Update README.md

README.md

@@ -37,7 +37,7 @@ 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 AutoFeatureExtractor, DeformableDetrForObjectDetection
+from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
 import torch
 from PIL import Image
 import requests
@@ -45,24 +45,23 @@ import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-feature_extractor = AutoFeatureExtractor.from_pretrained("SenseTime/deformable-detr-single-scale-dc5")
+processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr-single-scale-dc5")
 model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr-single-scale-dc5")
 
-inputs = feature_extractor(images=image, return_tensors="pt")
+inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
 
 # convert outputs (bounding boxes and class logits) to COCO API
+# let's only keep detections with score > 0.7
 target_sizes = torch.tensor([image.size[::-1]])
-results = feature_extractor.post_process(outputs, target_sizes=target_sizes)[0]
+results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
 
 for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
     box = [round(i, 2) for i in box.tolist()]
-    # let's only keep detections with score > 0.7
-    if score > 0.7:
-        print(
+    print(
             f"Detected {model.config.id2label[label.item()]} with confidence "
             f"{round(score.item(), 3)} at location {box}"
-        )
+    )
 ```
 
 Currently, both the feature extractor and model support PyTorch.