diff --git a/base_model.py b/base_model.py
index a58a2d0..6c520bd 100644
--- a/base_model.py
+++ b/base_model.py
@@ -157,14 +157,14 @@ def build_BAN(dataset, args, priotize_using_counter=False):
         if len(args.maml_nums) > 1:
             maml_v_emb = []
             for model_t in args.maml_nums:
-                weight_path = args.VQA_dir + '/' + 't%s_'%(model_t) + args.maml_model_path
+                weight_path = args.VQA_dir + '/maml/' + 't%s_'%(model_t) + args.maml_model_path
                 print('load initial weights MAML from: %s' % (weight_path))
                 # maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
                 maml_v_emb_temp = MAML(args.VQA_dir)
                 maml_v_emb_temp.load_state_dict(torch.load(weight_path))
                 maml_v_emb.append(maml_v_emb_temp)
         else:
-            weight_path = args.VQA_dir + '/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
+            weight_path = args.VQA_dir + '/maml/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
             print('load initial weights MAML from: %s' % (weight_path))
             # maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
             maml_v_emb = MAML(args.VQA_dir)
@@ -223,14 +223,14 @@ def build_SAN(dataset, args):
         if len(args.maml_nums) > 1:
             maml_v_emb = []
             for model_t in args.maml_nums:
-                weight_path = args.VQA_dir + '/' + 't%s_'%(model_t) + args.maml_model_path
+                weight_path = args.VQA_dir + '/maml/' + 't%s_'%(model_t) + args.maml_model_path
                 print('load initial weights MAML from: %s' % (weight_path))
                 # maml_v_emb = SimpleCNN32(weight_path, args.eps_cnn, args.momentum_cnn)
                 maml_v_emb_temp = MAML(args.VQA_dir)
                 maml_v_emb_temp.load_state_dict(torch.load(weight_path))
                 maml_v_emb.append(maml_v_emb_temp)
         else:
-            weight_path = args.VQA_dir + '/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
+            weight_path = args.VQA_dir + '/maml/' + 't%s_' % (args.maml_nums[0]) + args.maml_model_path
             print('load initial weights MAML from: %s' % (weight_path))
             maml_v_emb = MAML(args.VQA_dir)
             maml_v_emb.load_state_dict(torch.load(weight_path))