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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+DenseNet_121.caffemodel filter=lfs diff=lfs merge=lfs -text

DenseNet_121.caffemodel

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c6a6ec988d76c468c3f67501a23a39ec7bf6ebe6729fd99496a15d0e845478b2
+size 32303870

DenseNet_121.prototxt

@@ -0,0 +1,4762 @@
+name: "DENSENET_121"
+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 224
+input_dim: 224
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 64
+    bias_term: false
+    pad: 3
+    kernel_size: 7
+    stride: 2
+  }
+}
+layer {
+  name: "conv1/bn"
+  type: "BatchNorm"
+  bottom: "conv1"
+  top: "conv1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv1/scale"
+  type: "Scale"
+  bottom: "conv1/bn"
+  top: "conv1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1/bn"
+  top: "conv1/bn"
+}
+layer {
+  name: "pool1"
+  type: "Pooling"
+  bottom: "conv1/bn"
+  top: "pool1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 3
+    stride: 2
+    pad: 1
+    ceil_mode: false
+  }
+}
+layer {
+  name: "conv2_1/x1/bn"
+  type: "BatchNorm"
+  bottom: "pool1"
+  top: "conv2_1/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_1/x1/scale"
+  type: "Scale"
+  bottom: "conv2_1/x1/bn"
+  top: "conv2_1/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_1/x1"
+  type: "ReLU"
+  bottom: "conv2_1/x1/bn"
+  top: "conv2_1/x1/bn"
+}
+layer {
+  name: "conv2_1/x1"
+  type: "Convolution"
+  bottom: "conv2_1/x1/bn"
+  top: "conv2_1/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_1/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_1/x1"
+  top: "conv2_1/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_1/x2/scale"
+  type: "Scale"
+  bottom: "conv2_1/x2/bn"
+  top: "conv2_1/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_1/x2"
+  type: "ReLU"
+  bottom: "conv2_1/x2/bn"
+  top: "conv2_1/x2/bn"
+}
+layer {
+  name: "conv2_1/x2"
+  type: "Convolution"
+  bottom: "conv2_1/x2/bn"
+  top: "conv2_1/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_1"
+  type: "Concat"
+  bottom: "pool1"
+  bottom: "conv2_1/x2"
+  top: "concat_2_1"
+}
+layer {
+  name: "conv2_2/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_1"
+  top: "conv2_2/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_2/x1/scale"
+  type: "Scale"
+  bottom: "conv2_2/x1/bn"
+  top: "conv2_2/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_2/x1"
+  type: "ReLU"
+  bottom: "conv2_2/x1/bn"
+  top: "conv2_2/x1/bn"
+}
+layer {
+  name: "conv2_2/x1"
+  type: "Convolution"
+  bottom: "conv2_2/x1/bn"
+  top: "conv2_2/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_2/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_2/x1"
+  top: "conv2_2/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_2/x2/scale"
+  type: "Scale"
+  bottom: "conv2_2/x2/bn"
+  top: "conv2_2/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_2/x2"
+  type: "ReLU"
+  bottom: "conv2_2/x2/bn"
+  top: "conv2_2/x2/bn"
+}
+layer {
+  name: "conv2_2/x2"
+  type: "Convolution"
+  bottom: "conv2_2/x2/bn"
+  top: "conv2_2/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_2"
+  type: "Concat"
+  bottom: "concat_2_1"
+  bottom: "conv2_2/x2"
+  top: "concat_2_2"
+}
+layer {
+  name: "conv2_3/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_2"
+  top: "conv2_3/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_3/x1/scale"
+  type: "Scale"
+  bottom: "conv2_3/x1/bn"
+  top: "conv2_3/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_3/x1"
+  type: "ReLU"
+  bottom: "conv2_3/x1/bn"
+  top: "conv2_3/x1/bn"
+}
+layer {
+  name: "conv2_3/x1"
+  type: "Convolution"
+  bottom: "conv2_3/x1/bn"
+  top: "conv2_3/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_3/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_3/x1"
+  top: "conv2_3/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_3/x2/scale"
+  type: "Scale"
+  bottom: "conv2_3/x2/bn"
+  top: "conv2_3/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_3/x2"
+  type: "ReLU"
+  bottom: "conv2_3/x2/bn"
+  top: "conv2_3/x2/bn"
+}
+layer {
+  name: "conv2_3/x2"
+  type: "Convolution"
+  bottom: "conv2_3/x2/bn"
+  top: "conv2_3/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_3"
+  type: "Concat"
+  bottom: "concat_2_2"
+  bottom: "conv2_3/x2"
+  top: "concat_2_3"
+}
+layer {
+  name: "conv2_4/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_3"
+  top: "conv2_4/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_4/x1/scale"
+  type: "Scale"
+  bottom: "conv2_4/x1/bn"
+  top: "conv2_4/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_4/x1"
+  type: "ReLU"
+  bottom: "conv2_4/x1/bn"
+  top: "conv2_4/x1/bn"
+}
+layer {
+  name: "conv2_4/x1"
+  type: "Convolution"
+  bottom: "conv2_4/x1/bn"
+  top: "conv2_4/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_4/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_4/x1"
+  top: "conv2_4/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_4/x2/scale"
+  type: "Scale"
+  bottom: "conv2_4/x2/bn"
+  top: "conv2_4/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_4/x2"
+  type: "ReLU"
+  bottom: "conv2_4/x2/bn"
+  top: "conv2_4/x2/bn"
+}
+layer {
+  name: "conv2_4/x2"
+  type: "Convolution"
+  bottom: "conv2_4/x2/bn"
+  top: "conv2_4/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_4"
+  type: "Concat"
+  bottom: "concat_2_3"
+  bottom: "conv2_4/x2"
+  top: "concat_2_4"
+}
+layer {
+  name: "conv2_5/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_4"
+  top: "conv2_5/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_5/x1/scale"
+  type: "Scale"
+  bottom: "conv2_5/x1/bn"
+  top: "conv2_5/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_5/x1"
+  type: "ReLU"
+  bottom: "conv2_5/x1/bn"
+  top: "conv2_5/x1/bn"
+}
+layer {
+  name: "conv2_5/x1"
+  type: "Convolution"
+  bottom: "conv2_5/x1/bn"
+  top: "conv2_5/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_5/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_5/x1"
+  top: "conv2_5/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_5/x2/scale"
+  type: "Scale"
+  bottom: "conv2_5/x2/bn"
+  top: "conv2_5/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_5/x2"
+  type: "ReLU"
+  bottom: "conv2_5/x2/bn"
+  top: "conv2_5/x2/bn"
+}
+layer {
+  name: "conv2_5/x2"
+  type: "Convolution"
+  bottom: "conv2_5/x2/bn"
+  top: "conv2_5/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_5"
+  type: "Concat"
+  bottom: "concat_2_4"
+  bottom: "conv2_5/x2"
+  top: "concat_2_5"
+}
+layer {
+  name: "conv2_6/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_5"
+  top: "conv2_6/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_6/x1/scale"
+  type: "Scale"
+  bottom: "conv2_6/x1/bn"
+  top: "conv2_6/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_6/x1"
+  type: "ReLU"
+  bottom: "conv2_6/x1/bn"
+  top: "conv2_6/x1/bn"
+}
+layer {
+  name: "conv2_6/x1"
+  type: "Convolution"
+  bottom: "conv2_6/x1/bn"
+  top: "conv2_6/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv2_6/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv2_6/x1"
+  top: "conv2_6/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_6/x2/scale"
+  type: "Scale"
+  bottom: "conv2_6/x2/bn"
+  top: "conv2_6/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_6/x2"
+  type: "ReLU"
+  bottom: "conv2_6/x2/bn"
+  top: "conv2_6/x2/bn"
+}
+layer {
+  name: "conv2_6/x2"
+  type: "Convolution"
+  bottom: "conv2_6/x2/bn"
+  top: "conv2_6/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_2_6"
+  type: "Concat"
+  bottom: "concat_2_5"
+  bottom: "conv2_6/x2"
+  top: "concat_2_6"
+}
+layer {
+  name: "conv2_blk/bn"
+  type: "BatchNorm"
+  bottom: "concat_2_6"
+  top: "conv2_blk/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv2_blk/scale"
+  type: "Scale"
+  bottom: "conv2_blk/bn"
+  top: "conv2_blk/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu2_blk"
+  type: "ReLU"
+  bottom: "conv2_blk/bn"
+  top: "conv2_blk/bn"
+}
+layer {
+  name: "conv2_blk"
+  type: "Convolution"
+  bottom: "conv2_blk/bn"
+  top: "conv2_blk"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "pool2"
+  type: "Pooling"
+  bottom: "conv2_blk"
+  top: "pool2"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv3_1/x1/bn"
+  type: "BatchNorm"
+  bottom: "pool2"
+  top: "conv3_1/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_1/x1/scale"
+  type: "Scale"
+  bottom: "conv3_1/x1/bn"
+  top: "conv3_1/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_1/x1"
+  type: "ReLU"
+  bottom: "conv3_1/x1/bn"
+  top: "conv3_1/x1/bn"
+}
+layer {
+  name: "conv3_1/x1"
+  type: "Convolution"
+  bottom: "conv3_1/x1/bn"
+  top: "conv3_1/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_1/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_1/x1"
+  top: "conv3_1/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_1/x2/scale"
+  type: "Scale"
+  bottom: "conv3_1/x2/bn"
+  top: "conv3_1/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_1/x2"
+  type: "ReLU"
+  bottom: "conv3_1/x2/bn"
+  top: "conv3_1/x2/bn"
+}
+layer {
+  name: "conv3_1/x2"
+  type: "Convolution"
+  bottom: "conv3_1/x2/bn"
+  top: "conv3_1/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_1"
+  type: "Concat"
+  bottom: "pool2"
+  bottom: "conv3_1/x2"
+  top: "concat_3_1"
+}
+layer {
+  name: "conv3_2/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_1"
+  top: "conv3_2/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_2/x1/scale"
+  type: "Scale"
+  bottom: "conv3_2/x1/bn"
+  top: "conv3_2/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_2/x1"
+  type: "ReLU"
+  bottom: "conv3_2/x1/bn"
+  top: "conv3_2/x1/bn"
+}
+layer {
+  name: "conv3_2/x1"
+  type: "Convolution"
+  bottom: "conv3_2/x1/bn"
+  top: "conv3_2/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_2/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_2/x1"
+  top: "conv3_2/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_2/x2/scale"
+  type: "Scale"
+  bottom: "conv3_2/x2/bn"
+  top: "conv3_2/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_2/x2"
+  type: "ReLU"
+  bottom: "conv3_2/x2/bn"
+  top: "conv3_2/x2/bn"
+}
+layer {
+  name: "conv3_2/x2"
+  type: "Convolution"
+  bottom: "conv3_2/x2/bn"
+  top: "conv3_2/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_2"
+  type: "Concat"
+  bottom: "concat_3_1"
+  bottom: "conv3_2/x2"
+  top: "concat_3_2"
+}
+layer {
+  name: "conv3_3/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_2"
+  top: "conv3_3/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_3/x1/scale"
+  type: "Scale"
+  bottom: "conv3_3/x1/bn"
+  top: "conv3_3/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_3/x1"
+  type: "ReLU"
+  bottom: "conv3_3/x1/bn"
+  top: "conv3_3/x1/bn"
+}
+layer {
+  name: "conv3_3/x1"
+  type: "Convolution"
+  bottom: "conv3_3/x1/bn"
+  top: "conv3_3/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_3/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_3/x1"
+  top: "conv3_3/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_3/x2/scale"
+  type: "Scale"
+  bottom: "conv3_3/x2/bn"
+  top: "conv3_3/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_3/x2"
+  type: "ReLU"
+  bottom: "conv3_3/x2/bn"
+  top: "conv3_3/x2/bn"
+}
+layer {
+  name: "conv3_3/x2"
+  type: "Convolution"
+  bottom: "conv3_3/x2/bn"
+  top: "conv3_3/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_3"
+  type: "Concat"
+  bottom: "concat_3_2"
+  bottom: "conv3_3/x2"
+  top: "concat_3_3"
+}
+layer {
+  name: "conv3_4/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_3"
+  top: "conv3_4/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_4/x1/scale"
+  type: "Scale"
+  bottom: "conv3_4/x1/bn"
+  top: "conv3_4/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_4/x1"
+  type: "ReLU"
+  bottom: "conv3_4/x1/bn"
+  top: "conv3_4/x1/bn"
+}
+layer {
+  name: "conv3_4/x1"
+  type: "Convolution"
+  bottom: "conv3_4/x1/bn"
+  top: "conv3_4/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_4/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_4/x1"
+  top: "conv3_4/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_4/x2/scale"
+  type: "Scale"
+  bottom: "conv3_4/x2/bn"
+  top: "conv3_4/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_4/x2"
+  type: "ReLU"
+  bottom: "conv3_4/x2/bn"
+  top: "conv3_4/x2/bn"
+}
+layer {
+  name: "conv3_4/x2"
+  type: "Convolution"
+  bottom: "conv3_4/x2/bn"
+  top: "conv3_4/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_4"
+  type: "Concat"
+  bottom: "concat_3_3"
+  bottom: "conv3_4/x2"
+  top: "concat_3_4"
+}
+layer {
+  name: "conv3_5/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_4"
+  top: "conv3_5/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_5/x1/scale"
+  type: "Scale"
+  bottom: "conv3_5/x1/bn"
+  top: "conv3_5/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_5/x1"
+  type: "ReLU"
+  bottom: "conv3_5/x1/bn"
+  top: "conv3_5/x1/bn"
+}
+layer {
+  name: "conv3_5/x1"
+  type: "Convolution"
+  bottom: "conv3_5/x1/bn"
+  top: "conv3_5/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_5/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_5/x1"
+  top: "conv3_5/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_5/x2/scale"
+  type: "Scale"
+  bottom: "conv3_5/x2/bn"
+  top: "conv3_5/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_5/x2"
+  type: "ReLU"
+  bottom: "conv3_5/x2/bn"
+  top: "conv3_5/x2/bn"
+}
+layer {
+  name: "conv3_5/x2"
+  type: "Convolution"
+  bottom: "conv3_5/x2/bn"
+  top: "conv3_5/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_5"
+  type: "Concat"
+  bottom: "concat_3_4"
+  bottom: "conv3_5/x2"
+  top: "concat_3_5"
+}
+layer {
+  name: "conv3_6/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_5"
+  top: "conv3_6/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_6/x1/scale"
+  type: "Scale"
+  bottom: "conv3_6/x1/bn"
+  top: "conv3_6/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_6/x1"
+  type: "ReLU"
+  bottom: "conv3_6/x1/bn"
+  top: "conv3_6/x1/bn"
+}
+layer {
+  name: "conv3_6/x1"
+  type: "Convolution"
+  bottom: "conv3_6/x1/bn"
+  top: "conv3_6/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_6/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_6/x1"
+  top: "conv3_6/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_6/x2/scale"
+  type: "Scale"
+  bottom: "conv3_6/x2/bn"
+  top: "conv3_6/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_6/x2"
+  type: "ReLU"
+  bottom: "conv3_6/x2/bn"
+  top: "conv3_6/x2/bn"
+}
+layer {
+  name: "conv3_6/x2"
+  type: "Convolution"
+  bottom: "conv3_6/x2/bn"
+  top: "conv3_6/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_6"
+  type: "Concat"
+  bottom: "concat_3_5"
+  bottom: "conv3_6/x2"
+  top: "concat_3_6"
+}
+layer {
+  name: "conv3_7/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_6"
+  top: "conv3_7/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_7/x1/scale"
+  type: "Scale"
+  bottom: "conv3_7/x1/bn"
+  top: "conv3_7/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_7/x1"
+  type: "ReLU"
+  bottom: "conv3_7/x1/bn"
+  top: "conv3_7/x1/bn"
+}
+layer {
+  name: "conv3_7/x1"
+  type: "Convolution"
+  bottom: "conv3_7/x1/bn"
+  top: "conv3_7/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_7/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_7/x1"
+  top: "conv3_7/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_7/x2/scale"
+  type: "Scale"
+  bottom: "conv3_7/x2/bn"
+  top: "conv3_7/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_7/x2"
+  type: "ReLU"
+  bottom: "conv3_7/x2/bn"
+  top: "conv3_7/x2/bn"
+}
+layer {
+  name: "conv3_7/x2"
+  type: "Convolution"
+  bottom: "conv3_7/x2/bn"
+  top: "conv3_7/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_7"
+  type: "Concat"
+  bottom: "concat_3_6"
+  bottom: "conv3_7/x2"
+  top: "concat_3_7"
+}
+layer {
+  name: "conv3_8/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_7"
+  top: "conv3_8/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_8/x1/scale"
+  type: "Scale"
+  bottom: "conv3_8/x1/bn"
+  top: "conv3_8/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_8/x1"
+  type: "ReLU"
+  bottom: "conv3_8/x1/bn"
+  top: "conv3_8/x1/bn"
+}
+layer {
+  name: "conv3_8/x1"
+  type: "Convolution"
+  bottom: "conv3_8/x1/bn"
+  top: "conv3_8/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_8/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_8/x1"
+  top: "conv3_8/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_8/x2/scale"
+  type: "Scale"
+  bottom: "conv3_8/x2/bn"
+  top: "conv3_8/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_8/x2"
+  type: "ReLU"
+  bottom: "conv3_8/x2/bn"
+  top: "conv3_8/x2/bn"
+}
+layer {
+  name: "conv3_8/x2"
+  type: "Convolution"
+  bottom: "conv3_8/x2/bn"
+  top: "conv3_8/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_8"
+  type: "Concat"
+  bottom: "concat_3_7"
+  bottom: "conv3_8/x2"
+  top: "concat_3_8"
+}
+layer {
+  name: "conv3_9/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_8"
+  top: "conv3_9/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_9/x1/scale"
+  type: "Scale"
+  bottom: "conv3_9/x1/bn"
+  top: "conv3_9/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_9/x1"
+  type: "ReLU"
+  bottom: "conv3_9/x1/bn"
+  top: "conv3_9/x1/bn"
+}
+layer {
+  name: "conv3_9/x1"
+  type: "Convolution"
+  bottom: "conv3_9/x1/bn"
+  top: "conv3_9/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_9/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_9/x1"
+  top: "conv3_9/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_9/x2/scale"
+  type: "Scale"
+  bottom: "conv3_9/x2/bn"
+  top: "conv3_9/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_9/x2"
+  type: "ReLU"
+  bottom: "conv3_9/x2/bn"
+  top: "conv3_9/x2/bn"
+}
+layer {
+  name: "conv3_9/x2"
+  type: "Convolution"
+  bottom: "conv3_9/x2/bn"
+  top: "conv3_9/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_9"
+  type: "Concat"
+  bottom: "concat_3_8"
+  bottom: "conv3_9/x2"
+  top: "concat_3_9"
+}
+layer {
+  name: "conv3_10/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_9"
+  top: "conv3_10/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_10/x1/scale"
+  type: "Scale"
+  bottom: "conv3_10/x1/bn"
+  top: "conv3_10/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_10/x1"
+  type: "ReLU"
+  bottom: "conv3_10/x1/bn"
+  top: "conv3_10/x1/bn"
+}
+layer {
+  name: "conv3_10/x1"
+  type: "Convolution"
+  bottom: "conv3_10/x1/bn"
+  top: "conv3_10/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_10/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_10/x1"
+  top: "conv3_10/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_10/x2/scale"
+  type: "Scale"
+  bottom: "conv3_10/x2/bn"
+  top: "conv3_10/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_10/x2"
+  type: "ReLU"
+  bottom: "conv3_10/x2/bn"
+  top: "conv3_10/x2/bn"
+}
+layer {
+  name: "conv3_10/x2"
+  type: "Convolution"
+  bottom: "conv3_10/x2/bn"
+  top: "conv3_10/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_10"
+  type: "Concat"
+  bottom: "concat_3_9"
+  bottom: "conv3_10/x2"
+  top: "concat_3_10"
+}
+layer {
+  name: "conv3_11/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_10"
+  top: "conv3_11/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_11/x1/scale"
+  type: "Scale"
+  bottom: "conv3_11/x1/bn"
+  top: "conv3_11/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_11/x1"
+  type: "ReLU"
+  bottom: "conv3_11/x1/bn"
+  top: "conv3_11/x1/bn"
+}
+layer {
+  name: "conv3_11/x1"
+  type: "Convolution"
+  bottom: "conv3_11/x1/bn"
+  top: "conv3_11/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_11/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_11/x1"
+  top: "conv3_11/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_11/x2/scale"
+  type: "Scale"
+  bottom: "conv3_11/x2/bn"
+  top: "conv3_11/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_11/x2"
+  type: "ReLU"
+  bottom: "conv3_11/x2/bn"
+  top: "conv3_11/x2/bn"
+}
+layer {
+  name: "conv3_11/x2"
+  type: "Convolution"
+  bottom: "conv3_11/x2/bn"
+  top: "conv3_11/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_11"
+  type: "Concat"
+  bottom: "concat_3_10"
+  bottom: "conv3_11/x2"
+  top: "concat_3_11"
+}
+layer {
+  name: "conv3_12/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_11"
+  top: "conv3_12/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_12/x1/scale"
+  type: "Scale"
+  bottom: "conv3_12/x1/bn"
+  top: "conv3_12/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_12/x1"
+  type: "ReLU"
+  bottom: "conv3_12/x1/bn"
+  top: "conv3_12/x1/bn"
+}
+layer {
+  name: "conv3_12/x1"
+  type: "Convolution"
+  bottom: "conv3_12/x1/bn"
+  top: "conv3_12/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv3_12/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv3_12/x1"
+  top: "conv3_12/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_12/x2/scale"
+  type: "Scale"
+  bottom: "conv3_12/x2/bn"
+  top: "conv3_12/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_12/x2"
+  type: "ReLU"
+  bottom: "conv3_12/x2/bn"
+  top: "conv3_12/x2/bn"
+}
+layer {
+  name: "conv3_12/x2"
+  type: "Convolution"
+  bottom: "conv3_12/x2/bn"
+  top: "conv3_12/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_3_12"
+  type: "Concat"
+  bottom: "concat_3_11"
+  bottom: "conv3_12/x2"
+  top: "concat_3_12"
+}
+layer {
+  name: "conv3_blk/bn"
+  type: "BatchNorm"
+  bottom: "concat_3_12"
+  top: "conv3_blk/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv3_blk/scale"
+  type: "Scale"
+  bottom: "conv3_blk/bn"
+  top: "conv3_blk/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu3_blk"
+  type: "ReLU"
+  bottom: "conv3_blk/bn"
+  top: "conv3_blk/bn"
+}
+layer {
+  name: "conv3_blk"
+  type: "Convolution"
+  bottom: "conv3_blk/bn"
+  top: "conv3_blk"
+  convolution_param {
+    num_output: 256
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "pool3"
+  type: "Pooling"
+  bottom: "conv3_blk"
+  top: "pool3"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv4_1/x1/bn"
+  type: "BatchNorm"
+  bottom: "pool3"
+  top: "conv4_1/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_1/x1/scale"
+  type: "Scale"
+  bottom: "conv4_1/x1/bn"
+  top: "conv4_1/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_1/x1"
+  type: "ReLU"
+  bottom: "conv4_1/x1/bn"
+  top: "conv4_1/x1/bn"
+}
+layer {
+  name: "conv4_1/x1"
+  type: "Convolution"
+  bottom: "conv4_1/x1/bn"
+  top: "conv4_1/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_1/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_1/x1"
+  top: "conv4_1/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_1/x2/scale"
+  type: "Scale"
+  bottom: "conv4_1/x2/bn"
+  top: "conv4_1/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_1/x2"
+  type: "ReLU"
+  bottom: "conv4_1/x2/bn"
+  top: "conv4_1/x2/bn"
+}
+layer {
+  name: "conv4_1/x2"
+  type: "Convolution"
+  bottom: "conv4_1/x2/bn"
+  top: "conv4_1/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_1"
+  type: "Concat"
+  bottom: "pool3"
+  bottom: "conv4_1/x2"
+  top: "concat_4_1"
+}
+layer {
+  name: "conv4_2/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_1"
+  top: "conv4_2/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_2/x1/scale"
+  type: "Scale"
+  bottom: "conv4_2/x1/bn"
+  top: "conv4_2/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_2/x1"
+  type: "ReLU"
+  bottom: "conv4_2/x1/bn"
+  top: "conv4_2/x1/bn"
+}
+layer {
+  name: "conv4_2/x1"
+  type: "Convolution"
+  bottom: "conv4_2/x1/bn"
+  top: "conv4_2/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_2/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_2/x1"
+  top: "conv4_2/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_2/x2/scale"
+  type: "Scale"
+  bottom: "conv4_2/x2/bn"
+  top: "conv4_2/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_2/x2"
+  type: "ReLU"
+  bottom: "conv4_2/x2/bn"
+  top: "conv4_2/x2/bn"
+}
+layer {
+  name: "conv4_2/x2"
+  type: "Convolution"
+  bottom: "conv4_2/x2/bn"
+  top: "conv4_2/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_2"
+  type: "Concat"
+  bottom: "concat_4_1"
+  bottom: "conv4_2/x2"
+  top: "concat_4_2"
+}
+layer {
+  name: "conv4_3/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_2"
+  top: "conv4_3/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_3/x1/scale"
+  type: "Scale"
+  bottom: "conv4_3/x1/bn"
+  top: "conv4_3/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_3/x1"
+  type: "ReLU"
+  bottom: "conv4_3/x1/bn"
+  top: "conv4_3/x1/bn"
+}
+layer {
+  name: "conv4_3/x1"
+  type: "Convolution"
+  bottom: "conv4_3/x1/bn"
+  top: "conv4_3/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_3/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_3/x1"
+  top: "conv4_3/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_3/x2/scale"
+  type: "Scale"
+  bottom: "conv4_3/x2/bn"
+  top: "conv4_3/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_3/x2"
+  type: "ReLU"
+  bottom: "conv4_3/x2/bn"
+  top: "conv4_3/x2/bn"
+}
+layer {
+  name: "conv4_3/x2"
+  type: "Convolution"
+  bottom: "conv4_3/x2/bn"
+  top: "conv4_3/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_3"
+  type: "Concat"
+  bottom: "concat_4_2"
+  bottom: "conv4_3/x2"
+  top: "concat_4_3"
+}
+layer {
+  name: "conv4_4/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_3"
+  top: "conv4_4/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_4/x1/scale"
+  type: "Scale"
+  bottom: "conv4_4/x1/bn"
+  top: "conv4_4/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_4/x1"
+  type: "ReLU"
+  bottom: "conv4_4/x1/bn"
+  top: "conv4_4/x1/bn"
+}
+layer {
+  name: "conv4_4/x1"
+  type: "Convolution"
+  bottom: "conv4_4/x1/bn"
+  top: "conv4_4/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_4/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_4/x1"
+  top: "conv4_4/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_4/x2/scale"
+  type: "Scale"
+  bottom: "conv4_4/x2/bn"
+  top: "conv4_4/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_4/x2"
+  type: "ReLU"
+  bottom: "conv4_4/x2/bn"
+  top: "conv4_4/x2/bn"
+}
+layer {
+  name: "conv4_4/x2"
+  type: "Convolution"
+  bottom: "conv4_4/x2/bn"
+  top: "conv4_4/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_4"
+  type: "Concat"
+  bottom: "concat_4_3"
+  bottom: "conv4_4/x2"
+  top: "concat_4_4"
+}
+layer {
+  name: "conv4_5/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_4"
+  top: "conv4_5/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_5/x1/scale"
+  type: "Scale"
+  bottom: "conv4_5/x1/bn"
+  top: "conv4_5/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_5/x1"
+  type: "ReLU"
+  bottom: "conv4_5/x1/bn"
+  top: "conv4_5/x1/bn"
+}
+layer {
+  name: "conv4_5/x1"
+  type: "Convolution"
+  bottom: "conv4_5/x1/bn"
+  top: "conv4_5/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_5/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_5/x1"
+  top: "conv4_5/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_5/x2/scale"
+  type: "Scale"
+  bottom: "conv4_5/x2/bn"
+  top: "conv4_5/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_5/x2"
+  type: "ReLU"
+  bottom: "conv4_5/x2/bn"
+  top: "conv4_5/x2/bn"
+}
+layer {
+  name: "conv4_5/x2"
+  type: "Convolution"
+  bottom: "conv4_5/x2/bn"
+  top: "conv4_5/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_5"
+  type: "Concat"
+  bottom: "concat_4_4"
+  bottom: "conv4_5/x2"
+  top: "concat_4_5"
+}
+layer {
+  name: "conv4_6/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_5"
+  top: "conv4_6/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_6/x1/scale"
+  type: "Scale"
+  bottom: "conv4_6/x1/bn"
+  top: "conv4_6/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_6/x1"
+  type: "ReLU"
+  bottom: "conv4_6/x1/bn"
+  top: "conv4_6/x1/bn"
+}
+layer {
+  name: "conv4_6/x1"
+  type: "Convolution"
+  bottom: "conv4_6/x1/bn"
+  top: "conv4_6/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_6/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_6/x1"
+  top: "conv4_6/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_6/x2/scale"
+  type: "Scale"
+  bottom: "conv4_6/x2/bn"
+  top: "conv4_6/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_6/x2"
+  type: "ReLU"
+  bottom: "conv4_6/x2/bn"
+  top: "conv4_6/x2/bn"
+}
+layer {
+  name: "conv4_6/x2"
+  type: "Convolution"
+  bottom: "conv4_6/x2/bn"
+  top: "conv4_6/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_6"
+  type: "Concat"
+  bottom: "concat_4_5"
+  bottom: "conv4_6/x2"
+  top: "concat_4_6"
+}
+layer {
+  name: "conv4_7/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_6"
+  top: "conv4_7/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_7/x1/scale"
+  type: "Scale"
+  bottom: "conv4_7/x1/bn"
+  top: "conv4_7/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_7/x1"
+  type: "ReLU"
+  bottom: "conv4_7/x1/bn"
+  top: "conv4_7/x1/bn"
+}
+layer {
+  name: "conv4_7/x1"
+  type: "Convolution"
+  bottom: "conv4_7/x1/bn"
+  top: "conv4_7/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_7/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_7/x1"
+  top: "conv4_7/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_7/x2/scale"
+  type: "Scale"
+  bottom: "conv4_7/x2/bn"
+  top: "conv4_7/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_7/x2"
+  type: "ReLU"
+  bottom: "conv4_7/x2/bn"
+  top: "conv4_7/x2/bn"
+}
+layer {
+  name: "conv4_7/x2"
+  type: "Convolution"
+  bottom: "conv4_7/x2/bn"
+  top: "conv4_7/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_7"
+  type: "Concat"
+  bottom: "concat_4_6"
+  bottom: "conv4_7/x2"
+  top: "concat_4_7"
+}
+layer {
+  name: "conv4_8/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_7"
+  top: "conv4_8/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_8/x1/scale"
+  type: "Scale"
+  bottom: "conv4_8/x1/bn"
+  top: "conv4_8/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_8/x1"
+  type: "ReLU"
+  bottom: "conv4_8/x1/bn"
+  top: "conv4_8/x1/bn"
+}
+layer {
+  name: "conv4_8/x1"
+  type: "Convolution"
+  bottom: "conv4_8/x1/bn"
+  top: "conv4_8/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_8/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_8/x1"
+  top: "conv4_8/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_8/x2/scale"
+  type: "Scale"
+  bottom: "conv4_8/x2/bn"
+  top: "conv4_8/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_8/x2"
+  type: "ReLU"
+  bottom: "conv4_8/x2/bn"
+  top: "conv4_8/x2/bn"
+}
+layer {
+  name: "conv4_8/x2"
+  type: "Convolution"
+  bottom: "conv4_8/x2/bn"
+  top: "conv4_8/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_8"
+  type: "Concat"
+  bottom: "concat_4_7"
+  bottom: "conv4_8/x2"
+  top: "concat_4_8"
+}
+layer {
+  name: "conv4_9/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_8"
+  top: "conv4_9/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_9/x1/scale"
+  type: "Scale"
+  bottom: "conv4_9/x1/bn"
+  top: "conv4_9/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_9/x1"
+  type: "ReLU"
+  bottom: "conv4_9/x1/bn"
+  top: "conv4_9/x1/bn"
+}
+layer {
+  name: "conv4_9/x1"
+  type: "Convolution"
+  bottom: "conv4_9/x1/bn"
+  top: "conv4_9/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_9/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_9/x1"
+  top: "conv4_9/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_9/x2/scale"
+  type: "Scale"
+  bottom: "conv4_9/x2/bn"
+  top: "conv4_9/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_9/x2"
+  type: "ReLU"
+  bottom: "conv4_9/x2/bn"
+  top: "conv4_9/x2/bn"
+}
+layer {
+  name: "conv4_9/x2"
+  type: "Convolution"
+  bottom: "conv4_9/x2/bn"
+  top: "conv4_9/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_9"
+  type: "Concat"
+  bottom: "concat_4_8"
+  bottom: "conv4_9/x2"
+  top: "concat_4_9"
+}
+layer {
+  name: "conv4_10/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_9"
+  top: "conv4_10/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_10/x1/scale"
+  type: "Scale"
+  bottom: "conv4_10/x1/bn"
+  top: "conv4_10/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_10/x1"
+  type: "ReLU"
+  bottom: "conv4_10/x1/bn"
+  top: "conv4_10/x1/bn"
+}
+layer {
+  name: "conv4_10/x1"
+  type: "Convolution"
+  bottom: "conv4_10/x1/bn"
+  top: "conv4_10/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_10/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_10/x1"
+  top: "conv4_10/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_10/x2/scale"
+  type: "Scale"
+  bottom: "conv4_10/x2/bn"
+  top: "conv4_10/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_10/x2"
+  type: "ReLU"
+  bottom: "conv4_10/x2/bn"
+  top: "conv4_10/x2/bn"
+}
+layer {
+  name: "conv4_10/x2"
+  type: "Convolution"
+  bottom: "conv4_10/x2/bn"
+  top: "conv4_10/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_10"
+  type: "Concat"
+  bottom: "concat_4_9"
+  bottom: "conv4_10/x2"
+  top: "concat_4_10"
+}
+layer {
+  name: "conv4_11/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_10"
+  top: "conv4_11/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_11/x1/scale"
+  type: "Scale"
+  bottom: "conv4_11/x1/bn"
+  top: "conv4_11/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_11/x1"
+  type: "ReLU"
+  bottom: "conv4_11/x1/bn"
+  top: "conv4_11/x1/bn"
+}
+layer {
+  name: "conv4_11/x1"
+  type: "Convolution"
+  bottom: "conv4_11/x1/bn"
+  top: "conv4_11/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_11/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_11/x1"
+  top: "conv4_11/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_11/x2/scale"
+  type: "Scale"
+  bottom: "conv4_11/x2/bn"
+  top: "conv4_11/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_11/x2"
+  type: "ReLU"
+  bottom: "conv4_11/x2/bn"
+  top: "conv4_11/x2/bn"
+}
+layer {
+  name: "conv4_11/x2"
+  type: "Convolution"
+  bottom: "conv4_11/x2/bn"
+  top: "conv4_11/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_11"
+  type: "Concat"
+  bottom: "concat_4_10"
+  bottom: "conv4_11/x2"
+  top: "concat_4_11"
+}
+layer {
+  name: "conv4_12/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_11"
+  top: "conv4_12/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_12/x1/scale"
+  type: "Scale"
+  bottom: "conv4_12/x1/bn"
+  top: "conv4_12/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_12/x1"
+  type: "ReLU"
+  bottom: "conv4_12/x1/bn"
+  top: "conv4_12/x1/bn"
+}
+layer {
+  name: "conv4_12/x1"
+  type: "Convolution"
+  bottom: "conv4_12/x1/bn"
+  top: "conv4_12/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_12/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_12/x1"
+  top: "conv4_12/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_12/x2/scale"
+  type: "Scale"
+  bottom: "conv4_12/x2/bn"
+  top: "conv4_12/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_12/x2"
+  type: "ReLU"
+  bottom: "conv4_12/x2/bn"
+  top: "conv4_12/x2/bn"
+}
+layer {
+  name: "conv4_12/x2"
+  type: "Convolution"
+  bottom: "conv4_12/x2/bn"
+  top: "conv4_12/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_12"
+  type: "Concat"
+  bottom: "concat_4_11"
+  bottom: "conv4_12/x2"
+  top: "concat_4_12"
+}
+layer {
+  name: "conv4_13/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_12"
+  top: "conv4_13/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_13/x1/scale"
+  type: "Scale"
+  bottom: "conv4_13/x1/bn"
+  top: "conv4_13/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_13/x1"
+  type: "ReLU"
+  bottom: "conv4_13/x1/bn"
+  top: "conv4_13/x1/bn"
+}
+layer {
+  name: "conv4_13/x1"
+  type: "Convolution"
+  bottom: "conv4_13/x1/bn"
+  top: "conv4_13/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_13/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_13/x1"
+  top: "conv4_13/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_13/x2/scale"
+  type: "Scale"
+  bottom: "conv4_13/x2/bn"
+  top: "conv4_13/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_13/x2"
+  type: "ReLU"
+  bottom: "conv4_13/x2/bn"
+  top: "conv4_13/x2/bn"
+}
+layer {
+  name: "conv4_13/x2"
+  type: "Convolution"
+  bottom: "conv4_13/x2/bn"
+  top: "conv4_13/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_13"
+  type: "Concat"
+  bottom: "concat_4_12"
+  bottom: "conv4_13/x2"
+  top: "concat_4_13"
+}
+layer {
+  name: "conv4_14/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_13"
+  top: "conv4_14/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_14/x1/scale"
+  type: "Scale"
+  bottom: "conv4_14/x1/bn"
+  top: "conv4_14/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_14/x1"
+  type: "ReLU"
+  bottom: "conv4_14/x1/bn"
+  top: "conv4_14/x1/bn"
+}
+layer {
+  name: "conv4_14/x1"
+  type: "Convolution"
+  bottom: "conv4_14/x1/bn"
+  top: "conv4_14/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_14/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_14/x1"
+  top: "conv4_14/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_14/x2/scale"
+  type: "Scale"
+  bottom: "conv4_14/x2/bn"
+  top: "conv4_14/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_14/x2"
+  type: "ReLU"
+  bottom: "conv4_14/x2/bn"
+  top: "conv4_14/x2/bn"
+}
+layer {
+  name: "conv4_14/x2"
+  type: "Convolution"
+  bottom: "conv4_14/x2/bn"
+  top: "conv4_14/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_14"
+  type: "Concat"
+  bottom: "concat_4_13"
+  bottom: "conv4_14/x2"
+  top: "concat_4_14"
+}
+layer {
+  name: "conv4_15/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_14"
+  top: "conv4_15/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_15/x1/scale"
+  type: "Scale"
+  bottom: "conv4_15/x1/bn"
+  top: "conv4_15/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_15/x1"
+  type: "ReLU"
+  bottom: "conv4_15/x1/bn"
+  top: "conv4_15/x1/bn"
+}
+layer {
+  name: "conv4_15/x1"
+  type: "Convolution"
+  bottom: "conv4_15/x1/bn"
+  top: "conv4_15/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_15/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_15/x1"
+  top: "conv4_15/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_15/x2/scale"
+  type: "Scale"
+  bottom: "conv4_15/x2/bn"
+  top: "conv4_15/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_15/x2"
+  type: "ReLU"
+  bottom: "conv4_15/x2/bn"
+  top: "conv4_15/x2/bn"
+}
+layer {
+  name: "conv4_15/x2"
+  type: "Convolution"
+  bottom: "conv4_15/x2/bn"
+  top: "conv4_15/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_15"
+  type: "Concat"
+  bottom: "concat_4_14"
+  bottom: "conv4_15/x2"
+  top: "concat_4_15"
+}
+layer {
+  name: "conv4_16/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_15"
+  top: "conv4_16/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_16/x1/scale"
+  type: "Scale"
+  bottom: "conv4_16/x1/bn"
+  top: "conv4_16/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_16/x1"
+  type: "ReLU"
+  bottom: "conv4_16/x1/bn"
+  top: "conv4_16/x1/bn"
+}
+layer {
+  name: "conv4_16/x1"
+  type: "Convolution"
+  bottom: "conv4_16/x1/bn"
+  top: "conv4_16/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_16/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_16/x1"
+  top: "conv4_16/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_16/x2/scale"
+  type: "Scale"
+  bottom: "conv4_16/x2/bn"
+  top: "conv4_16/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_16/x2"
+  type: "ReLU"
+  bottom: "conv4_16/x2/bn"
+  top: "conv4_16/x2/bn"
+}
+layer {
+  name: "conv4_16/x2"
+  type: "Convolution"
+  bottom: "conv4_16/x2/bn"
+  top: "conv4_16/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_16"
+  type: "Concat"
+  bottom: "concat_4_15"
+  bottom: "conv4_16/x2"
+  top: "concat_4_16"
+}
+layer {
+  name: "conv4_17/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_16"
+  top: "conv4_17/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_17/x1/scale"
+  type: "Scale"
+  bottom: "conv4_17/x1/bn"
+  top: "conv4_17/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_17/x1"
+  type: "ReLU"
+  bottom: "conv4_17/x1/bn"
+  top: "conv4_17/x1/bn"
+}
+layer {
+  name: "conv4_17/x1"
+  type: "Convolution"
+  bottom: "conv4_17/x1/bn"
+  top: "conv4_17/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_17/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_17/x1"
+  top: "conv4_17/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_17/x2/scale"
+  type: "Scale"
+  bottom: "conv4_17/x2/bn"
+  top: "conv4_17/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_17/x2"
+  type: "ReLU"
+  bottom: "conv4_17/x2/bn"
+  top: "conv4_17/x2/bn"
+}
+layer {
+  name: "conv4_17/x2"
+  type: "Convolution"
+  bottom: "conv4_17/x2/bn"
+  top: "conv4_17/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_17"
+  type: "Concat"
+  bottom: "concat_4_16"
+  bottom: "conv4_17/x2"
+  top: "concat_4_17"
+}
+layer {
+  name: "conv4_18/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_17"
+  top: "conv4_18/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_18/x1/scale"
+  type: "Scale"
+  bottom: "conv4_18/x1/bn"
+  top: "conv4_18/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_18/x1"
+  type: "ReLU"
+  bottom: "conv4_18/x1/bn"
+  top: "conv4_18/x1/bn"
+}
+layer {
+  name: "conv4_18/x1"
+  type: "Convolution"
+  bottom: "conv4_18/x1/bn"
+  top: "conv4_18/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_18/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_18/x1"
+  top: "conv4_18/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_18/x2/scale"
+  type: "Scale"
+  bottom: "conv4_18/x2/bn"
+  top: "conv4_18/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_18/x2"
+  type: "ReLU"
+  bottom: "conv4_18/x2/bn"
+  top: "conv4_18/x2/bn"
+}
+layer {
+  name: "conv4_18/x2"
+  type: "Convolution"
+  bottom: "conv4_18/x2/bn"
+  top: "conv4_18/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_18"
+  type: "Concat"
+  bottom: "concat_4_17"
+  bottom: "conv4_18/x2"
+  top: "concat_4_18"
+}
+layer {
+  name: "conv4_19/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_18"
+  top: "conv4_19/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_19/x1/scale"
+  type: "Scale"
+  bottom: "conv4_19/x1/bn"
+  top: "conv4_19/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_19/x1"
+  type: "ReLU"
+  bottom: "conv4_19/x1/bn"
+  top: "conv4_19/x1/bn"
+}
+layer {
+  name: "conv4_19/x1"
+  type: "Convolution"
+  bottom: "conv4_19/x1/bn"
+  top: "conv4_19/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_19/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_19/x1"
+  top: "conv4_19/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_19/x2/scale"
+  type: "Scale"
+  bottom: "conv4_19/x2/bn"
+  top: "conv4_19/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_19/x2"
+  type: "ReLU"
+  bottom: "conv4_19/x2/bn"
+  top: "conv4_19/x2/bn"
+}
+layer {
+  name: "conv4_19/x2"
+  type: "Convolution"
+  bottom: "conv4_19/x2/bn"
+  top: "conv4_19/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_19"
+  type: "Concat"
+  bottom: "concat_4_18"
+  bottom: "conv4_19/x2"
+  top: "concat_4_19"
+}
+layer {
+  name: "conv4_20/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_19"
+  top: "conv4_20/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_20/x1/scale"
+  type: "Scale"
+  bottom: "conv4_20/x1/bn"
+  top: "conv4_20/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_20/x1"
+  type: "ReLU"
+  bottom: "conv4_20/x1/bn"
+  top: "conv4_20/x1/bn"
+}
+layer {
+  name: "conv4_20/x1"
+  type: "Convolution"
+  bottom: "conv4_20/x1/bn"
+  top: "conv4_20/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_20/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_20/x1"
+  top: "conv4_20/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_20/x2/scale"
+  type: "Scale"
+  bottom: "conv4_20/x2/bn"
+  top: "conv4_20/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_20/x2"
+  type: "ReLU"
+  bottom: "conv4_20/x2/bn"
+  top: "conv4_20/x2/bn"
+}
+layer {
+  name: "conv4_20/x2"
+  type: "Convolution"
+  bottom: "conv4_20/x2/bn"
+  top: "conv4_20/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_20"
+  type: "Concat"
+  bottom: "concat_4_19"
+  bottom: "conv4_20/x2"
+  top: "concat_4_20"
+}
+layer {
+  name: "conv4_21/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_20"
+  top: "conv4_21/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_21/x1/scale"
+  type: "Scale"
+  bottom: "conv4_21/x1/bn"
+  top: "conv4_21/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_21/x1"
+  type: "ReLU"
+  bottom: "conv4_21/x1/bn"
+  top: "conv4_21/x1/bn"
+}
+layer {
+  name: "conv4_21/x1"
+  type: "Convolution"
+  bottom: "conv4_21/x1/bn"
+  top: "conv4_21/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_21/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_21/x1"
+  top: "conv4_21/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_21/x2/scale"
+  type: "Scale"
+  bottom: "conv4_21/x2/bn"
+  top: "conv4_21/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_21/x2"
+  type: "ReLU"
+  bottom: "conv4_21/x2/bn"
+  top: "conv4_21/x2/bn"
+}
+layer {
+  name: "conv4_21/x2"
+  type: "Convolution"
+  bottom: "conv4_21/x2/bn"
+  top: "conv4_21/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_21"
+  type: "Concat"
+  bottom: "concat_4_20"
+  bottom: "conv4_21/x2"
+  top: "concat_4_21"
+}
+layer {
+  name: "conv4_22/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_21"
+  top: "conv4_22/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_22/x1/scale"
+  type: "Scale"
+  bottom: "conv4_22/x1/bn"
+  top: "conv4_22/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_22/x1"
+  type: "ReLU"
+  bottom: "conv4_22/x1/bn"
+  top: "conv4_22/x1/bn"
+}
+layer {
+  name: "conv4_22/x1"
+  type: "Convolution"
+  bottom: "conv4_22/x1/bn"
+  top: "conv4_22/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_22/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_22/x1"
+  top: "conv4_22/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_22/x2/scale"
+  type: "Scale"
+  bottom: "conv4_22/x2/bn"
+  top: "conv4_22/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_22/x2"
+  type: "ReLU"
+  bottom: "conv4_22/x2/bn"
+  top: "conv4_22/x2/bn"
+}
+layer {
+  name: "conv4_22/x2"
+  type: "Convolution"
+  bottom: "conv4_22/x2/bn"
+  top: "conv4_22/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_22"
+  type: "Concat"
+  bottom: "concat_4_21"
+  bottom: "conv4_22/x2"
+  top: "concat_4_22"
+}
+layer {
+  name: "conv4_23/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_22"
+  top: "conv4_23/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_23/x1/scale"
+  type: "Scale"
+  bottom: "conv4_23/x1/bn"
+  top: "conv4_23/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_23/x1"
+  type: "ReLU"
+  bottom: "conv4_23/x1/bn"
+  top: "conv4_23/x1/bn"
+}
+layer {
+  name: "conv4_23/x1"
+  type: "Convolution"
+  bottom: "conv4_23/x1/bn"
+  top: "conv4_23/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_23/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_23/x1"
+  top: "conv4_23/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_23/x2/scale"
+  type: "Scale"
+  bottom: "conv4_23/x2/bn"
+  top: "conv4_23/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_23/x2"
+  type: "ReLU"
+  bottom: "conv4_23/x2/bn"
+  top: "conv4_23/x2/bn"
+}
+layer {
+  name: "conv4_23/x2"
+  type: "Convolution"
+  bottom: "conv4_23/x2/bn"
+  top: "conv4_23/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_23"
+  type: "Concat"
+  bottom: "concat_4_22"
+  bottom: "conv4_23/x2"
+  top: "concat_4_23"
+}
+layer {
+  name: "conv4_24/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_23"
+  top: "conv4_24/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_24/x1/scale"
+  type: "Scale"
+  bottom: "conv4_24/x1/bn"
+  top: "conv4_24/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_24/x1"
+  type: "ReLU"
+  bottom: "conv4_24/x1/bn"
+  top: "conv4_24/x1/bn"
+}
+layer {
+  name: "conv4_24/x1"
+  type: "Convolution"
+  bottom: "conv4_24/x1/bn"
+  top: "conv4_24/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv4_24/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv4_24/x1"
+  top: "conv4_24/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_24/x2/scale"
+  type: "Scale"
+  bottom: "conv4_24/x2/bn"
+  top: "conv4_24/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_24/x2"
+  type: "ReLU"
+  bottom: "conv4_24/x2/bn"
+  top: "conv4_24/x2/bn"
+}
+layer {
+  name: "conv4_24/x2"
+  type: "Convolution"
+  bottom: "conv4_24/x2/bn"
+  top: "conv4_24/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_4_24"
+  type: "Concat"
+  bottom: "concat_4_23"
+  bottom: "conv4_24/x2"
+  top: "concat_4_24"
+}
+layer {
+  name: "conv4_blk/bn"
+  type: "BatchNorm"
+  bottom: "concat_4_24"
+  top: "conv4_blk/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv4_blk/scale"
+  type: "Scale"
+  bottom: "conv4_blk/bn"
+  top: "conv4_blk/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu4_blk"
+  type: "ReLU"
+  bottom: "conv4_blk/bn"
+  top: "conv4_blk/bn"
+}
+layer {
+  name: "conv4_blk"
+  type: "Convolution"
+  bottom: "conv4_blk/bn"
+  top: "conv4_blk"
+  convolution_param {
+    num_output: 512
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "pool4"
+  type: "Pooling"
+  bottom: "conv4_blk"
+  top: "pool4"
+  pooling_param {
+    pool: AVE
+    kernel_size: 2
+    stride: 2
+  }
+}
+layer {
+  name: "conv5_1/x1/bn"
+  type: "BatchNorm"
+  bottom: "pool4"
+  top: "conv5_1/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_1/x1/scale"
+  type: "Scale"
+  bottom: "conv5_1/x1/bn"
+  top: "conv5_1/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_1/x1"
+  type: "ReLU"
+  bottom: "conv5_1/x1/bn"
+  top: "conv5_1/x1/bn"
+}
+layer {
+  name: "conv5_1/x1"
+  type: "Convolution"
+  bottom: "conv5_1/x1/bn"
+  top: "conv5_1/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_1/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_1/x1"
+  top: "conv5_1/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_1/x2/scale"
+  type: "Scale"
+  bottom: "conv5_1/x2/bn"
+  top: "conv5_1/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_1/x2"
+  type: "ReLU"
+  bottom: "conv5_1/x2/bn"
+  top: "conv5_1/x2/bn"
+}
+layer {
+  name: "conv5_1/x2"
+  type: "Convolution"
+  bottom: "conv5_1/x2/bn"
+  top: "conv5_1/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_1"
+  type: "Concat"
+  bottom: "pool4"
+  bottom: "conv5_1/x2"
+  top: "concat_5_1"
+}
+layer {
+  name: "conv5_2/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_1"
+  top: "conv5_2/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_2/x1/scale"
+  type: "Scale"
+  bottom: "conv5_2/x1/bn"
+  top: "conv5_2/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_2/x1"
+  type: "ReLU"
+  bottom: "conv5_2/x1/bn"
+  top: "conv5_2/x1/bn"
+}
+layer {
+  name: "conv5_2/x1"
+  type: "Convolution"
+  bottom: "conv5_2/x1/bn"
+  top: "conv5_2/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_2/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_2/x1"
+  top: "conv5_2/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_2/x2/scale"
+  type: "Scale"
+  bottom: "conv5_2/x2/bn"
+  top: "conv5_2/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_2/x2"
+  type: "ReLU"
+  bottom: "conv5_2/x2/bn"
+  top: "conv5_2/x2/bn"
+}
+layer {
+  name: "conv5_2/x2"
+  type: "Convolution"
+  bottom: "conv5_2/x2/bn"
+  top: "conv5_2/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_2"
+  type: "Concat"
+  bottom: "concat_5_1"
+  bottom: "conv5_2/x2"
+  top: "concat_5_2"
+}
+layer {
+  name: "conv5_3/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_2"
+  top: "conv5_3/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_3/x1/scale"
+  type: "Scale"
+  bottom: "conv5_3/x1/bn"
+  top: "conv5_3/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_3/x1"
+  type: "ReLU"
+  bottom: "conv5_3/x1/bn"
+  top: "conv5_3/x1/bn"
+}
+layer {
+  name: "conv5_3/x1"
+  type: "Convolution"
+  bottom: "conv5_3/x1/bn"
+  top: "conv5_3/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_3/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_3/x1"
+  top: "conv5_3/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_3/x2/scale"
+  type: "Scale"
+  bottom: "conv5_3/x2/bn"
+  top: "conv5_3/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_3/x2"
+  type: "ReLU"
+  bottom: "conv5_3/x2/bn"
+  top: "conv5_3/x2/bn"
+}
+layer {
+  name: "conv5_3/x2"
+  type: "Convolution"
+  bottom: "conv5_3/x2/bn"
+  top: "conv5_3/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_3"
+  type: "Concat"
+  bottom: "concat_5_2"
+  bottom: "conv5_3/x2"
+  top: "concat_5_3"
+}
+layer {
+  name: "conv5_4/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_3"
+  top: "conv5_4/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_4/x1/scale"
+  type: "Scale"
+  bottom: "conv5_4/x1/bn"
+  top: "conv5_4/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_4/x1"
+  type: "ReLU"
+  bottom: "conv5_4/x1/bn"
+  top: "conv5_4/x1/bn"
+}
+layer {
+  name: "conv5_4/x1"
+  type: "Convolution"
+  bottom: "conv5_4/x1/bn"
+  top: "conv5_4/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_4/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_4/x1"
+  top: "conv5_4/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_4/x2/scale"
+  type: "Scale"
+  bottom: "conv5_4/x2/bn"
+  top: "conv5_4/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_4/x2"
+  type: "ReLU"
+  bottom: "conv5_4/x2/bn"
+  top: "conv5_4/x2/bn"
+}
+layer {
+  name: "conv5_4/x2"
+  type: "Convolution"
+  bottom: "conv5_4/x2/bn"
+  top: "conv5_4/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_4"
+  type: "Concat"
+  bottom: "concat_5_3"
+  bottom: "conv5_4/x2"
+  top: "concat_5_4"
+}
+layer {
+  name: "conv5_5/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_4"
+  top: "conv5_5/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_5/x1/scale"
+  type: "Scale"
+  bottom: "conv5_5/x1/bn"
+  top: "conv5_5/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_5/x1"
+  type: "ReLU"
+  bottom: "conv5_5/x1/bn"
+  top: "conv5_5/x1/bn"
+}
+layer {
+  name: "conv5_5/x1"
+  type: "Convolution"
+  bottom: "conv5_5/x1/bn"
+  top: "conv5_5/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_5/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_5/x1"
+  top: "conv5_5/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_5/x2/scale"
+  type: "Scale"
+  bottom: "conv5_5/x2/bn"
+  top: "conv5_5/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_5/x2"
+  type: "ReLU"
+  bottom: "conv5_5/x2/bn"
+  top: "conv5_5/x2/bn"
+}
+layer {
+  name: "conv5_5/x2"
+  type: "Convolution"
+  bottom: "conv5_5/x2/bn"
+  top: "conv5_5/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_5"
+  type: "Concat"
+  bottom: "concat_5_4"
+  bottom: "conv5_5/x2"
+  top: "concat_5_5"
+}
+layer {
+  name: "conv5_6/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_5"
+  top: "conv5_6/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_6/x1/scale"
+  type: "Scale"
+  bottom: "conv5_6/x1/bn"
+  top: "conv5_6/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_6/x1"
+  type: "ReLU"
+  bottom: "conv5_6/x1/bn"
+  top: "conv5_6/x1/bn"
+}
+layer {
+  name: "conv5_6/x1"
+  type: "Convolution"
+  bottom: "conv5_6/x1/bn"
+  top: "conv5_6/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_6/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_6/x1"
+  top: "conv5_6/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_6/x2/scale"
+  type: "Scale"
+  bottom: "conv5_6/x2/bn"
+  top: "conv5_6/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_6/x2"
+  type: "ReLU"
+  bottom: "conv5_6/x2/bn"
+  top: "conv5_6/x2/bn"
+}
+layer {
+  name: "conv5_6/x2"
+  type: "Convolution"
+  bottom: "conv5_6/x2/bn"
+  top: "conv5_6/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_6"
+  type: "Concat"
+  bottom: "concat_5_5"
+  bottom: "conv5_6/x2"
+  top: "concat_5_6"
+}
+layer {
+  name: "conv5_7/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_6"
+  top: "conv5_7/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_7/x1/scale"
+  type: "Scale"
+  bottom: "conv5_7/x1/bn"
+  top: "conv5_7/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_7/x1"
+  type: "ReLU"
+  bottom: "conv5_7/x1/bn"
+  top: "conv5_7/x1/bn"
+}
+layer {
+  name: "conv5_7/x1"
+  type: "Convolution"
+  bottom: "conv5_7/x1/bn"
+  top: "conv5_7/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_7/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_7/x1"
+  top: "conv5_7/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_7/x2/scale"
+  type: "Scale"
+  bottom: "conv5_7/x2/bn"
+  top: "conv5_7/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_7/x2"
+  type: "ReLU"
+  bottom: "conv5_7/x2/bn"
+  top: "conv5_7/x2/bn"
+}
+layer {
+  name: "conv5_7/x2"
+  type: "Convolution"
+  bottom: "conv5_7/x2/bn"
+  top: "conv5_7/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_7"
+  type: "Concat"
+  bottom: "concat_5_6"
+  bottom: "conv5_7/x2"
+  top: "concat_5_7"
+}
+layer {
+  name: "conv5_8/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_7"
+  top: "conv5_8/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_8/x1/scale"
+  type: "Scale"
+  bottom: "conv5_8/x1/bn"
+  top: "conv5_8/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_8/x1"
+  type: "ReLU"
+  bottom: "conv5_8/x1/bn"
+  top: "conv5_8/x1/bn"
+}
+layer {
+  name: "conv5_8/x1"
+  type: "Convolution"
+  bottom: "conv5_8/x1/bn"
+  top: "conv5_8/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_8/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_8/x1"
+  top: "conv5_8/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_8/x2/scale"
+  type: "Scale"
+  bottom: "conv5_8/x2/bn"
+  top: "conv5_8/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_8/x2"
+  type: "ReLU"
+  bottom: "conv5_8/x2/bn"
+  top: "conv5_8/x2/bn"
+}
+layer {
+  name: "conv5_8/x2"
+  type: "Convolution"
+  bottom: "conv5_8/x2/bn"
+  top: "conv5_8/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_8"
+  type: "Concat"
+  bottom: "concat_5_7"
+  bottom: "conv5_8/x2"
+  top: "concat_5_8"
+}
+layer {
+  name: "conv5_9/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_8"
+  top: "conv5_9/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_9/x1/scale"
+  type: "Scale"
+  bottom: "conv5_9/x1/bn"
+  top: "conv5_9/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_9/x1"
+  type: "ReLU"
+  bottom: "conv5_9/x1/bn"
+  top: "conv5_9/x1/bn"
+}
+layer {
+  name: "conv5_9/x1"
+  type: "Convolution"
+  bottom: "conv5_9/x1/bn"
+  top: "conv5_9/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_9/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_9/x1"
+  top: "conv5_9/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_9/x2/scale"
+  type: "Scale"
+  bottom: "conv5_9/x2/bn"
+  top: "conv5_9/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_9/x2"
+  type: "ReLU"
+  bottom: "conv5_9/x2/bn"
+  top: "conv5_9/x2/bn"
+}
+layer {
+  name: "conv5_9/x2"
+  type: "Convolution"
+  bottom: "conv5_9/x2/bn"
+  top: "conv5_9/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_9"
+  type: "Concat"
+  bottom: "concat_5_8"
+  bottom: "conv5_9/x2"
+  top: "concat_5_9"
+}
+layer {
+  name: "conv5_10/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_9"
+  top: "conv5_10/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_10/x1/scale"
+  type: "Scale"
+  bottom: "conv5_10/x1/bn"
+  top: "conv5_10/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_10/x1"
+  type: "ReLU"
+  bottom: "conv5_10/x1/bn"
+  top: "conv5_10/x1/bn"
+}
+layer {
+  name: "conv5_10/x1"
+  type: "Convolution"
+  bottom: "conv5_10/x1/bn"
+  top: "conv5_10/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_10/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_10/x1"
+  top: "conv5_10/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_10/x2/scale"
+  type: "Scale"
+  bottom: "conv5_10/x2/bn"
+  top: "conv5_10/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_10/x2"
+  type: "ReLU"
+  bottom: "conv5_10/x2/bn"
+  top: "conv5_10/x2/bn"
+}
+layer {
+  name: "conv5_10/x2"
+  type: "Convolution"
+  bottom: "conv5_10/x2/bn"
+  top: "conv5_10/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_10"
+  type: "Concat"
+  bottom: "concat_5_9"
+  bottom: "conv5_10/x2"
+  top: "concat_5_10"
+}
+layer {
+  name: "conv5_11/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_10"
+  top: "conv5_11/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_11/x1/scale"
+  type: "Scale"
+  bottom: "conv5_11/x1/bn"
+  top: "conv5_11/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_11/x1"
+  type: "ReLU"
+  bottom: "conv5_11/x1/bn"
+  top: "conv5_11/x1/bn"
+}
+layer {
+  name: "conv5_11/x1"
+  type: "Convolution"
+  bottom: "conv5_11/x1/bn"
+  top: "conv5_11/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_11/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_11/x1"
+  top: "conv5_11/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_11/x2/scale"
+  type: "Scale"
+  bottom: "conv5_11/x2/bn"
+  top: "conv5_11/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_11/x2"
+  type: "ReLU"
+  bottom: "conv5_11/x2/bn"
+  top: "conv5_11/x2/bn"
+}
+layer {
+  name: "conv5_11/x2"
+  type: "Convolution"
+  bottom: "conv5_11/x2/bn"
+  top: "conv5_11/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_11"
+  type: "Concat"
+  bottom: "concat_5_10"
+  bottom: "conv5_11/x2"
+  top: "concat_5_11"
+}
+layer {
+  name: "conv5_12/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_11"
+  top: "conv5_12/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_12/x1/scale"
+  type: "Scale"
+  bottom: "conv5_12/x1/bn"
+  top: "conv5_12/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_12/x1"
+  type: "ReLU"
+  bottom: "conv5_12/x1/bn"
+  top: "conv5_12/x1/bn"
+}
+layer {
+  name: "conv5_12/x1"
+  type: "Convolution"
+  bottom: "conv5_12/x1/bn"
+  top: "conv5_12/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_12/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_12/x1"
+  top: "conv5_12/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_12/x2/scale"
+  type: "Scale"
+  bottom: "conv5_12/x2/bn"
+  top: "conv5_12/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_12/x2"
+  type: "ReLU"
+  bottom: "conv5_12/x2/bn"
+  top: "conv5_12/x2/bn"
+}
+layer {
+  name: "conv5_12/x2"
+  type: "Convolution"
+  bottom: "conv5_12/x2/bn"
+  top: "conv5_12/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_12"
+  type: "Concat"
+  bottom: "concat_5_11"
+  bottom: "conv5_12/x2"
+  top: "concat_5_12"
+}
+layer {
+  name: "conv5_13/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_12"
+  top: "conv5_13/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_13/x1/scale"
+  type: "Scale"
+  bottom: "conv5_13/x1/bn"
+  top: "conv5_13/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_13/x1"
+  type: "ReLU"
+  bottom: "conv5_13/x1/bn"
+  top: "conv5_13/x1/bn"
+}
+layer {
+  name: "conv5_13/x1"
+  type: "Convolution"
+  bottom: "conv5_13/x1/bn"
+  top: "conv5_13/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_13/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_13/x1"
+  top: "conv5_13/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_13/x2/scale"
+  type: "Scale"
+  bottom: "conv5_13/x2/bn"
+  top: "conv5_13/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_13/x2"
+  type: "ReLU"
+  bottom: "conv5_13/x2/bn"
+  top: "conv5_13/x2/bn"
+}
+layer {
+  name: "conv5_13/x2"
+  type: "Convolution"
+  bottom: "conv5_13/x2/bn"
+  top: "conv5_13/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_13"
+  type: "Concat"
+  bottom: "concat_5_12"
+  bottom: "conv5_13/x2"
+  top: "concat_5_13"
+}
+layer {
+  name: "conv5_14/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_13"
+  top: "conv5_14/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_14/x1/scale"
+  type: "Scale"
+  bottom: "conv5_14/x1/bn"
+  top: "conv5_14/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_14/x1"
+  type: "ReLU"
+  bottom: "conv5_14/x1/bn"
+  top: "conv5_14/x1/bn"
+}
+layer {
+  name: "conv5_14/x1"
+  type: "Convolution"
+  bottom: "conv5_14/x1/bn"
+  top: "conv5_14/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_14/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_14/x1"
+  top: "conv5_14/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_14/x2/scale"
+  type: "Scale"
+  bottom: "conv5_14/x2/bn"
+  top: "conv5_14/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_14/x2"
+  type: "ReLU"
+  bottom: "conv5_14/x2/bn"
+  top: "conv5_14/x2/bn"
+}
+layer {
+  name: "conv5_14/x2"
+  type: "Convolution"
+  bottom: "conv5_14/x2/bn"
+  top: "conv5_14/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_14"
+  type: "Concat"
+  bottom: "concat_5_13"
+  bottom: "conv5_14/x2"
+  top: "concat_5_14"
+}
+layer {
+  name: "conv5_15/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_14"
+  top: "conv5_15/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_15/x1/scale"
+  type: "Scale"
+  bottom: "conv5_15/x1/bn"
+  top: "conv5_15/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_15/x1"
+  type: "ReLU"
+  bottom: "conv5_15/x1/bn"
+  top: "conv5_15/x1/bn"
+}
+layer {
+  name: "conv5_15/x1"
+  type: "Convolution"
+  bottom: "conv5_15/x1/bn"
+  top: "conv5_15/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_15/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_15/x1"
+  top: "conv5_15/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_15/x2/scale"
+  type: "Scale"
+  bottom: "conv5_15/x2/bn"
+  top: "conv5_15/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_15/x2"
+  type: "ReLU"
+  bottom: "conv5_15/x2/bn"
+  top: "conv5_15/x2/bn"
+}
+layer {
+  name: "conv5_15/x2"
+  type: "Convolution"
+  bottom: "conv5_15/x2/bn"
+  top: "conv5_15/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_15"
+  type: "Concat"
+  bottom: "concat_5_14"
+  bottom: "conv5_15/x2"
+  top: "concat_5_15"
+}
+layer {
+  name: "conv5_16/x1/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_15"
+  top: "conv5_16/x1/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_16/x1/scale"
+  type: "Scale"
+  bottom: "conv5_16/x1/bn"
+  top: "conv5_16/x1/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_16/x1"
+  type: "ReLU"
+  bottom: "conv5_16/x1/bn"
+  top: "conv5_16/x1/bn"
+}
+layer {
+  name: "conv5_16/x1"
+  type: "Convolution"
+  bottom: "conv5_16/x1/bn"
+  top: "conv5_16/x1"
+  convolution_param {
+    num_output: 128
+    bias_term: false
+    kernel_size: 1
+  }
+}
+layer {
+  name: "conv5_16/x2/bn"
+  type: "BatchNorm"
+  bottom: "conv5_16/x1"
+  top: "conv5_16/x2/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_16/x2/scale"
+  type: "Scale"
+  bottom: "conv5_16/x2/bn"
+  top: "conv5_16/x2/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_16/x2"
+  type: "ReLU"
+  bottom: "conv5_16/x2/bn"
+  top: "conv5_16/x2/bn"
+}
+layer {
+  name: "conv5_16/x2"
+  type: "Convolution"
+  bottom: "conv5_16/x2/bn"
+  top: "conv5_16/x2"
+  convolution_param {
+    num_output: 32
+    bias_term: false
+    pad: 1
+    kernel_size: 3
+  }
+}
+layer {
+  name: "concat_5_16"
+  type: "Concat"
+  bottom: "concat_5_15"
+  bottom: "conv5_16/x2"
+  top: "concat_5_16"
+}
+layer {
+  name: "conv5_blk/bn"
+  type: "BatchNorm"
+  bottom: "concat_5_16"
+  top: "conv5_blk/bn"
+  batch_norm_param {
+    eps: 1e-5
+  }
+}
+layer {
+  name: "conv5_blk/scale"
+  type: "Scale"
+  bottom: "conv5_blk/bn"
+  top: "conv5_blk/bn"
+  scale_param {
+    bias_term: true
+  }
+}
+layer {
+  name: "relu5_blk"
+  type: "ReLU"
+  bottom: "conv5_blk/bn"
+  top: "conv5_blk/bn"
+}
+layer {
+  name: "pool5"
+  type: "Pooling"
+  bottom: "conv5_blk/bn"
+  top: "pool5"
+  pooling_param {
+    pool: AVE
+    global_pooling: true
+  }
+}
+layer {
+  name: "fc6"
+  type: "Convolution"
+  bottom: "pool5"
+  top: "fc6"
+  convolution_param {
+    num_output: 1000
+    kernel_size: 1
+  }
+}

app.py

@@ -0,0 +1,100 @@
+from requests.models import MissingSchema
+import streamlit as st
+import cv2
+import numpy as np
+from PIL import Image, UnidentifiedImageError
+import requests
+from io import BytesIO
+
+# Create application title and file uploader widget.
+st.title("OpenCV Deep Learning based Image Classification")
+
+
+# @st.cache(allow_output_mutation=True)
+@st.cache_resource
+def load_model():
+    """Loads the DNN model."""
+
+    # Read the ImageNet class names.
+    with open('classification_classes_ILSVRC2012.txt', 'r') as f:
+        image_net_names = f.read().split('\n')
+
+    # Final class names, picking just the first name if multiple in the class.
+    class_names = [name.split(',')[0] for name in image_net_names]
+
+    # Load the neural network model.
+    model = cv2.dnn.readNet(
+        model='DenseNet_121.caffemodel',
+        config='DenseNet_121.prototxt',
+        framework='Caffe')
+    return model, class_names
+
+
+def classify(model, image, class_names):
+    """Performs inference and returns class name with highest confidence."""
+
+    # Remove alpha channel if found.
+    if image.shape[2] == 4:
+        image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
+
+    # Create blob from image using values specified by the model:
+    # https://github.com/shicai/DenseNet-Caffe
+    blob = cv2.dnn.blobFromImage(
+        image=image, scalefactor=0.017, size=(224, 224), mean=(104, 117, 123))
+
+    # Set the input blob for the neural network and pass through network.
+    model.setInput(blob)
+    outputs = model.forward()
+
+    final_outputs = outputs[0]
+    # Make all the outputs 1D.
+    final_outputs = final_outputs.reshape(1000, 1)
+    # get the class label
+    label_id = np.argmax(final_outputs)
+    # Convert the output scores to softmax probabilities.
+    probs = np.exp(final_outputs) / np.sum(np.exp(final_outputs))
+    # Get the final highest probability.
+    final_prob = np.max(probs) * 100.
+    # Map the max confidence to the class label names.
+    out_name = class_names[label_id]
+    out_text = f"Class: {out_name}, Confidence: {final_prob:.1f}%"
+    return out_text
+
+
+def header(text):
+    st.markdown(
+        '<p style="background-color:#0066cc;color:#33ff33;font-size:24px;'
+        f'border-radius:2%;" align="center">{text}</p>',
+        unsafe_allow_html=True)
+
+
+net, class_names = load_model()
+
+img_file_buffer = st.file_uploader("Choose a file or Camera", type=['jpg', 'jpeg', 'png'])
+st.text('OR')
+url = st.text_input('Enter URL')
+
+if img_file_buffer is not None:
+    # Read the file and convert it to opencv Image.
+    image = np.array(Image.open(img_file_buffer))
+    st.image(image)
+
+    # Call the classification model to detect faces in the image.
+    detections = classify(net, image, class_names)
+    header(detections)
+
+elif url != '':
+    try:
+        response = requests.get(url)
+        image = np.array(Image.open(BytesIO(response.content)))
+        st.image(image)
+
+        # Call the classification model to detect faces in the image.
+        detections = classify(net, image, class_names)
+        header(detections)
+    except MissingSchema as err:
+        st.header('Invalid URL, Try Again!')
+        print(err)
+    except UnidentifiedImageError as err:
+        st.header('URL has no Image, Try Again!')
+        print(err)

classification_classes_ILSVRC2012.txt

@@ -0,0 +1,1000 @@
+tench, Tinca tinca
+goldfish, Carassius auratus
+great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias
+tiger shark, Galeocerdo cuvieri
+hammerhead, hammerhead shark
+electric ray, crampfish, numbfish, torpedo
+stingray
+cock
+hen
+ostrich, Struthio camelus
+brambling, Fringilla montifringilla
+goldfinch, Carduelis carduelis
+house finch, linnet, Carpodacus mexicanus
+junco, snowbird
+indigo bunting, indigo finch, indigo bird, Passerina cyanea
+robin, American robin, Turdus migratorius
+bulbul
+jay
+magpie
+chickadee
+water ouzel, dipper
+kite
+bald eagle, American eagle, Haliaeetus leucocephalus
+vulture
+great grey owl, great gray owl, Strix nebulosa
+European fire salamander, Salamandra salamandra
+common newt, Triturus vulgaris
+eft
+spotted salamander, Ambystoma maculatum
+axolotl, mud puppy, Ambystoma mexicanum
+bullfrog, Rana catesbeiana
+tree frog, tree-frog
+tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui
+loggerhead, loggerhead turtle, Caretta caretta
+leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea
+mud turtle
+terrapin
+box turtle, box tortoise
+banded gecko
+common iguana, iguana, Iguana iguana
+American chameleon, anole, Anolis carolinensis
+whiptail, whiptail lizard
+agama
+frilled lizard, Chlamydosaurus kingi
+alligator lizard
+Gila monster, Heloderma suspectum
+green lizard, Lacerta viridis
+African chameleon, Chamaeleo chamaeleon
+Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis
+African crocodile, Nile crocodile, Crocodylus niloticus
+American alligator, Alligator mississipiensis
+triceratops
+thunder snake, worm snake, Carphophis amoenus
+ringneck snake, ring-necked snake, ring snake
+hognose snake, puff adder, sand viper
+green snake, grass snake
+king snake, kingsnake
+garter snake, grass snake
+water snake
+vine snake
+night snake, Hypsiglena torquata
+boa constrictor, Constrictor constrictor
+rock python, rock snake, Python sebae
+Indian cobra, Naja naja
+green mamba
+sea snake
+horned viper, cerastes, sand viper, horned asp, Cerastes cornutus
+diamondback, diamondback rattlesnake, Crotalus adamanteus
+sidewinder, horned rattlesnake, Crotalus cerastes
+trilobite
+harvestman, daddy longlegs, Phalangium opilio
+scorpion
+black and gold garden spider, Argiope aurantia
+barn spider, Araneus cavaticus
+garden spider, Aranea diademata
+black widow, Latrodectus mactans
+tarantula
+wolf spider, hunting spider
+tick
+centipede
+black grouse
+ptarmigan
+ruffed grouse, partridge, Bonasa umbellus
+prairie chicken, prairie grouse, prairie fowl
+peacock
+quail
+partridge
+African grey, African gray, Psittacus erithacus
+macaw
+sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita
+lorikeet
+coucal
+bee eater
+hornbill
+hummingbird
+jacamar
+toucan
+drake
+red-breasted merganser, Mergus serrator
+goose
+black swan, Cygnus atratus
+tusker
+echidna, spiny anteater, anteater
+platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus
+wallaby, brush kangaroo
+koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus
+wombat
+jellyfish
+sea anemone, anemone
+brain coral
+flatworm, platyhelminth
+nematode, nematode worm, roundworm
+conch
+snail
+slug
+sea slug, nudibranch
+chiton, coat-of-mail shell, sea cradle, polyplacophore
+chambered nautilus, pearly nautilus, nautilus
+Dungeness crab, Cancer magister
+rock crab, Cancer irroratus
+fiddler crab
+king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica
+American lobster, Northern lobster, Maine lobster, Homarus americanus
+spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish
+crayfish, crawfish, crawdad, crawdaddy
+hermit crab
+isopod
+white stork, Ciconia ciconia
+black stork, Ciconia nigra
+spoonbill
+flamingo
+little blue heron, Egretta caerulea
+American egret, great white heron, Egretta albus
+bittern
+crane
+limpkin, Aramus pictus
+European gallinule, Porphyrio porphyrio
+American coot, marsh hen, mud hen, water hen, Fulica americana
+bustard
+ruddy turnstone, Arenaria interpres
+red-backed sandpiper, dunlin, Erolia alpina
+redshank, Tringa totanus
+dowitcher
+oystercatcher, oyster catcher
+pelican
+king penguin, Aptenodytes patagonica
+albatross, mollymawk
+grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus
+killer whale, killer, orca, grampus, sea wolf, Orcinus orca
+dugong, Dugong dugon
+sea lion
+Chihuahua
+Japanese spaniel
+Maltese dog, Maltese terrier, Maltese
+Pekinese, Pekingese, Peke
+Shih-Tzu
+Blenheim spaniel
+papillon
+toy terrier
+Rhodesian ridgeback
+Afghan hound, Afghan
+basset, basset hound
+beagle
+bloodhound, sleuthhound
+bluetick
+black-and-tan coonhound
+Walker hound, Walker foxhound
+English foxhound
+redbone
+borzoi, Russian wolfhound
+Irish wolfhound
+Italian greyhound
+whippet
+Ibizan hound, Ibizan Podenco
+Norwegian elkhound, elkhound
+otterhound, otter hound
+Saluki, gazelle hound
+Scottish deerhound, deerhound
+Weimaraner
+Staffordshire bullterrier, Staffordshire bull terrier
+American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier
+Bedlington terrier
+Border terrier
+Kerry blue terrier
+Irish terrier
+Norfolk terrier
+Norwich terrier
+Yorkshire terrier
+wire-haired fox terrier
+Lakeland terrier
+Sealyham terrier, Sealyham
+Airedale, Airedale terrier
+cairn, cairn terrier
+Australian terrier
+Dandie Dinmont, Dandie Dinmont terrier
+Boston bull, Boston terrier
+miniature schnauzer
+giant schnauzer
+standard schnauzer
+Scotch terrier, Scottish terrier, Scottie
+Tibetan terrier, chrysanthemum dog
+silky terrier, Sydney silky
+soft-coated wheaten terrier
+West Highland white terrier
+Lhasa, Lhasa apso
+flat-coated retriever
+curly-coated retriever
+golden retriever
+Labrador retriever
+Chesapeake Bay retriever
+German short-haired pointer
+vizsla, Hungarian pointer
+English setter
+Irish setter, red setter
+Gordon setter
+Brittany spaniel
+clumber, clumber spaniel
+English springer, English springer spaniel
+Welsh springer spaniel
+cocker spaniel, English cocker spaniel, cocker
+Sussex spaniel
+Irish water spaniel
+kuvasz
+schipperke
+groenendael
+malinois
+briard
+kelpie
+komondor
+Old English sheepdog, bobtail
+Shetland sheepdog, Shetland sheep dog, Shetland
+collie
+Border collie
+Bouvier des Flandres, Bouviers des Flandres
+Rottweiler
+German shepherd, German shepherd dog, German police dog, alsatian
+Doberman, Doberman pinscher
+miniature pinscher
+Greater Swiss Mountain dog
+Bernese mountain dog
+Appenzeller
+EntleBucher
+boxer
+bull mastiff
+Tibetan mastiff
+French bulldog
+Great Dane
+Saint Bernard, St Bernard
+Eskimo dog, husky
+malamute, malemute, Alaskan malamute
+Siberian husky
+dalmatian, coach dog, carriage dog
+affenpinscher, monkey pinscher, monkey dog
+basenji
+pug, pug-dog
+Leonberg
+Newfoundland, Newfoundland dog
+Great Pyrenees
+Samoyed, Samoyede
+Pomeranian
+chow, chow chow
+keeshond
+Brabancon griffon
+Pembroke, Pembroke Welsh corgi
+Cardigan, Cardigan Welsh corgi
+toy poodle
+miniature poodle
+standard poodle
+Mexican hairless
+timber wolf, grey wolf, gray wolf, Canis lupus
+white wolf, Arctic wolf, Canis lupus tundrarum
+red wolf, maned wolf, Canis rufus, Canis niger
+coyote, prairie wolf, brush wolf, Canis latrans
+dingo, warrigal, warragal, Canis dingo
+dhole, Cuon alpinus
+African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus
+hyena, hyaena
+red fox, Vulpes vulpes
+kit fox, Vulpes macrotis
+Arctic fox, white fox, Alopex lagopus
+grey fox, gray fox, Urocyon cinereoargenteus
+tabby, tabby cat
+tiger cat
+Persian cat
+Siamese cat, Siamese
+Egyptian cat
+cougar, puma, catamount, mountain lion, painter, panther, Felis concolor
+lynx, catamount
+leopard, Panthera pardus
+snow leopard, ounce, Panthera uncia
+jaguar, panther, Panthera onca, Felis onca
+lion, king of beasts, Panthera leo
+tiger, Panthera tigris
+cheetah, chetah, Acinonyx jubatus
+brown bear, bruin, Ursus arctos
+American black bear, black bear, Ursus americanus, Euarctos americanus
+ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus
+sloth bear, Melursus ursinus, Ursus ursinus
+mongoose
+meerkat, mierkat
+tiger beetle
+ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle
+ground beetle, carabid beetle
+long-horned beetle, longicorn, longicorn beetle
+leaf beetle, chrysomelid
+dung beetle
+rhinoceros beetle
+weevil
+fly
+bee
+ant, emmet, pismire
+grasshopper, hopper
+cricket
+walking stick, walkingstick, stick insect
+cockroach, roach
+mantis, mantid
+cicada, cicala
+leafhopper
+lacewing, lacewing fly
+dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk
+damselfly
+admiral
+ringlet, ringlet butterfly
+monarch, monarch butterfly, milkweed butterfly, Danaus plexippus
+cabbage butterfly
+sulphur butterfly, sulfur butterfly
+lycaenid, lycaenid butterfly
+starfish, sea star
+sea urchin
+sea cucumber, holothurian
+wood rabbit, cottontail, cottontail rabbit
+hare
+Angora, Angora rabbit
+hamster
+porcupine, hedgehog
+fox squirrel, eastern fox squirrel, Sciurus niger
+marmot
+beaver
+guinea pig, Cavia cobaya
+sorrel
+zebra
+hog, pig, grunter, squealer, Sus scrofa
+wild boar, boar, Sus scrofa
+warthog
+hippopotamus, hippo, river horse, Hippopotamus amphibius
+ox
+water buffalo, water ox, Asiatic buffalo, Bubalus bubalis
+bison
+ram, tup
+bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis
+ibex, Capra ibex
+hartebeest
+impala, Aepyceros melampus
+gazelle
+Arabian camel, dromedary, Camelus dromedarius
+llama
+weasel
+mink
+polecat, fitch, foulmart, foumart, Mustela putorius
+black-footed ferret, ferret, Mustela nigripes
+otter
+skunk, polecat, wood pussy
+badger
+armadillo
+three-toed sloth, ai, Bradypus tridactylus
+orangutan, orang, orangutang, Pongo pygmaeus
+gorilla, Gorilla gorilla
+chimpanzee, chimp, Pan troglodytes
+gibbon, Hylobates lar
+siamang, Hylobates syndactylus, Symphalangus syndactylus
+guenon, guenon monkey
+patas, hussar monkey, Erythrocebus patas
+baboon
+macaque
+langur
+colobus, colobus monkey
+proboscis monkey, Nasalis larvatus
+marmoset
+capuchin, ringtail, Cebus capucinus
+howler monkey, howler
+titi, titi monkey
+spider monkey, Ateles geoffroyi
+squirrel monkey, Saimiri sciureus
+Madagascar cat, ring-tailed lemur, Lemur catta
+indri, indris, Indri indri, Indri brevicaudatus
+Indian elephant, Elephas maximus
+African elephant, Loxodonta africana
+lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens
+giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca
+barracouta, snoek
+eel
+coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch
+rock beauty, Holocanthus tricolor
+anemone fish
+sturgeon
+gar, garfish, garpike, billfish, Lepisosteus osseus
+lionfish
+puffer, pufferfish, blowfish, globefish
+abacus
+abaya
+academic gown, academic robe, judge's robe
+accordion, piano accordion, squeeze box
+acoustic guitar
+aircraft carrier, carrier, flattop, attack aircraft carrier
+airliner
+airship, dirigible
+altar
+ambulance
+amphibian, amphibious vehicle
+analog clock
+apiary, bee house
+apron
+ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin
+assault rifle, assault gun
+backpack, back pack, knapsack, packsack, rucksack, haversack
+bakery, bakeshop, bakehouse
+balance beam, beam
+balloon
+ballpoint, ballpoint pen, ballpen, Biro
+Band Aid
+banjo
+bannister, banister, balustrade, balusters, handrail
+barbell
+barber chair
+barbershop
+barn
+barometer
+barrel, cask
+barrow, garden cart, lawn cart, wheelbarrow
+baseball
+basketball
+bassinet
+bassoon
+bathing cap, swimming cap
+bath towel
+bathtub, bathing tub, bath, tub
+beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon
+beacon, lighthouse, beacon light, pharos
+beaker
+bearskin, busby, shako
+beer bottle
+beer glass
+bell cote, bell cot
+bib
+bicycle-built-for-two, tandem bicycle, tandem
+bikini, two-piece
+binder, ring-binder
+binoculars, field glasses, opera glasses
+birdhouse
+boathouse
+bobsled, bobsleigh, bob
+bolo tie, bolo, bola tie, bola
+bonnet, poke bonnet
+bookcase
+bookshop, bookstore, bookstall
+bottlecap
+bow
+bow tie, bow-tie, bowtie
+brass, memorial tablet, plaque
+brassiere, bra, bandeau
+breakwater, groin, groyne, mole, bulwark, seawall, jetty
+breastplate, aegis, egis
+broom
+bucket, pail
+buckle
+bulletproof vest
+bullet train, bullet
+butcher shop, meat market
+cab, hack, taxi, taxicab
+caldron, cauldron
+candle, taper, wax light
+cannon
+canoe
+can opener, tin opener
+cardigan
+car mirror
+carousel, carrousel, merry-go-round, roundabout, whirligig
+carpenter's kit, tool kit
+carton
+car wheel
+cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM
+cassette
+cassette player
+castle
+catamaran
+CD player
+cello, violoncello
+cellular telephone, cellular phone, cellphone, cell, mobile phone
+chain
+chainlink fence
+chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour
+chain saw, chainsaw
+chest
+chiffonier, commode
+chime, bell, gong
+china cabinet, china closet
+Christmas stocking
+church, church building
+cinema, movie theater, movie theatre, movie house, picture palace
+cleaver, meat cleaver, chopper
+cliff dwelling
+cloak
+clog, geta, patten, sabot
+cocktail shaker
+coffee mug
+coffeepot
+coil, spiral, volute, whorl, helix
+combination lock
+computer keyboard, keypad
+confectionery, confectionary, candy store
+container ship, containership, container vessel
+convertible
+corkscrew, bottle screw
+cornet, horn, trumpet, trump
+cowboy boot
+cowboy hat, ten-gallon hat
+cradle
+crane
+crash helmet
+crate
+crib, cot
+Crock Pot
+croquet ball
+crutch
+cuirass
+dam, dike, dyke
+desk
+desktop computer
+dial telephone, dial phone
+diaper, nappy, napkin
+digital clock
+digital watch
+dining table, board
+dishrag, dishcloth
+dishwasher, dish washer, dishwashing machine
+disk brake, disc brake
+dock, dockage, docking facility
+dogsled, dog sled, dog sleigh
+dome
+doormat, welcome mat
+drilling platform, offshore rig
+drum, membranophone, tympan
+drumstick
+dumbbell
+Dutch oven
+electric fan, blower
+electric guitar
+electric locomotive
+entertainment center
+envelope
+espresso maker
+face powder
+feather boa, boa
+file, file cabinet, filing cabinet
+fireboat
+fire engine, fire truck
+fire screen, fireguard
+flagpole, flagstaff
+flute, transverse flute
+folding chair
+football helmet
+forklift
+fountain
+fountain pen
+four-poster
+freight car
+French horn, horn
+frying pan, frypan, skillet
+fur coat
+garbage truck, dustcart
+gasmask, respirator, gas helmet
+gas pump, gasoline pump, petrol pump, island dispenser
+goblet
+go-kart
+golf ball
+golfcart, golf cart
+gondola
+gong, tam-tam
+gown
+grand piano, grand
+greenhouse, nursery, glasshouse
+grille, radiator grille
+grocery store, grocery, food market, market
+guillotine
+hair slide
+hair spray
+half track
+hammer
+hamper
+hand blower, blow dryer, blow drier, hair dryer, hair drier
+hand-held computer, hand-held microcomputer
+handkerchief, hankie, hanky, hankey
+hard disc, hard disk, fixed disk
+harmonica, mouth organ, harp, mouth harp
+harp
+harvester, reaper
+hatchet
+holster
+home theater, home theatre
+honeycomb
+hook, claw
+hoopskirt, crinoline
+horizontal bar, high bar
+horse cart, horse-cart
+hourglass
+iPod
+iron, smoothing iron
+jack-o'-lantern
+jean, blue jean, denim
+jeep, landrover
+jersey, T-shirt, tee shirt
+jigsaw puzzle
+jinrikisha, ricksha, rickshaw
+joystick
+kimono
+knee pad
+knot
+lab coat, laboratory coat
+ladle
+lampshade, lamp shade
+laptop, laptop computer
+lawn mower, mower
+lens cap, lens cover
+letter opener, paper knife, paperknife
+library
+lifeboat
+lighter, light, igniter, ignitor
+limousine, limo
+liner, ocean liner
+lipstick, lip rouge
+Loafer
+lotion
+loudspeaker, speaker, speaker unit, loudspeaker system, speaker system
+loupe, jeweler's loupe
+lumbermill, sawmill
+magnetic compass
+mailbag, postbag
+mailbox, letter box
+maillot
+maillot, tank suit
+manhole cover
+maraca
+marimba, xylophone
+mask
+matchstick
+maypole
+maze, labyrinth
+measuring cup
+medicine chest, medicine cabinet
+megalith, megalithic structure
+microphone, mike
+microwave, microwave oven
+military uniform
+milk can
+minibus
+miniskirt, mini
+minivan
+missile
+mitten
+mixing bowl
+mobile home, manufactured home
+Model T
+modem
+monastery
+monitor
+moped
+mortar
+mortarboard
+mosque
+mosquito net
+motor scooter, scooter
+mountain bike, all-terrain bike, off-roader
+mountain tent
+mouse, computer mouse
+mousetrap
+moving van
+muzzle
+nail
+neck brace
+necklace
+nipple
+notebook, notebook computer
+obelisk
+oboe, hautboy, hautbois
+ocarina, sweet potato
+odometer, hodometer, mileometer, milometer
+oil filter
+organ, pipe organ
+oscilloscope, scope, cathode-ray oscilloscope, CRO
+overskirt
+oxcart
+oxygen mask
+packet
+paddle, boat paddle
+paddlewheel, paddle wheel
+padlock
+paintbrush
+pajama, pyjama, pj's, jammies
+palace
+panpipe, pandean pipe, syrinx
+paper towel
+parachute, chute
+parallel bars, bars
+park bench
+parking meter
+passenger car, coach, carriage
+patio, terrace
+pay-phone, pay-station
+pedestal, plinth, footstall
+pencil box, pencil case
+pencil sharpener
+perfume, essence
+Petri dish
+photocopier
+pick, plectrum, plectron
+pickelhaube
+picket fence, paling
+pickup, pickup truck
+pier
+piggy bank, penny bank
+pill bottle
+pillow
+ping-pong ball
+pinwheel
+pirate, pirate ship
+pitcher, ewer
+plane, carpenter's plane, woodworking plane
+planetarium
+plastic bag
+plate rack
+plow, plough
+plunger, plumber's helper
+Polaroid camera, Polaroid Land camera
+pole
+police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria
+poncho
+pool table, billiard table, snooker table
+pop bottle, soda bottle
+pot, flowerpot
+potter's wheel
+power drill
+prayer rug, prayer mat
+printer
+prison, prison house
+projectile, missile
+projector
+puck, hockey puck
+punching bag, punch bag, punching ball, punchball
+purse
+quill, quill pen
+quilt, comforter, comfort, puff
+racer, race car, racing car
+racket, racquet
+radiator
+radio, wireless
+radio telescope, radio reflector
+rain barrel
+recreational vehicle, RV, R.V.
+reel
+reflex camera
+refrigerator, icebox
+remote control, remote
+restaurant, eating house, eating place, eatery
+revolver, six-gun, six-shooter
+rifle
+rocking chair, rocker
+rotisserie
+rubber eraser, rubber, pencil eraser
+rugby ball
+rule, ruler
+running shoe
+safe
+safety pin
+saltshaker, salt shaker
+sandal
+sarong
+sax, saxophone
+scabbard
+scale, weighing machine
+school bus
+schooner
+scoreboard
+screen, CRT screen
+screw
+screwdriver
+seat belt, seatbelt
+sewing machine
+shield, buckler
+shoe shop, shoe-shop, shoe store
+shoji
+shopping basket
+shopping cart
+shovel
+shower cap
+shower curtain
+ski
+ski mask
+sleeping bag
+slide rule, slipstick
+sliding door
+slot, one-armed bandit
+snorkel
+snowmobile
+snowplow, snowplough
+soap dispenser
+soccer ball
+sock
+solar dish, solar collector, solar furnace
+sombrero
+soup bowl
+space bar
+space heater
+space shuttle
+spatula
+speedboat
+spider web, spider's web
+spindle
+sports car, sport car
+spotlight, spot
+stage
+steam locomotive
+steel arch bridge
+steel drum
+stethoscope
+stole
+stone wall
+stopwatch, stop watch
+stove
+strainer
+streetcar, tram, tramcar, trolley, trolley car
+stretcher
+studio couch, day bed
+stupa, tope
+submarine, pigboat, sub, U-boat
+suit, suit of clothes
+sundial
+sunglass
+sunglasses, dark glasses, shades
+sunscreen, sunblock, sun blocker
+suspension bridge
+swab, swob, mop
+sweatshirt
+swimming trunks, bathing trunks
+swing
+switch, electric switch, electrical switch
+syringe
+table lamp
+tank, army tank, armored combat vehicle, armoured combat vehicle
+tape player
+teapot
+teddy, teddy bear
+television, television system
+tennis ball
+thatch, thatched roof
+theater curtain, theatre curtain
+thimble
+thresher, thrasher, threshing machine
+throne
+tile roof
+toaster
+tobacco shop, tobacconist shop, tobacconist
+toilet seat
+torch
+totem pole
+tow truck, tow car, wrecker
+toyshop
+tractor
+trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi
+tray
+trench coat
+tricycle, trike, velocipede
+trimaran
+tripod
+triumphal arch
+trolleybus, trolley coach, trackless trolley
+trombone
+tub, vat
+turnstile
+typewriter keyboard
+umbrella
+unicycle, monocycle
+upright, upright piano
+vacuum, vacuum cleaner
+vase
+vault
+velvet
+vending machine
+vestment
+viaduct
+violin, fiddle
+volleyball
+waffle iron
+wall clock
+wallet, billfold, notecase, pocketbook
+wardrobe, closet, press
+warplane, military plane
+washbasin, handbasin, washbowl, lavabo, wash-hand basin
+washer, automatic washer, washing machine
+water bottle
+water jug
+water tower
+whiskey jug
+whistle
+wig
+window screen
+window shade
+Windsor tie
+wine bottle
+wing
+wok
+wooden spoon
+wool, woolen, woollen
+worm fence, snake fence, snake-rail fence, Virginia fence
+wreck
+yawl
+yurt
+web site, website, internet site, site
+comic book
+crossword puzzle, crossword
+street sign
+traffic light, traffic signal, stoplight
+book jacket, dust cover, dust jacket, dust wrapper
+menu
+plate
+guacamole
+consomme
+hot pot, hotpot
+trifle
+ice cream, icecream
+ice lolly, lolly, lollipop, popsicle
+French loaf
+bagel, beigel
+pretzel
+cheeseburger
+hotdog, hot dog, red hot
+mashed potato
+head cabbage
+broccoli
+cauliflower
+zucchini, courgette
+spaghetti squash
+acorn squash
+butternut squash
+cucumber, cuke
+artichoke, globe artichoke
+bell pepper
+cardoon
+mushroom
+Granny Smith
+strawberry
+orange
+lemon
+fig
+pineapple, ananas
+banana
+jackfruit, jak, jack
+custard apple
+pomegranate
+hay
+carbonara
+chocolate sauce, chocolate syrup
+dough
+meat loaf, meatloaf
+pizza, pizza pie
+potpie
+burrito
+red wine
+espresso
+cup
+eggnog
+alp
+bubble
+cliff, drop, drop-off
+coral reef
+geyser
+lakeside, lakeshore
+promontory, headland, head, foreland
+sandbar, sand bar
+seashore, coast, seacoast, sea-coast
+valley, vale
+volcano
+ballplayer, baseball player
+groom, bridegroom
+scuba diver
+rapeseed
+daisy
+yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum
+corn
+acorn
+hip, rose hip, rosehip
+buckeye, horse chestnut, conker
+coral fungus
+agaric
+gyromitra
+stinkhorn, carrion fungus
+earthstar
+hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa
+bolete
+ear, spike, capitulum
+toilet tissue, toilet paper, bathroom tissue

packages.txt

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+libgl1