devices." But the Pi 4's Cortex-A72 cores aren't the Snapdragon 855 that most benchmarks assume. And TensorFlow Lite's XNNPACK delegate behaves very differently depending on which operations your model uses.

Here's what actually happens when you deploy both to a $35 board.

Photo by Alessandro Oliverio on Pexels

Setting Up the Benchmark Environment

The Raspberry Pi 4 Model B (4GB) runs Raspberry Pi OS Lite (64-bit, Debian Bookworm). I'm using TensorFlow Lite 2.15 with the XNNPACK delegate enabled — this matters because XNNPACK handles depthwise convolutions much better than the default interpreter.

python
import numpy as np

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*Continue reading the full article on [TildAlice](https://tildalice.io/mobilenet-v3-efficientnet-lite-raspberry-pi-benchmark/)*