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train_val.prototxt
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train_val.prototxt
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name: "Face_fine_age_net"
layer {
name: "data" type: "ImageData" top: "data" top: "label"
include { phase: TRAIN }
transform_param { mirror: true scale: 0.00390625 }
image_data_param {
source: "/media/crw/MyBook/MyDataset/Adience/train/age/train_8.list"
batch_size: 96
is_color: false
shuffle: true
}
}
layer {
name: "data" type: "ImageData" top: "data" top: "label"
include { phase: TEST }
transform_param { mirror: true scale: 0.00390625 }
image_data_param {
source: "/media/crw/MyBook/MyDataset/Adience/test/age/train_8.list"
batch_size: 96
is_color: false
shuffle: true
}
}
# --- stage 1, we use a little bit large filter ~
layer {
name: "conv1" type: "Convolution" bottom: "data" top: "conv1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param { num_output: 64 kernel_size: 7 stride: 1 pad: 3
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice1" type: "Slice" bottom: "conv1" top: "slice1_1" top: "slice1_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise1" type: "Eltwise" bottom: "slice1_1" bottom: "slice1_2" top: "eltwise1"
eltwise_param{ operation: MAX }
}
layer {
name:"pool1" type:"Pooling" bottom:"eltwise1" top:"pool1"
pooling_param{ pool:MAX kernel_size:2 stride:2 }
}
# --- stage 2, use maxout combine small filters
layer {
name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param { num_output: 128 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice2" type: "Slice" bottom: "conv2_1" top: "slice2_1" top: "slice2_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise2" type: "Eltwise" bottom: "slice2_1" bottom: "slice2_2" top: "eltwise2"
eltwise_param{ operation: MAX }
}
layer {
name: "conv2_2" type: "Convolution" bottom: "eltwise2" top: "conv2_2"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 128 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler {type: "constant" value: 0.1 }
}
}
layer {
name: "slice3" type: "Slice" bottom: "conv2_2" top: "slice3_1" top: "slice3_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise3" type: "Eltwise" bottom: "slice3_1" bottom: "slice3_2" top: "eltwise3"
eltwise_param{ operation: MAX }
}
layer {
name: "pool3" type: "Pooling" bottom: "eltwise3" top:"pool3"
pooling_param{ pool:MAX kernel_size:2 stride: 2 }
}
# --- stage 3
layer {
name: "conv3_1" type: "Convolution" bottom: "pool3" top: "conv3_1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 192 kernel_size: 3 stride: 1 pad:1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice4" type: "Slice" bottom: "conv3_1" top: "slice4_1" top: "slice4_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise4" type: "Eltwise" bottom: "slice4_1" bottom: "slice4_2" top: "eltwise4"
eltwise_param{ operation: MAX }
}
layer {
name: "conv3_2" type: "Convolution" bottom: "eltwise4" top: "conv3_2"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 192 kernel_size: 3 stride: 1 pad :1
weight_filler{ type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice5" type: "Slice" bottom: "conv3_2" top: "slice5_1" top: "slice5_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise5" type: "Eltwise" bottom: "slice5_1" bottom: "slice5_2" top: "eltwise5"
eltwise_param{ operation: MAX }
}
layer {
name:"pool5" type:"Pooling" bottom:"eltwise5" top:"pool5"
pooling_param{ pool:MAX kernel_size:2 stride:2 }
}
# --- stage 4
layer {
name: "conv4_1" type: "Convolution" bottom: "pool5" top: "conv4_1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256 kernel_size: 3 stride: 1 pad:1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice6" type: "Slice" bottom: "conv4_1" top: "slice6_1" top: "slice6_2"
slice_param { slice_dim: 1 }
}
layer {
name: "eltwise6" type: "Eltwise" bottom: "slice6_1" bottom: "slice6_2" top: "eltwise6"
eltwise_param {operation: MAX }
}
layer {
name: "conv4_2" type: "Convolution" bottom: "eltwise6" top: "conv4_2"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice7" type: "Slice" bottom: "conv4_2" top: "slice7_1" top: "slice7_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise7" type: "Eltwise" bottom: "slice7_1" bottom: "slice7_2" top: "eltwise7"
eltwise_param{ operation: MAX }
}
layer {
name: "conv4_3" type: "Convolution" bottom: "eltwise7" top: "conv4_3"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 256 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice7_3" type: "Slice" bottom: "conv4_3" top: "slice4_3_1" top: "slice4_3_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise7_3" type: "Eltwise" bottom: "slice4_3_1" bottom: "slice4_3_2" top: "eltwise7_3"
eltwise_param{ operation: MAX }
}
layer {
name:"pool7_3" type:"Pooling" bottom:"eltwise7_3" top:"pool7_3"
pooling_param { pool:MAX kernel_size:2 stride: 2 }
}
# --- stage 5
layer {
name: "conv5_1" type: "Convolution" bottom: "pool7_3" top: "conv5_1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 512 kernel_size: 3 stride: 1 pad: 1
weight_filler {type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice8" type: "Slice" bottom: "conv5_1" top: "slice8_1" top: "slice8_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise8" type: "Eltwise" bottom: "slice8_1" bottom: "slice8_2" top: "eltwise8"
eltwise_param{ operation: MAX }
}
layer {
name: "conv5_2" type: "Convolution" bottom: "eltwise8" top: "conv5_2"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 512 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice9" type: "Slice" bottom: "conv5_2" top: "slice9_1" top: "slice9_2"
slice_param { slice_dim: 1 }
}
layer {
name: "eltwise9" type: "Eltwise" bottom: "slice9_1" bottom: "slice9_2" top: "eltwise9"
eltwise_param { operation: MAX }
}
layer {
name: "conv5_3" type: "Convolution" bottom: "eltwise9" top: "conv5_3"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
convolution_param {
num_output: 512 kernel_size: 3 stride: 1 pad: 1
weight_filler { type: "xavier" }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice5_3" type: "Slice" bottom: "conv5_3" top: "slice5_3_1" top: "slice5_3_2"
slice_param { slice_dim: 1 }
}
layer {
name: "eltwise5_3" type: "Eltwise" bottom: "slice5_3_1" bottom: "slice5_3_2" top: "eltwise5_3"
eltwise_param { operation: MAX }
}
# --- final stage, use spp layer to generate multi-scale feature , better for face recognition
# plug in the spp layer ~~
layer {
name:"spp1" type:"SPP" bottom:"eltwise5_3" top:"spp1"
spp_param{ pool:MAX pyramid_height: 3 }
}
layer {
name: "drop_spp" type: "Dropout" bottom: "spp1" top: "drop_spp"
dropout_param { dropout_ratio : 0.5 }
}
layer {
name: "fc1_new" type: "InnerProduct" bottom: "drop_spp" top: "fc1"
param { lr_mult: 1 decay_mult: 1 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 512
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "slice10" type: "Slice" bottom: "fc1" top: "slice10_1" top: "slice10_2"
slice_param{ slice_dim: 1 }
}
layer {
name: "eltwise10" type: "Eltwise" bottom: "slice10_1" bottom: "slice10_2" top: "eltwise10"
eltwise_param{ operation: MAX }
}
layer {
name : "dropoutfc1" type: "Dropout" bottom: "eltwise10" top: "eltwise10"
dropout_param{ dropout_ratio: 0.5 }
}
layer {
name: "classfy_age" type: "InnerProduct" bottom: "eltwise10" top: "classfy_age"
param { lr_mult: 10 decay_mult: 10 }
param { lr_mult: 2 decay_mult: 0 }
inner_product_param {
num_output: 8
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0.1 }
}
}
layer {
name: "loss_age" type: "SoftmaxWithLoss" bottom: "classfy_age" bottom: "label"
}
layer {
include: { phase: TEST }
name: "accuracy_age" type: "Accuracy" bottom: "classfy_age" bottom: "label" top:"accuracy_age"
}