diff --git a/src/datasets/utilities.py b/src/datasets/utilities.py
index 4803ffc..b553727 100644
--- a/src/datasets/utilities.py
+++ b/src/datasets/utilities.py
@@ -13,7 +13,8 @@ Dataset2Class = {'cifar10': 10,
                  'ImageNet16' : 1000,
                  'ImageNet16-120': 120,
                  'ImageNet16-150': 150,
-                 'ImageNet16-200': 200}
+                 'ImageNet16-200': 200,
+                 'aircraft': 100}
 
 class RandChannel(object):
     # randomly pick channels from input
@@ -46,6 +47,10 @@ def get_datasets(name, root, input_size, cutout=-1):
     elif name.startswith('ImageNet16'):
         mean = [0.481098, 0.45749, 0.407882]
         std  = [0.247922, 0.240235, 0.255255]
+    elif name == 'aircraft':
+        mean = [0.4785, 0.5100, 0.5338]
+        std  = [0.1845, 0.1830, 0.2060]
+
     else:
         raise TypeError("Unknow dataset : {:}".format(name))
 
@@ -55,6 +60,12 @@ def get_datasets(name, root, input_size, cutout=-1):
         if cutout > 0 : lists += [CUTOUT(cutout)]
         train_transform = transforms.Compose(lists)
         test_transform  = transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean, std)])
+    elif name == 'aircraft':
+        lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std)]
+        if cutout > 0 : lists += [CUTOUT(cutout)]
+        train_transform = transforms.Compose(lists)
+        test_transform  = transforms.Compose([transforms.Resize((224,224)), transforms.ToTensor(), transforms.Normalize(mean, std)])
+
     elif name.startswith('ImageNet16'):
         lists = [transforms.RandomCrop(input_size[1], padding=0), transforms.ToTensor(), transforms.Normalize(mean, std), RandChannel(input_size[0])]
         if cutout > 0 : lists += [CUTOUT(cutout)]
@@ -86,9 +97,12 @@ def get_datasets(name, root, input_size, cutout=-1):
         train_data = dset.CIFAR100(root, train=True , transform=train_transform, download=True)
         test_data  = dset.CIFAR100(root, train=False, transform=test_transform , download=True)
         assert len(train_data) == 50000 and len(test_data) == 10000
+    elif name == 'aircraft':
+        train_data = dset.ImageFolder(osp.join(root, 'train_sorted_images'), train_transform)
+        test_data  = dset.ImageFolder(osp.join(root, 'test_sorted_images'),  test_transform)
     elif name.startswith('imagenet-1k'):
         train_data = dset.ImageFolder(osp.join(root, 'train'), train_transform)
-        test_data  = dset.ImageFolder(osp.join(root, 'val'),   test_transform)
+        test_data  = dset.ImageFolder(osp.join(root, 'test'),   test_transform)
     elif name == 'ImageNet16':
         root = osp.join(root, 'ImageNet16')
         train_data = ImageNet16(root, True , train_transform)