diff --git a/test/preprocess-bench.py b/test/preprocess-bench.py
index 85599362a73..9acf9e71e54 100644
--- a/test/preprocess-bench.py
+++ b/test/preprocess-bench.py
@@ -6,6 +6,11 @@
 import torch.utils.data
 import torchvision.transforms as transforms
 import torchvision.datasets as datasets
+try:
+    import accimage
+    print("Using accimage.Image")
+except ImportError:
+    print("Using PIL.Image")
 
 
 parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
diff --git a/torchvision/datasets/folder.py b/torchvision/datasets/folder.py
index 5eb3126ae96..9fb528f1614 100644
--- a/torchvision/datasets/folder.py
+++ b/torchvision/datasets/folder.py
@@ -1,6 +1,10 @@
 import torch.utils.data as data
 
 from PIL import Image
+try:
+    import accimage
+except ImportError:
+    accimage = None
 import os
 import os.path
 
@@ -47,7 +51,14 @@ def __init__(self, root, transform=None, target_transform=None):
 
     def __getitem__(self, index):
         path, target = self.imgs[index]
-        img = Image.open(os.path.join(self.root, path)).convert('RGB')
+        if accimage is None:
+            img = Image.open(os.path.join(self.root, path)).convert('RGB')
+        else:
+            try:
+                img = accimage.Image(os.path.join(self.root, path))
+            except IOError:
+                # Potentially a decoding problem, fall back to PIL.Image
+                img = Image.open(os.path.join(self.root, path)).convert('RGB')
         if self.transform is not None:
             img = self.transform(img)
         if self.target_transform is not None:
diff --git a/torchvision/transforms.py b/torchvision/transforms.py
index 48be812569b..9b42f30bb1d 100644
--- a/torchvision/transforms.py
+++ b/torchvision/transforms.py
@@ -3,6 +3,10 @@
 import math
 import random
 from PIL import Image, ImageOps
+try:
+    import accimage
+except ImportError:
+    accimage = None
 import numpy as np
 import numbers
 import types
@@ -28,7 +32,11 @@ class ToTensor(object):
     """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255]
     to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """
     def __call__(self, pic):
-        if isinstance(pic, np.ndarray):
+        if accimage is not None and isinstance(pic, accimage.Image):
+            nppic = np.empty([pic.channels, pic.height, pic.width])
+            pic.copyto(nppic)
+            img = torch.from_numpy(nppic)
+        elif isinstance(pic, np.ndarray):
             # handle numpy array
             img = torch.from_numpy(pic)
         else: