diff --git a/README.md b/README.md
index 44c4820..732708b 100644
--- a/README.md
+++ b/README.md
@@ -5,7 +5,7 @@
 [![lint](https://github.com/daisylab-bit/torchattack/actions/workflows/lint.yml/badge.svg)](https://github.com/daisylab-bit/torchattack/actions/workflows/lint.yml)
 [![GitHub release (latest by date)](https://img.shields.io/github/v/release/daisylab-bit/torchattack)](https://github.com/daisylab-bit/torchattack/releases/latest)
 
-A set of adversarial attacks implemented in PyTorch. _For internal use._
+A set of adversarial attacks implemented in PyTorch. For internal use, no support guaranteed.
 
 ```shell
 # Install from github source
@@ -18,22 +18,25 @@ python -m pip install git+https://gitee.com/daisylab-bit/torchattack
 ## Usage
 
 ```python
+import torch
+from torchattack import FGSM, MIFGSM
 from torchvision.models import resnet50
 from torchvision.transforms import transforms
 
-from torchattack import FGSM, MIFGSM
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # Load a model
 model = resnet50(weights='DEFAULT')
+model = model.to(device)
 
-# Define transforms (you are responsible for normalizing the data if needed)
-transform = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+# Define normalization (you are responsible for normalizing the data if needed)
+normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
 
 # Initialize an attack
-attack = FGSM(model, transform, eps=0.03)
+attack = FGSM(model, normalize, device)
 
 # Initialize an attack with extra params
-attack = MIFGSM(model, transform, eps=0.03, steps=10, decay=1.0)
+attack = MIFGSM(model, normalize, device, eps=0.03, steps=10, decay=1.0)
 ```
 
 Check out [`torchattack.utils.run_attack`](src/torchattack/utils.py) for a simple example.