class_docstr: Default SSD training transform which includes tons of image augmentations.
  
  Parameters
  ----------
  width : int
      Image width.
  height : int
      Image height.
  num_class : int
      Number of categories
  scale_factor : int, default is 4
      The downsampling scale factor between input image and output heatmap
  mean : array-like of size 3
      Mean pixel values to be subtracted from image tensor. Default is [0.485, 0.456, 0.406].
  std : array-like of size 3
      Standard deviation to be divided from image. Default is [0.229, 0.224, 0.225].
class_docstr_tok: ["Default","SSD","training","transform","which","includes","tons","of","image","augmentations",".","Parameters","--","--","--","--","--","width",":","int","Image","width",".","height",":","int","Image","height",".","num_class",":","int","Number","of","categories","scale_factor",":","int",",","default","is","4","The","downsampling","scale","factor","between","input","image","and","output","heatmap","mean",":","array-like","of","size","3","Mean","pixel","values","to","be","subtracted","from","image","tensor",".","Default","is","[","0.485",",","0.456",",","0.406","]",".","std",":","array-like","of","size","3","Standard","deviation","to","be","divided","from","image",".","Default","is","[","0.229",",","0.224",",","0.225","]","."]
code: def __init__(
      self,
      width,
      height,
      num_class,
      scale_factor=4,
      mean=(0.485, 0.456, 0.406),
      std=(0.229, 0.224, 0.225),
      **kwargs,
  ):
      self._kwargs = kwargs
      self._width = width
      self._height = height
      self._num_class = num_class
      self._scale_factor = scale_factor
      self._mean = np.array(mean, dtype=np.float32).reshape((1, 1, 3))
      self._std = np.array(std, dtype=np.float32).reshape((1, 1, 3))
      self._data_rng = np.random.RandomState(123)
      self._eig_val = np.array(
          [0.2141788, 0.01817699, 0.00341571], dtype=np.float32
      )
      self._eig_vec = np.array(
          [
              [-0.58752847, -0.69563484, 0.41340352],
              [-0.5832747, 0.00994535, -0.81221408],
              [-0.56089297, 0.71832671, 0.41158938],
          ],
          dtype=np.float32,
      )
      self._internal_target_generator = None
      self._target_width = width // scale_factor
      self._target_height = height // scale_factor
  
file_path: gluon-cv/gluoncv/data/transforms/presets/center_net.py
imports: from __future__ import absolute_import
  import numpy
  import mxnet
  from ..None import bbox
  from ..None import image
  from ..None import experimental
  from ....utils.filesystem import try_import_cv2
label_desc: Use image node_id 1 to create a new CenterNetDefaultTrainTransform object from inherited base classes: object with example: obj = CenterNetDefaultTrainTransform(width, height, num_class, scale_factor, mean, std)