EfficientNetV2: A New and Improved Convolutional Neural Network
EfficientNetV2 is a new type of convolutional neural network that has faster training speeds and better parameter efficiency than the previous models. Developed through a combination of training-aware neural architecture search and scaling, EfficientNetV2 aims to optimize the training speed of convolutional neural networks. By enriching the search space with new operations such as Fused-MBConv, EfficientNetV2 was able to develop models that are more efficient than previous models.
The Architecture of EfficientNetV2
The main differences in the architecture of EfficientNetV2 are as follows. Firstly, it extensively uses both MBConv and the newly added fused-MBConv in the early layers. Secondly, it prefers smaller expansion ratios for MBConv since smaller expansion ratios tend to have less memory access overhead. Thirdly, it prefers smaller 3x3 kernel sizes but adds more layers to compensate for the reduced receptive field resulted from the smaller kernel size. Finally, it completely removes the last stride-1 stage in the original EfficientNet, perhaps due to its large parameter size and memory access overhead.
The use of MBConv in the early layers of EfficientNetV2 is significant since these layers are where most of the computation occurs. MBConv stands for Mobile Inverted Residual Bottleneck Convolutional Block, and it is a type of convolutional block that consists of three key components: the inverted bottleneck, the depthwise convolution, and the pointwise convolution. The inverted bottleneck is responsible for reducing the number of channels in the input tensor, while the depthwise convolution applies a separate filter to each channel in the tensor. Finally, the pointwise convolution re-expands the tensor to a higher number of channels. By using MBConv in the early layers, EfficientNetV2 is able to optimize the computation that occurs in these layers, leading to faster training speeds and better parameter efficiency overall.
The Smaller Expansion Ratio
The preference for a smaller expansion ratio for MBConv in EfficientNetV2 is also significant. The expansion ratio refers to the ratio between the number of output channels and input channels in the inverted bottleneck. In general, a smaller expansion ratio means that the computation required is lower, which in turn leads to faster training times and better efficiency. By reducing the size of the expansion ratio, EfficientNetV2 is able to minimize the memory access overhead as well, leading to further improvements in efficiency.
The Smaller 3x3 Kernel Size
The preference for a smaller 3x3 kernel size in EfficientNetV2 is also significant. The kernel size refers to the size of the convolutional filter applied to each element in the tensor. In general, a smaller kernel size means that less computation is required to produce an output, which again leads to faster training times and better efficiency. However, it is worth noting that the smaller kernel size can result in a reduced receptive field, which can impact the overall accuracy of the model. To compensate for this, EfficientNetV2 adds more layers to the network, which in turn helps to maintain the accuracy of the model.
The Removal of the Last Stride-1 Stage
The removal of the last stride-1 stage in the original EfficientNet is also noteworthy. The size of the parameter and memory access overhead in the original EfficientNet made this stage particularly slow, leading to suboptimal training times and efficiency. By removing this stage completely in EfficientNetV2, the model is able to further optimize its training speed and parameter efficiency.
EfficientNetV2 is a new and improved convolutional neural network that is optimized for faster training speed and better parameter efficiency than previous models. By using a combination of training-aware neural architecture search and scaling, EfficientNetV2 is able to develop models that are more efficient than previous models. The architecture of EfficientNetV2 focuses on using MBConv in the early layers, preferring a smaller expansion ratio, using a smaller 3x3 kernel size, and removing the last stride-1 stage in the original EfficientNet. By doing so, EfficientNetV2 is able to minimize the memory access overhead and reduce the computation required to produce an output, leading to faster training times and better efficiency overall.
