[人脸识别]使用VGG Face Model微调（Fine tune）自己的数据集

关键词：人脸识别、Caffe、VGG Face Model

一、准备数据集

对于一个未经加工的数据集，基本情况如下图所示

图中每一个文件夹内是该类别的所有图像。我们需要对每一类划分训练集与测试集。这里可以写个脚本进行划分，训练集与测试集的比例自己把握，一般来说是训练集：测试集=4：1。
需要注意的是，最终只需要生成两个文件夹：train与val。train文件夹中包含所有训练图片，换句话说，就是把所有训练图片都放在一个文件夹内。val同理。
我们在使用脚本划分数据集时，同时需要新建两个文本：train.txt与val.txt，用来记录训练集与测试集中图片名称，以及类别。如下图所示：

需要注意的非常重要的一点是，train.txt和val.txt中的名称与类别中间分隔符是一个空格，其次，类别号要从0开始，否则后续处理会出现错误。

二、数据处理

经过上一步后，我们会得到两个文件夹：train与val、以及相对应的train.txt和val.txt。
接下来，我们需要将上述数据集处理成caffe能读取的数据格式LMDB。这部分有标准的处理脚本。

#!/usr/bin/env sh
# Create the imagenet lmdb inputs
# N.B. set the path to the imagenet train + val data dirs

EXAMPLE=    #修改
DATA=       #修改
TOOLS=      #修改

TRAIN_DATA_ROOT=$DATA/train/
VAL_DATA_ROOT=$DATA/val/
DBTYPE=lmdb

# Set RESIZE=true to resize the images to 224×224. Leave as false if images have
# already been resized using another tool.
RESIZE=true
if $RESIZE; then
  RESIZE_HEIGHT=224
  RESIZE_WIDTH=224
else
  RESIZE_HEIGHT=0
  RESIZE_WIDTH=0
fi

if [ ! -d "$TRAIN_DATA_ROOT" ]; then
  echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT"
  echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \
       "where the ImageNet training data is stored."
  exit 1
fi

if [ ! -d "$VAL_DATA_ROOT" ]; then
  echo "Error: VAL_DATA_ROOT is not a path to a directory: $VAL_DATA_ROOT"
  echo "Set the VAL_DATA_ROOT variable in create_imagenet.sh to the path" \
       "where the ImageNet validation data is stored."
  exit 1
fi

echo "Creating train lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset \
    --resize_height=$RESIZE_HEIGHT \
    --resize_width=$RESIZE_WIDTH \
    --shuffle \
    $TRAIN_DATA_ROOT \
    $DATA/train.txt \
    $EXAMPLE/train_lmdb

echo "Creating val lmdb..."

GLOG_logtostderr=1 $TOOLS/convert_imageset \
    --resize_height=$RESIZE_HEIGHT \
    --resize_width=$RESIZE_WIDTH \
    --shuffle \
    $VAL_DATA_ROOT \
    $DATA/val.txt \
    $EXAMPLE/val_lmdb

echo "Computing image mean..."
$TOOLS/compute_image_mean -backend=$DBTYPE \
  $EXAMPLE/train_lmdb $EXAMPLE/mean.binaryproto

echo "Done."

你需要更改的地方有：
1.EXAMPLE后面的路径，尽量设置为第一步中的train与test文件夹的父路径；
2.DATA为第一步中的train与test文件夹的父路径；
3.TOOLS的标准格式为：（caffe路径）/build/tools；
4.TRAIN_DATA_ROOT与VAL_DATA_ROOT不需要改变，分别对应train与val文件夹的路径。
其他参数的意义解释可参考：http://www.cnblogs.com/dupuleng/articles/4370236.html
确保上述路径设置正确后，便可将脚本拖入终端运行。
运行完成后，在你设置的EXAMPLE路径中会产生train_lmdb与val_lmdb两个文件夹，以及mean.binaryproto文件。

三、caffe训练相关配置文件修改

下载VGG Face Model，并解压，移动到（caffe路径）/models中：

接下来，我们需要修改vgg_face_caffe中的VGG_FACE_deploy.prototxt 文件，推荐你直接复制下面的文件

name: "VGG_FACE_16_Net"
layer {
  name: "data"
  type: "Data"  #这里注意
  top: "data"
  top: "label"
  data_param {
    source: "$/train_lmdb"   #这里修改
    backend:LMDB
    batch_size: 100   #这里修改
  }
  transform_param {
     mean_file: "$/mean.binaryproto"   #这里修改
     mirror: true
  }
  include: { phase: TRAIN }
}

layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  data_param {
    source: "$/val_lmdb"  #这里修改
    backend:LMDB
    batch_size: 25   #这里修改
  }
  transform_param {
    mean_file: "$/mean.binaryproto"   #这里修改
    mirror: true
  }
  include: { 
    phase: TEST 
  }
}
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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 
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  } 
}
layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  } 
  param {
    lr_mult: 2
    decay_mult: 0
  } 
  convolution_param {
    num_output: 128
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    } 
    bias_filler {
      type: "constant"
      value: 0
    } 
  } 
}
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
    decay_mult: 1
  } 
  param {
    lr_mult: 2
    decay_mult: 0
  } 
  convolution_param {
    num_output: 128
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01 
    } 
    bias_filler {
      type: "constant"
      value: 0
    }
  } 
}
layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  # Note that lr_mult can be set to 0 to disable any fine-tuning of this, and any other, layer
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8_flickr"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_flickr"
  # lr_mult is set to higher than for other layers, because this layer is starting from random while the others are already trained
  propagate_down: false
  inner_product_param {
    num_output: 356   #这里修改
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8_flickr"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8_flickr"
  bottom: "label"
  top: "loss"
}

如果你复制了上面的配置，你还需要修改7处，分别在第8、10、13、25、27、30、611行。
其中，第8、13、25、30分别修改为第二步产生的train_lmdb与val_lmdb两个文件夹路径，以及mean.binaryproto路径。
至于第10、27行batch_size的数值要根据你的GPU容量修改，可以根据128–64–32–16依次修改，至于batch_size的含义，可以查阅其他资料。注意，此处如果设置过大，训练时会提示内存不足！。另外，第10、27行两处的batch_size不必相同。
第611行需要需改为你训练的图片种类数
修改完VGG_FACE_deploy.prototxt后，还需新增一个文件solver.prototxt文件。
你可以拷贝models/finetune_flickr_style/solver.prototxt到models/vgg_face_caffe文件夹中，并将针对现问题进行修改，主要修改如下：

net: "models/vgg_face_caffe/VGG_FACE_deploy.prototxt"
test_iter: 100
test_interval: 1000
# lr for fine-tuning should be lower than when starting from scratch
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
# stepsize should also be lower, as we're closer to being done
stepsize: 20000
display: 20
max_iter: 100000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "models/vgg"
# uncomment the following to default to CPU mode solving
#solver_mode: CPU

该配置文件可以直接复制，具体参数意义可参考：
http://www.cnblogs.com/denny402/p/5074049.html

四、训练

在终端中输入下面两行命令：

cd (caffe目录)
./build/tools/caffe train -solver models/vgg_face_caffe/solver.prototxt -weights models/vgg_face_caffe/VGG_FACE.caffemodel -gpu 0

-solver 后面的代表上面我们拷贝并修改后的solver文件，-weights后面的路径表示最终生成的模型名称及路径。
以上便是使用VGG Face Model微调自己的数据集的方法。至于如何使用训练后模型对图片进行测试，可以看下一篇。

本文链接：https://blog.csdn.net/tlj893/article/details/78140946

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