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MobileNet caffe implementation for NVIDIA DIGITS
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| name: "MOBILENET" | |
| layer { | |
| name: "train-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| transform_param { | |
| mirror: true | |
| crop_size: 224 | |
| } | |
| data_param { | |
| batch_size: 32 | |
| } | |
| include { stage: "train" } | |
| } | |
| layer { | |
| name: "val-data" | |
| type: "Data" | |
| top: "data" | |
| top: "label" | |
| transform_param { | |
| mirror: false | |
| crop_size: 224 | |
| } | |
| data_param { | |
| batch_size: 16 | |
| } | |
| include { stage: "val" } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "data" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/bn" | |
| type: "BatchNorm" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv1/scale" | |
| type: "Scale" | |
| bottom: "conv1" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "conv2_1/dw" | |
| type: "Convolution" | |
| bottom: "conv1" | |
| top: "conv2_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 32 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/dw" | |
| type: "ReLU" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/dw" | |
| } | |
| layer { | |
| name: "conv2_1/sep" | |
| type: "Convolution" | |
| bottom: "conv2_1/dw" | |
| top: "conv2_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1/sep" | |
| type: "ReLU" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_1/sep" | |
| } | |
| layer { | |
| name: "conv2_2/dw" | |
| type: "Convolution" | |
| bottom: "conv2_1/sep" | |
| top: "conv2_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 64 | |
| engine: CAFFE | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/dw" | |
| type: "ReLU" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/dw" | |
| } | |
| layer { | |
| name: "conv2_2/sep" | |
| type: "Convolution" | |
| bottom: "conv2_2/dw" | |
| top: "conv2_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv2_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2/sep" | |
| type: "ReLU" | |
| bottom: "conv2_2/sep" | |
| top: "conv2_2/sep" | |
| } | |
| layer { | |
| name: "conv3_1/dw" | |
| type: "Convolution" | |
| bottom: "conv2_2/sep" | |
| top: "conv3_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/dw" | |
| type: "ReLU" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/dw" | |
| } | |
| layer { | |
| name: "conv3_1/sep" | |
| type: "Convolution" | |
| bottom: "conv3_1/dw" | |
| top: "conv3_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1/sep" | |
| type: "ReLU" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_1/sep" | |
| } | |
| layer { | |
| name: "conv3_2/dw" | |
| type: "Convolution" | |
| bottom: "conv3_1/sep" | |
| top: "conv3_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 128 | |
| engine: CAFFE | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/dw" | |
| type: "ReLU" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/dw" | |
| } | |
| layer { | |
| name: "conv3_2/sep" | |
| type: "Convolution" | |
| bottom: "conv3_2/dw" | |
| top: "conv3_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv3_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2/sep" | |
| type: "ReLU" | |
| bottom: "conv3_2/sep" | |
| top: "conv3_2/sep" | |
| } | |
| layer { | |
| name: "conv4_1/dw" | |
| type: "Convolution" | |
| bottom: "conv3_2/sep" | |
| top: "conv4_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/dw" | |
| type: "ReLU" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/dw" | |
| } | |
| layer { | |
| name: "conv4_1/sep" | |
| type: "Convolution" | |
| bottom: "conv4_1/dw" | |
| top: "conv4_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1/sep" | |
| type: "ReLU" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_1/sep" | |
| } | |
| layer { | |
| name: "conv4_2/dw" | |
| type: "Convolution" | |
| bottom: "conv4_1/sep" | |
| top: "conv4_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 256 | |
| engine: CAFFE | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/dw" | |
| type: "ReLU" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/dw" | |
| } | |
| layer { | |
| name: "conv4_2/sep" | |
| type: "Convolution" | |
| bottom: "conv4_2/dw" | |
| top: "conv4_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv4_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2/sep" | |
| type: "ReLU" | |
| bottom: "conv4_2/sep" | |
| top: "conv4_2/sep" | |
| } | |
| layer { | |
| name: "conv5_1/dw" | |
| type: "Convolution" | |
| bottom: "conv4_2/sep" | |
| top: "conv5_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/dw" | |
| top: "conv5_1/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/dw" | |
| top: "conv5_1/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_1/dw" | |
| type: "ReLU" | |
| bottom: "conv5_1/dw" | |
| top: "conv5_1/dw" | |
| } | |
| layer { | |
| name: "conv5_1/sep" | |
| type: "Convolution" | |
| bottom: "conv5_1/dw" | |
| top: "conv5_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_1/sep" | |
| top: "conv5_1/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_1/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_1/sep" | |
| top: "conv5_1/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_1/sep" | |
| type: "ReLU" | |
| bottom: "conv5_1/sep" | |
| top: "conv5_1/sep" | |
| } | |
| layer { | |
| name: "conv5_2/dw" | |
| type: "Convolution" | |
| bottom: "conv5_1/sep" | |
| top: "conv5_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/dw" | |
| top: "conv5_2/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_2/dw" | |
| top: "conv5_2/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_2/dw" | |
| type: "ReLU" | |
| bottom: "conv5_2/dw" | |
| top: "conv5_2/dw" | |
| } | |
| layer { | |
| name: "conv5_2/sep" | |
| type: "Convolution" | |
| bottom: "conv5_2/dw" | |
| top: "conv5_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_2/sep" | |
| top: "conv5_2/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_2/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_2/sep" | |
| top: "conv5_2/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_2/sep" | |
| type: "ReLU" | |
| bottom: "conv5_2/sep" | |
| top: "conv5_2/sep" | |
| } | |
| layer { | |
| name: "conv5_3/dw" | |
| type: "Convolution" | |
| bottom: "conv5_2/sep" | |
| top: "conv5_3/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/dw" | |
| top: "conv5_3/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_3/dw" | |
| top: "conv5_3/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_3/dw" | |
| type: "ReLU" | |
| bottom: "conv5_3/dw" | |
| top: "conv5_3/dw" | |
| } | |
| layer { | |
| name: "conv5_3/sep" | |
| type: "Convolution" | |
| bottom: "conv5_3/dw" | |
| top: "conv5_3/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_3/sep" | |
| top: "conv5_3/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_3/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_3/sep" | |
| top: "conv5_3/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_3/sep" | |
| type: "ReLU" | |
| bottom: "conv5_3/sep" | |
| top: "conv5_3/sep" | |
| } | |
| layer { | |
| name: "conv5_4/dw" | |
| type: "Convolution" | |
| bottom: "conv5_3/sep" | |
| top: "conv5_4/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/dw" | |
| top: "conv5_4/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_4/dw" | |
| top: "conv5_4/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_4/dw" | |
| type: "ReLU" | |
| bottom: "conv5_4/dw" | |
| top: "conv5_4/dw" | |
| } | |
| layer { | |
| name: "conv5_4/sep" | |
| type: "Convolution" | |
| bottom: "conv5_4/dw" | |
| top: "conv5_4/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_4/sep" | |
| top: "conv5_4/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_4/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_4/sep" | |
| top: "conv5_4/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_4/sep" | |
| type: "ReLU" | |
| bottom: "conv5_4/sep" | |
| top: "conv5_4/sep" | |
| } | |
| layer { | |
| name: "conv5_5/dw" | |
| type: "Convolution" | |
| bottom: "conv5_4/sep" | |
| top: "conv5_5/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_5/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_5/dw" | |
| top: "conv5_5/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_5/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_5/dw" | |
| top: "conv5_5/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_5/dw" | |
| type: "ReLU" | |
| bottom: "conv5_5/dw" | |
| top: "conv5_5/dw" | |
| } | |
| layer { | |
| name: "conv5_5/sep" | |
| type: "Convolution" | |
| bottom: "conv5_5/dw" | |
| top: "conv5_5/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_5/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_5/sep" | |
| top: "conv5_5/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_5/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_5/sep" | |
| top: "conv5_5/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_5/sep" | |
| type: "ReLU" | |
| bottom: "conv5_5/sep" | |
| top: "conv5_5/sep" | |
| } | |
| layer { | |
| name: "conv5_6/dw" | |
| type: "Convolution" | |
| bottom: "conv5_5/sep" | |
| top: "conv5_6/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 512 | |
| engine: CAFFE | |
| stride: 2 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_6/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_6/dw" | |
| top: "conv5_6/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_6/dw/scale" | |
| type: "Scale" | |
| bottom: "conv5_6/dw" | |
| top: "conv5_6/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_6/dw" | |
| type: "ReLU" | |
| bottom: "conv5_6/dw" | |
| top: "conv5_6/dw" | |
| } | |
| layer { | |
| name: "conv5_6/sep" | |
| type: "Convolution" | |
| bottom: "conv5_6/dw" | |
| top: "conv5_6/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5_6/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv5_6/sep" | |
| top: "conv5_6/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv5_6/sep/scale" | |
| type: "Scale" | |
| bottom: "conv5_6/sep" | |
| top: "conv5_6/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5_6/sep" | |
| type: "ReLU" | |
| bottom: "conv5_6/sep" | |
| top: "conv5_6/sep" | |
| } | |
| layer { | |
| name: "conv6/dw" | |
| type: "Convolution" | |
| bottom: "conv5_6/sep" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 1 | |
| kernel_size: 3 | |
| group: 1024 | |
| engine: CAFFE | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/dw/bn" | |
| type: "BatchNorm" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv6/dw/scale" | |
| type: "Scale" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/dw" | |
| type: "ReLU" | |
| bottom: "conv6/dw" | |
| top: "conv6/dw" | |
| } | |
| layer { | |
| name: "conv6/sep" | |
| type: "Convolution" | |
| bottom: "conv6/dw" | |
| top: "conv6/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| convolution_param { | |
| num_output: 1024 | |
| bias_term: false | |
| pad: 0 | |
| kernel_size: 1 | |
| stride: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv6/sep/bn" | |
| type: "BatchNorm" | |
| bottom: "conv6/sep" | |
| top: "conv6/sep" | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: true | |
| eps: 1e-5 | |
| } | |
| } | |
| layer { | |
| name: "conv6/sep/scale" | |
| type: "Scale" | |
| bottom: "conv6/sep" | |
| top: "conv6/sep" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 0 | |
| } | |
| scale_param { | |
| filler { | |
| value: 1 | |
| } | |
| bias_term: true | |
| bias_filler { | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6/sep" | |
| type: "ReLU" | |
| bottom: "conv6/sep" | |
| top: "conv6/sep" | |
| } | |
| layer { | |
| name: "pool6" | |
| type: "Pooling" | |
| bottom: "conv6/sep" | |
| top: "pool6" | |
| pooling_param { | |
| pool: AVE | |
| global_pooling: true | |
| } | |
| } | |
| layer { | |
| name: "fc7" | |
| type: "Convolution" | |
| bottom: "pool6" | |
| top: "fc7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| #num_output: 1000 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "fc8" | |
| type: "InnerProduct" | |
| bottom: "fc7" | |
| top: "fc8" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| # Since num_output is unset, DIGITS will automatically set it to the | |
| # number of classes in your dataset. | |
| # Uncomment this line to set it explicitly: | |
| #num_output: 1000 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "fc8" | |
| bottom: "label" | |
| top: "accuracy" | |
| include { stage: "val" } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc8" | |
| bottom: "label" | |
| top: "loss" | |
| exclude { stage: "deploy" } | |
| } | |
| layer { | |
| name: "softmax" | |
| type: "Softmax" | |
| bottom: "fc8" | |
| top: "softmax" | |
| include { stage: "deploy" } | |
| } |
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