name: "SE-ResNeXt-50 (32 x 4d)"
# mean_value: 104, 117, 123
layer {
  name: "se_resnext_50"
  type: "MemoryData"
  top: "data"
  top: "label"
  memory_data_param {
    batch_size: 1
    channels: 3
    height: 224
    width: 224
  }
}
layer {
  name: "conv1/7x7_s2"
  type: "Convolution"
  bottom: "data"
  top: "conv1/7x7_s2"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 3
    kernel_size: 7
    stride: 2
  }
}
layer {
  name: "conv1/7x7_s2/bn"
  type: "BatchNorm"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv1/7x7_s2/bn/scale"
  type: "Scale"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv1/relu_7x7_s2"
  type: "ReLU"
  bottom: "conv1/7x7_s2"
  top: "conv1/7x7_s2"
}
layer {
  name: "pool1/3x3_s2"
  type: "Pooling"
  bottom: "conv1/7x7_s2"
  top: "pool1/3x3_s2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2_1_1x1_reduce"
  type: "Convolution"
  bottom: "pool1/3x3_s2"
  top: "conv2_1_1x1_reduce"
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_1_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv2_1_1x1_reduce"
  top: "conv2_1_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv2_1_1x1_reduce"
  top: "conv2_1_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv2_1_1x1_reduce"
  top: "conv2_1_1x1_reduce"
}
layer {
  name: "conv2_1_3x3"
  type: "Convolution"
  bottom: "conv2_1_1x1_reduce"
  top: "conv2_1_3x3"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv2_1_3x3/bn"
  type: "BatchNorm"
  bottom: "conv2_1_3x3"
  top: "conv2_1_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_3x3/bn/scale"
  type: "Scale"
  bottom: "conv2_1_3x3"
  top: "conv2_1_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1_3x3/relu"
  type: "ReLU"
  bottom: "conv2_1_3x3"
  top: "conv2_1_3x3"
}
layer {
  name: "conv2_1_1x1_increase"
  type: "Convolution"
  bottom: "conv2_1_3x3"
  top: "conv2_1_1x1_increase"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_1_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv2_1_1x1_increase"
  top: "conv2_1_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv2_1_1x1_increase"
  top: "conv2_1_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1_global_pool"
  type: "Pooling"
  bottom: "conv2_1_1x1_increase"
  top: "conv2_1_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv2_1_1x1_down"
  type: "Convolution"
  bottom: "conv2_1_global_pool"
  top: "conv2_1_1x1_down"
  convolution_param {
    num_output: 16
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_1_1x1_down/relu"
  type: "ReLU"
  bottom: "conv2_1_1x1_down"
  top: "conv2_1_1x1_down"
}
layer {
  name: "conv2_1_1x1_up"
  type: "Convolution"
  bottom: "conv2_1_1x1_down"
  top: "conv2_1_1x1_up"
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_1_prob"
  type: "Sigmoid"
  bottom: "conv2_1_1x1_up"
  top: "conv2_1_1x1_up"
}
layer {
  name: "conv2_1_1x1_proj"
  type: "Convolution"
  bottom: "pool1/3x3_s2"
  top: "conv2_1_1x1_proj"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_1_1x1_proj/bn"
  type: "BatchNorm"
  bottom: "conv2_1_1x1_proj"
  top: "conv2_1_1x1_proj"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_1_1x1_proj/bn/scale"
  type: "Scale"
  bottom: "conv2_1_1x1_proj"
  top: "conv2_1_1x1_proj"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_1"
  type: "Axpy"
  bottom: "conv2_1_1x1_up"
  bottom: "conv2_1_1x1_increase"
  bottom: "conv2_1_1x1_proj"
  top: "conv2_1"
}
layer {
  name: "conv2_1/relu"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2_1x1_reduce"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2_1x1_reduce"
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_2_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv2_2_1x1_reduce"
  top: "conv2_2_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_2_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv2_2_1x1_reduce"
  top: "conv2_2_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_2_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv2_2_1x1_reduce"
  top: "conv2_2_1x1_reduce"
}
layer {
  name: "conv2_2_3x3"
  type: "Convolution"
  bottom: "conv2_2_1x1_reduce"
  top: "conv2_2_3x3"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv2_2_3x3/bn"
  type: "BatchNorm"
  bottom: "conv2_2_3x3"
  top: "conv2_2_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_2_3x3/bn/scale"
  type: "Scale"
  bottom: "conv2_2_3x3"
  top: "conv2_2_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_2_3x3/relu"
  type: "ReLU"
  bottom: "conv2_2_3x3"
  top: "conv2_2_3x3"
}
layer {
  name: "conv2_2_1x1_increase"
  type: "Convolution"
  bottom: "conv2_2_3x3"
  top: "conv2_2_1x1_increase"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_2_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv2_2_1x1_increase"
  top: "conv2_2_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_2_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv2_2_1x1_increase"
  top: "conv2_2_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_2_global_pool"
  type: "Pooling"
  bottom: "conv2_2_1x1_increase"
  top: "conv2_2_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv2_2_1x1_down"
  type: "Convolution"
  bottom: "conv2_2_global_pool"
  top: "conv2_2_1x1_down"
  convolution_param {
    num_output: 16
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_2_1x1_down/relu"
  type: "ReLU"
  bottom: "conv2_2_1x1_down"
  top: "conv2_2_1x1_down"
}
layer {
  name: "conv2_2_1x1_up"
  type: "Convolution"
  bottom: "conv2_2_1x1_down"
  top: "conv2_2_1x1_up"
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_2_prob"
  type: "Sigmoid"
  bottom: "conv2_2_1x1_up"
  top: "conv2_2_1x1_up"
}
layer {
  name: "conv2_2"
  type: "Axpy"
  bottom: "conv2_2_1x1_up"
  bottom: "conv2_2_1x1_increase"
  bottom: "conv2_1"
  top: "conv2_2"
}
layer {
  name: "conv2_2/relu"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "conv2_3_1x1_reduce"
  type: "Convolution"
  bottom: "conv2_2"
  top: "conv2_3_1x1_reduce"
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_3_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv2_3_1x1_reduce"
  top: "conv2_3_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_3_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv2_3_1x1_reduce"
  top: "conv2_3_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_3_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv2_3_1x1_reduce"
  top: "conv2_3_1x1_reduce"
}
layer {
  name: "conv2_3_3x3"
  type: "Convolution"
  bottom: "conv2_3_1x1_reduce"
  top: "conv2_3_3x3"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv2_3_3x3/bn"
  type: "BatchNorm"
  bottom: "conv2_3_3x3"
  top: "conv2_3_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_3_3x3/bn/scale"
  type: "Scale"
  bottom: "conv2_3_3x3"
  top: "conv2_3_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_3_3x3/relu"
  type: "ReLU"
  bottom: "conv2_3_3x3"
  top: "conv2_3_3x3"
}
layer {
  name: "conv2_3_1x1_increase"
  type: "Convolution"
  bottom: "conv2_3_3x3"
  top: "conv2_3_1x1_increase"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_3_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv2_3_1x1_increase"
  top: "conv2_3_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv2_3_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv2_3_1x1_increase"
  top: "conv2_3_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv2_3_global_pool"
  type: "Pooling"
  bottom: "conv2_3_1x1_increase"
  top: "conv2_3_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv2_3_1x1_down"
  type: "Convolution"
  bottom: "conv2_3_global_pool"
  top: "conv2_3_1x1_down"
  convolution_param {
    num_output: 16
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_3_1x1_down/relu"
  type: "ReLU"
  bottom: "conv2_3_1x1_down"
  top: "conv2_3_1x1_down"
}
layer {
  name: "conv2_3_1x1_up"
  type: "Convolution"
  bottom: "conv2_3_1x1_down"
  top: "conv2_3_1x1_up"
  convolution_param {
    num_output: 256
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv2_3_prob"
  type: "Sigmoid"
  bottom: "conv2_3_1x1_up"
  top: "conv2_3_1x1_up"
}
layer {
  name: "conv2_3"
  type: "Axpy"
  bottom: "conv2_3_1x1_up"
  bottom: "conv2_3_1x1_increase"
  bottom: "conv2_2"
  top: "conv2_3"
}
layer {
  name: "conv2_3/relu"
  type: "ReLU"
  bottom: "conv2_3"
  top: "conv2_3"
}
layer {
  name: "conv3_1_1x1_reduce"
  type: "Convolution"
  bottom: "conv2_3"
  top: "conv3_1_1x1_reduce"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_1_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv3_1_1x1_reduce"
  top: "conv3_1_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv3_1_1x1_reduce"
  top: "conv3_1_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv3_1_1x1_reduce"
  top: "conv3_1_1x1_reduce"
}
layer {
  name: "conv3_1_3x3"
  type: "Convolution"
  bottom: "conv3_1_1x1_reduce"
  top: "conv3_1_3x3"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 2
  }
}
layer {
  name: "conv3_1_3x3/bn"
  type: "BatchNorm"
  bottom: "conv3_1_3x3"
  top: "conv3_1_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_3x3/bn/scale"
  type: "Scale"
  bottom: "conv3_1_3x3"
  top: "conv3_1_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1_3x3/relu"
  type: "ReLU"
  bottom: "conv3_1_3x3"
  top: "conv3_1_3x3"
}
layer {
  name: "conv3_1_1x1_increase"
  type: "Convolution"
  bottom: "conv3_1_3x3"
  top: "conv3_1_1x1_increase"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_1_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv3_1_1x1_increase"
  top: "conv3_1_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv3_1_1x1_increase"
  top: "conv3_1_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1_global_pool"
  type: "Pooling"
  bottom: "conv3_1_1x1_increase"
  top: "conv3_1_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv3_1_1x1_down"
  type: "Convolution"
  bottom: "conv3_1_global_pool"
  top: "conv3_1_1x1_down"
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_1_1x1_down/relu"
  type: "ReLU"
  bottom: "conv3_1_1x1_down"
  top: "conv3_1_1x1_down"
}
layer {
  name: "conv3_1_1x1_up"
  type: "Convolution"
  bottom: "conv3_1_1x1_down"
  top: "conv3_1_1x1_up"
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_1_prob"
  type: "Sigmoid"
  bottom: "conv3_1_1x1_up"
  top: "conv3_1_1x1_up"
}
layer {
  name: "conv3_1_1x1_proj"
  type: "Convolution"
  bottom: "conv2_3"
  top: "conv3_1_1x1_proj"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 2
  }
}
layer {
  name: "conv3_1_1x1_proj/bn"
  type: "BatchNorm"
  bottom: "conv3_1_1x1_proj"
  top: "conv3_1_1x1_proj"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_1_1x1_proj/bn/scale"
  type: "Scale"
  bottom: "conv3_1_1x1_proj"
  top: "conv3_1_1x1_proj"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_1"
  type: "Axpy"
  bottom: "conv3_1_1x1_up"
  bottom: "conv3_1_1x1_increase"
  bottom: "conv3_1_1x1_proj"
  top: "conv3_1"
}
layer {
  name: "conv3_1/relu"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2_1x1_reduce"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2_1x1_reduce"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_2_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv3_2_1x1_reduce"
  top: "conv3_2_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_2_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv3_2_1x1_reduce"
  top: "conv3_2_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_2_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv3_2_1x1_reduce"
  top: "conv3_2_1x1_reduce"
}
layer {
  name: "conv3_2_3x3"
  type: "Convolution"
  bottom: "conv3_2_1x1_reduce"
  top: "conv3_2_3x3"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv3_2_3x3/bn"
  type: "BatchNorm"
  bottom: "conv3_2_3x3"
  top: "conv3_2_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_2_3x3/bn/scale"
  type: "Scale"
  bottom: "conv3_2_3x3"
  top: "conv3_2_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_2_3x3/relu"
  type: "ReLU"
  bottom: "conv3_2_3x3"
  top: "conv3_2_3x3"
}
layer {
  name: "conv3_2_1x1_increase"
  type: "Convolution"
  bottom: "conv3_2_3x3"
  top: "conv3_2_1x1_increase"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_2_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv3_2_1x1_increase"
  top: "conv3_2_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_2_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv3_2_1x1_increase"
  top: "conv3_2_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_2_global_pool"
  type: "Pooling"
  bottom: "conv3_2_1x1_increase"
  top: "conv3_2_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv3_2_1x1_down"
  type: "Convolution"
  bottom: "conv3_2_global_pool"
  top: "conv3_2_1x1_down"
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_2_1x1_down/relu"
  type: "ReLU"
  bottom: "conv3_2_1x1_down"
  top: "conv3_2_1x1_down"
}
layer {
  name: "conv3_2_1x1_up"
  type: "Convolution"
  bottom: "conv3_2_1x1_down"
  top: "conv3_2_1x1_up"
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_2_prob"
  type: "Sigmoid"
  bottom: "conv3_2_1x1_up"
  top: "conv3_2_1x1_up"
}
layer {
  name: "conv3_2"
  type: "Axpy"
  bottom: "conv3_2_1x1_up"
  bottom: "conv3_2_1x1_increase"
  bottom: "conv3_1"
  top: "conv3_2"
}
layer {
  name: "conv3_2/relu"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3_1x1_reduce"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3_1x1_reduce"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_3_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv3_3_1x1_reduce"
  top: "conv3_3_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_3_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv3_3_1x1_reduce"
  top: "conv3_3_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv3_3_1x1_reduce"
  top: "conv3_3_1x1_reduce"
}
layer {
  name: "conv3_3_3x3"
  type: "Convolution"
  bottom: "conv3_3_1x1_reduce"
  top: "conv3_3_3x3"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv3_3_3x3/bn"
  type: "BatchNorm"
  bottom: "conv3_3_3x3"
  top: "conv3_3_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_3_3x3/bn/scale"
  type: "Scale"
  bottom: "conv3_3_3x3"
  top: "conv3_3_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3_3x3/relu"
  type: "ReLU"
  bottom: "conv3_3_3x3"
  top: "conv3_3_3x3"
}
layer {
  name: "conv3_3_1x1_increase"
  type: "Convolution"
  bottom: "conv3_3_3x3"
  top: "conv3_3_1x1_increase"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_3_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv3_3_1x1_increase"
  top: "conv3_3_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_3_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv3_3_1x1_increase"
  top: "conv3_3_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_3_global_pool"
  type: "Pooling"
  bottom: "conv3_3_1x1_increase"
  top: "conv3_3_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv3_3_1x1_down"
  type: "Convolution"
  bottom: "conv3_3_global_pool"
  top: "conv3_3_1x1_down"
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_3_1x1_down/relu"
  type: "ReLU"
  bottom: "conv3_3_1x1_down"
  top: "conv3_3_1x1_down"
}
layer {
  name: "conv3_3_1x1_up"
  type: "Convolution"
  bottom: "conv3_3_1x1_down"
  top: "conv3_3_1x1_up"
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_3_prob"
  type: "Sigmoid"
  bottom: "conv3_3_1x1_up"
  top: "conv3_3_1x1_up"
}
layer {
  name: "conv3_3"
  type: "Axpy"
  bottom: "conv3_3_1x1_up"
  bottom: "conv3_3_1x1_increase"
  bottom: "conv3_2"
  top: "conv3_3"
}
layer {
  name: "conv3_3/relu"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_4_1x1_reduce"
  type: "Convolution"
  bottom: "conv3_3"
  top: "conv3_4_1x1_reduce"
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_4_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv3_4_1x1_reduce"
  top: "conv3_4_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_4_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv3_4_1x1_reduce"
  top: "conv3_4_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_4_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv3_4_1x1_reduce"
  top: "conv3_4_1x1_reduce"
}
layer {
  name: "conv3_4_3x3"
  type: "Convolution"
  bottom: "conv3_4_1x1_reduce"
  top: "conv3_4_3x3"
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv3_4_3x3/bn"
  type: "BatchNorm"
  bottom: "conv3_4_3x3"
  top: "conv3_4_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_4_3x3/bn/scale"
  type: "Scale"
  bottom: "conv3_4_3x3"
  top: "conv3_4_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_4_3x3/relu"
  type: "ReLU"
  bottom: "conv3_4_3x3"
  top: "conv3_4_3x3"
}
layer {
  name: "conv3_4_1x1_increase"
  type: "Convolution"
  bottom: "conv3_4_3x3"
  top: "conv3_4_1x1_increase"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_4_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv3_4_1x1_increase"
  top: "conv3_4_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv3_4_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv3_4_1x1_increase"
  top: "conv3_4_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv3_4_global_pool"
  type: "Pooling"
  bottom: "conv3_4_1x1_increase"
  top: "conv3_4_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv3_4_1x1_down"
  type: "Convolution"
  bottom: "conv3_4_global_pool"
  top: "conv3_4_1x1_down"
  convolution_param {
    num_output: 32
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_4_1x1_down/relu"
  type: "ReLU"
  bottom: "conv3_4_1x1_down"
  top: "conv3_4_1x1_down"
}
layer {
  name: "conv3_4_1x1_up"
  type: "Convolution"
  bottom: "conv3_4_1x1_down"
  top: "conv3_4_1x1_up"
  convolution_param {
    num_output: 512
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv3_4_prob"
  type: "Sigmoid"
  bottom: "conv3_4_1x1_up"
  top: "conv3_4_1x1_up"
}
layer {
  name: "conv3_4"
  type: "Axpy"
  bottom: "conv3_4_1x1_up"
  bottom: "conv3_4_1x1_increase"
  bottom: "conv3_3"
  top: "conv3_4"
}
layer {
  name: "conv3_4/relu"
  type: "ReLU"
  bottom: "conv3_4"
  top: "conv3_4"
}
layer {
  name: "conv4_1_1x1_reduce"
  type: "Convolution"
  bottom: "conv3_4"
  top: "conv4_1_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_1_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_1_1x1_reduce"
  top: "conv4_1_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_1_1x1_reduce"
  top: "conv4_1_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_1_1x1_reduce"
  top: "conv4_1_1x1_reduce"
}
layer {
  name: "conv4_1_3x3"
  type: "Convolution"
  bottom: "conv4_1_1x1_reduce"
  top: "conv4_1_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 2
  }
}
layer {
  name: "conv4_1_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_1_3x3"
  top: "conv4_1_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_1_3x3"
  top: "conv4_1_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1_3x3/relu"
  type: "ReLU"
  bottom: "conv4_1_3x3"
  top: "conv4_1_3x3"
}
layer {
  name: "conv4_1_1x1_increase"
  type: "Convolution"
  bottom: "conv4_1_3x3"
  top: "conv4_1_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_1_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_1_1x1_increase"
  top: "conv4_1_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_1_1x1_increase"
  top: "conv4_1_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1_global_pool"
  type: "Pooling"
  bottom: "conv4_1_1x1_increase"
  top: "conv4_1_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_1_1x1_down"
  type: "Convolution"
  bottom: "conv4_1_global_pool"
  top: "conv4_1_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_1_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_1_1x1_down"
  top: "conv4_1_1x1_down"
}
layer {
  name: "conv4_1_1x1_up"
  type: "Convolution"
  bottom: "conv4_1_1x1_down"
  top: "conv4_1_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_1_prob"
  type: "Sigmoid"
  bottom: "conv4_1_1x1_up"
  top: "conv4_1_1x1_up"
}
layer {
  name: "conv4_1_1x1_proj"
  type: "Convolution"
  bottom: "conv3_4"
  top: "conv4_1_1x1_proj"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 2
  }
}
layer {
  name: "conv4_1_1x1_proj/bn"
  type: "BatchNorm"
  bottom: "conv4_1_1x1_proj"
  top: "conv4_1_1x1_proj"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_1_1x1_proj/bn/scale"
  type: "Scale"
  bottom: "conv4_1_1x1_proj"
  top: "conv4_1_1x1_proj"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_1"
  type: "Axpy"
  bottom: "conv4_1_1x1_up"
  bottom: "conv4_1_1x1_increase"
  bottom: "conv4_1_1x1_proj"
  top: "conv4_1"
}
layer {
  name: "conv4_1/relu"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_2_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_2_1x1_reduce"
  top: "conv4_2_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_2_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_2_1x1_reduce"
  top: "conv4_2_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_2_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_2_1x1_reduce"
  top: "conv4_2_1x1_reduce"
}
layer {
  name: "conv4_2_3x3"
  type: "Convolution"
  bottom: "conv4_2_1x1_reduce"
  top: "conv4_2_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv4_2_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_2_3x3"
  top: "conv4_2_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_2_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_2_3x3"
  top: "conv4_2_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_2_3x3/relu"
  type: "ReLU"
  bottom: "conv4_2_3x3"
  top: "conv4_2_3x3"
}
layer {
  name: "conv4_2_1x1_increase"
  type: "Convolution"
  bottom: "conv4_2_3x3"
  top: "conv4_2_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_2_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_2_1x1_increase"
  top: "conv4_2_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_2_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_2_1x1_increase"
  top: "conv4_2_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_2_global_pool"
  type: "Pooling"
  bottom: "conv4_2_1x1_increase"
  top: "conv4_2_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_2_1x1_down"
  type: "Convolution"
  bottom: "conv4_2_global_pool"
  top: "conv4_2_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_2_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_2_1x1_down"
  top: "conv4_2_1x1_down"
}
layer {
  name: "conv4_2_1x1_up"
  type: "Convolution"
  bottom: "conv4_2_1x1_down"
  top: "conv4_2_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_2_prob"
  type: "Sigmoid"
  bottom: "conv4_2_1x1_up"
  top: "conv4_2_1x1_up"
}
layer {
  name: "conv4_2"
  type: "Axpy"
  bottom: "conv4_2_1x1_up"
  bottom: "conv4_2_1x1_increase"
  bottom: "conv4_1"
  top: "conv4_2"
}
layer {
  name: "conv4_2/relu"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_3_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_3_1x1_reduce"
  top: "conv4_3_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_3_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_3_1x1_reduce"
  top: "conv4_3_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_3_1x1_reduce"
  top: "conv4_3_1x1_reduce"
}
layer {
  name: "conv4_3_3x3"
  type: "Convolution"
  bottom: "conv4_3_1x1_reduce"
  top: "conv4_3_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv4_3_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_3_3x3"
  top: "conv4_3_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_3_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_3_3x3"
  top: "conv4_3_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3_3x3/relu"
  type: "ReLU"
  bottom: "conv4_3_3x3"
  top: "conv4_3_3x3"
}
layer {
  name: "conv4_3_1x1_increase"
  type: "Convolution"
  bottom: "conv4_3_3x3"
  top: "conv4_3_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_3_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_3_1x1_increase"
  top: "conv4_3_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_3_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_3_1x1_increase"
  top: "conv4_3_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_3_global_pool"
  type: "Pooling"
  bottom: "conv4_3_1x1_increase"
  top: "conv4_3_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_3_1x1_down"
  type: "Convolution"
  bottom: "conv4_3_global_pool"
  top: "conv4_3_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_3_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_3_1x1_down"
  top: "conv4_3_1x1_down"
}
layer {
  name: "conv4_3_1x1_up"
  type: "Convolution"
  bottom: "conv4_3_1x1_down"
  top: "conv4_3_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_3_prob"
  type: "Sigmoid"
  bottom: "conv4_3_1x1_up"
  top: "conv4_3_1x1_up"
}
layer {
  name: "conv4_3"
  type: "Axpy"
  bottom: "conv4_3_1x1_up"
  bottom: "conv4_3_1x1_increase"
  bottom: "conv4_2"
  top: "conv4_3"
}
layer {
  name: "conv4_3/relu"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv4_4_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_3"
  top: "conv4_4_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_4_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_4_1x1_reduce"
  top: "conv4_4_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_4_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_4_1x1_reduce"
  top: "conv4_4_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_4_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_4_1x1_reduce"
  top: "conv4_4_1x1_reduce"
}
layer {
  name: "conv4_4_3x3"
  type: "Convolution"
  bottom: "conv4_4_1x1_reduce"
  top: "conv4_4_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv4_4_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_4_3x3"
  top: "conv4_4_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_4_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_4_3x3"
  top: "conv4_4_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_4_3x3/relu"
  type: "ReLU"
  bottom: "conv4_4_3x3"
  top: "conv4_4_3x3"
}
layer {
  name: "conv4_4_1x1_increase"
  type: "Convolution"
  bottom: "conv4_4_3x3"
  top: "conv4_4_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_4_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_4_1x1_increase"
  top: "conv4_4_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_4_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_4_1x1_increase"
  top: "conv4_4_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_4_global_pool"
  type: "Pooling"
  bottom: "conv4_4_1x1_increase"
  top: "conv4_4_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_4_1x1_down"
  type: "Convolution"
  bottom: "conv4_4_global_pool"
  top: "conv4_4_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_4_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_4_1x1_down"
  top: "conv4_4_1x1_down"
}
layer {
  name: "conv4_4_1x1_up"
  type: "Convolution"
  bottom: "conv4_4_1x1_down"
  top: "conv4_4_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_4_prob"
  type: "Sigmoid"
  bottom: "conv4_4_1x1_up"
  top: "conv4_4_1x1_up"
}
layer {
  name: "conv4_4"
  type: "Axpy"
  bottom: "conv4_4_1x1_up"
  bottom: "conv4_4_1x1_increase"
  bottom: "conv4_3"
  top: "conv4_4"
}
layer {
  name: "conv4_4/relu"
  type: "ReLU"
  bottom: "conv4_4"
  top: "conv4_4"
}
layer {
  name: "conv4_5_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_4"
  top: "conv4_5_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_5_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_5_1x1_reduce"
  top: "conv4_5_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_5_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_5_1x1_reduce"
  top: "conv4_5_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_5_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_5_1x1_reduce"
  top: "conv4_5_1x1_reduce"
}
layer {
  name: "conv4_5_3x3"
  type: "Convolution"
  bottom: "conv4_5_1x1_reduce"
  top: "conv4_5_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv4_5_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_5_3x3"
  top: "conv4_5_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_5_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_5_3x3"
  top: "conv4_5_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_5_3x3/relu"
  type: "ReLU"
  bottom: "conv4_5_3x3"
  top: "conv4_5_3x3"
}
layer {
  name: "conv4_5_1x1_increase"
  type: "Convolution"
  bottom: "conv4_5_3x3"
  top: "conv4_5_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_5_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_5_1x1_increase"
  top: "conv4_5_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_5_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_5_1x1_increase"
  top: "conv4_5_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_5_global_pool"
  type: "Pooling"
  bottom: "conv4_5_1x1_increase"
  top: "conv4_5_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_5_1x1_down"
  type: "Convolution"
  bottom: "conv4_5_global_pool"
  top: "conv4_5_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_5_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_5_1x1_down"
  top: "conv4_5_1x1_down"
}
layer {
  name: "conv4_5_1x1_up"
  type: "Convolution"
  bottom: "conv4_5_1x1_down"
  top: "conv4_5_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_5_prob"
  type: "Sigmoid"
  bottom: "conv4_5_1x1_up"
  top: "conv4_5_1x1_up"
}
layer {
  name: "conv4_5"
  type: "Axpy"
  bottom: "conv4_5_1x1_up"
  bottom: "conv4_5_1x1_increase"
  bottom: "conv4_4"
  top: "conv4_5"
}
layer {
  name: "conv4_5/relu"
  type: "ReLU"
  bottom: "conv4_5"
  top: "conv4_5"
}
layer {
  name: "conv4_6_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_5"
  top: "conv4_6_1x1_reduce"
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_6_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv4_6_1x1_reduce"
  top: "conv4_6_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_6_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv4_6_1x1_reduce"
  top: "conv4_6_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_6_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv4_6_1x1_reduce"
  top: "conv4_6_1x1_reduce"
}
layer {
  name: "conv4_6_3x3"
  type: "Convolution"
  bottom: "conv4_6_1x1_reduce"
  top: "conv4_6_3x3"
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv4_6_3x3/bn"
  type: "BatchNorm"
  bottom: "conv4_6_3x3"
  top: "conv4_6_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_6_3x3/bn/scale"
  type: "Scale"
  bottom: "conv4_6_3x3"
  top: "conv4_6_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_6_3x3/relu"
  type: "ReLU"
  bottom: "conv4_6_3x3"
  top: "conv4_6_3x3"
}
layer {
  name: "conv4_6_1x1_increase"
  type: "Convolution"
  bottom: "conv4_6_3x3"
  top: "conv4_6_1x1_increase"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_6_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv4_6_1x1_increase"
  top: "conv4_6_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv4_6_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv4_6_1x1_increase"
  top: "conv4_6_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4_6_global_pool"
  type: "Pooling"
  bottom: "conv4_6_1x1_increase"
  top: "conv4_6_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv4_6_1x1_down"
  type: "Convolution"
  bottom: "conv4_6_global_pool"
  top: "conv4_6_1x1_down"
  convolution_param {
    num_output: 64
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_6_1x1_down/relu"
  type: "ReLU"
  bottom: "conv4_6_1x1_down"
  top: "conv4_6_1x1_down"
}
layer {
  name: "conv4_6_1x1_up"
  type: "Convolution"
  bottom: "conv4_6_1x1_down"
  top: "conv4_6_1x1_up"
  convolution_param {
    num_output: 1024
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv4_6_prob"
  type: "Sigmoid"
  bottom: "conv4_6_1x1_up"
  top: "conv4_6_1x1_up"
}
layer {
  name: "conv4_6"
  type: "Axpy"
  bottom: "conv4_6_1x1_up"
  bottom: "conv4_6_1x1_increase"
  bottom: "conv4_5"
  top: "conv4_6"
}
layer {
  name: "conv4_6/relu"
  type: "ReLU"
  bottom: "conv4_6"
  top: "conv4_6"
}
layer {
  name: "conv5_1_1x1_reduce"
  type: "Convolution"
  bottom: "conv4_6"
  top: "conv5_1_1x1_reduce"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_1_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv5_1_1x1_reduce"
  top: "conv5_1_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv5_1_1x1_reduce"
  top: "conv5_1_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv5_1_1x1_reduce"
  top: "conv5_1_1x1_reduce"
}
layer {
  name: "conv5_1_3x3"
  type: "Convolution"
  bottom: "conv5_1_1x1_reduce"
  top: "conv5_1_3x3"
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 2
  }
}
layer {
  name: "conv5_1_3x3/bn"
  type: "BatchNorm"
  bottom: "conv5_1_3x3"
  top: "conv5_1_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_3x3/bn/scale"
  type: "Scale"
  bottom: "conv5_1_3x3"
  top: "conv5_1_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1_3x3/relu"
  type: "ReLU"
  bottom: "conv5_1_3x3"
  top: "conv5_1_3x3"
}
layer {
  name: "conv5_1_1x1_increase"
  type: "Convolution"
  bottom: "conv5_1_3x3"
  top: "conv5_1_1x1_increase"
  convolution_param {
    num_output: 2048
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_1_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv5_1_1x1_increase"
  top: "conv5_1_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv5_1_1x1_increase"
  top: "conv5_1_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1_global_pool"
  type: "Pooling"
  bottom: "conv5_1_1x1_increase"
  top: "conv5_1_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv5_1_1x1_down"
  type: "Convolution"
  bottom: "conv5_1_global_pool"
  top: "conv5_1_1x1_down"
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_1_1x1_down/relu"
  type: "ReLU"
  bottom: "conv5_1_1x1_down"
  top: "conv5_1_1x1_down"
}
layer {
  name: "conv5_1_1x1_up"
  type: "Convolution"
  bottom: "conv5_1_1x1_down"
  top: "conv5_1_1x1_up"
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_1_prob"
  type: "Sigmoid"
  bottom: "conv5_1_1x1_up"
  top: "conv5_1_1x1_up"
}
layer {
  name: "conv5_1_1x1_proj"
  type: "Convolution"
  bottom: "conv4_6"
  top: "conv5_1_1x1_proj"
  convolution_param {
    num_output: 2048
    bias_term: false
    kernel_size: 1
    stride: 2
  }
}
layer {
  name: "conv5_1_1x1_proj/bn"
  type: "BatchNorm"
  bottom: "conv5_1_1x1_proj"
  top: "conv5_1_1x1_proj"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_1_1x1_proj/bn/scale"
  type: "Scale"
  bottom: "conv5_1_1x1_proj"
  top: "conv5_1_1x1_proj"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_1"
  type: "Axpy"
  bottom: "conv5_1_1x1_up"
  bottom: "conv5_1_1x1_increase"
  bottom: "conv5_1_1x1_proj"
  top: "conv5_1"
}
layer {
  name: "conv5_1/relu"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2_1x1_reduce"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2_1x1_reduce"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_2_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv5_2_1x1_reduce"
  top: "conv5_2_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_2_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv5_2_1x1_reduce"
  top: "conv5_2_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_2_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv5_2_1x1_reduce"
  top: "conv5_2_1x1_reduce"
}
layer {
  name: "conv5_2_3x3"
  type: "Convolution"
  bottom: "conv5_2_1x1_reduce"
  top: "conv5_2_3x3"
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv5_2_3x3/bn"
  type: "BatchNorm"
  bottom: "conv5_2_3x3"
  top: "conv5_2_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_2_3x3/bn/scale"
  type: "Scale"
  bottom: "conv5_2_3x3"
  top: "conv5_2_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_2_3x3/relu"
  type: "ReLU"
  bottom: "conv5_2_3x3"
  top: "conv5_2_3x3"
}
layer {
  name: "conv5_2_1x1_increase"
  type: "Convolution"
  bottom: "conv5_2_3x3"
  top: "conv5_2_1x1_increase"
  convolution_param {
    num_output: 2048
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_2_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv5_2_1x1_increase"
  top: "conv5_2_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_2_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv5_2_1x1_increase"
  top: "conv5_2_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_2_global_pool"
  type: "Pooling"
  bottom: "conv5_2_1x1_increase"
  top: "conv5_2_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv5_2_1x1_down"
  type: "Convolution"
  bottom: "conv5_2_global_pool"
  top: "conv5_2_1x1_down"
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_2_1x1_down/relu"
  type: "ReLU"
  bottom: "conv5_2_1x1_down"
  top: "conv5_2_1x1_down"
}
layer {
  name: "conv5_2_1x1_up"
  type: "Convolution"
  bottom: "conv5_2_1x1_down"
  top: "conv5_2_1x1_up"
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_2_prob"
  type: "Sigmoid"
  bottom: "conv5_2_1x1_up"
  top: "conv5_2_1x1_up"
}
layer {
  name: "conv5_2"
  type: "Axpy"
  bottom: "conv5_2_1x1_up"
  bottom: "conv5_2_1x1_increase"
  bottom: "conv5_1"
  top: "conv5_2"
}
layer {
  name: "conv5_2/relu"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3_1x1_reduce"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3_1x1_reduce"
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_3_1x1_reduce/bn"
  type: "BatchNorm"
  bottom: "conv5_3_1x1_reduce"
  top: "conv5_3_1x1_reduce"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_3_1x1_reduce/bn/scale"
  type: "Scale"
  bottom: "conv5_3_1x1_reduce"
  top: "conv5_3_1x1_reduce"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3_1x1_reduce/relu"
  type: "ReLU"
  bottom: "conv5_3_1x1_reduce"
  top: "conv5_3_1x1_reduce"
}
layer {
  name: "conv5_3_3x3"
  type: "Convolution"
  bottom: "conv5_3_1x1_reduce"
  top: "conv5_3_3x3"
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
  }
}
layer {
  name: "conv5_3_3x3/bn"
  type: "BatchNorm"
  bottom: "conv5_3_3x3"
  top: "conv5_3_3x3"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_3_3x3/bn/scale"
  type: "Scale"
  bottom: "conv5_3_3x3"
  top: "conv5_3_3x3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3_3x3/relu"
  type: "ReLU"
  bottom: "conv5_3_3x3"
  top: "conv5_3_3x3"
}
layer {
  name: "conv5_3_1x1_increase"
  type: "Convolution"
  bottom: "conv5_3_3x3"
  top: "conv5_3_1x1_increase"
  convolution_param {
    num_output: 2048
    bias_term: false
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_3_1x1_increase/bn"
  type: "BatchNorm"
  bottom: "conv5_3_1x1_increase"
  top: "conv5_3_1x1_increase"
  batch_norm_param {
    use_global_stats: true
  }
}
layer {
  name: "conv5_3_1x1_increase/bn/scale"
  type: "Scale"
  bottom: "conv5_3_1x1_increase"
  top: "conv5_3_1x1_increase"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv5_3_global_pool"
  type: "Pooling"
  bottom: "conv5_3_1x1_increase"
  top: "conv5_3_global_pool"
  pooling_param {
    pool: AVE
    engine: CAFFE
    global_pooling: true
  }
}
layer {
  name: "conv5_3_1x1_down"
  type: "Convolution"
  bottom: "conv5_3_global_pool"
  top: "conv5_3_1x1_down"
  convolution_param {
    num_output: 128
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_3_1x1_down/relu"
  type: "ReLU"
  bottom: "conv5_3_1x1_down"
  top: "conv5_3_1x1_down"
}
layer {
  name: "conv5_3_1x1_up"
  type: "Convolution"
  bottom: "conv5_3_1x1_down"
  top: "conv5_3_1x1_up"
  convolution_param {
    num_output: 2048
    kernel_size: 1
    stride: 1
  }
}
layer {
  name: "conv5_3_prob"
  type: "Sigmoid"
  bottom: "conv5_3_1x1_up"
  top: "conv5_3_1x1_up"
}
layer {
  name: "conv5_3"
  type: "Axpy"
  bottom: "conv5_3_1x1_up"
  bottom: "conv5_3_1x1_increase"
  bottom: "conv5_2"
  top: "conv5_3"
}
layer {
  name: "conv5_3/relu"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5/7x7_s1"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5/7x7_s1"
  pooling_param {
    pool: AVE
    kernel_size: 7
    stride: 1
  }
}
layer {
  name: "classifier"
  type: "InnerProduct"
  bottom: "pool5/7x7_s1"
  top: "classifier"
  inner_product_param {
    num_output: 1000
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "classifier"
  top: "prob"
}