diff --git a/README.md b/README.md
index 9b7e467..991a5b9 100644
--- a/README.md
+++ b/README.md
@@ -89,7 +89,7 @@ pip install pointnet2_ops_lib/.
 
 By default, it will create a folder named "checkpoints/{modelName}-{msg}-{randomseed}", which includes args.txt, best_checkpoint.pth, last_checkpoint.pth, log.txt, out.txt.
 ```bash
-cd pointMLP-pytorch/classification_ModelNet40
+cd classification_ModelNet40
 # train pointMLP
 python main.py --model pointMLP
 # train pointMLP-elite
@@ -111,6 +111,7 @@ The dataset will be automatically downloaded
 
 - Train pointMLP/pointMLPElite 
 ```bash
+cd classification_ScanObjectNN
 # train pointMLP
 python main.py --model pointMLP
 # train pointMLP-elite
@@ -124,7 +125,7 @@ By default, it will create a fold named "checkpoints/{modelName}-{msg}-{randomse
 
 - Make data folder and download the dataset
 ```bash
-cd pointMLP-pytorch/part_segmentation
+cd part_segmentation
 mkdir data
 cd data
 wget https://shapenet.cs.stanford.edu/media/shapenetcore_partanno_segmentation_benchmark_v0_normal.zip --no-check-certificate
diff --git a/requirements.txt b/requirements.txt
index 3c44c85..5473c3e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,13 +1,12 @@
-cudatoolkit=10.2.89
-cycler=0.10.0
-einops=0.3.0
-h5py=3.2.1
-matplotlib=3.4.2
-numpy=1.20.2
-numpy-base=1.20.2
-pytorch=1.8.1
-pyyaml=5.4.1
-scikit-learn=0.24.2
-scipy=1.6.3
-torchvision=0.9.1
-tqdm=4.61.1
+torch
+torchvision
+cudatoolkit
+cycler
+einops
+h5py
+matplotlib==3.4.2
+pytorch
+pyyaml==5.4.1
+scikit-learn==0.24.2
+scipy
+tqdm