MobileNetV2 Paper Walkthrough: The Smarter Tiny Giant

MobileNetV2 improves upon its predecessor by introducing inverted residuals and linear bottlenecks. Inverted residuals utilize a narrow-to-wide-to-narrow structure, expanding channel dimensions within a block to help the lightweight model learn more complex patterns. The linear bottleneck concept involves removing the final ReLU activation function in each block to prevent information loss when projecting to a lower-dimensional tensor. The architecture also employs ReLU6 for better performance on low-precision devices and maintains adjustable parameters like a width multiplier and input resolution for further customization.