Update README.md

README.md

@@ -35,17 +35,17 @@ You can use the raw model for object detection. See the [model hub](https://hugg
 Here is how to use this model:
 
 ```python
-from transformers import YolosFeatureExtractor, YolosForObjectDetection
+from transformers import YolosImageProcessor, YolosForObjectDetection
 from PIL import Image
 import requests
 
 url = 'http://images.cocodataset.org/val2017/000000039769.jpg'
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small-300')
+image_processor = YolosImageProcessor.from_pretrained('hustvl/yolos-small-300')
 model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small-300')
 
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = image_processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 
 # model predicts bounding boxes and corresponding COCO classes
@@ -53,7 +53,7 @@ logits = outputs.logits
 bboxes = outputs.pred_boxes
 ```
 
-Currently, both the feature extractor and model support PyTorch. 
+Currently, both the image processor and model support PyTorch. 
 
 ## Training data