Update README.md

README.md

@@ -59,7 +59,7 @@ Here is a quick example of using the model. Since this model is an image matchin
 The raw outputs contain the list of keypoints detected by the backbone as well as the list of matches with their corresponding 
 matching scores.
 ```python
-from transformers import AutoImageProcessor, AutoModel
+from transformers import AutoImageProcessor, AutoModelForKeypointMatching
 import torch
 from PIL import Image
 import requests
@@ -72,7 +72,7 @@ image2 = Image.open(requests.get(url_image2, stream=True).raw)
 images = [image1, image2]
 
 processor = AutoImageProcessor.from_pretrained("zju-community/efficientloftr")
-model = AutoModel.from_pretrained("zju-community/efficientloftr")
+model = AutoModelForKeypointMatching.from_pretrained("zju-community/efficientloftr")
 
 inputs = processor(images, return_tensors="pt")
 with torch.no_grad():