Update README.md

README.md

@@ -2,44 +2,76 @@
 license: apache-2.0
 pipeline_tag: object-detection
 ---
+<repository>
+  <title>📌 Nepal Vehicle License Plates Detection Model (Version 3)</title>
 
-  <title>Nepal Vehicle License Plates Detection Model (Version 3)</title>
-  <description>
-    This repository contains the **Version 3** of the Nepal Vehicle License Plates Detection Model.
-    This version detects **individual characters** in Nepalese vehicle license plates and draws bounding boxes around each one.
-    It is an improvement over the earlier version, which detected license plates as a whole.
-  </description>
-  
-  <features>
-    <feature>
-      <title>Character-Wise Detection</title>
-      <details>
-        Unlike the earlier model, this version detects each character in the license plate individually and draws separate bounding boxes.
-      </details>
-    </feature>
-    <feature>
-      <title>Improved Accuracy</title>
-      <details>
-        Trained with refined datasets and configurations, resulting in higher precision and recall.
-      </details>
-    </feature>
-    <feature>
-      <title>Real-Time Inference</title>
-      <details>
-        Optimized for fast and accurate predictions, suitable for deployment in real-world applications.
-      </details>
-    </feature>
-  </features>
-  
-  <previousModel>
-    <title>Earlier Model</title>
-    <link>
-      <url>https://huggingface.co/krishnamishra8848/Nepal-Vehicle-License-Plate-Detection</url>
-      <description>
-        The earlier version of this project detected license plates as a whole. Click the link above to explore it.
-      </description>
-    </link>
-  </previousModel>
+  <section>
+    <heading>🔍 Description</heading>
+    <content>
+      **Version 3** of the Nepal Vehicle License Plates Detection Model offers advanced functionality to detect **individual characters** in Nepalese vehicle license plates and draw bounding boxes around each one. 
+      This version enhances the accuracy and granularity of detection compared to earlier versions, making it ideal for real-world applications.
+    </content>
+  </section>
+
+  <section>
+    <heading>✨ Key Features</heading>
+    <features>
+      <feature>
+        <name>📝 **Character-Wise Detection**</name>
+        <summary>Detects and draws bounding boxes for each character in a license plate for precise recognition.</summary>
+      </feature>
+      <feature>
+        <name>🎯 **High Precision and Recall**</name>
+        <summary>Achieves industry-leading metrics, ensuring accurate and reliable performance.</summary>
+      </feature>
+      <feature>
+        <name>⚡ **Real-Time Inference**</name>
+        <summary>Optimized for fast predictions, making it suitable for live detection applications.</summary>
+      </feature>
+    </features>
+  </section>
+
+  <section>
+    <heading>📜 Earlier Model</heading>
+    <content>
+      Explore the earlier version of this project, which detects license plates as a whole:
+      [Nepal Vehicle License Plate Detection](https://huggingface.co/krishnamishra8848/Nepal-Vehicle-License-Plate-Detection)
+    </content>
+  </section>
+
+  <section>
+    <heading>📊 Model Performance Metrics</heading>
+    <metrics>
+      <metric>
+        <name>**Precision**</name>
+        <value>0.985</value>
+        <description>Percentage of correct bounding box predictions among all predictions.</description>
+      </metric>
+      <metric>
+        <name>**Recall**</name>
+        <value>0.984</value>
+        <description>Percentage of ground truth objects successfully detected.</description>
+      </metric>
+      <metric>
+        <name>**mAP@50**</name>
+        <value>0.994</value>
+        <description>Mean Average Precision at IoU threshold 0.5.</description>
+      </metric>
+      <metric>
+        <name>**mAP@50-95**</name>
+        <value>0.861</value>
+        <description>Mean Average Precision across multiple IoU thresholds (0.5 to 0.95).</description>
+      </metric>
+      <metric>
+        <name>**Inference Speed**</name>
+        <value>~2.4ms per image</value>
+        <description>Time taken to process a single image during inference.</description>
+      </metric>
+    </metrics>
+  </section>
+
+ 
+</repository>
 
 ```python
 # Example Code: Test this code on colab