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<!doctype html>
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<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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<div class='site_name'>MegaPixels</div>
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<a href="/datasets/">Datasets</a>
<a href="/about/">About</a>
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<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><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 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>
</div> -->
<div class="applet" data-payload="{"command": "chart"}"></div>
</section><section>
<p>
These pie charts show overall totals based on country and institution type.
</p>
</section>
<section class="applet_container">
<div class="applet" data-payload="{"command": "piechart"}"></div>
</section><section>
<h3>Information Supply Chain</h3>
<!--
<div class="map-sidebar right-sidebar">
<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>
</div>
-->
<p>
To understand how MsCeleb has been used around the world...
affected global research on computer vision, surveillance, defense, and consumer technology, the and where this dataset has been used the locations of each organization that used or referenced the datast
</p>
</section>
<section class="applet_container">
<div class="applet" data-payload="{"command": "map"}"></div>
</section>
<div class="caption">
<ul class="map-legend">
<li class="edu">Academic</li>
<li class="com">Industry</li>
<li class="gov">Government / Military</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>
<div class="hr-wave-holder">
<div class="hr-wave-line hr-wave-line1"></div>
<div class="hr-wave-line hr-wave-line2"></div>
</div>
<h2>Supplementary Information</h2>
</section><section class="applet_container">
<h3>Citations</h3>
<p>
Citations were collected from <a href="https://www.semanticscholar.org">Semantic Scholar</a>, a website which aggregates
and indexes research papers. The citations were 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 and/or test machine learning algorithms.
</p>
<p>
Add [button/link] to download CSV. Add search input field to filter. Expand number of rows to 10. Reduce URL text to show only the domain (ie https://arxiv.org/pdf/123456 --> arxiv.org)
</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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