mirror of
https://github.com/opencv/opencv.git
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1899 lines
30 KiB
Plaintext
1899 lines
30 KiB
Plaintext
layer {
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name: "data"
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type: "AnnotatedData"
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top: "data"
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top: "label"
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include {
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phase: TRAIN
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}
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transform_param {
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mirror: true
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mean_value: 104
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mean_value: 117
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mean_value: 123
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resize_param {
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prob: 1
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resize_mode: WARP
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height: 300
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width: 300
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interp_mode: LINEAR
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interp_mode: AREA
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interp_mode: NEAREST
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interp_mode: CUBIC
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interp_mode: LANCZOS4
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}
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emit_constraint {
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emit_type: CENTER
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}
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distort_param {
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brightness_prob: 0.5
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brightness_delta: 32
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contrast_prob: 0.5
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contrast_lower: 0.5
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contrast_upper: 1.5
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hue_prob: 0.5
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hue_delta: 18
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saturation_prob: 0.5
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saturation_lower: 0.5
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saturation_upper: 1.5
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random_order_prob: 0.0
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}
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expand_param {
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prob: 0.5
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max_expand_ratio: 4.0
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}
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}
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data_param {
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source: "train_lmdb/"
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batch_size: 16
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backend: LMDB
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}
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annotated_data_param {
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batch_sampler {
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max_sample: 1
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max_trials: 1
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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min_jaccard_overlap: 0.1
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}
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max_sample: 1
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max_trials: 50
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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min_jaccard_overlap: 0.3
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}
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max_sample: 1
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max_trials: 50
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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min_jaccard_overlap: 0.5
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}
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max_sample: 1
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max_trials: 50
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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min_jaccard_overlap: 0.7
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}
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max_sample: 1
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max_trials: 50
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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min_jaccard_overlap: 0.9
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}
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max_sample: 1
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max_trials: 50
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}
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batch_sampler {
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sampler {
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min_scale: 0.3
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max_scale: 1.0
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min_aspect_ratio: 0.5
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max_aspect_ratio: 2.0
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}
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sample_constraint {
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max_jaccard_overlap: 1.0
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}
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max_sample: 1
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max_trials: 50
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}
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}
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}
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layer {
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name: "data_bn"
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type: "BatchNorm"
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bottom: "data"
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top: "data_bn"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "data_scale"
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type: "Scale"
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bottom: "data_bn"
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top: "data_bn"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "conv1_h"
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type: "Convolution"
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bottom: "data_bn"
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top: "conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 32
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pad: 3
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kernel_size: 7
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stride: 2
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weight_filler {
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type: "msra"
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variance_norm: FAN_OUT
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "conv1_bn_h"
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type: "BatchNorm"
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bottom: "conv1_h"
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top: "conv1_h"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "conv1_scale_h"
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type: "Scale"
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bottom: "conv1_h"
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top: "conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "conv1_relu"
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type: "ReLU"
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bottom: "conv1_h"
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top: "conv1_h"
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}
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layer {
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name: "conv1_pool"
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type: "Pooling"
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bottom: "conv1_h"
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top: "conv1_pool"
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pooling_param {
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kernel_size: 3
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stride: 2
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}
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}
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layer {
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name: "layer_64_1_conv1_h"
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type: "Convolution"
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bottom: "conv1_pool"
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top: "layer_64_1_conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 32
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bias_term: false
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pad: 1
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kernel_size: 3
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stride: 1
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "layer_64_1_bn2_h"
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type: "BatchNorm"
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bottom: "layer_64_1_conv1_h"
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top: "layer_64_1_conv1_h"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "layer_64_1_scale2_h"
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type: "Scale"
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bottom: "layer_64_1_conv1_h"
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top: "layer_64_1_conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "layer_64_1_relu2"
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type: "ReLU"
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bottom: "layer_64_1_conv1_h"
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top: "layer_64_1_conv1_h"
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}
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layer {
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name: "layer_64_1_conv2_h"
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type: "Convolution"
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bottom: "layer_64_1_conv1_h"
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top: "layer_64_1_conv2_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 32
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bias_term: false
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pad: 1
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kernel_size: 3
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stride: 1
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "layer_64_1_sum"
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type: "Eltwise"
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bottom: "layer_64_1_conv2_h"
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bottom: "conv1_pool"
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top: "layer_64_1_sum"
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}
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layer {
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name: "layer_128_1_bn1_h"
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type: "BatchNorm"
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bottom: "layer_64_1_sum"
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top: "layer_128_1_bn1_h"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "layer_128_1_scale1_h"
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type: "Scale"
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bottom: "layer_128_1_bn1_h"
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top: "layer_128_1_bn1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "layer_128_1_relu1"
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type: "ReLU"
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bottom: "layer_128_1_bn1_h"
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top: "layer_128_1_bn1_h"
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}
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layer {
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name: "layer_128_1_conv1_h"
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type: "Convolution"
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bottom: "layer_128_1_bn1_h"
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top: "layer_128_1_conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 128
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bias_term: false
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pad: 1
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kernel_size: 3
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stride: 2
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "layer_128_1_bn2"
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type: "BatchNorm"
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bottom: "layer_128_1_conv1_h"
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top: "layer_128_1_conv1_h"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "layer_128_1_scale2"
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type: "Scale"
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bottom: "layer_128_1_conv1_h"
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top: "layer_128_1_conv1_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "layer_128_1_relu2"
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type: "ReLU"
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bottom: "layer_128_1_conv1_h"
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top: "layer_128_1_conv1_h"
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}
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layer {
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name: "layer_128_1_conv2"
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type: "Convolution"
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bottom: "layer_128_1_conv1_h"
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top: "layer_128_1_conv2"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 128
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bias_term: false
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pad: 1
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kernel_size: 3
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stride: 1
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "layer_128_1_conv_expand_h"
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type: "Convolution"
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bottom: "layer_128_1_bn1_h"
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top: "layer_128_1_conv_expand_h"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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convolution_param {
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num_output: 128
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bias_term: false
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pad: 0
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kernel_size: 1
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stride: 2
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weight_filler {
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type: "msra"
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}
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bias_filler {
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type: "constant"
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value: 0.0
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}
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}
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}
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layer {
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name: "layer_128_1_sum"
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type: "Eltwise"
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bottom: "layer_128_1_conv2"
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bottom: "layer_128_1_conv_expand_h"
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top: "layer_128_1_sum"
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}
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layer {
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name: "layer_256_1_bn1"
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type: "BatchNorm"
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bottom: "layer_128_1_sum"
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top: "layer_256_1_bn1"
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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param {
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lr_mult: 0.0
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}
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}
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layer {
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name: "layer_256_1_scale1"
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type: "Scale"
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bottom: "layer_256_1_bn1"
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top: "layer_256_1_bn1"
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param {
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lr_mult: 1.0
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decay_mult: 1.0
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}
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param {
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lr_mult: 2.0
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decay_mult: 1.0
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}
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scale_param {
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bias_term: true
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}
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}
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layer {
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name: "layer_256_1_relu1"
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type: "ReLU"
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|
bottom: "layer_256_1_bn1"
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top: "layer_256_1_bn1"
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}
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layer {
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name: "layer_256_1_conv1"
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type: "Convolution"
|
|
bottom: "layer_256_1_bn1"
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top: "layer_256_1_conv1"
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param {
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lr_mult: 1.0
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|
decay_mult: 1.0
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}
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|
convolution_param {
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num_output: 256
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bias_term: false
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|
pad: 1
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|
kernel_size: 3
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|
stride: 2
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|
weight_filler {
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|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
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value: 0.0
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}
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}
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}
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|
layer {
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name: "layer_256_1_bn2"
|
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type: "BatchNorm"
|
|
bottom: "layer_256_1_conv1"
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top: "layer_256_1_conv1"
|
|
param {
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|
lr_mult: 0.0
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}
|
|
param {
|
|
lr_mult: 0.0
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}
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|
param {
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lr_mult: 0.0
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}
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}
|
|
layer {
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|
name: "layer_256_1_scale2"
|
|
type: "Scale"
|
|
bottom: "layer_256_1_conv1"
|
|
top: "layer_256_1_conv1"
|
|
param {
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|
lr_mult: 1.0
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decay_mult: 1.0
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}
|
|
param {
|
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lr_mult: 2.0
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|
decay_mult: 1.0
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}
|
|
scale_param {
|
|
bias_term: true
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|
}
|
|
}
|
|
layer {
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|
name: "layer_256_1_relu2"
|
|
type: "ReLU"
|
|
bottom: "layer_256_1_conv1"
|
|
top: "layer_256_1_conv1"
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|
}
|
|
layer {
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|
name: "layer_256_1_conv2"
|
|
type: "Convolution"
|
|
bottom: "layer_256_1_conv1"
|
|
top: "layer_256_1_conv2"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
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|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
bias_term: false
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
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|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_256_1_conv_expand"
|
|
type: "Convolution"
|
|
bottom: "layer_256_1_bn1"
|
|
top: "layer_256_1_conv_expand"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
bias_term: false
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_256_1_sum"
|
|
type: "Eltwise"
|
|
bottom: "layer_256_1_conv2"
|
|
bottom: "layer_256_1_conv_expand"
|
|
top: "layer_256_1_sum"
|
|
}
|
|
layer {
|
|
name: "layer_512_1_bn1"
|
|
type: "BatchNorm"
|
|
bottom: "layer_256_1_sum"
|
|
top: "layer_512_1_bn1"
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_scale1"
|
|
type: "Scale"
|
|
bottom: "layer_512_1_bn1"
|
|
top: "layer_512_1_bn1"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 1.0
|
|
}
|
|
scale_param {
|
|
bias_term: true
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_relu1"
|
|
type: "ReLU"
|
|
bottom: "layer_512_1_bn1"
|
|
top: "layer_512_1_bn1"
|
|
}
|
|
layer {
|
|
name: "layer_512_1_conv1_h"
|
|
type: "Convolution"
|
|
bottom: "layer_512_1_bn1"
|
|
top: "layer_512_1_conv1_h"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
bias_term: false
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1 # 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_bn2_h"
|
|
type: "BatchNorm"
|
|
bottom: "layer_512_1_conv1_h"
|
|
top: "layer_512_1_conv1_h"
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_scale2_h"
|
|
type: "Scale"
|
|
bottom: "layer_512_1_conv1_h"
|
|
top: "layer_512_1_conv1_h"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 1.0
|
|
}
|
|
scale_param {
|
|
bias_term: true
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_relu2"
|
|
type: "ReLU"
|
|
bottom: "layer_512_1_conv1_h"
|
|
top: "layer_512_1_conv1_h"
|
|
}
|
|
layer {
|
|
name: "layer_512_1_conv2_h"
|
|
type: "Convolution"
|
|
bottom: "layer_512_1_conv1_h"
|
|
top: "layer_512_1_conv2_h"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
bias_term: false
|
|
pad: 2 # 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
dilation: 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_conv_expand_h"
|
|
type: "Convolution"
|
|
bottom: "layer_512_1_bn1"
|
|
top: "layer_512_1_conv_expand_h"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
bias_term: false
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 1 # 2
|
|
weight_filler {
|
|
type: "msra"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0.0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "layer_512_1_sum"
|
|
type: "Eltwise"
|
|
bottom: "layer_512_1_conv2_h"
|
|
bottom: "layer_512_1_conv_expand_h"
|
|
top: "layer_512_1_sum"
|
|
}
|
|
layer {
|
|
name: "last_bn_h"
|
|
type: "BatchNorm"
|
|
bottom: "layer_512_1_sum"
|
|
top: "layer_512_1_sum"
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
param {
|
|
lr_mult: 0.0
|
|
}
|
|
}
|
|
layer {
|
|
name: "last_scale_h"
|
|
type: "Scale"
|
|
bottom: "layer_512_1_sum"
|
|
top: "layer_512_1_sum"
|
|
param {
|
|
lr_mult: 1.0
|
|
decay_mult: 1.0
|
|
}
|
|
param {
|
|
lr_mult: 2.0
|
|
decay_mult: 1.0
|
|
}
|
|
scale_param {
|
|
bias_term: true
|
|
}
|
|
}
|
|
layer {
|
|
name: "last_relu"
|
|
type: "ReLU"
|
|
bottom: "layer_512_1_sum"
|
|
top: "fc7"
|
|
}
|
|
|
|
layer {
|
|
name: "conv6_1_h"
|
|
type: "Convolution"
|
|
bottom: "fc7"
|
|
top: "conv6_1_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_1_relu"
|
|
type: "ReLU"
|
|
bottom: "conv6_1_h"
|
|
top: "conv6_1_h"
|
|
}
|
|
layer {
|
|
name: "conv6_2_h"
|
|
type: "Convolution"
|
|
bottom: "conv6_1_h"
|
|
top: "conv6_2_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 256
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_relu"
|
|
type: "ReLU"
|
|
bottom: "conv6_2_h"
|
|
top: "conv6_2_h"
|
|
}
|
|
layer {
|
|
name: "conv7_1_h"
|
|
type: "Convolution"
|
|
bottom: "conv6_2_h"
|
|
top: "conv7_1_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 64
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_1_relu"
|
|
type: "ReLU"
|
|
bottom: "conv7_1_h"
|
|
top: "conv7_1_h"
|
|
}
|
|
layer {
|
|
name: "conv7_2_h"
|
|
type: "Convolution"
|
|
bottom: "conv7_1_h"
|
|
top: "conv7_2_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 2
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_relu"
|
|
type: "ReLU"
|
|
bottom: "conv7_2_h"
|
|
top: "conv7_2_h"
|
|
}
|
|
layer {
|
|
name: "conv8_1_h"
|
|
type: "Convolution"
|
|
bottom: "conv7_2_h"
|
|
top: "conv8_1_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 64
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_1_relu"
|
|
type: "ReLU"
|
|
bottom: "conv8_1_h"
|
|
top: "conv8_1_h"
|
|
}
|
|
layer {
|
|
name: "conv8_2_h"
|
|
type: "Convolution"
|
|
bottom: "conv8_1_h"
|
|
top: "conv8_2_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_relu"
|
|
type: "ReLU"
|
|
bottom: "conv8_2_h"
|
|
top: "conv8_2_h"
|
|
}
|
|
layer {
|
|
name: "conv9_1_h"
|
|
type: "Convolution"
|
|
bottom: "conv8_2_h"
|
|
top: "conv9_1_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 64
|
|
pad: 0
|
|
kernel_size: 1
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_1_relu"
|
|
type: "ReLU"
|
|
bottom: "conv9_1_h"
|
|
top: "conv9_1_h"
|
|
}
|
|
layer {
|
|
name: "conv9_2_h"
|
|
type: "Convolution"
|
|
bottom: "conv9_1_h"
|
|
top: "conv9_2_h"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 128
|
|
pad: 0
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_relu"
|
|
type: "ReLU"
|
|
bottom: "conv9_2_h"
|
|
top: "conv9_2_h"
|
|
}
|
|
layer {
|
|
name: "conv4_3_norm"
|
|
type: "Normalize"
|
|
bottom: "layer_256_1_bn1"
|
|
top: "conv4_3_norm"
|
|
norm_param {
|
|
across_spatial: false
|
|
scale_filler {
|
|
type: "constant"
|
|
value: 20
|
|
}
|
|
channel_shared: false
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv4_3_norm_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv4_3_norm"
|
|
top: "conv4_3_norm_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 16
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 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: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
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: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv4_3_norm_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv4_3_norm"
|
|
top: "conv4_3_norm_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 8 # 84
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 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: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
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: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv4_3_norm_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv4_3_norm"
|
|
bottom: "data"
|
|
top: "conv4_3_norm_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 30.0
|
|
max_size: 60.0
|
|
aspect_ratio: 2
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 8
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "fc7"
|
|
top: "fc7_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "fc7_mbox_loc"
|
|
top: "fc7_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "fc7_mbox_loc_perm"
|
|
top: "fc7_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "fc7"
|
|
top: "fc7_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 12 # 126
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "fc7_mbox_conf"
|
|
top: "fc7_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "fc7_mbox_conf_perm"
|
|
top: "fc7_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "fc7_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "fc7"
|
|
bottom: "data"
|
|
top: "fc7_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 60.0
|
|
max_size: 111.0
|
|
aspect_ratio: 2
|
|
aspect_ratio: 3
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 16
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv6_2_h"
|
|
top: "conv6_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv6_2_mbox_loc"
|
|
top: "conv6_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv6_2_mbox_loc_perm"
|
|
top: "conv6_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv6_2_h"
|
|
top: "conv6_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 12 # 126
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv6_2_mbox_conf"
|
|
top: "conv6_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv6_2_mbox_conf_perm"
|
|
top: "conv6_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv6_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv6_2_h"
|
|
bottom: "data"
|
|
top: "conv6_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 111.0
|
|
max_size: 162.0
|
|
aspect_ratio: 2
|
|
aspect_ratio: 3
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 32
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv7_2_h"
|
|
top: "conv7_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 24
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv7_2_mbox_loc"
|
|
top: "conv7_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv7_2_mbox_loc_perm"
|
|
top: "conv7_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv7_2_h"
|
|
top: "conv7_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 12 # 126
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv7_2_mbox_conf"
|
|
top: "conv7_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv7_2_mbox_conf_perm"
|
|
top: "conv7_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv7_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv7_2_h"
|
|
bottom: "data"
|
|
top: "conv7_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 162.0
|
|
max_size: 213.0
|
|
aspect_ratio: 2
|
|
aspect_ratio: 3
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 64
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv8_2_h"
|
|
top: "conv8_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 16
|
|
pad: 0
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv8_2_mbox_loc"
|
|
top: "conv8_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv8_2_mbox_loc_perm"
|
|
top: "conv8_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv8_2_h"
|
|
top: "conv8_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 8 # 84
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv8_2_mbox_conf"
|
|
top: "conv8_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv8_2_mbox_conf_perm"
|
|
top: "conv8_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv8_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv8_2_h"
|
|
bottom: "data"
|
|
top: "conv8_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 213.0
|
|
max_size: 264.0
|
|
aspect_ratio: 2
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 100
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_loc"
|
|
type: "Convolution"
|
|
bottom: "conv9_2_h"
|
|
top: "conv9_2_mbox_loc"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 16
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_loc_perm"
|
|
type: "Permute"
|
|
bottom: "conv9_2_mbox_loc"
|
|
top: "conv9_2_mbox_loc_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_loc_flat"
|
|
type: "Flatten"
|
|
bottom: "conv9_2_mbox_loc_perm"
|
|
top: "conv9_2_mbox_loc_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_conf"
|
|
type: "Convolution"
|
|
bottom: "conv9_2_h"
|
|
top: "conv9_2_mbox_conf"
|
|
param {
|
|
lr_mult: 1
|
|
decay_mult: 1
|
|
}
|
|
param {
|
|
lr_mult: 2
|
|
decay_mult: 0
|
|
}
|
|
convolution_param {
|
|
num_output: 8 # 84
|
|
pad: 1
|
|
kernel_size: 3
|
|
stride: 1
|
|
weight_filler {
|
|
type: "xavier"
|
|
}
|
|
bias_filler {
|
|
type: "constant"
|
|
value: 0
|
|
}
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_conf_perm"
|
|
type: "Permute"
|
|
bottom: "conv9_2_mbox_conf"
|
|
top: "conv9_2_mbox_conf_perm"
|
|
permute_param {
|
|
order: 0
|
|
order: 2
|
|
order: 3
|
|
order: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_conf_flat"
|
|
type: "Flatten"
|
|
bottom: "conv9_2_mbox_conf_perm"
|
|
top: "conv9_2_mbox_conf_flat"
|
|
flatten_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "conv9_2_mbox_priorbox"
|
|
type: "PriorBox"
|
|
bottom: "conv9_2_h"
|
|
bottom: "data"
|
|
top: "conv9_2_mbox_priorbox"
|
|
prior_box_param {
|
|
min_size: 264.0
|
|
max_size: 315.0
|
|
aspect_ratio: 2
|
|
flip: true
|
|
clip: false
|
|
variance: 0.1
|
|
variance: 0.1
|
|
variance: 0.2
|
|
variance: 0.2
|
|
step: 300
|
|
offset: 0.5
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_loc"
|
|
type: "Concat"
|
|
bottom: "conv4_3_norm_mbox_loc_flat"
|
|
bottom: "fc7_mbox_loc_flat"
|
|
bottom: "conv6_2_mbox_loc_flat"
|
|
bottom: "conv7_2_mbox_loc_flat"
|
|
bottom: "conv8_2_mbox_loc_flat"
|
|
bottom: "conv9_2_mbox_loc_flat"
|
|
top: "mbox_loc"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_conf"
|
|
type: "Concat"
|
|
bottom: "conv4_3_norm_mbox_conf_flat"
|
|
bottom: "fc7_mbox_conf_flat"
|
|
bottom: "conv6_2_mbox_conf_flat"
|
|
bottom: "conv7_2_mbox_conf_flat"
|
|
bottom: "conv8_2_mbox_conf_flat"
|
|
bottom: "conv9_2_mbox_conf_flat"
|
|
top: "mbox_conf"
|
|
concat_param {
|
|
axis: 1
|
|
}
|
|
}
|
|
layer {
|
|
name: "mbox_priorbox"
|
|
type: "Concat"
|
|
bottom: "conv4_3_norm_mbox_priorbox"
|
|
bottom: "fc7_mbox_priorbox"
|
|
bottom: "conv6_2_mbox_priorbox"
|
|
bottom: "conv7_2_mbox_priorbox"
|
|
bottom: "conv8_2_mbox_priorbox"
|
|
bottom: "conv9_2_mbox_priorbox"
|
|
top: "mbox_priorbox"
|
|
concat_param {
|
|
axis: 2
|
|
}
|
|
}
|
|
|
|
layer {
|
|
name: "mbox_loss"
|
|
type: "MultiBoxLoss"
|
|
bottom: "mbox_loc"
|
|
bottom: "mbox_conf"
|
|
bottom: "mbox_priorbox"
|
|
bottom: "label"
|
|
top: "mbox_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: 2 # 21
|
|
share_location: true
|
|
match_type: PER_PREDICTION
|
|
overlap_threshold: 0.5
|
|
use_prior_for_matching: true
|
|
background_label_id: 0
|
|
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
|
|
}
|
|
}
|