Blog

The latest from Google Research

Introducing the Next Generation of On-Device Vision Models: MobileNetV3 and MobileNetEdgeTPU

Wednesday, November 13, 2019

Posted by Andrew Howard and Suyog Gupta, Software Engineers, Google Researchalgorithmically-efficientGoogle Pixel 4Pixel Neural CoreEdge TPUedge computingMobileNetsMobileNetV3MobileNetEdgeTPUAutoMLMobileNetV2Building MobileNetV3previous version of MobileNetMnasNetNetAdaptreinforcement learning

Comparison of accuracy vs. latency for mobile models on the ImageNet classification task using the Google Pixel 4 CPU.

MobileNetV3 Search SpaceSwish

squeeze-and-excitationsigmoid functioninverted bottleneck structure

MobileNetV3 extends the MobileNetV2 inverted bottleneck structure by adding h-swish and mobile friendly squeeze-and-excitation as searchable options.

Size of expansion layer

Degree of squeeze-excite compression

Choice of activation function: h-swish or ReLU

Number of layers for each resolution block

MobileNetV3 Object Detection and Semantic SegmentationCOCO datasetsemantic segmentationCityscapes DatasetMobileNet for Edge TPUsCoralaccelerator-aware AutoMLDSPsGPUsEfficientNet-EdgeTPU

Left: Comparison of the accuracy on the ImageNet classification task between MobileNetEdgeTPU and other image classification networks designed for mobile when running on Pixel4 Edge TPU. MobileNetEdgeTPU achieves higher accuracy and lower latency compared with other models. Right: Average Edge TPU power in Watts for different classification models running at 30 frames per second (fps).

Objection Detection Using MobileNetEdgeTPUCOCO14 minival datasetThe Need for Hardware-Aware Models

MobileNetV3 is still the best performing network when using mobile CPU as the deployment target.

For Researchers and DevelopersMobileNet github pageTensorflow Object Detection APIDeepLabAcknowledgementsThis work is made possible through a collaboration spanning several teams across Google. We’d like to acknowledge contributions from Berkin Akin, Okan Arikan, Gabriel Bender, Bo Chen, Liang-Chieh Chen, Grace Chu, Eddy Hsu, John Joseph, Pieter-jan Kindermans, Quoc Le, Owen Lin, Hanxiao Liu, Yun Long, Ravi Narayanaswami, Ruoming Pang, Mark Sandler, Mingxing Tan, Vijay Vasudevan, Weijun Wang, Dong Hyuk Woo, Dmitry Kalenichenko, Yunyang Xiong, Yukun Zhu and support from Hartwig Adam, Blaise Agüera y Arcas, Chidu Krishnan and Steve Molloy.

Labels

Archive

2022
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2021
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2020
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2019
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2018
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2017
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2016
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2015
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2014
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2013
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2012
- Dec
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2011
- Dec
- Nov
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2010
- Dec
- Nov
- Oct
- Sep
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2009
- Dec
- Nov
- Aug
- Jul
- Jun
- May
- Apr
- Mar
- Feb
- Jan

2008
- Dec
- Nov
- Oct
- Sep
- Jul
- May
- Apr
- Mar
- Feb

2007
- Oct
- Sep
- Aug
- Jul
- Jun
- Feb

2006
- Dec
- Nov
- Sep
- Aug
- Jul
- Jun
- Apr
- Mar
- Feb

Feed

Give us feedback in our Product Forums.

Google
Privacy
Terms