VGG very deep 19 layer prototxt

1. 改动版本

name: "VGG_ILSVRC_19_layer"layer {  name: "data"  type: "ImageData"  top: "data"  top: "label"  include {    phase: TRAIN  }   image_data_param {    batch_size: 12    source: "../../fine_tuning_data/HAT_fineTuning_data/train_data_fineTuning.txt"    root_folder: "../../fine_tuning_data/HAT_fineTuning_data/train_data/"  }}layer {  name: "data"  type: "ImageData"  top: "data"  top: "label"  include {    phase: TEST  }  transform_param {    mirror: false  }  image_data_param {    batch_size: 10    source: "../../fine_tuning_data/HAT_fineTuning_data/test_data_fineTuning.txt"    root_folder: "../../fine_tuning_data/HAT_fineTuning_data/test_data/"  }}layer {  bottom:"data"   top:"conv1_1"   name:"conv1_1"   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"   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"   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"   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"  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"   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"   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:"conv3_4"   name:"conv3_4"   type:"Convolution"   convolution_param {    num_output:256    pad:1    kernel_size:3  }}layer {  bottom:"conv3_4"   top:"conv3_4"   name:"relu3_4"   type:"ReLU" }layer {  bottom:"conv3_4"   top:"pool3"   name:"pool3"   type:"Pooling"   pooling_param {    pool:MAX     kernel_size: 2    stride: 2  }}layer {  bottom:"pool3"   top:"conv4_1"   name:"conv4_1"   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"   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"   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:"conv4_4"   name:"conv4_4"   type:"Convolution"   convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layer {  bottom:"conv4_4"   top:"conv4_4"   name:"relu4_4"   type:"ReLU" }layer {  bottom:"conv4_4"   top:"pool4"   name:"pool4"   type:"Pooling"   pooling_param {    pool:MAX    kernel_size: 2    stride: 2  }}layer {  bottom:"pool4"   top:"conv5_1"   name:"conv5_1"   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"   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"   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:"conv5_4"   name:"conv5_4"   type:"Convolution"   convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layer {  bottom:"conv5_4"   top:"conv5_4"   name:"relu5_4"   type:"ReLU" }layer {  bottom:"conv5_4"   top:"pool5"   name:"pool5"   type:"Pooling"   pooling_param {    pool:MAX     kernel_size: 2    stride: 2  }}layer {  bottom:"pool5"   top:"fc6_"   name:"fc6_"   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"   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_"   name:"fc8_"   type:"InnerProduct"   inner_product_param {    num_output: 27  }}layer {  name: "sigmoid"  type: "Sigmoid"  bottom: "fc8_"  top: "fc8_"} layer {   name: "accuracy"   type: "Accuracy"   bottom: "fc8_"   bottom: "label"   top: "accuracy"   include {     phase: TEST   } }layer {  name: "loss"  type: "EuclideanLoss"  bottom: "fc8_"  bottom: "label"  top: "loss"}

2.原版

VGG_ILSVRC_19_layers_train_val.prototxtname: "VGG_ILSVRC_19_layers"layers {  name: "data"  type: DATA  include {    phase: TRAIN  } transform_param {    crop_size: 224    mean_value: 104    mean_value: 117    mean_value: 123    mirror: true } data_param {    source: "data/ilsvrc12/ilsvrc12_train_lmdb"    batch_size: 64    backend: LMDB  }  top: "data"  top: "label"}layers {  name: "data"  type: DATA  include {    phase: TEST  } transform_param {    crop_size: 224    mean_value: 104    mean_value: 117    mean_value: 123    mirror: false } data_param {    source: "data/ilsvrc12/ilsvrc12_val_lmdb"    batch_size: 50    backend: LMDB  }  top: "data"  top: "label"}layers {  bottom: "data"  top: "conv1_1"  name: "conv1_1"  type: CONVOLUTION  convolution_param {    num_output: 64    pad: 1    kernel_size: 3  } }layers {  bottom: "conv1_1"  top: "conv1_1"  name: "relu1_1"  type: RELU}layers {  bottom: "conv1_1"  top: "conv1_2"  name: "conv1_2"  type: CONVOLUTION  convolution_param {    num_output: 64    pad: 1    kernel_size: 3  }}layers {  bottom: "conv1_2"  top: "conv1_2"  name: "relu1_2"  type: RELU}layers {  bottom: "conv1_2"  top: "pool1"  name: "pool1"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool1"  top: "conv2_1"  name: "conv2_1"  type: CONVOLUTION  convolution_param {    num_output: 128    pad: 1    kernel_size: 3  } }layers {  bottom: "conv2_1"  top: "conv2_1"  name: "relu2_1"  type: RELU}layers {  bottom: "conv2_1"  top: "conv2_2"  name: "conv2_2"  type: CONVOLUTION  convolution_param {    num_output: 128    pad: 1    kernel_size: 3  }}layers {  bottom: "conv2_2"  top: "conv2_2"  name: "relu2_2"  type: RELU}layers {  bottom: "conv2_2"  top: "pool2"  name: "pool2"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool2"  top: "conv3_1"  name: "conv3_1"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_1"  top: "conv3_1"  name: "relu3_1"  type: RELU}layers {  bottom: "conv3_1"  top: "conv3_2"  name: "conv3_2"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_2"  top: "conv3_2"  name: "relu3_2"  type: RELU}layers {  bottom: "conv3_2"  top: "conv3_3"  name: "conv3_3"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_3"  top: "conv3_3"  name: "relu3_3"  type: RELU}layers {  bottom: "conv3_3"  top: "conv3_4"  name: "conv3_4"  type: CONVOLUTION  convolution_param {    num_output: 256    pad: 1    kernel_size: 3  }}layers {  bottom: "conv3_4"  top: "conv3_4"  name: "relu3_4"  type: RELU}layers {  bottom: "conv3_4"  top: "pool3"  name: "pool3"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool3"  top: "conv4_1"  name: "conv4_1"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv4_1"  top: "conv4_1"  name: "relu4_1"  type: RELU}layers {  bottom: "conv4_1"  top: "conv4_2"  name: "conv4_2"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv4_2"  top: "conv4_2"  name: "relu4_2"  type: RELU}layers {  bottom: "conv4_2"  top: "conv4_3"  name: "conv4_3"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv4_3"  top: "conv4_3"  name: "relu4_3"  type: RELU}layers {  bottom: "conv4_3"  top: "conv4_4"  name: "conv4_4"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv4_4"  top: "conv4_4"  name: "relu4_4"  type: RELU}layers {  bottom: "conv4_4"  top: "pool4"  name: "pool4"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool4"  top: "conv5_1"  name: "conv5_1"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  }}layers {  bottom: "conv5_1"  top: "conv5_1"  name: "relu5_1"  type: RELU}layers {  bottom: "conv5_1"  top: "conv5_2"  name: "conv5_2"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv5_2"  top: "conv5_2"  name: "relu5_2"  type: RELU}layers {  bottom: "conv5_2"  top: "conv5_3"  name: "conv5_3"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv5_3"  top: "conv5_3"  name: "relu5_3"  type: RELU}layers {  bottom: "conv5_3"  top: "conv5_4"  name: "conv5_4"  type: CONVOLUTION  convolution_param {    num_output: 512    pad: 1    kernel_size: 3  } }layers {  bottom: "conv5_4"  top: "conv5_4"  name: "relu5_4"  type: RELU}layers {  bottom: "conv5_4"  top: "pool5"  name: "pool5"  type: POOLING  pooling_param {    pool: MAX    kernel_size: 2    stride: 2  }}layers {  bottom: "pool5"  top: "fc6"  name: "fc6"  type: INNER_PRODUCT  inner_product_param {    num_output: 4096  }}layers {  bottom: "fc6"  top: "fc6"  name: "relu6"  type: RELU}layers {  bottom: "fc6"  top: "fc6"  name: "drop6"  type: DROPOUT  dropout_param {    dropout_ratio: 0.5  }}layers {  bottom: "fc6"  top: "fc7"  name: "fc7"  type: INNER_PRODUCT  inner_product_param {    num_output: 4096  }}layers {  bottom: "fc7"  top: "fc7"  name: "relu7"  type: RELU}layers {  bottom: "fc7"  top: "fc7"  name: "drop7"  type: DROPOUT  dropout_param {    dropout_ratio: 0.5  }}layers {  name: "fc8"  bottom: "fc7"  top: "fc8"  type: INNER_PRODUCT  inner_product_param {    num_output: 1000  }}layers {  name: "loss"  type: SOFTMAX_LOSS  bottom: "fc8"  bottom: "label"  top: "loss/loss"}layers {  name: "accuracy/top1"  type: ACCURACY  bottom: "fc8"  bottom: "label"  top: "accuracy@1"  include: { phase: TEST }  accuracy_param {    top_k: 1  }}layers {  name: "accuracy/top5"  type: ACCURACY  bottom: "fc8"  bottom: "label"  top: "accuracy@5"  include: { phase: TEST }  accuracy_param {    top_k: 5  }}