计算机科学
延迟(音频)
推论
移动设备
失败
毫秒
分割
人工神经网络
蜂窝网络
分布式计算
计算机体系结构
人工智能
实时计算
计算机网络
并行计算
操作系统
电信
物理
天文
作者
Pavan Kumar Anasosalu Vasu,James Gabriel,JinJiang Zhu,Oncel Tuzel,Anurag Ranjan
标识
DOI:10.1109/cvpr52729.2023.00764
摘要
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 38×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 https://github.com/apple/ml-mobileone
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