Update README.md

README.md

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