class_docstr: The ``ObjectDetectionData`` class is a :class:`~flash.core.data.data_module.DataModule` with a set of
  classmethods for loading data for object detection.
class_docstr_tok: ["The","``","ObjectDetectionData","``","class","is","a",":","class",":","`","~flash.core.data.data_module.DataModule","`","with","a","set","of","classmethods","for","loading","data","for","object","detection","."]
docstr: Creates a :class:`~flash.image.detection.data.ObjectDetectionData` object from the given data folders This is
  currently support only for the predicting stage.
  
  Args:
      predict_folder: The folder containing the predict data.
      predict_transform: The dictionary of transforms to use during predicting which maps
      data_module_kwargs: The keywords arguments for creating the datamodule.
  
  Returns:
      The constructed data module.
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code: def from_folders(
      cls,
      predict_folder: Optional[str] = None,
      predict_transform: INPUT_TRANSFORM_TYPE = IceVisionInputTransform,
      input_cls: Type[Input] = IceVisionInput,
      transform_kwargs: Optional[Dict] = None,
      **data_module_kwargs: Any,
  ) -> "DataModule":
      """Creates a :class:`~flash.image.detection.data.ObjectDetectionData` object from the given data folders This is
      currently support only for the predicting stage.
  
      Args:
          predict_folder: The folder containing the predict data.
          predict_transform: The dictionary of transforms to use during predicting which maps
          data_module_kwargs: The keywords arguments for creating the datamodule.
  
      Returns:
          The constructed data module.
  
      """
      return cls(
          predict_input=input_cls(
              RunningStage.PREDICTING, predict_folder
          ),
          transform=predict_transform,
          transform_kwargs=transform_kwargs,
          **data_module_kwargs,
      )
  
imports: from functools import partial
  from typing import Any, Callable, Collection, Dict, List, Optional, Sequence, Type, Union
  import numpy
  import torch
  from flash.core.data.data_module import DataModule
  from flash.core.data.io.input import Input
  from flash.core.data.utilities.classification import TargetFormatter
  from flash.core.data.utilities.sort import sorted_alphanumeric
  from flash.core.integrations.icevision.data import IceVisionInput
  from flash.core.integrations.icevision.transforms import IceVisionInputTransform
  from flash.core.utilities.imports import _FIFTYONE_AVAILABLE, _ICEVISION_AVAILABLE, _TOPIC_IMAGE_AVAILABLE, Image, requires
  from flash.core.utilities.stages import RunningStage
  from flash.core.utilities.types import INPUT_TRANSFORM_TYPE
  from flash.image.detection.input import ObjectDetectionFiftyOneInput, ObjectDetectionFilesInput, ObjectDetectionImageInput, ObjectDetectionNumpyInput, ObjectDetectionTensorInput
label_desc: Use image node_id 10 for calling the ObjectDetectionData obj's underlying member method code with example usage: obj.from_folders(cls, predict_folder, predict_transform, input_cls, transform_kwargs) and returns: cls