Facts about EfficientNet
- 09
Neural architecture search on a small baseline network called EfficientNet-B0 underpins the entire EfficientNet family, with B0 itself designed using MnasNet's multi-objective search framework.
- 08
Transfer learning with EfficientNet-B0 achieves 98.7% accuracy on Stanford Dogs dataset after fine-tuning for only 10 epochs, demonstrating exceptional performance on downstream classification tasks with minimal domain-specific training.
- 07
EfficientNet models demonstrate 2 to 11 times better accuracy-to-latency trade-offs compared to ResNet, MobileNet, and Inception architectures across mobile and desktop deployment scenarios.
- 06
Progressive image resolution augmentation during EfficientNet training, starting at 128x128 pixels and gradually increasing to full resolution, reduces training time by approximately 23% compared to training with maximum resolution from the beginning.
- 05
Training EfficientNet-B0 from scratch requires approximately 4.7 GPU days on a TPUv3 pod to reach 77.1% ImageNet top-1 accuracy, making it significantly faster to train than comparable ResNet architectures.
- 04
Compound scaling in EfficientNet adjusts network depth, width, and resolution proportionally using a mathematical formula rather than manual tuning, enabling efficient scaling across seven model sizes from B0 to B7.
- 03
Mobile deployment of EfficientNet requires 5.1 times less memory than MobileNetV2 while delivering superior accuracy on edge devices with limited computational resources.
- 02
EfficientNet-B7, the largest variant in the family, achieves 84.4% top-1 accuracy on ImageNet while using only 66 million parameters compared to ResNet-50's 25.5 million parameters for lower accuracy.
- 01
Google researchers introduced EfficientNet in 2019 using compound scaling to improve ImageNet accuracy from 84.3% to 88.5% with 10 times fewer parameters.