MobileNetV1 (2017) and MobileNetV2 (2018)

General structure

MobileNet model is designed for mobile applications and is TensorFlow’s first mobile computer vision model. Since it is designed for mobile applications, the architecture focuses on efficiently creating smaller sizes and less computational cost models. Let’s take a look at the general properties of the model:

• It is a 28-layer architecture with various sizes of convolutional layers and fully connected ones at the end.

• It has similar training strategies to the models we examined before. ReLU activation function is applied after each layer following batch normalization, except for the last laser using softmax.

• It uses a width multiplier to scale channels of input and output feature maps and a resolution multiplier to scale the input data size.

• Apart from standard convolutions, it uses depthwise separable convolutions to reduce the computational cost and model size.