diff --git a/README.md b/README.md
index 2300e31..d990c95 100644
--- a/README.md
+++ b/README.md
@@ -34,11 +34,25 @@ Overview of one stage in PointMLP. Given an input point cloud, PointMLP progress
     }
 
 ## Model Zoo
-- The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
 
-- On ModelNet40, fixed pointMLP achieves a result of **91.5% mAcc** and **94.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/modelnet40/pointMLP-20220209053148-404/).
-- On ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/pointMLP-20220204021453/). Fixed pointMLP-elite achieves a result of **81.7% mAcc** and **84.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/model313Elite-20220220015842-2956/).
-- Stay tuned. More elite versions and voting results will be uploaded.
+  **Questions on ModelNet40 classification results (a common issue for ModelNet40 dataset in the community)**
+  
+  The performance on ModelNet40 of almost all methods are not stable, see (https://github.com/CVMI-Lab/PAConv/issues/9#issuecomment-873371422).<br>
+  If you run the same codes for several times, you will get different results (even with fixed seed).<br>
+  The best way to reproduce the results is to test with a pretrained model for ModelNet40. <br>
+  Also, the randomness of ModelNet40 is our motivation to experiment on ScanObjectNN, and to report the mean/std results of several runs.
+
+
+
+------
+
+The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
+
+On ModelNet40, fixed pointMLP achieves a result of **91.5% mAcc** and **94.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/modelnet40/pointMLP-20220209053148-404/).
+
+On ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/pointMLP-20220204021453/). Fixed pointMLP-elite achieves a result of **81.7% mAcc** and **84.1% OA** without voting, logs and pretrained models can be found [[here]](https://web.northeastern.edu/smilelab/xuma/pointMLP/checkpoints/fixstd/scanobjectnn/model313Elite-20220220015842-2956/).
+
+Stay tuned. More elite versions and voting results will be uploaded.
 
 
 
@@ -52,13 +66,7 @@ Overview of one stage in PointMLP. Given an input point cloud, PointMLP progress
 
 :point_right::point_right::point_right:**NOTE:** The codes/models/logs for submission version (without bug fixed) can be found here [commit:d2b8dbaa](http://github.com/13952522076/pointMLP-pytorch/tree/d2b8dbaa06eb6176b222dcf2ad248f8438582026).
 
-<details>
-  <summary>:fast_forward: Questions on ModelNet40 classification reproduction</summary>
-  The performance on ModelNet40 of almost all methods are not stable, see (https://github.com/CVMI-Lab/PAConv/issues/9#issuecomment-873371422).
-  If you run the same codes for several times, you will get different results.
-  Also, the randomness of ModelNet40 is our motivation to experiment on ScanObjectNN benchmark, and to report the mean/std results of several runs.
 
-</details>
 
 
 ## Install