diff --git a/utils/data_utils.py b/utils/data_utils.py
index 0507615..9ecfe0c 100755
--- a/utils/data_utils.py
+++ b/utils/data_utils.py
@@ -13,39 +13,6 @@ from .autoaugment import AutoAugImageNetPolicy
 logger = logging.getLogger(__name__)
 
 
-def get_loader_new():
-        
-    train_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
-                                    transforms.RandomCrop((320, 320)), #448
-                                    transforms.RandomHorizontalFlip(),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
-    test_transform = transforms.Compose([transforms.Resize((320, 320), Image.BILINEAR),
-                                    transforms.ToTensor(),
-                                    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
-    trainset = emptyJudge(root='./emptyJudge5', is_train=True, transform=train_transform)
-    testset = emptyJudge(root='./emptyJudge5', is_train=False, transform=test_transform)
-
-
-    train_sampler = RandomSampler(trainset) 
-    test_sampler = SequentialSampler(testset) 
-    train_loader = DataLoader(trainset,
-                              sampler=train_sampler,
-                              batch_size=8,
-                              num_workers=4,
-                              drop_last=True,
-                              pin_memory=True)
-    test_loader = DataLoader(testset,
-                             sampler=test_sampler,
-                             batch_size=8,
-                             num_workers=4,
-                             pin_memory=True) if testset is not None else None
-
-    print('emptyJudge5 getdataloader ok!')
-    return train_loader, test_loader
-
-
-#根据不同数据集，获取data_loader
 def get_loader(args):
     if args.local_rank not in [-1, 0]:
         torch.distributed.barrier()
@@ -132,9 +99,9 @@ def get_loader(args):
                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
         trainset = INat2017(args.data_root, 'train', train_transform)
         testset = INat2017(args.data_root, 'val', test_transform)
-    elif args.dataset == 'emptyJudge5':
+    elif args.dataset == 'emptyJudge5' or args.dataset == 'emptyJudge4':
         train_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
-                                    transforms.RandomCrop((600, 600)), #448
+                                    transforms.RandomCrop((320, 320)),
                                     transforms.RandomHorizontalFlip(),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
@@ -142,7 +109,7 @@ def get_loader(args):
         #                             transforms.CenterCrop((448, 448)),
         #                             transforms.ToTensor(),
         #                             transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
-        test_transform = transforms.Compose([transforms.Resize((600, 600), Image.BILINEAR),
+        test_transform = transforms.Compose([transforms.Resize((320, 320), Image.BILINEAR),
                                     transforms.ToTensor(),
                                     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
         trainset = emptyJudge(root=args.data_root, is_train=True, transform=train_transform)
@@ -165,5 +132,4 @@ def get_loader(args):
                              num_workers=4,
                              pin_memory=True) if testset is not None else None
 
-    print('emptyJudge5 getdataloader ok!')
     return train_loader, test_loader