The release of large, publicly available image datasets, such as ImageNet, Open Images and Conceptual Captions, has been one of the factors driving the tremendous progress in the field of computer vision. While these datasets are a necessary and critical part of developing useful machine learning (ML) models, some open source data sets have been found to be geographically skewed based on how they were collected. Because the shape of a dataset informs what an ML model learns, such skew may cause the research community to inadvertently develop models that may perform less well on images drawn from geographical regions under-represented in those data sets. For example, the images below show one standard open-source image classifier trained on the Open Images dataset that does not properly apply “wedding” related labels to images of wedding traditions from different parts of the world.
|Wedding photographs (donated by Googlers), labeled by a classifier trained on the Open Images dataset. The classifier’s label predictions are recorded below each image.|
While Google is focusing on building even more representative datasets, we also want to encourage additional research in the field around ways that machine learning methods can be more robust and inclusive when learning from imperfect data sources. This is an important research challenge, and one that pushes the boundaries of ways that machine learning models are currently created. Good solutions will help ensure that even when some data sources aren’t fully inclusive, the models developed with them can be.
In support of this effort and to spur further progress in developing inclusive ML models, we are happy to announce the Inclusive Images Competition on Kaggle. Developed in partnership with the Conference on Neural Information Processing Systems Competition Track, this competition challenges you to use Open Images, a large, multilabel, publicly-available image classification dataset that is majority-sampled from North America and Europe, to train a model that will be evaluated on images collected from a different set of geographic regions across the globe.
For model evaluation, we have created two Challenge datasets via our Crowdsource project, where we asked our volunteers from across the globe to participate in contributing photos of their surroundings. We hope that these datasets, built by donations from Google’s global community, will provide a challenging geographically-based stress test for this competition. We also plan to release a larger set of images at the end of the competition to further encourage inclusive development, with more inclusive data.
|Examples of labeled images from the challenge dataset. Clockwise from top left, image donation by Peter Tester, Mukesh Kumhar, HeeYoung Moon, Sudipta Pramanik, jaturan amnatbuddee, Tomi Familoni and Anu Subhi|
The Inclusive Images Competition officially started September 5th with the available training data & first stage Challenge data set. The deadline for submitting your results will be Monday, November 5th, and the test set will be released on Tuesday, November 6th. For more details and timelines, please visit the Inclusive Images Competition website.
The results of the competition will be presented at the 2018 Conference on Neural Information Processing Systems, and we will provide top-ranking competitors with travel grants to attend the conference (see this page for full details). We look forward to being part of the community’s development of more inclusive, global image classification algorithms!
We would like to thank the following individuals for making the Inclusive Image Competition and dataset possible: James Atwood, Pallavi Baljekar, Parker Barnes, Anurag Batra, Eric Breck, Peggy Chi, Tulsee Doshi, Julia Elliott, Gursheesh Kaur, Akshay Gaur, Yoni Halpern, Henry Jicha, Matthew Long, Jigyasa Saxena, and D. Sculley.