class_docstr: Zero shot object detection pipeline using `OwlViTForObjectDetection`. This pipeline predicts bounding boxes of
  objects when you provide an image and a set of `candidate_labels`.
  
  This object detection pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"zero-shot-object-detection"`.
  
  See the list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=zero-shot-object-detection).
class_docstr_tok: ["Zero","shot","object","detection","pipeline","using","`","OwlViTForObjectDetection","`",".","This","pipeline","predicts","bounding","boxes","of","objects","when","you","provide","an","image","and","a","set","of","`","candidate_labels","`",".","This","object","detection","pipeline","can","currently","be","loaded","from","[","`","pipeline","`","]","using","the","following","task","identifier",":","`","``","zero-shot-object-detection","''","`",".","See","the","list","of","available","models","on","[","huggingface.co\/models","]","(","https",":","\/\/huggingface.co\/models","?","filter=zero-shot-object-detection",")","."]
code: def __init__(self, **kwargs):
      super().__init__(**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_ZERO_SHOT_OBJECT_DETECTION_MAPPING
      )
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/pipelines/zero_shot_object_detection.py
imports: from typing import Dict, List, Union
  import numpy
  from ..tokenization_utils_base import BatchEncoding
  from ..utils import add_end_docstrings, is_tf_available, 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 ZeroShotObjectDetectionPipeline object from inherited base classes: Pipeline with example: obj = ZeroShotObjectDetectionPipeline()