diff --git a/pdseg/train.py b/pdseg/train.py
index 7b155a2b44e0dad56cd818150e75d8de5fb81ee9..3a717889d13784cbddfb0aa00979a69b6eb53a17 100644
--- a/pdseg/train.py
+++ b/pdseg/train.py
@@ -178,6 +178,11 @@ def load_checkpoint(exe, program):
 
     return begin_epoch
 
+def update_best_model(ckpt_dir):
+    best_model_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model')
+    if os.path.exists(best_model_dir):
+        shutil.rmtree(best_model_dir)
+    shutil.copytree(ckpt_dir, best_model_dir)
 
 def print_info(*msg):
     if cfg.TRAINER_ID == 0:
@@ -341,6 +346,8 @@ def train(cfg):
     all_step *= (cfg.SOLVER.NUM_EPOCHS - begin_epoch + 1)
 
     avg_loss = 0.0
+    best_mIoU = 0.0
+
     timer = Timer()
     timer.start()
     if begin_epoch > cfg.SOLVER.NUM_EPOCHS:
@@ -353,9 +360,6 @@ def train(cfg):
     else:
         print_info("Use multi-thread reader")
 
-    # 存储评估时最高mIoU
-    best_mIoU = 0
-
     for epoch in range(begin_epoch, cfg.SOLVER.NUM_EPOCHS + 1):
         py_reader.start()
         while True:
@@ -448,13 +452,11 @@ def train(cfg):
                     log_writer.add_scalar('Evaluate/mean_acc', mean_acc,
                                           global_step)
 
-                # 将最优模型拷贝一份至best_model中
                 if mean_iou > best_mIoU:
                     best_mIoU = mean_iou
-                    best_model_dir = os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model')
-                    if os.path.exists(best_model_dir):
-                        shutil.rmtree(best_model_dir)
-                    shutil.copytree(ckpt_dir, best_model_dir)
+                    update_best_model(ckpt_dir)
+                    print_info("Model {} has best mIoU, save it in {}".format(ckpt_dir,
+                        os.path.join(cfg.TRAIN.MODEL_SAVE_DIR, 'best_model')))
 
             # Use Tensorboard to visualize results
             if args.use_tb and cfg.DATASET.VIS_FILE_LIST is not None: