refinedet网络结构
refinedet只预测4个层,并且只有conv6_1、conv6_2,没有ssd中的conv7、8、9
refinedet的4个层都只有1个aspect ratio和1个min_size,所以每层每个点只有3个anchor,arm中做location的conv4_3_norm_mbox_loc等层都是3*4个channel,做confidence的conv4_3_norm_mbox_conf都是6个channel,因为这里变成了2分类,每个anchor必须要有negative和positive的概率
refinedet是两步都要回归bounding box的框,refinedet中的odm_loss就相当于ssd中的mbox_loss,mbox_loss获得了anchor的坐标后会加上回归再进行训练,odm_loss获得anchor的坐标后先要加上arm_loc的回归,再加odm_loc的回归,这样再去进行loss计算.
name: "vgg_1/8"
layer {
name: "data"
type: "AnnotatedData"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
mirror: true
mean_value: 104.0
mean_value: 117.0
mean_value: 123.0
resize_param {
prob: 1.0
resize_mode: WARP
height:
width:
interp_mode: LINEAR
interp_mode: AREA
interp_mode: NEAREST
interp_mode: CUBIC
interp_mode: LANCZOS4
}
emit_constraint {
emit_type: CENTER
}
distort_param {
brightness_prob: 0.5
brightness_delta: 32.0
contrast_prob: 0.5
contrast_lower: 0.5
contrast_upper: 1.5
hue_prob: 0.5
hue_delta: 18.0
saturation_prob: 0.5
saturation_lower: 0.5
saturation_upper: 1.5
random_order_prob: 0.0
}
expand_param {
prob: 0.5
max_expand_ratio: 4.0
}
}
data_param {
source:"examples/cityscapes/cityscapes_train_lmdb"
batch_size:
backend: LMDB
}
annotated_data_param {
batch_sampler {
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.10000000149
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.300000011921
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.5
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.699999988079
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
min_jaccard_overlap: 0.899999976158
}
max_sample:
max_trials:
}
batch_sampler {
sampler {
min_scale: 0.300000011921
max_scale: 1.0
min_aspect_ratio: 0.5
max_aspect_ratio: 2.0
}
sample_constraint {
max_jaccard_overlap: 1.0
}
max_sample:
max_trials:
}
label_map_file: "data/cityscapes/labelmap_cityscapes.prototxt"
}
}
layer {
name: "conv1_1"
type: "Convolution"
bottom: "data"
top: "conv1_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_1"
type: "ReLU"
bottom: "conv1_1"
top: "conv1_1"
}
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_2"
type: "ReLU"
bottom: "conv1_2"
top: "conv1_2"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1_2"
top: "pool1"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv2_1"
type: "Convolution"
bottom: "pool1"
top: "conv2_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_1"
type: "ReLU"
bottom: "conv2_1"
top: "conv2_1"
}
layer {
name: "conv2_2"
type: "Convolution"
bottom: "conv2_1"
top: "conv2_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_2"
type: "ReLU"
bottom: "conv2_2"
top: "conv2_2"
}
layer {
name: "pool2"
type: "Pooling"
bottom: "conv2_2"
top: "pool2"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv3_1"
type: "Convolution"
bottom: "pool2"
top: "conv3_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_1"
type: "ReLU"
bottom: "conv3_1"
top: "conv3_1"
}
layer {
name: "conv3_2"
type: "Convolution"
bottom: "conv3_1"
top: "conv3_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_2"
type: "ReLU"
bottom: "conv3_2"
top: "conv3_2"
}
layer {
name: "conv3_3"
type: "Convolution"
bottom: "conv3_2"
top: "conv3_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_3"
type: "ReLU"
bottom: "conv3_3"
top: "conv3_3"
}
layer {
name: "pool3"
type: "Pooling"
bottom: "conv3_3"
top: "pool3"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv4_1"
type: "Convolution"
bottom: "pool3"
top: "conv4_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_1"
type: "ReLU"
bottom: "conv4_1"
top: "conv4_1"
}
layer {
name: "conv4_2"
type: "Convolution"
bottom: "conv4_1"
top: "conv4_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_2"
type: "ReLU"
bottom: "conv4_2"
top: "conv4_2"
}
layer {
name: "conv4_3"
type: "Convolution"
bottom: "conv4_2"
top: "conv4_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_3"
type: "ReLU"
bottom: "conv4_3"
top: "conv4_3"
}
layer {
name: "pool4"
type: "Pooling"
bottom: "conv4_3"
top: "pool4"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv5_1"
type: "Convolution"
bottom: "pool4"
top: "conv5_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_1"
type: "ReLU"
bottom: "conv5_1"
top: "conv5_1"
}
layer {
name: "conv5_2"
type: "Convolution"
bottom: "conv5_1"
top: "conv5_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_2"
type: "ReLU"
bottom: "conv5_2"
top: "conv5_2"
}
layer {
name: "conv5_3"
type: "Convolution"
bottom: "conv5_2"
top: "conv5_3"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu5_3"
type: "ReLU"
bottom: "conv5_3"
top: "conv5_3"
}
layer {
name: "pool5"
type: "Pooling"
bottom: "conv5_3"
top: "pool5"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "fc6"
type: "Convolution"
bottom: "pool5"
top: "fc6"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu6"
type: "ReLU"
bottom: "fc6"
top: "fc6"
}
layer {
name: "fc7"
type: "Convolution"
bottom: "fc6"
top: "fc7"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu7"
type: "ReLU"
bottom: "fc7"
top: "fc7"
}
layer {
name: "conv6_1"
type: "Convolution"
bottom: "fc7"
top: "conv6_1"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_1_relu"
type: "ReLU"
bottom: "conv6_1"
top: "conv6_1"
}
layer {
name: "conv6_2"
type: "Convolution"
bottom: "conv6_1"
top: "conv6_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_relu"
type: "ReLU"
bottom: "conv6_2"
top: "conv6_2"
}
layer {
name: "conv4_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_3_norm_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_loc"
top: "conv4_3_norm_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv4_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_loc_perm"
top: "conv4_3_norm_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv4_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv4_3"
top: "conv4_3_norm_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv4_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv4_3_norm_mbox_conf"
top: "conv4_3_norm_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv4_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv4_3_norm_mbox_conf_perm"
top: "conv4_3_norm_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv4_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv4_3"
bottom: "data"
top: "conv4_3_norm_mbox_priorbox"
prior_box_param {
min_size: 16.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 8.0
offset: 0.5
}
}
layer {
name: "conv5_3_norm_mbox_loc"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_norm_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5_3_norm_mbox_loc_perm"
type: "Permute"
bottom: "conv5_3_norm_mbox_loc"
top: "conv5_3_norm_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv5_3_norm_mbox_loc_flat"
type: "Flatten"
bottom: "conv5_3_norm_mbox_loc_perm"
top: "conv5_3_norm_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv5_3_norm_mbox_conf"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_norm_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv5_3_norm_mbox_conf_perm"
type: "Permute"
bottom: "conv5_3_norm_mbox_conf"
top: "conv5_3_norm_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv5_3_norm_mbox_conf_flat"
type: "Flatten"
bottom: "conv5_3_norm_mbox_conf_perm"
top: "conv5_3_norm_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv5_3_norm_mbox_priorbox"
type: "PriorBox"
bottom: "conv5_3"
bottom: "data"
top: "conv5_3_norm_mbox_priorbox"
prior_box_param {
min_size: 32.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 16.0
offset: 0.5
}
}
layer {
name: "fc7_mbox_loc"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "fc7_mbox_loc_perm"
type: "Permute"
bottom: "fc7_mbox_loc"
top: "fc7_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "fc7_mbox_loc_flat"
type: "Flatten"
bottom: "fc7_mbox_loc_perm"
top: "fc7_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "fc7_mbox_conf"
type: "Convolution"
bottom: "fc7"
top: "fc7_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "fc7_mbox_conf_perm"
type: "Permute"
bottom: "fc7_mbox_conf"
top: "fc7_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "fc7_mbox_conf_flat"
type: "Flatten"
bottom: "fc7_mbox_conf_perm"
top: "fc7_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "fc7_mbox_priorbox"
type: "PriorBox"
bottom: "fc7"
bottom: "data"
top: "fc7_mbox_priorbox"
prior_box_param {
min_size: 64.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 32.0
offset: 0.5
}
}
layer {
name: "conv6_2_mbox_loc"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_mbox_loc_perm"
type: "Permute"
bottom: "conv6_2_mbox_loc"
top: "conv6_2_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv6_2_mbox_loc_flat"
type: "Flatten"
bottom: "conv6_2_mbox_loc_perm"
top: "conv6_2_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "conv6_2_mbox_conf"
type: "Convolution"
bottom: "conv6_2"
top: "conv6_2_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "conv6_2_mbox_conf_perm"
type: "Permute"
bottom: "conv6_2_mbox_conf"
top: "conv6_2_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "conv6_2_mbox_conf_flat"
type: "Flatten"
bottom: "conv6_2_mbox_conf_perm"
top: "conv6_2_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "conv6_2_mbox_priorbox"
type: "PriorBox"
bottom: "conv6_2"
bottom: "data"
top: "conv6_2_mbox_priorbox"
prior_box_param {
min_size: 128.0
aspect_ratio: 2.0
flip: true
clip: false
variance: 0.10000000149
variance: 0.10000000149
variance: 0.20000000298
variance: 0.20000000298
step: 64.0
offset: 0.5
}
}
layer {
name: "arm_loc"
type: "Concat"
bottom: "conv4_3_norm_mbox_loc_flat"
bottom: "conv5_3_norm_mbox_loc_flat"
bottom: "fc7_mbox_loc_flat"
bottom: "conv6_2_mbox_loc_flat"
top: "arm_loc"
concat_param {
axis:
}
}
layer {
name: "arm_conf"
type: "Concat"
bottom: "conv4_3_norm_mbox_conf_flat"
bottom: "conv5_3_norm_mbox_conf_flat"
bottom: "fc7_mbox_conf_flat"
bottom: "conv6_2_mbox_conf_flat"
top: "arm_conf"
concat_param {
axis:
}
}
layer {
name: "arm_priorbox"
type: "Concat"
bottom: "conv4_3_norm_mbox_priorbox"
bottom: "conv5_3_norm_mbox_priorbox"
bottom: "fc7_mbox_priorbox"
bottom: "conv6_2_mbox_priorbox"
top: "arm_priorbox"
concat_param {
axis:
}
}
layer {
name: "P3_mbox_loc_p"
type: "Convolution"
bottom: "conv4_3"
top: "P3_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P3_mbox_loc_perm"
type: "Permute"
bottom: "P3_mbox_loc"
top: "P3_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P3_mbox_loc_flat"
type: "Flatten"
bottom: "P3_mbox_loc_perm"
top: "P3_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P3_mbox_conf_p"
type: "Convolution"
bottom: "conv4_3"
top: "P3_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P3_mbox_conf_perm"
type: "Permute"
bottom: "P3_mbox_conf"
top: "P3_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P3_mbox_conf_flat"
type: "Flatten"
bottom: "P3_mbox_conf_perm"
top: "P3_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P4_mbox_loc_p"
type: "Convolution"
bottom: "conv5_3"
top: "P4_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P4_mbox_loc_perm"
type: "Permute"
bottom: "P4_mbox_loc"
top: "P4_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P4_mbox_loc_flat"
type: "Flatten"
bottom: "P4_mbox_loc_perm"
top: "P4_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P4_mbox_conf_p"
type: "Convolution"
bottom: "conv5_3"
top: "P4_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P4_mbox_conf_perm"
type: "Permute"
bottom: "P4_mbox_conf"
top: "P4_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P4_mbox_conf_flat"
type: "Flatten"
bottom: "P4_mbox_conf_perm"
top: "P4_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P5_mbox_loc_p"
type: "Convolution"
bottom: "fc7"
top: "P5_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P5_mbox_loc_perm"
type: "Permute"
bottom: "P5_mbox_loc"
top: "P5_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P5_mbox_loc_flat"
type: "Flatten"
bottom: "P5_mbox_loc_perm"
top: "P5_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P5_mbox_conf_p"
type: "Convolution"
bottom: "fc7"
top: "P5_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P5_mbox_conf_perm"
type: "Permute"
bottom: "P5_mbox_conf"
top: "P5_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P5_mbox_conf_flat"
type: "Flatten"
bottom: "P5_mbox_conf_perm"
top: "P5_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "P6_mbox_loc_p"
type: "Convolution"
bottom: "conv6_2"
top: "P6_mbox_loc"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P6_mbox_loc_perm"
type: "Permute"
bottom: "P6_mbox_loc"
top: "P6_mbox_loc_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P6_mbox_loc_flat"
type: "Flatten"
bottom: "P6_mbox_loc_perm"
top: "P6_mbox_loc_flat"
flatten_param {
axis:
}
}
layer {
name: "P6_mbox_conf_p"
type: "Convolution"
bottom: "conv6_2"
top: "P6_mbox_conf"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output:
pad:
kernel_size:
stride:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "P6_mbox_conf_perm"
type: "Permute"
bottom: "P6_mbox_conf"
top: "P6_mbox_conf_perm"
permute_param {
order:
order:
order:
order:
}
}
layer {
name: "P6_mbox_conf_flat"
type: "Flatten"
bottom: "P6_mbox_conf_perm"
top: "P6_mbox_conf_flat"
flatten_param {
axis:
}
}
layer {
name: "odm_loc"
type: "Concat"
bottom: "P3_mbox_loc_flat"
bottom: "P4_mbox_loc_flat"
bottom: "P5_mbox_loc_flat"
bottom: "P6_mbox_loc_flat"
top: "odm_loc"
concat_param {
axis:
}
}
layer {
name: "odm_conf"
type: "Concat"
bottom: "P3_mbox_conf_flat"
bottom: "P4_mbox_conf_flat"
bottom: "P5_mbox_conf_flat"
bottom: "P6_mbox_conf_flat"
top: "odm_conf"
concat_param {
axis:
}
}
layer {
name: "arm_loss"
type: "MultiBoxLoss"
bottom: "arm_loc"
bottom: "arm_conf"
bottom: "arm_priorbox"
bottom: "label"
top: "arm_loss"
include {
phase: TRAIN
}
propagate_down: true
propagate_down: true
propagate_down: false
propagate_down: false
loss_param {
normalization: VALID
}
multibox_loss_param {
loc_loss_type: SMOOTH_L1
conf_loss_type: SOFTMAX
loc_weight: 1.0
num_classes:
share_location: true
match_type: PER_PREDICTION
overlap_threshold: 0.5
use_prior_for_matching: true
background_label_id:
use_difficult_gt: true
neg_pos_ratio: 3.0
neg_overlap: 0.5
code_type: CENTER_SIZE
ignore_cross_boundary_bbox: false
mining_type: MAX_NEGATIVE
objectness_score: 0.00999999977648
}
}
layer {
name: "arm_conf_reshape"
type: "Reshape"
bottom: "arm_conf"
top: "arm_conf_reshape"
reshape_param {
shape {
dim:
dim: -
dim:
}
}
}
layer {
name: "arm_conf_softmax"
type: "Softmax"
bottom: "arm_conf_reshape"
top: "arm_conf_softmax"
softmax_param {
axis:
}
}
layer {
name: "arm_conf_flatten"
type: "Flatten"
bottom: "arm_conf_softmax"
top: "arm_conf_flatten"
flatten_param {
axis:
}
}
layer {
name: "odm_loss"
type: "MultiBoxLoss"
bottom: "odm_loc"
bottom: "odm_conf"
bottom: "arm_priorbox"
bottom: "label"
bottom: "arm_conf_flatten"
bottom: "arm_loc"
top: "odm_loss"
include {
phase: TRAIN
}
propagate_down: true
propagate_down: true
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
loss_param {
normalization: VALID
}
multibox_loss_param {
loc_loss_type: SMOOTH_L1
conf_loss_type: SOFTMAX
loc_weight: 1.0
num_classes:
share_location: true
match_type: PER_PREDICTION
overlap_threshold: 0.5
use_prior_for_matching: true
background_label_id:
use_difficult_gt: true
neg_pos_ratio: 3.0
neg_overlap: 0.5
code_type: CENTER_SIZE
ignore_cross_boundary_bbox: false
mining_type: MAX_NEGATIVE
objectness_score: 0.00999999977648
}
} layer {
name: "conv1_1_t"
type: "Convolution"
bottom: "data"
top: "conv1_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_1_t"
type: "ReLU"
bottom: "conv1_1_t"
top: "conv1_1_t"
}
layer {
name: "conv1_2_t"
type: "Convolution"
bottom: "conv1_1_t"
top: "conv1_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu1_2_t"
type: "ReLU"
bottom: "conv1_2_t"
top: "conv1_2_t"
}
layer {
name: "pool1_t"
type: "Pooling"
bottom: "conv1_2_t"
top: "pool1_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv2_1_t"
type: "Convolution"
bottom: "pool1_t"
top: "conv2_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_1_t"
type: "ReLU"
bottom: "conv2_1_t"
top: "conv2_1_t"
}
layer {
name: "conv2_2_t"
type: "Convolution"
bottom: "conv2_1_t"
top: "conv2_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu2_2_t"
type: "ReLU"
bottom: "conv2_2_t"
top: "conv2_2_t"
}
layer {
name: "pool2_t"
type: "Pooling"
bottom: "conv2_2_t"
top: "pool2_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv3_1_t"
type: "Convolution"
bottom: "pool2_t"
top: "conv3_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_1_t"
type: "ReLU"
bottom: "conv3_1_t"
top: "conv3_1_t"
}
layer {
name: "conv3_2_t"
type: "Convolution"
bottom: "conv3_1_t"
top: "conv3_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_2_t"
type: "ReLU"
bottom: "conv3_2_t"
top: "conv3_2_t"
}
layer {
name: "conv3_3_t"
type: "Convolution"
bottom: "conv3_2_t"
top: "conv3_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu3_3_t"
type: "ReLU"
bottom: "conv3_3_t"
top: "conv3_3_t"
}
layer {
name: "pool3_t"
type: "Pooling"
bottom: "conv3_3_t"
top: "pool3_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv4_1_t"
type: "Convolution"
bottom: "pool3_t"
top: "conv4_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_1_t"
type: "ReLU"
bottom: "conv4_1_t"
top: "conv4_1_t"
}
layer {
name: "conv4_2_t"
type: "Convolution"
bottom: "conv4_1_t"
top: "conv4_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_2_t"
type: "ReLU"
bottom: "conv4_2_t"
top: "conv4_2_t"
}
layer {
name: "conv4_3_t"
type: "Convolution"
bottom: "conv4_2_t"
top: "conv4_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
layer {
name: "relu4_3_t"
type: "ReLU"
bottom: "conv4_3_t"
top: "conv4_3_t"
}
layer {
name: "pool4_t"
type: "Pooling"
bottom: "conv4_3_t"
top: "pool4_t"
pooling_param {
pool: MAX
kernel_size:
stride:
}
}
layer {
name: "conv5_1_t"
type: "Convolution"
bottom: "pool4_t"
top: "conv5_1_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_1_t"
type: "ReLU"
bottom: "conv5_1_t"
top: "conv5_1_t"
}
layer {
name: "conv5_2_t"
type: "Convolution"
bottom: "conv5_1_t"
top: "conv5_2_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_2_t"
type: "ReLU"
bottom: "conv5_2_t"
top: "conv5_2_t"
}
layer {
name: "conv5_3_t"
type: "Convolution"
bottom: "conv5_2_t"
top: "conv5_3_t"
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
pad:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
dilation:
}
}
layer {
name: "relu5_3_t"
type: "ReLU"
bottom: "conv5_3_t"
top: "conv5_3_t"
} layer {
name: "conv5_3_m"
type: "Convolution"
bottom: "conv5_3"
top: "conv5_3_m"
propagate_down: true
param {
lr_mult:
decay_mult:
}
param {
lr_mult:
decay_mult:
}
convolution_param {
num_output:
kernel_size:
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value:
}
}
} layer {
name: "relu5_3_m"
type: "ReLU"
bottom: "conv5_3_m"
top: "conv5_3_m"
} layer {
name: "roi_pool_t"
type: "ROIPooling"
bottom: "conv5_3_t"
bottom: "label"
top: "pool_t"
roi_pooling_param {
pooled_w:
pooled_h:
}
propagate_down: false
propagate_down: false
}
layer {
name: "roi_pool_s"
type: "ROIPooling"
bottom: "conv5_3_m"
bottom: "label"
top: "pool_s"
roi_pooling_param {
pooled_w:
pooled_h:
}
propagate_down: true
propagate_down: false
} layer {
name: "mimic_loss"
type: "EuclideanLoss"
bottom: "pool_t"
bottom: "pool_s"
top: "mimic_loss"
propagate_down: false
propagate_down: true
loss_weight:
include {
phase: TRAIN
}
}
refinedet网络结构的更多相关文章
- RefineDet算法笔记
---恢复内容开始--- 一.创新点 针对two-stage的速度慢以及one-stage精度不足提出的方法,refinedet 包括三个核心部分:使用TCB来转换ARM的特征,送入ODM中进行检测: ...
- 论文阅读 | RefineDet：Single-Shot Refinement Neural Network for Object Detection
论文链接:https://arxiv.org/abs/1711.06897 代码链接:https://github.com/sfzhang15/RefineDet 摘要 RefineDet是CVPR ...
- 目标检测之RefineDet
RefineDet 一.相关背景 中科院自动化所最新成果,CVPR 2018 <Single-Shot Refinement Neural Network for Object Detectio ...
- Sparse Filtering 学习笔记（一）网络结构与特征矩阵
Sparse Filtering 是一个用于提取特征的无监督学习算法,与通常特征学习算法试图建模训练数据的分布的做法不同,Sparse Filtering 直接对训练数据的特征分布进行分析,在所谓 ...
- 深度神经网络结构以及Pre-Training的理解
Logistic回归.传统多层神经网络 1.1 线性回归.线性神经网络.Logistic/Softmax回归 线性回归是用于数据拟合的常规手段,其任务是优化目标函数:$h(\theta )=\thet ...
- 受限玻尔兹曼机（RBM）学习笔记（二）网络结构
去年 6 月份写的博文<Yusuke Sugomori 的 C 语言 Deep Learning 程序解读>是囫囵吞枣地读完一个关于 DBN 算法的开源代码后的笔记,当时对其中涉及的算 ...
- OpenWrt网络结构
原文链接:http://www.freezhongzi.info/?p=104 OpenWrt网络结构 OpenWrt的网络配置很丰富,在我看来几乎可以完成任何网络结构.下图为一个支持OpenWrt的 ...
- CNN(卷积神经网络)、RNN(循环神经网络)、DNN(深度神经网络)的内部网络结构有什么区别？
https://www.zhihu.com/question/34681168 CNN(卷积神经网络).RNN(循环神经网络).DNN(深度神经网络)的内部网络结构有什么区别?修改 CNN(卷积神经网 ...
- 神经网络结构在命名实体识别（NER）中的应用
神经网络结构在命名实体识别(NER)中的应用 近年来,基于神经网络的深度学习方法在自然语言处理领域已经取得了不少进展.作为NLP领域的基础任务-命名实体识别(Named Entity Recognit ...
随机推荐
- shell中if的可判断的类型
-d :判断制定的是否为目录-z:判断制定的变量是否存在值-f:判断制定的是否为文件-L:判断制定的是否为符号链接-r:判断制定的是否可读-w:判断制定的是否可写-x:判断存在的对象是否可以执行!:测 ...
- CSS 专业的技巧
目录 专业的技巧 支持情况 贡献准则 专业的技巧 使用CSS复位 继承 box-sizing 使用 :not() 选择器来决定表单是否显示边框 为 body 元素添加行高 垂直居中任何元素 逗号分 ...
- [转]ASP.NET MVC 5 List Editor with Bootstrap Modals
本文转自:https://www.codeproject.com/articles/786085/asp-net-mvc-list-editor-with-bootstrap-modals With ...
- 搭建一个最简单的node服务器
搭建一个最简单的node服务器 1.创建一个Http服务并监听8888端口 2.使用url模块 获取请求的路由和请求参数 var http = require('http'); var url = r ...
- mysql 查两个表相同的值
比如一个数据库 表A和表B 都有一个username字段, 现查出与表A中username值相同的表B的username和password数据 select B.username,B.password ...
- Angular6路由复用与延迟加载的冲突解决——看看有备无患
结论: 结论放最上面,送给匆匆查资料的你: 同时使用延迟加载 + 路由复用,一定不能使用route.routeConfig.path做key去缓存,否则会死得难看. 经实测(我没有完全去解读源代码 ...
- Java - > for, while 及 do.while循环
为什么要用到循环结构: 按顺序结构执行程序语句(方法或者代码块)只能被执行一次.如果要多次执行,就需要使用到循环结构(循环结构是指在程序中需要反复执行某个功能而设置的一种程序结构) 布尔表达式:是一段 ...
- js之方法
原文 在一个对象中绑定函数,称为这个对象的方法. 在JavaScript中,对象的定义是这样的: var xiaoming = { name: '小明', birth: 1990 }; 但是,如果我们 ...
- Tomcat配置https之 JDK SSL证书生成与验证
关于证书 SSL证书通过在客户端浏览器和Web服务器之间建立一条SSL安全通道(Secure socket layer(SSL),安全协议是由Netscape Communication公司设计开发. ...
- link 和 import 导入外部样式的区别
差别一:link 属于 XHTML 标签,而 @import 完全是 CSS 提供的 一种方式.link标签除了可以加载 CSS 外,还可以做很多事情,比如定义 RSS ,定义 rel 链接属性等. ...