眾所周知，人臉識別中有一項重要的任務-人臉關鍵點預測，通過這個環節對齊，才能進行人臉識別，提高人臉識別的準確率。另外，一些活體檢測/人臉狀態分析也需要利用該方案進行實現。

    經典的人臉檢測模型MTCNN中具有人臉關鍵點的預測功能，但其關鍵預測精度比較差，對於大角度、模糊、遮擋、小尺度等情況的人臉效果下降更加嚴重。因此我結合openpose的關鍵點預測模型，自行設計瞭如下的人臉關鍵點熱圖預測模型，經過驗證可以很好的實現人臉關鍵點預測的效果。

訓練網路設計

    由於經典的openpose框架中heatmaps的熱圖有6個stage進行預測和中間監督，但我按照原始的框架發現到達後面幾個stage後，loss基本和前面的stage一致，但多個stage對模型的速度會有影響，因此設計了3個stage和4個stage的版本，這裡主要介紹4個stage的版本。

利用netscope可以將設計的網路視覺化如下（如果看不清，可以利用我下面提供的proto內容自行驗證）：

所設計的網路proto檔案內容如下：

name: "landmarks-net"

input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 112
  dim: 112
}

layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1_stage1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1_stage1"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2_stage1"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2_stage1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2_stage1"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_4"
  type: "Convolution"
  bottom: "conv3_3"
  top: "conv3_4"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu3_4"
  type: "ReLU"
  bottom: "conv3_4"
  top: "conv3_4"
}


layer {
  name: "conv4_4_CPM"
  type: "Convolution"
  bottom: "conv3_4"
  top: "conv4_4_CPM"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu4_4_CPM"
  type: "ReLU"
  bottom: "conv4_4_CPM"
  top: "conv4_4_CPM"
}


layer {
  name: "conv5_1_CPM_new"
  type: "Convolution"
  bottom: "conv4_4_CPM"
  top: "conv5_1_CPM_new"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5_1_CPM_new"
  type: "ReLU"
  bottom: "conv5_1_CPM_new"
  top: "conv5_1_CPM_new"
}

layer {
  name: "conv5_2_CPM_new"
  type: "Convolution"
  bottom: "conv5_1_CPM_new"
  top: "conv5_2_CPM_new"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5_2_CPM_new"
  type: "ReLU"
  bottom: "conv5_2_CPM_new"
  top: "conv5_2_CPM_new"
}

layer {
  name: "conv5_3_CPM_new"
  type: "Convolution"
  bottom: "conv5_2_CPM_new"
  top: "conv5_3_CPM_new"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5_3_CPM_new"
  type: "ReLU"
  bottom: "conv5_3_CPM_new"
  top: "conv5_3_CPM_new"
}

layer {
  name: "conv5_4_CPM_new"
  type: "Convolution"
  bottom: "conv5_3_CPM_new"
  top: "conv5_4_CPM_new"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu5_4_CPM_new"
  type: "ReLU"
  bottom: "conv5_4_CPM_new"
  top: "conv5_4_CPM_new"
}

layer {
  name: "conv5_5_CPM_new"
  type: "Convolution"
  bottom: "conv5_4_CPM_new"
  top: "conv5_5_CPM_new"
  param {
    lr_mult: 1.0
    decay_mult: 1
  }
  param {
    lr_mult: 2.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 5
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}


layer {
  name: "concat_stage2"
  type: "Concat"
  bottom: "conv5_5_CPM_new"
  bottom: "conv4_4_CPM"
  top: "concat_stage2"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage2_new"
  type: "Convolution"
  bottom: "concat_stage2"
  top: "Mconv1_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu1_stage2_new"
  type: "ReLU"
  bottom: "Mconv1_stage2_new"
  top: "Mconv1_stage2_new"
}

layer {
  name: "Mconv2_stage2_new"
  type: "Convolution"
  bottom: "Mconv1_stage2_new"
  top: "Mconv2_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu2_stage2_new"
  type: "ReLU"
  bottom: "Mconv2_stage2_new"
  top: "Mconv2_stage2_new"
}

layer {
  name: "Mconv3_stage2_new"
  type: "Convolution"
  bottom: "Mconv2_stage2_new"
  top: "Mconv3_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu3_stage2_new"
  type: "ReLU"
  bottom: "Mconv3_stage2_new"
  top: "Mconv3_stage2_new"
}

layer {
  name: "Mconv4_stage2_new"
  type: "Convolution"
  bottom: "Mconv3_stage2_new"
  top: "Mconv4_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu4_stage2_new"
  type: "ReLU"
  bottom: "Mconv4_stage2_new"
  top: "Mconv4_stage2_new"
}

layer {
  name: "Mconv5_stage2_new"
  type: "Convolution"
  bottom: "Mconv4_stage2_new"
  top: "Mconv5_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu5_stage2_new"
  type: "ReLU"
  bottom: "Mconv5_stage2_new"
  top: "Mconv5_stage2_new"
}

layer {
  name: "Mconv6_stage2_new"
  type: "Convolution"
  bottom: "Mconv5_stage2_new"
  top: "Mconv6_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu6_stage2_new"
  type: "ReLU"
  bottom: "Mconv6_stage2_new"
  top: "Mconv6_stage2_new"
}

layer {
  name: "Mconv7_stage2_new"
  type: "Convolution"
  bottom: "Mconv6_stage2_new"
  top: "Mconv7_stage2_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 5
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}



layer {
  name: "concat_stage3"
  type: "Concat"
  bottom: "Mconv7_stage2_new"
  bottom: "conv4_4_CPM"
  top: "concat_stage3"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage3_new"
  type: "Convolution"
  bottom: "concat_stage3"
  top: "Mconv1_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu1_stage3_new"
  type: "ReLU"
  bottom: "Mconv1_stage3_new"
  top: "Mconv1_stage3_new"
}

layer {
  name: "Mconv2_stage3_new"
  type: "Convolution"
  bottom: "Mconv1_stage3_new"
  top: "Mconv2_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu2_stage3_new"
  type: "ReLU"
  bottom: "Mconv2_stage3_new"
  top: "Mconv2_stage3_new"
}

layer {
  name: "Mconv3_stage3_new"
  type: "Convolution"
  bottom: "Mconv2_stage3_new"
  top: "Mconv3_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu3_stage3_new"
  type: "ReLU"
  bottom: "Mconv3_stage3_new"
  top: "Mconv3_stage3_new"
}

layer {
  name: "Mconv4_stage3_new"
  type: "Convolution"
  bottom: "Mconv3_stage3_new"
  top: "Mconv4_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu4_stage3_new"
  type: "ReLU"
  bottom: "Mconv4_stage3_new"
  top: "Mconv4_stage3_new"
}

layer {
  name: "Mconv5_stage3_new"
  type: "Convolution"
  bottom: "Mconv4_stage3_new"
  top: "Mconv5_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu5_stage3_new"
  type: "ReLU"
  bottom: "Mconv5_stage3_new"
  top: "Mconv5_stage3_new"
}

layer {
  name: "Mconv6_stage3_new"
  type: "Convolution"
  bottom: "Mconv5_stage3_new"
  top: "Mconv6_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu6_stage3_new"
  type: "ReLU"
  bottom: "Mconv6_stage3_new"
  top: "Mconv6_stage3_new"
}

layer {
  name: "Mconv7_stage3_new"
  type: "Convolution"
  bottom: "Mconv6_stage3_new"
  top: "Mconv7_stage3_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 5
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}



layer {
  name: "concat_stage4"
  type: "Concat"
  bottom: "Mconv7_stage3_new"
  bottom: "conv4_4_CPM"
  top: "concat_stage4"
  concat_param {
    axis: 1
  }
}
layer {
  name: "Mconv1_stage4_new"
  type: "Convolution"
  bottom: "concat_stage4"
  top: "Mconv1_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu1_stage4_new"
  type: "ReLU"
  bottom: "Mconv1_stage4_new"
  top: "Mconv1_stage4_new"
}

layer {
  name: "Mconv2_stage4_new"
  type: "Convolution"
  bottom: "Mconv1_stage4_new"
  top: "Mconv2_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu2_stage4_new"
  type: "ReLU"
  bottom: "Mconv2_stage4_new"
  top: "Mconv2_stage4_new"
}

layer {
  name: "Mconv3_stage4_new"
  type: "Convolution"
  bottom: "Mconv2_stage4_new"
  top: "Mconv3_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu3_stage4_new"
  type: "ReLU"
  bottom: "Mconv3_stage4_new"
  top: "Mconv3_stage4_new"
}

layer {
  name: "Mconv4_stage4_new"
  type: "Convolution"
  bottom: "Mconv3_stage4_new"
  top: "Mconv4_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu4_stage4_new"
  type: "ReLU"
  bottom: "Mconv4_stage4_new"
  top: "Mconv4_stage4_new"
}

layer {
  name: "Mconv5_stage4_new"
  type: "Convolution"
  bottom: "Mconv4_stage4_new"
  top: "Mconv5_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 3
    kernel_size: 7
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu5_stage4_new"
  type: "ReLU"
  bottom: "Mconv5_stage4_new"
  top: "Mconv5_stage4_new"
}

layer {
  name: "Mconv6_stage4_new"
  type: "Convolution"
  bottom: "Mconv5_stage4_new"
  top: "Mconv6_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Mrelu6_stage4_new"
  type: "ReLU"
  bottom: "Mconv6_stage4_new"
  top: "Mconv6_stage4_new"
}

layer {
  name: "Mconv7_stage4_new"
  type: "Convolution"
  bottom: "Mconv6_stage4_new"
  top: "Mconv7_stage4_new"
  param {
    lr_mult: 4.0
    decay_mult: 1
  }
  param {
    lr_mult: 8.0
    decay_mult: 0
  }
  convolution_param {
    num_output: 5
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
    }
  }
}
 

訓練資料

    我主要使用了資料集CelebA，並且按照1:1的比例，從中抽取了正臉和大角度的人臉作為訓練資料。

    標籤檔案：我根據CelebA中人臉的關鍵點標籤，為每個關鍵點生成了單峰的heatmaps標籤檔案。標籤檔案視覺化後如下圖樣子：

    需要可以私信我交流。

    最終訓練結果如下：

I0906 05:13:55.329042  5065 solver.cpp:243] Iteration 41960, loss = 0.5419
I0906 05:13:55.329210  5065 solver.cpp:259]     Train net output #0: land_loss_stage = 0.281634 (* 1 = 0.281634 loss)
I0906 05:13:55.329221  5065 solver.cpp:259]     Train net output #1: land_loss_stage2 = 0.0835331 (* 1 = 0.0835331 loss)
I0906 05:13:55.329238  5065 solver.cpp:259]     Train net output #2: land_loss_stage3 = 0.0827685 (* 1 = 0.0827685 loss)
I0906 05:13:55.329244  5065 solver.cpp:259]     Train net output #3: land_loss_stage4 = 0.0939632 (* 1 = 0.0939632 loss)
I0906 05:13:55.329252  5065 sgd_solver.cpp:138] Iteration 41960, lr = 8.1e-07
I0906 05:14:18.058833  5065 solver.cpp:243] Iteration 41980, loss = 0.487881
I0906 05:14:18.058876  5065 solver.cpp:259]     Train net output #0: land_loss_stage = 0.261268 (* 1 = 0.261268 loss)
I0906 05:14:18.058885  5065 solver.cpp:259]     Train net output #1: land_loss_stage2 = 0.0769336 (* 1 = 0.0769336 loss)
I0906 05:14:18.058892  5065 solver.cpp:259]     Train net output #2: land_loss_stage3 = 0.0663182 (* 1 = 0.0663182 loss)
I0906 05:14:18.058897  5065 solver.cpp:259]     Train net output #3: land_loss_stage4 = 0.0833601 (* 1 = 0.0833601 loss)
I0906 05:14:18.058903  5065 sgd_solver.cpp:138] Iteration 41980, lr = 8.1e-07
I0906 05:14:39.636696  5065 solver.cpp:596] Snapshotting to binary proto file /home/work/glenn/gitmodel/caffe-face/face_example/landmarks_data/train_map/snapshot_iter_42000.caffemodel
I0906 05:14:40.695569  5065 sgd_solver.cpp:307] Snapshotting solver state to binary proto file /home/work/glenn/gitmodel/caffe-face/face_example/landmarks_data/train_map/snapshot_iter_42000.solverstate
I0906 05:14:41.087797  5065 solver.cpp:332] Iteration 42000, loss = 0.826902
I0906 05:14:41.087839  5065 solver.cpp:337] Optimization Done.
I0906 05:14:41.087843  5065 caffe.cpp:254] Optimization Done.
 

模型測試

      經過約4w步迭代，模型的預測結果如下：