update model file and improved accuracy

app.py

@@ -10,6 +10,8 @@ import os
 import requests
 import gdown
 from skimage import io
+from torchvision.ops import box_iou
+import torch
 
 # Initialize Flask app
 app = Flask(__name__)
@@ -90,12 +92,34 @@ def upload():
     instances = outputs["instances"].to("cpu")
     class_names = MetadataCatalog.get(cfg.DATASETS.TEST[0]).thing_classes
 
-    # Initialize total cost
+    # Extract bounding boxes and class IDs
+    boxes = instances.pred_boxes.tensor.numpy()
+    class_ids = instances.pred_classes.numpy()
+
+    # Filter overlapping boxes using IoU
+    iou_threshold = 0.5
+    keep_indices = []
+    merged_boxes = set()
+
+    for i in range(len(boxes)):
+        if i in merged_boxes:
+            continue
+        keep_indices.append(i)
+        for j in range(i + 1, len(boxes)):
+            if j in merged_boxes:
+                continue
+            iou = box_iou(
+                torch.tensor(boxes[i]).unsqueeze(0), torch.tensor(boxes[j]).unsqueeze(0)
+            ).item()
+            if iou > iou_threshold:
+                merged_boxes.add(j)
+
+    # Calculate total cost based on non-overlapping boxes
     total_cost = 0
     damage_details = []
 
-    for j in range(len(instances)):
-        class_id = instances.pred_classes[j].item()
+    for idx in keep_indices:
+        class_id = class_ids[idx]
         damaged_part = (
             class_names[class_id] if class_id < len(class_names) else "unknown"
         )