diff --git a/test/test_utils.py b/test/test_utils.py
index ebe35a8cd14..4e3fa401e89 100644
--- a/test/test_utils.py
+++ b/test/test_utils.py
@@ -124,7 +124,7 @@ def test_draw_boxes_vanilla():
     img = torch.full((3, 100, 100), 0, dtype=torch.uint8)
     img_cp = img.clone()
     boxes_cp = boxes.clone()
-    result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7)
+    result = utils.draw_bounding_boxes(img, boxes, fill=False, width=7, colors="white")
 
     path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets", "fakedata", "draw_boxes_vanilla.png")
     if not os.path.exists(path):
@@ -149,7 +149,11 @@ def test_draw_invalid_boxes():
     img_tp = ((1, 1, 1), (1, 2, 3))
     img_wrong1 = torch.full((3, 5, 5), 255, dtype=torch.float)
     img_wrong2 = torch.full((1, 3, 5, 5), 255, dtype=torch.uint8)
+    img_correct = torch.zeros((3, 10, 10), dtype=torch.uint8)
     boxes = torch.tensor([[0, 0, 20, 20], [0, 0, 0, 0], [10, 15, 30, 35], [23, 35, 93, 95]], dtype=torch.float)
+    labels_wrong = ["one", "two"]
+    colors_wrong = ["pink", "blue"]
+
     with pytest.raises(TypeError, match="Tensor expected"):
         utils.draw_bounding_boxes(img_tp, boxes)
     with pytest.raises(ValueError, match="Tensor uint8 expected"):
@@ -158,6 +162,10 @@ def test_draw_invalid_boxes():
         utils.draw_bounding_boxes(img_wrong2, boxes)
     with pytest.raises(ValueError, match="Only grayscale and RGB images are supported"):
         utils.draw_bounding_boxes(img_wrong2[0][:2], boxes)
+    with pytest.raises(ValueError, match="Number of boxes"):
+        utils.draw_bounding_boxes(img_correct, boxes, labels_wrong)
+    with pytest.raises(ValueError, match="Number of colors"):
+        utils.draw_bounding_boxes(img_correct, boxes, colors=colors_wrong)
 
 
 @pytest.mark.parametrize(
diff --git a/torchvision/utils.py b/torchvision/utils.py
index 855f132d645..34e36c553dd 100644
--- a/torchvision/utils.py
+++ b/torchvision/utils.py
@@ -176,6 +176,7 @@ def draw_bounding_boxes(
         colors (color or list of colors, optional): List containing the colors
             of the boxes or single color for all boxes. The color can be represented as
             PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
+            By default, random colors are generated for boxes.
         fill (bool): If `True` fills the bounding box with specified color.
         width (int): Width of bounding box.
         font (str): A filename containing a TrueType font. If the file is not found in this filename, the loader may
@@ -198,45 +199,50 @@ def draw_bounding_boxes(
     elif image.size(0) not in {1, 3}:
         raise ValueError("Only grayscale and RGB images are supported")
 
+    num_boxes = boxes.shape[0]
+
+    if labels is None:
+        labels: Union[List[str], List[None]] = [None] * num_boxes  # type: ignore[no-redef]
+    elif len(labels) != num_boxes:
+        raise ValueError(
+            f"Number of boxes ({num_boxes}) and labels ({len(labels)}) mismatch. Please specify labels for each box."
+        )
+
+    if colors is None:
+        colors = _generate_color_palette(num_boxes)
+    elif isinstance(colors, list):
+        if len(colors) < num_boxes:
+            raise ValueError(f"Number of colors ({len(colors)}) is less than number of boxes ({num_boxes}). ")
+    else:  # colors specifies a single color for all boxes
+        colors = [colors] * num_boxes
+
+    colors = [(ImageColor.getrgb(color) if isinstance(color, str) else color) for color in colors]
+
+    # Handle Grayscale images
     if image.size(0) == 1:
         image = torch.tile(image, (3, 1, 1))
 
     ndarr = image.permute(1, 2, 0).cpu().numpy()
     img_to_draw = Image.fromarray(ndarr)
-
     img_boxes = boxes.to(torch.int64).tolist()
 
     if fill:
         draw = ImageDraw.Draw(img_to_draw, "RGBA")
-
     else:
         draw = ImageDraw.Draw(img_to_draw)
 
     txt_font = ImageFont.load_default() if font is None else ImageFont.truetype(font=font, size=font_size)
 
-    for i, bbox in enumerate(img_boxes):
-        if colors is None:
-            color = None
-        elif isinstance(colors, list):
-            color = colors[i]
-        else:
-            color = colors
-
+    for bbox, color, label in zip(img_boxes, colors, labels):  # type: ignore[arg-type]
         if fill:
-            if color is None:
-                fill_color = (255, 255, 255, 100)
-            elif isinstance(color, str):
-                # This will automatically raise Error if rgb cannot be parsed.
-                fill_color = ImageColor.getrgb(color) + (100,)
-            elif isinstance(color, tuple):
-                fill_color = color + (100,)
+            fill_color = color + (100,)
             draw.rectangle(bbox, width=width, outline=color, fill=fill_color)
         else:
             draw.rectangle(bbox, width=width, outline=color)
 
-        if labels is not None:
+        if label is not None:
             margin = width + 1
-            draw.text((bbox[0] + margin, bbox[1] + margin), labels[i], fill=color, font=txt_font)
+            draw.text((bbox[0] + margin, bbox[1] + margin), label, fill=color, font=txt_font)
 
     return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)
 
@@ -505,9 +511,9 @@ def _make_colorwheel() -> torch.Tensor:
     return colorwheel
 
 
-def _generate_color_palette(num_masks: int):
+def _generate_color_palette(num_objects: int):
     palette = torch.tensor([2 ** 25 - 1, 2 ** 15 - 1, 2 ** 21 - 1])
-    return [tuple((i * palette) % 255) for i in range(num_masks)]
+    return [tuple((i * palette) % 255) for i in range(num_objects)]
 
 
 def _log_api_usage_once(obj: Any) -> None: