class_docstr: Base class for  Light-Head R-CNN.
  
  This is a base class for Light-Head R-CNN links supporting object detection
  API [#]_. The following three stages constitute Light-Head R-CNN.
  
  1. **Feature extraction**: Images are taken and their         feature maps are calculated.
  2. **Region Proposal Networks**: Given the feature maps calculated in         the previous stage, produce set of RoIs around objects.
  3. **Localization and Classification Heads**: Using feature maps that         belong to the proposed RoIs, classify the categories of the objects         in the RoIs and improve localizations.
  
  Each stage is carried out by one of the callable
  :class:`chainer.Chain` objects :obj:`feature`, :obj:`rpn` and :obj:`head`.
  
  There are two functions :meth:`predict` and :meth:`__call__` to conduct
  object detection.
  :meth:`predict` takes images and returns bounding boxes that are converted
  to image coordinates. This will be useful for a scenario when
  Light-Head R-CNN is treated as a black box function, for instance.
  :meth:`__call__` is provided for a scnerario when intermediate outputs
  are needed, for instance, for training and debugging.
  
  Links that support obejct detection API have method :meth:`predict` with
  the same interface. Please refer to :meth:`predict` for
  further details.
  
  .. [#] Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng,     Jian Sun. Light-Head R-CNN: In Defense of Two-Stage Object Detector.     arXiv preprint arXiv:1711.07264.
  
  Args:
      extractor (callable Chain): A callable that takes a BCHW image
          array and returns feature maps.
      rpn (callable Chain): A callable that has the same interface as
          :class:`~chainercv.links.model.faster_rcnn.RegionProposalNetwork`.
          Please refer to the documentation found there.
      head (callable Chain): A callable that takes
          a BCHW array, RoIs and batch indices for RoIs. This returns class
          dependent localization paramters and class scores.
      mean (numpy.ndarray): A value to be subtracted from an image
          in :meth:`prepare`.
      min_size (int): A preprocessing paramter for :meth:`prepare`. Please
          refer to a docstring found for :meth:`prepare`.
      max_size (int): A preprocessing paramter for :meth:`prepare`.
      loc_normalize_mean (tuple of four floats): Mean values of
          localization estimates.
      loc_normalize_std (tupler of four floats): Standard deviation
          of localization estimates.
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docstr: Use the given preset during prediction.
  
  This method changes values of :obj:`self.nms_thresh` and
  :obj:`self.score_thresh`. These values are a threshold value
  used for non maximum suppression and a threshold value
  to discard low confidence proposals in :meth:`predict`,
  respectively.
  
  If the attributes need to be changed to something
  other than the values provided in the presets, please modify
  them by directly accessing the public attributes.
  
  Args:
      preset ({'visualize', 'evaluate'): A string to determine the
          preset to use.
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code: def use_preset(self, preset):
      """Use the given preset during prediction.
  
      This method changes values of :obj:`self.nms_thresh` and
      :obj:`self.score_thresh`. These values are a threshold value
      used for non maximum suppression and a threshold value
      to discard low confidence proposals in :meth:`predict`,
      respectively.
  
      If the attributes need to be changed to something
      other than the values provided in the presets, please modify
      them by directly accessing the public attributes.
  
      Args:
          preset ({'visualize', 'evaluate'): A string to determine the
              preset to use.
  
      """
      if preset == "visualize":
          self.nms_thresh = 0.5
          self.score_thresh = 0.7
      elif preset == "evaluate":
          self.nms_thresh = 0.5
          self.score_thresh = 0.001
      else:
          raise ValueError("preset must be visualize or evaluate")
  
file_path: chainercv/chainercv/links/model/light_head_rcnn/light_head_rcnn.py
imports: from __future__ import division
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainercv.links.model.faster_rcnn.utils.loc2bbox import loc2bbox
  from chainercv.transforms.image.resize import resize
  from chainercv.utils.bbox.non_maximum_suppression import non_maximum_suppression
label_desc: Use image node_id 4 for calling the LightHeadRCNN obj's underlying member method code with example usage: obj.use_preset(preset) without return types