diff --git a/vision_agent/tools/__init__.py b/vision_agent/tools/__init__.py
index e5b7c334..da74f677 100644
--- a/vision_agent/tools/__init__.py
+++ b/vision_agent/tools/__init__.py
@@ -45,7 +45,6 @@
     loca_zero_shot_counting,
     ocr,
     overlay_bounding_boxes,
-    overlay_counting_results,
     overlay_heat_map,
     overlay_segmentation_masks,
     owl_v2_image,
diff --git a/vision_agent/tools/tools.py b/vision_agent/tools/tools.py
index f83132a5..b2b8a985 100644
--- a/vision_agent/tools/tools.py
+++ b/vision_agent/tools/tools.py
@@ -13,7 +13,7 @@
 import cv2
 import numpy as np
 import requests
-from PIL import Image, ImageDraw, ImageEnhance, ImageFont
+from PIL import Image, ImageDraw, ImageFont
 from pillow_heif import register_heif_opener  # type: ignore
 from pytube import YouTube  # type: ignore
 
@@ -1917,30 +1917,36 @@ def overlay_bounding_boxes(
         bboxes = bbox_int[i]
         bboxes = sorted(bboxes, key=lambda x: x["label"], reverse=True)
 
-        width, height = pil_image.size
-        fontsize = max(12, int(min(width, height) / 40))
-        draw = ImageDraw.Draw(pil_image)
-        font = ImageFont.truetype(
-            str(
-                resources.files("vision_agent.fonts").joinpath("default_font_ch_en.ttf")
-            ),
-            fontsize,
-        )
-
-        for elt in bboxes:
-            label = elt["label"]
-            box = elt["bbox"]
-            scores = elt["score"]
-
-            # denormalize the box if it is normalized
-            box = denormalize_bbox(box, (height, width))
-            draw.rectangle(box, outline=color[label], width=4)
-            text = f"{label}: {scores:.2f}"
-            text_box = draw.textbbox((box[0], box[1]), text=text, font=font)
-            draw.rectangle(
-                (box[0], box[1], text_box[2], text_box[3]), fill=color[label]
+        if len(bboxes) > 20:
+            pil_image = _plot_counting(pil_image, bboxes, color)
+        else:
+            width, height = pil_image.size
+            fontsize = max(12, int(min(width, height) / 40))
+            draw = ImageDraw.Draw(pil_image)
+            font = ImageFont.truetype(
+                str(
+                    resources.files("vision_agent.fonts").joinpath(
+                        "default_font_ch_en.ttf"
+                    )
+                ),
+                fontsize,
             )
-            draw.text((box[0], box[1]), text, fill="black", font=font)
+
+            for elt in bboxes:
+                label = elt["label"]
+                box = elt["bbox"]
+                scores = elt["score"]
+
+                # denormalize the box if it is normalized
+                box = denormalize_bbox(box, (height, width))
+                draw.rectangle(box, outline=color[label], width=4)
+                text = f"{label}: {scores:.2f}"
+                text_box = draw.textbbox((box[0], box[1]), text=text, font=font)
+                draw.rectangle(
+                    (box[0], box[1], text_box[2], text_box[3]), fill=color[label]
+                )
+                draw.text((box[0], box[1]), text, fill="black", font=font)
+
         frame_out.append(np.array(pil_image))
     return frame_out[0] if len(frame_out) == 1 else frame_out
 
@@ -2099,39 +2105,19 @@ def overlay_heat_map(
     return np.array(combined)
 
 
-def overlay_counting_results(
-    image: np.ndarray, instances: List[Dict[str, Any]]
-) -> np.ndarray:
-    """'overlay_counting_results' is a utility function that displays counting results on
-    an image.
-
-    Parameters:
-        image (np.ndarray): The image to display the bounding boxes on.
-        instances (List[Dict[str, Any]]): A list of dictionaries containing the bounding
-            box information of each instance
-
-    Returns:
-        np.ndarray: The image with the instance_id dislpayed
-
-    Example
-    -------
-        >>> image_with_bboxes = overlay_counting_results(
-            image, [{'score': 0.99, 'label': 'object', 'bbox': [0.1, 0.11, 0.35, 0.4]}],
-        )
-    """
-    pil_image = Image.fromarray(image.astype(np.uint8)).convert("RGB")
-    color = (158, 218, 229)
-
-    width, height = pil_image.size
+def _plot_counting(
+    image: Image.Image,
+    bboxes: List[Dict[str, Any]],
+    colors: Dict[str, Tuple[int, int, int]],
+) -> Image.Image:
+    width, height = image.size
     fontsize = max(10, int(min(width, height) / 80))
-    pil_image = ImageEnhance.Brightness(pil_image).enhance(0.5)
-    draw = ImageDraw.Draw(pil_image)
+    draw = ImageDraw.Draw(image)
     font = ImageFont.truetype(
         str(resources.files("vision_agent.fonts").joinpath("default_font_ch_en.ttf")),
         fontsize,
     )
-
-    for i, elt in enumerate(instances, 1):
+    for i, elt in enumerate(bboxes, 1):
         label = f"{i}"
         box = elt["bbox"]
 
@@ -2153,7 +2139,7 @@ def overlay_counting_results(
         text_y1 = cy + text_height / 2
 
         # Draw the rectangle encapsulating the text
-        draw.rectangle((text_x0, text_y0, text_x1, text_y1), fill=color)
+        draw.rectangle((text_x0, text_y0, text_x1, text_y1), fill=colors[elt["label"]])
 
         # Draw the text at the center of the bounding box
         draw.text(
@@ -2164,7 +2150,7 @@ def overlay_counting_results(
             anchor="lt",
         )
 
-    return np.array(pil_image)
+    return image
 
 
 FUNCTION_TOOLS = [
@@ -2197,7 +2183,6 @@ def overlay_counting_results(
     overlay_bounding_boxes,
     overlay_segmentation_masks,
     overlay_heat_map,
-    overlay_counting_results,
 ]
 
 TOOLS = FUNCTION_TOOLS + UTIL_TOOLS