Update README.md

README.md

@@ -173,20 +173,26 @@ We use the following _statistical-based metrics_:
 
   - **Scoring Formula**:
 
-    \[
+    ![\[
     \text{Score} = \frac{|A \cap B|}{n} \times \left( 1 - \frac{1}{p} \times \min\left(\frac{\text{dist}(A, B)}{\operatorname{max}(n, m)}, p \right)\right)
-    \]
+    \]](https://cdn-uploads.huggingface.co/production/uploads/67571051d39ac252085797ca/KF53yea-wyQe9Av2Zn0-q.png)
 
     - Ensures a score of 0 when no correct images are present.
     - \( \frac{|A \cap B|}{n} \): Normalization factor.
 
   - **Details**:
     - Here, dist(A, B) represents the weighted edit distance between string A and string B , i.e., the minimum total cost to transform string B into string A through the following three operations:
+   
       - **String Insertion**: If B is missing certain images, insert an image from A into a specific position in B
 . The operation cost is p_1.
+
       - **String Deletion**: If B contains extra irrelevant images, delete them. The operation cost is p_2.
+     
+      - 
       - **String Substitution**: If the positions of images in B do not match A, substitute the image in B with the corresponding image from A. The operation cost is p_3.
+     
     - The weights generally satisfy p_1 > p_2 > p_3, and p >= p_1 ensures the final score falls within the range \[0, 1\].
+    
     - Weighted edit distance can be computed using dynamic programming, with a time complexity of O(mn).
 
 - **Rouge-L** is a text generation evaluation metric based on the longest common subsequence, measuring the structural similarity between the answer and the ground truth.