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rexlow/SE-ResNet-50.prototxt

Last active December 12, 2018 07:10
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SE-ResNet-50.prototxt
name: "SE-ResNet-50"
# mean_value: 104, 117, 123
layer {
name: "data"
type: "Input"
top: "data"
input_param: {
shape: {
dim: 1
dim: 3
dim: 224
dim: 224
}
}
}
layer {
name: "conv1/7x7_s2"
type: "Convolution"
bottom: "data"
top: "conv1/7x7_s2"
convolution_param {
num_output: 64
bias_term: false
pad: 3
kernel_size: 7
stride: 2
}
}
layer {
name: "conv1/7x7_s2/bn"
type: "BatchNorm"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv1/7x7_s2/bn/scale"
type: "Scale"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2"
scale_param {
bias_term: true
}
}
layer {
name: "conv1/relu_7x7_s2"
type: "ReLU"
bottom: "conv1/7x7_s2"
top: "conv1/7x7_s2"
}
layer {
name: "pool1/3x3_s2"
type: "Pooling"
bottom: "conv1/7x7_s2"
top: "pool1/3x3_s2"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
}
}
layer {
name: "conv2_1_1x1_reduce"
type: "Convolution"
bottom: "pool1/3x3_s2"
top: "conv2_1_1x1_reduce"
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_1_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv2_1_1x1_reduce"
top: "conv2_1_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_1_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv2_1_1x1_reduce"
top: "conv2_1_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1_1x1_reduce/relu"
type: "ReLU"
bottom: "conv2_1_1x1_reduce"
top: "conv2_1_1x1_reduce"
}
layer {
name: "conv2_1_3x3"
type: "Convolution"
bottom: "conv2_1_1x1_reduce"
top: "conv2_1_3x3"
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv2_1_3x3/bn"
type: "BatchNorm"
bottom: "conv2_1_3x3"
top: "conv2_1_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_1_3x3/bn/scale"
type: "Scale"
bottom: "conv2_1_3x3"
top: "conv2_1_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1_3x3/relu"
type: "ReLU"
bottom: "conv2_1_3x3"
top: "conv2_1_3x3"
}
layer {
name: "conv2_1_1x1_increase"
type: "Convolution"
bottom: "conv2_1_3x3"
top: "conv2_1_1x1_increase"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_1_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv2_1_1x1_increase"
top: "conv2_1_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_1_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv2_1_1x1_increase"
top: "conv2_1_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1_global_pool"
type: "Pooling"
bottom: "conv2_1_1x1_increase"
top: "conv2_1_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv2_1_1x1_down"
type: "Convolution"
bottom: "conv2_1_global_pool"
top: "conv2_1_1x1_down"
convolution_param {
num_output: 16
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_1_1x1_down/relu"
type: "ReLU"
bottom: "conv2_1_1x1_down"
top: "conv2_1_1x1_down"
}
layer {
name: "conv2_1_1x1_up"
type: "Convolution"
bottom: "conv2_1_1x1_down"
top: "conv2_1_1x1_up"
convolution_param {
num_output: 256
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_1_prob"
type: "Sigmoid"
bottom: "conv2_1_1x1_up"
top: "conv2_1_1x1_up"
}
layer {
name: "conv2_1_1x1_proj"
type: "Convolution"
bottom: "pool1/3x3_s2"
top: "conv2_1_1x1_proj"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_1_1x1_proj/bn"
type: "BatchNorm"
bottom: "conv2_1_1x1_proj"
top: "conv2_1_1x1_proj"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_1_1x1_proj/bn/scale"
type: "Scale"
bottom: "conv2_1_1x1_proj"
top: "conv2_1_1x1_proj"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_1"
type: "Axpy"
bottom: "conv2_1_1x1_up"
bottom: "conv2_1_1x1_increase"
bottom: "conv2_1_1x1_proj"
top: "conv2_1"
}
layer {
name: "conv2_1/relu"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2_1x1_reduce"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2_1x1_reduce"
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_2_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv2_2_1x1_reduce"
top: "conv2_2_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_2_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv2_2_1x1_reduce"
top: "conv2_2_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_2_1x1_reduce/relu"
type: "ReLU"
bottom: "conv2_2_1x1_reduce"
top: "conv2_2_1x1_reduce"
}
layer {
name: "conv2_2_3x3"
type: "Convolution"
bottom: "conv2_2_1x1_reduce"
top: "conv2_2_3x3"
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv2_2_3x3/bn"
type: "BatchNorm"
bottom: "conv2_2_3x3"
top: "conv2_2_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_2_3x3/bn/scale"
type: "Scale"
bottom: "conv2_2_3x3"
top: "conv2_2_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_2_3x3/relu"
type: "ReLU"
bottom: "conv2_2_3x3"
top: "conv2_2_3x3"
}
layer {
name: "conv2_2_1x1_increase"
type: "Convolution"
bottom: "conv2_2_3x3"
top: "conv2_2_1x1_increase"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_2_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv2_2_1x1_increase"
top: "conv2_2_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_2_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv2_2_1x1_increase"
top: "conv2_2_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_2_global_pool"
type: "Pooling"
bottom: "conv2_2_1x1_increase"
top: "conv2_2_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv2_2_1x1_down"
type: "Convolution"
bottom: "conv2_2_global_pool"
top: "conv2_2_1x1_down"
convolution_param {
num_output: 16
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_2_1x1_down/relu"
type: "ReLU"
bottom: "conv2_2_1x1_down"
top: "conv2_2_1x1_down"
}
layer {
name: "conv2_2_1x1_up"
type: "Convolution"
bottom: "conv2_2_1x1_down"
top: "conv2_2_1x1_up"
convolution_param {
num_output: 256
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_2_prob"
type: "Sigmoid"
bottom: "conv2_2_1x1_up"
top: "conv2_2_1x1_up"
}
layer {
name: "conv2_2"
type: "Axpy"
bottom: "conv2_2_1x1_up"
bottom: "conv2_2_1x1_increase"
bottom: "conv2_1"
top: "conv2_2"
}
layer {
name: "conv2_2/relu"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "conv2_3_1x1_reduce"
type: "Convolution"
bottom: "conv2_2"
top: "conv2_3_1x1_reduce"
convolution_param {
num_output: 64
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_3_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv2_3_1x1_reduce"
top: "conv2_3_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_3_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv2_3_1x1_reduce"
top: "conv2_3_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv2_3_1x1_reduce"
top: "conv2_3_1x1_reduce"
}
layer {
name: "conv2_3_3x3"
type: "Convolution"
bottom: "conv2_3_1x1_reduce"
top: "conv2_3_3x3"
convolution_param {
num_output: 64
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv2_3_3x3/bn"
type: "BatchNorm"
bottom: "conv2_3_3x3"
top: "conv2_3_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_3_3x3/bn/scale"
type: "Scale"
bottom: "conv2_3_3x3"
top: "conv2_3_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_3_3x3/relu"
type: "ReLU"
bottom: "conv2_3_3x3"
top: "conv2_3_3x3"
}
layer {
name: "conv2_3_1x1_increase"
type: "Convolution"
bottom: "conv2_3_3x3"
top: "conv2_3_1x1_increase"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_3_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv2_3_1x1_increase"
top: "conv2_3_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv2_3_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv2_3_1x1_increase"
top: "conv2_3_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv2_3_global_pool"
type: "Pooling"
bottom: "conv2_3_1x1_increase"
top: "conv2_3_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv2_3_1x1_down"
type: "Convolution"
bottom: "conv2_3_global_pool"
top: "conv2_3_1x1_down"
convolution_param {
num_output: 16
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_3_1x1_down/relu"
type: "ReLU"
bottom: "conv2_3_1x1_down"
top: "conv2_3_1x1_down"
}
layer {
name: "conv2_3_1x1_up"
type: "Convolution"
bottom: "conv2_3_1x1_down"
top: "conv2_3_1x1_up"
convolution_param {
num_output: 256
kernel_size: 1
stride: 1
}
}
layer {
name: "conv2_3_prob"
type: "Sigmoid"
bottom: "conv2_3_1x1_up"
top: "conv2_3_1x1_up"
}
layer {
name: "conv2_3"
type: "Axpy"
bottom: "conv2_3_1x1_up"
bottom: "conv2_3_1x1_increase"
bottom: "conv2_2"
top: "conv2_3"
}
layer {
name: "conv2_3/relu"
type: "ReLU"
bottom: "conv2_3"
top: "conv2_3"
}
layer {
name: "conv3_1_1x1_reduce"
type: "Convolution"
bottom: "conv2_3"
top: "conv3_1_1x1_reduce"
convolution_param {
num_output: 128
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv3_1_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv3_1_1x1_reduce"
top: "conv3_1_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_1_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv3_1_1x1_reduce"
top: "conv3_1_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1_1x1_reduce/relu"
type: "ReLU"
bottom: "conv3_1_1x1_reduce"
top: "conv3_1_1x1_reduce"
}
layer {
name: "conv3_1_3x3"
type: "Convolution"
bottom: "conv3_1_1x1_reduce"
top: "conv3_1_3x3"
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv3_1_3x3/bn"
type: "BatchNorm"
bottom: "conv3_1_3x3"
top: "conv3_1_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_1_3x3/bn/scale"
type: "Scale"
bottom: "conv3_1_3x3"
top: "conv3_1_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1_3x3/relu"
type: "ReLU"
bottom: "conv3_1_3x3"
top: "conv3_1_3x3"
}
layer {
name: "conv3_1_1x1_increase"
type: "Convolution"
bottom: "conv3_1_3x3"
top: "conv3_1_1x1_increase"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_1_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv3_1_1x1_increase"
top: "conv3_1_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_1_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv3_1_1x1_increase"
top: "conv3_1_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1_global_pool"
type: "Pooling"
bottom: "conv3_1_1x1_increase"
top: "conv3_1_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv3_1_1x1_down"
type: "Convolution"
bottom: "conv3_1_global_pool"
top: "conv3_1_1x1_down"
convolution_param {
num_output: 32
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_1_1x1_down/relu"
type: "ReLU"
bottom: "conv3_1_1x1_down"
top: "conv3_1_1x1_down"
}
layer {
name: "conv3_1_1x1_up"
type: "Convolution"
bottom: "conv3_1_1x1_down"
top: "conv3_1_1x1_up"
convolution_param {
num_output: 512
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_1_prob"
type: "Sigmoid"
bottom: "conv3_1_1x1_up"
top: "conv3_1_1x1_up"
}
layer {
name: "conv3_1_1x1_proj"
type: "Convolution"
bottom: "conv2_3"
top: "conv3_1_1x1_proj"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv3_1_1x1_proj/bn"
type: "BatchNorm"
bottom: "conv3_1_1x1_proj"
top: "conv3_1_1x1_proj"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_1_1x1_proj/bn/scale"
type: "Scale"
bottom: "conv3_1_1x1_proj"
top: "conv3_1_1x1_proj"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_1"
type: "Axpy"
bottom: "conv3_1_1x1_up"
bottom: "conv3_1_1x1_increase"
bottom: "conv3_1_1x1_proj"
top: "conv3_1"
}
layer {
name: "conv3_1/relu"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2_1x1_reduce"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2_1x1_reduce"
convolution_param {
num_output: 128
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_2_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv3_2_1x1_reduce"
top: "conv3_2_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_2_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv3_2_1x1_reduce"
top: "conv3_2_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2_1x1_reduce/relu"
type: "ReLU"
bottom: "conv3_2_1x1_reduce"
top: "conv3_2_1x1_reduce"
}
layer {
name: "conv3_2_3x3"
type: "Convolution"
bottom: "conv3_2_1x1_reduce"
top: "conv3_2_3x3"
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv3_2_3x3/bn"
type: "BatchNorm"
bottom: "conv3_2_3x3"
top: "conv3_2_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_2_3x3/bn/scale"
type: "Scale"
bottom: "conv3_2_3x3"
top: "conv3_2_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2_3x3/relu"
type: "ReLU"
bottom: "conv3_2_3x3"
top: "conv3_2_3x3"
}
layer {
name: "conv3_2_1x1_increase"
type: "Convolution"
bottom: "conv3_2_3x3"
top: "conv3_2_1x1_increase"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_2_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv3_2_1x1_increase"
top: "conv3_2_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_2_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv3_2_1x1_increase"
top: "conv3_2_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_2_global_pool"
type: "Pooling"
bottom: "conv3_2_1x1_increase"
top: "conv3_2_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv3_2_1x1_down"
type: "Convolution"
bottom: "conv3_2_global_pool"
top: "conv3_2_1x1_down"
convolution_param {
num_output: 32
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_2_1x1_down/relu"
type: "ReLU"
bottom: "conv3_2_1x1_down"
top: "conv3_2_1x1_down"
}
layer {
name: "conv3_2_1x1_up"
type: "Convolution"
bottom: "conv3_2_1x1_down"
top: "conv3_2_1x1_up"
convolution_param {
num_output: 512
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_2_prob"
type: "Sigmoid"
bottom: "conv3_2_1x1_up"
top: "conv3_2_1x1_up"
}
layer {
name: "conv3_2"
type: "Axpy"
bottom: "conv3_2_1x1_up"
bottom: "conv3_2_1x1_increase"
bottom: "conv3_1"
top: "conv3_2"
}
layer {
name: "conv3_2/relu"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3_1x1_reduce"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3_1x1_reduce"
convolution_param {
num_output: 128
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_3_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv3_3_1x1_reduce"
top: "conv3_3_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_3_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv3_3_1x1_reduce"
top: "conv3_3_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv3_3_1x1_reduce"
top: "conv3_3_1x1_reduce"
}
layer {
name: "conv3_3_3x3"
type: "Convolution"
bottom: "conv3_3_1x1_reduce"
top: "conv3_3_3x3"
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv3_3_3x3/bn"
type: "BatchNorm"
bottom: "conv3_3_3x3"
top: "conv3_3_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_3_3x3/bn/scale"
type: "Scale"
bottom: "conv3_3_3x3"
top: "conv3_3_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3_3x3/relu"
type: "ReLU"
bottom: "conv3_3_3x3"
top: "conv3_3_3x3"
}
layer {
name: "conv3_3_1x1_increase"
type: "Convolution"
bottom: "conv3_3_3x3"
top: "conv3_3_1x1_increase"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_3_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv3_3_1x1_increase"
top: "conv3_3_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_3_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv3_3_1x1_increase"
top: "conv3_3_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_3_global_pool"
type: "Pooling"
bottom: "conv3_3_1x1_increase"
top: "conv3_3_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv3_3_1x1_down"
type: "Convolution"
bottom: "conv3_3_global_pool"
top: "conv3_3_1x1_down"
convolution_param {
num_output: 32
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_3_1x1_down/relu"
type: "ReLU"
bottom: "conv3_3_1x1_down"
top: "conv3_3_1x1_down"
}
layer {
name: "conv3_3_1x1_up"
type: "Convolution"
bottom: "conv3_3_1x1_down"
top: "conv3_3_1x1_up"
convolution_param {
num_output: 512
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_3_prob"
type: "Sigmoid"
bottom: "conv3_3_1x1_up"
top: "conv3_3_1x1_up"
}
layer {
name: "conv3_3"
type: "Axpy"
bottom: "conv3_3_1x1_up"
bottom: "conv3_3_1x1_increase"
bottom: "conv3_2"
top: "conv3_3"
}
layer {
name: "conv3_3/relu"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "conv3_4_1x1_reduce"
type: "Convolution"
bottom: "conv3_3"
top: "conv3_4_1x1_reduce"
convolution_param {
num_output: 128
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_4_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv3_4_1x1_reduce"
top: "conv3_4_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_4_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv3_4_1x1_reduce"
top: "conv3_4_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_4_1x1_reduce/relu"
type: "ReLU"
bottom: "conv3_4_1x1_reduce"
top: "conv3_4_1x1_reduce"
}
layer {
name: "conv3_4_3x3"
type: "Convolution"
bottom: "conv3_4_1x1_reduce"
top: "conv3_4_3x3"
convolution_param {
num_output: 128
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv3_4_3x3/bn"
type: "BatchNorm"
bottom: "conv3_4_3x3"
top: "conv3_4_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_4_3x3/bn/scale"
type: "Scale"
bottom: "conv3_4_3x3"
top: "conv3_4_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_4_3x3/relu"
type: "ReLU"
bottom: "conv3_4_3x3"
top: "conv3_4_3x3"
}
layer {
name: "conv3_4_1x1_increase"
type: "Convolution"
bottom: "conv3_4_3x3"
top: "conv3_4_1x1_increase"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_4_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv3_4_1x1_increase"
top: "conv3_4_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv3_4_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv3_4_1x1_increase"
top: "conv3_4_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv3_4_global_pool"
type: "Pooling"
bottom: "conv3_4_1x1_increase"
top: "conv3_4_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv3_4_1x1_down"
type: "Convolution"
bottom: "conv3_4_global_pool"
top: "conv3_4_1x1_down"
convolution_param {
num_output: 32
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_4_1x1_down/relu"
type: "ReLU"
bottom: "conv3_4_1x1_down"
top: "conv3_4_1x1_down"
}
layer {
name: "conv3_4_1x1_up"
type: "Convolution"
bottom: "conv3_4_1x1_down"
top: "conv3_4_1x1_up"
convolution_param {
num_output: 512
kernel_size: 1
stride: 1
}
}
layer {
name: "conv3_4_prob"
type: "Sigmoid"
bottom: "conv3_4_1x1_up"
top: "conv3_4_1x1_up"
}
layer {
name: "conv3_4"
type: "Axpy"
bottom: "conv3_4_1x1_up"
bottom: "conv3_4_1x1_increase"
bottom: "conv3_3"
top: "conv3_4"
}
layer {
name: "conv3_4/relu"
type: "ReLU"
bottom: "conv3_4"
top: "conv3_4"
}
layer {
name: "conv4_1_1x1_reduce"
type: "Convolution"
bottom: "conv3_4"
top: "conv4_1_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv4_1_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_1_1x1_reduce"
top: "conv4_1_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_1_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_1_1x1_reduce"
top: "conv4_1_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_1_1x1_reduce"
top: "conv4_1_1x1_reduce"
}
layer {
name: "conv4_1_3x3"
type: "Convolution"
bottom: "conv4_1_1x1_reduce"
top: "conv4_1_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_1_3x3/bn"
type: "BatchNorm"
bottom: "conv4_1_3x3"
top: "conv4_1_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_1_3x3/bn/scale"
type: "Scale"
bottom: "conv4_1_3x3"
top: "conv4_1_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1_3x3/relu"
type: "ReLU"
bottom: "conv4_1_3x3"
top: "conv4_1_3x3"
}
layer {
name: "conv4_1_1x1_increase"
type: "Convolution"
bottom: "conv4_1_3x3"
top: "conv4_1_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_1_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_1_1x1_increase"
top: "conv4_1_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_1_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_1_1x1_increase"
top: "conv4_1_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1_global_pool"
type: "Pooling"
bottom: "conv4_1_1x1_increase"
top: "conv4_1_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_1_1x1_down"
type: "Convolution"
bottom: "conv4_1_global_pool"
top: "conv4_1_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_1_1x1_down/relu"
type: "ReLU"
bottom: "conv4_1_1x1_down"
top: "conv4_1_1x1_down"
}
layer {
name: "conv4_1_1x1_up"
type: "Convolution"
bottom: "conv4_1_1x1_down"
top: "conv4_1_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_1_prob"
type: "Sigmoid"
bottom: "conv4_1_1x1_up"
top: "conv4_1_1x1_up"
}
layer {
name: "conv4_1_1x1_proj"
type: "Convolution"
bottom: "conv3_4"
top: "conv4_1_1x1_proj"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv4_1_1x1_proj/bn"
type: "BatchNorm"
bottom: "conv4_1_1x1_proj"
top: "conv4_1_1x1_proj"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_1_1x1_proj/bn/scale"
type: "Scale"
bottom: "conv4_1_1x1_proj"
top: "conv4_1_1x1_proj"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_1"
type: "Axpy"
bottom: "conv4_1_1x1_up"
bottom: "conv4_1_1x1_increase"
bottom: "conv4_1_1x1_proj"
top: "conv4_1"
}
layer {
name: "conv4_1/relu"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2_1x1_reduce"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_2_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_2_1x1_reduce"
top: "conv4_2_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_2_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_2_1x1_reduce"
top: "conv4_2_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_2_1x1_reduce"
top: "conv4_2_1x1_reduce"
}
layer {
name: "conv4_2_3x3"
type: "Convolution"
bottom: "conv4_2_1x1_reduce"
top: "conv4_2_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_2_3x3/bn"
type: "BatchNorm"
bottom: "conv4_2_3x3"
top: "conv4_2_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_2_3x3/bn/scale"
type: "Scale"
bottom: "conv4_2_3x3"
top: "conv4_2_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2_3x3/relu"
type: "ReLU"
bottom: "conv4_2_3x3"
top: "conv4_2_3x3"
}
layer {
name: "conv4_2_1x1_increase"
type: "Convolution"
bottom: "conv4_2_3x3"
top: "conv4_2_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_2_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_2_1x1_increase"
top: "conv4_2_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_2_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_2_1x1_increase"
top: "conv4_2_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_2_global_pool"
type: "Pooling"
bottom: "conv4_2_1x1_increase"
top: "conv4_2_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_2_1x1_down"
type: "Convolution"
bottom: "conv4_2_global_pool"
top: "conv4_2_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_2_1x1_down/relu"
type: "ReLU"
bottom: "conv4_2_1x1_down"
top: "conv4_2_1x1_down"
}
layer {
name: "conv4_2_1x1_up"
type: "Convolution"
bottom: "conv4_2_1x1_down"
top: "conv4_2_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_2_prob"
type: "Sigmoid"
bottom: "conv4_2_1x1_up"
top: "conv4_2_1x1_up"
}
layer {
name: "conv4_2"
type: "Axpy"
bottom: "conv4_2_1x1_up"
bottom: "conv4_2_1x1_increase"
bottom: "conv4_1"
top: "conv4_2"
}
layer {
name: "conv4_2/relu"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3_1x1_reduce"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_3_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_1x1_reduce"
}
layer {
name: "conv4_3_3x3"
type: "Convolution"
bottom: "conv4_3_1x1_reduce"
top: "conv4_3_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_3_3x3/bn"
type: "BatchNorm"
bottom: "conv4_3_3x3"
top: "conv4_3_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_3_3x3/bn/scale"
type: "Scale"
bottom: "conv4_3_3x3"
top: "conv4_3_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3_3x3/relu"
type: "ReLU"
bottom: "conv4_3_3x3"
top: "conv4_3_3x3"
}
layer {
name: "conv4_3_1x1_increase"
type: "Convolution"
bottom: "conv4_3_3x3"
top: "conv4_3_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_3_1x1_increase"
top: "conv4_3_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_3_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_3_1x1_increase"
top: "conv4_3_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_3_global_pool"
type: "Pooling"
bottom: "conv4_3_1x1_increase"
top: "conv4_3_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_3_1x1_down"
type: "Convolution"
bottom: "conv4_3_global_pool"
top: "conv4_3_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_1x1_down/relu"
type: "ReLU"
bottom: "conv4_3_1x1_down"
top: "conv4_3_1x1_down"
}
layer {
name: "conv4_3_1x1_up"
type: "Convolution"
bottom: "conv4_3_1x1_down"
top: "conv4_3_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_3_prob"
type: "Sigmoid"
bottom: "conv4_3_1x1_up"
top: "conv4_3_1x1_up"
}
layer {
name: "conv4_3"
type: "Axpy"
bottom: "conv4_3_1x1_up"
bottom: "conv4_3_1x1_increase"
bottom: "conv4_2"
top: "conv4_3"
}
layer {
name: "conv4_3/relu"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "conv4_4_1x1_reduce"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_4_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_4_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_4_1x1_reduce"
top: "conv4_4_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_4_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_4_1x1_reduce"
top: "conv4_4_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_4_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_4_1x1_reduce"
top: "conv4_4_1x1_reduce"
}
layer {
name: "conv4_4_3x3"
type: "Convolution"
bottom: "conv4_4_1x1_reduce"
top: "conv4_4_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_4_3x3/bn"
type: "BatchNorm"
bottom: "conv4_4_3x3"
top: "conv4_4_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_4_3x3/bn/scale"
type: "Scale"
bottom: "conv4_4_3x3"
top: "conv4_4_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_4_3x3/relu"
type: "ReLU"
bottom: "conv4_4_3x3"
top: "conv4_4_3x3"
}
layer {
name: "conv4_4_1x1_increase"
type: "Convolution"
bottom: "conv4_4_3x3"
top: "conv4_4_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_4_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_4_1x1_increase"
top: "conv4_4_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_4_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_4_1x1_increase"
top: "conv4_4_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_4_global_pool"
type: "Pooling"
bottom: "conv4_4_1x1_increase"
top: "conv4_4_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_4_1x1_down"
type: "Convolution"
bottom: "conv4_4_global_pool"
top: "conv4_4_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_4_1x1_down/relu"
type: "ReLU"
bottom: "conv4_4_1x1_down"
top: "conv4_4_1x1_down"
}
layer {
name: "conv4_4_1x1_up"
type: "Convolution"
bottom: "conv4_4_1x1_down"
top: "conv4_4_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_4_prob"
type: "Sigmoid"
bottom: "conv4_4_1x1_up"
top: "conv4_4_1x1_up"
}
layer {
name: "conv4_4"
type: "Axpy"
bottom: "conv4_4_1x1_up"
bottom: "conv4_4_1x1_increase"
bottom: "conv4_3"
top: "conv4_4"
}
layer {
name: "conv4_4/relu"
type: "ReLU"
bottom: "conv4_4"
top: "conv4_4"
}
layer {
name: "conv4_5_1x1_reduce"
type: "Convolution"
bottom: "conv4_4"
top: "conv4_5_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_5_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_5_1x1_reduce"
top: "conv4_5_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_5_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_5_1x1_reduce"
top: "conv4_5_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_5_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_5_1x1_reduce"
top: "conv4_5_1x1_reduce"
}
layer {
name: "conv4_5_3x3"
type: "Convolution"
bottom: "conv4_5_1x1_reduce"
top: "conv4_5_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_5_3x3/bn"
type: "BatchNorm"
bottom: "conv4_5_3x3"
top: "conv4_5_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_5_3x3/bn/scale"
type: "Scale"
bottom: "conv4_5_3x3"
top: "conv4_5_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_5_3x3/relu"
type: "ReLU"
bottom: "conv4_5_3x3"
top: "conv4_5_3x3"
}
layer {
name: "conv4_5_1x1_increase"
type: "Convolution"
bottom: "conv4_5_3x3"
top: "conv4_5_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_5_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_5_1x1_increase"
top: "conv4_5_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_5_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_5_1x1_increase"
top: "conv4_5_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_5_global_pool"
type: "Pooling"
bottom: "conv4_5_1x1_increase"
top: "conv4_5_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_5_1x1_down"
type: "Convolution"
bottom: "conv4_5_global_pool"
top: "conv4_5_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_5_1x1_down/relu"
type: "ReLU"
bottom: "conv4_5_1x1_down"
top: "conv4_5_1x1_down"
}
layer {
name: "conv4_5_1x1_up"
type: "Convolution"
bottom: "conv4_5_1x1_down"
top: "conv4_5_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_5_prob"
type: "Sigmoid"
bottom: "conv4_5_1x1_up"
top: "conv4_5_1x1_up"
}
layer {
name: "conv4_5"
type: "Axpy"
bottom: "conv4_5_1x1_up"
bottom: "conv4_5_1x1_increase"
bottom: "conv4_4"
top: "conv4_5"
}
layer {
name: "conv4_5/relu"
type: "ReLU"
bottom: "conv4_5"
top: "conv4_5"
}
layer {
name: "conv4_6_1x1_reduce"
type: "Convolution"
bottom: "conv4_5"
top: "conv4_6_1x1_reduce"
convolution_param {
num_output: 256
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_6_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv4_6_1x1_reduce"
top: "conv4_6_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_6_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv4_6_1x1_reduce"
top: "conv4_6_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_6_1x1_reduce/relu"
type: "ReLU"
bottom: "conv4_6_1x1_reduce"
top: "conv4_6_1x1_reduce"
}
layer {
name: "conv4_6_3x3"
type: "Convolution"
bottom: "conv4_6_1x1_reduce"
top: "conv4_6_3x3"
convolution_param {
num_output: 256
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv4_6_3x3/bn"
type: "BatchNorm"
bottom: "conv4_6_3x3"
top: "conv4_6_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_6_3x3/bn/scale"
type: "Scale"
bottom: "conv4_6_3x3"
top: "conv4_6_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_6_3x3/relu"
type: "ReLU"
bottom: "conv4_6_3x3"
top: "conv4_6_3x3"
}
layer {
name: "conv4_6_1x1_increase"
type: "Convolution"
bottom: "conv4_6_3x3"
top: "conv4_6_1x1_increase"
convolution_param {
num_output: 1024
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_6_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv4_6_1x1_increase"
top: "conv4_6_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv4_6_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv4_6_1x1_increase"
top: "conv4_6_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv4_6_global_pool"
type: "Pooling"
bottom: "conv4_6_1x1_increase"
top: "conv4_6_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv4_6_1x1_down"
type: "Convolution"
bottom: "conv4_6_global_pool"
top: "conv4_6_1x1_down"
convolution_param {
num_output: 64
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_6_1x1_down/relu"
type: "ReLU"
bottom: "conv4_6_1x1_down"
top: "conv4_6_1x1_down"
}
layer {
name: "conv4_6_1x1_up"
type: "Convolution"
bottom: "conv4_6_1x1_down"
top: "conv4_6_1x1_up"
convolution_param {
num_output: 1024
kernel_size: 1
stride: 1
}
}
layer {
name: "conv4_6_prob"
type: "Sigmoid"
bottom: "conv4_6_1x1_up"
top: "conv4_6_1x1_up"
}
layer {
name: "conv4_6"
type: "Axpy"
bottom: "conv4_6_1x1_up"
bottom: "conv4_6_1x1_increase"
bottom: "conv4_5"
top: "conv4_6"
}
layer {
name: "conv4_6/relu"
type: "ReLU"
bottom: "conv4_6"
top: "conv4_6"
}
layer {
name: "conv5_1_1x1_reduce"
type: "Convolution"
bottom: "conv4_6"
top: "conv5_1_1x1_reduce"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv5_1_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv5_1_1x1_reduce"
top: "conv5_1_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_1_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv5_1_1x1_reduce"
top: "conv5_1_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1_1x1_reduce/relu"
type: "ReLU"
bottom: "conv5_1_1x1_reduce"
top: "conv5_1_1x1_reduce"
}
layer {
name: "conv5_1_3x3"
type: "Convolution"
bottom: "conv5_1_1x1_reduce"
top: "conv5_1_3x3"
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv5_1_3x3/bn"
type: "BatchNorm"
bottom: "conv5_1_3x3"
top: "conv5_1_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_1_3x3/bn/scale"
type: "Scale"
bottom: "conv5_1_3x3"
top: "conv5_1_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1_3x3/relu"
type: "ReLU"
bottom: "conv5_1_3x3"
top: "conv5_1_3x3"
}
layer {
name: "conv5_1_1x1_increase"
type: "Convolution"
bottom: "conv5_1_3x3"
top: "conv5_1_1x1_increase"
convolution_param {
num_output: 2048
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_1_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv5_1_1x1_increase"
top: "conv5_1_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_1_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv5_1_1x1_increase"
top: "conv5_1_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1_global_pool"
type: "Pooling"
bottom: "conv5_1_1x1_increase"
top: "conv5_1_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv5_1_1x1_down"
type: "Convolution"
bottom: "conv5_1_global_pool"
top: "conv5_1_1x1_down"
convolution_param {
num_output: 128
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_1_1x1_down/relu"
type: "ReLU"
bottom: "conv5_1_1x1_down"
top: "conv5_1_1x1_down"
}
layer {
name: "conv5_1_1x1_up"
type: "Convolution"
bottom: "conv5_1_1x1_down"
top: "conv5_1_1x1_up"
convolution_param {
num_output: 2048
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_1_prob"
type: "Sigmoid"
bottom: "conv5_1_1x1_up"
top: "conv5_1_1x1_up"
}
layer {
name: "conv5_1_1x1_proj"
type: "Convolution"
bottom: "conv4_6"
top: "conv5_1_1x1_proj"
convolution_param {
num_output: 2048
bias_term: false
kernel_size: 1
stride: 2
}
}
layer {
name: "conv5_1_1x1_proj/bn"
type: "BatchNorm"
bottom: "conv5_1_1x1_proj"
top: "conv5_1_1x1_proj"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_1_1x1_proj/bn/scale"
type: "Scale"
bottom: "conv5_1_1x1_proj"
top: "conv5_1_1x1_proj"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_1"
type: "Axpy"
bottom: "conv5_1_1x1_up"
bottom: "conv5_1_1x1_increase"
bottom: "conv5_1_1x1_proj"
top: "conv5_1"
}
layer {
name: "conv5_1/relu"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2_1x1_reduce"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2_1x1_reduce"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_2_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv5_2_1x1_reduce"
top: "conv5_2_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_2_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv5_2_1x1_reduce"
top: "conv5_2_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_2_1x1_reduce/relu"
type: "ReLU"
bottom: "conv5_2_1x1_reduce"
top: "conv5_2_1x1_reduce"
}
layer {
name: "conv5_2_3x3"
type: "Convolution"
bottom: "conv5_2_1x1_reduce"
top: "conv5_2_3x3"
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv5_2_3x3/bn"
type: "BatchNorm"
bottom: "conv5_2_3x3"
top: "conv5_2_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_2_3x3/bn/scale"
type: "Scale"
bottom: "conv5_2_3x3"
top: "conv5_2_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_2_3x3/relu"
type: "ReLU"
bottom: "conv5_2_3x3"
top: "conv5_2_3x3"
}
layer {
name: "conv5_2_1x1_increase"
type: "Convolution"
bottom: "conv5_2_3x3"
top: "conv5_2_1x1_increase"
convolution_param {
num_output: 2048
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_2_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv5_2_1x1_increase"
top: "conv5_2_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_2_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv5_2_1x1_increase"
top: "conv5_2_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_2_global_pool"
type: "Pooling"
bottom: "conv5_2_1x1_increase"
top: "conv5_2_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv5_2_1x1_down"
type: "Convolution"
bottom: "conv5_2_global_pool"
top: "conv5_2_1x1_down"
convolution_param {
num_output: 128
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_2_1x1_down/relu"
type: "ReLU"
bottom: "conv5_2_1x1_down"
top: "conv5_2_1x1_down"
}
layer {
name: "conv5_2_1x1_up"
type: "Convolution"
bottom: "conv5_2_1x1_down"
top: "conv5_2_1x1_up"
convolution_param {
num_output: 2048
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_2_prob"
type: "Sigmoid"
bottom: "conv5_2_1x1_up"
top: "conv5_2_1x1_up"
}
layer {
name: "conv5_2"
type: "Axpy"
bottom: "conv5_2_1x1_up"
bottom: "conv5_2_1x1_increase"
bottom: "conv5_1"
top: "conv5_2"
}
layer {
name: "conv5_2/relu"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3_1x1_reduce"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3_1x1_reduce"
convolution_param {
num_output: 512
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_1x1_reduce/bn"
type: "BatchNorm"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_1x1_reduce"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_3_1x1_reduce/bn/scale"
type: "Scale"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_1x1_reduce"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_3_1x1_reduce/relu"
type: "ReLU"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_1x1_reduce"
}
layer {
name: "conv5_3_3x3"
type: "Convolution"
bottom: "conv5_3_1x1_reduce"
top: "conv5_3_3x3"
convolution_param {
num_output: 512
bias_term: false
pad: 1
kernel_size: 3
stride: 1
}
}
layer {
name: "conv5_3_3x3/bn"
type: "BatchNorm"
bottom: "conv5_3_3x3"
top: "conv5_3_3x3"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_3_3x3/bn/scale"
type: "Scale"
bottom: "conv5_3_3x3"
top: "conv5_3_3x3"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_3_3x3/relu"
type: "ReLU"
bottom: "conv5_3_3x3"
top: "conv5_3_3x3"
}
layer {
name: "conv5_3_1x1_increase"
type: "Convolution"
bottom: "conv5_3_3x3"
top: "conv5_3_1x1_increase"
convolution_param {
num_output: 2048
bias_term: false
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_1x1_increase/bn"
type: "BatchNorm"
bottom: "conv5_3_1x1_increase"
top: "conv5_3_1x1_increase"
batch_norm_param {
use_global_stats: true
}
}
layer {
name: "conv5_3_1x1_increase/bn/scale"
type: "Scale"
bottom: "conv5_3_1x1_increase"
top: "conv5_3_1x1_increase"
scale_param {
bias_term: true
}
}
layer {
name: "conv5_3_global_pool"
type: "Pooling"
bottom: "conv5_3_1x1_increase"
top: "conv5_3_global_pool"
pooling_param {
pool: AVE
engine: CAFFE
global_pooling: true
}
}
layer {
name: "conv5_3_1x1_down"
type: "Convolution"
bottom: "conv5_3_global_pool"
top: "conv5_3_1x1_down"
convolution_param {
num_output: 128
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_1x1_down/relu"
type: "ReLU"
bottom: "conv5_3_1x1_down"
top: "conv5_3_1x1_down"
}
layer {
name: "conv5_3_1x1_up"
type: "Convolution"
bottom: "conv5_3_1x1_down"
top: "conv5_3_1x1_up"
convolution_param {
num_output: 2048
kernel_size: 1
stride: 1
}
}
layer {
name: "conv5_3_prob"
type: "Sigmoid"
bottom: "conv5_3_1x1_up"
top: "conv5_3_1x1_up"
}
layer {
name: "conv5_3"
type: "Axpy"
bottom: "conv5_3_1x1_up"
bottom: "conv5_3_1x1_increase"
bottom: "conv5_2"
top: "conv5_3"
}
layer {
name: "conv5_3/relu"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5/7x7_s1"
type: "Pooling"
bottom: "conv5_3"
top: "pool5/7x7_s1"
pooling_param {
pool: AVE
kernel_size: 7
stride: 1
}
}
layer {
name: "classifier"
type: "InnerProduct"
bottom: "pool5/7x7_s1"
top: "classifier"
inner_product_param {
num_output: 1000
}
}
layer {
name: "prob"
type: "Softmax"
bottom: "classifier"
top: "prob"
}
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