border

3 files changed, 9 insertions, 30 deletions

diff --git a/client/index.js b/client/index.js
index 2c003888..c9335f14 100644
--- a/client/index.js
+++ b/client/index.js
@@ -36,7 +36,7 @@ function appendApplets(applets) {
         appendTable(el, payload)
         break
       case 'map':
-        el.parentNode.classList.add('fullwidth')
+        el.parentNode.classList.add('wide')
         appendMap(el, payload)
         el.classList.add('loaded')
         break
diff --git a/site/assets/css/applets.css b/site/assets/css/applets.css
index e450b46e..f437d1e8 100644
--- a/site/assets/css/applets.css
+++ b/site/assets/css/applets.css
@@ -132,6 +132,12 @@
   max-width: 40px;
 }
 
+.map, .map .applet {
+  height: 500px;
+}
+.map {
+  margin-bottom: 20px;
+}
 
 /* tabulator */
 
diff --git a/site/public/datasets/lfw/index.html b/site/public/datasets/lfw/index.html
index 6526c4f8..057413ae 100644
--- a/site/public/datasets/lfw/index.html
+++ b/site/public/datasets/lfw/index.html
@@ -66,40 +66,13 @@
 <p>According to BiometricUpdate.com [^lfw_pingan], LFW is "the most widely used evaluation set in the field of facial recognition, LFW attracts a few dozen teams from around the globe including Google, Facebook, Microsoft Research Asia, Baidu, Tencent, SenseTime, Face++ and Chinese University of Hong Kong."</p>
 <p>According to researchers at the Baidu Research – Institute of Deep Learning "LFW has been the most popular evaluation benchmark for face recognition, and played a very important role in facilitating the face recognition society to improve algorithm. [^lfw_baidu]."</p>
 <p>In addition to commercial use as an evaluation tool, alll of the faces in LFW dataset are prepackaged into a popular machine learning code framework called scikit-learn.</p>
-<table>
-<thead><tr>
-<th style="text-align:left">Company</th>
-<th style="text-align:left">Country</th>
-<th style="text-align:left">Industries</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td>
-<td style="text-align:left">China</td>
-<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td>
-</tr>
-<tr>
-<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td>
-<td style="text-align:left">China</td>
-<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td>
-</tr>
-<tr>
-<td style="text-align:left"><a href="http://www.aratek.co">Aratek</a></td>
-<td style="text-align:left">China</td>
-<td style="text-align:left">Biometric sensors for telecom, civil identification, finance, education, POS, and transportation</td>
-</tr>
-</tbody>
-</table>
-<p>Add 2-4 screenshots of companies mentioning LFW here</p>
 </section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_01.jpg' alt=' "PING AN Tech facial recognition receives high score in latest LFW test results"'><div class='caption'> "PING AN Tech facial recognition receives high score in latest LFW test results"</div></div>
 <div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_02.jpg' alt=' "Face Recognition Performance in LFW benchmark"'><div class='caption'> "Face Recognition Performance in LFW benchmark"</div></div>
 <div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/lfw_screenshot_03.jpg' alt=' "The 1st place in face verification challenge, LFW"'><div class='caption'> "The 1st place in face verification challenge, LFW"</div></div></section><section><p>In benchmarking, companies use a dataset to evaluate their algorithms which are typically trained on other data. After training, researchers will use LFW as a benchmark to compare results with other algorithms.</p>
 <p>For example, Baidu (est. net worth $13B) uses LFW to report results for their "Targeting Ultimate Accuracy: Face Recognition via Deep Embedding". According to the three Baidu researchers who produced the paper:</p>
 <h3>Citations</h3>
-<p>Overall, LFW has at least 456 citations from 123 countries. 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. Nemo enim ipsam voluptatem, quia voluptas sit, aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos.</p>
-<p>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. Nemo enim ipsam voluptatem, quia voluptas sit, aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos.</p>
-</section><section class='images'><div class='image'><img src='https://nyc3.digitaloceanspaces.com/megapixels/v1/datasets/lfw/assets/temp_graph.jpg' alt='Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset'><div class='caption'>Distribution of citations per year per country for the top 5 countries with citations for the LFW Dataset</div></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "map"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "citations"}'></div></section><section><h3>Conclusion</h3>
+<p>Overall, LFW has at least 116 citations from 11 countries.</p>
+</section><section class='applet_container'><div class='applet' data-payload='{"command": "map"}'></div></section><section class='applet_container'><div class='applet' data-payload='{"command": "citations"}'></div></section><section><h3>Conclusion</h3>
 <p>The LFW face recognition training and evaluation dataset is a historically important face dataset as it was the first popular dataset to be created entirely from Internet images, paving the way for a global trend towards downloading anyone’s face from the Internet and adding it to a dataset. As will be evident with other datasets, LFW’s approach has now become the norm.</p>
 <p>For all the 5,000 people in this datasets, their face is forever a part of facial recognition history. It would be impossible to remove anyone from the dataset because it is so ubiquitous. For their rest of the lives and forever after, these 5,000 people will continue to be used for training facial recognition surveillance.</p>
 <h2>Code</h2>