人脸识别---利用caffe实现多层特征学习人脸识别网络
来源：互联网 发布：32bit安装tensorflow 编辑：程序博客网 时间：2024/04/30 10:33
摘要：本文主要讲解如何利用caffe搭建自己的网络，本文主要讲利用caffe搭建一种Hierarchical Feature Representation的网络。网络如下图：
数据库：CASIA-WebFace数据集，可以到我的网盘中下载:http://pan.baidu.com/s/1nuWsju5。在我的试验中我选用1000人的样本训练，在CASIA-WebFace数据中1000人包含图片最多，大概加起来有10W+的数据。
参看上篇博文：http://blog.csdn.net/hlx371240/article/details/51388022
1. 建立训练集和验证集
配置好ourfaceNet中的路径，执行ourfaceNet可以得到两个文件夹face_train_lmdb和face_val_lmdbface_val_lmdb.
ourfaceNet.sh
#!/usr/bin/env sh# Create the imagenet lmdb inputs# N.B. set the path to the imagenet train + val data dirsEXAMPLE=facenetDATA=facenetTOOLS=./build/toolsTRAIN_DATA_ROOT=ourfacenet/data/VAL_DATA_ROOT=ourfacenet/data/# Set RESIZE=true to resize the images to 256x256. Leave as false if images have# already been resized using another tool.RESIZE=trueif $RESIZE; then RESIZE_HEIGHT=128 RESIZE_WIDTH=128else RESIZE_HEIGHT=0 RESIZE_WIDTH=0fiif [ ! -d "$TRAIN_DATA_ROOT" ]; then echo "Error: TRAIN_DATA_ROOT is not a path to a directory: $TRAIN_DATA_ROOT" echo "Set the TRAIN_DATA_ROOT variable in create_imagenet.sh to the path" \ "where the ImageNet training data is stored." exit 1fiif [ ! -d "$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 1fiecho "Creating train lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \ --resize_height=$RESIZE_HEIGHT \ --resize_width=$RESIZE_WIDTH \ --shuffle \ $TRAIN_DATA_ROOT \ $DATA/train.txt \ $EXAMPLE/face_train_lmdb1echo "Creating val lmdb..."GLOG_logtostderr=1 $TOOLS/convert_imageset.bin \ --resize_height=$RESIZE_HEIGHT \ --resize_width=$RESIZE_WIDTH \ --shuffle \ $VAL_DATA_ROOT \ $DATA/val.txt \ $EXAMPLE/face_val_lmdb1echo "Done."- 求均值文件
make_mean.sh
EXAMPLE=facenetDATA=facenetTOOLS=./build/tools$TOOLS/compute_image_mean $EXAMPLE/face_train_lmdb \ $DATA/face_mean.binaryprotoecho "Done."这样可以得到face_mean.binaryproto
3.根据上图搭建自己的框架
我搭建的是分层式的网络，可以得到卷积层具有区分性的特征
train_val.prototxt
name: "train_val.prototxt"layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TRAIN } transform_param { mirror: true crop_size: 128 mean_file: "facenet/face_mean.binaryproto" } data_param { source: "facenet/face_train_lmdb" batch_size: 50 backend: LMDB }}layer { name: "data" type: "Data" top: "data" top: "label" include { phase: TEST } transform_param { mirror: false crop_size: 128 mean_file: "facenet/face_mean.binaryproto" } data_param { source: "facenet/face_val_lmdb" batch_size: 50 backend: LMDB }}layer { name: "conv1/7x7_s2" type: "Convolution" bottom: "data" top: "conv1/7x7_s2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 pad: 3 kernel_size: 7 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "conv1/relu_7x7" 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: 2 stride: 2 }}layer { name: "pool1/norm1" type: "LRN" bottom: "pool1/3x3_s2" top: "pool1/norm1" lrn_param { local_size: 5 alpha: 0.0001 beta: 0.75 }}layer { name: "conv2/7x7_s2" type: "Convolution" bottom: "pool1/norm1" top: "conv2/7x7_s2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 pad: 2 kernel_size: 5 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "conv2/relu_7x7" type: "ReLU" bottom: "conv2/7x7_s2" top: "conv2/7x7_s2"}layer { name: "pool2/3x3_s2" type: "Pooling" bottom: "conv2/7x7_s2" top: "pool2/3x3_s2" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: "loss1/classifier" type: "InnerProduct" bottom: "pool2/3x3_s2" top: "loss1/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}layer { name: "loss1/loss" type: "SoftmaxWithLoss" bottom: "loss1/classifier" bottom: "label" top: "loss1/loss1" loss_weight: 0.3}layer { name: "loss1/top-1" type: "Accuracy" bottom: "loss1/classifier" bottom: "label" top: "loss1/top-1" include { phase: TEST }}layer { name: "loss2/conv" type: "Convolution" bottom: "pool1/norm1" top: "loss2/conv" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 pad: 2 kernel_size: 5 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "loss2/relu_conv" type: "ReLU" bottom: "loss2/conv" top: "loss2/conv"}layer { name: "loss2/pool" type: "Pooling" bottom: "loss2/conv" top: "loss2/pool" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: "loss2/classifier" type: "InnerProduct" bottom: "loss2/pool" top: "loss2/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}layer { name: "loss2/loss" type: "SoftmaxWithLoss" bottom: "loss2/classifier" bottom: "label" top: "loss2/loss1" loss_weight: 0.3}layer { name: "loss2/top-1" type: "Accuracy" bottom: "loss2/classifier" bottom: "label" top: "loss2/top-1" include { phase: TEST }}layer { name: "loss3/conv" type: "Convolution" bottom: "loss2/pool" top: "loss3/conv" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 pad: 2 kernel_size: 5 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "loss3/relu_conv" type: "ReLU" bottom: "loss3/conv" top: "loss3/conv"}layer { name: "loss3/pool" type: "Pooling" bottom: "loss3/conv" top: "loss3/pool" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: "loss3/classifier" type: "InnerProduct" bottom: "loss3/pool" top: "loss3/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}layer { name: "loss3/loss" type: "SoftmaxWithLoss" bottom: "loss3/classifier" bottom: "label" top: "loss3/loss1" loss_weight: 0.3}layer { name: "loss3/top-1" type: "Accuracy" bottom: "loss3/classifier" bottom: "label" top: "loss3/top-1" include { phase: TEST }}layer { name: "loss4/conv" type: "Convolution" bottom: "loss3/pool" top: "loss4/conv" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 40 pad: 2 kernel_size: 5 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "loss4/relu_conv" type: "ReLU" bottom: "loss4/conv" top: "loss4/conv"}layer { name: "loss4/pool" type: "Pooling" bottom: "loss4/conv" top: "loss4/pool" pooling_param { pool: MAX kernel_size: 2 stride: 2 }}layer { name: "loss4/fc" type: "InnerProduct" bottom: "loss4/pool" top: "loss4/fc" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1024 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0.2 } }}layer { name: "loss4/relu_fc" type: "ReLU" bottom: "loss4/fc" top: "loss4/fc"}layer { name: "loss4/drop_fc" type: "Dropout" bottom: "loss4/fc" top: "loss4/fc" dropout_param { dropout_ratio: 0.7 }}layer { name: "loss4/classifier" type: "InnerProduct" bottom: "loss4/fc" top: "loss4/classifier" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 1000 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } }}layer { name: "loss4/loss" type: "SoftmaxWithLoss" bottom: "loss4/classifier" bottom: "label" top: "loss4/loss1" loss_weight: 1}layer { name: "loss4/top-1" type: "Accuracy" bottom: "loss4/classifier" bottom: "label" top: "loss4/top-1" include { phase: TEST }}4. 接下来就是写自己的solver.prototxt
solver1.prototxt
net: "facenet/train_val.prototxt"test_iter: 1000test_interval: 4000test_initialization: falsedisplay: 100average_loss: 100base_lr: 0.005lr_policy: "step"stepsize: 320000gamma: 0.96max_iter: 100000momentum: 0.9weight_decay: 0.0002snapshot: 4000snapshot_prefix: "facenet/face_model"solver_mode: GPUsolver2.prototxt
net: "facenet/train_val.prototxt"test_iter: 1000test_interval: 4000test_initialization: falsedisplay: 100average_loss: 100base_lr: 0.001lr_policy: "step"stepsize: 320000gamma: 0.96max_iter: 100000momentum: 0.9weight_decay: 0.0002snapshot: 4000snapshot_prefix: "facenet/face_model"solver_mode: GPUsolver3.prototxt
net: "facenet/train_val.prototxt"test_iter: 1000test_interval: 4000test_initialization: falsedisplay: 100average_loss: 100base_lr: 0.0001lr_policy: "step"stepsize: 320000gamma: 0.96max_iter: 100000momentum: 0.9weight_decay: 0.0002snapshot: 4000snapshot_prefix: "facenet/face_model"solver_mode: GPU包括了三个solver.prototxt，三个solver.prototxt有不同的学习率，都学习了10w次迭代。
5.建立自己的训练脚本
./build/tools/caffe train \ --solver=facenet/solver.prototxt \./build/tools/caffe train \ --solver=facenet/solver.prototxt \ --weights=facenet/face_model_iter_100000.caffemodel -gpu=0./build/tools/caffe train \ --solver=facenet/solver.prototxt \ --weights=facenet/face_model_iter_200000.caffemodel -gpu=0最后执行各个脚本就可以把自己的网络搭建起来了。当然我们可以根据自己的需求去搭建我们的网络，我们只要记住name,top,bottom这三个就可以轻松搭建任意的网络去完成我们的任务。
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