class_docstr: Object detection pipeline using any `AutoModelForObjectDetection`. This pipeline predicts bounding boxes of objects
  and their classes.
  
  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")
      self.check_model_type(MODEL_FOR_OBJECT_DETECTION_MAPPING)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/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()