mirror of
https://github.com/rendies/People-Counting-in-Real-Time.git
synced 2025-10-31 12:23:04 +07:00
1913 lines
29 KiB
Plaintext
1913 lines
29 KiB
Plaintext
name: "MobileNet-SSD"
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input: "data"
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input_shape {
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dim: 1
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dim: 3
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dim: 300
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dim: 300
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}
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layer {
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name: "conv0"
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type: "Convolution"
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bottom: "data"
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top: "conv0"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 32
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pad: 1
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kernel_size: 3
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stride: 2
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv0/relu"
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type: "ReLU"
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bottom: "conv0"
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top: "conv0"
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}
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layer {
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name: "conv1/dw"
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type: "Convolution"
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bottom: "conv0"
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top: "conv1/dw"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 32
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pad: 1
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kernel_size: 3
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group: 32
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engine: CAFFE
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv1/dw/relu"
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type: "ReLU"
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bottom: "conv1/dw"
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top: "conv1/dw"
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}
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layer {
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name: "conv1"
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type: "Convolution"
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bottom: "conv1/dw"
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top: "conv1"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 64
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kernel_size: 1
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv1/relu"
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type: "ReLU"
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bottom: "conv1"
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top: "conv1"
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}
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layer {
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name: "conv2/dw"
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type: "Convolution"
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bottom: "conv1"
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top: "conv2/dw"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 64
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pad: 1
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kernel_size: 3
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stride: 2
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group: 64
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engine: CAFFE
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv2/dw/relu"
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type: "ReLU"
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bottom: "conv2/dw"
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top: "conv2/dw"
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}
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layer {
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name: "conv2"
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type: "Convolution"
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bottom: "conv2/dw"
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top: "conv2"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 128
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kernel_size: 1
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv2/relu"
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type: "ReLU"
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bottom: "conv2"
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top: "conv2"
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}
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layer {
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name: "conv3/dw"
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type: "Convolution"
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bottom: "conv2"
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top: "conv3/dw"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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group: 128
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engine: CAFFE
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv3/dw/relu"
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type: "ReLU"
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bottom: "conv3/dw"
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top: "conv3/dw"
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}
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layer {
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name: "conv3"
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type: "Convolution"
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bottom: "conv3/dw"
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top: "conv3"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 128
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kernel_size: 1
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weight_filler {
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|
type: "msra"
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|
}
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bias_filler {
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|
type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv3/relu"
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type: "ReLU"
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bottom: "conv3"
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top: "conv3"
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}
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layer {
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name: "conv4/dw"
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type: "Convolution"
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bottom: "conv3"
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top: "conv4/dw"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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convolution_param {
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num_output: 128
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pad: 1
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kernel_size: 3
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stride: 2
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group: 128
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engine: CAFFE
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|
weight_filler {
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|
type: "msra"
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|
}
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|
bias_filler {
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|
type: "constant"
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|
value: 0.0
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}
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|
}
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}
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layer {
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name: "conv4/dw/relu"
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type: "ReLU"
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bottom: "conv4/dw"
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top: "conv4/dw"
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}
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layer {
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name: "conv4"
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type: "Convolution"
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bottom: "conv4/dw"
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top: "conv4"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 0.0
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}
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|
convolution_param {
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num_output: 256
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kernel_size: 1
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weight_filler {
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|
type: "msra"
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|
}
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|
bias_filler {
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|
type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv4/relu"
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type: "ReLU"
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bottom: "conv4"
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top: "conv4"
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}
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layer {
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name: "conv5/dw"
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type: "Convolution"
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bottom: "conv4"
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top: "conv5/dw"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
|
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lr_mult: 2.0
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decay_mult: 0.0
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}
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|
convolution_param {
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num_output: 256
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pad: 1
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kernel_size: 3
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group: 256
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engine: CAFFE
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|
weight_filler {
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|
type: "msra"
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|
}
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|
bias_filler {
|
|
type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv5/dw/relu"
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type: "ReLU"
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bottom: "conv5/dw"
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top: "conv5/dw"
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}
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layer {
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name: "conv5"
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type: "Convolution"
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bottom: "conv5/dw"
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top: "conv5"
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param {
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lr_mult: 1.0
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|
decay_mult: 1.0
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|
}
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|
param {
|
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lr_mult: 2.0
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decay_mult: 0.0
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|
}
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|
convolution_param {
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|
num_output: 256
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|
kernel_size: 1
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|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
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|
}
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|
}
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}
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layer {
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name: "conv5/relu"
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type: "ReLU"
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bottom: "conv5"
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top: "conv5"
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}
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|
layer {
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name: "conv6/dw"
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|
type: "Convolution"
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bottom: "conv5"
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top: "conv6/dw"
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|
param {
|
|
lr_mult: 1.0
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|
decay_mult: 1.0
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}
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|
param {
|
|
lr_mult: 2.0
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decay_mult: 0.0
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}
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|
convolution_param {
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|
num_output: 256
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|
pad: 1
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|
kernel_size: 3
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|
stride: 2
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|
group: 256
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|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
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|
}
|
|
}
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}
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|
layer {
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name: "conv6/dw/relu"
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type: "ReLU"
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bottom: "conv6/dw"
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top: "conv6/dw"
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}
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|
layer {
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|
name: "conv6"
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|
type: "Convolution"
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|
bottom: "conv6/dw"
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|
top: "conv6"
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|
param {
|
|
lr_mult: 1.0
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|
decay_mult: 1.0
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|
}
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|
param {
|
|
lr_mult: 2.0
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|
decay_mult: 0.0
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|
}
|
|
convolution_param {
|
|
num_output: 512
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|
kernel_size: 1
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|
weight_filler {
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|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
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|
value: 0.0
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}
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}
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}
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layer {
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name: "conv6/relu"
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type: "ReLU"
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bottom: "conv6"
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top: "conv6"
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}
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layer {
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name: "conv7/dw"
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|
type: "Convolution"
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bottom: "conv6"
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top: "conv7/dw"
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param {
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lr_mult: 1.0
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|
decay_mult: 1.0
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}
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|
param {
|
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lr_mult: 2.0
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|
decay_mult: 0.0
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|
}
|
|
convolution_param {
|
|
num_output: 512
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|
pad: 1
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|
kernel_size: 3
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group: 512
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|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
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|
value: 0.0
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}
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}
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}
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layer {
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name: "conv7/dw/relu"
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type: "ReLU"
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bottom: "conv7/dw"
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top: "conv7/dw"
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}
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layer {
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name: "conv7"
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type: "Convolution"
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bottom: "conv7/dw"
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|
top: "conv7"
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|
param {
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lr_mult: 1.0
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|
decay_mult: 1.0
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|
}
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|
param {
|
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lr_mult: 2.0
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|
decay_mult: 0.0
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}
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|
convolution_param {
|
|
num_output: 512
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|
kernel_size: 1
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|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
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|
}
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|
}
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}
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|
layer {
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name: "conv7/relu"
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|
type: "ReLU"
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|
bottom: "conv7"
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|
top: "conv7"
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}
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|
layer {
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|
name: "conv8/dw"
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|
type: "Convolution"
|
|
bottom: "conv7"
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|
top: "conv8/dw"
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|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
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|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
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|
pad: 1
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|
kernel_size: 3
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|
group: 512
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|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8/dw/relu"
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|
type: "ReLU"
|
|
bottom: "conv8/dw"
|
|
top: "conv8/dw"
|
|
}
|
|
layer {
|
|
name: "conv8"
|
|
type: "Convolution"
|
|
bottom: "conv8/dw"
|
|
top: "conv8"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8/relu"
|
|
type: "ReLU"
|
|
bottom: "conv8"
|
|
top: "conv8"
|
|
}
|
|
layer {
|
|
name: "conv9/dw"
|
|
type: "Convolution"
|
|
bottom: "conv8"
|
|
top: "conv9/dw"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 512
|
|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9/dw/relu"
|
|
type: "ReLU"
|
|
bottom: "conv9/dw"
|
|
top: "conv9/dw"
|
|
}
|
|
layer {
|
|
name: "conv9"
|
|
type: "Convolution"
|
|
bottom: "conv9/dw"
|
|
top: "conv9"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9/relu"
|
|
type: "ReLU"
|
|
bottom: "conv9"
|
|
top: "conv9"
|
|
}
|
|
layer {
|
|
name: "conv10/dw"
|
|
type: "Convolution"
|
|
bottom: "conv9"
|
|
top: "conv10/dw"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 512
|
|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv10/dw/relu"
|
|
type: "ReLU"
|
|
bottom: "conv10/dw"
|
|
top: "conv10/dw"
|
|
}
|
|
layer {
|
|
name: "conv10"
|
|
type: "Convolution"
|
|
bottom: "conv10/dw"
|
|
top: "conv10"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv10/relu"
|
|
type: "ReLU"
|
|
bottom: "conv10"
|
|
top: "conv10"
|
|
}
|
|
layer {
|
|
name: "conv11/dw"
|
|
type: "Convolution"
|
|
bottom: "conv10"
|
|
top: "conv11/dw"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 512
|
|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11/dw/relu"
|
|
type: "ReLU"
|
|
bottom: "conv11/dw"
|
|
top: "conv11/dw"
|
|
}
|
|
layer {
|
|
name: "conv11"
|
|
type: "Convolution"
|
|
bottom: "conv11/dw"
|
|
top: "conv11"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11/relu"
|
|
type: "ReLU"
|
|
bottom: "conv11"
|
|
top: "conv11"
|
|
}
|
|
layer {
|
|
name: "conv12/dw"
|
|
type: "Convolution"
|
|
bottom: "conv11"
|
|
top: "conv12/dw"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
group: 512
|
|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv12/dw/relu"
|
|
type: "ReLU"
|
|
bottom: "conv12/dw"
|
|
top: "conv12/dw"
|
|
}
|
|
layer {
|
|
name: "conv12"
|
|
type: "Convolution"
|
|
bottom: "conv12/dw"
|
|
top: "conv12"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 1024
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv12/relu"
|
|
type: "ReLU"
|
|
bottom: "conv12"
|
|
top: "conv12"
|
|
}
|
|
layer {
|
|
name: "conv13/dw"
|
|
type: "Convolution"
|
|
bottom: "conv12"
|
|
top: "conv13/dw"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 1024
|
|
pad: 1
|
|
kernel_size: 3
|
|
group: 1024
|
|
engine: CAFFE
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13/dw/relu"
|
|
type: "ReLU"
|
|
bottom: "conv13/dw"
|
|
top: "conv13/dw"
|
|
}
|
|
layer {
|
|
name: "conv13"
|
|
type: "Convolution"
|
|
bottom: "conv13/dw"
|
|
top: "conv13"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 1024
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13/relu"
|
|
type: "ReLU"
|
|
bottom: "conv13"
|
|
top: "conv13"
|
|
}
|
|
layer {
|
|
name: "conv14_1"
|
|
type: "Convolution"
|
|
bottom: "conv13"
|
|
top: "conv14_1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_1/relu"
|
|
type: "ReLU"
|
|
bottom: "conv14_1"
|
|
top: "conv14_1"
|
|
}
|
|
layer {
|
|
name: "conv14_2"
|
|
type: "Convolution"
|
|
bottom: "conv14_1"
|
|
top: "conv14_2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 512
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2/relu"
|
|
type: "ReLU"
|
|
bottom: "conv14_2"
|
|
top: "conv14_2"
|
|
}
|
|
layer {
|
|
name: "conv15_1"
|
|
type: "Convolution"
|
|
bottom: "conv14_2"
|
|
top: "conv15_1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_1/relu"
|
|
type: "ReLU"
|
|
bottom: "conv15_1"
|
|
top: "conv15_1"
|
|
}
|
|
layer {
|
|
name: "conv15_2"
|
|
type: "Convolution"
|
|
bottom: "conv15_1"
|
|
top: "conv15_2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2/relu"
|
|
type: "ReLU"
|
|
bottom: "conv15_2"
|
|
top: "conv15_2"
|
|
}
|
|
layer {
|
|
name: "conv16_1"
|
|
type: "Convolution"
|
|
bottom: "conv15_2"
|
|
top: "conv16_1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_1/relu"
|
|
type: "ReLU"
|
|
bottom: "conv16_1"
|
|
top: "conv16_1"
|
|
}
|
|
layer {
|
|
name: "conv16_2"
|
|
type: "Convolution"
|
|
bottom: "conv16_1"
|
|
top: "conv16_2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2/relu"
|
|
type: "ReLU"
|
|
bottom: "conv16_2"
|
|
top: "conv16_2"
|
|
}
|
|
layer {
|
|
name: "conv17_1"
|
|
type: "Convolution"
|
|
bottom: "conv16_2"
|
|
top: "conv17_1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 64
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_1/relu"
|
|
type: "ReLU"
|
|
bottom: "conv17_1"
|
|
top: "conv17_1"
|
|
}
|
|
layer {
|
|
name: "conv17_2"
|
|
type: "Convolution"
|
|
bottom: "conv17_1"
|
|
top: "conv17_2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2/relu"
|
|
type: "ReLU"
|
|
bottom: "conv17_2"
|
|
top: "conv17_2"
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv11"
|
|
top: "conv11_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 12
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv11_mbox_loc"
|
|
top: "conv11_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv11_mbox_loc_perm"
|
|
top: "conv11_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv11"
|
|
top: "conv11_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 63
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv11_mbox_conf"
|
|
top: "conv11_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv11_mbox_conf_perm"
|
|
top: "conv11_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv11_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv11"
|
|
bottom: "data"
|
|
top: "conv11_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 60.0
|
|
aspect_ratio: 2.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv13"
|
|
top: "conv13_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv13_mbox_loc"
|
|
top: "conv13_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv13_mbox_loc_perm"
|
|
top: "conv13_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv13"
|
|
top: "conv13_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 126
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv13_mbox_conf"
|
|
top: "conv13_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv13_mbox_conf_perm"
|
|
top: "conv13_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv13_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv13"
|
|
bottom: "data"
|
|
top: "conv13_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 105.0
|
|
max_size: 150.0
|
|
aspect_ratio: 2.0
|
|
aspect_ratio: 3.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv14_2"
|
|
top: "conv14_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv14_2_mbox_loc"
|
|
top: "conv14_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv14_2_mbox_loc_perm"
|
|
top: "conv14_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv14_2"
|
|
top: "conv14_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 126
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv14_2_mbox_conf"
|
|
top: "conv14_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv14_2_mbox_conf_perm"
|
|
top: "conv14_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv14_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv14_2"
|
|
bottom: "data"
|
|
top: "conv14_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 150.0
|
|
max_size: 195.0
|
|
aspect_ratio: 2.0
|
|
aspect_ratio: 3.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv15_2"
|
|
top: "conv15_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv15_2_mbox_loc"
|
|
top: "conv15_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv15_2_mbox_loc_perm"
|
|
top: "conv15_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv15_2"
|
|
top: "conv15_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 126
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv15_2_mbox_conf"
|
|
top: "conv15_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv15_2_mbox_conf_perm"
|
|
top: "conv15_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv15_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv15_2"
|
|
bottom: "data"
|
|
top: "conv15_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 195.0
|
|
max_size: 240.0
|
|
aspect_ratio: 2.0
|
|
aspect_ratio: 3.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv16_2"
|
|
top: "conv16_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv16_2_mbox_loc"
|
|
top: "conv16_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv16_2_mbox_loc_perm"
|
|
top: "conv16_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv16_2"
|
|
top: "conv16_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 126
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv16_2_mbox_conf"
|
|
top: "conv16_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv16_2_mbox_conf_perm"
|
|
top: "conv16_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv16_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv16_2"
|
|
bottom: "data"
|
|
top: "conv16_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 240.0
|
|
max_size: 285.0
|
|
aspect_ratio: 2.0
|
|
aspect_ratio: 3.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv17_2"
|
|
top: "conv17_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv17_2_mbox_loc"
|
|
top: "conv17_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv17_2_mbox_loc_perm"
|
|
top: "conv17_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv17_2"
|
|
top: "conv17_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 0.0
|
|
}
|
|
convolution_param {
|
|
num_output: 126
|
|
kernel_size: 1
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv17_2_mbox_conf"
|
|
top: "conv17_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv17_2_mbox_conf_perm"
|
|
top: "conv17_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv17_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv17_2"
|
|
bottom: "data"
|
|
top: "conv17_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 285.0
|
|
max_size: 300.0
|
|
aspect_ratio: 2.0
|
|
aspect_ratio: 3.0
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_loc"
|
|
type: "Concat"
|
|
bottom: "conv11_mbox_loc_flat"
|
|
bottom: "conv13_mbox_loc_flat"
|
|
bottom: "conv14_2_mbox_loc_flat"
|
|
bottom: "conv15_2_mbox_loc_flat"
|
|
bottom: "conv16_2_mbox_loc_flat"
|
|
bottom: "conv17_2_mbox_loc_flat"
|
|
top: "mbox_loc"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_conf"
|
|
type: "Concat"
|
|
bottom: "conv11_mbox_conf_flat"
|
|
bottom: "conv13_mbox_conf_flat"
|
|
bottom: "conv14_2_mbox_conf_flat"
|
|
bottom: "conv15_2_mbox_conf_flat"
|
|
bottom: "conv16_2_mbox_conf_flat"
|
|
bottom: "conv17_2_mbox_conf_flat"
|
|
top: "mbox_conf"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_priorbox"
|
|
type: "Concat"
|
|
bottom: "conv11_mbox_priorbox"
|
|
bottom: "conv13_mbox_priorbox"
|
|
bottom: "conv14_2_mbox_priorbox"
|
|
bottom: "conv15_2_mbox_priorbox"
|
|
bottom: "conv16_2_mbox_priorbox"
|
|
bottom: "conv17_2_mbox_priorbox"
|
|
top: "mbox_priorbox"
|
|
concat_param {
|
|
axis: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_conf_reshape"
|
|
type: "Reshape"
|
|
bottom: "mbox_conf"
|
|
top: "mbox_conf_reshape"
|
|
reshape_param {
|
|
shape {
|
|
dim: 0
|
|
dim: -1
|
|
dim: 21
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_conf_softmax"
|
|
type: "Softmax"
|
|
bottom: "mbox_conf_reshape"
|
|
top: "mbox_conf_softmax"
|
|
softmax_param {
|
|
axis: 2
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_conf_flatten"
|
|
type: "Flatten"
|
|
bottom: "mbox_conf_softmax"
|
|
top: "mbox_conf_flatten"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "detection_out"
|
|
type: "DetectionOutput"
|
|
bottom: "mbox_loc"
|
|
bottom: "mbox_conf_flatten"
|
|
bottom: "mbox_priorbox"
|
|
top: "detection_out"
|
|
include {
|
|
phase: TEST
|
|
}
|
|
detection_output_param {
|
|
num_classes: 21
|
|
share_location: true
|
|
background_label_id: 0
|
|
nms_param {
|
|
nms_threshold: 0.45
|
|
top_k: 100
|
|
}
|
|
code_type: CENTER_SIZE
|
|
keep_top_k: 100
|
|
confidence_threshold: 0.25
|
|
}
|
|
}
|