Blog
The latest news from Google AI
Introducing the iNaturalist 2018 Challenge
Friday, March 9, 2018
Posted by Yang Song, Staff Software Engineer and Serge Belongie, Visiting Faculty, Google Research
Thanks to recent advances in
deep learning
, the visual recognition abilities of machines have improved dramatically, permitting the practical application of computer vision to tasks ranging from pedestrian detection for
self-driving cars
to expression recognition in
virtual reality
. One area that remains challenging for computers, however, is
fine-grained
and
instance-level recognition
. Earlier this month, we posted an
instance-level landmark recognition challenge
for identifying individual landmarks. Here we focus on fine-grained visual recognition, which is to distinguish species of animals and plants, car and motorcycle models, architectural styles, etc. For computers, discriminating fine-grained categories is challenging because many categories have relatively few training examples (i.e., the
long tail
problem), the examples that do exist often lack authoritative training labels, and there is variability in illumination, viewing angle and object occlusion.
To help confront these hurdles, we are excited to announce the
2018 iNaturalist Challenge
(iNat-2018), a species classification competition offered in partnership with
iNaturalist
and
Visipedia
(short for Visual Encyclopedia), a project for which Caltech and Cornell Tech received a Google Focused Research Award. This is a flagship challenge for the 5th International Workshop on Fine Grained Visual Categorization (
FGVC5
) at
CVPR 2018
. Building upon the first iNaturalist challenge,
iNat-2017
, iNat-2018 spans over 8000 categories of plants, animals, and fungi, with a total of more than 450,000 training images. We invite participants to enter the competition on
Kaggle
, with final submissions due in early June. Training data, annotations, and links to pretrained models can be found on our
GitHub repo
.
iNaturalist has emerged as a world leader for citizen scientists to share observations of species and connect with nature since its founding in 2008. It hosts research-grade photos and annotations submitted by a thriving, engaged community of users. Consider
the following photo
from iNaturalist:
The map on the right shows where the photo was taken. Image credit: Serge Belongie
.
You may notice that the photo on the left contains a turtle. But did you also know this is a
Trachemys scripta
, common name “Pond Slider?” If you knew the latter, you possess knowledge of
fine-grained
or
subordinate
categories.
In contrast to other image classification datasets such as ImageNet, the dataset in the iNaturalist challenge exhibits a
long-tailed distribution
, with many species having relatively few images. It is important to enable machine learning models to handle categories in the long-tail, as the natural world is heavily imbalanced – some species are more abundant and easier to photograph than others. The iNaturalist challenge will encourage progress because the training distribution of iNat-2018 has an even longer tail than iNat-2017.
Distribution of training images per species for iNat-2017 and iNat-2018, plotted on a log-linear scale, illustrating the long-tail behavior typical of fine-grained classification problems. Image Credit:
Grant Van Horn and Oisin Mac Aodha
.
Along with iNat-2018,
FGVC5
will also host the iMaterialist 2018 challenge (including a
furniture categorization challenge
and a fashion attributes challenge for product images) and a set of “
FGVCx
” challenges representing smaller scale – but still significant – challenges, featuring content such as food and modern art.
FGVC5
will be showcased on the main stage at
CVPR 2018
, thereby ensuring broad exposure for the top performing teams. This project will advance the state-of-the-art in automatic image classification for real world, fine-grained categories, with heavy class imbalances, and large numbers of classes. We cordially invite you to participate in these
competitions
and help move the field forward!
Acknowledgements
We’d like to thank our colleagues and friends at
iNaturalist
,
Visipedia
, and
FGVC5
for working together to advance this important area. At Google we would like to thank Hartwig Adam, Weijun Wang, Nathan Frey, Andrew Howard, Alessandro Fin, Yuning Chai, Xiao Zhang, Jack Sim, Yuan Li, Grant Van Horn, Yin Cui, Chen Sun, Yanan Qian, Grace Vesom, Tanya Birch, Celeste Chung, Wendy Kan, and Maggie Demkin.
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
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
.
