VGG-19 和 VGG-16 的 prototxt文件

VGG-19 和 VGG-16 的 prototxt文件
 

VGG-19 和 VGG-16 的 prototxt文件

VGG-16:
prototxt 地址：https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
caffemodel 地址：http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel

VGG-19:
prototxt 地址：https://gist.github.com/ksimonyan/3785162f95cd2d5fee77#file-readme-md
caffemodel 地址：http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel

VGG_16.prototxt 文件：

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: 43
}
}

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"
}

　　

name: "VGG_ILSVRC_16_layer"
layers {
name: "data"
type: IMAGE_DATA
top: "data"
top: "label"
include {
phase: TRAIN
}

image_data_param {
batch_size: 80
source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Labeled_Train_0.5_.txt"
root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/train_image_sun_256_256/"
new_height: 224
new_ 224
}
}

layers {
name: "data"
type: IMAGE_DATA
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
mirror: false
}
image_data_param {
batch_size: 10
source: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/Sun_100_Test_0.5_.txt"
root_folder: "/home/wangxiao/SUN397_part/selected_sun/Sun-100/test_image_sun_227_227/"
new_height:224
new_224
}
}

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: "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: "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: "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 {
  bottom: "fc7"
  top: "fc8_"
  name: "fc8_"
  type: INNER_PRODUCT
  inner_product_param {
    num_output: 88
  }
}
layers {
  name: "accuracy"
  type: ACCURACY
  bottom: "fc8_"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layers{
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "fc8_"
  bottom: "label"
  top: "loss"
}

　　

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原文地址：https://www.cnblogs.com/wangxiaocvpr/p/5712800.html