EfficientNetV2
EfficientNetV2
**EfficientNetV2** is a type convolutional neural network that has faster training speed and better parameter efficiency than [previous models](https://paperswithcode.com/method/efficientnet). To develop these models, the authors use a combination of training-aware [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) and scaling, to jointly optimize training speed. The models were searched from the search space enriched with new ops such as [Fused-MBConv](https://ai.googleblog.com/2019/08/efficientnet-edgetpu-creating.html).

Architecturally the main differences are:

- EfficientNetV2 extensively uses both [MBConv](https://paperswithcode.com/method/inverted-residual-block)  and the newly added fused-MBConv in the early layers.
- EfficientNetV2 prefers smaller expansion ratio for [MBConv](https://paperswithcode.com/method/inverted-residual-block) since smaller expansion ratios tend to have less memory access overhead.
- EfficientNetV2 prefers smaller 3x3 kernel sizes, but it adds more layers to compensate the reduced receptive field resulted from the smaller kernel size. 
- EfficientNetV2 completely removes the last stride-1 stage in the original EfficientNet, wperhaps due to its large parameter size and memory access overhead.
EfficientNetV2: Smaller Models and Faster Training
https://paperswithcode.com/paper/efficientnetv2-smaller-models-and-faster
EfficientNetV2: Smaller Models and Faster Training