diff --git a/predict.py b/predict.py
index 49641a1..4717952 100644
--- a/predict.py
+++ b/predict.py
@@ -67,11 +67,13 @@ def main():
     dataset_test = MelanomaDataset(df_test, 'test', meta_features, transform=transforms_val)
     test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=args.batch_size, num_workers=args.num_workers)
 
-    PROBS = []
+    # load model
+    models = []
     for fold in range(5):
 
         if args.eval == 'best':
-            model_file = os.path.join(args.model_dir, f'{args.kernel_type}_best_fold{fold}.pth')
+            model_file = 
+            os.path.join(args.model_dir, f'{args.kernel_type}_best_fold{fold}.pth')
         elif args.eval == 'best_20':
             model_file = os.path.join(args.model_dir, f'{args.kernel_type}_best_20_fold{fold}.pth')
         if args.eval == 'final':
@@ -96,31 +98,36 @@ def main():
             model = torch.nn.DataParallel(model)
 
         model.eval()
+        models.append(model)
 
-        PROBS = []
-        with torch.no_grad():
-            for (data) in tqdm(test_loader):
-
-                if args.use_meta:
-                    data, meta = data
-                    data, meta = data.to(device), meta.to(device)
-                    probs = torch.zeros((data.shape[0], args.out_dim)).to(device)
+    # predict
+    PROBS = []
+    with torch.no_grad():
+        for (data) in tqdm(test_loader):
+            if args.use_meta:
+                data, meta = data
+                data, meta = data.to(device), meta.to(device)
+                probs = torch.zeros((data.shape[0], args.out_dim)).to(device)
+                for model in models:
                     for I in range(args.n_test):
                         l = model(get_trans(data, I), meta)
                         probs += l.softmax(1)
-                else:   
-                    data = data.to(device)
-                    probs = torch.zeros((data.shape[0], args.out_dim)).to(device)
+            else:   
+                data = data.to(device)
+                probs = torch.zeros((data.shape[0], args.out_dim)).to(device)
+                for model in models:
                     for I in range(args.n_test):
                         l = model(get_trans(data, I))
                         probs += l.softmax(1)
 
-                probs /= args.n_test
+            probs /= args.n_test
+            probs /= len(models)
 
-                PROBS.append(probs.detach().cpu())
+            PROBS.append(probs.detach().cpu())
 
-        PROBS = torch.cat(PROBS).numpy()
+    PROBS = torch.cat(PROBS).numpy()
 
+    # save cvs
     df_test['target'] = PROBS[:, mel_idx]
     df_test[['image_name', 'target']].to_csv(os.path.join(args.sub_dir, f'sub_{args.kernel_type}_{args.eval}.csv'), index=False)