diff --git a/config/config.py b/config/config.py
index 648796581937c1e5a86e13b630d2d07949973256..74a9dd34d61281e9fe92d79e0e048f44cd3342c4 100644
--- a/config/config.py
+++ b/config/config.py
@@ -9,8 +9,8 @@ def load_args():
     parser.add_argument('--eval_inter', type=int, default=1)
     parser.add_argument('--noise_threshold', type=int, default=0)
     parser.add_argument('--lr', type=float, default=0.001)
-    parser.add_argument('--batch_size', type=int, default=64)
-    parser.add_argument('--model', type=str, default='ResNet18')
+    parser.add_argument('--batch_size', type=int, default=8)
+    parser.add_argument('--model', type=str, default='ResNet50')
     parser.add_argument('--model_type', type=str, default='duo')
     parser.add_argument('--dataset_dir', type=str, default='data/processed_data/npy_image/data_training')
     parser.add_argument('--output', type=str, default='output/out.csv')
diff --git a/image_ref/config.py b/image_ref/config.py
index 3f96b9eb85e5be7b488e249318e3a753377e08a5..7a6ca0f915dbdc413a568b1527b028ed32d875c9 100644
--- a/image_ref/config.py
+++ b/image_ref/config.py
@@ -9,12 +9,12 @@ def load_args_contrastive():
     parser.add_argument('--eval_inter', type=int, default=1)
     parser.add_argument('--noise_threshold', type=int, default=0)
     parser.add_argument('--lr', type=float, default=0.001)
-    parser.add_argument('--batch_size', type=int, default=64)
+    parser.add_argument('--batch_size', type=int, default=16)
     parser.add_argument('--positive_prop', type=int, default=None)
-    parser.add_argument('--model', type=str, default='ResNet18')
-    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_ref_dir', type=str, default='image_ref/img_ref')
+    parser.add_argument('--model', type=str, default='ResNet50')
+    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_ref_dir', type=str, default='../image_ref/img_ref')
     parser.add_argument('--output', type=str, default='output/out_contrastive.csv')
     parser.add_argument('--save_path', type=str, default='output/best_model_constrastive.pt')
     parser.add_argument('--pretrain_path', type=str, default=None)
diff --git a/image_ref/main.py b/image_ref/main.py
index 0d109924a702ab54673efe14e823f09a5b9487cb..ef6382ea822a3061bd47818011fe3f27aa26abfc 100644
--- a/image_ref/main.py
+++ b/image_ref/main.py
@@ -84,6 +84,7 @@ def run_duo(args):
         load_model(model,args.pretrain_path)
     #move parameters to GPU
     if torch.cuda.is_available():
+        print('model loaded on GPU')
         model = model.cuda()
 
     #init accumulators
diff --git a/image_ref/model.py b/image_ref/model.py
index e73ef1b0cedaaf52ac08dfef10ea3b6855b1adbf..5302e3b122d79d71372a781aee834f805840f5a4 100644
--- a/image_ref/model.py
+++ b/image_ref/model.py
@@ -281,6 +281,10 @@ class Classification_model_duo_contrastive(nn.Module):
         self.n_class = n_class
         if model =='ResNet18':
             self.im_encoder = resnet18(num_classes=2, in_channels=2)
+        if model =='ResNet34':
+            self.im_encoder = resnet34(num_classes=2, in_channels=2)
+        if model =='ResNet50':
+            self.im_encoder = resnet34(num_classes=2, in_channels=2)
 
         self.predictor = nn.Linear(in_features=2*2,out_features=2)
 
diff --git a/models/model.py b/models/model.py
index f0a3d836adecc0160a2c2068306b54b945bfe13c..ce4e396c7a821d5a2406cb96d829a82b317dfd2c 100644
--- a/models/model.py
+++ b/models/model.py
@@ -281,6 +281,8 @@ class Classification_model_duo(nn.Module):
         self.n_class = n_class
         if model =='ResNet18':
             self.im_encoder = resnet18(num_classes=self.n_class, in_channels=1)
+        if model =='ResNet50':
+            self.im_encoder = resnet50(num_classes=self.n_class, in_channels=1)
 
         self.predictor = nn.Linear(in_features=self.n_class*2,out_features=self.n_class)