old_contents: from keras import backend as K
  
  
  class Config:
  
  	def __init__(self):
  
  		self.verbose = True
  
  		self.network = 'vgg'
  
  		# setting for data augmentation
  		self.use_horizontal_flips = False
  		self.use_vertical_flips = False
  		self.rot_90 = False
  
  		# anchor box scales
  		self.anchor_box_scales = [32]
  
  		# anchor box ratios
  		self.anchor_box_ratios = [[1, 1]]
  
  		# size to resize the smallest side of the image
  		self.im_size = 60
  
  		# image channel-wise mean to subtract
  		self.img_channel_mean = [103.939, 116.779, 123.68]
  		self.img_scaling_factor = 1.0
  
  		# number of ROIs at once
  		self.num_rois = 4
  
  		# stride at the RPN (this depends on the network configuration)
  		self.rpn_stride = 4
  
  		self.balanced_classes = False
  
  		# scaling the stdev
  		self.std_scaling = 4.0
  		self.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]
  
  		# overlaps for RPN
  		self.rpn_min_overlap = 0.3
  		self.rpn_max_overlap = 0.7
  
  		# overlaps for classifier ROIs
  		self.classifier_min_overlap = 0.1
  		self.classifier_max_overlap = 0.5
  
  		# placeholder for the class mapping, automatically generated by the parser
  		self.class_mapping = None
  
  		#location of pretrained weights for the base network 
  		# weight files can be found at:
  		# https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5
  		# https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
  
  		self.model_path = 'model_frcnn.vgg.hdf5'
  
new_contents: from keras import backend as K
  
  
  class Config:
  
  	def __init__(self):
  
  		self.verbose = True
  
  		self.network = 'vgg'
  
  		# setting for data augmentation
  		self.use_horizontal_flips = False
  		self.use_vertical_flips = False
  		self.rot_90 = False
  
  		# anchor box scales
  		self.anchor_box_scales = [32]
  
  		# anchor box ratios
  		self.anchor_box_ratios = [[1, 1]]
  
  		# size to resize the smallest side of the image
  		self.im_size = 60
  
  		# image channel-wise mean to subtract
  		self.img_channel_mean = [103.939, 116.779, 123.68]
  		self.img_scaling_factor = 1.0
  
  		# number of ROIs at once
  		self.num_rois = 4
  
  		# stride at the RPN (this depends on the network configuration)
  		self.rpn_stride = 7
  
  		self.balanced_classes = False
  
  		# scaling the stdev
  		self.std_scaling = 4.0
  		self.classifier_regr_std = [8.0, 8.0, 4.0, 4.0]
  
  		# overlaps for RPN
  		self.rpn_min_overlap = 0.3
  		self.rpn_max_overlap = 0.7
  
  		# overlaps for classifier ROIs
  		self.classifier_min_overlap = 0.1
  		self.classifier_max_overlap = 0.5
  
  		# placeholder for the class mapping, automatically generated by the parser
  		self.class_mapping = None
  
  		#location of pretrained weights for the base network 
  		# weight files can be found at:
  		# https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_th_dim_ordering_th_kernels_notop.h5
  		# https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5
  
  		self.model_path = 'model_frcnn.vgg.hdf5'
  
subject: Use another rpn stride