Blog
The latest news from Google AI
Train your own image classifier with Inception in TensorFlow
Wednesday, March 9, 2016
Posted by Jon Shlens, Senior Research Scientist
At the end of last year we released code that allows a user
to classify images with TensorFlow
models. This code demonstrated how to build an image classification system by employing a deep learning model that we had previously trained. This model was known to classify an image across 1000 categories supplied by the
ImageNet
academic competition with an error rate that approached
human performance
. After all, what self-respecting computer vision system would fail to recognize a cute puppy?
Image via
Wikipedia
Well, thankfully the image classification model would recognize this image as a
retriever
with 79.3% confidence. But, more spectacularly, it would also be able to distinguish between a
spotted salamander
and
fire salamander
with high confidence – a task that might be quite difficult for those not experts in
herpetology
. Can
you
tell the difference?
Images via
Wikipedia
The deep learning model we released,
Inception-v3
, is described in our Arxiv preprint "
Rethinking the Inception Architecture for Computer Vision
” and can be visualized with this schematic diagram:
Schematic diagram of Inception-v3
As described in the preprint, this model achieves 5.64% top-5 error while an ensemble of four of these models achieves 3.58% top-5 error on the validation set of the ImageNet whole image
ILSVRC 2012
classification task. Furthermore, in the
2015 ImageNet Challenge
, an ensemble of 4 of these models came in 2nd in the image classification task.
After the release of this model, many people in the TensorFlow community voiced their preference on having an Inception-v3 model that they can train themselves, rather than using our pre-trained model. We could not agree more, since a system for training an Inception-v3 model provides many opportunities, including:
Exploration of different variants of this model architecture in order to improve the image classification system.
Comparison of optimization algorithms and hardware setups for training this model faster or to a higher degree of predictive performance.
Retraining/fine-tuning the Inception-v3 model on a distinct image classification task or as a component of a larger network tasked with object detection or multi-modal learning.
The last topic is often referred to as
transfer learning
, and has been an area of particular excitement in the field of deep networks in the context of vision. A common prescription to a computer vision problem is to first train an image classification model with the ImageNet Challenge data set, and then transfer this model’s knowledge to a distinct task. This has been done for
object detection
,
zero-shot learning
,
image captioning
,
video analysis
and multitudes of other applications.
Today we are happy to announce that
we are releasing libraries and code for training
Inception-v3
on one or multiple GPU’s. Some features of this code include:
Training an Inception-v3 model with synchronous updates across multiple GPUs.
Employing batch normalization to speed up training of the model.
Leveraging many distortions of the image to augment model training.
Releasing a new (still experimental) high-level language for specifying complex model architectures, which we call
TensorFlow-Slim
.
Demonstrating how to perform transfer learning by taking a pre-trained Inception-v3 model and fine-tuning it for another task.
We can train a model from scratch to its best performance on a desktop with 8 NVIDIA Tesla K40s in about 2 weeks. In order to make research progress faster, we are additionally supplying a new version of a pre-trained Inception-v3 model that is ready to be fine-tuned or adapted to a new task. We demonstrate how to use this model for transfer learning on a simple flower classification task. Hopefully, this provides a useful didactic example for employing this Inception model on wide range of vision tasks.
Want to get started? See the accompanying
instructions
on how to
train
,
evaluate
or
fine-tune
a network.
Releasing this code has been a huge team effort. These efforts have taken several months with contributions from many individuals spanning research at Google. We wish to especially acknowledge the following people who contributed to this project:
Model Architecture
– Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Jon Shlens and Zbigniew Wojna
Systems Infrastructure
– Sherry Moore, Martin Wicke, David Andersen, Matthieu Devin, Manjunath Kudlur and Nishant Patil
TensorFlow-Slim
– Sergio Guadarrama and Nathan Silberman
Model Visualization
– Fernanda Viégas, Martin Wattenberg and James Wexler
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
2020
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
.
