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"
}
