diff --git a/torchvision/transforms/transforms.py b/torchvision/transforms/transforms.py
index fcba9dcd6bd..27616e5515f 100644
--- a/torchvision/transforms/transforms.py
+++ b/torchvision/transforms/transforms.py
@@ -728,20 +728,44 @@ class ColorJitter(object):
     """Randomly change the brightness, contrast and saturation of an image.
 
     Args:
-        brightness (float): How much to jitter brightness. brightness_factor
-            is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
-        contrast (float): How much to jitter contrast. contrast_factor
-            is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
-        saturation (float): How much to jitter saturation. saturation_factor
-            is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
-        hue(float): How much to jitter hue. hue_factor is chosen uniformly from
-            [-hue, hue]. Should be >=0 and <= 0.5.
+        brightness (float or tuple of float (min, max)): How much to jitter brightness.
+            brightness_factor is chosen uniformly from [max(0, 1 - brightness), 1 + brightness]
+            or the given [min, max]. Should be non negative numbers.
+        contrast (float or tuple of float (min, max)): How much to jitter contrast.
+            contrast_factor is chosen uniformly from [max(0, 1 - contrast), 1 + contrast]
+            or the given [min, max]. Should be non negative numbers.
+        saturation (float or tuple of float (min, max)): How much to jitter saturation.
+            saturation_factor is chosen uniformly from [max(0, 1 - saturation), 1 + saturation]
+            or the given [min, max]. Should be non negative numbers.
+        hue (float or tuple of float (min, max)): How much to jitter hue.
+            hue_factor is chosen uniformly from [-hue, hue] or the given [min, max].
+            Should have 0<= hue <= 0.5 or -0.5 <= min <= max <= 0.5.
     """
     def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
-        self.brightness = brightness
-        self.contrast = contrast
-        self.saturation = saturation
-        self.hue = hue
+        self.brightness = self._check_input(brightness, 'brightness')
+        self.contrast = self._check_input(contrast, 'contrast')
+        self.saturation = self._check_input(saturation, 'saturation')
+        self.hue = self._check_input(hue, 'hue', center=0, bound=(-0.5, 0.5),
+                                     clip_first_on_zero=False)
+
+    def _check_input(self, value, name, center=1, bound=(0, float('inf')), clip_first_on_zero=True):
+        if isinstance(value, numbers.Number):
+            if value < 0:
+                raise ValueError("If {} is a single number, it must be non negative.".format(name))
+            value = [center - value, center + value]
+            if clip_first_on_zero:
+                value[0] = max(value[0], 0)
+        elif isinstance(value, (tuple, list)) and len(value) == 2:
+            if not bound[0] <= value[0] <= value[1] <= bound[1]:
+                raise ValueError("{} values should be between {}".format(name, bound))
+        else:
+            raise TypeError("{} should be a single number or a list/tuple with lenght 2.".format(name))
+
+        # 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
 
     @staticmethod
     def get_params(brightness, contrast, saturation, hue):
@@ -754,20 +778,21 @@ def get_params(brightness, contrast, saturation, hue):
             saturation in a random order.
         """
         transforms = []
-        if brightness > 0:
-            brightness_factor = random.uniform(max(0, 1 - brightness), 1 + brightness)
+
+        if brightness is not None:
+            brightness_factor = random.uniform(brightness[0], brightness[1])
             transforms.append(Lambda(lambda img: F.adjust_brightness(img, brightness_factor)))
 
-        if contrast > 0:
-            contrast_factor = random.uniform(max(0, 1 - contrast), 1 + contrast)
+        if contrast is not None:
+            contrast_factor = random.uniform(contrast[0], contrast[1])
             transforms.append(Lambda(lambda img: F.adjust_contrast(img, contrast_factor)))
 
-        if saturation > 0:
-            saturation_factor = random.uniform(max(0, 1 - saturation), 1 + saturation)
+        if saturation is not None:
+            saturation_factor = random.uniform(saturation[0], saturation[1])
             transforms.append(Lambda(lambda img: F.adjust_saturation(img, saturation_factor)))
 
-        if hue > 0:
-            hue_factor = random.uniform(-hue, hue)
+        if hue is not None:
+            hue_factor = random.uniform(hue[0], hue[1])
             transforms.append(Lambda(lambda img: F.adjust_hue(img, hue_factor)))
 
         random.shuffle(transforms)