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<!doctype html>
<html>
<head>
<title>MegaPixels</title>
<meta charset="utf-8" />
<meta name="author" content="Adam Harvey" />
<meta name="description" content="MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms" />
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<header>
<a class='slogan' href="/">
<div class='logo'></div>
<div class='site_name'>MegaPixels</div>
<div class='splash'>MsCeleb</div>
</a>
<div class='links'>
<a href="/datasets/">Datasets</a>
<a href="/about/">About</a>
</div>
</header>
<div class="content content-dataset">
<section class='intro_section' style='background-image: url(https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/msceleb/assets/background.jpg)'><div class='inner'><div class='hero_desc'><span class='bgpad'>MS Celeb is a dataset of web images used for training and evaluating face recognition algorithms</span></div><div class='hero_subdesc'><span class='bgpad'>The MS Celeb dataset includes over 10,000,000 images and 93,000 identities of semi-public figures collected using the Bing search engine
</span></div></div></section><section><div class='left-sidebar'><div class='meta'>
<div class='gray'>Published</div>
<div>2016</div>
</div><div class='meta'>
<div class='gray'>Images</div>
<div>1,000,000 </div>
</div><div class='meta'>
<div class='gray'>Identities</div>
<div>100,000 </div>
</div><div class='meta'>
<div class='gray'>Purpose</div>
<div>Large-scale face recognition</div>
</div><div class='meta'>
<div class='gray'>Created by</div>
<div>Microsoft Research</div>
</div><div class='meta'>
<div class='gray'>Funded by</div>
<div>Microsoft Research</div>
</div><div class='meta'>
<div class='gray'>Website</div>
<div><a href='http://www.msceleb.org/' target='_blank' rel='nofollow noopener'>msceleb.org</a></div>
</div><div class='meta'><div><div class='gray'>Published</div><div>TBD</div></div><div><div class='gray'>Images</div><div>TBD</div></div><div><div class='gray'>Faces</div><div>TBD</div></div><div><div class='gray'>Created by</div><div>TBD</div></div></div></div><h2>Microsoft Celeb Dataset (MS Celeb)</h2>
<p>(PAGE UNDER DEVELOPMENT)</p>
<p>At vero eos et accusamus et iusto odio dignissimos ducimus, qui blanditiis praesentium voluptatum deleniti atque corrupti, quos dolores et quas molestias excepturi sint, obcaecati cupiditate non-provident, similique sunt in culpa, qui officia deserunt mollitia animi, id est laborum et dolorum fuga. Et harum quidem rerum facilis est et expedita distinctio.</p>
<p>Nam libero tempore, cum soluta nobis est eligendi optio, cumque nihil impedit, quo minus id, quod maxime placeat, facere possimus, omnis voluptas assumenda est, omnis dolor repellendus. Temporibus autem quibusdam et aut officiis debitis aut rerum necessitatibus saepe eveniet, ut et voluptates repudiandae sint et molestiae non-recusandae. Itaque earum rerum hic tenetur a sapiente delectus, ut aut reiciendis voluptatibus maiores alias consequatur aut perferendis doloribus asperiores repellat</p>
</section><section>
<h3>Who used MsCeleb?</h3>
<p>
This bar chart presents a ranking of the top countries where dataset citations originated. Mouse over individual columns to see yearly totals. These charts show at most the top 10 countries.
</p>
</section>
<section class="applet_container">
<!-- <div style="position: absolute;top: 0px;right: -55px;width: 180px;font-size: 14px;">Labeled Faces in the Wild Dataset<br><span class="numc" style="font-size: 11px;">20 citations</span>
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<h3>Biometric Trade Routes</h3>
<!--
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<h3>Legend</h3>
<ul>
<li><span style="color: #f2f293">■</span> Industry</li>
<li><span style="color: #f30000">■</span> Academic</li>
<li><span style="color: #3264f6">■</span> Government</li>
</ul>
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-->
<p>
To help understand how MsCeleb has been used around the world for commercial, military and academic research; publicly available research citing Microsoft Celebrity Dataset is collected, verified, and geocoded to show the biometric trade routes of people appearing in the images. Click on the markers to reveal reserach projects at that location.
</p>
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<div class="caption">
<ul class="map-legend">
<li class="edu">Academic</li>
<li class="com">Commercial</li>
<li class="gov">Military / Government</li>
<li class="source">Citation data is collected using <a href="https://semanticscholar.org" target="_blank">SemanticScholar.org</a> then dataset usage verified and geolocated.</li>
</ul>
</div>
<!-- <section>
<p class='subp'>
[section under development] MsCeleb ... Standardized paragraph of text about the map. Sed ut perspiciatis, unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo.
</p>
</section>
--><section><p>Add more analysis here</p>
</section><section>
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<h3>Supplementary Information</h3>
</section><section class="applet_container">
<h3>Dataset Citations</h3>
<p>
The dataset citations used in the visualizations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates and indexes research papers. Each citation was geocoded using names of institutions found in the PDF front matter, or as listed on other resources. These papers have been manually verified to show that researchers downloaded and used the dataset to train or test machine learning algorithms.
</p>
<div class="applet" data-payload="{"command": "citations"}"></div>
</section><section><h3>Additional Information</h3>
<ul>
<li>The dataset author spoke about his research at the CVPR conference in 2016 <a href="https://www.youtube.com/watch?v=Nl2fBKxwusQ">https://www.youtube.com/watch?v=Nl2fBKxwusQ</a></li>
</ul>
</section><section><ul class="footnotes"><li><a name="[^readme]" class="footnote_shim"></a><span class="backlinks"></span><p>"readme.txt" <a href="https://exhibits.stanford.edu/data/catalog/sx925dc9385">https://exhibits.stanford.edu/data/catalog/sx925dc9385</a>.</p>
</li><li><a name="[^localized_region_context]" class="footnote_shim"></a><span class="backlinks"></span><p>Li, Y. and Dou, Y. and Liu, X. and Li, T. Localized Region Context and Object Feature Fusion for People Head Detection. ICIP16 Proceedings. 2016. Pages 594-598.</p>
</li><li><a name="[^replacement_algorithm]" class="footnote_shim"></a><span class="backlinks"></span><p>Zhao. X, Wang Y, Dou, Y. A Replacement Algorithm of Non-Maximum Suppression Base on Graph Clustering.</p>
</li></ul></section>
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