[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
![[人脸识别]使用VGG Face Model微调（Fine tune）自己的数据集_第1张图片](http://img.e-com-net.com/image/info8/ca1b9ada2b2544ab90152b56db7e1a31.jpg)
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：
![[人脸识别]使用VGG Face Model微调（Fine tune）自己的数据集_第2张图片](http://img.e-com-net.com/image/info8/e0300b7635334af48abd48b67ec0ad38.jpg)
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 $EXAMPLE/mean.binaryproto
echo "Done." 你需要更改的地方有：
1.EXAMPLE后面的路径，尽量设置为第一步中的train与test文件夹的父路径；
2.DATA为第一步中的train与test文件夹的父路径；
3.TOOLS的标准格式为：（caffe路径）/build/tools；
4.TRAIN_DATA_ROOT与VAL_DATA_ROOT不需要改变，分别对应train与val文件夹的路径。
其他参数的意义解释可参考：http://www.cnblogs.com/dupuleng/articles/4370236.html
确保上述路径设置正确后，便可将脚本拖入终端运行。
运行完成后，在你设置的EXAMPLE路径中会产生train_lmdb与val_lmdb两个文件夹，以及mean.binaryproto文件。
三、caffe训练相关配置文件修改
下载VGG Face Model，并解压，移动到（caffe路径）/models中：
![[人脸识别]使用VGG Face Model微调（Fine tune）自己的数据集_第3张图片](http://img.e-com-net.com/image/info8/7438b42a1abd4ad7b6870941cb9904bc.jpg)
接下来，我们需要修改vgg_face_caffe中的VGG_FACE_deploy.prototxt 文件，推荐你直接复制下面的文件
name: "VGG_FACE_16_Net"
layer {
name: "data"
type: "Data" #这里注意
top: "data"
top: "label"
data_param {
source: "$/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。