Add code example

README.md

@@ -28,16 +28,22 @@ 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 ViTFeatureExtractor, ViTModel
+from transformers import DetrFeatureExtractor, DetrForObjectDetection
 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 = ViTFeatureExtractor.from_pretrained('google/vit-base-patch16-224-in21k')
-model = ViTModel.from_pretrained('google/vit-base-patch16-224-in21k')
+
+feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50')
+model = DetrForObjectDetection.from_pretrained('facebook/detr-resnet-50')
+
 inputs = feature_extractor(images=image, return_tensors="pt")
 outputs = model(**inputs)
-last_hidden_states = outputs.last_hidden_state
+
+# model predicts bounding boxes and corresponding COCO classes
+logits = outputs.logits
+bboxes = outputs.pred_boxes
 ```
 
 Currently, both the feature extractor and model support PyTorch.