fix bug

config.yaml

@@ -1,6 +1,6 @@
 model:
   path: 'VietCat/GTSRB-Model/models/GTSRB.pt' # Path to the YOLO model on Hugging Face Hub (will be downloaded automatically)
-  confidence_threshold: 0.01 # Minimum confidence for detections (lowered for debugging)
+  confidence_threshold: 0.25 # Minimum confidence for detections
 
 inference:
   box_color: (128, 0, 128) # Purple color for bounding boxes (BGR format)

model.py

@@ -114,8 +114,8 @@ class TrafficSignDetector:
         image = self._preprocess(image)
         
         # Use imgsz=640 to match training size
-        # Run with conf=0.0 to get ALL detections
-        results = self.model(image, conf=0.0, imgsz=640)
+        # Use iou_threshold for NMS (Non-Maximum Suppression) to remove overlapping boxes
+        results = self.model(image, conf=self.conf_threshold, imgsz=640, iou=0.45)
         print(f"Number of results: {len(results)}")
 
         # Get original dimensions for coordinate transformation
@@ -123,16 +123,14 @@ class TrafficSignDetector:
         
         for result in results:
             boxes = result.boxes
-            print(f"Total boxes detected (all confidences): {len(boxes)}")
+            print(f"Total boxes detected (confidence >= {self.conf_threshold}): {len(boxes)}")
             
-            # Debug: print all detection confidences
+            # Debug: print summary
             if len(boxes) > 0:
-                all_confidences = [float(box.conf[0]) for box in boxes]
-                print(f"All detected confidences: {all_confidences}")
-                print(f"Max confidence: {max(all_confidences):.4f}, Min: {min(all_confidences):.4f}")
+                confidences = [float(box.conf[0]) for box in boxes]
+                print(f"Confidence range: {min(confidences):.4f} - {max(confidences):.4f}")
             else:
-                print(f"No detections at all (even with conf=0.0)")
-                print(f"Model may not have detected any objects. Check if model was trained correctly.")
+                print(f"No detections above threshold {self.conf_threshold}")
             
             for box in boxes:
                 # Get bounding box coordinates from letterboxed image