The Inverted Residual Block, also known as an MBConv Block, is a type of residual block used for image models that follows an inverted structure for efficiency reasons. This type of block was originally proposed for the MobileNetV2 CNN architecture and has since been widely used for several mobile-optimized CNNs.
Traditional Residual Block Structure
A traditional Residual Block has a structure that starts with a wide input, which is then compressed with a 1x1 convolution to a narrower size, and then expanded again with another 1x1 convolution so the input and output can be added. This creates a structure that looks like wide -> narrow -> wide.
This structure has been commonly used in many CNN architectures, but it has some disadvantages. One of these disadvantages is that it requires a larger number of parameters, which can make the model size large and slow down its computation.
Inverted Residual Block Structure
The Inverted Residual Block follows a narrow -> wide -> narrow approach, hence the inversion. We first start with a 1x1 convolution to narrow the input channels. Then, we apply a 3x3 depthwise convolution, which greatly reduces the number of parameters, making the model faster and more efficient. Finally, we use another 1x1 convolution to increase the number of channels to match the input so the input and output can be added again.
This approach has some advantages over the traditional residual block structure. First, it is more efficient since it requires fewer parameters for computation, making it faster and more accurate. Second, the inverted structure can lead to better gradient flow, which is important for training large models.
Why is the Inverted Residual Block Important for Mobile Devices?
Mobile devices have limited resources, including processing power and memory. Therefore, it is important to have models that are lightweight and can run on mobile devices with low latency. The Inverted Residual Block is important for this reason.
It has been widely used in many mobile-optimized CNNs, such as MobileNetV2 and EfficientNet. These models have been shown to have high accuracy while keeping their model size and computational requirements low. This makes them perfect for running on mobile devices, where limited resources are available.
The Inverted Residual Block is a type of residual block used for image models that is efficient, accurate, and lightweight. It has been widely used in many mobile-optimized CNNs and has shown to be very effective at improving the performance of these models. By using an inverted structure, fewer parameters are needed, making the model faster and more efficient. This makes it perfect for running on mobile devices with limited resources.
