We have explored MobileNet V2 architecture in depth. MobileNet V2 model has 53 convolution layers and 1 AvgPool with nearly 350 GFLOP. It has two main components:

• Inverted Residual Block
• Bottleneck Residual Block

There are two types of Convolution layers in MobileNet V2 architecture:

• 1x1 Convolution
• 3x3 Depthwise Convolution

These are the two different components in MobileNet V2 model:

Each block has 3 different layers:

• 1x1 Convolution with Relu6
• Depthwise Convolution
• 1x1 Convolution without any linearity

There are Stride 1 Blocks and Stride 2 Blocks. The internal components of the two blocks are as follows:

Stride 1 Block:

• Input
• 1x1 Convolution with Relu6
• Depthwise Convolution with Relu6
• 1x1 Convolution without any linearity
• Add

Stride 2 Block:

• Input
• 1x1 Convolution with Relu6
• Depthwise Convolution with stride=2 and Relu6
• 1x1 Convolution without any linearity

Layers in MobileNetV2

Bottleneck is either Inverted Residual Block or Bottleneck Residual Block or Stride 1 or Stride 2 block.

Convolutions in MobileNetV2

Following is the list of the 53 Convolution layers in MobileNetV2 architecture with details of different parameters like Input height, Input width, Kernel height and more:

The parameters of each Convolution layer in order are:

• Input Height and width
• Input Channel
• Kernel Height and Width
• Stride Height/ Width
• Padding Height/ Width
• Output Height/ Width
• Output Channel

With this, you have the complete idea about the architecture of MobileNetV2 model. Enjoy.