How to change VGG16 SSD 300*300 deploy.prototxt to work with 38 classes

[Ram-Godavarthi]

Hello,

I want to train caffe models using my dataset. I have 38 classes. I wan to use VGG 16 deploy prototxt.
I would like to change few things in it to match my dataset.

What should I change in it to make it work with 38 classes.
Here is the file contents:

name: "VGG_VOC0712_SSD_300x300_deploy" input: "data" input_shape { dim: 1 dim: 3 dim: 300 dim: 300 } layer { name: "conv1_1" type: "Convolution" bottom: "data" top: "conv1_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 64 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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: 2 stride: 2 } } layer { name: "conv2_1" type: "Convolution" bottom: "pool1" top: "conv2_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 128 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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: 2 stride: 2 } } layer { name: "conv3_1" type: "Convolution" bottom: "pool2" top: "conv3_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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: 2 stride: 2 } } layer { name: "conv4_1" type: "Convolution" bottom: "pool3" top: "conv4_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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: 2 stride: 2 } } layer { name: "conv5_1" type: "Convolution" bottom: "pool4" top: "conv5_1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 pad: 1 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 1 } } 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: 3 stride: 1 pad: 1 } } layer { name: "fc6" type: "Convolution" bottom: "pool5" top: "fc6" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 pad: 6 kernel_size: 3 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 0 } dilation: 6 } } layer { name: "relu6" type: "ReLU" bottom: "fc6" top: "fc6" } layer { name: "fc7" type: "Convolution" bottom: "fc6" top: "fc7" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 1024 kernel_size: 1 weight_filler { type: "xavier" } bias_filler { type: "constant" value: 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 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 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" top: "conv6_1" } layer { name: "conv6_2" type: "Convolution" bottom: "conv6_1" top: "conv6_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 512 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" top: "conv6_2" } layer { name: "conv7_1" type: "Convolution" bottom: "conv6_2" top: "conv7_1" 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: "conv7_1_relu" type: "ReLU" bottom: "conv7_1" top: "conv7_1" } layer { name: "conv7_2" type: "Convolution" bottom: "conv7_1" top: "conv7_2" 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: "conv7_2_relu" type: "ReLU" bottom: "conv7_2" top: "conv7_2" } layer { name: "conv8_1" type: "Convolution" bottom: "conv7_2" top: "conv8_1" 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: "conv8_1_relu" type: "ReLU" bottom: "conv8_1" top: "conv8_1" } layer { name: "conv8_2" type: "Convolution" bottom: "conv8_1" top: "conv8_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 pad: 0 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" top: "conv8_2" } layer { name: "conv9_1" type: "Convolution" bottom: "conv8_2" top: "conv9_1" 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: "conv9_1_relu" type: "ReLU" bottom: "conv9_1" top: "conv9_1" } layer { name: "conv9_2" type: "Convolution" bottom: "conv9_1" top: "conv9_2" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 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" top: "conv9_2" } layer { name: "conv4_3_norm" type: "Normalize" bottom: "conv4_3" 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: 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: 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" 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" top: "conv6_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 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" 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" 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" top: "conv7_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 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" 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" 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: 1 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" top: "conv8_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 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" 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" 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" top: "conv9_2_mbox_conf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 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" 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_conf_reshape" type: "Reshape" bottom: "mbox_conf" top: "mbox_conf_reshape" reshape_param { shape { dim: 0 dim: -1 dim: 21 } } } layer { name: "mbox_conf_softmax" type: "Softmax" bottom: "mbox_conf_reshape" top: "mbox_conf_softmax" softmax_param { axis: 2 } } layer { name: "mbox_conf_flatten" type: "Flatten" bottom: "mbox_conf_softmax" top: "mbox_conf_flatten" flatten_param { axis: 1 } } layer { name: "detection_out" type: "DetectionOutput" bottom: "mbox_loc" bottom: "mbox_conf_flatten" bottom: "mbox_priorbox" top: "detection_out" include { phase: TEST } detection_output_param { num_classes: 21 share_location: true background_label_id: 0 nms_param { nms_threshold: 0.45 top_k: 400 } save_output_param { label_map_file: "data/VOC0712/labelmap_voc.prototxt" } code_type: CENTER_SIZE keep_top_k: 200 confidence_threshold: 0.01 } }
I facing problem with it.

Please help me out with this.

Thank You