diff --git a/caffe_model/colorization.caffemodel b/caffe_model/colorization.caffemodel
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index 0000000..aee45de
Binary files /dev/null and b/caffe_model/colorization.caffemodel differ
diff --git a/caffe_model/colorization.prototxt b/caffe_model/colorization.prototxt
new file mode 100644
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+++ b/caffe_model/colorization.prototxt
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+name: "LtoAB"
+
+layer {
+  name: "data_l"
+  type: "Input"
+  top: "data_l"
+  input_param {
+    shape { dim: 1 dim: 1 dim: 224 dim: 224 }
+  }
+}
+
+# *****************
+# ***** conv1 *****
+# *****************
+layer {
+  name: "bw_conv1_1"
+  type: "Convolution"
+  bottom: "data_l"
+  top: "conv1_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu1_1"
+  type: "ReLU"
+  bottom: "conv1_1"
+  top: "conv1_1"
+}
+layer {
+  name: "conv1_2"
+  type: "Convolution"
+  bottom: "conv1_1"
+  top: "conv1_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 64
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu1_2"
+  type: "ReLU"
+  bottom: "conv1_2"
+  top: "conv1_2"
+}
+layer {
+  name: "conv1_2norm"
+  type: "BatchNorm"
+  bottom: "conv1_2"
+  top: "conv1_2norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv2 *****
+# *****************
+layer {
+  name: "conv2_1"
+  type: "Convolution"
+  # bottom: "conv1_2"
+  bottom: "conv1_2norm"
+  # bottom: "pool1"
+  top: "conv2_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu2_1"
+  type: "ReLU"
+  bottom: "conv2_1"
+  top: "conv2_1"
+}
+layer {
+  name: "conv2_2"
+  type: "Convolution"
+  bottom: "conv2_1"
+  top: "conv2_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 128
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu2_2"
+  type: "ReLU"
+  bottom: "conv2_2"
+  top: "conv2_2"
+}
+layer {
+  name: "conv2_2norm"
+  type: "BatchNorm"
+  bottom: "conv2_2"
+  top: "conv2_2norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv3 *****
+# *****************
+layer {
+  name: "conv3_1"
+  type: "Convolution"
+  # bottom: "conv2_2"
+  bottom: "conv2_2norm"
+  # bottom: "pool2"
+  top: "conv3_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_1"
+  type: "ReLU"
+  bottom: "conv3_1"
+  top: "conv3_1"
+}
+layer {
+  name: "conv3_2"
+  type: "Convolution"
+  bottom: "conv3_1"
+  top: "conv3_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "relu3_2"
+  type: "ReLU"
+  bottom: "conv3_2"
+  top: "conv3_2"
+}
+layer {
+  name: "conv3_3"
+  type: "Convolution"
+  bottom: "conv3_2"
+  top: "conv3_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 256
+    pad: 1
+    kernel_size: 3
+    stride: 2
+  }
+}
+layer {
+  name: "relu3_3"
+  type: "ReLU"
+  bottom: "conv3_3"
+  top: "conv3_3"
+}
+layer {
+  name: "conv3_3norm"
+  type: "BatchNorm"
+  bottom: "conv3_3"
+  top: "conv3_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv4 *****
+# *****************
+layer {
+  name: "conv4_1"
+  type: "Convolution"
+  # bottom: "conv3_3"
+  bottom: "conv3_3norm"
+  # bottom: "pool3"
+  top: "conv4_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_1"
+  type: "ReLU"
+  bottom: "conv4_1"
+  top: "conv4_1"
+}
+layer {
+  name: "conv4_2"
+  type: "Convolution"
+  bottom: "conv4_1"
+  top: "conv4_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_2"
+  type: "ReLU"
+  bottom: "conv4_2"
+  top: "conv4_2"
+}
+layer {
+  name: "conv4_3"
+  type: "Convolution"
+  bottom: "conv4_2"
+  top: "conv4_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu4_3"
+  type: "ReLU"
+  bottom: "conv4_3"
+  top: "conv4_3"
+}
+layer {
+  name: "conv4_3norm"
+  type: "BatchNorm"
+  bottom: "conv4_3"
+  top: "conv4_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv5 *****
+# *****************
+layer {
+  name: "conv5_1"
+  type: "Convolution"
+  # bottom: "conv4_3"
+  bottom: "conv4_3norm"
+  # bottom: "pool4"
+  top: "conv5_1"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0}   
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_1"
+  type: "ReLU"
+  bottom: "conv5_1"
+  top: "conv5_1"
+}
+layer {
+  name: "conv5_2"
+  type: "Convolution"
+  bottom: "conv5_1"
+  top: "conv5_2"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_2"
+  type: "ReLU"
+  bottom: "conv5_2"
+  top: "conv5_2"
+}
+layer {
+  name: "conv5_3"
+  type: "Convolution"
+  bottom: "conv5_2"
+  top: "conv5_3"
+  # param {lr_mult: 0 decay_mult: 0} 
+  # param {lr_mult: 0 decay_mult: 0} 
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    stride: 1
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu5_3"
+  type: "ReLU"
+  bottom: "conv5_3"
+  top: "conv5_3"
+}
+layer {
+  name: "conv5_3norm"
+  type: "BatchNorm"
+  bottom: "conv5_3"
+  top: "conv5_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv6 *****
+# *****************
+layer {
+  name: "conv6_1"
+  type: "Convolution"
+  bottom: "conv5_3norm"
+  top: "conv6_1"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_1"
+  type: "ReLU"
+  bottom: "conv6_1"
+  top: "conv6_1"
+}
+layer {
+  name: "conv6_2"
+  type: "Convolution"
+  bottom: "conv6_1"
+  top: "conv6_2"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_2"
+  type: "ReLU"
+  bottom: "conv6_2"
+  top: "conv6_2"
+}
+layer {
+  name: "conv6_3"
+  type: "Convolution"
+  bottom: "conv6_2"
+  top: "conv6_3"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 2
+    dilation: 2
+  }
+}
+layer {
+  name: "relu6_3"
+  type: "ReLU"
+  bottom: "conv6_3"
+  top: "conv6_3"
+}
+layer {
+  name: "conv6_3norm"
+  type: "BatchNorm"
+  bottom: "conv6_3"
+  top: "conv6_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv7 *****
+# *****************
+layer {
+  name: "conv7_1"
+  type: "Convolution"
+  bottom: "conv6_3norm"
+  top: "conv7_1"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_1"
+  type: "ReLU"
+  bottom: "conv7_1"
+  top: "conv7_1"
+}
+layer {
+  name: "conv7_2"
+  type: "Convolution"
+  bottom: "conv7_1"
+  top: "conv7_2"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_2"
+  type: "ReLU"
+  bottom: "conv7_2"
+  top: "conv7_2"
+}
+layer {
+  name: "conv7_3"
+  type: "Convolution"
+  bottom: "conv7_2"
+  top: "conv7_3"
+  convolution_param {
+    num_output: 512
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu7_3"
+  type: "ReLU"
+  bottom: "conv7_3"
+  top: "conv7_3"
+}
+layer {
+  name: "conv7_3norm"
+  type: "BatchNorm"
+  bottom: "conv7_3"
+  top: "conv7_3norm"
+  batch_norm_param{ }
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+  param {lr_mult: 0 decay_mult: 0}
+}
+# *****************
+# ***** conv8 *****
+# *****************
+layer {
+  name: "conv8_1"
+  type: "Deconvolution"
+  bottom: "conv7_3norm"
+  top: "conv8_1"
+  convolution_param {
+    num_output: 256
+    kernel_size: 4
+    pad: 1
+    dilation: 1
+    stride: 2
+  }
+}
+layer {
+  name: "relu8_1"
+  type: "ReLU"
+  bottom: "conv8_1"
+  top: "conv8_1"
+}
+layer {
+  name: "conv8_2"
+  type: "Convolution"
+  bottom: "conv8_1"
+  top: "conv8_2"
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu8_2"
+  type: "ReLU"
+  bottom: "conv8_2"
+  top: "conv8_2"
+}
+layer {
+  name: "conv8_3"
+  type: "Convolution"
+  bottom: "conv8_2"
+  top: "conv8_3"
+  convolution_param {
+    num_output: 256
+    kernel_size: 3
+    pad: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "relu8_3"
+  type: "ReLU"
+  bottom: "conv8_3"
+  top: "conv8_3"
+}
+# *******************
+# ***** Softmax *****
+# *******************
+layer {
+  name: "conv8_313"
+  type: "Convolution"
+  bottom: "conv8_3"
+  top: "conv8_313"
+  convolution_param {
+    num_output: 313
+    kernel_size: 1
+    stride: 1
+    dilation: 1
+  }
+}
+layer {
+  name: "conv8_313_rh"
+  type: "Scale"
+  bottom: "conv8_313"
+  top: "conv8_313_rh"
+  scale_param {
+    bias_term: false
+    filler {      type: 'constant'      value: 2.606    }
+  }
+}
+layer {
+  name: "class8_313_rh"
+  type: "Softmax"
+  bottom: "conv8_313_rh"
+  top: "class8_313_rh"
+}
+# ********************
+# ***** Decoding *****
+# ********************
+layer {
+  name: "class8_ab"
+  type: "Convolution"
+  bottom: "class8_313_rh"
+  top: "class8_ab"
+  convolution_param {
+    num_output: 2
+    kernel_size: 1
+    stride: 1
+    dilation: 1
+  }
+}
+#layer {
+#  name: "Silence"
+#  type: "Silence"
+#  bottom: "class8_ab"
+#}