class_docstr: Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects
  and their classes.
  
  Example:
  
  ```python
  >>> from transformers import pipeline
  
  >>> detector = pipeline(model="facebook/detr-resnet-50")
  >>> detector("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png")
  [{'score': 0.997, 'label': 'bird', 'box': {'xmin': 69, 'ymin': 171, 'xmax': 396, 'ymax': 507}}, {'score': 0.999, 'label': 'bird', 'box': {'xmin': 398, 'ymin': 105, 'xmax': 767, 'ymax': 507}}]
  
  >>> # x, y  are expressed relative to the top left hand corner.
  ```
  
  Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
  
  This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"object-detection"`.
  
  See the list of available models on [huggingface.co/models](https://huggingface.co/models?filter=object-detection).
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      if self.framework == "tf":
          raise ValueError(
              f"The {self.__class__} is only available in PyTorch."
          )
  
      requires_backends(self, "vision")
      mapping = MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES.copy()
      mapping.update(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES)
      self.check_model_type(mapping)
  
file_path: transformers/src/transformers/pipelines/object_detection.py
imports: from typing import Any, Dict, List, Union
  from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 1 to create a new ObjectDetectionPipeline object from inherited base classes: Pipeline with example: obj = ObjectDetectionPipeline()