The paper presents MobileNetV3, an advanced version of MobileNet designed for mobile devices, focusing on improving accuracy and efficiency. MobileNetV3 combines complementary search techniques, including hardware-aware network architecture search (NAS) and the NetAdapt algorithm, with novel architectural improvements. The authors introduce two new models: MobileNetV3-Large and MobileNetV3-Small, tailored for high and low resource usage, respectively. These models are evaluated on tasks such as object detection and semantic segmentation, achieving state-of-the-art results. Key contributions include a new efficient segmentation decoder, Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and optimized nonlinearities like h-swish, which are more efficient for mobile devices. The paper also discusses the trade-offs between accuracy and latency, providing detailed experimental results and ablation studies to demonstrate the effectiveness of the proposed techniques.The paper presents MobileNetV3, an advanced version of MobileNet designed for mobile devices, focusing on improving accuracy and efficiency. MobileNetV3 combines complementary search techniques, including hardware-aware network architecture search (NAS) and the NetAdapt algorithm, with novel architectural improvements. The authors introduce two new models: MobileNetV3-Large and MobileNetV3-Small, tailored for high and low resource usage, respectively. These models are evaluated on tasks such as object detection and semantic segmentation, achieving state-of-the-art results. Key contributions include a new efficient segmentation decoder, Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and optimized nonlinearities like h-swish, which are more efficient for mobile devices. The paper also discusses the trade-offs between accuracy and latency, providing detailed experimental results and ablation studies to demonstrate the effectiveness of the proposed techniques.