diff --git a/models/ModelBase.py b/models/ModelBase.py
index fe13698..4cb44b9 100644
--- a/models/ModelBase.py
+++ b/models/ModelBase.py
@@ -140,21 +140,23 @@ class ModelBase(object):
 
         if ask_batch_size and (self.iter == 0 or ask_override):
             default_batch_size = 0 if self.iter == 0 else self.options.get('batch_size',0)
-            self.options['batch_cap'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % self.options.get('batch_cap', 16),self.options.get('batch_cap', 16),
+            self.options['batch_cap'] = max(0, io.input_int("Batch_size (?:help skip:%d) : " % self.options.get('batch_cap', 1),self.options.get('batch_cap', 1),
                                                              help_message="Larger batch size is better for NN's"
                                                                           " generalization, but it can cause Out of"
                                                                           " Memory error. Tune this value for your"
                                                                           " videocard manually."))
             self.options['ping_pong'] = io.input_bool(
-                "Enable ping-pong? (y/n ?:help skip:%s) : " % self.options.get('ping_pong', False),
+                "Enable ping-pong? (y/n ?:help skip:%s) : " % yn_str[self.options.get('ping_pong', False)],
                 self.options.get('ping_pong', False),
                 help_message="Cycles batch size between 1 and chosen batch size, simulating super convergence")
             self.options['paddle'] = self.options.get('paddle','ping')
             if self.options.get('ping_pong',False):
                 self.options['ping_pong_iter'] = max(0, io.input_int("Ping-pong iteration (skip:1000/default) : ", 1000))
+            else:
+                self.options['batch_size'] = self.options.get('batch_cap', 1)
 
         else:
-            self.options['batch_cap'] = self.options.get('batch_cap', 16)
+            self.options['batch_cap'] = self.options.get('batch_cap', 1)
             self.options['ping_pong'] = self.options.get('ping_pong', False)
             self.options['ping_pong_iter'] = self.options.get('ping_pong_iter',1000)
 
@@ -197,13 +199,13 @@ class ModelBase(object):
             self.options.pop('target_iter')
 
         self.batch_size = self.options.get('batch_size', 8)
-        self.batch_cap = self.options.get('batch_cap',16)
+        self.batch_cap = self.options.get('batch_cap',1)
         self.ping_pong_iter = self.options.get('ping_pong_iter',1000)
         self.sort_by_yaw = self.options.get('sort_by_yaw',False)
         self.random_flip = self.options.get('random_flip',True)
         if self.batch_cap == 0:
             self.options['batch_cap'] = self.batch_size
-            self.batch_cap = self.options.get('batch_cap',16)
+            self.batch_cap = self.options.get('batch_cap',1)
 
         self.src_scale_mod = self.options.get('src_scale_mod',0)
         if self.src_scale_mod == 0 and 'src_scale_mod' in self.options: