diff --git a/README.md b/README.md
index 07c33e3..d2011a9 100644
--- a/README.md
+++ b/README.md
@@ -6,11 +6,12 @@ This is the PyTorch implementation for our [technical report](https://arxiv.org/
 ```
 pip install -r requirements.txt
 ```
+We are using Python 3.5.2. And as pointed out by [Issue #3](https://github.com/art-programmer/MASC/issues/3), please consider using Python 3.6 and refer to [SparseConvNet](https://github.com/facebookresearch/SparseConvNet) for related issues.
 
 ## Data preparation
 To prepare training data from ScanNet mesh models, please run:
 ```
-python train.py --task=prepare --dataFolder=[SCANNET_PATH] ----labelFile=[SCANNET_LABEL_FILE_PATH (i.e., scannetv2-labels.combined.tsv)]
+python train.py --task=prepare --dataFolder=[SCANNET_PATH] --labelFile=[SCANNET_LABEL_FILE_PATH (i.e., scannetv2-labels.combined.tsv)]
 ```
 
 ## Training
@@ -42,13 +43,13 @@ The "task" option can contain any combinations of these three tasks, but the ear
 ## Write results for the final evaluation
 To train the instance confidence model, please first generate the instance segmentation results:
 ```
-python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster split=val
-python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster split=train
+python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster --split=val
+python inference.py --dataFolder=[SCANNET_PATH] --task=predict_cluster --split=train
 ```
 
 Then train the confidence model:
 ```
-python train.py --restore=0 --dataFolder=[SCANNET_PATH]
+python train_confidence.py --restore=0 --dataFolder=[SCANNET_PATH]
 ```
 
 Predict instance confidence, add additional instances for certain semantic labels, and write instance segmentation results:
diff --git a/inference.py b/inference.py
index 6ba5d33..31f76c1 100644
--- a/inference.py
+++ b/inference.py
@@ -187,11 +187,7 @@ def write(filename, model, validator, test_dir, num_scales, augment=True, num_cr
     filenames = []
     split = options.split
 
-    if split == 'test':
-        data_folder = '/gruvi/Data/chenliu/ScanNet/scans_test/'
-    else:
-        data_folder = '/gruvi/Data/chenliu/ScanNet/scans/'
-        pass
+    data_folder = options.dataFolder
 
     test_dir = 'test/' + options.keyname + '/inference/' + split + '/'
     if not os.path.exists(test_dir):