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).
docstr: Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
  
  Args:
      images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
          The pipeline handles three types of images:
  
          - A string containing an HTTP(S) link pointing to an image
          - A string containing a local path to an image
          - An image loaded in PIL directly
  
          The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
          same format: all as HTTP(S) links, all as local paths, or all as PIL images.
      threshold (`float`, *optional*, defaults to 0.9):
          The probability necessary to make a prediction.
      timeout (`float`, *optional*, defaults to None):
          The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
          the call may block forever.
  
  Return:
      A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single
      image, will return a list of dictionaries, if the input is a list of several images, will return a list of
      list of dictionaries corresponding to each image.
  
      The dictionaries contain the following keys:
  
      - **label** (`str`) -- The class label identified by the model.
      - **score** (`float`) -- The score attributed by the model for that label.
      - **box** (`List[Dict[str, int]]`) -- The bounding box of detected object in image's original size.
code: def __call__(
      self, *args, **kwargs
  ) -> Union[Predictions, List[Prediction]]:
      """
      Detect objects (bounding boxes & classes) in the image(s) passed as inputs.
  
      Args:
          images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
              The pipeline handles three types of images:
  
              - A string containing an HTTP(S) link pointing to an image
              - A string containing a local path to an image
              - An image loaded in PIL directly
  
              The pipeline accepts either a single image or a batch of images. Images in a batch must all be in the
              same format: all as HTTP(S) links, all as local paths, or all as PIL images.
          threshold (`float`, *optional*, defaults to 0.9):
              The probability necessary to make a prediction.
          timeout (`float`, *optional*, defaults to None):
              The maximum time in seconds to wait for fetching images from the web. If None, no timeout is set and
              the call may block forever.
  
      Return:
          A list of dictionaries or a list of list of dictionaries containing the result. If the input is a single
          image, will return a list of dictionaries, if the input is a list of several images, will return a list of
          list of dictionaries corresponding to each image.
  
          The dictionaries contain the following keys:
  
          - **label** (`str`) -- The class label identified by the model.
          - **score** (`float`) -- The score attributed by the model for that label.
          - **box** (`List[Dict[str, int]]`) -- The bounding box of detected object in image's original size.
      """
  
      return super().__call__(*args, **kwargs)
  
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 3 for calling the ObjectDetectionPipeline obj's underlying member method code with example usage: obj.__call__() and returns: super