Blog
The latest news from Google AI
Announcing Open Images V4 and the ECCV 2018 Open Images Challenge
Monday, April 30, 2018
Posted by Vittorio Ferrari, Research Scientist, Machine Perception
In 2016, we introduced
Open Images
, a collaborative release of ~9 million images annotated with labels spanning thousands of object categories. Since its initial release, we've been hard at work
updating
and
refining
the dataset, in order to provide a useful resource for the computer vision community to develop new models.
Today, we are happy to announce
Open Images V4
, containing 15.4M bounding-boxes for 600 categories on 1.9M images, making it the
largest existing dataset
with object location annotations. The boxes have been largely manually drawn by professional annotators to ensure accuracy and consistency. The images are very diverse and often contain complex scenes with several objects (8 per image on average;
visualizer
).
Annotated images from the Open Images dataset. Left:
Mark Paul Gosselaar plays the guitar
by
Rhys A.
Right:
Civilization
by
Paul Downey
. Both images used under
CC BY 2.0
license.
In conjunction with this release, we are also introducing the
Open Images Challenge
, a new object detection challenge to be held at the
2018 European Conference on Computer Vision
(ECCV 2018). The Open Images Challenge follows in the tradition of
PASCAL VOC
,
ImageNet
and
COCO
, but at an unprecedented scale.
This challenge is unique in several ways:
12.2M bounding-box annotations for 500 categories on 1.7M training images,
A broader range of categories than previous detection challenges, including new objects such as “fedora” and “snowman”.
In addition to the object detection main track, the challenge includes a
Visual Relationship Detection
track, on detecting pairs of objects in particular relations, e.g. “woman playing guitar”.
The training set is available now. A test set of 100k images will be released on July 1st 2018 by Kaggle. Deadline for submission of results is on September 1st 2018. We hope that the very large training set will stimulate research into more sophisticated detection models that will exceed current state-of-the-art performance, and that the 500 categories will enable a more precise assessment of where different detectors perform best. Furthermore, having a large set of images with many objects annotated enables to explore Visual Relationship Detection, which is a hot emerging topic with a growing sub-community.
In addition to the above, Open Images V4 also contains 30.1M human-verified image-level labels for 19,794 categories, which are not part of the Challenge. The dataset includes 5.5M image-level labels generated by tens of thousands of users from all over the world at
crowdsource.google.com
.
Labels
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
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
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
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
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
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
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
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
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
YouTube
Archive
2018
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
Google
on
Follow @googleai
Give us feedback in our
Product Forums
.
