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October 23, 2020 09:07
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| name: "MobileNet-SSD" | |
| input: "data" | |
| input_shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 300 | |
| dim: 300 | |
| } | |
| layer { | |
| name: "conv0" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv0" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| mean_value: 127.5 | |
| mean_value: 127.5 | |
| mean_value: 127.5 | |
| std: 0.007843 | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv0/bn" | |
| type: "BatchNorm" | |
| bottom: "conv0" | |
| top: "conv0" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv0/scale" | |
| type: "Scale" | |
| bottom: "conv0" | |
| top: "conv0" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv0/relu" | |
| type: "ReLU" | |
| bottom: "conv0" | |
| top: "conv0" | |
| } | |
| layer { | |
| name: "conv1/dw" | |
| type: "Convolution" | |
| bottom: "conv0" | |
| top: "conv1/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 32 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1/dw" | |
| top: "conv1/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv1/dw" | |
| top: "conv1/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv1/dw" | |
| top: "conv1/dw" | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "conv1/dw" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv1/scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/relu" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "conv2/dw" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "conv2/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 64 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2/dw" | |
| top: "conv2/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv2/dw" | |
| top: "conv2/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv2/dw" | |
| top: "conv2/dw" | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "conv2/dw" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv2/scale" | |
| type: "Scale" | |
| bottom: "conv2" | |
| top: "conv2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "conv3/dw" | |
| type: "Convolution" | |
| bottom: "conv2" | |
| top: "conv3/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3/dw" | |
| top: "conv3/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv3/dw/scale" | |
| type: "Scale" | |
| bottom: "conv3/dw" | |
| top: "conv3/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv3/dw" | |
| top: "conv3/dw" | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "conv3/dw" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv3/scale" | |
| type: "Scale" | |
| bottom: "conv3" | |
| top: "conv3" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3/relu" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "conv4/dw" | |
| type: "Convolution" | |
| bottom: "conv3" | |
| top: "conv4/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4/dw" | |
| top: "conv4/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv4/dw/scale" | |
| type: "Scale" | |
| bottom: "conv4/dw" | |
| top: "conv4/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv4/dw" | |
| top: "conv4/dw" | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "conv4/dw" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv4/scale" | |
| type: "Scale" | |
| bottom: "conv4" | |
| top: "conv4" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4/relu" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "conv5/dw" | |
| type: "Convolution" | |
| bottom: "conv4" | |
| top: "conv5/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5/dw" | |
| top: "conv5/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5/dw" | |
| top: "conv5/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv5/dw" | |
| top: "conv5/dw" | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "conv5/dw" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv5/scale" | |
| type: "Scale" | |
| bottom: "conv5" | |
| top: "conv5" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5/relu" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "conv6/dw" | |
| type: "Convolution" | |
| bottom: "conv5" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6/dw/scale" | |
| type: "Scale" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| } | |
| layer { | |
| name: "conv6" | |
| type: "Convolution" | |
| bottom: "conv6/dw" | |
| top: "conv6" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/bn" | |
| type: "BatchNorm" | |
| bottom: "conv6" | |
| top: "conv6" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv6/scale" | |
| type: "Scale" | |
| bottom: "conv6" | |
| top: "conv6" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/relu" | |
| type: "ReLU" | |
| bottom: "conv6" | |
| top: "conv6" | |
| } | |
| layer { | |
| name: "conv7/dw" | |
| type: "Convolution" | |
| bottom: "conv6" | |
| top: "conv7/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv7/dw" | |
| top: "conv7/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv7/dw/scale" | |
| type: "Scale" | |
| bottom: "conv7/dw" | |
| top: "conv7/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv7/dw" | |
| top: "conv7/dw" | |
| } | |
| layer { | |
| name: "conv7" | |
| type: "Convolution" | |
| bottom: "conv7/dw" | |
| top: "conv7" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7/bn" | |
| type: "BatchNorm" | |
| bottom: "conv7" | |
| top: "conv7" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv7/scale" | |
| type: "Scale" | |
| bottom: "conv7" | |
| top: "conv7" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv7/relu" | |
| type: "ReLU" | |
| bottom: "conv7" | |
| top: "conv7" | |
| } | |
| layer { | |
| name: "conv8/dw" | |
| type: "Convolution" | |
| bottom: "conv7" | |
| top: "conv8/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv8/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv8/dw" | |
| top: "conv8/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv8/dw/scale" | |
| type: "Scale" | |
| bottom: "conv8/dw" | |
| top: "conv8/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv8/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv8/dw" | |
| top: "conv8/dw" | |
| } | |
| layer { | |
| name: "conv8" | |
| type: "Convolution" | |
| bottom: "conv8/dw" | |
| top: "conv8" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv8/bn" | |
| type: "BatchNorm" | |
| bottom: "conv8" | |
| top: "conv8" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv8/scale" | |
| type: "Scale" | |
| bottom: "conv8" | |
| top: "conv8" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv8/relu" | |
| type: "ReLU" | |
| bottom: "conv8" | |
| top: "conv8" | |
| } | |
| layer { | |
| name: "conv9/dw" | |
| type: "Convolution" | |
| bottom: "conv8" | |
| top: "conv9/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv9/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv9/dw" | |
| top: "conv9/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv9/dw/scale" | |
| type: "Scale" | |
| bottom: "conv9/dw" | |
| top: "conv9/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv9/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv9/dw" | |
| top: "conv9/dw" | |
| } | |
| layer { | |
| name: "conv9" | |
| type: "Convolution" | |
| bottom: "conv9/dw" | |
| top: "conv9" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv9/bn" | |
| type: "BatchNorm" | |
| bottom: "conv9" | |
| top: "conv9" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv9/scale" | |
| type: "Scale" | |
| bottom: "conv9" | |
| top: "conv9" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv9/relu" | |
| type: "ReLU" | |
| bottom: "conv9" | |
| top: "conv9" | |
| } | |
| layer { | |
| name: "conv10/dw" | |
| type: "Convolution" | |
| bottom: "conv9" | |
| top: "conv10/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv10/dw" | |
| top: "conv10/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv10/dw/scale" | |
| type: "Scale" | |
| bottom: "conv10/dw" | |
| top: "conv10/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv10/dw" | |
| top: "conv10/dw" | |
| } | |
| layer { | |
| name: "conv10" | |
| type: "Convolution" | |
| bottom: "conv10/dw" | |
| top: "conv10" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10/bn" | |
| type: "BatchNorm" | |
| bottom: "conv10" | |
| top: "conv10" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv10/scale" | |
| type: "Scale" | |
| bottom: "conv10" | |
| top: "conv10" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10/relu" | |
| type: "ReLU" | |
| bottom: "conv10" | |
| top: "conv10" | |
| } | |
| layer { | |
| name: "conv11/dw" | |
| type: "Convolution" | |
| bottom: "conv10" | |
| top: "conv11/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv11/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv11/dw" | |
| top: "conv11/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv11/dw/scale" | |
| type: "Scale" | |
| bottom: "conv11/dw" | |
| top: "conv11/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv11/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv11/dw" | |
| top: "conv11/dw" | |
| } | |
| layer { | |
| name: "conv11" | |
| type: "Convolution" | |
| bottom: "conv11/dw" | |
| top: "conv11" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv11/bn" | |
| type: "BatchNorm" | |
| bottom: "conv11" | |
| top: "conv11" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv11/scale" | |
| type: "Scale" | |
| bottom: "conv11" | |
| top: "conv11" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv11/relu" | |
| type: "ReLU" | |
| bottom: "conv11" | |
| top: "conv11" | |
| } | |
| layer { | |
| name: "conv12/dw" | |
| type: "Convolution" | |
| bottom: "conv11" | |
| top: "conv12/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv12/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv12/dw" | |
| top: "conv12/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv12/dw/scale" | |
| type: "Scale" | |
| bottom: "conv12/dw" | |
| top: "conv12/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv12/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv12/dw" | |
| top: "conv12/dw" | |
| } | |
| layer { | |
| name: "conv12" | |
| type: "Convolution" | |
| bottom: "conv12/dw" | |
| top: "conv12" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv12/bn" | |
| type: "BatchNorm" | |
| bottom: "conv12" | |
| top: "conv12" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv12/scale" | |
| type: "Scale" | |
| bottom: "conv12" | |
| top: "conv12" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv12/relu" | |
| type: "ReLU" | |
| bottom: "conv12" | |
| top: "conv12" | |
| } | |
| layer { | |
| name: "conv13/dw" | |
| type: "Convolution" | |
| bottom: "conv12" | |
| top: "conv13/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 1024 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv13/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv13/dw" | |
| top: "conv13/dw" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv13/dw/scale" | |
| type: "Scale" | |
| bottom: "conv13/dw" | |
| top: "conv13/dw" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv13/dw/relu" | |
| type: "ReLU" | |
| bottom: "conv13/dw" | |
| top: "conv13/dw" | |
| } | |
| layer { | |
| name: "conv13" | |
| type: "Convolution" | |
| bottom: "conv13/dw" | |
| top: "conv13" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv13/bn" | |
| type: "BatchNorm" | |
| bottom: "conv13" | |
| top: "conv13" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv13/scale" | |
| type: "Scale" | |
| bottom: "conv13" | |
| top: "conv13" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv13/relu" | |
| type: "ReLU" | |
| bottom: "conv13" | |
| top: "conv13" | |
| } | |
| layer { | |
| name: "conv14_1" | |
| type: "Convolution" | |
| bottom: "conv13" | |
| top: "conv14_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv14_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv14_1" | |
| top: "conv14_1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv14_1/scale" | |
| type: "Scale" | |
| bottom: "conv14_1" | |
| top: "conv14_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv14_1/relu" | |
| type: "ReLU" | |
| bottom: "conv14_1" | |
| top: "conv14_1" | |
| } | |
| layer { | |
| name: "conv14_2" | |
| type: "Convolution" | |
| bottom: "conv14_1" | |
| top: "conv14_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv14_2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv14_2" | |
| top: "conv14_2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv14_2/scale" | |
| type: "Scale" | |
| bottom: "conv14_2" | |
| top: "conv14_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv14_2/relu" | |
| type: "ReLU" | |
| bottom: "conv14_2" | |
| top: "conv14_2" | |
| } | |
| layer { | |
| name: "conv15_1" | |
| type: "Convolution" | |
| bottom: "conv14_2" | |
| top: "conv15_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv15_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv15_1" | |
| top: "conv15_1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv15_1/scale" | |
| type: "Scale" | |
| bottom: "conv15_1" | |
| top: "conv15_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv15_1/relu" | |
| type: "ReLU" | |
| bottom: "conv15_1" | |
| top: "conv15_1" | |
| } | |
| layer { | |
| name: "conv15_2" | |
| type: "Convolution" | |
| bottom: "conv15_1" | |
| top: "conv15_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv15_2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv15_2" | |
| top: "conv15_2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv15_2/scale" | |
| type: "Scale" | |
| bottom: "conv15_2" | |
| top: "conv15_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv15_2/relu" | |
| type: "ReLU" | |
| bottom: "conv15_2" | |
| top: "conv15_2" | |
| } | |
| layer { | |
| name: "conv16_1" | |
| type: "Convolution" | |
| bottom: "conv15_2" | |
| top: "conv16_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv16_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv16_1" | |
| top: "conv16_1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv16_1/scale" | |
| type: "Scale" | |
| bottom: "conv16_1" | |
| top: "conv16_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv16_1/relu" | |
| type: "ReLU" | |
| bottom: "conv16_1" | |
| top: "conv16_1" | |
| } | |
| layer { | |
| name: "conv16_2" | |
| type: "Convolution" | |
| bottom: "conv16_1" | |
| top: "conv16_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv16_2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv16_2" | |
| top: "conv16_2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv16_2/scale" | |
| type: "Scale" | |
| bottom: "conv16_2" | |
| top: "conv16_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv16_2/relu" | |
| type: "ReLU" | |
| bottom: "conv16_2" | |
| top: "conv16_2" | |
| } | |
| layer { | |
| name: "conv17_1" | |
| type: "Convolution" | |
| bottom: "conv16_2" | |
| top: "conv17_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv17_1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv17_1" | |
| top: "conv17_1" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv17_1/scale" | |
| type: "Scale" | |
| bottom: "conv17_1" | |
| top: "conv17_1" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv17_1/relu" | |
| type: "ReLU" | |
| bottom: "conv17_1" | |
| top: "conv17_1" | |
| } | |
| layer { | |
| name: "conv17_2" | |
| type: "Convolution" | |
| bottom: "conv17_1" | |
| top: "conv17_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv17_2/bn" | |
| type: "BatchNorm" | |
| bottom: "conv17_2" | |
| top: "conv17_2" | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| } | |
| layer { | |
| name: "conv17_2/scale" | |
| type: "Scale" | |
| bottom: "conv17_2" | |
| top: "conv17_2" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| decay_mult: 0.0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1.0 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv17_2/relu" | |
| type: "ReLU" | |
| bottom: "conv17_2" | |
| top: "conv17_2" | |
| } | |
| layer { | |
| name: "conv11_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv11" | |
| top: "conv11_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv11_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv11" | |
| top: "conv11_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 63 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv13_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv13" | |
| top: "conv13_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv13_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv13" | |
| top: "conv13_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 126 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv14_2_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv14_2" | |
| top: "conv14_2_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv14_2_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv14_2" | |
| top: "conv14_2_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 126 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv15_2_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv15_2" | |
| top: "conv15_2_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv15_2_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv15_2" | |
| top: "conv15_2_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 126 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv16_2_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv16_2" | |
| top: "conv16_2_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv16_2_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv16_2" | |
| top: "conv16_2_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 126 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv17_2_mbox_loc" | |
| type: "Convolution" | |
| bottom: "conv17_2" | |
| top: "conv17_2_mbox_loc" | |
| param { | |
| lr_mult: 0.10000000149 | |
| decay_mult: 0.10000000149 | |
| } | |
| param { | |
| lr_mult: 0.20000000298 | |
| 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: "conv17_2_mbox_conf" | |
| type: "Convolution" | |
| bottom: "conv17_2" | |
| top: "conv17_2_mbox_conf" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 126 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "detection_out" | |
| type: "SsdDetection" | |
| bottom: "conv11_mbox_loc" | |
| bottom: "conv13_mbox_loc" | |
| bottom: "conv14_2_mbox_loc" | |
| bottom: "conv15_2_mbox_loc" | |
| bottom: "conv16_2_mbox_loc" | |
| bottom: "conv17_2_mbox_loc" | |
| bottom: "conv11_mbox_conf" | |
| bottom: "conv13_mbox_conf" | |
| bottom: "conv14_2_mbox_conf" | |
| bottom: "conv15_2_mbox_conf" | |
| bottom: "conv16_2_mbox_conf" | |
| bottom: "conv17_2_mbox_conf" | |
| bottom: "conv11" | |
| bottom: "conv13" | |
| bottom: "conv14_2" | |
| bottom: "conv15_2" | |
| bottom: "conv16_2" | |
| bottom: "conv17_2" | |
| bottom: "data" | |
| top: "detection_out" | |
| detection_output_param { | |
| num_classes: 21 | |
| share_location: true | |
| background_label_id: 0 | |
| nms_param { | |
| nms_threshold: 0.449999988079 | |
| top_k: 100 | |
| } | |
| code_type: CENTER_SIZE | |
| keep_top_k: 100 | |
| confidence_threshold: 0.01 | |
| } | |
| priorbox_params { | |
| min_size: 60.0 | |
| aspect_ratio: 2.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| priorbox_params { | |
| min_size: 105.0 | |
| max_size: 150.0 | |
| aspect_ratio: 2.0 | |
| aspect_ratio: 3.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| priorbox_params { | |
| min_size: 150.0 | |
| max_size: 195.0 | |
| aspect_ratio: 2.0 | |
| aspect_ratio: 3.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| priorbox_params { | |
| min_size: 195.0 | |
| max_size: 240.0 | |
| aspect_ratio: 2.0 | |
| aspect_ratio: 3.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| priorbox_params { | |
| min_size: 240.0 | |
| max_size: 285.0 | |
| aspect_ratio: 2.0 | |
| aspect_ratio: 3.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| priorbox_params { | |
| min_size: 285.0 | |
| max_size: 300.0 | |
| aspect_ratio: 2.0 | |
| aspect_ratio: 3.0 | |
| flip: true | |
| clip: false | |
| variance: 0.10000000149 | |
| variance: 0.10000000149 | |
| variance: 0.20000000298 | |
| variance: 0.20000000298 | |
| offset: 0.5 | |
| } | |
| } | |
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http://dgschwend.github.io/netscope/#/gist/e4fbf176b63f6e38fefe66cf65b4567a