 Image.open(requests.get(url, stream=True).raw)

        >>> image_processor = AutoImageProcessor.from_pretrained("facebook/detr-resnet-50")
        >>> model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50")

        >>> inputs = image_processor(images=image, return_tensors="pt")
        >>> outputs = model(**inputs)

        >>> # convert outputs (bounding boxes and class logits) to COCO API
        >>> target_sizes = torch.tensor([image.size[::-1]])
        >>> results = image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[
        ...     0
        ... ]

        >>> for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
        ...     box = [round(i, 2) for i in box.tolist()]
        ...     print(
        ...         f"Detected {model.config.id2label[label.item()]} with confidence "
        ...         f"{round(score.item(), 3)} at location {box}"
        ...     )
        Detected remote with confidence 0.998 at location [40.16, 70.81, 175.55, 117.98]
        Detected remote with confidence 0.996 at location [333.24, 72.55, 368.33, 187.66]
        Detected couch with confidence 0.995 at location [-0.02, 1.15, 639.73, 473.76]
        Detected cat with confidence 0.999 at location [13.24, 52.05, 314.02, 470.93]
        Detected cat with confidence 0.999 at location [345.4, 23.85, 640.37, 368.72]
        ```N)rt