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_width: 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_width: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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