Blog
The latest news from Google AI
MobileNetV2: The Next Generation of On-Device Computer Vision Networks
Tuesday, April 3, 2018
Posted by Mark Sandler and Andrew Howard, Google Research
Last year we introduced
MobileNetV1
, a family of general purpose computer vision neural networks designed with mobile devices in mind to support classification, detection and more. The ability to run deep networks on personal mobile devices improves user experience, offering anytime, anywhere access, with additional benefits for security, privacy, and energy consumption. As new applications emerge allowing users to interact with the real world in real time, so does the need for ever more efficient neural networks.
Today, we are pleased to announce the availability of
MobileNetV2
to power the next generation of mobile vision applications. MobileNetV2 is a significant improvement over MobileNetV1 and pushes the state of the art for mobile visual recognition including classification, object detection and semantic segmentation. MobileNetV2 is released as part of
TensorFlow-Slim Image Classification Library
, or you can start exploring MobileNetV2 right away in
Colaboratory
. Alternately, you can
download
the notebook and explore it locally using
Jupyter
. MobileNetV2 is also available as
modules
on TF-Hub, and pretrained checkpoints can be found
on github
.
MobileNetV2 builds upon the ideas from MobileNetV1 [1], using depthwise separable convolution as efficient building blocks. However, V2 introduces two new features to the architecture: 1) linear bottlenecks between the layers, and 2) shortcut connections between the bottlenecks
1
. The basic structure is shown below.
Overview of MobileNetV2 Architecture. Blue blocks represent composite convolutional building blocks as shown above.
The intuition is that the bottlenecks encode the model’s intermediate inputs and outputs while the inner layer encapsulates the model’s ability to transform from lower-level concepts such as pixels to higher level descriptors such as image categories. Finally, as with traditional residual connections, shortcuts enable faster training and better accuracy. You can learn more about the technical details in our paper, “
MobileNet V2: Inverted Residuals and Linear Bottlenecks
”.
How does it compare to the first generation of MobileNets?
Overall, the MobileNetV2 models are faster for the same accuracy across the entire latency spectrum. In particular, the new models use 2x fewer operations, need 30% fewer parameters and are about 30-40% faster on a Google Pixel phone than MobileNetV1 models, all while achieving higher accuracy.
MobileNetV2 improves speed (reduced latency) and increased ImageNet Top 1 accuracy
MobileNetV2 is a very effective feature extractor for object detection and segmentation. For example, for detection when paired with the newly introduced SSDLite [2] the new model is about 35% faster with the same accuracy than MobileNetV1. We have open sourced the model under the
Tensorflow Object Detection API
[4].
Model
Params
Multiply-Adds
mAP
Mobile CPU
MobileNetV1 + SSDLite
5.1M
1.3B
22.2%
270ms
MobileNetV2 + SSDLite
4.3M
0.8B
22.1%
200ms
To enable on-device semantic segmentation, we employ MobileNetV2 as a feature extractor in a reduced form of DeepLabv3 [3], that was
announced recently
. On the semantic segmentation benchmark,
PASCAL VOC 2012
, our resulting model attains a similar performance as employing MobileNetV1 as feature extractor, but requires 5.3 times fewer parameters and 5.2 times fewer operations in terms of Multiply-Adds.
Model
Params
Multiply-Adds
mIOU
MobileNetV1 + DeepLabV3
11.15M
14.25B
75.29%
MobileNetV2 + DeepLabV3
2.11M
2.75B
75.32%
As we have seen MobileNetV2 provides a very efficient mobile-oriented model that can be used as a base for many visual recognition tasks. We hope by sharing it with the broader academic and open-source community we can help to advance research and application development.
Acknowledgements:
We would like to acknowledge our core contributors Menglong Zhu, Andrey Zhmoginov and Liang-Chieh Chen. We also give special thanks to Bo Chen, Dmitry Kalenichenko, Skirmantas Kligys, Mathew Tang, Weijun Wang, Benoit Jacob, George Papandreou, Zhichao Lu, Vivek Rathod, Jonathan Huang, Yukun Zhu, and Hartwig Adam.
References
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
,
Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H, arXiv:1704.04861, 2017.
MobileNetV2: Inverted Residuals and Linear Bottlenecks
,
Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. arXiv preprint. arXiv:1801.04381, 2018.
Rethinking Atrous Convolution for Semantic Image Segmentation
,
Chen LC, Papandreou G, Schroff F, Adam H. arXiv:1706.05587, 2017.
Speed/accuracy trade-offs for modern convolutional object detectors
,
Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017.
Deep Residual Learning for Image Recognition
,
He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. arXiv:1512.03385,2015
1
The shortcut (also known as skip) connections, popularized by ResNets[5] are commonly used to connect the non-bottleneck layers. MobilenNetV2 inverts this notion and connects the bottlenecks directly.
↩
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2021
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
Follow @googleai
Give us feedback in our
Product Forums
.
