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Update README.md

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  1. README.md +2 -4
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@@ -50,18 +50,16 @@ outputs = model(**inputs)
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  # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
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  target_sizes = torch.Tensor([image.size[::-1]])
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  # Convert outputs (bounding boxes and class logits) to COCO API
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- results = processor.post_process_object_detection(outputs=outputs, target_sizes=target_sizes)
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  i = 0 # Retrieve predictions for the first image for the corresponding text queries
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  text = texts[i]
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  boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
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  # Print detected objects and rescaled box coordinates
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- score_threshold = 0.1
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  for box, score, label in zip(boxes, scores, labels):
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  box = [round(i, 2) for i in box.tolist()]
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- if score >= score_threshold:
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- print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
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  ```
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  # Target image sizes (height, width) to rescale box predictions [batch_size, 2]
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  target_sizes = torch.Tensor([image.size[::-1]])
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  # Convert outputs (bounding boxes and class logits) to COCO API
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+ results = processor.post_process_object_detection(outputs=outputs, threshold=0.1, target_sizes=target_sizes)
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  i = 0 # Retrieve predictions for the first image for the corresponding text queries
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  text = texts[i]
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  boxes, scores, labels = results[i]["boxes"], results[i]["scores"], results[i]["labels"]
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  # Print detected objects and rescaled box coordinates
 
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  for box, score, label in zip(boxes, scores, labels):
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  box = [round(i, 2) for i in box.tolist()]
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+ print(f"Detected {text[label]} with confidence {round(score.item(), 3)} at location {box}")
 
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  ```
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