EfficientNetV2 vs ResNet: 11x Faster Training Explained

#efficientnetv2 #resnet #paperreview #progressivelearning

Why ResNet's Dominance Is Finally Over

EfficientNetV2 trains 11x faster than ResNet while achieving better accuracy. That's not a typo. When I first saw the numbers from Tan and Le's 2021 paper, I assumed there was a catch — maybe a different dataset, different augmentation, some asterisk buried in the appendix. But the benchmarks hold up. You can read the full paper here.

The real story isn't just speed. It's that we've been training ImageNet models wrong for years.

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The Progressive Learning Trick Nobody Uses

Here's what makes EfficientNetV2 actually work: progressive learning. Start with small images (128×128), weak augmentation (basic crops, flips). As training progresses, gradually increase both image size AND regularization strength. By the final epochs, you're at 380×380 with aggressive RandAugment and Mixup.

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