From 76f36c6c5dafe754b066903b1ee8ecdd1b92dcab Mon Sep 17 00:00:00 2001
From: Jules Laplace <julescarbon@gmail.com>
Date: Sun, 16 Dec 2018 20:01:23 +0100
Subject: faceSearch client

---
 site/public/datasets/vgg_face2/index.html | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

(limited to 'site/public/datasets/vgg_face2')

diff --git a/site/public/datasets/vgg_face2/index.html b/site/public/datasets/vgg_face2/index.html
index 63715a4f..6e6c7ac1 100644
--- a/site/public/datasets/vgg_face2/index.html
+++ b/site/public/datasets/vgg_face2/index.html
@@ -30,7 +30,7 @@
     
   <section><h1>VGG Faces2</h1>
 </section><section><div class='meta'><div><div class='gray'>Created</div><div>2018</div></div><div><div class='gray'>Images</div><div>3.3M</div></div><div><div class='gray'>People</div><div>9,000</div></div><div><div class='gray'>Created From</div><div>Scraping search engines</div></div><div><div class='gray'>Search available</div><div>[Searchable](#)</div></div></div></section><section><p>VGG Face2 is the updated version of the VGG Face dataset and now includes over 3.3M face images from over 9K people. The identities were selected by taking the top 500K identities in Google's Knowledge Graph of celebrities and then selecting only the names that yielded enough training images. The dataset was created in the UK but funded by Office of Director of National Intelligence in the United States.</p>
-</section><section class='applet_container'><div class='applet' data-payload='{"command": "face_search"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "name_search"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "load_file assets/lfw_names_gender_kg_min.csv", "fields": ["Name, Images, Gender, Description"]}'></div></section><section><h2>VGG Face2 by the Numbers</h2>
+</section><section class='applet_container'><div class='applet' data-payload='{"command": "face_search"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "name_search"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "load_file assets/lfw_names_gender_kg_min.csv", "fields": ["Name, Images, Gender, Description"]}'></div></section><section><h3>VGG Face2 by the Numbers</h3>
 <ul>
 <li>1,331 actresses, 139 presidents</li>
 <li>3 husbands and 16 wives</li>
@@ -39,14 +39,14 @@
 <li>1 pornographic actress</li>
 <li>3 computer programmer</li>
 </ul>
-<h1>Names and descriptions</h1>
+<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>
-<h1>TODO</h1>
+<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>
-- 
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