diff --git a/demo/predictor.py b/demo/predictor.py
index 189ec79..b13eca8 100644
--- a/demo/predictor.py
+++ b/demo/predictor.py
@@ -15,19 +15,21 @@
 
 
 class VisualizationDemo(object):
-    def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False):
+    def __init__(self, cfg, instance_mode=ColorMode.IMAGE, parallel=False, confidence_threshold=0.5):
         """
         Args:
             cfg (CfgNode):
             instance_mode (ColorMode):
             parallel (bool): whether to run the model in different processes from visualization.
                 Useful since the visualization logic can be slow.
+            confidence_threshold (float): minimum score for instance predictions to be shown
         """
         self.metadata = MetadataCatalog.get(
             cfg.DATASETS.TEST[0] if len(cfg.DATASETS.TEST) else "__unused"
         )
         self.cpu_device = torch.device("cpu")
         self.instance_mode = instance_mode
+        self.confidence_threshold = confidence_threshold
 
         self.parallel = parallel
         if parallel:
@@ -60,9 +62,16 @@ def run_on_image(self, image):
                 vis_output = visualizer.draw_sem_seg(
                     predictions["sem_seg"].argmax(dim=0).to(self.cpu_device)
                 )
-            if "instances" in predictions:
-                instances = predictions["instances"].to(self.cpu_device)
-                vis_output = visualizer.draw_instance_predictions(predictions=instances)
+        if "instances" in predictions:
+            instances = predictions["instances"].to(self.cpu_device)
+            
+            # Filter instances by confidence threshold
+            if self.confidence_threshold > 0:
+                scores = instances.scores
+                keep = scores > self.confidence_threshold
+                instances = instances[keep]
+                
+            vis_output = visualizer.draw_instance_predictions(predictions=instances)
 
         return predictions, vis_output
 
@@ -94,6 +103,13 @@ def process_predictions(frame, predictions):
                 )
             elif "instances" in predictions:
                 predictions = predictions["instances"].to(self.cpu_device)
+                
+                # Filter instances by confidence threshold
+                if self.confidence_threshold > 0:
+                    scores = predictions.scores
+                    keep = scores > self.confidence_threshold
+                    predictions = predictions[keep]
+                    
                 vis_frame = video_visualizer.draw_instance_predictions(frame, predictions)
             elif "sem_seg" in predictions:
                 vis_frame = video_visualizer.draw_sem_seg(