diff --git a/README.md b/README.md
index 2fac4f9..5ab71e0 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,36 @@ python -m pip install glob2
 
 Then, download the model weights from <a href="https://drive.google.com/drive/folders/1t3LMJIIrSnCGwOEf53Cyg0lkSXd3M4Hm?usp=drive_link" target="_blank">this link</a> and save it under `./checkpoints/`.
 
+## Use torch hub to predcit normal
+```
+import torch
+import cv2
+import numpy as np
+
+# Load the normal predictor model from torch hub
+normal_predictor = torch.hub.load("hugoycj/DSINE-hub", "DSINE", trust_repo=True)
+
+# Load the input image using OpenCV
+image = cv2.imread(args.input, cv2.IMREAD_COLOR)
+h, w = image.shape[:2]
+
+# Use the model to infer the normal map from the input image
+with torch.inference_mode():
+    normal = normal_predictor.infer_cv2(image)[0]  # Output shape: (H, W, 3)
+    normal = (normal + 1) / 2  # Convert values to the range [0, 1]
+
+# Convert the normal map to a displayable format
+normal = (normal * 255).cpu().numpy().astype(np.uint8).transpose(1, 2, 0)
+normal = cv2.cvtColor(normal, cv2.COLOR_RGB2BGR)
+
+# Save the output normal map to a file
+cv2.imwrite(args.output, normal)
+```
+If the network is unavailable to retrieve weights, you can use local weights for torch hub as shown below:
+```
+normal_predictor = torch.hub.load("hugoycj/DSINE-hub", "DSINE", local_file_path='./checkpoints/dsine.pt', trust_repo=True)
+```
+
 ## Test on images
 
 * Run `python test.py` to generate predictions for the images under `./samples/img/`. The result will be saved under `./samples/output/`.