MobileNetV3: Improving Performance Through Separable Convolutions, Adaptive Width & Resolution, SE Modules, and Hard-Swish

The MobileNetV3 model improves performance through several key approaches. Firstly, it replaces traditional convolution operations with separable convolutions, which decompose the convolution into depth-wise and point-wise operations for channel-specific feature extraction and fusion, respectively. This approach reduces computational complexity while improving model accuracy. Secondly, the model incorporates an adaptive method for adjusting network width and resolution based on input image size and complexity. This includes a network width multiplier and a resolution multiplier, which enable flexible adaptation to different hardware platforms and application scenarios. Thirdly, the model introduces a Squeeze-and-Excitation (SE) module to adaptively adjust the importance of channel features. This module calculates the global average value of each channel to determine its importance, and uses the resulting weight as a scaling factor for channel features. This approach improves model performance without adding significant computational cost. Finally, the model adopts a new activation function called Hard-Swish, which balances computational complexity and accuracy better than traditional ReLU and Sigmoid functions. Hard-Swish has very low computational complexity while maintaining good non-linear characteristics.