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twtygqyy/DesNet-Deepfashion

Last active July 26, 2017 15:00
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DesNet-Deepfashion
name: "Joe-DesNet-Deepfashion"
layer {
name: "data"
type: "Python"
top: "data"
top: "texture_label"
top: "fabric_label"
top: "shape_label"
top: "part_label"
top: "style_label"
top: "class_label"
python_param {
module: "deepfashion_multitask_datalayers"
layer: "MultilabelDataLayerAsync_Multitask"
param_str: "{\'image_root\': \'/data0/xujiu/proj/deep-fashion-desnet/\', \'im_shape\': [224, 224], \'split\': \'clean_train\', \'batch_size\': 9}"
}
}
layer {
name: "conv1"
type: "Convolution"
bottom: "data"
top: "conv1"
convolution_param {
num_output: 96
bias_term: false
pad: 3
kernel_size: 7
stride: 2
}
}
layer {
name: "conv1/bn"
type: "BatchNorm"
bottom: "conv1"
top: "conv1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv1/scale"
type: "Scale"
bottom: "conv1/bn"
top: "conv1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1/bn"
top: "conv1/bn"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1/bn"
top: "pool1"
pooling_param {
pool: MAX
kernel_size: 3
stride: 2
pad: 1
}
}
layer {
name: "conv2_1/x1/bn"
type: "BatchNorm"
bottom: "pool1"
top: "conv2_1/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_1/x1/scale"
type: "Scale"
bottom: "conv2_1/x1/bn"
top: "conv2_1/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_1/x1"
type: "ReLU"
bottom: "conv2_1/x1/bn"
top: "conv2_1/x1/bn"
}
layer {
name: "conv2_1/x1"
type: "Convolution"
bottom: "conv2_1/x1/bn"
top: "conv2_1/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_1/x2/bn"
type: "BatchNorm"
bottom: "conv2_1/x1"
top: "conv2_1/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_1/x2/scale"
type: "Scale"
bottom: "conv2_1/x2/bn"
top: "conv2_1/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_1/x2"
type: "ReLU"
bottom: "conv2_1/x2/bn"
top: "conv2_1/x2/bn"
}
layer {
name: "conv2_1/x2"
type: "Convolution"
bottom: "conv2_1/x2/bn"
top: "conv2_1/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_1"
type: "Concat"
bottom: "pool1"
bottom: "conv2_1/x2"
top: "concat_2_1"
}
layer {
name: "conv2_2/x1/bn"
type: "BatchNorm"
bottom: "concat_2_1"
top: "conv2_2/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_2/x1/scale"
type: "Scale"
bottom: "conv2_2/x1/bn"
top: "conv2_2/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_2/x1"
type: "ReLU"
bottom: "conv2_2/x1/bn"
top: "conv2_2/x1/bn"
}
layer {
name: "conv2_2/x1"
type: "Convolution"
bottom: "conv2_2/x1/bn"
top: "conv2_2/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_2/x2/bn"
type: "BatchNorm"
bottom: "conv2_2/x1"
top: "conv2_2/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_2/x2/scale"
type: "Scale"
bottom: "conv2_2/x2/bn"
top: "conv2_2/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_2/x2"
type: "ReLU"
bottom: "conv2_2/x2/bn"
top: "conv2_2/x2/bn"
}
layer {
name: "conv2_2/x2"
type: "Convolution"
bottom: "conv2_2/x2/bn"
top: "conv2_2/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_2"
type: "Concat"
bottom: "concat_2_1"
bottom: "conv2_2/x2"
top: "concat_2_2"
}
layer {
name: "conv2_3/x1/bn"
type: "BatchNorm"
bottom: "concat_2_2"
top: "conv2_3/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_3/x1/scale"
type: "Scale"
bottom: "conv2_3/x1/bn"
top: "conv2_3/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_3/x1"
type: "ReLU"
bottom: "conv2_3/x1/bn"
top: "conv2_3/x1/bn"
}
layer {
name: "conv2_3/x1"
type: "Convolution"
bottom: "conv2_3/x1/bn"
top: "conv2_3/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_3/x2/bn"
type: "BatchNorm"
bottom: "conv2_3/x1"
top: "conv2_3/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_3/x2/scale"
type: "Scale"
bottom: "conv2_3/x2/bn"
top: "conv2_3/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_3/x2"
type: "ReLU"
bottom: "conv2_3/x2/bn"
top: "conv2_3/x2/bn"
}
layer {
name: "conv2_3/x2"
type: "Convolution"
bottom: "conv2_3/x2/bn"
top: "conv2_3/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_3"
type: "Concat"
bottom: "concat_2_2"
bottom: "conv2_3/x2"
top: "concat_2_3"
}
layer {
name: "conv2_4/x1/bn"
type: "BatchNorm"
bottom: "concat_2_3"
top: "conv2_4/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_4/x1/scale"
type: "Scale"
bottom: "conv2_4/x1/bn"
top: "conv2_4/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_4/x1"
type: "ReLU"
bottom: "conv2_4/x1/bn"
top: "conv2_4/x1/bn"
}
layer {
name: "conv2_4/x1"
type: "Convolution"
bottom: "conv2_4/x1/bn"
top: "conv2_4/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_4/x2/bn"
type: "BatchNorm"
bottom: "conv2_4/x1"
top: "conv2_4/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_4/x2/scale"
type: "Scale"
bottom: "conv2_4/x2/bn"
top: "conv2_4/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_4/x2"
type: "ReLU"
bottom: "conv2_4/x2/bn"
top: "conv2_4/x2/bn"
}
layer {
name: "conv2_4/x2"
type: "Convolution"
bottom: "conv2_4/x2/bn"
top: "conv2_4/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_4"
type: "Concat"
bottom: "concat_2_3"
bottom: "conv2_4/x2"
top: "concat_2_4"
}
layer {
name: "conv2_5/x1/bn"
type: "BatchNorm"
bottom: "concat_2_4"
top: "conv2_5/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_5/x1/scale"
type: "Scale"
bottom: "conv2_5/x1/bn"
top: "conv2_5/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_5/x1"
type: "ReLU"
bottom: "conv2_5/x1/bn"
top: "conv2_5/x1/bn"
}
layer {
name: "conv2_5/x1"
type: "Convolution"
bottom: "conv2_5/x1/bn"
top: "conv2_5/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_5/x2/bn"
type: "BatchNorm"
bottom: "conv2_5/x1"
top: "conv2_5/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_5/x2/scale"
type: "Scale"
bottom: "conv2_5/x2/bn"
top: "conv2_5/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_5/x2"
type: "ReLU"
bottom: "conv2_5/x2/bn"
top: "conv2_5/x2/bn"
}
layer {
name: "conv2_5/x2"
type: "Convolution"
bottom: "conv2_5/x2/bn"
top: "conv2_5/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_5"
type: "Concat"
bottom: "concat_2_4"
bottom: "conv2_5/x2"
top: "concat_2_5"
}
layer {
name: "conv2_6/x1/bn"
type: "BatchNorm"
bottom: "concat_2_5"
top: "conv2_6/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_6/x1/scale"
type: "Scale"
bottom: "conv2_6/x1/bn"
top: "conv2_6/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_6/x1"
type: "ReLU"
bottom: "conv2_6/x1/bn"
top: "conv2_6/x1/bn"
}
layer {
name: "conv2_6/x1"
type: "Convolution"
bottom: "conv2_6/x1/bn"
top: "conv2_6/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv2_6/x2/bn"
type: "BatchNorm"
bottom: "conv2_6/x1"
top: "conv2_6/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_6/x2/scale"
type: "Scale"
bottom: "conv2_6/x2/bn"
top: "conv2_6/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_6/x2"
type: "ReLU"
bottom: "conv2_6/x2/bn"
top: "conv2_6/x2/bn"
}
layer {
name: "conv2_6/x2"
type: "Convolution"
bottom: "conv2_6/x2/bn"
top: "conv2_6/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_2_6"
type: "Concat"
bottom: "concat_2_5"
bottom: "conv2_6/x2"
top: "concat_2_6"
}
layer {
name: "conv2_blk/bn"
type: "BatchNorm"
bottom: "concat_2_6"
top: "conv2_blk/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv2_blk/scale"
type: "Scale"
bottom: "conv2_blk/bn"
top: "conv2_blk/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu2_blk"
type: "ReLU"
bottom: "conv2_blk/bn"
top: "conv2_blk/bn"
}
layer {
name: "conv2_blk"
type: "Convolution"
bottom: "conv2_blk/bn"
top: "conv2_blk"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_blk"
top: "pool2"
pooling_param {
pool: AVE
kernel_size: 2
stride: 2
}
}
layer {
name: "conv3_1/x1/bn"
type: "BatchNorm"
bottom: "pool2"
top: "conv3_1/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_1/x1/scale"
type: "Scale"
bottom: "conv3_1/x1/bn"
top: "conv3_1/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_1/x1"
type: "ReLU"
bottom: "conv3_1/x1/bn"
top: "conv3_1/x1/bn"
}
layer {
name: "conv3_1/x1"
type: "Convolution"
bottom: "conv3_1/x1/bn"
top: "conv3_1/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_1/x2/bn"
type: "BatchNorm"
bottom: "conv3_1/x1"
top: "conv3_1/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_1/x2/scale"
type: "Scale"
bottom: "conv3_1/x2/bn"
top: "conv3_1/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_1/x2"
type: "ReLU"
bottom: "conv3_1/x2/bn"
top: "conv3_1/x2/bn"
}
layer {
name: "conv3_1/x2"
type: "Convolution"
bottom: "conv3_1/x2/bn"
top: "conv3_1/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_1"
type: "Concat"
bottom: "pool2"
bottom: "conv3_1/x2"
top: "concat_3_1"
}
layer {
name: "conv3_2/x1/bn"
type: "BatchNorm"
bottom: "concat_3_1"
top: "conv3_2/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_2/x1/scale"
type: "Scale"
bottom: "conv3_2/x1/bn"
top: "conv3_2/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_2/x1"
type: "ReLU"
bottom: "conv3_2/x1/bn"
top: "conv3_2/x1/bn"
}
layer {
name: "conv3_2/x1"
type: "Convolution"
bottom: "conv3_2/x1/bn"
top: "conv3_2/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_2/x2/bn"
type: "BatchNorm"
bottom: "conv3_2/x1"
top: "conv3_2/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_2/x2/scale"
type: "Scale"
bottom: "conv3_2/x2/bn"
top: "conv3_2/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_2/x2"
type: "ReLU"
bottom: "conv3_2/x2/bn"
top: "conv3_2/x2/bn"
}
layer {
name: "conv3_2/x2"
type: "Convolution"
bottom: "conv3_2/x2/bn"
top: "conv3_2/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_2"
type: "Concat"
bottom: "concat_3_1"
bottom: "conv3_2/x2"
top: "concat_3_2"
}
layer {
name: "conv3_3/x1/bn"
type: "BatchNorm"
bottom: "concat_3_2"
top: "conv3_3/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_3/x1/scale"
type: "Scale"
bottom: "conv3_3/x1/bn"
top: "conv3_3/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_3/x1"
type: "ReLU"
bottom: "conv3_3/x1/bn"
top: "conv3_3/x1/bn"
}
layer {
name: "conv3_3/x1"
type: "Convolution"
bottom: "conv3_3/x1/bn"
top: "conv3_3/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_3/x2/bn"
type: "BatchNorm"
bottom: "conv3_3/x1"
top: "conv3_3/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_3/x2/scale"
type: "Scale"
bottom: "conv3_3/x2/bn"
top: "conv3_3/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_3/x2"
type: "ReLU"
bottom: "conv3_3/x2/bn"
top: "conv3_3/x2/bn"
}
layer {
name: "conv3_3/x2"
type: "Convolution"
bottom: "conv3_3/x2/bn"
top: "conv3_3/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_3"
type: "Concat"
bottom: "concat_3_2"
bottom: "conv3_3/x2"
top: "concat_3_3"
}
layer {
name: "conv3_4/x1/bn"
type: "BatchNorm"
bottom: "concat_3_3"
top: "conv3_4/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_4/x1/scale"
type: "Scale"
bottom: "conv3_4/x1/bn"
top: "conv3_4/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_4/x1"
type: "ReLU"
bottom: "conv3_4/x1/bn"
top: "conv3_4/x1/bn"
}
layer {
name: "conv3_4/x1"
type: "Convolution"
bottom: "conv3_4/x1/bn"
top: "conv3_4/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_4/x2/bn"
type: "BatchNorm"
bottom: "conv3_4/x1"
top: "conv3_4/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_4/x2/scale"
type: "Scale"
bottom: "conv3_4/x2/bn"
top: "conv3_4/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_4/x2"
type: "ReLU"
bottom: "conv3_4/x2/bn"
top: "conv3_4/x2/bn"
}
layer {
name: "conv3_4/x2"
type: "Convolution"
bottom: "conv3_4/x2/bn"
top: "conv3_4/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_4"
type: "Concat"
bottom: "concat_3_3"
bottom: "conv3_4/x2"
top: "concat_3_4"
}
layer {
name: "conv3_5/x1/bn"
type: "BatchNorm"
bottom: "concat_3_4"
top: "conv3_5/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_5/x1/scale"
type: "Scale"
bottom: "conv3_5/x1/bn"
top: "conv3_5/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_5/x1"
type: "ReLU"
bottom: "conv3_5/x1/bn"
top: "conv3_5/x1/bn"
}
layer {
name: "conv3_5/x1"
type: "Convolution"
bottom: "conv3_5/x1/bn"
top: "conv3_5/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_5/x2/bn"
type: "BatchNorm"
bottom: "conv3_5/x1"
top: "conv3_5/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_5/x2/scale"
type: "Scale"
bottom: "conv3_5/x2/bn"
top: "conv3_5/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_5/x2"
type: "ReLU"
bottom: "conv3_5/x2/bn"
top: "conv3_5/x2/bn"
}
layer {
name: "conv3_5/x2"
type: "Convolution"
bottom: "conv3_5/x2/bn"
top: "conv3_5/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_5"
type: "Concat"
bottom: "concat_3_4"
bottom: "conv3_5/x2"
top: "concat_3_5"
}
layer {
name: "conv3_6/x1/bn"
type: "BatchNorm"
bottom: "concat_3_5"
top: "conv3_6/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_6/x1/scale"
type: "Scale"
bottom: "conv3_6/x1/bn"
top: "conv3_6/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_6/x1"
type: "ReLU"
bottom: "conv3_6/x1/bn"
top: "conv3_6/x1/bn"
}
layer {
name: "conv3_6/x1"
type: "Convolution"
bottom: "conv3_6/x1/bn"
top: "conv3_6/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_6/x2/bn"
type: "BatchNorm"
bottom: "conv3_6/x1"
top: "conv3_6/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_6/x2/scale"
type: "Scale"
bottom: "conv3_6/x2/bn"
top: "conv3_6/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_6/x2"
type: "ReLU"
bottom: "conv3_6/x2/bn"
top: "conv3_6/x2/bn"
}
layer {
name: "conv3_6/x2"
type: "Convolution"
bottom: "conv3_6/x2/bn"
top: "conv3_6/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_6"
type: "Concat"
bottom: "concat_3_5"
bottom: "conv3_6/x2"
top: "concat_3_6"
}
layer {
name: "conv3_7/x1/bn"
type: "BatchNorm"
bottom: "concat_3_6"
top: "conv3_7/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_7/x1/scale"
type: "Scale"
bottom: "conv3_7/x1/bn"
top: "conv3_7/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_7/x1"
type: "ReLU"
bottom: "conv3_7/x1/bn"
top: "conv3_7/x1/bn"
}
layer {
name: "conv3_7/x1"
type: "Convolution"
bottom: "conv3_7/x1/bn"
top: "conv3_7/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_7/x2/bn"
type: "BatchNorm"
bottom: "conv3_7/x1"
top: "conv3_7/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_7/x2/scale"
type: "Scale"
bottom: "conv3_7/x2/bn"
top: "conv3_7/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_7/x2"
type: "ReLU"
bottom: "conv3_7/x2/bn"
top: "conv3_7/x2/bn"
}
layer {
name: "conv3_7/x2"
type: "Convolution"
bottom: "conv3_7/x2/bn"
top: "conv3_7/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_7"
type: "Concat"
bottom: "concat_3_6"
bottom: "conv3_7/x2"
top: "concat_3_7"
}
layer {
name: "conv3_8/x1/bn"
type: "BatchNorm"
bottom: "concat_3_7"
top: "conv3_8/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_8/x1/scale"
type: "Scale"
bottom: "conv3_8/x1/bn"
top: "conv3_8/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_8/x1"
type: "ReLU"
bottom: "conv3_8/x1/bn"
top: "conv3_8/x1/bn"
}
layer {
name: "conv3_8/x1"
type: "Convolution"
bottom: "conv3_8/x1/bn"
top: "conv3_8/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_8/x2/bn"
type: "BatchNorm"
bottom: "conv3_8/x1"
top: "conv3_8/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_8/x2/scale"
type: "Scale"
bottom: "conv3_8/x2/bn"
top: "conv3_8/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_8/x2"
type: "ReLU"
bottom: "conv3_8/x2/bn"
top: "conv3_8/x2/bn"
}
layer {
name: "conv3_8/x2"
type: "Convolution"
bottom: "conv3_8/x2/bn"
top: "conv3_8/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_8"
type: "Concat"
bottom: "concat_3_7"
bottom: "conv3_8/x2"
top: "concat_3_8"
}
layer {
name: "conv3_9/x1/bn"
type: "BatchNorm"
bottom: "concat_3_8"
top: "conv3_9/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_9/x1/scale"
type: "Scale"
bottom: "conv3_9/x1/bn"
top: "conv3_9/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_9/x1"
type: "ReLU"
bottom: "conv3_9/x1/bn"
top: "conv3_9/x1/bn"
}
layer {
name: "conv3_9/x1"
type: "Convolution"
bottom: "conv3_9/x1/bn"
top: "conv3_9/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_9/x2/bn"
type: "BatchNorm"
bottom: "conv3_9/x1"
top: "conv3_9/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_9/x2/scale"
type: "Scale"
bottom: "conv3_9/x2/bn"
top: "conv3_9/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_9/x2"
type: "ReLU"
bottom: "conv3_9/x2/bn"
top: "conv3_9/x2/bn"
}
layer {
name: "conv3_9/x2"
type: "Convolution"
bottom: "conv3_9/x2/bn"
top: "conv3_9/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_9"
type: "Concat"
bottom: "concat_3_8"
bottom: "conv3_9/x2"
top: "concat_3_9"
}
layer {
name: "conv3_10/x1/bn"
type: "BatchNorm"
bottom: "concat_3_9"
top: "conv3_10/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_10/x1/scale"
type: "Scale"
bottom: "conv3_10/x1/bn"
top: "conv3_10/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_10/x1"
type: "ReLU"
bottom: "conv3_10/x1/bn"
top: "conv3_10/x1/bn"
}
layer {
name: "conv3_10/x1"
type: "Convolution"
bottom: "conv3_10/x1/bn"
top: "conv3_10/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_10/x2/bn"
type: "BatchNorm"
bottom: "conv3_10/x1"
top: "conv3_10/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_10/x2/scale"
type: "Scale"
bottom: "conv3_10/x2/bn"
top: "conv3_10/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_10/x2"
type: "ReLU"
bottom: "conv3_10/x2/bn"
top: "conv3_10/x2/bn"
}
layer {
name: "conv3_10/x2"
type: "Convolution"
bottom: "conv3_10/x2/bn"
top: "conv3_10/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_10"
type: "Concat"
bottom: "concat_3_9"
bottom: "conv3_10/x2"
top: "concat_3_10"
}
layer {
name: "conv3_11/x1/bn"
type: "BatchNorm"
bottom: "concat_3_10"
top: "conv3_11/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_11/x1/scale"
type: "Scale"
bottom: "conv3_11/x1/bn"
top: "conv3_11/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_11/x1"
type: "ReLU"
bottom: "conv3_11/x1/bn"
top: "conv3_11/x1/bn"
}
layer {
name: "conv3_11/x1"
type: "Convolution"
bottom: "conv3_11/x1/bn"
top: "conv3_11/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_11/x2/bn"
type: "BatchNorm"
bottom: "conv3_11/x1"
top: "conv3_11/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_11/x2/scale"
type: "Scale"
bottom: "conv3_11/x2/bn"
top: "conv3_11/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_11/x2"
type: "ReLU"
bottom: "conv3_11/x2/bn"
top: "conv3_11/x2/bn"
}
layer {
name: "conv3_11/x2"
type: "Convolution"
bottom: "conv3_11/x2/bn"
top: "conv3_11/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_11"
type: "Concat"
bottom: "concat_3_10"
bottom: "conv3_11/x2"
top: "concat_3_11"
}
layer {
name: "conv3_12/x1/bn"
type: "BatchNorm"
bottom: "concat_3_11"
top: "conv3_12/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_12/x1/scale"
type: "Scale"
bottom: "conv3_12/x1/bn"
top: "conv3_12/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_12/x1"
type: "ReLU"
bottom: "conv3_12/x1/bn"
top: "conv3_12/x1/bn"
}
layer {
name: "conv3_12/x1"
type: "Convolution"
bottom: "conv3_12/x1/bn"
top: "conv3_12/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv3_12/x2/bn"
type: "BatchNorm"
bottom: "conv3_12/x1"
top: "conv3_12/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_12/x2/scale"
type: "Scale"
bottom: "conv3_12/x2/bn"
top: "conv3_12/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_12/x2"
type: "ReLU"
bottom: "conv3_12/x2/bn"
top: "conv3_12/x2/bn"
}
layer {
name: "conv3_12/x2"
type: "Convolution"
bottom: "conv3_12/x2/bn"
top: "conv3_12/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_3_12"
type: "Concat"
bottom: "concat_3_11"
bottom: "conv3_12/x2"
top: "concat_3_12"
}
layer {
name: "conv3_blk/bn"
type: "BatchNorm"
bottom: "concat_3_12"
top: "conv3_blk/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv3_blk/scale"
type: "Scale"
bottom: "conv3_blk/bn"
top: "conv3_blk/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu3_blk"
type: "ReLU"
bottom: "conv3_blk/bn"
top: "conv3_blk/bn"
}
layer {
name: "conv3_blk"
type: "Convolution"
bottom: "conv3_blk/bn"
top: "conv3_blk"
convolution_param {
num_output: 384
bias_term: false
kernel_size: 1
}
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_blk"
top: "pool3"
pooling_param {
pool: AVE
kernel_size: 2
stride: 2
}
}
layer {
name: "conv4_1/x1/bn"
type: "BatchNorm"
bottom: "pool3"
top: "conv4_1/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_1/x1/scale"
type: "Scale"
bottom: "conv4_1/x1/bn"
top: "conv4_1/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_1/x1"
type: "ReLU"
bottom: "conv4_1/x1/bn"
top: "conv4_1/x1/bn"
}
layer {
name: "conv4_1/x1"
type: "Convolution"
bottom: "conv4_1/x1/bn"
top: "conv4_1/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_1/x2/bn"
type: "BatchNorm"
bottom: "conv4_1/x1"
top: "conv4_1/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_1/x2/scale"
type: "Scale"
bottom: "conv4_1/x2/bn"
top: "conv4_1/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_1/x2"
type: "ReLU"
bottom: "conv4_1/x2/bn"
top: "conv4_1/x2/bn"
}
layer {
name: "conv4_1/x2"
type: "Convolution"
bottom: "conv4_1/x2/bn"
top: "conv4_1/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_1"
type: "Concat"
bottom: "pool3"
bottom: "conv4_1/x2"
top: "concat_4_1"
}
layer {
name: "conv4_2/x1/bn"
type: "BatchNorm"
bottom: "concat_4_1"
top: "conv4_2/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_2/x1/scale"
type: "Scale"
bottom: "conv4_2/x1/bn"
top: "conv4_2/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_2/x1"
type: "ReLU"
bottom: "conv4_2/x1/bn"
top: "conv4_2/x1/bn"
}
layer {
name: "conv4_2/x1"
type: "Convolution"
bottom: "conv4_2/x1/bn"
top: "conv4_2/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_2/x2/bn"
type: "BatchNorm"
bottom: "conv4_2/x1"
top: "conv4_2/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_2/x2/scale"
type: "Scale"
bottom: "conv4_2/x2/bn"
top: "conv4_2/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_2/x2"
type: "ReLU"
bottom: "conv4_2/x2/bn"
top: "conv4_2/x2/bn"
}
layer {
name: "conv4_2/x2"
type: "Convolution"
bottom: "conv4_2/x2/bn"
top: "conv4_2/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_2"
type: "Concat"
bottom: "concat_4_1"
bottom: "conv4_2/x2"
top: "concat_4_2"
}
layer {
name: "conv4_3/x1/bn"
type: "BatchNorm"
bottom: "concat_4_2"
top: "conv4_3/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_3/x1/scale"
type: "Scale"
bottom: "conv4_3/x1/bn"
top: "conv4_3/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_3/x1"
type: "ReLU"
bottom: "conv4_3/x1/bn"
top: "conv4_3/x1/bn"
}
layer {
name: "conv4_3/x1"
type: "Convolution"
bottom: "conv4_3/x1/bn"
top: "conv4_3/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_3/x2/bn"
type: "BatchNorm"
bottom: "conv4_3/x1"
top: "conv4_3/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_3/x2/scale"
type: "Scale"
bottom: "conv4_3/x2/bn"
top: "conv4_3/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_3/x2"
type: "ReLU"
bottom: "conv4_3/x2/bn"
top: "conv4_3/x2/bn"
}
layer {
name: "conv4_3/x2"
type: "Convolution"
bottom: "conv4_3/x2/bn"
top: "conv4_3/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_3"
type: "Concat"
bottom: "concat_4_2"
bottom: "conv4_3/x2"
top: "concat_4_3"
}
layer {
name: "conv4_4/x1/bn"
type: "BatchNorm"
bottom: "concat_4_3"
top: "conv4_4/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_4/x1/scale"
type: "Scale"
bottom: "conv4_4/x1/bn"
top: "conv4_4/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_4/x1"
type: "ReLU"
bottom: "conv4_4/x1/bn"
top: "conv4_4/x1/bn"
}
layer {
name: "conv4_4/x1"
type: "Convolution"
bottom: "conv4_4/x1/bn"
top: "conv4_4/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_4/x2/bn"
type: "BatchNorm"
bottom: "conv4_4/x1"
top: "conv4_4/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_4/x2/scale"
type: "Scale"
bottom: "conv4_4/x2/bn"
top: "conv4_4/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_4/x2"
type: "ReLU"
bottom: "conv4_4/x2/bn"
top: "conv4_4/x2/bn"
}
layer {
name: "conv4_4/x2"
type: "Convolution"
bottom: "conv4_4/x2/bn"
top: "conv4_4/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_4"
type: "Concat"
bottom: "concat_4_3"
bottom: "conv4_4/x2"
top: "concat_4_4"
}
layer {
name: "conv4_5/x1/bn"
type: "BatchNorm"
bottom: "concat_4_4"
top: "conv4_5/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_5/x1/scale"
type: "Scale"
bottom: "conv4_5/x1/bn"
top: "conv4_5/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_5/x1"
type: "ReLU"
bottom: "conv4_5/x1/bn"
top: "conv4_5/x1/bn"
}
layer {
name: "conv4_5/x1"
type: "Convolution"
bottom: "conv4_5/x1/bn"
top: "conv4_5/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_5/x2/bn"
type: "BatchNorm"
bottom: "conv4_5/x1"
top: "conv4_5/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_5/x2/scale"
type: "Scale"
bottom: "conv4_5/x2/bn"
top: "conv4_5/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_5/x2"
type: "ReLU"
bottom: "conv4_5/x2/bn"
top: "conv4_5/x2/bn"
}
layer {
name: "conv4_5/x2"
type: "Convolution"
bottom: "conv4_5/x2/bn"
top: "conv4_5/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_5"
type: "Concat"
bottom: "concat_4_4"
bottom: "conv4_5/x2"
top: "concat_4_5"
}
layer {
name: "conv4_6/x1/bn"
type: "BatchNorm"
bottom: "concat_4_5"
top: "conv4_6/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_6/x1/scale"
type: "Scale"
bottom: "conv4_6/x1/bn"
top: "conv4_6/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_6/x1"
type: "ReLU"
bottom: "conv4_6/x1/bn"
top: "conv4_6/x1/bn"
}
layer {
name: "conv4_6/x1"
type: "Convolution"
bottom: "conv4_6/x1/bn"
top: "conv4_6/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_6/x2/bn"
type: "BatchNorm"
bottom: "conv4_6/x1"
top: "conv4_6/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_6/x2/scale"
type: "Scale"
bottom: "conv4_6/x2/bn"
top: "conv4_6/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_6/x2"
type: "ReLU"
bottom: "conv4_6/x2/bn"
top: "conv4_6/x2/bn"
}
layer {
name: "conv4_6/x2"
type: "Convolution"
bottom: "conv4_6/x2/bn"
top: "conv4_6/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_6"
type: "Concat"
bottom: "concat_4_5"
bottom: "conv4_6/x2"
top: "concat_4_6"
}
layer {
name: "conv4_7/x1/bn"
type: "BatchNorm"
bottom: "concat_4_6"
top: "conv4_7/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_7/x1/scale"
type: "Scale"
bottom: "conv4_7/x1/bn"
top: "conv4_7/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_7/x1"
type: "ReLU"
bottom: "conv4_7/x1/bn"
top: "conv4_7/x1/bn"
}
layer {
name: "conv4_7/x1"
type: "Convolution"
bottom: "conv4_7/x1/bn"
top: "conv4_7/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_7/x2/bn"
type: "BatchNorm"
bottom: "conv4_7/x1"
top: "conv4_7/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_7/x2/scale"
type: "Scale"
bottom: "conv4_7/x2/bn"
top: "conv4_7/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_7/x2"
type: "ReLU"
bottom: "conv4_7/x2/bn"
top: "conv4_7/x2/bn"
}
layer {
name: "conv4_7/x2"
type: "Convolution"
bottom: "conv4_7/x2/bn"
top: "conv4_7/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_7"
type: "Concat"
bottom: "concat_4_6"
bottom: "conv4_7/x2"
top: "concat_4_7"
}
layer {
name: "conv4_8/x1/bn"
type: "BatchNorm"
bottom: "concat_4_7"
top: "conv4_8/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_8/x1/scale"
type: "Scale"
bottom: "conv4_8/x1/bn"
top: "conv4_8/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_8/x1"
type: "ReLU"
bottom: "conv4_8/x1/bn"
top: "conv4_8/x1/bn"
}
layer {
name: "conv4_8/x1"
type: "Convolution"
bottom: "conv4_8/x1/bn"
top: "conv4_8/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_8/x2/bn"
type: "BatchNorm"
bottom: "conv4_8/x1"
top: "conv4_8/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_8/x2/scale"
type: "Scale"
bottom: "conv4_8/x2/bn"
top: "conv4_8/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_8/x2"
type: "ReLU"
bottom: "conv4_8/x2/bn"
top: "conv4_8/x2/bn"
}
layer {
name: "conv4_8/x2"
type: "Convolution"
bottom: "conv4_8/x2/bn"
top: "conv4_8/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_8"
type: "Concat"
bottom: "concat_4_7"
bottom: "conv4_8/x2"
top: "concat_4_8"
}
layer {
name: "conv4_9/x1/bn"
type: "BatchNorm"
bottom: "concat_4_8"
top: "conv4_9/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_9/x1/scale"
type: "Scale"
bottom: "conv4_9/x1/bn"
top: "conv4_9/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_9/x1"
type: "ReLU"
bottom: "conv4_9/x1/bn"
top: "conv4_9/x1/bn"
}
layer {
name: "conv4_9/x1"
type: "Convolution"
bottom: "conv4_9/x1/bn"
top: "conv4_9/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_9/x2/bn"
type: "BatchNorm"
bottom: "conv4_9/x1"
top: "conv4_9/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_9/x2/scale"
type: "Scale"
bottom: "conv4_9/x2/bn"
top: "conv4_9/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_9/x2"
type: "ReLU"
bottom: "conv4_9/x2/bn"
top: "conv4_9/x2/bn"
}
layer {
name: "conv4_9/x2"
type: "Convolution"
bottom: "conv4_9/x2/bn"
top: "conv4_9/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_9"
type: "Concat"
bottom: "concat_4_8"
bottom: "conv4_9/x2"
top: "concat_4_9"
}
layer {
name: "conv4_10/x1/bn"
type: "BatchNorm"
bottom: "concat_4_9"
top: "conv4_10/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_10/x1/scale"
type: "Scale"
bottom: "conv4_10/x1/bn"
top: "conv4_10/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_10/x1"
type: "ReLU"
bottom: "conv4_10/x1/bn"
top: "conv4_10/x1/bn"
}
layer {
name: "conv4_10/x1"
type: "Convolution"
bottom: "conv4_10/x1/bn"
top: "conv4_10/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_10/x2/bn"
type: "BatchNorm"
bottom: "conv4_10/x1"
top: "conv4_10/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_10/x2/scale"
type: "Scale"
bottom: "conv4_10/x2/bn"
top: "conv4_10/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_10/x2"
type: "ReLU"
bottom: "conv4_10/x2/bn"
top: "conv4_10/x2/bn"
}
layer {
name: "conv4_10/x2"
type: "Convolution"
bottom: "conv4_10/x2/bn"
top: "conv4_10/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_10"
type: "Concat"
bottom: "concat_4_9"
bottom: "conv4_10/x2"
top: "concat_4_10"
}
layer {
name: "conv4_11/x1/bn"
type: "BatchNorm"
bottom: "concat_4_10"
top: "conv4_11/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_11/x1/scale"
type: "Scale"
bottom: "conv4_11/x1/bn"
top: "conv4_11/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_11/x1"
type: "ReLU"
bottom: "conv4_11/x1/bn"
top: "conv4_11/x1/bn"
}
layer {
name: "conv4_11/x1"
type: "Convolution"
bottom: "conv4_11/x1/bn"
top: "conv4_11/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_11/x2/bn"
type: "BatchNorm"
bottom: "conv4_11/x1"
top: "conv4_11/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_11/x2/scale"
type: "Scale"
bottom: "conv4_11/x2/bn"
top: "conv4_11/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_11/x2"
type: "ReLU"
bottom: "conv4_11/x2/bn"
top: "conv4_11/x2/bn"
}
layer {
name: "conv4_11/x2"
type: "Convolution"
bottom: "conv4_11/x2/bn"
top: "conv4_11/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_11"
type: "Concat"
bottom: "concat_4_10"
bottom: "conv4_11/x2"
top: "concat_4_11"
}
layer {
name: "conv4_12/x1/bn"
type: "BatchNorm"
bottom: "concat_4_11"
top: "conv4_12/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_12/x1/scale"
type: "Scale"
bottom: "conv4_12/x1/bn"
top: "conv4_12/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_12/x1"
type: "ReLU"
bottom: "conv4_12/x1/bn"
top: "conv4_12/x1/bn"
}
layer {
name: "conv4_12/x1"
type: "Convolution"
bottom: "conv4_12/x1/bn"
top: "conv4_12/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_12/x2/bn"
type: "BatchNorm"
bottom: "conv4_12/x1"
top: "conv4_12/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_12/x2/scale"
type: "Scale"
bottom: "conv4_12/x2/bn"
top: "conv4_12/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_12/x2"
type: "ReLU"
bottom: "conv4_12/x2/bn"
top: "conv4_12/x2/bn"
}
layer {
name: "conv4_12/x2"
type: "Convolution"
bottom: "conv4_12/x2/bn"
top: "conv4_12/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_12"
type: "Concat"
bottom: "concat_4_11"
bottom: "conv4_12/x2"
top: "concat_4_12"
}
layer {
name: "conv4_13/x1/bn"
type: "BatchNorm"
bottom: "concat_4_12"
top: "conv4_13/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_13/x1/scale"
type: "Scale"
bottom: "conv4_13/x1/bn"
top: "conv4_13/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_13/x1"
type: "ReLU"
bottom: "conv4_13/x1/bn"
top: "conv4_13/x1/bn"
}
layer {
name: "conv4_13/x1"
type: "Convolution"
bottom: "conv4_13/x1/bn"
top: "conv4_13/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_13/x2/bn"
type: "BatchNorm"
bottom: "conv4_13/x1"
top: "conv4_13/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_13/x2/scale"
type: "Scale"
bottom: "conv4_13/x2/bn"
top: "conv4_13/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_13/x2"
type: "ReLU"
bottom: "conv4_13/x2/bn"
top: "conv4_13/x2/bn"
}
layer {
name: "conv4_13/x2"
type: "Convolution"
bottom: "conv4_13/x2/bn"
top: "conv4_13/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_13"
type: "Concat"
bottom: "concat_4_12"
bottom: "conv4_13/x2"
top: "concat_4_13"
}
layer {
name: "conv4_14/x1/bn"
type: "BatchNorm"
bottom: "concat_4_13"
top: "conv4_14/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_14/x1/scale"
type: "Scale"
bottom: "conv4_14/x1/bn"
top: "conv4_14/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_14/x1"
type: "ReLU"
bottom: "conv4_14/x1/bn"
top: "conv4_14/x1/bn"
}
layer {
name: "conv4_14/x1"
type: "Convolution"
bottom: "conv4_14/x1/bn"
top: "conv4_14/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_14/x2/bn"
type: "BatchNorm"
bottom: "conv4_14/x1"
top: "conv4_14/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_14/x2/scale"
type: "Scale"
bottom: "conv4_14/x2/bn"
top: "conv4_14/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_14/x2"
type: "ReLU"
bottom: "conv4_14/x2/bn"
top: "conv4_14/x2/bn"
}
layer {
name: "conv4_14/x2"
type: "Convolution"
bottom: "conv4_14/x2/bn"
top: "conv4_14/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_14"
type: "Concat"
bottom: "concat_4_13"
bottom: "conv4_14/x2"
top: "concat_4_14"
}
layer {
name: "conv4_15/x1/bn"
type: "BatchNorm"
bottom: "concat_4_14"
top: "conv4_15/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_15/x1/scale"
type: "Scale"
bottom: "conv4_15/x1/bn"
top: "conv4_15/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_15/x1"
type: "ReLU"
bottom: "conv4_15/x1/bn"
top: "conv4_15/x1/bn"
}
layer {
name: "conv4_15/x1"
type: "Convolution"
bottom: "conv4_15/x1/bn"
top: "conv4_15/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_15/x2/bn"
type: "BatchNorm"
bottom: "conv4_15/x1"
top: "conv4_15/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_15/x2/scale"
type: "Scale"
bottom: "conv4_15/x2/bn"
top: "conv4_15/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_15/x2"
type: "ReLU"
bottom: "conv4_15/x2/bn"
top: "conv4_15/x2/bn"
}
layer {
name: "conv4_15/x2"
type: "Convolution"
bottom: "conv4_15/x2/bn"
top: "conv4_15/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_15"
type: "Concat"
bottom: "concat_4_14"
bottom: "conv4_15/x2"
top: "concat_4_15"
}
layer {
name: "conv4_16/x1/bn"
type: "BatchNorm"
bottom: "concat_4_15"
top: "conv4_16/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_16/x1/scale"
type: "Scale"
bottom: "conv4_16/x1/bn"
top: "conv4_16/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_16/x1"
type: "ReLU"
bottom: "conv4_16/x1/bn"
top: "conv4_16/x1/bn"
}
layer {
name: "conv4_16/x1"
type: "Convolution"
bottom: "conv4_16/x1/bn"
top: "conv4_16/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_16/x2/bn"
type: "BatchNorm"
bottom: "conv4_16/x1"
top: "conv4_16/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_16/x2/scale"
type: "Scale"
bottom: "conv4_16/x2/bn"
top: "conv4_16/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_16/x2"
type: "ReLU"
bottom: "conv4_16/x2/bn"
top: "conv4_16/x2/bn"
}
layer {
name: "conv4_16/x2"
type: "Convolution"
bottom: "conv4_16/x2/bn"
top: "conv4_16/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_16"
type: "Concat"
bottom: "concat_4_15"
bottom: "conv4_16/x2"
top: "concat_4_16"
}
layer {
name: "conv4_17/x1/bn"
type: "BatchNorm"
bottom: "concat_4_16"
top: "conv4_17/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_17/x1/scale"
type: "Scale"
bottom: "conv4_17/x1/bn"
top: "conv4_17/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_17/x1"
type: "ReLU"
bottom: "conv4_17/x1/bn"
top: "conv4_17/x1/bn"
}
layer {
name: "conv4_17/x1"
type: "Convolution"
bottom: "conv4_17/x1/bn"
top: "conv4_17/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_17/x2/bn"
type: "BatchNorm"
bottom: "conv4_17/x1"
top: "conv4_17/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_17/x2/scale"
type: "Scale"
bottom: "conv4_17/x2/bn"
top: "conv4_17/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_17/x2"
type: "ReLU"
bottom: "conv4_17/x2/bn"
top: "conv4_17/x2/bn"
}
layer {
name: "conv4_17/x2"
type: "Convolution"
bottom: "conv4_17/x2/bn"
top: "conv4_17/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_17"
type: "Concat"
bottom: "concat_4_16"
bottom: "conv4_17/x2"
top: "concat_4_17"
}
layer {
name: "conv4_18/x1/bn"
type: "BatchNorm"
bottom: "concat_4_17"
top: "conv4_18/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_18/x1/scale"
type: "Scale"
bottom: "conv4_18/x1/bn"
top: "conv4_18/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_18/x1"
type: "ReLU"
bottom: "conv4_18/x1/bn"
top: "conv4_18/x1/bn"
}
layer {
name: "conv4_18/x1"
type: "Convolution"
bottom: "conv4_18/x1/bn"
top: "conv4_18/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_18/x2/bn"
type: "BatchNorm"
bottom: "conv4_18/x1"
top: "conv4_18/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_18/x2/scale"
type: "Scale"
bottom: "conv4_18/x2/bn"
top: "conv4_18/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_18/x2"
type: "ReLU"
bottom: "conv4_18/x2/bn"
top: "conv4_18/x2/bn"
}
layer {
name: "conv4_18/x2"
type: "Convolution"
bottom: "conv4_18/x2/bn"
top: "conv4_18/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_18"
type: "Concat"
bottom: "concat_4_17"
bottom: "conv4_18/x2"
top: "concat_4_18"
}
layer {
name: "conv4_19/x1/bn"
type: "BatchNorm"
bottom: "concat_4_18"
top: "conv4_19/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_19/x1/scale"
type: "Scale"
bottom: "conv4_19/x1/bn"
top: "conv4_19/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_19/x1"
type: "ReLU"
bottom: "conv4_19/x1/bn"
top: "conv4_19/x1/bn"
}
layer {
name: "conv4_19/x1"
type: "Convolution"
bottom: "conv4_19/x1/bn"
top: "conv4_19/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_19/x2/bn"
type: "BatchNorm"
bottom: "conv4_19/x1"
top: "conv4_19/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_19/x2/scale"
type: "Scale"
bottom: "conv4_19/x2/bn"
top: "conv4_19/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_19/x2"
type: "ReLU"
bottom: "conv4_19/x2/bn"
top: "conv4_19/x2/bn"
}
layer {
name: "conv4_19/x2"
type: "Convolution"
bottom: "conv4_19/x2/bn"
top: "conv4_19/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_19"
type: "Concat"
bottom: "concat_4_18"
bottom: "conv4_19/x2"
top: "concat_4_19"
}
layer {
name: "conv4_20/x1/bn"
type: "BatchNorm"
bottom: "concat_4_19"
top: "conv4_20/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_20/x1/scale"
type: "Scale"
bottom: "conv4_20/x1/bn"
top: "conv4_20/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_20/x1"
type: "ReLU"
bottom: "conv4_20/x1/bn"
top: "conv4_20/x1/bn"
}
layer {
name: "conv4_20/x1"
type: "Convolution"
bottom: "conv4_20/x1/bn"
top: "conv4_20/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_20/x2/bn"
type: "BatchNorm"
bottom: "conv4_20/x1"
top: "conv4_20/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_20/x2/scale"
type: "Scale"
bottom: "conv4_20/x2/bn"
top: "conv4_20/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_20/x2"
type: "ReLU"
bottom: "conv4_20/x2/bn"
top: "conv4_20/x2/bn"
}
layer {
name: "conv4_20/x2"
type: "Convolution"
bottom: "conv4_20/x2/bn"
top: "conv4_20/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_20"
type: "Concat"
bottom: "concat_4_19"
bottom: "conv4_20/x2"
top: "concat_4_20"
}
layer {
name: "conv4_21/x1/bn"
type: "BatchNorm"
bottom: "concat_4_20"
top: "conv4_21/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_21/x1/scale"
type: "Scale"
bottom: "conv4_21/x1/bn"
top: "conv4_21/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_21/x1"
type: "ReLU"
bottom: "conv4_21/x1/bn"
top: "conv4_21/x1/bn"
}
layer {
name: "conv4_21/x1"
type: "Convolution"
bottom: "conv4_21/x1/bn"
top: "conv4_21/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_21/x2/bn"
type: "BatchNorm"
bottom: "conv4_21/x1"
top: "conv4_21/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_21/x2/scale"
type: "Scale"
bottom: "conv4_21/x2/bn"
top: "conv4_21/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_21/x2"
type: "ReLU"
bottom: "conv4_21/x2/bn"
top: "conv4_21/x2/bn"
}
layer {
name: "conv4_21/x2"
type: "Convolution"
bottom: "conv4_21/x2/bn"
top: "conv4_21/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_21"
type: "Concat"
bottom: "concat_4_20"
bottom: "conv4_21/x2"
top: "concat_4_21"
}
layer {
name: "conv4_22/x1/bn"
type: "BatchNorm"
bottom: "concat_4_21"
top: "conv4_22/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_22/x1/scale"
type: "Scale"
bottom: "conv4_22/x1/bn"
top: "conv4_22/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_22/x1"
type: "ReLU"
bottom: "conv4_22/x1/bn"
top: "conv4_22/x1/bn"
}
layer {
name: "conv4_22/x1"
type: "Convolution"
bottom: "conv4_22/x1/bn"
top: "conv4_22/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_22/x2/bn"
type: "BatchNorm"
bottom: "conv4_22/x1"
top: "conv4_22/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_22/x2/scale"
type: "Scale"
bottom: "conv4_22/x2/bn"
top: "conv4_22/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_22/x2"
type: "ReLU"
bottom: "conv4_22/x2/bn"
top: "conv4_22/x2/bn"
}
layer {
name: "conv4_22/x2"
type: "Convolution"
bottom: "conv4_22/x2/bn"
top: "conv4_22/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_22"
type: "Concat"
bottom: "concat_4_21"
bottom: "conv4_22/x2"
top: "concat_4_22"
}
layer {
name: "conv4_23/x1/bn"
type: "BatchNorm"
bottom: "concat_4_22"
top: "conv4_23/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_23/x1/scale"
type: "Scale"
bottom: "conv4_23/x1/bn"
top: "conv4_23/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_23/x1"
type: "ReLU"
bottom: "conv4_23/x1/bn"
top: "conv4_23/x1/bn"
}
layer {
name: "conv4_23/x1"
type: "Convolution"
bottom: "conv4_23/x1/bn"
top: "conv4_23/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_23/x2/bn"
type: "BatchNorm"
bottom: "conv4_23/x1"
top: "conv4_23/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_23/x2/scale"
type: "Scale"
bottom: "conv4_23/x2/bn"
top: "conv4_23/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_23/x2"
type: "ReLU"
bottom: "conv4_23/x2/bn"
top: "conv4_23/x2/bn"
}
layer {
name: "conv4_23/x2"
type: "Convolution"
bottom: "conv4_23/x2/bn"
top: "conv4_23/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_23"
type: "Concat"
bottom: "concat_4_22"
bottom: "conv4_23/x2"
top: "concat_4_23"
}
layer {
name: "conv4_24/x1/bn"
type: "BatchNorm"
bottom: "concat_4_23"
top: "conv4_24/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_24/x1/scale"
type: "Scale"
bottom: "conv4_24/x1/bn"
top: "conv4_24/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_24/x1"
type: "ReLU"
bottom: "conv4_24/x1/bn"
top: "conv4_24/x1/bn"
}
layer {
name: "conv4_24/x1"
type: "Convolution"
bottom: "conv4_24/x1/bn"
top: "conv4_24/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_24/x2/bn"
type: "BatchNorm"
bottom: "conv4_24/x1"
top: "conv4_24/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_24/x2/scale"
type: "Scale"
bottom: "conv4_24/x2/bn"
top: "conv4_24/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_24/x2"
type: "ReLU"
bottom: "conv4_24/x2/bn"
top: "conv4_24/x2/bn"
}
layer {
name: "conv4_24/x2"
type: "Convolution"
bottom: "conv4_24/x2/bn"
top: "conv4_24/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_24"
type: "Concat"
bottom: "concat_4_23"
bottom: "conv4_24/x2"
top: "concat_4_24"
}
layer {
name: "conv4_25/x1/bn"
type: "BatchNorm"
bottom: "concat_4_24"
top: "conv4_25/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_25/x1/scale"
type: "Scale"
bottom: "conv4_25/x1/bn"
top: "conv4_25/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_25/x1"
type: "ReLU"
bottom: "conv4_25/x1/bn"
top: "conv4_25/x1/bn"
}
layer {
name: "conv4_25/x1"
type: "Convolution"
bottom: "conv4_25/x1/bn"
top: "conv4_25/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_25/x2/bn"
type: "BatchNorm"
bottom: "conv4_25/x1"
top: "conv4_25/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_25/x2/scale"
type: "Scale"
bottom: "conv4_25/x2/bn"
top: "conv4_25/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_25/x2"
type: "ReLU"
bottom: "conv4_25/x2/bn"
top: "conv4_25/x2/bn"
}
layer {
name: "conv4_25/x2"
type: "Convolution"
bottom: "conv4_25/x2/bn"
top: "conv4_25/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_25"
type: "Concat"
bottom: "concat_4_24"
bottom: "conv4_25/x2"
top: "concat_4_25"
}
layer {
name: "conv4_26/x1/bn"
type: "BatchNorm"
bottom: "concat_4_25"
top: "conv4_26/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_26/x1/scale"
type: "Scale"
bottom: "conv4_26/x1/bn"
top: "conv4_26/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_26/x1"
type: "ReLU"
bottom: "conv4_26/x1/bn"
top: "conv4_26/x1/bn"
}
layer {
name: "conv4_26/x1"
type: "Convolution"
bottom: "conv4_26/x1/bn"
top: "conv4_26/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_26/x2/bn"
type: "BatchNorm"
bottom: "conv4_26/x1"
top: "conv4_26/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_26/x2/scale"
type: "Scale"
bottom: "conv4_26/x2/bn"
top: "conv4_26/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_26/x2"
type: "ReLU"
bottom: "conv4_26/x2/bn"
top: "conv4_26/x2/bn"
}
layer {
name: "conv4_26/x2"
type: "Convolution"
bottom: "conv4_26/x2/bn"
top: "conv4_26/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_26"
type: "Concat"
bottom: "concat_4_25"
bottom: "conv4_26/x2"
top: "concat_4_26"
}
layer {
name: "conv4_27/x1/bn"
type: "BatchNorm"
bottom: "concat_4_26"
top: "conv4_27/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_27/x1/scale"
type: "Scale"
bottom: "conv4_27/x1/bn"
top: "conv4_27/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_27/x1"
type: "ReLU"
bottom: "conv4_27/x1/bn"
top: "conv4_27/x1/bn"
}
layer {
name: "conv4_27/x1"
type: "Convolution"
bottom: "conv4_27/x1/bn"
top: "conv4_27/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_27/x2/bn"
type: "BatchNorm"
bottom: "conv4_27/x1"
top: "conv4_27/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_27/x2/scale"
type: "Scale"
bottom: "conv4_27/x2/bn"
top: "conv4_27/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_27/x2"
type: "ReLU"
bottom: "conv4_27/x2/bn"
top: "conv4_27/x2/bn"
}
layer {
name: "conv4_27/x2"
type: "Convolution"
bottom: "conv4_27/x2/bn"
top: "conv4_27/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_27"
type: "Concat"
bottom: "concat_4_26"
bottom: "conv4_27/x2"
top: "concat_4_27"
}
layer {
name: "conv4_28/x1/bn"
type: "BatchNorm"
bottom: "concat_4_27"
top: "conv4_28/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_28/x1/scale"
type: "Scale"
bottom: "conv4_28/x1/bn"
top: "conv4_28/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_28/x1"
type: "ReLU"
bottom: "conv4_28/x1/bn"
top: "conv4_28/x1/bn"
}
layer {
name: "conv4_28/x1"
type: "Convolution"
bottom: "conv4_28/x1/bn"
top: "conv4_28/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_28/x2/bn"
type: "BatchNorm"
bottom: "conv4_28/x1"
top: "conv4_28/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_28/x2/scale"
type: "Scale"
bottom: "conv4_28/x2/bn"
top: "conv4_28/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_28/x2"
type: "ReLU"
bottom: "conv4_28/x2/bn"
top: "conv4_28/x2/bn"
}
layer {
name: "conv4_28/x2"
type: "Convolution"
bottom: "conv4_28/x2/bn"
top: "conv4_28/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_28"
type: "Concat"
bottom: "concat_4_27"
bottom: "conv4_28/x2"
top: "concat_4_28"
}
layer {
name: "conv4_29/x1/bn"
type: "BatchNorm"
bottom: "concat_4_28"
top: "conv4_29/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_29/x1/scale"
type: "Scale"
bottom: "conv4_29/x1/bn"
top: "conv4_29/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_29/x1"
type: "ReLU"
bottom: "conv4_29/x1/bn"
top: "conv4_29/x1/bn"
}
layer {
name: "conv4_29/x1"
type: "Convolution"
bottom: "conv4_29/x1/bn"
top: "conv4_29/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_29/x2/bn"
type: "BatchNorm"
bottom: "conv4_29/x1"
top: "conv4_29/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_29/x2/scale"
type: "Scale"
bottom: "conv4_29/x2/bn"
top: "conv4_29/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_29/x2"
type: "ReLU"
bottom: "conv4_29/x2/bn"
top: "conv4_29/x2/bn"
}
layer {
name: "conv4_29/x2"
type: "Convolution"
bottom: "conv4_29/x2/bn"
top: "conv4_29/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_29"
type: "Concat"
bottom: "concat_4_28"
bottom: "conv4_29/x2"
top: "concat_4_29"
}
layer {
name: "conv4_30/x1/bn"
type: "BatchNorm"
bottom: "concat_4_29"
top: "conv4_30/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_30/x1/scale"
type: "Scale"
bottom: "conv4_30/x1/bn"
top: "conv4_30/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_30/x1"
type: "ReLU"
bottom: "conv4_30/x1/bn"
top: "conv4_30/x1/bn"
}
layer {
name: "conv4_30/x1"
type: "Convolution"
bottom: "conv4_30/x1/bn"
top: "conv4_30/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_30/x2/bn"
type: "BatchNorm"
bottom: "conv4_30/x1"
top: "conv4_30/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_30/x2/scale"
type: "Scale"
bottom: "conv4_30/x2/bn"
top: "conv4_30/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_30/x2"
type: "ReLU"
bottom: "conv4_30/x2/bn"
top: "conv4_30/x2/bn"
}
layer {
name: "conv4_30/x2"
type: "Convolution"
bottom: "conv4_30/x2/bn"
top: "conv4_30/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_30"
type: "Concat"
bottom: "concat_4_29"
bottom: "conv4_30/x2"
top: "concat_4_30"
}
layer {
name: "conv4_31/x1/bn"
type: "BatchNorm"
bottom: "concat_4_30"
top: "conv4_31/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_31/x1/scale"
type: "Scale"
bottom: "conv4_31/x1/bn"
top: "conv4_31/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_31/x1"
type: "ReLU"
bottom: "conv4_31/x1/bn"
top: "conv4_31/x1/bn"
}
layer {
name: "conv4_31/x1"
type: "Convolution"
bottom: "conv4_31/x1/bn"
top: "conv4_31/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_31/x2/bn"
type: "BatchNorm"
bottom: "conv4_31/x1"
top: "conv4_31/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_31/x2/scale"
type: "Scale"
bottom: "conv4_31/x2/bn"
top: "conv4_31/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_31/x2"
type: "ReLU"
bottom: "conv4_31/x2/bn"
top: "conv4_31/x2/bn"
}
layer {
name: "conv4_31/x2"
type: "Convolution"
bottom: "conv4_31/x2/bn"
top: "conv4_31/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_31"
type: "Concat"
bottom: "concat_4_30"
bottom: "conv4_31/x2"
top: "concat_4_31"
}
layer {
name: "conv4_32/x1/bn"
type: "BatchNorm"
bottom: "concat_4_31"
top: "conv4_32/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_32/x1/scale"
type: "Scale"
bottom: "conv4_32/x1/bn"
top: "conv4_32/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_32/x1"
type: "ReLU"
bottom: "conv4_32/x1/bn"
top: "conv4_32/x1/bn"
}
layer {
name: "conv4_32/x1"
type: "Convolution"
bottom: "conv4_32/x1/bn"
top: "conv4_32/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_32/x2/bn"
type: "BatchNorm"
bottom: "conv4_32/x1"
top: "conv4_32/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_32/x2/scale"
type: "Scale"
bottom: "conv4_32/x2/bn"
top: "conv4_32/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_32/x2"
type: "ReLU"
bottom: "conv4_32/x2/bn"
top: "conv4_32/x2/bn"
}
layer {
name: "conv4_32/x2"
type: "Convolution"
bottom: "conv4_32/x2/bn"
top: "conv4_32/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_32"
type: "Concat"
bottom: "concat_4_31"
bottom: "conv4_32/x2"
top: "concat_4_32"
}
layer {
name: "conv4_33/x1/bn"
type: "BatchNorm"
bottom: "concat_4_32"
top: "conv4_33/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_33/x1/scale"
type: "Scale"
bottom: "conv4_33/x1/bn"
top: "conv4_33/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_33/x1"
type: "ReLU"
bottom: "conv4_33/x1/bn"
top: "conv4_33/x1/bn"
}
layer {
name: "conv4_33/x1"
type: "Convolution"
bottom: "conv4_33/x1/bn"
top: "conv4_33/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_33/x2/bn"
type: "BatchNorm"
bottom: "conv4_33/x1"
top: "conv4_33/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_33/x2/scale"
type: "Scale"
bottom: "conv4_33/x2/bn"
top: "conv4_33/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_33/x2"
type: "ReLU"
bottom: "conv4_33/x2/bn"
top: "conv4_33/x2/bn"
}
layer {
name: "conv4_33/x2"
type: "Convolution"
bottom: "conv4_33/x2/bn"
top: "conv4_33/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_33"
type: "Concat"
bottom: "concat_4_32"
bottom: "conv4_33/x2"
top: "concat_4_33"
}
layer {
name: "conv4_34/x1/bn"
type: "BatchNorm"
bottom: "concat_4_33"
top: "conv4_34/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_34/x1/scale"
type: "Scale"
bottom: "conv4_34/x1/bn"
top: "conv4_34/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_34/x1"
type: "ReLU"
bottom: "conv4_34/x1/bn"
top: "conv4_34/x1/bn"
}
layer {
name: "conv4_34/x1"
type: "Convolution"
bottom: "conv4_34/x1/bn"
top: "conv4_34/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_34/x2/bn"
type: "BatchNorm"
bottom: "conv4_34/x1"
top: "conv4_34/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_34/x2/scale"
type: "Scale"
bottom: "conv4_34/x2/bn"
top: "conv4_34/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_34/x2"
type: "ReLU"
bottom: "conv4_34/x2/bn"
top: "conv4_34/x2/bn"
}
layer {
name: "conv4_34/x2"
type: "Convolution"
bottom: "conv4_34/x2/bn"
top: "conv4_34/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_34"
type: "Concat"
bottom: "concat_4_33"
bottom: "conv4_34/x2"
top: "concat_4_34"
}
layer {
name: "conv4_35/x1/bn"
type: "BatchNorm"
bottom: "concat_4_34"
top: "conv4_35/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_35/x1/scale"
type: "Scale"
bottom: "conv4_35/x1/bn"
top: "conv4_35/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_35/x1"
type: "ReLU"
bottom: "conv4_35/x1/bn"
top: "conv4_35/x1/bn"
}
layer {
name: "conv4_35/x1"
type: "Convolution"
bottom: "conv4_35/x1/bn"
top: "conv4_35/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_35/x2/bn"
type: "BatchNorm"
bottom: "conv4_35/x1"
top: "conv4_35/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_35/x2/scale"
type: "Scale"
bottom: "conv4_35/x2/bn"
top: "conv4_35/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_35/x2"
type: "ReLU"
bottom: "conv4_35/x2/bn"
top: "conv4_35/x2/bn"
}
layer {
name: "conv4_35/x2"
type: "Convolution"
bottom: "conv4_35/x2/bn"
top: "conv4_35/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_35"
type: "Concat"
bottom: "concat_4_34"
bottom: "conv4_35/x2"
top: "concat_4_35"
}
layer {
name: "conv4_36/x1/bn"
type: "BatchNorm"
bottom: "concat_4_35"
top: "conv4_36/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_36/x1/scale"
type: "Scale"
bottom: "conv4_36/x1/bn"
top: "conv4_36/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_36/x1"
type: "ReLU"
bottom: "conv4_36/x1/bn"
top: "conv4_36/x1/bn"
}
layer {
name: "conv4_36/x1"
type: "Convolution"
bottom: "conv4_36/x1/bn"
top: "conv4_36/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv4_36/x2/bn"
type: "BatchNorm"
bottom: "conv4_36/x1"
top: "conv4_36/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_36/x2/scale"
type: "Scale"
bottom: "conv4_36/x2/bn"
top: "conv4_36/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_36/x2"
type: "ReLU"
bottom: "conv4_36/x2/bn"
top: "conv4_36/x2/bn"
}
layer {
name: "conv4_36/x2"
type: "Convolution"
bottom: "conv4_36/x2/bn"
top: "conv4_36/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_4_36"
type: "Concat"
bottom: "concat_4_35"
bottom: "conv4_36/x2"
top: "concat_4_36"
}
layer {
name: "conv4_blk/bn"
type: "BatchNorm"
bottom: "concat_4_36"
top: "conv4_blk/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv4_blk/scale"
type: "Scale"
bottom: "conv4_blk/bn"
top: "conv4_blk/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu4_blk"
type: "ReLU"
bottom: "conv4_blk/bn"
top: "conv4_blk/bn"
}
layer {
name: "conv4_blk"
type: "Convolution"
bottom: "conv4_blk/bn"
top: "conv4_blk"
convolution_param {
num_output: 1056
bias_term: false
kernel_size: 1
}
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_blk"
top: "pool4"
pooling_param {
pool: AVE
kernel_size: 2
stride: 2
}
}
layer {
name: "conv5_1/x1/bn"
type: "BatchNorm"
bottom: "pool4"
top: "conv5_1/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_1/x1/scale"
type: "Scale"
bottom: "conv5_1/x1/bn"
top: "conv5_1/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_1/x1"
type: "ReLU"
bottom: "conv5_1/x1/bn"
top: "conv5_1/x1/bn"
}
layer {
name: "conv5_1/x1"
type: "Convolution"
bottom: "conv5_1/x1/bn"
top: "conv5_1/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_1/x2/bn"
type: "BatchNorm"
bottom: "conv5_1/x1"
top: "conv5_1/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_1/x2/scale"
type: "Scale"
bottom: "conv5_1/x2/bn"
top: "conv5_1/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_1/x2"
type: "ReLU"
bottom: "conv5_1/x2/bn"
top: "conv5_1/x2/bn"
}
layer {
name: "conv5_1/x2"
type: "Convolution"
bottom: "conv5_1/x2/bn"
top: "conv5_1/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_1"
type: "Concat"
bottom: "pool4"
bottom: "conv5_1/x2"
top: "concat_5_1"
}
layer {
name: "conv5_2/x1/bn"
type: "BatchNorm"
bottom: "concat_5_1"
top: "conv5_2/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_2/x1/scale"
type: "Scale"
bottom: "conv5_2/x1/bn"
top: "conv5_2/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_2/x1"
type: "ReLU"
bottom: "conv5_2/x1/bn"
top: "conv5_2/x1/bn"
}
layer {
name: "conv5_2/x1"
type: "Convolution"
bottom: "conv5_2/x1/bn"
top: "conv5_2/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_2/x2/bn"
type: "BatchNorm"
bottom: "conv5_2/x1"
top: "conv5_2/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_2/x2/scale"
type: "Scale"
bottom: "conv5_2/x2/bn"
top: "conv5_2/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_2/x2"
type: "ReLU"
bottom: "conv5_2/x2/bn"
top: "conv5_2/x2/bn"
}
layer {
name: "conv5_2/x2"
type: "Convolution"
bottom: "conv5_2/x2/bn"
top: "conv5_2/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_2"
type: "Concat"
bottom: "concat_5_1"
bottom: "conv5_2/x2"
top: "concat_5_2"
}
layer {
name: "conv5_3/x1/bn"
type: "BatchNorm"
bottom: "concat_5_2"
top: "conv5_3/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_3/x1/scale"
type: "Scale"
bottom: "conv5_3/x1/bn"
top: "conv5_3/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_3/x1"
type: "ReLU"
bottom: "conv5_3/x1/bn"
top: "conv5_3/x1/bn"
}
layer {
name: "conv5_3/x1"
type: "Convolution"
bottom: "conv5_3/x1/bn"
top: "conv5_3/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_3/x2/bn"
type: "BatchNorm"
bottom: "conv5_3/x1"
top: "conv5_3/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_3/x2/scale"
type: "Scale"
bottom: "conv5_3/x2/bn"
top: "conv5_3/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_3/x2"
type: "ReLU"
bottom: "conv5_3/x2/bn"
top: "conv5_3/x2/bn"
}
layer {
name: "conv5_3/x2"
type: "Convolution"
bottom: "conv5_3/x2/bn"
top: "conv5_3/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_3"
type: "Concat"
bottom: "concat_5_2"
bottom: "conv5_3/x2"
top: "concat_5_3"
}
layer {
name: "conv5_4/x1/bn"
type: "BatchNorm"
bottom: "concat_5_3"
top: "conv5_4/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_4/x1/scale"
type: "Scale"
bottom: "conv5_4/x1/bn"
top: "conv5_4/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_4/x1"
type: "ReLU"
bottom: "conv5_4/x1/bn"
top: "conv5_4/x1/bn"
}
layer {
name: "conv5_4/x1"
type: "Convolution"
bottom: "conv5_4/x1/bn"
top: "conv5_4/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_4/x2/bn"
type: "BatchNorm"
bottom: "conv5_4/x1"
top: "conv5_4/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_4/x2/scale"
type: "Scale"
bottom: "conv5_4/x2/bn"
top: "conv5_4/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_4/x2"
type: "ReLU"
bottom: "conv5_4/x2/bn"
top: "conv5_4/x2/bn"
}
layer {
name: "conv5_4/x2"
type: "Convolution"
bottom: "conv5_4/x2/bn"
top: "conv5_4/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_4"
type: "Concat"
bottom: "concat_5_3"
bottom: "conv5_4/x2"
top: "concat_5_4"
}
layer {
name: "conv5_5/x1/bn"
type: "BatchNorm"
bottom: "concat_5_4"
top: "conv5_5/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_5/x1/scale"
type: "Scale"
bottom: "conv5_5/x1/bn"
top: "conv5_5/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_5/x1"
type: "ReLU"
bottom: "conv5_5/x1/bn"
top: "conv5_5/x1/bn"
}
layer {
name: "conv5_5/x1"
type: "Convolution"
bottom: "conv5_5/x1/bn"
top: "conv5_5/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_5/x2/bn"
type: "BatchNorm"
bottom: "conv5_5/x1"
top: "conv5_5/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_5/x2/scale"
type: "Scale"
bottom: "conv5_5/x2/bn"
top: "conv5_5/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_5/x2"
type: "ReLU"
bottom: "conv5_5/x2/bn"
top: "conv5_5/x2/bn"
}
layer {
name: "conv5_5/x2"
type: "Convolution"
bottom: "conv5_5/x2/bn"
top: "conv5_5/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_5"
type: "Concat"
bottom: "concat_5_4"
bottom: "conv5_5/x2"
top: "concat_5_5"
}
layer {
name: "conv5_6/x1/bn"
type: "BatchNorm"
bottom: "concat_5_5"
top: "conv5_6/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_6/x1/scale"
type: "Scale"
bottom: "conv5_6/x1/bn"
top: "conv5_6/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_6/x1"
type: "ReLU"
bottom: "conv5_6/x1/bn"
top: "conv5_6/x1/bn"
}
layer {
name: "conv5_6/x1"
type: "Convolution"
bottom: "conv5_6/x1/bn"
top: "conv5_6/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_6/x2/bn"
type: "BatchNorm"
bottom: "conv5_6/x1"
top: "conv5_6/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_6/x2/scale"
type: "Scale"
bottom: "conv5_6/x2/bn"
top: "conv5_6/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_6/x2"
type: "ReLU"
bottom: "conv5_6/x2/bn"
top: "conv5_6/x2/bn"
}
layer {
name: "conv5_6/x2"
type: "Convolution"
bottom: "conv5_6/x2/bn"
top: "conv5_6/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_6"
type: "Concat"
bottom: "concat_5_5"
bottom: "conv5_6/x2"
top: "concat_5_6"
}
layer {
name: "conv5_7/x1/bn"
type: "BatchNorm"
bottom: "concat_5_6"
top: "conv5_7/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_7/x1/scale"
type: "Scale"
bottom: "conv5_7/x1/bn"
top: "conv5_7/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_7/x1"
type: "ReLU"
bottom: "conv5_7/x1/bn"
top: "conv5_7/x1/bn"
}
layer {
name: "conv5_7/x1"
type: "Convolution"
bottom: "conv5_7/x1/bn"
top: "conv5_7/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_7/x2/bn"
type: "BatchNorm"
bottom: "conv5_7/x1"
top: "conv5_7/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_7/x2/scale"
type: "Scale"
bottom: "conv5_7/x2/bn"
top: "conv5_7/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_7/x2"
type: "ReLU"
bottom: "conv5_7/x2/bn"
top: "conv5_7/x2/bn"
}
layer {
name: "conv5_7/x2"
type: "Convolution"
bottom: "conv5_7/x2/bn"
top: "conv5_7/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_7"
type: "Concat"
bottom: "concat_5_6"
bottom: "conv5_7/x2"
top: "concat_5_7"
}
layer {
name: "conv5_8/x1/bn"
type: "BatchNorm"
bottom: "concat_5_7"
top: "conv5_8/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_8/x1/scale"
type: "Scale"
bottom: "conv5_8/x1/bn"
top: "conv5_8/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_8/x1"
type: "ReLU"
bottom: "conv5_8/x1/bn"
top: "conv5_8/x1/bn"
}
layer {
name: "conv5_8/x1"
type: "Convolution"
bottom: "conv5_8/x1/bn"
top: "conv5_8/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_8/x2/bn"
type: "BatchNorm"
bottom: "conv5_8/x1"
top: "conv5_8/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_8/x2/scale"
type: "Scale"
bottom: "conv5_8/x2/bn"
top: "conv5_8/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_8/x2"
type: "ReLU"
bottom: "conv5_8/x2/bn"
top: "conv5_8/x2/bn"
}
layer {
name: "conv5_8/x2"
type: "Convolution"
bottom: "conv5_8/x2/bn"
top: "conv5_8/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_8"
type: "Concat"
bottom: "concat_5_7"
bottom: "conv5_8/x2"
top: "concat_5_8"
}
layer {
name: "conv5_9/x1/bn"
type: "BatchNorm"
bottom: "concat_5_8"
top: "conv5_9/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_9/x1/scale"
type: "Scale"
bottom: "conv5_9/x1/bn"
top: "conv5_9/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_9/x1"
type: "ReLU"
bottom: "conv5_9/x1/bn"
top: "conv5_9/x1/bn"
}
layer {
name: "conv5_9/x1"
type: "Convolution"
bottom: "conv5_9/x1/bn"
top: "conv5_9/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_9/x2/bn"
type: "BatchNorm"
bottom: "conv5_9/x1"
top: "conv5_9/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_9/x2/scale"
type: "Scale"
bottom: "conv5_9/x2/bn"
top: "conv5_9/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_9/x2"
type: "ReLU"
bottom: "conv5_9/x2/bn"
top: "conv5_9/x2/bn"
}
layer {
name: "conv5_9/x2"
type: "Convolution"
bottom: "conv5_9/x2/bn"
top: "conv5_9/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_9"
type: "Concat"
bottom: "concat_5_8"
bottom: "conv5_9/x2"
top: "concat_5_9"
}
layer {
name: "conv5_10/x1/bn"
type: "BatchNorm"
bottom: "concat_5_9"
top: "conv5_10/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_10/x1/scale"
type: "Scale"
bottom: "conv5_10/x1/bn"
top: "conv5_10/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_10/x1"
type: "ReLU"
bottom: "conv5_10/x1/bn"
top: "conv5_10/x1/bn"
}
layer {
name: "conv5_10/x1"
type: "Convolution"
bottom: "conv5_10/x1/bn"
top: "conv5_10/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_10/x2/bn"
type: "BatchNorm"
bottom: "conv5_10/x1"
top: "conv5_10/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_10/x2/scale"
type: "Scale"
bottom: "conv5_10/x2/bn"
top: "conv5_10/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_10/x2"
type: "ReLU"
bottom: "conv5_10/x2/bn"
top: "conv5_10/x2/bn"
}
layer {
name: "conv5_10/x2"
type: "Convolution"
bottom: "conv5_10/x2/bn"
top: "conv5_10/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_10"
type: "Concat"
bottom: "concat_5_9"
bottom: "conv5_10/x2"
top: "concat_5_10"
}
layer {
name: "conv5_11/x1/bn"
type: "BatchNorm"
bottom: "concat_5_10"
top: "conv5_11/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_11/x1/scale"
type: "Scale"
bottom: "conv5_11/x1/bn"
top: "conv5_11/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_11/x1"
type: "ReLU"
bottom: "conv5_11/x1/bn"
top: "conv5_11/x1/bn"
}
layer {
name: "conv5_11/x1"
type: "Convolution"
bottom: "conv5_11/x1/bn"
top: "conv5_11/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_11/x2/bn"
type: "BatchNorm"
bottom: "conv5_11/x1"
top: "conv5_11/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_11/x2/scale"
type: "Scale"
bottom: "conv5_11/x2/bn"
top: "conv5_11/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_11/x2"
type: "ReLU"
bottom: "conv5_11/x2/bn"
top: "conv5_11/x2/bn"
}
layer {
name: "conv5_11/x2"
type: "Convolution"
bottom: "conv5_11/x2/bn"
top: "conv5_11/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_11"
type: "Concat"
bottom: "concat_5_10"
bottom: "conv5_11/x2"
top: "concat_5_11"
}
layer {
name: "conv5_12/x1/bn"
type: "BatchNorm"
bottom: "concat_5_11"
top: "conv5_12/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_12/x1/scale"
type: "Scale"
bottom: "conv5_12/x1/bn"
top: "conv5_12/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_12/x1"
type: "ReLU"
bottom: "conv5_12/x1/bn"
top: "conv5_12/x1/bn"
}
layer {
name: "conv5_12/x1"
type: "Convolution"
bottom: "conv5_12/x1/bn"
top: "conv5_12/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_12/x2/bn"
type: "BatchNorm"
bottom: "conv5_12/x1"
top: "conv5_12/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_12/x2/scale"
type: "Scale"
bottom: "conv5_12/x2/bn"
top: "conv5_12/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_12/x2"
type: "ReLU"
bottom: "conv5_12/x2/bn"
top: "conv5_12/x2/bn"
}
layer {
name: "conv5_12/x2"
type: "Convolution"
bottom: "conv5_12/x2/bn"
top: "conv5_12/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_12"
type: "Concat"
bottom: "concat_5_11"
bottom: "conv5_12/x2"
top: "concat_5_12"
}
layer {
name: "conv5_13/x1/bn"
type: "BatchNorm"
bottom: "concat_5_12"
top: "conv5_13/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_13/x1/scale"
type: "Scale"
bottom: "conv5_13/x1/bn"
top: "conv5_13/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_13/x1"
type: "ReLU"
bottom: "conv5_13/x1/bn"
top: "conv5_13/x1/bn"
}
layer {
name: "conv5_13/x1"
type: "Convolution"
bottom: "conv5_13/x1/bn"
top: "conv5_13/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_13/x2/bn"
type: "BatchNorm"
bottom: "conv5_13/x1"
top: "conv5_13/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_13/x2/scale"
type: "Scale"
bottom: "conv5_13/x2/bn"
top: "conv5_13/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_13/x2"
type: "ReLU"
bottom: "conv5_13/x2/bn"
top: "conv5_13/x2/bn"
}
layer {
name: "conv5_13/x2"
type: "Convolution"
bottom: "conv5_13/x2/bn"
top: "conv5_13/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_13"
type: "Concat"
bottom: "concat_5_12"
bottom: "conv5_13/x2"
top: "concat_5_13"
}
layer {
name: "conv5_14/x1/bn"
type: "BatchNorm"
bottom: "concat_5_13"
top: "conv5_14/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_14/x1/scale"
type: "Scale"
bottom: "conv5_14/x1/bn"
top: "conv5_14/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_14/x1"
type: "ReLU"
bottom: "conv5_14/x1/bn"
top: "conv5_14/x1/bn"
}
layer {
name: "conv5_14/x1"
type: "Convolution"
bottom: "conv5_14/x1/bn"
top: "conv5_14/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_14/x2/bn"
type: "BatchNorm"
bottom: "conv5_14/x1"
top: "conv5_14/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_14/x2/scale"
type: "Scale"
bottom: "conv5_14/x2/bn"
top: "conv5_14/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_14/x2"
type: "ReLU"
bottom: "conv5_14/x2/bn"
top: "conv5_14/x2/bn"
}
layer {
name: "conv5_14/x2"
type: "Convolution"
bottom: "conv5_14/x2/bn"
top: "conv5_14/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_14"
type: "Concat"
bottom: "concat_5_13"
bottom: "conv5_14/x2"
top: "concat_5_14"
}
layer {
name: "conv5_15/x1/bn"
type: "BatchNorm"
bottom: "concat_5_14"
top: "conv5_15/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_15/x1/scale"
type: "Scale"
bottom: "conv5_15/x1/bn"
top: "conv5_15/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_15/x1"
type: "ReLU"
bottom: "conv5_15/x1/bn"
top: "conv5_15/x1/bn"
}
layer {
name: "conv5_15/x1"
type: "Convolution"
bottom: "conv5_15/x1/bn"
top: "conv5_15/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_15/x2/bn"
type: "BatchNorm"
bottom: "conv5_15/x1"
top: "conv5_15/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_15/x2/scale"
type: "Scale"
bottom: "conv5_15/x2/bn"
top: "conv5_15/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_15/x2"
type: "ReLU"
bottom: "conv5_15/x2/bn"
top: "conv5_15/x2/bn"
}
layer {
name: "conv5_15/x2"
type: "Convolution"
bottom: "conv5_15/x2/bn"
top: "conv5_15/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_15"
type: "Concat"
bottom: "concat_5_14"
bottom: "conv5_15/x2"
top: "concat_5_15"
}
layer {
name: "conv5_16/x1/bn"
type: "BatchNorm"
bottom: "concat_5_15"
top: "conv5_16/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_16/x1/scale"
type: "Scale"
bottom: "conv5_16/x1/bn"
top: "conv5_16/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_16/x1"
type: "ReLU"
bottom: "conv5_16/x1/bn"
top: "conv5_16/x1/bn"
}
layer {
name: "conv5_16/x1"
type: "Convolution"
bottom: "conv5_16/x1/bn"
top: "conv5_16/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_16/x2/bn"
type: "BatchNorm"
bottom: "conv5_16/x1"
top: "conv5_16/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_16/x2/scale"
type: "Scale"
bottom: "conv5_16/x2/bn"
top: "conv5_16/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_16/x2"
type: "ReLU"
bottom: "conv5_16/x2/bn"
top: "conv5_16/x2/bn"
}
layer {
name: "conv5_16/x2"
type: "Convolution"
bottom: "conv5_16/x2/bn"
top: "conv5_16/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_16"
type: "Concat"
bottom: "concat_5_15"
bottom: "conv5_16/x2"
top: "concat_5_16"
}
layer {
name: "conv5_17/x1/bn"
type: "BatchNorm"
bottom: "concat_5_16"
top: "conv5_17/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_17/x1/scale"
type: "Scale"
bottom: "conv5_17/x1/bn"
top: "conv5_17/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_17/x1"
type: "ReLU"
bottom: "conv5_17/x1/bn"
top: "conv5_17/x1/bn"
}
layer {
name: "conv5_17/x1"
type: "Convolution"
bottom: "conv5_17/x1/bn"
top: "conv5_17/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_17/x2/bn"
type: "BatchNorm"
bottom: "conv5_17/x1"
top: "conv5_17/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_17/x2/scale"
type: "Scale"
bottom: "conv5_17/x2/bn"
top: "conv5_17/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_17/x2"
type: "ReLU"
bottom: "conv5_17/x2/bn"
top: "conv5_17/x2/bn"
}
layer {
name: "conv5_17/x2"
type: "Convolution"
bottom: "conv5_17/x2/bn"
top: "conv5_17/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_17"
type: "Concat"
bottom: "concat_5_16"
bottom: "conv5_17/x2"
top: "concat_5_17"
}
layer {
name: "conv5_18/x1/bn"
type: "BatchNorm"
bottom: "concat_5_17"
top: "conv5_18/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_18/x1/scale"
type: "Scale"
bottom: "conv5_18/x1/bn"
top: "conv5_18/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_18/x1"
type: "ReLU"
bottom: "conv5_18/x1/bn"
top: "conv5_18/x1/bn"
}
layer {
name: "conv5_18/x1"
type: "Convolution"
bottom: "conv5_18/x1/bn"
top: "conv5_18/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_18/x2/bn"
type: "BatchNorm"
bottom: "conv5_18/x1"
top: "conv5_18/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_18/x2/scale"
type: "Scale"
bottom: "conv5_18/x2/bn"
top: "conv5_18/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_18/x2"
type: "ReLU"
bottom: "conv5_18/x2/bn"
top: "conv5_18/x2/bn"
}
layer {
name: "conv5_18/x2"
type: "Convolution"
bottom: "conv5_18/x2/bn"
top: "conv5_18/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_18"
type: "Concat"
bottom: "concat_5_17"
bottom: "conv5_18/x2"
top: "concat_5_18"
}
layer {
name: "conv5_19/x1/bn"
type: "BatchNorm"
bottom: "concat_5_18"
top: "conv5_19/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_19/x1/scale"
type: "Scale"
bottom: "conv5_19/x1/bn"
top: "conv5_19/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_19/x1"
type: "ReLU"
bottom: "conv5_19/x1/bn"
top: "conv5_19/x1/bn"
}
layer {
name: "conv5_19/x1"
type: "Convolution"
bottom: "conv5_19/x1/bn"
top: "conv5_19/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_19/x2/bn"
type: "BatchNorm"
bottom: "conv5_19/x1"
top: "conv5_19/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_19/x2/scale"
type: "Scale"
bottom: "conv5_19/x2/bn"
top: "conv5_19/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_19/x2"
type: "ReLU"
bottom: "conv5_19/x2/bn"
top: "conv5_19/x2/bn"
}
layer {
name: "conv5_19/x2"
type: "Convolution"
bottom: "conv5_19/x2/bn"
top: "conv5_19/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_19"
type: "Concat"
bottom: "concat_5_18"
bottom: "conv5_19/x2"
top: "concat_5_19"
}
layer {
name: "conv5_20/x1/bn"
type: "BatchNorm"
bottom: "concat_5_19"
top: "conv5_20/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_20/x1/scale"
type: "Scale"
bottom: "conv5_20/x1/bn"
top: "conv5_20/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_20/x1"
type: "ReLU"
bottom: "conv5_20/x1/bn"
top: "conv5_20/x1/bn"
}
layer {
name: "conv5_20/x1"
type: "Convolution"
bottom: "conv5_20/x1/bn"
top: "conv5_20/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_20/x2/bn"
type: "BatchNorm"
bottom: "conv5_20/x1"
top: "conv5_20/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_20/x2/scale"
type: "Scale"
bottom: "conv5_20/x2/bn"
top: "conv5_20/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_20/x2"
type: "ReLU"
bottom: "conv5_20/x2/bn"
top: "conv5_20/x2/bn"
}
layer {
name: "conv5_20/x2"
type: "Convolution"
bottom: "conv5_20/x2/bn"
top: "conv5_20/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_20"
type: "Concat"
bottom: "concat_5_19"
bottom: "conv5_20/x2"
top: "concat_5_20"
}
layer {
name: "conv5_21/x1/bn"
type: "BatchNorm"
bottom: "concat_5_20"
top: "conv5_21/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_21/x1/scale"
type: "Scale"
bottom: "conv5_21/x1/bn"
top: "conv5_21/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_21/x1"
type: "ReLU"
bottom: "conv5_21/x1/bn"
top: "conv5_21/x1/bn"
}
layer {
name: "conv5_21/x1"
type: "Convolution"
bottom: "conv5_21/x1/bn"
top: "conv5_21/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_21/x2/bn"
type: "BatchNorm"
bottom: "conv5_21/x1"
top: "conv5_21/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_21/x2/scale"
type: "Scale"
bottom: "conv5_21/x2/bn"
top: "conv5_21/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_21/x2"
type: "ReLU"
bottom: "conv5_21/x2/bn"
top: "conv5_21/x2/bn"
}
layer {
name: "conv5_21/x2"
type: "Convolution"
bottom: "conv5_21/x2/bn"
top: "conv5_21/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_21"
type: "Concat"
bottom: "concat_5_20"
bottom: "conv5_21/x2"
top: "concat_5_21"
}
layer {
name: "conv5_22/x1/bn"
type: "BatchNorm"
bottom: "concat_5_21"
top: "conv5_22/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_22/x1/scale"
type: "Scale"
bottom: "conv5_22/x1/bn"
top: "conv5_22/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_22/x1"
type: "ReLU"
bottom: "conv5_22/x1/bn"
top: "conv5_22/x1/bn"
}
layer {
name: "conv5_22/x1"
type: "Convolution"
bottom: "conv5_22/x1/bn"
top: "conv5_22/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_22/x2/bn"
type: "BatchNorm"
bottom: "conv5_22/x1"
top: "conv5_22/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_22/x2/scale"
type: "Scale"
bottom: "conv5_22/x2/bn"
top: "conv5_22/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_22/x2"
type: "ReLU"
bottom: "conv5_22/x2/bn"
top: "conv5_22/x2/bn"
}
layer {
name: "conv5_22/x2"
type: "Convolution"
bottom: "conv5_22/x2/bn"
top: "conv5_22/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_22"
type: "Concat"
bottom: "concat_5_21"
bottom: "conv5_22/x2"
top: "concat_5_22"
}
layer {
name: "conv5_23/x1/bn"
type: "BatchNorm"
bottom: "concat_5_22"
top: "conv5_23/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_23/x1/scale"
type: "Scale"
bottom: "conv5_23/x1/bn"
top: "conv5_23/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_23/x1"
type: "ReLU"
bottom: "conv5_23/x1/bn"
top: "conv5_23/x1/bn"
}
layer {
name: "conv5_23/x1"
type: "Convolution"
bottom: "conv5_23/x1/bn"
top: "conv5_23/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_23/x2/bn"
type: "BatchNorm"
bottom: "conv5_23/x1"
top: "conv5_23/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_23/x2/scale"
type: "Scale"
bottom: "conv5_23/x2/bn"
top: "conv5_23/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_23/x2"
type: "ReLU"
bottom: "conv5_23/x2/bn"
top: "conv5_23/x2/bn"
}
layer {
name: "conv5_23/x2"
type: "Convolution"
bottom: "conv5_23/x2/bn"
top: "conv5_23/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_23"
type: "Concat"
bottom: "concat_5_22"
bottom: "conv5_23/x2"
top: "concat_5_23"
}
layer {
name: "conv5_24/x1/bn"
type: "BatchNorm"
bottom: "concat_5_23"
top: "conv5_24/x1/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_24/x1/scale"
type: "Scale"
bottom: "conv5_24/x1/bn"
top: "conv5_24/x1/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_24/x1"
type: "ReLU"
bottom: "conv5_24/x1/bn"
top: "conv5_24/x1/bn"
}
layer {
name: "conv5_24/x1"
type: "Convolution"
bottom: "conv5_24/x1/bn"
top: "conv5_24/x1"
convolution_param {
num_output: 192
bias_term: false
kernel_size: 1
}
}
layer {
name: "conv5_24/x2/bn"
type: "BatchNorm"
bottom: "conv5_24/x1"
top: "conv5_24/x2/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_24/x2/scale"
type: "Scale"
bottom: "conv5_24/x2/bn"
top: "conv5_24/x2/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_24/x2"
type: "ReLU"
bottom: "conv5_24/x2/bn"
top: "conv5_24/x2/bn"
}
layer {
name: "conv5_24/x2"
type: "Convolution"
bottom: "conv5_24/x2/bn"
top: "conv5_24/x2"
convolution_param {
num_output: 48
bias_term: false
pad: 1
kernel_size: 3
}
}
layer {
name: "concat_5_24"
type: "Concat"
bottom: "concat_5_23"
bottom: "conv5_24/x2"
top: "concat_5_24"
}
layer {
name: "conv5_blk/bn"
type: "BatchNorm"
bottom: "concat_5_24"
top: "conv5_blk/bn"
batch_norm_param {
eps: 1e-5
}
}
layer {
name: "conv5_blk/scale"
type: "Scale"
bottom: "conv5_blk/bn"
top: "conv5_blk/bn"
scale_param {
bias_term: true
}
}
layer {
name: "relu5_blk"
type: "ReLU"
bottom: "conv5_blk/bn"
top: "conv5_blk/bn"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_blk/bn"
top: "pool5"
pooling_param {
pool: AVE
global_pooling: true
}
}
layer {
bottom: "pool5"
top: "local"
name: "local"
type: "InnerProduct"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 1024
}
}
layer {
bottom: "local"
top: "local"
name: "relu-local"
type: "ReLU"
}
layer {
bottom: "pool5"
top: "global"
name: "global"
type: "InnerProduct"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 1024
}
}
layer {
bottom: "global"
top: "global"
name: "relu-globall"
type: "ReLU"
}
layer {
bottom: "global"
top: "fc50"
name: "fc50"
type: "InnerProduct"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 50
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "class_loss"
type: "SoftmaxWithLoss"
bottom: "fc50"
bottom: "class_label"
top: "class_loss"
loss_weight: 1
}
#layer {
# name: "svm_class_label"
# type: "HingeLoss"
# bottom: "fc50"
# bottom: "class_label"
# top: "svm_class_label"
# loss_weight: 1
#}
layer {
name: "texture-label-score"
type: "InnerProduct"
bottom: "local"
top: "texture-label-score"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 156
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "texture-label-loss"
type: "SigmoidCrossEntropyLoss"
bottom: "texture-label-score"
bottom: "texture_label"
top: "texture-label-loss"
}
layer {
name: "fabric-label-score"
type: "InnerProduct"
bottom: "local"
top: "fabric-label-score"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 218
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "fabric-label-loss"
type: "SigmoidCrossEntropyLoss"
bottom: "fabric-label-score"
bottom: "fabric_label"
top: "fabric-label-loss"
}
layer {
name: "shape-label-score"
type: "InnerProduct"
bottom: "global"
top: "shape-label-score"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 180
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "shape-label-loss"
type: "SigmoidCrossEntropyLoss"
bottom: "shape-label-score"
bottom: "shape_label"
top: "shape-label-loss"
}
layer {
name: "part-label-score"
type: "InnerProduct"
bottom: "local"
top: "part-label-score"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 216
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "part-label-loss"
type: "SigmoidCrossEntropyLoss"
bottom: "part-label-score"
bottom: "part_label"
top: "part-label-loss"
}
layer {
name: "style-label-score"
type: "InnerProduct"
bottom: "global"
top: "style-label-score"
param {
lr_mult: 10
decay_mult: 1
}
param {
lr_mult: 20
decay_mult: 0
}
inner_product_param {
num_output: 230
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0
}
}
}
layer {
name: "style-label-loss"
type: "SigmoidCrossEntropyLoss"
bottom: "style-label-score"
bottom: "style_label"
top: "style-label-loss"
}
#layer {
# type: 'Python'
# name: 'weighted-multi-label-loss'
# top: 'weighted-multi-label-loss'
# bottom: "multi-label-score"
# bottom: "label"
# python_param {
# # the module name -- usually the filename -- that needs to be in $PYTHONPATH
# module: 'weightedsigmoidcrossentropyloss'
# # the layer name -- the class name in the module
# layer: 'WeightedSigmoidCrossentropyLoss'
# }
# loss_weight: 1
#}
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