class_docstr: A Darknet53 based feature extractor for YOLOv3.
  
  This is a feature extractor for :class:`~chainercv.links.model.yolo.YOLOv3`
class_docstr_tok: ["A","Darknet53","based","feature","extractor","for","YOLOv3",".","This","is","a","feature","extractor","for",":","class",":","`","~chainercv.links.model.yolo.YOLOv3","`"]
code: def __init__(self):
      super(Darknet53Extractor, self).__init__()
  
      # Darknet53
      self.append(Conv2DBNActiv(32, 3, pad=1, activ=_leaky_relu))
      for k, n_block in enumerate((1, 2, 8, 8, 4)):
          self.append(
              Conv2DBNActiv(
                  32 << (k + 1), 3, stride=2, pad=1, activ=_leaky_relu
              )
          )
          for _ in range(n_block):
              self.append(
                  ResidualBlock(
                      Conv2DBNActiv(32 << k, 1, activ=_leaky_relu),
                      Conv2DBNActiv(
                          32 << (k + 1), 3, pad=1, activ=_leaky_relu
                      ),
                  )
              )
  
      # additional links
      for i, n in enumerate((512, 256, 128)):
          if i > 0:
              self.append(Conv2DBNActiv(n, 1, activ=_leaky_relu))
          self.append(Conv2DBNActiv(n, 1, activ=_leaky_relu))
          self.append(Conv2DBNActiv(n * 2, 3, pad=1, activ=_leaky_relu))
          self.append(Conv2DBNActiv(n, 1, activ=_leaky_relu))
          self.append(Conv2DBNActiv(n * 2, 3, pad=1, activ=_leaky_relu))
          self.append(Conv2DBNActiv(n, 1, activ=_leaky_relu))
  
imports: from __future__ import division
  import itertools
  import numpy
  import chainer
  from chainer.backends import cuda
  import chainer.functions
  from chainer.links import Convolution2D
  from chainercv.links import Conv2DBNActiv
  from chainercv import utils
  from chainercv.links.model.yolo.yolo_base import YOLOBase
label_desc: Use image node_id 1 to create a new Darknet53Extractor object from inherited base classes: chainer with example: obj = Darknet53Extractor()