MobileNetV2 is a mobile-friendly neural network architecture that improves performance on multiple tasks and benchmarks while maintaining efficiency. It introduces an inverted residual structure with linear bottlenecks, which allows for efficient computation and memory usage. The architecture uses depthwise separable convolutions and linear bottlenecks to reduce computational cost while maintaining accuracy. The inverted residual structure connects bottlenecks directly, enabling efficient inference and reducing memory requirements. MobileNetV2 is evaluated on ImageNet, COCO, and VOC datasets, showing improved performance compared to previous models. It is also used to build efficient object detection and semantic segmentation models. The architecture is efficient, with a small number of parameters and operations, making it suitable for mobile and embedded applications. MobileNetV2 outperforms other models in terms of accuracy and efficiency, particularly in real-time applications. The design allows for a clear separation between the input/output domains and the transformation expressiveness, enabling further analysis and optimization. The model is implemented in TensorFlow-Slim and has been shown to be effective in various tasks, including object detection and semantic segmentation.MobileNetV2 is a mobile-friendly neural network architecture that improves performance on multiple tasks and benchmarks while maintaining efficiency. It introduces an inverted residual structure with linear bottlenecks, which allows for efficient computation and memory usage. The architecture uses depthwise separable convolutions and linear bottlenecks to reduce computational cost while maintaining accuracy. The inverted residual structure connects bottlenecks directly, enabling efficient inference and reducing memory requirements. MobileNetV2 is evaluated on ImageNet, COCO, and VOC datasets, showing improved performance compared to previous models. It is also used to build efficient object detection and semantic segmentation models. The architecture is efficient, with a small number of parameters and operations, making it suitable for mobile and embedded applications. MobileNetV2 outperforms other models in terms of accuracy and efficiency, particularly in real-time applications. The design allows for a clear separation between the input/output domains and the transformation expressiveness, enabling further analysis and optimization. The model is implemented in TensorFlow-Slim and has been shown to be effective in various tasks, including object detection and semantic segmentation.