Add raw predictions debug info and scale factor warnings

model.py

@@ -175,6 +175,11 @@ class TrafficSignDetector:
         print(f"  - Scale factor: {scale:.3f}")
         print(f"  - Padding X: {pad_x}, Y: {pad_y}")
         
+        # Warning if scale is too small (objects might be too small to detect)
+        if scale < 0.5:
+            print(f"  ⚠️  WARNING: Scale factor < 0.5 - objects may be too small!")
+            print(f"     Original size: {original_image.shape[:2]} → Resized: {int(original_image.shape[1]*scale)}x{int(original_image.shape[0]*scale)}")
+        
         # Normalize pixel values for inference
         print(f"\n[STEP 3] IMAGE NORMALIZATION")
         image = self._preprocess(image)
@@ -188,8 +193,17 @@ class TrafficSignDetector:
         print(f"  - Input shape to model: {image.shape}")
         print(f"  - Confidence threshold: {self.conf_threshold}")
         print(f"  - IOU threshold: 0.45")
+        
+        # Run with conf=0.0 to get raw predictions (before filtering)
+        results_raw = self.model(image, conf=0.0, imgsz=640, iou=0.45)
+        print(f"  - Raw detections (conf=0.0): {len(results_raw[0].boxes) if results_raw else 0}")
+        if results_raw and len(results_raw[0].boxes) > 0:
+            raw_confs = [float(box.conf[0]) for box in results_raw[0].boxes[:5]]  # First 5
+            print(f"  - Top 5 raw confidences: {[f'{c:.4f}' for c in raw_confs]}")
+        
+        # Now run with actual threshold
         results = self.model(image, conf=self.conf_threshold, imgsz=640, iou=0.45)
-        print(f"  - Number of results: {len(results)}")
+        print(f"  - Filtered detections (conf={self.conf_threshold}): {len(results)}")
 
         # Get original dimensions for coordinate transformation
         orig_h, orig_w = original_image.shape[:2]
@@ -239,5 +253,18 @@ class TrafficSignDetector:
 
         print(f"\n{'='*80}")
         print(f"DETECTION PIPELINE COMPLETE")
-        print(f"{'='*80}\n")
+        print(f"{'='*80}")
+        
+        # Analysis and recommendations
+        if scale < 0.5:
+            print(f"\n📋 ANALYSIS & RECOMMENDATIONS:")
+            print(f"  1. Objects are too small after resizing (scale={scale:.2f})")
+            print(f"  2. Possible solutions:")
+            print(f"     a) Use larger target size (imgsz=1024 or 1280)")
+            print(f"     b) Use multi-scale detection (detect multiple regions)")
+            print(f"     c) Reduce input image size to avoid extreme scaling")
+            print(f"     d) Check if model was trained with your image dimensions")
+            print(f"  3. Current: {original_image.shape} → {image.shape} (scale {scale:.2f})")
+        
+        print()
         return original_image, preprocessed_display