diff --git a/learn2learn/vision/benchmarks/fc100_benchmark.py b/learn2learn/vision/benchmarks/fc100_benchmark.py
index 5ac5a714..22fbbff5 100644
--- a/learn2learn/vision/benchmarks/fc100_benchmark.py
+++ b/learn2learn/vision/benchmarks/fc100_benchmark.py
@@ -5,6 +5,7 @@
 
 from learn2learn.data.transforms import NWays, KShots, LoadData, RemapLabels, ConsecutiveLabels
 
+from torchvision.transforms import Compose
 
 def fc100_tasksets(
     train_ways=5,
@@ -12,21 +13,68 @@ def fc100_tasksets(
     test_ways=5,
     test_samples=10,
     root='~/data',
+    data_augmentation=None,
     device=None,
     **kwargs,
 ):
     """Tasksets for FC100 benchmarks."""
-    data_transform = tv.transforms.ToTensor()
+    if data_augmentation is None:
+        train_data_transforms = tv.transforms.ToTensor()
+        test_data_transforms = tv.transforms.ToTensor()
+    elif data_augmentation == 'normalize':
+        train_data_transforms = Compose([
+            lambda x: x / 255.0,
+        ])
+        test_data_transforms = train_data_transforms
+    elif data_augmentation == 'rfs2020':
+        """
+        # original
+    if augment:
+        transform = transforms.Compose([
+            lambda x: Image.fromarray(x),
+            transforms.RandomCrop(32, padding=4),
+            transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
+            transforms.RandomHorizontalFlip(),
+            lambda x: np.asarray(x),
+            transforms.ToTensor(),
+            normalize_cifar100
+        ])
+    else:
+        transform = transforms.Compose([
+            lambda x: Image.fromarray(x),
+            transforms.ToTensor(),
+            normalize_cifar100
+        ])
+    return transform
+    """
+        mean = [0.5071, 0.4867, 0.4408]
+        std = [0.2675, 0.2565, 0.2761]
+        normalize = tv.transforms.Normalize(mean=mean, std=std)
+        train_data_transforms = Compose([
+            ToPILImage(),
+            RandomCrop(32, padding=4),
+            ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
+            RandomHorizontalFlip(),
+            ToTensor(),
+            normalize,
+        ])
+        test_data_transforms = Compose([
+            ToTensor(),
+            normalize,
+        ])
+    else:
+        raise('Invalid data_augmentation argument.')
+        
     train_dataset = l2l.vision.datasets.FC100(root=root,
-                                              transform=data_transform,
+                                              transform=train_data_transforms,
                                               mode='train',
                                               download=True)
     valid_dataset = l2l.vision.datasets.FC100(root=root,
-                                              transform=data_transform,
+                                              transform=train_data_transforms,
                                               mode='validation',
                                               download=True)
     test_dataset = l2l.vision.datasets.FC100(root=root,
-                                             transform=data_transform,
+                                             transform=test_data_transforms,
                                              mode='test',
                                              download=True)
     if device is not None: