Kaggle Data Scientists Contest & Chance to Win Wikipedia-Branded Merchandise

Kaggle invites data scientists to participate in a competition to build a model that automatically retrieves the text closest to an image. Specifically, you'll train your model to associate given images with article titles or complex captions, in multiple languages. The best models will account for the semantic granularity of Wikipedia images. If successful, you'll be contributing to the accessibility of the largest online encyclopedia. The millions of Wikipedia readers and editors will be able to more easily understand, search, and describe media at scale.

The top three winning teams will receive Wikipedia-branded merchandise.

• The competition is open to residents of the United States and all countries of the world except for countries subject to sanctions from the USA such as Cuba, Iran, Syria, North Korea and Sudan.
• You must be at least 18 years of age or the age of majority depending on the country in which you reside.
• Each person must have an individual account registered with Kaggle
• Participants may submit up to a maximum of five Submissions per day.

The Submission must meet the following criteria:

• Not contain material that violates or infringes any rights of any other party, including but not limited to the copyright, trademark, patent, privacy, publicity, or any other intellectual property rights;
• Not contain material that is inappropriate, indecent, obscene hateful, tortious, defamatory, slanderous, or libelous;
• Not contain material that promotes bigotry, racism, hatred, or harm against any group or individual or promotes discrimination based on race, gender, religion, nationality, disability, sexual orientation, or age;
• Not have been developed or created for or on behalf of a client of a Participant and must not have won any kind of award, competition, or contest.

About Kaggle:

Kaggle, a subsidiary of Google LLC, is an online community of data scientists and machine learning practitioners. Kaggle allows users to find and publish data sets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges.

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