diff --git a/faq/index.html b/faq/index.html
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@@ -72,7 +72,7 @@ <h1><a href="/" class="no-decoration">Computer Vision Model Leaderboard</a></h1>
         </div>
 
         <h2>Frequently Asked Questions</h2>
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         <h3>What is mAP?</h3>
         <p>
             Mean Average Precision (mAP) is a metric used to evaluate the object detection models. It is the average of the precision-recall curves at different IoU thresholds.
@@ -95,7 +95,7 @@ <h3>What is F1 Score?</h3>
             Precision measures how many of the detected objects are correct. If the model found a box on some cars in an image, but classified 20% as bicycles, precision is 80%, regardless of how many were found.
 
             What if you want high recall and high precision? F1 Score simply combines the two into a single metric. With a high F1 score, you can be sure that model produced both high precision and recall in its results.
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         <p>Here is the formula for F1 Score, where P is precision and R is recall:</p>
 
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diff --git a/methodology/index.html b/methodology/index.html
index 94b7ae1..72984ea 100644
--- a/methodology/index.html
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@@ -87,7 +87,7 @@ <h2>Methodology</h2>
         <p>
             The Roboflow 100 benchmark was designed to measure model performance across domains. If you are interested in learning more about domain-specific model benchmarking, refer to the <a href="https://www.rf100.org/">Roboflow 100 website</a>.
         </p>
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     </div>
 
     <div class="tooltip"></div>
diff --git a/static/common.css b/static/common.css
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