MobileDets

MobileDets is a specialized family of object detection models developed by Google Research, specifically designed to optimize the latency-accuracy trade-off on mobile hardware. By utilizing Neural Architecture Search (NAS), MobileDets moves beyond the 'one-size-fits-all' approach of depthwise separable convolutions used in previous MobileNet iterations. The architecture identifies the optimal placement of inverted bottleneck (IBN) layers and fused convolutions based on the specific target hardware, such as mobile CPUs, DSPs, and Edge TPUs. In the 2026 market, MobileDets serves as a critical backbone for developers who require real-time, high-precision detection without the computational overhead of Transformer-based architectures. Its design specifically addresses memory access patterns that often bottleneck mobile processors, allowing it to outperform MobileNetV3-based SSDLite models in Mean Average Precision (mAP) while maintaining lower latency. As part of the TensorFlow Object Detection API, it supports seamless export to TensorFlow Lite, making it an industry standard for production-grade on-device computer vision in sectors ranging from robotics to mobile-first consumer applications.