MobileNet Ran First, EfficientNet-Lite Ran Better
Most Raspberry Pi tutorials default to MobileNet v1 because it's everywhere in TFLite examples. I followed that path for my first edge deployment — a simple object detector that had to run under 100ms on a Pi 4. MobileNet v1 1.0_224 hit 87ms inference on the CPU. Good enough to ship, until I tried EfficientNet-Lite0 and watched the same task drop to 49ms.
That 38ms difference turned into the gap between "barely usable" and "actually responsive" for real-time detection. The frustrating part? Both models have roughly the same parameter count (4.2M vs 4.6M) and similar ImageNet accuracy (70.4% vs 75.1% top-1). The speed gap comes down to how operations map to ARM NEON instructions and how TFLite optimizes depthwise separable convolutions versus inverted residuals with squeeze-excitation.
This isn't about one being strictly better. MobileNet v1 is still the right starting point if you need minimal setup friction or you're porting legacy TF1.x code. But if you're starting fresh in 2026 and latency matters, EfficientNet-Lite deserves the first benchmark slot.
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