本文用记录基于Caffe的人脸性别识别过程。基于imdb-wiki模型做finetune，imdb-wiki数据集合模型可从这里下载：https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/。

准备训练环境

（1）准备OS：Ubuntu16.04

（2）安装Nvidia GPU Driver

https://www.nvidia.com/Download/index.aspx?lang=en-us

（3）安装CUDA

https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html

查看cuda版本的方法：

cat /usr/local/cuda/version.txt

（4）安装cnDNN(可选)

https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html

查看cudnn版本的方法：

cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2

（5）安装Docker（可选）

https://docs.docker.com/install/linux/docker-ce/ubuntu/#set-up-the-repository

（6）安装Nvidia Docker（可选）

https://github.com/NVIDIA/nvidia-docker

（7）准备Docker Image（可选）

进入Container的方式之一：，

nvidia-docker exec -it $ContainerID /bin/bash

用nvidia-docker ps查看ContainerID。

准备模型及训练数据集

（1）     下载Imdb-wiki模型

https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/gender.caffemodel

https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/gender_train.prototxt

如果下载了imdb-wiki的数据集，可以通过如下方式读取数据集的描述文件：

import scipy.io as sio

mat_contents = sio.loadmat('wiki.mat')

（2）     下载celeba数据集

CelebA是CelebFaces Attribute的缩写，意即名人人脸属性数据集，其包含10,177个名人身份的202,599张人脸图片，每张图片都做好了特征标记，包含人脸bbox标注框、5个人脸特征点坐标以及40个属性标记，CelebA由香港中文大学开放提供，广泛用于人脸相关的计算机视觉训练任务，可用于人脸属性标识训练、人脸检测训练以及landmark标记等，可以从http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html下载img_align_celeba.zip即可。

（3）     生成celeba数据集的训练和测试描述文件

删除list_attr_celeba文件第一行后，提取性别属性：

cat list_attr_celeba | awk -F ' ' '{print $1,$22}' >gender.txt

计算图片文件数量：

cat list_attr_celeba | wc -l

对gender.txt文件行做shuffle：

cat gender.txt | awk  -F"\3" 'BEGIN{srand();}{value=int(rand()*图片文件数量); print value"\3"$0 }' | sort | awk -F"\3" '{print $2}' >> shuffled

生成训练集：

head -n 图片文件数量*0.9 shuffled > train.txt

tail -n 图片文件数量*0.1 shuffled > test.txt

修改图片路径可能用到的VI命令：1,$ s/old/new/g

(4) 为了更好的识别亚洲人的性别，还可以通过爬取等方式收集标注来补充亚洲人的数据。

 训练模型

（1）准备solver.prototxt

Solver文件解释可参考：

https://github.com/BVLC/caffe/wiki/Solver-Prototxt

（更全面）https://zhuanlan.zhihu.com/p/48462756

net: “gender.prototxt”
test_iter: 100
test_interval: 500
test_compute_loss: true
base_lr: 0.00001
momentum: 0.95
type: “SGD”
weight_decay: 0.0005
lr_policy: “step”
gamma: 0.9
stepsize: 200
display: 100
max_iter: 20000
snapshot: 2000
snapshot_prefix: “gender”
solver_mode: GPU

（2） 修改gender.prototxt

name: "VGG_ILSVRC_16_layers"
layer {
  top: "data"
  type: "ImageData"
  top: "label"
  name: "data"
  transform_param {
    mirror: true
    crop_size: 224
    mean_file: "imagenet_mean.binaryproto"
  }
  image_data_param {
    source: "train.txt"
    batch_size: 32
    new_height: 256
    new_width: 256
  }
  include: { phase: TRAIN }
}
layer {
  top: "data"
  top: "label"
  name: "data"
  type: "ImageData"
  image_data_param {
    new_height: 256
    new_width: 256
    source: "train.txt"
    batch_size: 10
  }
  transform_param {
    crop_size: 224
    mirror: false
    mean_file: "imagenet_mean.binaryproto"
  }
  include: { phase: TEST }
}
layer {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_1"
  top: "conv1_1"
  name: "relu1_1"
  type: "ReLU"
}
layer {
  bottom: "conv1_1"
  top: "conv1_2"
  name: "conv1_2"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv1_2"
  top: "conv1_2"
  name: "relu1_2"
  type: "ReLU"
}
layer {
  bottom: "conv1_2"
  top: "pool1"
  name: "pool1"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool1"
  top: "conv2_1"
  name: "conv2_1"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_1"
  top: "conv2_1"
  name: "relu2_1"
  type: "ReLU"
}
layer {
  bottom: "conv2_1"
  top: "conv2_2"
  name: "conv2_2"

 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv2_2"
  top: "conv2_2"
  name: "relu2_2"
  type: "ReLU"
}
layer {
  bottom: "conv2_2"
  top: "pool2"
  name: "pool2"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool2"
  top: "conv3_1"
  name: "conv3_1"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_1"
  top: "conv3_1"
  name: "relu3_1"
  type: "ReLU"
}
layer {
  bottom: "conv3_1"
  top: "conv3_2"
  name: "conv3_2"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_2"
  top: "conv3_2"
  name: "relu3_2"
  type: "ReLU"
}
layer {
  bottom: "conv3_2"
  top: "conv3_3"
  name: "conv3_3"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: "ReLU"
}
layer {
  bottom: "conv3_3"
  top: "pool3"
  name: "pool3"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool3"
  top: "conv4_1"
  name: "conv4_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }

  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_1"
  top: "conv4_1"
  name: "relu4_1"
  type: "ReLU"
}
layer {
  bottom: "conv4_1"
  top: "conv4_2"
  name: "conv4_2"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_2"
  top: "conv4_2"
  name: "relu4_2"
  type: "ReLU"
}
layer {
  bottom: "conv4_2"
  top: "conv4_3"
  name: "conv4_3"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }

  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: "ReLU"
}
layer {
  bottom: "conv4_3"
  top: "pool4"
  name: "pool4"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool4"
  top: "conv5_1"
  name: "conv5_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }

  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_1"
  top: "conv5_1"
  name: "relu5_1"
  type: "ReLU"
}
layer {
  bottom: "conv5_1"
  top: "conv5_2"
  name: "conv5_2"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  type: "Convolution"
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_2"
  top: "conv5_2"
  name: "relu5_2"
  type: "ReLU"
}
layer {
  bottom: "conv5_2"
  top: "conv5_3"
  name: "conv5_3"
  type: "Convolution"
 param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    pad: 1
    kernel_size: 3
  }
}
layer {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: "ReLU"
}
layer {
  bottom: "conv5_3"
  top: "pool5"
  name: "pool5"
  type: "Pooling"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "relu6"
  type: "ReLU"
}
layer {
  bottom: "fc6"
  top: "fc6"
  name: "drop6"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  bottom: "fc6"
  top: "fc7"
  name: "fc7"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"
  inner_product_param {
    num_output: 4096
  }
}
layer {
  bottom: "fc7"
  top: "fc7"
  name: "relu7"
  type: "ReLU"
}
layer {
  bottom: "fc7"
  top: "fc7"
  name: "drop7"
  type: "Dropout"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  bottom: "fc7"
  top: "fc8-2"
  name: "fc8-2"
 param {
    lr_mult: 10
    decay_mult: 1
  }
  param {
    lr_mult: 20
    decay_mult: 0
  }
  type: "InnerProduct"
  inner_product_param {
    num_output: 2
  weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  bottom: "fc8-2"
  bottom: "label"
  name: "loss"
  type: "SoftmaxWithLoss"
  include: { phase: TRAIN }
}

layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc8-2"
  top: "prob"
    include {
    phase: TEST
  }
}
layer {
  name: "accuracy_train_top01"
  type: "Accuracy"
  bottom: "fc8-2"
  bottom: "label"
  top: "accuracy_train_top01"
  include {
    phase: TEST
  }
}

imagenet_mean.binaryproto 文件的生成可参考https://github.com/BVLC/caffe/blob/master/examples/imagenet/make_imagenet_mean.sh

或直接从网上下载。

（3）启动训练

caffe train –sovler=pathto/solver.prototxt –weight=pathtto/gender.caffemodel –gpu all

（4）使用训练的模型

实际应用中，我们首先采用人脸检测技术检测人脸，将图片中的人脸取裁剪出来送入训练好的模型进行性别识别。人脸检测技术可以采用dlib库，dlib库人脸检测支持根据具体应用场景进行finetune。

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