diff --git a/grounded_sam2_gd1.5_demo.py b/grounded_sam2_gd1.5_demo.py
index 95bbf1b..e699498 100644
--- a/grounded_sam2_gd1.5_demo.py
+++ b/grounded_sam2_gd1.5_demo.py
@@ -6,19 +6,38 @@ from dds_cloudapi_sdk import TextPrompt
 from dds_cloudapi_sdk import DetectionModel
 from dds_cloudapi_sdk import DetectionTarget
 
+import os
 import cv2
+import json
 import torch
 import numpy as np
 import supervision as sv
+import pycocotools.mask as mask_util
+from pathlib import Path
 from PIL import Image
 from sam2.build_sam import build_sam2
 from sam2.sam2_image_predictor import SAM2ImagePredictor
 
+"""
+Hyper parameters
+"""
+API_TOKEN = "Your API token"
+TEXT_PROMPT = "car"
+IMG_PATH = "notebooks/images/cars.jpg"
+SAM2_CHECKPOINT = "./checkpoints/sam2_hiera_large.pt"
+SAM2_MODEL_CONFIG = "sam2_hiera_l.yaml"
+GROUNDING_MODEL = DetectionModel.GDino1_5_Pro # DetectionModel.GDino1_6_Pro
+OUTPUT_DIR = Path("outputs/grounded_sam2_gd1.5_demo")
+DUMP_JSON_RESULTS = True
+
+# create output directory
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
 """
 Prompt Grounding DINO 1.5 with Text for Box Prompt Generation with Cloud API
 """
 # Step 1: initialize the config
-token = "Your API token"
+token = API_TOKEN
 config = Config(token)
 
 # Step 2: initialize the client
@@ -27,14 +46,14 @@ client = Client(config)
 # Step 3: run the task by DetectionTask class
 # image_url = "https://algosplt.oss-cn-shenzhen.aliyuncs.com/test_files/tasks/detection/iron_man.jpg"
 # if you are processing local image file, upload them to DDS server to get the image url
-img_path = "notebooks/images/cars.jpg"
+img_path = IMG_PATH
 image_url = client.upload_file(img_path)
 
 task = DetectionTask(
     image_url=image_url,
-    prompts=[TextPrompt(text="car")],
+    prompts=[TextPrompt(text=TEXT_PROMPT)],
     targets=[DetectionTarget.BBox],  # detect bbox
-    model=DetectionModel.GDino1_5_Pro,  # detect with GroundingDino-1.5-Pro model
+    model=GROUNDING_MODEL,  # detect with GroundingDino-1.5-Pro model
 )
 
 client.run_task(task)
@@ -68,8 +87,8 @@ if torch.cuda.get_device_properties(0).major >= 8:
     torch.backends.cudnn.allow_tf32 = True
 
 # build SAM2 image predictor
-sam2_checkpoint = "./checkpoints/sam2_hiera_large.pt"
-model_cfg = "sam2_hiera_l.yaml"
+sam2_checkpoint = SAM2_CHECKPOINT
+model_cfg = SAM2_MODEL_CONFIG
 sam2_model = build_sam2(model_cfg, sam2_checkpoint, device="cuda")
 sam2_predictor = SAM2ImagePredictor(sam2_model)
 
@@ -120,8 +139,43 @@ 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("groundingdino_annotated_image.jpg", annotated_frame)
+cv2.imwrite(os.path.join(OUTPUT_DIR, "groundingdino_annotated_image.jpg"), annotated_frame)
 
 mask_annotator = sv.MaskAnnotator()
 annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
-cv2.imwrite("grounded_sam2_annotated_image_with_mask.jpg", annotated_frame)
+cv2.imwrite(os.path.join(OUTPUT_DIR, "grounded_sam2_annotated_image_with_mask.jpg"), annotated_frame)
+
+"""
+Dump the results in standard format and save as json files
+"""
+
+def single_mask_to_rle(mask):
+    rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+    rle["counts"] = rle["counts"].decode("utf-8")
+    return rle
+
+if DUMP_JSON_RESULTS:
+    # convert mask into rle format
+    mask_rles = [single_mask_to_rle(mask) for mask in masks]
+
+    input_boxes = input_boxes.tolist()
+    scores = scores.tolist()
+    # save the results in standard format
+    results = {
+        "image_path": img_path,
+        "annotations" : [
+            {
+                "class_name": class_name,
+                "bbox": box,
+                "segmentation": mask_rle,
+                "score": score,
+            }
+            for class_name, box, mask_rle, score in zip(class_names, input_boxes, mask_rles, scores)
+        ],
+        "box_format": "xyxy",
+        "img_width": image.width,
+        "img_height": image.height,
+    }
+    
+    with open(os.path.join(OUTPUT_DIR, "grounded_sam2_gd1.5_image_demo_results.json"), "w") as f:
+        json.dump(results, f, indent=4)