diff --git a/ppocr/utils/save_load.py b/ppocr/utils/save_load.py
index 02814d6208aba7ddfa6eac338229502b18b535da..e69b330f0344321d88e7d175ae093cd9e51296aa 100644
--- a/ppocr/utils/save_load.py
+++ b/ppocr/utils/save_load.py
@@ -121,7 +121,7 @@ def init_model(config, model, logger, optimizer=None, lr_scheduler=None):
     return best_model_dict
 
 
-def save_model(net,
+def save_model(model,
                optimizer,
                model_path,
                logger,
@@ -133,7 +133,7 @@ def save_model(net,
     """
     _mkdir_if_not_exist(model_path, logger)
     model_prefix = os.path.join(model_path, prefix)
-    paddle.save(net.state_dict(), model_prefix + '.pdparams')
+    paddle.save(model.state_dict(), model_prefix + '.pdparams')
     paddle.save(optimizer.state_dict(), model_prefix + '.pdopt')
 
     # save metric and config
diff --git a/tools/program.py b/tools/program.py
index d4c3583829f5946c73fde06d0838d9b4d9376858..0aa3307e38982985519e3156214c0b4991ef54bc 100755
--- a/tools/program.py
+++ b/tools/program.py
@@ -159,6 +159,8 @@ def train(config,
     eval_batch_step = config['Global']['eval_batch_step']
 
     global_step = 0
+    if 'global_step' in pre_best_model_dict:
+        global_step = pre_best_model_dict['global_step']
     start_eval_step = 0
     if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
         start_eval_step = eval_batch_step[0]
@@ -285,7 +287,8 @@ def train(config,
                         is_best=True,
                         prefix='best_accuracy',
                         best_model_dict=best_model_dict,
-                        epoch=epoch)
+                        epoch=epoch,
+                        global_step=global_step)
                 best_str = 'best metric, {}'.format(', '.join([
                     '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                 ]))
@@ -307,7 +310,8 @@ def train(config,
                 is_best=False,
                 prefix='latest',
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
         if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
             save_model(
                 model,
@@ -317,7 +321,8 @@ def train(config,
                 is_best=False,
                 prefix='iter_epoch_{}'.format(epoch),
                 best_model_dict=best_model_dict,
-                epoch=epoch)
+                epoch=epoch,
+                global_step=global_step)
     best_str = 'best metric, {}'.format(', '.join(
         ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
     logger.info(best_str)