Add margin check to multi-ref matching to reduce false positives

The multi-ref matching method was missing a margin check against other
logos, causing excessive false positives. This fix adds:

- margin parameter to find_best_match_multi_ref() that requires the
  best logo's score to exceed the second-best by a minimum margin
- Test script now passes --margin to both matching methods
- Updated documentation to reflect margin applies to both methods

Also adds run_comparison_tests.sh to run all three matching methods
and compare results.

logo_detection_detr.py

@@ -401,6 +401,7 @@ class DetectLogosDETR:
         similarity_threshold: float = 0.85,
         min_matching_refs: int = 1,
         use_mean_similarity: bool = True,
+        margin: float = 0.0,
     ) -> Optional[Tuple[str, float, int]]:
         """
         Find the best matching reference logo using multiple reference embeddings per logo.
@@ -414,6 +415,7 @@ class DetectLogosDETR:
             similarity_threshold: Minimum similarity to consider a match (0-1)
             min_matching_refs: Minimum number of references that must match above threshold
             use_mean_similarity: If True, use mean similarity across all refs; if False, use max
+            margin: Required margin between best and second-best logo scores (0-1)
 
         Returns:
             Tuple of (label, similarity, num_matching_refs) for best match,
@@ -422,9 +424,8 @@ class DetectLogosDETR:
         if not reference_embeddings:
             return None
 
-        best_score = -1.0
-        best_label = None
-        best_num_matches = 0
+        # Calculate scores for all logos that meet the min_matching_refs requirement
+        logo_scores = []
 
         for label, ref_embedding_list in reference_embeddings.items():
             if not ref_embedding_list:
@@ -445,17 +446,30 @@ class DetectLogosDETR:
             else:
                 score = max(similarities)
 
-            # Check if this logo meets the minimum matching refs requirement
-            if num_matches >= min_matching_refs and score > best_score:
-                best_score = score
-                best_label = label
-                best_num_matches = num_matches
+            # Only consider logos that meet the minimum matching refs requirement
+            if num_matches >= min_matching_refs:
+                logo_scores.append((label, score, num_matches))
 
-        if best_label is not None and best_score >= similarity_threshold:
-            return (best_label, best_score, best_num_matches)
-        else:
+        if not logo_scores:
             return None
 
+        # Sort by score descending
+        logo_scores.sort(key=lambda x: x[1], reverse=True)
+
+        best_label, best_score, best_num_matches = logo_scores[0]
+
+        # Check if best score meets threshold
+        if best_score < similarity_threshold:
+            return None
+
+        # Check margin against second-best logo (if exists)
+        if margin > 0 and len(logo_scores) > 1:
+            second_best_score = logo_scores[1][1]
+            if best_score - second_best_score < margin:
+                return None  # Not confident enough
+
+        return (best_label, best_score, best_num_matches)
+
     def find_best_match_with_margin(
         self,
         detected_embedding: torch.Tensor,