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Resnet 网络结构:
layer {
name: "res2c"
type: "Eltwise"
bottom: "res2b"
bottom: "res2c_branch2c"
top: "res2c"
}
layer {
name: "res2c_relu"
type: "ReLU"
bottom: "res2c"
top: "res2c"
}
layer {
name: "res3a_branch1"
type: "Convolution"
bottom: "res2c"
top: "res3a_branch1"
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "bn3a_branch1"
type: "BatchNorm"
bottom: "res3a_branch1"
top: "res3a_branch1"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
}
layer {
name: "scale3a_branch1"
type: "Scale"
bottom: "res3a_branch1"
top: "res3a_branch1"
scale_param {
bias_term: true
}
}
layer {
name: "res3a_branch2a"
type: "Convolution"
bottom: "res2c"
top: "res3a_branch2a"
convolution_param {
num_output: 128
bias_term: false
pad: 0
kernel_size: 1
stride: 2
weight_filler {
type: "msra"
}
}
}
layer {
name: "bn3a_branch2a"
type: "BatchNorm"
bottom: "res3a_branch2a"
top: "res3a_branch2a"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
}
layer {
name: "scale3a_branch2a"
type: "Scale"
bottom: "res3a_branch2a"
top: "res3a_branch2a"
scale_param {
bias_term: true
}
}
layer {
name: "res3a_branch2a_relu"
type: "ReLU"
bottom: "res3a_branch2a"
top: "res3a_branch2a"
}
layer {
name: "res3a_branch2b"
type: "Convolution"
bottom: "res3a_branch2a"
top: "res3a_branch2b"
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "bn3a_branch2b"
type: "BatchNorm"
bottom: "res3a_branch2b"
top: "res3a_branch2b"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
}
layer {
name: "scale3a_branch2b"
type: "Scale"
bottom: "res3a_branch2b"
top: "res3a_branch2b"
scale_param {
bias_term: true
}
}
layer {
name: "res3a_branch2b_relu"
type: "ReLU"
bottom: "res3a_branch2b"
top: "res3a_branch2b"
}
layer {
name: "res3a_branch2c"
type: "Convolution"
bottom: "res3a_branch2b"
top: "res3a_branch2c"
convolution_param {
num_output: 512
bias_term: false
pad: 0
kernel_size: 1
stride: 1
weight_filler {
type: "msra"
}
}
}
layer {
name: "bn3a_branch2c"
type: "BatchNorm"
bottom: "res3a_branch2c"
top: "res3a_branch2c"
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
param {
lr_mult: 0
decay_mult: 0
}
}
layer {
name: "scale3a_branch2c"
type: "Scale"
bottom: "res3a_branch2c"
top: "res3a_branch2c"
scale_param {
bias_term: true
}
}
layer {
name: "res3a"
type: "Eltwise"
bottom: "res3a_branch1"
bottom: "res3a_branch2c"
top: "res3a"
}
layer {
name: "res3a_relu"
type: "ReLU"
bottom: "res3a"
top: "res3a"
}
proto结构:
如果是SSD的最后第八层,则出现卷积是3*3的卷积pading =0;步长是1的卷积,最后生成1*1的卷积核;384的输入的话!