Caffe Multi-label Training _caffe

Recently contacted Caffe got a caffe multiple tags encounter a variety of egg pain to share with you.

A verification code used here to prepare the data to generate a 4-digit verification Code 0-9+26 letters

The Second Amendment Caffe source code involves the modification of the file has

Caffe.proto,

Convert_imageset.cpp,

Data_layer.cpp,

Io.cpp,

DATA_LAYER.HPP,

Io.hpp

Specific changes will not be introduced to the following address to download the modified file and then replace the original Caffe in the

File Download Address: Https://pan.baidu.com/s/1eSP1RUi

Here we understand, Caffe originally does not support the multi-label classification task, the main change here is to Caffe support multiple label classification, we know Caffe label original specified is an integer type and only 1, Read Caffe.proto inside write: Datum is our data layer, then the data Layer label is Int32, which restricts the data label input must be an integer, then modify his starting point is to start from the Proto, plus a labels, array type

Modified and then then modified Caffe use datum part of the code to achieve support for labels

Finally modify the convert_imageset.cpp and let him implement it for example as follows:

Imgs/abc.jpg 1 2 3 4 5

This type of multiple label do support

Finish this change and compile it again.

Three Production data labels

Picture path + corresponding label Samples/myl1.bmp 22 34 21 1

Four write a multiple classification network

The structure is as follows:

      Name: "Lenet" layer{ name: "Mnist"  type: "Data"  top: "Data"  top: "Label"   include{   phase:train  }  transform_param{   scale:0.003921568627451  }   data_param{   source: "Train_lmdb"    batch_size:64    backend:lmdb  }} layer{&nbsp ; Name: "Mnist"  type: "Data"  top: "Data"  top: "label"  include{   phase:test  }  transform_param{   scale:0.003921568627451  }  data_param{   source: "Val_lmdb"    batch_size:64    backend:lmdb &nbsp}} layer{ name: "Conv1"  type: "Convolution"  bottom: "Data"  top: "Conv1"  param{   lr_mult:1  }  param{   lr_mult:2 & nbsp;}  convolution_param{   num_output:128    kernel_size:7    stride:1     weight_filler{     type: "Xavier"   &NBSP}    bias_filler{     type: "Constant"    }  } layer{ name: "Pool1"  type: "Pooling"  bottom: "Conv1"  top: "Pool1"  pooling_param{   pool:max     Kernel_size:2    stride:2 &nbsp}} layer{ name: "Conv2"  type: "Convolution"  bottom: "Pool1" & Nbsp;top: "Conv2"  param{   lr_mult:1  }  param{   lr_mult:2  }   convolution_param{   num_output:128    kernel_size:5    stride:1    weight_ filler{     type: "Xavier"        bias_filler{     type: "Constant" &N Bsp &NBSP} &nbsp}} layer{ name: "Pool2"  type: "Pooling"  bottom: "Conv2"  top: "Pool2"  pooling_ param{   pool:max    kernel_size:2    stride:1  } layer{ name: "Relu"  ty PE: "Relu"  bottom: "Pool2"  top: "Pool2"layer{ name: "Conv3"  type: "Convolution"  bottom: "Pool2"  top: "Conv3"  param{    Lr_mult:1  }  param{   lr_mult:2  }  convolution_param{   num_output:128    kernel_size:3    stride:1    weight_filler{     type: "Xavier"   & nbsp;}    bias_filler{     type: "Constant"      } layer{ name: "RELU2"  ty PE: "Relu"  bottom: "Conv3"  top: "Conv3"} layer{ name: "Conv4"  type: "Convolution"  bottom: " Conv3 " top: Conv4"  param{   lr_mult:1  }  param{   lr_mult:2  }   convolution_param{   num_output:128    kernel_size:3    stride:1    weight_ filler{     type: "Xavier"        bias_filler{     type: "Constant" &N Bsp &NBSP} &nbsp}} layer{ name: "RELU3"  type: "Relu"  bottom: "Conv4"  top: "Conv4"} layer{ name: "Conv5"  type: "Convolution"   Bottom: "conv4"  top: "conv5"  param{   lr_mult:1  }  param{   lr_mult:2    convolution_param{   num_output:128    kernel_size:3    stride:1     weight_filler{     type: "Xavier"        bias_filler{     type: " Constant "   } &nbsp}} layer{ name:" fc81 " type:" Innerproduct " bottom:" Conv5 " top:" Fc81 " param{   lr_mult:1  }  param{   lr_mult:2  }  inner_product_param {   num_output:36    weight_filler{     type: "Xavier"    }    bi as_filler{     type: "Constant"      } layer{ name: "Fc82"  type: " Innerproduct " bottom:" Conv5 " top:" Fc82 " param{    lr_mult:1  }  param{   lr_mult:2  }  inner_product_param{   num_ output:36    weight_filler{     type: "Xavier"    }    bias_filler{  & nbsp  type: "Constant"    } &nbsp} layer{ name: "fc83"  type: "Innerproduct"  bottom: "CONV5"  top: "fc83"  param{   lr_mult:1  }  param{   lr_mult:2  }  inner_ product_param{   num_output:36    weight_filler{     type: "Xavier"     nbsp  bias_filler{     type: "Constant"      } layer{ name: "fc84"  type: " Innerproduct " bottom: Conv5"  top: "fc84"  param{   lr_mult:1  }  param{  & Nbsp;lr_mult:2 &nbsp}  inner_product_param{   num_output:36    weight_filler{     type: "Xavier"    } &nbSp  bias_filler{     type: "Constant"      } layer{ name: "Slice2"  type: " Slice " bottom: Label"  top: "L1"  top: "L2"  top: "L3"  top: "L4"  slice_param{   axis:1    slice_point:1    slice_point:2    slice_point:3  } layer{  Name: "Accuracy1"  type: "Accuracy"  bottom: "fc81"  bottom: "L1"  top: "Accuracy1"  include{    phase:test &nbsp} layer{ name: "Accuracy2"  type: "Accuracy"  bottom: "Fc82"  bottom : "L2"  top: "Accuracy2"  include{   phase:test  }} layer{ name: "Accuracy3"  type: " Accuracy " bottom: fc83"  bottom: "L3"  top: "Accuracy3"  include{   phase:test  } layer{ name: "Accuracy4"  type: "Accuracy"  bottom: "fc84"  bottom: "L4"  top: "Accuracy4"  include{   phase:test &nbsp}} layer{&nbsP;name: "Loss1"  type: "Softmaxwithloss"  bottom: "fc81"  bottom: "L1"  top: "Loss1"  loss_ weight:0.1} layer{ name: "Loss2"  type: "Softmaxwithloss"  bottom: "Fc82"  bottom: "L2"  top: " Loss2 " loss_weight:0.1} layer{ name:" LOSS3 " type:" Softmaxwithloss " bottom:" fc83 "  Bottom: "L3"  top: "Loss3"  loss_weight:0.1} layer{ name: "Loss4"  type: "Softmaxwithloss"   Bottom: "fc84"  bottom: "L4"  top: "Loss4"  loss_weight:0.1}

And then we can start training.

The results are as follows: