diff --git a/README.md b/README.md
index 8875762..f0706dc 100644
--- a/README.md
+++ b/README.md
@@ -1,48 +1,40 @@
 # Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework （ICLR 2022）
 
-
-
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-modelnet40)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-modelnet40?p=rethinking-network-design-and-local-geometry-1)
 [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/rethinking-network-design-and-local-geometry-1/3d-point-cloud-classification-on-scanobjectnn)](https://paperswithcode.com/sota/3d-point-cloud-classification-on-scanobjectnn?p=rethinking-network-design-and-local-geometry-1)
 
-
 [![github](https://img.shields.io/github/stars/ma-xu/pointMLP-pytorch?style=social)](https://github.com/ma-xu/pointMLP-pytorch)
 
+![](images/smile.png)
+![](images/neu.png)
+![](images/columbia.png)
 
-<div align="left">
-    <a><img src="images/smile.png"  height="70px" ></a>
-    <a><img src="images/neu.png"  height="70px" ></a>
-    <a><img src="images/columbia.png"  height="70px" ></a>
-</div>
+[open review](https://openreview.net/forum?id=3Pbra-_u76D) | [arXiv](https://arxiv.org/abs/2202.07123) | Primary contact: [Xu Ma](mailto:ma.xu1@northeastern.edu)
 
- [open review](https://openreview.net/forum?id=3Pbra-_u76D) | [arXiv](https://arxiv.org/abs/2202.07123) | Primary contact: [Xu Ma](mailto:ma.xu1@northeastern.edu)
+![](images/overview.png)
 
-<div align="center">
-  <img src="images/overview.png" width="650px" height="300px">
-</div>
 
 Overview of one stage in PointMLP. Given an input point cloud, PointMLP progressively extracts local features using residual point MLP blocks. In each stage, we first transform the local point using a geometric affine module, and then local points are extracted before and after aggregation, respectively. By repeating multiple stages, PointMLP progressively enlarges the receptive field and models entire point cloud geometric information.
 
-
 ## BibTeX
 
-    @article{ma2022rethinking,
-        title={Rethinking network design and local geometry in point cloud: A simple residual MLP framework},
-        author={Ma, Xu and Qin, Can and You, Haoxuan and Ran, Haoxi and Fu, Yun},
-        journal={arXiv preprint arXiv:2202.07123},
-        year={2022}
-    }
+```bibtex
+@article{ma2022rethinking,
+    title={Rethinking network design and local geometry in point cloud: A simple residual MLP framework},
+    author={Ma, Xu and Qin, Can and You, Haoxuan and Ran, Haoxi and Fu, Yun},
+    journal={arXiv preprint arXiv:2202.07123},
+    year={2022}
+}
+```
 
 ## Model Zoo
 
-  **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.
-
+**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.
 
 ------
 
@@ -54,8 +46,6 @@ On ScanObjectNN, fixed pointMLP achieves a result of **84.4% mAcc** and **86.1%
 
 Stay tuned. More elite versions and voting results will be uploaded.
 
-
-
 ## News & Updates:
 
 - [x] fix the uncomplete utils in partseg by Mar/10, caused by error uplaoded folder.
@@ -66,9 +56,6 @@ Stay tuned. More elite versions and voting results will be uploaded.
 
 :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).
 
-
-
-
 ## Install
 
 ```bash
@@ -92,7 +79,6 @@ pip install cycler einops h5py pyyaml==5.4.1 scikit-learn==0.24.2 scipy tqdm mat
 pip install pointnet2_ops_lib/.
 ```
 
-
 ## Usage
 
 ### Classification ModelNet40
@@ -120,7 +106,7 @@ python voting.py --model pointMLP --msg demo
 
 The dataset will be automatically downloaded
 
-- Train pointMLP/pointMLPElite 
+- Train pointMLP/pointMLPElite
 ```bash
 cd classification_ScanObjectNN
 # train pointMLP
@@ -161,10 +147,5 @@ Our implementation is mainly based on the following codebases. We gratefully tha
 [Pointnet2_PyTorch](https://github.com/erikwijmans/Pointnet2_PyTorch)
 
 ## LICENSE
-PointMLP is under the Apache-2.0 license. 
-
-
-
-
-
 
+PointMLP is under the Apache-2.0 license.