This paper provides an extensive analysis of the performance of the
EfficientNet image classifiers with several recent training procedures, in
particular one that corrects the discrepancy between train and test images. The
resulting network, called FixEfficientNet, significantly outperforms the
initial architecture with the same number of parameters.
For instance, our FixEfficientNet-B0 trained without additional training data
achieves 79.3% top-1 accuracy on ImageNet with 5.3M parameters. This is a +0.5%
absolute improvement over the Noisy student EfficientNet-B0 trained with 300M
unlabeled images. An EfficientNet-L2 pre-trained with weak supervision on 300M
unlabeled images and further optimized with FixRes achieves 88.5% top-1
accuracy (top-5: 98.7%), which establishes the new state of the art for
ImageNet with a single crop.
These improvements are thoroughly evaluated with cleaner protocols than the
one usually employed for Imagenet, and particular we show that our improvement
remains in the experimental setting of ImageNet-v2, that is less prone to
overfitting, and with ImageNet Real Labels. In both cases we also establish the
new state of the art.

本篇论文通过对 EfficientNet 图像分类器的性能进行了详细分析，提出了一种修复训练集和测试集图像不一致问题的新训练方法 FixEfficientNet，并以 ImageNet 数据集进行了测试，证明了 FixEfficientNet 在相同参数下性能明显优于原始架构。