diff --git a/README.md b/README.md
index 9a7d8a4..da18ea0 100644
--- a/README.md
+++ b/README.md
@@ -199,6 +199,12 @@ After running our demo code, you can get the tracking results as follows:
 
 [![Video Name](./assets/tracking_car_mask_1.jpg)](https://github.com/user-attachments/assets/d3f91ad0-3d32-43c4-a0dc-0bed661415f4)
 
+If you want to try `Grounding DINO 1.5` model, you can run the following scripts after setting your API token:
+
+```bash
+python grounded_sam2_tracking_demo_with_continuous_id_gd1.5.py
+```
+
 ### Citation
 
 If you find this project helpful for your research, please consider citing the following BibTeX entry.
diff --git a/grounded_sam2_tracking_demo_with_continuous_id_gd1.5.py b/grounded_sam2_tracking_demo_with_continuous_id_gd1.5.py
new file mode 100644
index 0000000..5e39a1a
--- /dev/null
+++ b/grounded_sam2_tracking_demo_with_continuous_id_gd1.5.py
@@ -0,0 +1,216 @@
+# dds cloudapi for Grounding DINO 1.5
+from dds_cloudapi_sdk import Config
+from dds_cloudapi_sdk import Client
+from dds_cloudapi_sdk import DetectionTask
+from dds_cloudapi_sdk import TextPrompt
+from dds_cloudapi_sdk import DetectionModel
+from dds_cloudapi_sdk import DetectionTarget
+
+
+import os
+import torch
+import numpy as np
+from PIL import Image
+from sam2.build_sam import build_sam2_video_predictor, build_sam2
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection 
+from utils.video_utils import create_video_from_images
+from utils.common_utils import CommonUtils
+from utils.mask_dictionary_model import MaskDictionatyModel, ObjectInfo
+import json
+import copy
+
+"""
+Step 1: Environment settings and model initialization
+"""
+# use bfloat16 for the entire notebook
+torch.autocast(device_type="cuda", dtype=torch.bfloat16).__enter__()
+
+if torch.cuda.get_device_properties(0).major >= 8:
+    # turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+# init sam image predictor and video predictor model
+sam2_checkpoint = "./checkpoints/sam2_hiera_large.pt"
+model_cfg = "sam2_hiera_l.yaml"
+device = "cuda" if torch.cuda.is_available() else "cpu"
+print("device", device)
+
+video_predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint)
+sam2_image_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
+image_predictor = SAM2ImagePredictor(sam2_image_model)
+
+
+# init grounding dino model from huggingface
+model_id = "IDEA-Research/grounding-dino-tiny"
+processor = AutoProcessor.from_pretrained(model_id)
+grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+
+
+# setup the input image and text prompt for SAM 2 and Grounding DINO
+# VERY important: text queries need to be lowercased + end with a dot
+text = "car."
+
+# `video_dir` a directory of JPEG frames with filenames like `<frame_index>.jpg`  
+video_dir = "notebooks/videos/car"
+# 'output_dir' is the directory to save the annotated frames
+output_dir = "./outputs"
+# 'output_video_path' is the path to save the final video
+output_video_path = "./outputs/output.mp4"
+# create the output directory
+CommonUtils.creat_dirs(output_dir)
+mask_data_dir = os.path.join(output_dir, "mask_data")
+json_data_dir = os.path.join(output_dir, "json_data")
+result_dir = os.path.join(output_dir, "result")
+CommonUtils.creat_dirs(mask_data_dir)
+CommonUtils.creat_dirs(json_data_dir)
+# scan all the JPEG frame names in this directory
+frame_names = [
+    p for p in os.listdir(video_dir)
+    if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
+]
+frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
+
+# init video predictor state
+inference_state = video_predictor.init_state(video_path=video_dir)
+step = 10 # the step to sample frames for Grounding DINO predictor
+
+sam2_masks = MaskDictionatyModel()
+PROMPT_TYPE_FOR_VIDEO = "mask" # box, mask or point
+objects_count = 0
+
+"""
+Step 2: Prompt Grounding DINO and SAM image predictor to get the box and mask for all frames
+"""
+print("Total frames:", len(frame_names))
+for start_frame_idx in range(0, len(frame_names), step):
+# prompt grounding dino to get the box coordinates on specific frame
+    print("start_frame_idx", start_frame_idx)
+    # continue
+    img_path = os.path.join(video_dir, frame_names[start_frame_idx])
+    image = Image.open(img_path)
+    image_base_name = frame_names[start_frame_idx].split(".")[0]
+    mask_dict = MaskDictionatyModel(promote_type = PROMPT_TYPE_FOR_VIDEO, mask_name = f"mask_{image_base_name}.npy")
+
+    # run Grounding DINO 1.5 on the image
+
+    API_TOKEN_FOR_GD1_5 = "Your API token"
+
+    config = Config(API_TOKEN_FOR_GD1_5)
+    # Step 2: initialize the client
+    client = Client(config)
+    
+    image_url = client.upload_file(img_path)
+    task = DetectionTask(
+        image_url=image_url,
+        prompts=[TextPrompt(text=text)],
+        targets=[DetectionTarget.BBox],  # detect bbox
+        model=DetectionModel.GDino1_6_Pro,  # detect with GroundingDino-1.5-Pro model
+    )
+    client.run_task(task)
+    result = task.result
+
+    objects = result.objects  # the list of detected objects
+    input_boxes = []
+    confidences = []
+    class_names = []
+
+    for idx, obj in enumerate(objects):
+        input_boxes.append(obj.bbox)
+        confidences.append(obj.score)
+        class_names.append(obj.category)
+
+    input_boxes = np.array(input_boxes)
+    OBJECTS = class_names
+
+    # prompt SAM image predictor to get the mask for the object
+    image_predictor.set_image(np.array(image.convert("RGB")))
+
+    # prompt SAM 2 image predictor to get the mask for the object
+    masks, scores, logits = image_predictor.predict(
+        point_coords=None,
+        point_labels=None,
+        box=input_boxes,
+        multimask_output=False,
+    )
+    # convert the mask shape to (n, H, W)
+    if masks.ndim == 2:
+        masks = masks[None]
+        scores = scores[None]
+        logits = logits[None]
+    elif masks.ndim == 4:
+        masks = masks.squeeze(1)
+
+    """
+    Step 3: Register each object's positive points to video predictor
+    """
+
+    # If you are using point prompts, we uniformly sample positive points based on the mask
+    if mask_dict.promote_type == "mask":
+        mask_dict.add_new_frame_annotation(mask_list=torch.tensor(masks).to(device), box_list=torch.tensor(input_boxes), label_list=OBJECTS)
+    else:
+        raise NotImplementedError("SAM 2 video predictor only support mask prompts")
+
+
+    """
+    Step 4: Propagate the video predictor to get the segmentation results for each frame
+    """
+    objects_count = mask_dict.update_masks(tracking_annotation_dict=sam2_masks, iou_threshold=0.8, objects_count=objects_count)
+    print("objects_count", objects_count)
+    video_predictor.reset_state(inference_state)
+    if len(mask_dict.labels) == 0:
+        print("No object detected in the frame, skip the frame {}".format(start_frame_idx))
+        continue
+    video_predictor.reset_state(inference_state)
+
+    for object_id, object_info in mask_dict.labels.items():
+        frame_idx, out_obj_ids, out_mask_logits = video_predictor.add_new_mask(
+                inference_state,
+                start_frame_idx,
+                object_id,
+                object_info.mask,
+            )
+    
+    video_segments = {}  # output the following {step} frames tracking masks
+    for out_frame_idx, out_obj_ids, out_mask_logits in video_predictor.propagate_in_video(inference_state, max_frame_num_to_track=step, start_frame_idx=start_frame_idx):
+        frame_masks = MaskDictionatyModel()
+        
+        for i, out_obj_id in enumerate(out_obj_ids):
+            out_mask = (out_mask_logits[i] > 0.0) # .cpu().numpy()
+            object_info = ObjectInfo(instance_id = out_obj_id, mask = out_mask[0], class_name = mask_dict.get_target_class_name(out_obj_id))
+            object_info.update_box()
+            frame_masks.labels[out_obj_id] = object_info
+            image_base_name = frame_names[out_frame_idx].split(".")[0]
+            frame_masks.mask_name = f"mask_{image_base_name}.npy"
+            frame_masks.mask_height = out_mask.shape[-2]
+            frame_masks.mask_width = out_mask.shape[-1]
+
+        video_segments[out_frame_idx] = frame_masks
+        sam2_masks = copy.deepcopy(frame_masks)
+
+    print("video_segments:", len(video_segments))
+    """
+    Step 5: save the tracking masks and json files
+    """
+    for frame_idx, frame_masks_info in video_segments.items():
+        mask = frame_masks_info.labels
+        mask_img = torch.zeros(frame_masks_info.mask_height, frame_masks_info.mask_width)
+        for obj_id, obj_info in mask.items():
+            mask_img[obj_info.mask == True] = obj_id
+
+        mask_img = mask_img.numpy().astype(np.uint16)
+        np.save(os.path.join(mask_data_dir, frame_masks_info.mask_name), mask_img)
+
+        json_data = frame_masks_info.to_dict()
+        json_data_path = os.path.join(json_data_dir, frame_masks_info.mask_name.replace(".npy", ".json"))
+        with open(json_data_path, "w") as f:
+            json.dump(json_data, f)
+
+
+"""
+Step 6: Draw the results and save the video
+"""
+CommonUtils.draw_masks_and_box_with_supervision(video_dir, mask_data_dir, json_data_dir, result_dir)
+
+create_video_from_images(result_dir, output_video_path, frame_rate=30)
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