diff --git a/README.md b/README.md
index 8e3fe51..0048f36 100644
--- a/README.md
+++ b/README.md
@@ -283,6 +283,13 @@ python grounded_sam2_image_demo_florence2.py \
     --text_input "The left red car."
 ```
 
+**Open-Vocabulary Detection and Segmentation**
+```bash
+python grounded_sam2_image_demo_florence2.py \
+    --pipeline open_vocabulary_detection_segmentation \
+    --image_path ./notebooks/images/cars.jpg \
+    --text_input "two cars"
+```
 
 ### Citation
 
diff --git a/grounded_sam2_image_demo_florence2.py b/grounded_sam2_image_demo_florence2.py
index 4a19548..28a3507 100644
--- a/grounded_sam2_image_demo_florence2.py
+++ b/grounded_sam2_image_demo_florence2.py
@@ -513,6 +513,81 @@ def referring_expression_segmentation(
     print(f'Successfully save sam2 annotated image to "{output_dir}"')
 
 
+"""
+Pipeline 6: Open-Vocabulary Detection + Segmentation
+"""
+def open_vocabulary_detection_and_segmentation(
+    florence2_model,
+    florence2_processor,
+    sam2_predictor,
+    image_path,
+    task_prompt="<OPEN_VOCABULARY_DETECTION>",
+    text_input=None,
+    output_dir=OUTPUT_DIR
+):
+    # run florence-2 object detection in demo
+    image = Image.open(image_path).convert("RGB")
+    results = run_florence2(task_prompt, text_input, florence2_model, florence2_processor, image)
+    
+    """ Florence-2 Open-Vocabulary Detection Output Format
+    {'<OPEN_VOCABULARY_DETECTION>': 
+        {
+            'bboxes': 
+                [
+                    [34.23999786376953, 159.1199951171875, 582.0800170898438, 374.6399841308594]
+                ], 
+            'bboxes_labels': ['A green car'],
+            'polygons': [], 
+            'polygons_labels': []
+        }
+    }
+    """
+    assert text_input is not None, "Text input should not be none when calling open-vocabulary detection pipeline."
+    results = results[task_prompt]
+    # parse florence-2 detection results
+    input_boxes = np.array(results["bboxes"])
+    print(results)
+    class_names = results["bboxes_labels"]
+    class_ids = np.array(list(range(len(class_names))))
+    
+    # predict mask with SAM 2
+    sam2_predictor.set_image(np.array(image))
+    masks, scores, logits = sam2_predictor.predict(
+        point_coords=None,
+        point_labels=None,
+        box=input_boxes,
+        multimask_output=False,
+    )
+    
+    if masks.ndim == 4:
+        masks = masks.squeeze(1)
+    
+    # specify labels
+    labels = [
+        f"{class_name}" for class_name in class_names
+    ]
+    
+    # visualization results
+    img = cv2.imread(image_path)
+    detections = sv.Detections(
+        xyxy=input_boxes,
+        mask=masks.astype(bool),
+        class_id=class_ids
+    )
+    
+    box_annotator = sv.BoxAnnotator()
+    annotated_frame = box_annotator.annotate(scene=img.copy(), detections=detections)
+    
+    label_annotator = sv.LabelAnnotator()
+    annotated_frame = label_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+    cv2.imwrite(os.path.join(output_dir, "grounded_sam2_florence2_open_vocabulary_detection.jpg"), annotated_frame)
+    
+    mask_annotator = sv.MaskAnnotator()
+    annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+    cv2.imwrite(os.path.join(output_dir, "grounded_sam2_florence2_open_vocabulary_detection_with_mask.jpg"), annotated_frame)
+
+    print(f'Successfully save annotated image to "{output_dir}"')
+
 if __name__ == "__main__":
 
     parser = argparse.ArgumentParser("Grounded SAM 2 Florence-2 Demos", add_help=True)
@@ -561,7 +636,7 @@ if __name__ == "__main__":
             text_input=INPUT_TEXT
         )
     elif PIPELINE == "referring_expression_segmentation":
-        # pipeline-5: referring segmentation + sam2 segmentation
+        # pipeline-5: referring segmentation + segmentation
         referring_expression_segmentation(
             florence2_model=florence2_model,
             florence2_processor=florence2_processor,
@@ -569,5 +644,14 @@ if __name__ == "__main__":
             image_path=IMAGE_PATH,
             text_input=INPUT_TEXT
         )
+    elif PIPELINE == "open_vocabulary_detection_segmentation":
+        # pipeline-6: open-vocabulary detection + segmentation
+        open_vocabulary_detection_and_segmentation(
+            florence2_model=florence2_model,
+            florence2_processor=florence2_processor,
+            sam2_predictor=sam2_predictor,
+            image_path=IMAGE_PATH,
+            text_input=INPUT_TEXT
+        )
     else:
         raise NotImplementedError(f"Pipeline: {args.pipeline} is not implemented at this time")
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