diff --git a/test/test_prototype_transforms_consistency.py b/test/test_prototype_transforms_consistency.py
index b416dae20e0..f76c0f93d5e 100644
--- a/test/test_prototype_transforms_consistency.py
+++ b/test/test_prototype_transforms_consistency.py
@@ -317,7 +317,7 @@ def __init__(
             ArgsKwargs(saturation=(0.8, 0.9)),
             ArgsKwargs(hue=0.3),
             ArgsKwargs(hue=(-0.1, 0.2)),
-            ArgsKwargs(brightness=0.1, contrast=0.4, saturation=0.5, hue=0.6),
+            ArgsKwargs(brightness=0.1, contrast=0.4, saturation=0.5, hue=0.3),
         ],
         closeness_kwargs={"atol": 1e-5, "rtol": 1e-5},
     ),
diff --git a/test/test_transforms.py b/test/test_transforms.py
index 0c388dbb519..0340f9f3f15 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -1798,6 +1798,12 @@ def test_color_jitter():
     color_jitter.__repr__()
 
 
+@pytest.mark.parametrize("hue", [1, (-1, 1)])
+def test_color_jitter_hue_out_of_bounds(hue):
+    with pytest.raises(ValueError, match=re.escape("hue values should be between (-0.5, 0.5)")):
+        transforms.ColorJitter(hue=hue)
+
+
 @pytest.mark.parametrize("seed", range(10))
 @pytest.mark.skipif(stats is None, reason="scipy.stats not available")
 def test_random_erasing(seed):
diff --git a/torchvision/prototype/transforms/_color.py b/torchvision/prototype/transforms/_color.py
index 6ab997b1e93..17b02e36953 100644
--- a/torchvision/prototype/transforms/_color.py
+++ b/torchvision/prototype/transforms/_color.py
@@ -77,12 +77,12 @@ def _check_input(
             value = [center - value, center + value]
             if clip_first_on_zero:
                 value[0] = max(value[0], 0.0)
-        elif isinstance(value, collections.abc.Sequence) and len(value) == 2:
-            if not bound[0] <= value[0] <= value[1] <= bound[1]:
-                raise ValueError(f"{name} values should be between {bound}")
-        else:
+        elif not (isinstance(value, collections.abc.Sequence) and len(value) == 2):
             raise TypeError(f"{name} should be a single number or a sequence with length 2.")
 
+        if not bound[0] <= value[0] <= value[1] <= bound[1]:
+            raise ValueError(f"{name} values should be between {bound}, but got {value}.")
+
         return None if value[0] == value[1] == center else (float(value[0]), float(value[1]))
 
     @staticmethod
diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index 62e36a06f98..573791b4151 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -1195,16 +1195,19 @@ def _check_input(self, value, name, center=1, bound=(0, float("inf")), clip_firs
             if clip_first_on_zero:
                 value[0] = max(value[0], 0.0)
         elif isinstance(value, (tuple, list)) and len(value) == 2:
-            if not bound[0] <= value[0] <= value[1] <= bound[1]:
-                raise ValueError(f"{name} values should be between {bound}")
+            value = [float(value[0]), float(value[1])]
         else:
             raise TypeError(f"{name} should be a single number or a list/tuple with length 2.")
 
+        if not bound[0] <= value[0] <= value[1] <= bound[1]:
+            raise ValueError(f"{name} values should be between {bound}, but got {value}.")
+
         # if value is 0 or (1., 1.) for brightness/contrast/saturation
         # or (0., 0.) for hue, do nothing
         if value[0] == value[1] == center:
-            value = None
-        return value
+            return None
+        else:
+            return tuple(value)
 
     @staticmethod
     def get_params(