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train_val_maxmin_cnn.prototxt
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name: "CaffeNet"
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TRAIN
}
image_data_param {
source: "/home/huangdian/data/idcard/train.txt"
batch_size: 128
new_height: 32
new_width: 32
is_color: false
shuffle: true
}
transform_param {
mirror: false
}
}
layer {
name: "data"
type: "ImageData"
top: "data"
top: "label"
include {
phase: TEST
}
image_data_param {
source: "/home/huangdian/data/idcard/val_all.txt"
batch_size: 128
new_height: 32
new_width: 32
is_color: false
shuffle: true
}
transform_param {
mirror: false
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1.0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 48
kernel_size: 3
stride: 1
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "conv1_bn"
type: "BN"
bottom: "conv1"
top: "conv1_bn"
param {
lr_mult: 1.00001
decay_mult: 0
}
param {
lr_mult: 1.00001
decay_mult: 0
}
}
layer {
bottom: "conv1_bn"
top: "sacle_conv1"
name: "sacle_conv1"
type: "Scale"
scale_param {
filler {
value: -1 }
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "concat_conv1"
bottom: "conv1_bn"
bottom: "sacle_conv1"
top: "concat_conv1"
type: "Concat"
concat_param{
axis: 1
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "concat_conv1"
top: "relu1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "relu1"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2"
type: "Convolution"
bottom: "pool1"
top: "conv2"
param {
lr_mult: 1
decay_mult: 1.0
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "conv2_bn"
type: "BN"
bottom: "conv2"
top: "conv2_bn"
param {
lr_mult: 1.00001
decay_mult: 0
}
param {
lr_mult: 1.00001
decay_mult: 0
}
}
layer {
bottom: "conv2_bn"
top: "sacle_conv2"
name: "sacle_conv2"
type: "Scale"
scale_param {
filler {
value: -1 }
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "concat_conv2"
bottom: "conv2_bn"
bottom: "sacle_conv2"
top: "concat_conv2"
type: "Concat"
concat_param{
axis: 1
}
}
layer {
name: "relu2"
type: "ReLU"
bottom: "concat_conv2"
top: "relu2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "relu2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv3"
type: "Convolution"
bottom: "pool2"
top: "conv3"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "conv3_bn"
type: "BN"
bottom: "conv3"
top: "conv3_bn"
param {
lr_mult: 1.00001
decay_mult: 0
}
param {
lr_mult: 1.00001
decay_mult: 0
}
}
layer {
bottom: "conv3_bn"
top: "sacle_conv3"
name: "sacle_conv3"
type: "Scale"
scale_param {
filler {
value: -1 }
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "concat_conv3"
bottom: "conv3_bn"
bottom: "sacle_conv3"
top: "concat_conv3"
type: "Concat"
concat_param{
axis: 1
}
}
layer {
name: "relu4"
type: "ReLU"
bottom: "concat_conv3"
top: "relu4"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "relu4"
top: "pool4"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv4"
type: "Convolution"
bottom: "pool4"
top: "conv4"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 96
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "conv4_bn"
type: "BN"
bottom: "conv4"
top: "conv4_bn"
param {
lr_mult: 1.00001
decay_mult: 0
}
param {
lr_mult: 1.00001
decay_mult: 0
}
}
layer {
bottom: "conv4_bn"
top: "sacle_conv4"
name: "sacle_conv4"
type: "Scale"
scale_param {
filler {
value: -1 }
bias_term: true
bias_filler {
value: 0
}
}
}
layer {
name: "concat_conv4"
bottom: "conv4_bn"
bottom: "sacle_conv4"
top: "concat_conv4"
type: "Concat"
concat_param{
axis: 1
}
}
layer {
name: "relu5"
type: "ReLU"
bottom: "concat_conv4"
top: "relu5"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "relu5"
top: "pool5"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "fc6"
type: "InnerProduct"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1
decay_mult: 0.5
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 256
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 1
}
}
}
layer {
name: "fc6_bn"
type: "BN"
bottom: "fc6"
top: "fc6"
param {
lr_mult: 1.00001
decay_mult: 0
}
param {
lr_mult: 1.00001
decay_mult: 0
}
}
layer {
name: "fc6_relu"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "drop6"
type: "Dropout"
bottom: "fc6"
top: "fc6"
dropout_param {
dropout_ratio: 0.5
}
}
layer {
name: "fc8"
type: "InnerProduct"
bottom: "fc6"
top: "fc8"
param {
lr_mult: 1
decay_mult: 0.5
}
param {
lr_mult: 2
decay_mult: 0
}
inner_product_param {
num_output: 6915
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "fc8"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "fc8"
bottom: "label"
top: "loss"
}