This paper introduces EfficientNetV2, a new family of convolutional networks that have faster training speed and better parameter efficiency than previous models. To develop this family of models, we use a combination of training-aware neural architecture search and scaling, to jointly optimize training speed and parameter efficiency. The models were searched from the search space enriched with new ops such as Fused-MBConv. Our experiments show that EfficientNetV2 models train much faster than state-of-the-art models while being up to 6.8x smaller. Our training can be further sped up by progressively increasing the image size during training, but it often causes a drop in accuracy. To compensate for this accuracy drop, we propose to adaptively adjust regularization (e.g., dropout and data augmentation) as well, such that we can achieve both fast training and good accuracy. With progressive learning, our EfficientNetV2 significantly outperforms previous models on ImageNet and CIFAR/Cars/Flowers datasets. By pretraining on the same ImageNet21k, our EfficientNetV2 achieves 87.3% top-1 accuracy on ImageNet ILSVRC2012, outperforming the recent ViT by 2.0% accuracy while training 5x-11x faster using the same computing resources. Code will be available at https://github.com/google/automl/efficientnetv2.

本文介绍了EfficientNetV2, 这是一种新的卷积网络家族，具有比以前的模型更快的训练速度和更好的参数效率，同时采用了神经结构搜索和缩放相结合的方法，以共同优化训练速度和参数效率。通过逐渐增加图像大小来训练，我们的EfficientNetV2在ImageNet和CIFAR / Cars / Flowers数据集上显著优于以前的模型。

EfficientNetV2: 更小的模型和更快的训练