diff --git a/README.md b/README.md
index 2cc9a23..44600d0 100644
--- a/README.md
+++ b/README.md
@@ -1,15 +1,15 @@
 # Efficient feature extraction network for image super-resolution(EFEN)
 Yinggan Tang, Quanwei Hu, Chunning BU
 
-## Environment
+## Environment in our experiments
 
-[BasicSR >= 1.4.2]
+[BasicSR 1.4.2]
 
-[PyTorch >= 1.13.0]
+[PyTorch 1.13.0]
 
-[Torchvision >= 0.14.0]
+[Torchvision 0.14.0]
 
-[Cuda >= 11.7]
+[Cuda 11.7]
 
 ### Installation
 ```
@@ -20,7 +20,9 @@ python setup.py develop
 
 ## How To Test
 · Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model.  
+
 · The pretrained models are available at [Google Drive] or [Baidu Netdisk]. Place the pretrained models in ./experiments/pretrained_models/  
+
 · Then run the follwing codes (taking EFENx4.pth as an example):  
 
 ```
@@ -30,8 +32,11 @@ The testing results will be saved in the ./results folder.
 
 ## How To Train
 · Refer to ./options/train for the configuration file of the model to train.  
+
 · Preparation of training data can refer to this page. All datasets can be downloaded at the official website.  
+
 · Note that the default training dataset is based on lmdb, refer to [docs in BasicSR](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md) to learn how to generate the training datasets.  
+
 · The training command is like  
 ```
 python basicsr/train.py -opt options/train/train_EFEN_x4.yml