Mobie_TinySSD_train.prototxt

name: "MobileNet_SSD" 
layer {
    name: "data"
    type: "AnnotatedData"
    top: "data"
    top: "label"
    include{
        phase: TRAIN
    }
    transform_param{
        scale: 0.007843     
        mirror: true
        mean_value: 104
        mean_value: 117
        mean_value: 123
        resize_param{
          prob: 1.0
          resize_mode: WARP
          height: 300
          width: 300
          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: "E:/VOCdevkit/VOC0712/lmdb/VOC0712_train_lmdb"
        batch_size: 32
        backend: LMDB
    }
    annotated_data_param{
        batch_sampler {
            max_sample: 1
            max_trials: 1
        }
        batch_sampler {
            sampler {
                min_scale: 0.3
                max_scale: 1.0
                min_aspect_ratio: 0.5
                max_aspect_ratio: 2.0
            }
            sample_constraint {
                min_jaccard_overlap: 0.1
             }
          max_sample: 1
          max_trials: 50
        }
        batch_sampler {
          sampler {
            min_scale: 0.3
            max_scale: 1.0
            min_aspect_ratio: 0.5
            max_aspect_ratio: 2.0
          }
          sample_constraint {
            min_jaccard_overlap: 0.3
          }
          max_sample: 1
          max_trials: 50
        }
        batch_sampler {
          sampler {
            min_scale: 0.3
            max_scale: 1.0
            min_aspect_ratio: 0.5
            max_aspect_ratio: 2.0
          }
          sample_constraint {
            min_jaccard_overlap: 0.5
          }
          max_sample: 1
          max_trials: 50
        }
        batch_sampler {
          sampler {
            min_scale: 0.3
            max_scale: 1.0
            min_aspect_ratio: 0.5
            max_aspect_ratio: 2.0
          }
          sample_constraint {
            min_jaccard_overlap: 0.7
          }
          max_sample: 1
          max_trials: 50
        }
        batch_sampler {
          sampler {
            min_scale: 0.3
            max_scale: 1.0
            min_aspect_ratio: 0.5
            max_aspect_ratio: 2.0
          }
          sample_constraint {
            min_jaccard_overlap: 0.9
          }
          max_sample: 1
          max_trials: 50
        }
        batch_sampler {
          sampler {
            min_scale: 0.3
            max_scale: 1.0
            min_aspect_ratio: 0.5
            max_aspect_ratio: 2.0
          }
          sample_constraint {
            max_jaccard_overlap: 1.0
          }
          max_sample: 1
          max_trials: 50
        }
        label_map_file: "D:/pi_caffe/labelmap.prototxt"
    }
}
layer {
    name: "conv1_1"
    type: "Convolution"
    bottom: "data"
    top: "conv1_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 16
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv1_1/bn"
    type: "BatchNorm"
    bottom: "conv1_1"
    top: "conv1_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv1_1/scale"
    type: "Scale"
    bottom: "conv1_1"
    top: "conv1_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv1_1/relu"
    type: "ReLU"
    bottom: "conv1_1"
    top: "conv1_1"
}
layer {
    name: "conv1"
    type: "Convolution"
    bottom: "conv1_1"
    top: "conv1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 16
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv1/bn"
    type: "BatchNorm"
    bottom: "conv1"
    top: "conv1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv1/scale"
    type: "Scale"
    bottom: "conv1"
    top: "conv1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv1/relu"
    type: "ReLU"
    bottom: "conv1"
    top: "conv1"
}
layer{
    name: "pool1"
    type: "Pooling"
    bottom: "conv1"
    top: "pool1"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer {
    name: "conv2_1"
    type: "Convolution"
    bottom: "pool1"
    top: "conv2_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 32
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv2_1/bn"
    type: "BatchNorm"
    bottom: "conv2_1"
    top: "conv2_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv2_1/scale"
    type: "Scale"
    bottom: "conv2_1"
    top: "conv2_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv2_1/relu"
    type: "ReLU"
    bottom: "conv2_1"
    top: "conv2_1"
}
layer {
    name: "conv2"
    type: "Convolution"
    bottom: "conv2_1"
    top: "conv2"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 32
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv2/bn"
    type: "BatchNorm"
    bottom: "conv2"
    top: "conv2"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv2/scale"
    type: "Scale"
    bottom: "conv2"
    top: "conv2"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv2/relu"
    type: "ReLU"
    bottom: "conv2"
    top: "conv2"
}
layer{
    name: "pool2"
    type: "Pooling"
    bottom: "conv2"
    top: "pool2"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer {
    name: "conv3_1"
    type: "Convolution"
    bottom: "pool2"
    top: "conv3_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 64
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv3_1/bn"
    type: "BatchNorm"
    bottom: "conv3_1"
    top: "conv3_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv3_1/scale"
    type: "Scale"
    bottom: "conv3_1"
    top: "conv3_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv3_1/relu"
    type: "ReLU"
    bottom: "conv3_1"
    top: "conv3_1"
}
layer {
    name: "conv3"
    type: "Convolution"
    bottom: "conv3_1"
    top: "conv3"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 64
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv3/bn"
    type: "BatchNorm"
    bottom: "conv3"
    top: "conv3"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv3/scale"
    type: "Scale"
    bottom: "conv3"
    top: "conv3"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv3/relu"
    type: "ReLU"
    bottom: "conv3"
    top: "conv3"
}
layer{
    name: "pool3"
    type: "Pooling"
    bottom: "conv3"
    top: "pool3"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer {
    name: "conv4_1"
    type: "Convolution"
    bottom: "pool3"
    top: "conv4_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv4_1/bn"
    type: "BatchNorm"
    bottom: "conv4_1"
    top: "conv4_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv4_1/scale"
    type: "Scale"
    bottom: "conv4_1"
    top: "conv4_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv4_1/relu"
    type: "ReLU"
    bottom: "conv4_1"
    top: "conv4_1"
}
layer {
    name: "conv4"
    type: "Convolution"
    bottom: "conv4_1"
    top: "conv4"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv4/bn"
    type: "BatchNorm"
    bottom: "conv4"
    top: "conv4"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv4/scale"
    type: "Scale"
    bottom: "conv4"
    top: "conv4"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv4/relu"
    type: "ReLU"
    bottom: "conv4"
    top: "conv4"
}
layer{
    name: "pool4"
    type: "Pooling"
    bottom: "conv4"
    top: "pool4"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer {
    name: "conv5_1"
    type: "Convolution"
    bottom: "pool4"
    top: "conv5_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv5_1/bn"
    type: "BatchNorm"
    bottom: "conv5_1"
    top: "conv5_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv5_1/scale"
    type: "Scale"
    bottom: "conv5_1"
    top: "conv5_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv5_1/relu"
    type: "ReLU"
    bottom: "conv5_1"
    top: "conv5_1"
}
layer {
    name: "conv5"
    type: "Convolution"
    bottom: "conv5_1"
    top: "conv5"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv5/bn"
    type: "BatchNorm"
    bottom: "conv5"
    top: "conv5"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv5/scale"
    type: "Scale"
    bottom: "conv5"
    top: "conv5"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv5/relu"
    type: "ReLU"
    bottom: "conv5"
    top: "conv5"
}
layer{
    name: "pool5"
    type: "Pooling"
    bottom: "conv5"
    top: "pool5"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer {
    name: "conv6_1"
    type: "Convolution"
    bottom: "pool5"
    top: "conv6_1"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv6_1/bn"
    type: "BatchNorm"
    bottom: "conv6_1"
    top: "conv6_1"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv6_1/scale"
    type: "Scale"
    bottom: "conv6_1"
    top: "conv6_1"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv6_1/relu"
    type: "ReLU"
    bottom: "conv6_1"
    top: "conv6_1"
}
layer {
    name: "conv6"
    type: "Convolution"
    bottom: "conv6_1"
    top: "conv6"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    convolution_param {
        num_output: 128
        bias_term: false
        kernel_size: 3
        pad: 1 
        weight_filler{
            type: "msra"
        }
    }
}
layer {
    name: "conv6/bn"
    type: "BatchNorm"
    bottom: "conv6"
    top: "conv6"
    param{
        lr_mult: 0.0
        decay_mult: 0.0
    }
}
layer {
    name: "conv6/scale"
    type: "Scale"
    bottom: "conv6"
    top: "conv6"
    param{
        lr_mult: 2.0
        decay_mult: 0.0
    }
    param{
        lr_mult: 1.0
        decay_mult: 0.0
    }       
    scale_param{
        filler{
            value: 1
        }
        bias_term: true
        bias_filler{
            value: 0
        }
    }    
}
layer {
    name: "conv6/relu"
    type: "ReLU"
    bottom: "conv6"
    top: "conv6"
}
layer{
    name: "pool6"
    type: "Pooling"
    bottom: "conv6"
    top: "pool6"
    pooling_param{
        pool: MAX
        kernel_size: 2
        stride: 2
    }
}
layer{
    name: "pool"
    type: "Pooling"
    bottom: "pool6"
    top: "pool"
    pooling_param{
        pool: MAX
        global_pooling: true
    }
}
layer {
    name: "pool3_mbox_loc"
    type: "Convolution"
    bottom: "pool3"
    top: "pool3_mbox_loc"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 24
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool3_mbox_loc_perm"
    type: "Permute"
    bottom: "pool3_mbox_loc"
    top: "pool3_mbox_loc_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool3_mbox_loc_flat"
    type: "Flatten"
    bottom: "pool3_mbox_loc_perm"
    top: "pool3_mbox_loc_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool3_mbox_conf"
    type: "Convolution"
    bottom: "pool3"
    top: "pool3_mbox_conf"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 12
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool3_mbox_conf_perm"
    type: "Permute"
    bottom: "pool3_mbox_conf"
    top: "pool3_mbox_conf_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool3_mbox_conf_flat"
    type: "Flatten"
    bottom: "pool3_mbox_conf_perm"
    top: "pool3_mbox_conf_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool3_mbox_priorbox"
    type: "PriorBox"
    bottom: "pool3"
    bottom: "data"
    top: "pool3_mbox_priorbox"
    prior_box_param{
        min_size: 60.0
        max_size: 95.0
        aspect_ratio: 2.0
        aspect_ratio: 3.0
        flip: true
        clip: false
        variance: 0.1
        variance: 0.1
        variance: 0.2
        variance: 0.2
        offset: 0.5
    }
}
layer {
    name: "pool4_mbox_loc"
    type: "Convolution"
    bottom: "pool4"
    top: "pool4_mbox_loc"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 24
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool4_mbox_loc_perm"
    type: "Permute"
    bottom: "pool4_mbox_loc"
    top: "pool4_mbox_loc_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool4_mbox_loc_flat"
    type: "Flatten"
    bottom: "pool4_mbox_loc_perm"
    top: "pool4_mbox_loc_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool4_mbox_conf"
    type: "Convolution"
    bottom: "pool4"
    top: "pool4_mbox_conf"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 12
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool4_mbox_conf_perm"
    type: "Permute"
    bottom: "pool4_mbox_conf"
    top: "pool4_mbox_conf_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool4_mbox_conf_flat"
    type: "Flatten"
    bottom: "pool4_mbox_conf_perm"
    top: "pool4_mbox_conf_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool4_mbox_priorbox"
    type: "PriorBox"
    bottom: "pool4"
    bottom: "data"
    top: "pool4_mbox_priorbox"
    prior_box_param{
        min_size: 95.0
        max_size: 130.0
        aspect_ratio: 2.0
        aspect_ratio: 3.0
        flip: true
        clip: false
        variance: 0.1
        variance: 0.1
        variance: 0.2
        variance: 0.2
        offset: 0.5
    }
}
layer {
    name: "pool5_mbox_loc"
    type: "Convolution"
    bottom: "pool5"
    top: "pool5_mbox_loc"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 24
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool5_mbox_loc_perm"
    type: "Permute"
    bottom: "pool5_mbox_loc"
    top: "pool5_mbox_loc_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool5_mbox_loc_flat"
    type: "Flatten"
    bottom: "pool5_mbox_loc_perm"
    top: "pool5_mbox_loc_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool5_mbox_conf"
    type: "Convolution"
    bottom: "pool5"
    top: "pool5_mbox_conf"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 12
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool5_mbox_conf_perm"
    type: "Permute"
    bottom: "pool5_mbox_conf"
    top: "pool5_mbox_conf_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool5_mbox_conf_flat"
    type: "Flatten"
    bottom: "pool5_mbox_conf_perm"
    top: "pool5_mbox_conf_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool5_mbox_priorbox"
    type: "PriorBox"
    bottom: "pool5"
    bottom: "data"
    top: "pool5_mbox_priorbox"
    prior_box_param{
        min_size: 130.0
        max_size: 165.0
        aspect_ratio: 2.0
        aspect_ratio: 3.0
        flip: true
        clip: false
        variance: 0.1
        variance: 0.1
        variance: 0.2
        variance: 0.2
        offset: 0.5
    }
}
layer {
    name: "pool6_mbox_loc"
    type: "Convolution"
    bottom: "pool6"
    top: "pool6_mbox_loc"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 24
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool6_mbox_loc_perm"
    type: "Permute"
    bottom: "pool6_mbox_loc"
    top: "pool6_mbox_loc_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool6_mbox_loc_flat"
    type: "Flatten"
    bottom: "pool6_mbox_loc_perm"
    top: "pool6_mbox_loc_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool6_mbox_conf"
    type: "Convolution"
    bottom: "pool6"
    top: "pool6_mbox_conf"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 12
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool6_mbox_conf_perm"
    type: "Permute"
    bottom: "pool6_mbox_conf"
    top: "pool6_mbox_conf_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool6_mbox_conf_flat"
    type: "Flatten"
    bottom: "pool6_mbox_conf_perm"
    top: "pool6_mbox_conf_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool6_mbox_priorbox"
    type: "PriorBox"
    bottom: "pool6"
    bottom: "data"
    top: "pool6_mbox_priorbox"
    prior_box_param{
        min_size: 165.0
        max_size: 200.0
        aspect_ratio: 2.0
        aspect_ratio: 3.0
        flip: true
        clip: false
        variance: 0.1
        variance: 0.1
        variance: 0.2
        variance: 0.2
        offset: 0.5
    }
}
layer {
    name: "pool_mbox_loc"
    type: "Convolution"
    bottom: "pool"
    top: "pool_mbox_loc"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 24
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool_mbox_loc_perm"
    type: "Permute"
    bottom: "pool_mbox_loc"
    top: "pool_mbox_loc_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool_mbox_loc_flat"
    type: "Flatten"
    bottom: "pool_mbox_loc_perm"
    top: "pool_mbox_loc_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool_mbox_conf"
    type: "Convolution"
    bottom: "pool"
    top: "pool_mbox_conf"
    param{
        lr_mult: 1.0
        decay_mult: 1.0  
    }
    param{
        lr_mult: 2.0
        decay_mult: 0.0   
    }
    convolution_param {
        num_output: 12
        kernel_size: 1 
        weight_filler{
            type: "msra"
        }
        bias_filler{
            type: "constant"
            value: 0.0
        }
    }
}
layer {
    name: "pool_mbox_conf_perm"
    type: "Permute"
    bottom: "pool_mbox_conf"
    top: "pool_mbox_conf_perm"
    permute_param {
        order: 0
        order: 2
        order: 3
        order: 1
    }
}
layer {
    name: "pool_mbox_conf_flat"
    type: "Flatten"
    bottom: "pool_mbox_conf_perm"
    top: "pool_mbox_conf_flat"
    flatten_param {
        axis: 1
    }
}
layer {
    name: "pool_mbox_priorbox"
    type: "PriorBox"
    bottom: "pool"
    bottom: "data"
    top: "pool_mbox_priorbox"
    prior_box_param{
        min_size: 200.0
        max_size: 235.0
        aspect_ratio: 2.0
        aspect_ratio: 3.0
        flip: true
        clip: false
        variance: 0.1
        variance: 0.1
        variance: 0.2
        variance: 0.2
        offset: 0.5
    }
}
layer {
    name: "mbox_loc"
    type: "Concat"
    bottom: "pool3_mbox_loc_flat"
    bottom: "pool4_mbox_loc_flat"
    bottom: "pool5_mbox_loc_flat"
    bottom: "pool6_mbox_loc_flat"
    bottom: "pool_mbox_loc_flat"
    top: "mbox_loc" 
    concat_param{
        axis: 1
    }
}
layer {
    name: "mbox_conf"
    type: "Concat"
    bottom: "pool3_mbox_conf_flat"
    bottom: "pool4_mbox_conf_flat"
    bottom: "pool5_mbox_conf_flat"
    bottom: "pool6_mbox_conf_flat"
    bottom: "pool_mbox_conf_flat"
    top: "mbox_conf" 
    concat_param{
        axis: 1
    }
}
layer {
    name: "mbox_priorbox"
    type: "Concat"
    bottom: "pool3_mbox_priorbox"
    bottom: "pool4_mbox_priorbox"
    bottom: "pool5_mbox_priorbox"
    bottom: "pool6_mbox_priorbox"
    bottom: "pool_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
        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
    }
}