diff --git a/test/test_transforms.py b/test/test_transforms.py
index cb458f6e4fe..a53f14d4d92 100644
--- a/test/test_transforms.py
+++ b/test/test_transforms.py
@@ -102,10 +102,12 @@ def test_ten_crop(self):
                 if single_dim:
                     crop_h = min(crop_h, crop_w)
                     crop_w = crop_h
-                    transform = transforms.TenCrop(crop_h, vflip=should_vflip)
+                    transform = transforms.TenCrop(crop_h,
+                                                   vertical_flip=should_vflip)
                     five_crop = transforms.FiveCrop(crop_h)
                 else:
-                    transform = transforms.TenCrop((crop_h, crop_w), vflip=should_vflip)
+                    transform = transforms.TenCrop((crop_h, crop_w),
+                                                   vertical_flip=should_vflip)
                     five_crop = transforms.FiveCrop((crop_h, crop_w))
 
                 img = to_pil_image(torch.FloatTensor(3, h, w).uniform_())
diff --git a/torchvision/transforms.py b/torchvision/transforms.py
index 27fe4e2d1bc..deff17115d2 100644
--- a/torchvision/transforms.py
+++ b/torchvision/transforms.py
@@ -281,6 +281,73 @@ def vflip(img):
     return img.transpose(Image.FLIP_TOP_BOTTOM)
 
 
+def five_crop(img, size):
+    """Crop the given PIL.Image into four corners and the central crop.
+
+    Note: this transform returns a tuple of images and there may be a mismatch in the number of
+    inputs and targets your `Dataset` returns.
+
+    Args:
+       size (sequence or int): Desired output size of the crop. If size is an
+           int instead of sequence like (h, w), a square crop (size, size) is
+           made.
+    Returns:
+        tuple: tuple (tl, tr, bl, br, center) corresponding top left,
+            top right, bottom left, bottom right and center crop.
+    """
+    if isinstance(size, numbers.Number):
+        size = (int(size), int(size))
+    else:
+        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
+
+    w, h = img.size
+    crop_h, crop_w = size
+    if crop_w > w or crop_h > h:
+        raise ValueError("Requested crop size {} is bigger than input size {}".format(size,
+                                                                                      (h, w)))
+    tl = img.crop((0, 0, crop_w, crop_h))
+    tr = img.crop((w - crop_w, 0, w, crop_h))
+    bl = img.crop((0, h - crop_h, crop_w, h))
+    br = img.crop((w - crop_w, h - crop_h, w, h))
+    center = CenterCrop((crop_h, crop_w))(img)
+    return (tl, tr, bl, br, center)
+
+
+def ten_crop(img, size, vertical_flip=False):
+    """Crop the given PIL.Image into four corners and the central crop plus the
+       flipped version of these (horizontal flipping is used by default).
+
+       Note: this transform returns a tuple of images and there may be a mismatch in the number of
+       inputs and targets your `Dataset` returns.
+
+       Args:
+           size (sequence or int): Desired output size of the crop. If size is an
+               int instead of sequence like (h, w), a square crop (size, size) is
+               made.
+           vertical_flip (bool): Use vertical flipping instead of horizontal
+
+        Returns:
+            tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip,
+                br_flip, center_flip) corresponding top left, top right,
+                bottom left, bottom right and center crop and same for the
+                flipped image.
+    """
+    if isinstance(size, numbers.Number):
+        size = (int(size), int(size))
+    else:
+        assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
+
+    first_five = five_crop(img, size)
+
+    if vertical_flip:
+        img = vflip(img)
+    else:
+        img = hflip(img)
+
+    second_five = five_crop(img, size)
+    return first_five + second_five
+
+
 class Compose(object):
     """Composes several transforms together.
 
@@ -661,17 +728,7 @@ def __init__(self, size):
             self.size = size
 
     def __call__(self, img):
-        w, h = img.size
-        crop_h, crop_w = self.size
-        if crop_w > w or crop_h > h:
-            raise ValueError("Requested crop size {} is bigger than input size {}".format(self.size,
-                                                                                          (h, w)))
-        tl = img.crop((0, 0, crop_w, crop_h))
-        tr = img.crop((w - crop_w, 0, w, crop_h))
-        bl = img.crop((0, h - crop_h, crop_w, h))
-        br = img.crop((w - crop_w, h - crop_h, w, h))
-        center = CenterCrop((crop_h, crop_w))(img)
-        return (tl, tr, bl, br, center)
+        return five_crop(img, self.size)
 
 
 class TenCrop(object):
@@ -685,25 +742,17 @@ class TenCrop(object):
            size (sequence or int): Desired output size of the crop. If size is an
                int instead of sequence like (h, w), a square crop (size, size) is
                made.
-           vflip bool: Use vertical flipping instead of horizontal
+           vertical_flip(bool): Use vertical flipping instead of horizontal
     """
 
-    def __init__(self, size, vflip=False):
+    def __init__(self, size, vertical_flip=False):
         self.size = size
         if isinstance(size, numbers.Number):
             self.size = (int(size), int(size))
         else:
             assert len(size) == 2, "Please provide only two dimensions (h, w) for size."
             self.size = size
-        self.vflip = vflip
+        self.vertical_flip = vertical_flip
 
     def __call__(self, img):
-        five_crop = FiveCrop(self.size)
-        first_five = five_crop(img)
-        if self.vflip:
-            img = img.transpose(Image.FLIP_TOP_BOTTOM)
-        else:
-            img = img.transpose(Image.FLIP_LEFT_RIGHT)
-
-        second_five = five_crop(img)
-        return first_five + second_five
+        return ten_crop(img, self.size, self.vertical_flip)