Efficient neural network backbones for mobile devices are often optimized for
metrics such as FLOPs or parameter count. However, these metrics may not
correlate well with latency of the network when deployed on a mobile device.
Therefore, we perform extensive analysis of different metrics by deploying
several mobile-friendly networks on a mobile device. We identify and analyze
architectural and optimization bottlenecks in recent efficient neural networks
and provide ways to mitigate these bottlenecks. To this end, we design an
efficient backbone MobileOne, with variants achieving an inference time under 1
ms on an iPhone12 with 75.9% top-1 accuracy on ImageNet. We show that MobileOne
achieves state-of-the-art performance within the efficient architectures while
being many times faster on mobile. Our best model obtains similar performance
on ImageNet as MobileFormer while being 38x faster. Our model obtains 2.3%
better top-1 accuracy on ImageNet than EfficientNet at similar latency.
Furthermore, we show that our model generalizes to multiple tasks - image
classification, object detection, and semantic segmentation with significant
improvements in latency and accuracy as compared to existing efficient
architectures when deployed on a mobile device. Code and models are available
at this https URL

通过对多个移动设备上的网络进行部署和分析，提出了有效神经网络的优化瓶颈，设计了一种高效的 MobileOne 神经网络，该网络在 iPhone12 上获得了 1 ms 以下的推断时间，并在性能和速度方面超越了现有的语义分割网络。