diff --git a/README.md b/README.md
index 6e3e146..434bf9f 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
 # Unlearnable Examples
-Code for ICLR2021 Paper ["Unlearnable Examples: Making Personal Data Unexploitable "](https://openreview.net/forum?id=iAmZUo0DxC0) by Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, Yisen Wang.
 
 ## Quick Start
 ##### Use the QuickStart.ipynb notebook for a quick start.
 In the notebook, you can find the minimal implementation for generating sample-wise unlearnable examples on CIFAR-10.
+Please remove `mlconfig` from `models/__init__.py` if you only using the notebook and copy paste the model to the notebook.
+
+
 
 ## Experiments in the paper.
 Check scripts folder for *.sh for each corresponding experiments.
@@ -62,13 +64,3 @@ python3 -u main.py    --version                 resnet18                       \
                       --perturb_tensor_filepath path/to/your/experiment/folder/perturbation.pt \
                       --train
 ```
-
-## Citing this work
-```
-@inproceedings{huang2021unlearnable,
-    title={Unlearnable Examples: Making Personal Data Unexploitable},
-    author={Hanxun Huang and Xingjun Ma and Sarah Monazam Erfani and James Bailey and Yisen Wang},
-    booktitle={ICLR},
-    year={2021}
-}
-```
diff --git a/models/svhn/dense121.yaml b/models/svhn/dense121.yaml
deleted file mode 100644
index 365634c..0000000
--- a/models/svhn/dense121.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-num_classes: 10
-epochs: 30
-grad_clip: 5.0
-log_frequency: 100
-
-model:
-  name: DenseNet121
-  num_classes: 10
-
-criterion:
-  name: CrossEntropyLoss
-
-optimizer:
-  name: SGD
-  lr: 0.1
-  weight_decay: 5.e-4
-  momentum: 0.9
-
-scheduler:
-  name: CosineAnnealingLR
-  T_max: $epochs
-  eta_min: 0.0
-
-dataset:
-  name: DatasetGenerator
-  train_batch_size: 96
-  eval_batch_size: 128
diff --git a/models/svhn/resnet18.yaml b/models/svhn/resnet18.yaml
deleted file mode 100644
index 04b2b2e..0000000
--- a/models/svhn/resnet18.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-num_classes: 10
-epochs: 30
-grad_clip: 5.0
-log_frequency: 100
-
-model:
-  name: ResNet18
-  num_classes: 10
-
-criterion:
-  name: CrossEntropyLoss
-
-optimizer:
-  name: SGD
-  lr: 0.1
-  weight_decay: 5.e-4
-  momentum: 0.9
-
-scheduler:
-  name: CosineAnnealingLR
-  T_max: $epochs
-  eta_min: 0.0
-
-dataset:
-  name: DatasetGenerator
-  train_batch_size: 128
-  eval_batch_size: 128
diff --git a/models/svhn/resnet18_madrys.yaml b/models/svhn/resnet18_madrys.yaml
deleted file mode 100644
index 5530fa4..0000000
--- a/models/svhn/resnet18_madrys.yaml
+++ /dev/null
@@ -1,30 +0,0 @@
-num_classes: 100
-epochs: 100
-grad_clip: 5.0
-log_frequency: 100
-
-model:
-  name: ResNet18
-  num_classes: 10
-
-criterion:
-  name: MadrysLoss
-  epsilon: 0.03137254901
-  perturb_steps: 10
-  step_size: 0.00784313725
-
-optimizer:
-  name: SGD
-  lr: 0.1
-  weight_decay: 5.e-4
-  momentum: 0.9
-
-scheduler:
-  name: MultiStepLR
-  milestones: [75, 90, 100]
-  gamma: 0.1
-
-dataset:
-  name: DatasetGenerator
-  train_batch_size: 128
-  eval_batch_size: 128
diff --git a/models/svhn/resnet50.yaml b/models/svhn/resnet50.yaml
deleted file mode 100644
index 30a5fd8..0000000
--- a/models/svhn/resnet50.yaml
+++ /dev/null
@@ -1,27 +0,0 @@
-num_classes: 10
-epochs: 30
-grad_clip: 5.0
-log_frequency: 100
-
-model:
-  name: ResNet50
-  num_classes: 10
-
-criterion:
-  name: CrossEntropyLoss
-
-optimizer:
-  name: SGD
-  lr: 0.1
-  weight_decay: 5.e-4
-  momentum: 0.9
-
-scheduler:
-  name: CosineAnnealingLR
-  T_max: $epochs
-  eta_min: 0.0
-
-dataset:
-  name: DatasetGenerator
-  train_batch_size: 128
-  eval_batch_size: 128
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..d66c2b2
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,3 @@
+torch
+torchvision
+mlconfig