diff --git a/README.md b/README.md
index d7fbf1f..a0b437c 100644
--- a/README.md
+++ b/README.md
@@ -51,7 +51,7 @@ The SMPL human body layer for Pytorch is from the [smplpytorch](https://github.c
 
 ### 1. Executing Code
 
-You can start the fitting procedure by the following code and the configuration file in *fit/configs* corresponding to the dataset_name will be loaded:
+You can start the fitting procedure by the following code and the configuration file in *fit/configs* corresponding to the dataset_name will be loaded (the dataset_path can also be set in the configuration file):
 
 ```
 python fit/tools/main.py --dataset_name [DATASET NAME] --dataset_path [DATASET PATH]
diff --git a/fit/tools/main.py b/fit/tools/main.py
index e05473f..095f5d3 100644
--- a/fit/tools/main.py
+++ b/fit/tools/main.py
@@ -1,3 +1,4 @@
+import numpy as np
 import torch
 from tensorboardX import SummaryWriter
 from easydict import EasyDict as edict
@@ -9,13 +10,13 @@ import logging
 import argparse
 import json
 sys.path.append(os.getcwd())
-from smplpytorch.pytorch.smpl_layer import SMPL_Layer
-from train import train
-from transform import transform
-from save import save_pic, save_params
 from load import load
-import numpy as np
-torch.backends.cudnn.benchmark=True
+from save import save_pic, save_params
+from transform import transform
+from train import train
+from smplpytorch.pytorch.smpl_layer import SMPL_Layer
+torch.backends.cudnn.benchmark = True
+
 
 def parse_args():
     parser = argparse.ArgumentParser(description='Fit SMPL')
@@ -31,8 +32,9 @@ def parse_args():
     args = parser.parse_args()
     return args
 
+
 def get_config(args):
-    config_path='fit/configs/{}.json'.format(args.dataset_name)
+    config_path = 'fit/configs/{}.json'.format(args.dataset_name)
     with open(config_path, 'r') as f:
         data = json.load(f)
     cfg = edict(data.copy())
@@ -40,6 +42,7 @@ def get_config(args):
         cfg.DATASET.PATH = args.dataset_path
     return cfg
 
+
 def set_device(USE_GPU):
     if USE_GPU and torch.cuda.is_available():
         device = torch.device('cuda')
@@ -47,6 +50,7 @@ def set_device(USE_GPU):
         device = torch.device('cpu')
     return device
 
+
 def get_logger(cur_path):
     logger = logging.getLogger(__name__)
     logger.setLevel(level=logging.INFO)
@@ -69,6 +73,7 @@ def get_logger(cur_path):
 
     return logger, writer
 
+
 if __name__ == "__main__":
     args = parse_args()
 
@@ -84,26 +89,23 @@ if __name__ == "__main__":
 
     device = set_device(USE_GPU=cfg.USE_GPU)
     logger.info('using device: {}'.format(device))
-    
+
     smpl_layer = SMPL_Layer(
-        center_idx = 0,
+        center_idx=0,
         gender=cfg.MODEL.GENDER,
         model_root='smplpytorch/native/models')
-    
+
     file_num = 0
-    for root,dirs,files in os.walk(cfg.DATASET.PATH):
+    for root, dirs, files in os.walk(cfg.DATASET.PATH):
         for file in files:
             file_num += 1
-            logger.info('Processing file: {}   [{} / {}]'.format(file,file_num,len(files)))
-            target = torch.from_numpy(transform(args.dataset_name, 
-                                                load(args.dataset_name,
-                                                        os.path.join(root,file)))).float()
-            
-            
-            res = train(smpl_layer,target,
-                logger,writer,device,
-                args,cfg)
-            
+            logger.info('Processing file: {}    [{} / {}]'.format(file, file_num, len(files)))
+            target = torch.from_numpy(transform(args.dataset_name,load(args.dataset_name,
+                                                                    os.path.join(root, file)))).float()
+
+            res = train(smpl_layer, target,
+                        logger, writer, device,
+                        args, cfg)
+
             # save_pic(res,smpl_layer,file,logger,args.dataset_name,target)
-            save_params(res,file,logger, args.dataset_name)
-        
\ No newline at end of file
+            save_params(res, file, logger, args.dataset_name)