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Introducing the Open Images Dataset
Friday, September 30, 2016
Posted by Ivan Krasin and Tom Duerig, Software Engineers
In the last few years, advances in machine learning have enabled
Computer Vision
to progress rapidly, allowing for systems that can
automatically caption images
to apps that can create
natural language replies in response to shared photos
. Much of this progress can be attributed to publicly available image datasets, such as
ImageNet
and
COCO
for supervised learning, and
YFCC100M
for unsupervised learning.
Today, we introduce
Open Images
, a dataset consisting of ~9 million URLs to images that have been annotated with labels spanning over 6000 categories. We tried to make the dataset as practical as possible: the labels cover more real-life entities than the 1000 ImageNet classes, there are enough images to train a deep neural network from scratch and the images are listed as having a
Creative Commons Attribution
license
*
.
The image-level annotations have been populated automatically with a vision model similar to
Google Cloud Vision API
. For the validation set, we had human raters verify these automated labels to find and remove false positives. On average, each image has about 8 labels assigned. Here are some examples:
Annotated images form the Open Images dataset.
Left:
Ghost Arches
by
Kevin Krejci
.
Right:
Some Silverware
by
J B
. Both images used under
CC BY 2.0
license
We have trained an Inception v3 model based on Open Images annotations alone, and the model is good enough to be used for fine-tuning applications as well as for other things, like
DeepDream
or
artistic style transfer
which require a well developed hierarchy of filters. We hope to improve the quality of the annotations in Open Images the coming months, and therefore the quality of models which can be trained.
The dataset is a product of a collaboration between Google, CMU and Cornell universities, and there are a number of research papers built on top of the Open Images dataset in the works. It is our hope that datasets like
Open Images
and the
recently released YouTube-8M
will be useful tools for the machine learning community.
*
While we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself.
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