diff --git a/image_ref/config.py b/image_ref/config.py
index d2e1166031db045cf350403d86c1d251e6554e18..7137615b664c287ce52958539a0c2dd691ff5e45 100644
--- a/image_ref/config.py
+++ b/image_ref/config.py
@@ -16,7 +16,6 @@ def load_args_contrastive():
     parser.add_argument('--dataset_train_dir', type=str, default='data/processed_data/npy_image/data_training_contrastive')
     parser.add_argument('--dataset_val_dir', type=str, default='data/processed_data/npy_image/data_test_contrastive')
     parser.add_argument('--dataset_test_dir', type=str, default=None)
-    parser.add_argument('--base_dir', type=str, default=None)
     parser.add_argument('--base_out', type=str, default='output/baseline')
     parser.add_argument('--dataset_ref_dir', type=str, default='image_ref/img_ref')
     parser.add_argument('--output', type=str, default='output/out_contrastive.csv')
diff --git a/image_ref/dataset_ref.py b/image_ref/dataset_ref.py
index c73c3c47b33d8f774c2217f3b26fcd267f731642..c48992f6b1e3b0de54b312ca3d38aa2b55915f52 100644
--- a/image_ref/dataset_ref.py
+++ b/image_ref/dataset_ref.py
@@ -131,19 +131,15 @@ def make_dataset_custom(
 
 class ImageFolderDuo(data.Dataset):
     def __init__(self, root, transform=None, target_transform=None,
-                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, positive_prop=None, ref_transform=None, base_dir=None):
+                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, positive_prop=None, ref_transform=None):
         self.root = root
+        self.imlist = flist_reader(root)
         self.transform = transform
         self.target_transform = target_transform
         self.ref_transform = ref_transform
         self.loader = loader
         self.classes = torchvision.datasets.folder.find_classes(root)[0]
-        if base_dir is not None :
-            self.ref_dir = os.path.join(base_dir,ref_dir)
-            self.imlist = flist_reader(os.path.join(base_dir,root))
-        else :
-            self.ref_dir = ref_dir
-            self.imlist = flist_reader(root)
+        self.ref_dir = ref_dir
         self.positive_prop = positive_prop
 
     def __getitem__(self, index):
@@ -173,7 +169,7 @@ class ImageFolderDuo(data.Dataset):
     def __len__(self):
         return len(self.imlist)
 
-def load_data_duo(base_dir_train, base_dir_val, base_dir_test, batch_size, shuffle=True, noise_threshold=0, ref_dir = None, positive_prop=None, sampler=None, base_dir=None):
+def load_data_duo(base_dir_train, base_dir_val, base_dir_test, batch_size, shuffle=True, noise_threshold=0, ref_dir = None, positive_prop=None, sampler=None):
 
 
 
@@ -200,12 +196,12 @@ def load_data_duo(base_dir_train, base_dir_val, base_dir_test, batch_size, shuff
 
     print('Default val transform')
 
-    train_dataset = ImageFolderDuo(root=base_dir_train, transform=train_transform, ref_dir = ref_dir, positive_prop=positive_prop, ref_transform=ref_transform, base_dir=base_dir)
-    val_dataset = ImageFolderDuo_Batched(root=base_dir_val, transform=val_transform, ref_dir = ref_dir, ref_transform=ref_transform, base_dir=base_dir)
+    train_dataset = ImageFolderDuo(root=base_dir_train, transform=train_transform, ref_dir = ref_dir, positive_prop=positive_prop, ref_transform=ref_transform)
+    val_dataset = ImageFolderDuo_Batched(root=base_dir_val, transform=val_transform, ref_dir = ref_dir, ref_transform=ref_transform)
 
     if base_dir_test is not None :
         test_dataset = ImageFolderDuo_Batched(root=base_dir_test, transform=val_transform, ref_dir=ref_dir,
-                                             ref_transform=ref_transform, base_dir=base_dir)
+                                             ref_transform=ref_transform)
 
     if sampler =='balanced' :
         y_train_label = np.array([i for (_,_,i)in train_dataset.imlist])
@@ -260,19 +256,15 @@ def load_data_duo(base_dir_train, base_dir_val, base_dir_test, batch_size, shuff
 
 class ImageFolderDuo_Batched(data.Dataset):
     def __init__(self, root, transform=None, target_transform=None,
-                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, ref_transform=None, base_dir=None):
+                 flist_reader=make_dataset_custom, loader=npy_loader, ref_dir = None, ref_transform=None):
         self.root = root
-        if base_dir is not None:
-            self.ref_dir = os.path.join(base_dir, ref_dir)
-            self.imlist = flist_reader(os.path.join(base_dir, root))
-        else:
-            self.ref_dir = ref_dir
-            self.imlist = flist_reader(root)
+        self.imlist = flist_reader(root)
         self.transform = transform
         self.ref_transform = ref_transform
         self.target_transform = target_transform
         self.loader = loader
         self.classes = torchvision.datasets.folder.find_classes(root)[0]
+        self.ref_dir = ref_dir
 
     def __getitem__(self, index):
         impathAER, impathANA, target = self.imlist[index]
diff --git a/image_ref/main_ray.py b/image_ref/main_ray.py
index 04ab5a1a22d688a88b00c951f9583cb32f1ea9d3..90f45583da74f8374e5aca0fda7bb61c0043b812 100644
--- a/image_ref/main_ray.py
+++ b/image_ref/main_ray.py
@@ -152,7 +152,6 @@ def test_model(best_result, args):
     _, data_val_batch, _ = load_data_duo(base_dir_train=args.dataset_train_dir,
                                          base_dir_val=args.dataset_val_dir,
                                          base_dir_test=args.dataset_test_dir,
-                                         base_dir=args.base_dir,
                                          batch_size=args.batch_size,
                                          ref_dir=args.dataset_ref_dir,
                                          noise_threshold=best_result.config['noise'],