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<meta name="author" content="Adam Harvey" />
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<section><h1>VGG Face 2</h1>
</section><section><div class='meta'><div><div class='gray'>Years</div><div>TBD</div></div><div><div class='gray'>Images</div><div>TBD</div></div><div><div class='gray'>Identities</div><div>TBD</div></div><div><div class='gray'>Origin</div><div>TBD</div></div><div><div class='gray'>Funding</div><div>IARPA</div></div></div><section><section class='fullwidth'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/vgg_face2/assets/vgg_face2_index.gif' alt='...'><div class='caption'>...</div></div></section><section><h3>Analysis</h3>
<ul>
<li>The VGG Face 2 dataset includes approximately 1,331 actresses, 139 presidents, 16 wives, 3 husbands, 2 snooker player, and 1 guru</li>
</ul>
<h3>Names and descriptions</h3>
<ul>
<li>The original VGGF2 name list has been updated with the results returned from Google Knowledge</li>
<li>Names with a similarity score greater than 0.75 where automatically updated. Scores computed using <code>import difflib; seq = difflib.SequenceMatcher(a=a.lower(), b=b.lower()); score = seq.ratio()</code></li>
<li>The 97 names with a score of 0.75 or lower were manually reviewed and includes name changes validating using Wikipedia.org results for names such as "Bruce Jenner" to "Caitlyn Jenner", spousal last-name changes, and discretionary changes to improve search results such as combining nicknames with full name when appropriate, for example changing "Aleksandar Petrović" to "Aleksandar 'Aco' Petrović" and minor changes such as "Mohammad Ali" to "Muhammad Ali"</li>
<li>The 'Description' text was automatically added when the Knowledge Graph score was greater than 250</li>
</ul>
<h2>TODO</h2>
<ul>
<li>create name list, and populate with Knowledge graph information like LFW</li>
<li>make list of interesting number stats, by the numbers</li>
<li>make list of interesting important facts</li>
<li>write intro abstract</li>
<li>write analysis of usage</li>
<li>find examples, citations, and screenshots of useage</li>
<li>find list of companies using it for table</li>
<li>create montages of the dataset, like LFW</li>
<li>create right to removal information</li>
</ul>
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