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README.md

@@ -29,7 +29,7 @@ The recently-developed DETR approach applies the transformer encoder and decoder
 
 ## Intended uses & limitations
 
-You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=microsoft/conditional-detr) to look for all available Conditional DETR models.
+You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=conditional-detr) to look for all available Conditional DETR models.
 
 ### How to use
 
@@ -44,8 +44,8 @@ import requests
 url = "http://images.cocodataset.org/val2017/000000039769.jpg"
 image = Image.open(requests.get(url, stream=True).raw)
 
-processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50")
-model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50")
+processor = AutoImageProcessor.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5")
+model = ConditionalDetrForObjectDetection.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5")
 
 inputs = processor(images=image, return_tensors="pt")
 outputs = model(**inputs)
@@ -64,13 +64,12 @@ for score, label, box in zip(results["scores"], results["labels"], results["boxe
 ```
 This should output:
 ```
-Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45]
-Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0]
-Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95]
+Detected cat with confidence 0.865 at location [13.95, 64.98, 327.14, 478.82]
+Detected remote with confidence 0.849 at location [39.37, 83.18, 187.67, 125.02]
+Detected cat with confidence 0.743 at location [327.22, 35.17, 637.54, 377.04]
+Detected remote with confidence 0.737 at location [329.36, 89.47, 376.42, 197.53]
 ```
 
-Currently, both the feature extractor and model support PyTorch. 
-
 ## Training data
 
 The Conditional DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.