diff --git a/model_zoo/research/cv/FaceQualityAssessment/train.py b/model_zoo/research/cv/FaceQualityAssessment/train.py
index 47db3843bb1..0d6a3db4360 100644
--- a/model_zoo/research/cv/FaceQualityAssessment/train.py
+++ b/model_zoo/research/cv/FaceQualityAssessment/train.py
@@ -102,7 +102,7 @@ def main(args):
             else:
                 param_dict_new[key] = values
         load_param_into_net(network, param_dict_new)
-        cfg.logger.info('load model %s success' % str(cfg.pretrained))
+        cfg.logger.info('load model %s success.' % cfg.pretrained)
 
     # optimizer and lr scheduler
     lr = warmup_step(cfg, gamma=0.9)
diff --git a/model_zoo/research/cv/FaceRecognition/eval.py b/model_zoo/research/cv/FaceRecognition/eval.py
index 003da916b2a..b13a7dd5d0f 100644
--- a/model_zoo/research/cv/FaceRecognition/eval.py
+++ b/model_zoo/research/cv/FaceRecognition/eval.py
@@ -328,6 +328,6 @@ if __name__ == '__main__':
     log_path = os.path.join(arg.ckpt_path, 'logs')
     arg.logger = get_logger(log_path, arg.local_rank)
 
-    arg.logger.info('Config\n\n%s\n' % str(pformat(arg)))
+    arg.logger.info('Config\n\n{}\n'.format(pformat(arg)))
 
     main(arg)
diff --git a/model_zoo/research/cv/FaceRecognitionForTracking/train.py b/model_zoo/research/cv/FaceRecognitionForTracking/train.py
index 39f0af39511..273d9e01b55 100644
--- a/model_zoo/research/cv/FaceRecognitionForTracking/train.py
+++ b/model_zoo/research/cv/FaceRecognitionForTracking/train.py
@@ -44,8 +44,8 @@ context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs
 random.seed(1)
 np.random.seed(1)
 
-
-def main():
+def init_argument():
+    """init config argument."""
     parser = argparse.ArgumentParser(description='Cifar10 classification')
     parser.add_argument('--is_distributed', type=int, default=0, help='if multi device')
     parser.add_argument('--data_dir', type=str, default='', help='image label list file, e.g. /home/label.txt')
@@ -78,23 +78,25 @@ def main():
     # logger
     cfg.outputs_dir = os.path.join(cfg.ckpt_path, datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
     cfg.logger = get_logger(cfg.outputs_dir, cfg.local_rank)
-    loss_meter = AverageMeter('loss')
 
     # Show cfg
     cfg.logger.save_args(cfg)
+    return cfg
 
+def main():
+    cfg = init_argument()
+    loss_meter = AverageMeter('loss')
     # dataloader
     cfg.logger.info('start create dataloader')
     de_dataset, steps_per_epoch, class_num = get_de_dataset(cfg)
     cfg.steps_per_epoch = steps_per_epoch
-    cfg.logger.info('step per epoch: ' + str(cfg.steps_per_epoch))
+    cfg.logger.info('step per epoch: %d' % cfg.steps_per_epoch)
     de_dataloader = de_dataset.create_tuple_iterator()
-
-    cfg.logger.info('class num original: ' + str(class_num))
+    cfg.logger.info('class num original: %d' % class_num)
     if class_num % 16 != 0:
         class_num = (class_num // 16 + 1) * 16
     cfg.class_num = class_num
-    cfg.logger.info('change the class num to :' + str(cfg.class_num))
+    cfg.logger.info('change the class num to: %d' % cfg.class_num)
     cfg.logger.info('end create dataloader')
 
     # backbone and loss
@@ -117,7 +119,7 @@ def main():
             else:
                 param_dict_new[key] = values
         load_param_into_net(network, param_dict_new)
-        cfg.logger.info('load model {} success'.format(cfg.pretrained))
+        cfg.logger.info('load model %s success' % cfg.pretrained)
 
     # mixed precision training
     network.add_flags_recursive(fp16=True)