MnasFPN：学习用于移动设备上的对象检测的感知延迟的金字塔体系结构

尽管在资源受限的环境中进行视觉任务的体系结构搜索取得了巨大的成功，但设备上对象检测体系结构的设计大部分还是手动的。少数自动搜索工作要么围绕非移动友好的搜索空间展开，要么不受设备上延迟的影响。..

MnasFPN: Learning Latency-aware Pyramid Architecture for Object Detection on Mobile Devices

Despite the blooming success of architecture search for vision tasks in resource-constrained environments, the design of on-device object detection architectures have mostly been manual. The few automated search efforts are either centered around non-mobile-friendly search spaces or not guided by on-device latency.We propose MnasFPN, a mobile-friendly search space for the detection head, and combine it with latency-aware architecture search to produce efficient object detection models. The learned MnasFPN head, when paired with MobileNetV2 body, outperforms MobileNetV3+SSDLite by 1.8 mAP at similar latency on Pixel. It is also both 1.0 mAP more accurate and 10% faster than NAS-FPNLite. Ablation studies show that the majority of the performance gain comes from innovations in the search space. Further explorations reveal an interesting coupling between the search space design and the search algorithm, and that the complexity of MnasFPN search space may be at a local optimum.