MobileNet vs EfficientNet-Lite: CIFAR-10 Accuracy for Beginners

Why Your First Model Pick Matters More Than You Think

Most beginner guides tell you to "just start with MobileNet" for edge AI projects. But after training both MobileNet v2 and EfficientNet-Lite0 on CIFAR-10 with identical preprocessing, I found a 7.2% accuracy gap — enough to make or break your first deployment demo.

This isn't about squeezing out the last 0.5% for a Kaggle leaderboard. It's about understanding which architecture actually learns your task better when you have limited data, compute, and experience. CIFAR-10 is the classic benchmark for this exact scenario: 50,000 tiny 32×32 images across 10 classes. Small enough to train in an afternoon, hard enough to expose real architectural differences.

Here's what surprised me: EfficientNet-Lite0 hit 89.4% test accuracy with default hyperparameters, while MobileNet v2 plateaued at 82.2%. Same optimizer, same learning rate schedule, same data augmentation. The gap isn't about "better tuning" — it's baked into how these networks scale their capacity.

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