name: "segnet"
layer {
 name: "data"
 type: "DenseImageData"
 top: "data"
 top: "label"
 dense_image_data_param {
 source: "/SegNet/CamVid/train.txt"	# Change this to the absolute path to your data file
 batch_size: 4 			# Change this number to a batch size that will fit on your GPU
 shuffle: true
 }
}
layer {
 name: "norm"
 type: "LRN"
 bottom: "data"
 top: "norm"
 lrn_param {
 local_size: 5
 alpha: 0.0001
 beta: 0.75
 }
}
layer {
 name: "conv1"
 type: "Convolution"
 bottom: "norm"
 top: "conv1"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv1"
 top: "conv1"
 name: "conv1_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "relu1"
 type: "ReLU"
 bottom: "conv1"
 top: "conv1"
}
layer {
 name: "pool1"
 type: "Pooling"
 bottom: "conv1"
 top: "pool1"
 top: "pool1_mask"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "conv2"
 type: "Convolution"
 bottom: "pool1"
 top: "conv2"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv2"
 top: "conv2"
 name: "conv2_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "relu2"
 type: "ReLU"
 bottom: "conv2"
 top: "conv2"
}
layer {
 name: "pool2"
 type: "Pooling"
 bottom: "conv2"
 top: "pool2"
 top: "pool2_mask"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "conv3"
 type: "Convolution"
 bottom: "pool2"
 top: "conv3"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv3"
 top: "conv3"
 name: "conv3_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "relu3"
 type: "ReLU"
 bottom: "conv3"
 top: "conv3"
}
layer {
 name: "pool3"
 type: "Pooling"
 bottom: "conv3"
 top: "pool3"
 top: "pool3_mask"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "conv4"
 type: "Convolution"
 bottom: "pool3"
 top: "conv4"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv4"
 top: "conv4"
 name: "conv4_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "relu4"
 type: "ReLU"
 bottom: "conv4"
 top: "conv4"
}
layer {
 name: "pool4"
 type: "Pooling"
 bottom: "conv4"
 top: "pool4"
 top: "pool4_mask"
 pooling_param {
 pool: MAX
 kernel_size: 2
 stride: 2
 }
}
layer {
 name: "upsample4"
 type: "Upsample"
 bottom: "pool4"
 bottom: "pool4_mask"
 top: "upsample4"
 upsample_param {
 scale: 2
 pad_out_h: true
 }
}
layer {
 name: "conv_decode4"
 type: "Convolution"
 bottom: "upsample4"
 top: "conv_decode4"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv_decode4"
 top: "conv_decode4"
 name: "conv_decode4_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "upsample3"
 type: "Upsample"
 bottom: "conv_decode4"
 bottom: "pool3_mask"
 top: "upsample3"
 upsample_param {
 scale: 2
 }
}
layer {
 name: "conv_decode3"
 type: "Convolution"
 bottom: "upsample3"
 top: "conv_decode3"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv_decode3"
 top: "conv_decode3"
 name: "conv_decode3_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "upsample2"
 type: "Upsample"
 bottom: "conv_decode3"
 bottom: "pool2_mask"
 top: "upsample2"
 upsample_param {
 scale: 2
 }
}
layer {
 name: "conv_decode2"
 type: "Convolution"
 bottom: "upsample2"
 top: "conv_decode2"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv_decode2"
 top: "conv_decode2"
 name: "conv_decode2_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "upsample1"
 type: "Upsample"
 bottom: "conv_decode2"
 bottom: "pool1_mask"
 top: "upsample1"
 upsample_param {
 scale: 2
 }
}
layer {
 name: "conv_decode1"
 type: "Convolution"
 bottom: "upsample1"
 top: "conv_decode1"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 64
 kernel_size: 7
 pad: 3
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 bottom: "conv_decode1"
 top: "conv_decode1"
 name: "conv_decode1_bn"
 type: "BN"
 bn_param {
	scale_filler {
	 type: "constant"
	 value: 1
	}
	shift_filler {
	 type: "constant"
	 value: 0.001
	}
 }
}
layer {
 name: "conv_classifier"
 type: "Convolution"
 bottom: "conv_decode1"
 top: "conv_classifier"
 param {
 lr_mult: 1
 decay_mult: 1
 }
 param {
 lr_mult: 2
 decay_mult: 0
 }
 convolution_param {
 num_output: 11
 kernel_size: 1
 weight_filler {
 type: "msra"
 }
 bias_filler {
 type: "constant"
 }
 }
}
layer {
 name: "loss"
 type: "SoftmaxWithLoss"
 bottom: "conv_classifier"
 bottom: "label"
 top: "loss"
 softmax_param {engine: CAFFE}
 loss_param: {
 weight_by_label_freqs: true
 ignore_label: 11
 class_weighting: 0.2595
 class_weighting: 0.1826
 class_weighting: 4.5640
 class_weighting: 0.1417
 class_weighting: 0.9051
 class_weighting: 0.3826
 class_weighting: 9.6446
 class_weighting: 1.8418
 class_weighting: 0.6823
 class_weighting: 6.2478
 class_weighting: 7.3614
 }
}
layer {
 name: "accuracy"
 type: "Accuracy"
 bottom: "conv_classifier"
 bottom: "label"
 top: "accuracy"
 top: "per_class_accuracy"
}