在自己的数据集上微调Light CNN

遇到的坑

light cnn 加载的是灰度图，所以在 data 层要添加参数：

is_color:false
示例如下：

layer {  name: "data"  type: "ImageData"  top: "data"  top: "label"  image_data_param{      source: "/home/code/caffe-master/lightCNNFace/val.txt"      batch_size: 20      is_color:false    }  transform_param {    scale: 0.00390625    crop_size: 128    mirror: false  }  include: { phase: TEST }}

1.编写微调的网络结构lightcnn_train_test.prototxt

2.准备训练图像数据，分成训练集和验证集合

注意 要将图像灰度化

3.运行训练脚本文件

code/caffe-master/build/tools/caffe train \    --solver=code/caffe-master/lightCNNFace/LCNN_solver.prototxt \    --weights=code/caffe-master/lightCNNFace/LightenedCNN_B.caffemodel -gpu=0

4.微调时报错

  Cannot copy param 0 weights from layer 'conv1'; shape mismatch.  Source param shape is 96 1 5 5 (2400); target param shape is 96 3 5 5 (7200). To learn this layer's parameters from scratch rather than copying from a saved net, rename the layer

5.附 lightcnn_train_test.prototxt

有过微调light cnn的网友吗？求解决方案，另外 如何进一步减小模型大小？

name: "DeepFace_set003_net"layer {  name: "data"  type:"ImageData"  top: "data"  top: "label"  image_data_param{      source: "/home/himon/code/caffe-master/lightCNNFace/train.txt"      batch_size: 20      shuffle: true    }  transform_param {    scale: 0.00390625    crop_size: 128    mirror: true  }  include: { phase: TRAIN }}layer {  name: "data"  type: "ImageData"  top: "data"  top: "label"  image_data_param{      source: "/home/himon/code/caffe-master/lightCNNFace/val.txt"      batch_size: 20    }  transform_param {    scale: 0.00390625    crop_size: 128    mirror: false  }  include: { phase: TEST }}layer{  name: "conv1"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 96    kernel_size: 5    stride: 1    pad: 2    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }  }  bottom: "data"  top: "conv1"}layer{  name: "slice1"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv1"  top: "slice1_1"  top: "slice1_2"}layer{  name: "etlwise1"  type: "Eltwise"  bottom: "slice1_1"  bottom: "slice1_2"  top: "eltwise1"  eltwise_param {    operation: MAX  }}layer{  name: "pool1"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }  bottom: "eltwise1"  top: "pool1"}layer{  name: "conv2a"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 96    kernel_size: 1    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }  }  bottom: "pool1"  top: "conv2a"}layer{  name: "slice2a"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv2a"  top: "slice2a_1"  top: "slice2a_2"}layer{  name: "etlwise2a"  type: "Eltwise"  bottom: "slice2a_1"  bottom: "slice2a_2"  top: "eltwise2a"  eltwise_param {    operation: MAX  }}layer{  name: "conv2"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 192    kernel_size: 3    stride: 1    pad: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }  }  bottom: "eltwise2a"  top: "conv2"}layer{  name: "slice2"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv2"  top: "slice2_1"  top: "slice2_2"}layer{  name: "etlwise2"  type: "Eltwise"  bottom: "slice2_1"  bottom: "slice2_2"  top: "eltwise2"  eltwise_param {    operation: MAX  }}layer{  name: "pool2"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }  bottom: "eltwise2"  top: "pool2"}layer{  name: "conv3a"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 192    kernel_size: 1    stride: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }  }  bottom: "pool2"  top: "conv3a"}layer{  name: "slice3a"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv3a"  top: "slice3a_1"  top: "slice3a_2"}layer{  name: "etlwise3a"  type: "Eltwise"  bottom: "slice3a_1"  bottom: "slice3a_2"  top: "eltwise3a"  eltwise_param {    operation: MAX  }}layer{  name: "conv3"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param {    num_output: 384    kernel_size: 3    stride: 1    pad: 1    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }  }  bottom: "eltwise3a"  top: "conv3"}layer{  name: "slice3"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv3"  top: "slice3_1"  top: "slice3_2"}layer{  name: "etlwise3"  type: "Eltwise"  bottom: "slice3_1"  bottom: "slice3_2"  top: "eltwise3"  eltwise_param {    operation: MAX  }}layer{  name: "pool3"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }  bottom: "eltwise3"  top: "pool3"}layer{  name: "conv4a"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param{    num_output: 384    kernel_size: 1    stride: 1    weight_filler{      type:"xavier"    }    bias_filler{      type: "constant"      value: 0.1        }  }  bottom: "pool3"  top: "conv4a"}layer{  name: "slice4a"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv4a"  top: "slice4a_1"  top: "slice4a_2"}layer{  name: "etlwise4a"  type: "Eltwise"  bottom: "slice4a_1"  bottom: "slice4a_2"  top: "eltwise4a"  eltwise_param {    operation: MAX  }}layer{  name: "conv4"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param{    num_output: 256    kernel_size: 3    stride: 1    pad: 1    weight_filler{      type:"xavier"    }    bias_filler{      type: "constant"      value: 0.1        }  }  bottom: "eltwise4a"  top: "conv4"}layer{  name: "slice4"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv4"  top: "slice4_1"  top: "slice4_2"}layer{  name: "etlwise4"  type: "Eltwise"  bottom: "slice4_1"  bottom: "slice4_2"  top: "eltwise4"  eltwise_param {    operation: MAX  }}layer{  name: "conv5a"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param{    num_output: 256    kernel_size: 1    stride: 1    weight_filler{      type:"xavier"    }    bias_filler{      type: "constant"      value: 0.1        }  }  bottom: "eltwise4"  top: "conv5a"}layer{  name: "slice5a"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv5a"  top: "slice5a_1"  top: "slice5a_2"}layer{  name: "etlwise5a"  type: "Eltwise"  bottom: "slice5a_1"  bottom: "slice5a_2"  top: "eltwise5a"  eltwise_param {    operation: MAX  }}layer{  name: "conv5"  type: "Convolution"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  convolution_param{    num_output: 256    kernel_size: 3    stride: 1    pad: 1    weight_filler{      type:"xavier"    }    bias_filler{      type: "constant"      value: 0.1        }  }  bottom: "eltwise5a"  top: "conv5"}layer{  name: "slice5"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "conv5"  top: "slice5_1"  top: "slice5_2"}layer{  name: "etlwise5"  type: "Eltwise"  bottom: "slice5_1"  bottom: "slice5_2"  top: "eltwise5"  eltwise_param {    operation: MAX  }}layer{  name: "pool4"  type: "Pooling"  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }  bottom: "eltwise5"  top: "pool4"}layer{  name: "fc1"  type: "InnerProduct"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 512    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }     }    bottom: "pool4"  top: "fc1"}layer{  name: "slice_fc1"  type:"Slice"  slice_param {    slice_dim: 1  }  bottom: "fc1"  top: "slice_fc1_1"  top: "slice_fc1_2"}layer{  name: "etlwise_fc1"  type: "Eltwise"  bottom: "slice_fc1_1"  bottom: "slice_fc1_2"  top: "eltwise_fc1"  eltwise_param {    operation: MAX  }}layer{  name: "drop1"  type: "Dropout"  dropout_param{    dropout_ratio: 0.7  }  bottom: "eltwise_fc1"  top: "eltwise_fc1"}layer{  name: "fnc2"  type: "InnerProduct"  inner_product_param{    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"      value: 0.1    }     }  bottom: "eltwise_fc1"  top: "fnc2"}layer {  name: "accuracy"  type: "Accuracy"  bottom: "fnc2"  bottom: "label"  top: "accuracy"  include: { phase: TEST }}layer {  name: "softmaxloss"  type: "SoftmaxWithLoss"  bottom: "fnc2"  bottom: "label"  top: "loss"}