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sphereface_20 train prototxt, netscope: http://ethereon.github.io/netscope/#/gist/7b413902e7c9e3d64ec2e02e8d23c734
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| name: "SpherefaceNet-20" | |
| layer { | |
| name: "data" | |
| type: "ImageData" | |
| top: "data" | |
| top: "label" | |
| transform_param { | |
| mean_value: 127.5 | |
| mean_value: 127.5 | |
| mean_value: 127.5 | |
| scale: 0.0078125 | |
| mirror: true | |
| } | |
| image_data_param { | |
| source: "data/CASIA-WebFace-112X96.txt" | |
| batch_size: 256 | |
| shuffle: true | |
| } | |
| } | |
| ############## CNN Architecture ############### | |
| 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 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_1" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_2" | |
| type: "PReLU" | |
| bottom: "conv1_2" | |
| top: "conv1_2" | |
| } | |
| layer { | |
| name: "conv1_3" | |
| type: "Convolution" | |
| bottom: "conv1_2" | |
| top: "conv1_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1_3" | |
| type: "PReLU" | |
| bottom: "conv1_3" | |
| top: "conv1_3" | |
| } | |
| layer { | |
| name: "res1_3" | |
| type: "Eltwise" | |
| bottom: "conv1_1" | |
| bottom: "conv1_3" | |
| top: "res1_3" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_1" | |
| type: "Convolution" | |
| bottom: "res1_3" | |
| top: "conv2_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_1" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_2" | |
| type: "PReLU" | |
| bottom: "conv2_2" | |
| top: "conv2_2" | |
| } | |
| layer { | |
| name: "conv2_3" | |
| type: "Convolution" | |
| bottom: "conv2_2" | |
| top: "conv2_3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_3" | |
| type: "PReLU" | |
| bottom: "conv2_3" | |
| top: "conv2_3" | |
| } | |
| layer { | |
| name: "res2_3" | |
| type: "Eltwise" | |
| bottom: "conv2_1" | |
| bottom: "conv2_3" | |
| top: "res2_3" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv2_4" | |
| type: "Convolution" | |
| bottom: "res2_3" | |
| top: "conv2_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_4" | |
| type: "PReLU" | |
| bottom: "conv2_4" | |
| top: "conv2_4" | |
| } | |
| layer { | |
| name: "conv2_5" | |
| type: "Convolution" | |
| bottom: "conv2_4" | |
| top: "conv2_5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2_5" | |
| type: "PReLU" | |
| bottom: "conv2_5" | |
| top: "conv2_5" | |
| } | |
| layer { | |
| name: "res2_5" | |
| type: "Eltwise" | |
| bottom: "res2_3" | |
| bottom: "conv2_5" | |
| top: "res2_5" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_1" | |
| type: "Convolution" | |
| bottom: "res2_5" | |
| top: "conv3_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_1" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_2" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_3" | |
| type: "PReLU" | |
| bottom: "conv3_3" | |
| top: "conv3_3" | |
| } | |
| layer { | |
| name: "res3_3" | |
| type: "Eltwise" | |
| bottom: "conv3_1" | |
| bottom: "conv3_3" | |
| top: "res3_3" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_4" | |
| type: "Convolution" | |
| bottom: "res3_3" | |
| top: "conv3_4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_4" | |
| type: "PReLU" | |
| bottom: "conv3_4" | |
| top: "conv3_4" | |
| } | |
| layer { | |
| name: "conv3_5" | |
| type: "Convolution" | |
| bottom: "conv3_4" | |
| top: "conv3_5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_5" | |
| type: "PReLU" | |
| bottom: "conv3_5" | |
| top: "conv3_5" | |
| } | |
| layer { | |
| name: "res3_5" | |
| type: "Eltwise" | |
| bottom: "res3_3" | |
| bottom: "conv3_5" | |
| top: "res3_5" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_6" | |
| type: "Convolution" | |
| bottom: "res3_5" | |
| top: "conv3_6" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_6" | |
| type: "PReLU" | |
| bottom: "conv3_6" | |
| top: "conv3_6" | |
| } | |
| layer { | |
| name: "conv3_7" | |
| type: "Convolution" | |
| bottom: "conv3_6" | |
| top: "conv3_7" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_7" | |
| type: "PReLU" | |
| bottom: "conv3_7" | |
| top: "conv3_7" | |
| } | |
| layer { | |
| name: "res3_7" | |
| type: "Eltwise" | |
| bottom: "res3_5" | |
| bottom: "conv3_7" | |
| top: "res3_7" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv3_8" | |
| type: "Convolution" | |
| bottom: "res3_7" | |
| top: "conv3_8" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_8" | |
| type: "PReLU" | |
| bottom: "conv3_8" | |
| top: "conv3_8" | |
| } | |
| layer { | |
| name: "conv3_9" | |
| type: "Convolution" | |
| bottom: "conv3_8" | |
| top: "conv3_9" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3_9" | |
| type: "PReLU" | |
| bottom: "conv3_9" | |
| top: "conv3_9" | |
| } | |
| layer { | |
| name: "res3_9" | |
| type: "Eltwise" | |
| bottom: "res3_7" | |
| bottom: "conv3_9" | |
| top: "res3_9" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv4_1" | |
| type: "Convolution" | |
| bottom: "res3_9" | |
| top: "conv4_1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 2 | |
| pad: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_1" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_2" | |
| type: "PReLU" | |
| 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: 0 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 512 | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| weight_filler { | |
| type: "gaussian" | |
| std: 0.01 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4_3" | |
| type: "PReLU" | |
| bottom: "conv4_3" | |
| top: "conv4_3" | |
| } | |
| layer { | |
| name: "res4_3" | |
| type: "Eltwise" | |
| bottom: "conv4_1" | |
| bottom: "conv4_3" | |
| top: "res4_3" | |
| eltwise_param { | |
| operation: 1 | |
| } | |
| } | |
| layer { | |
| name: "fc5" | |
| type: "InnerProduct" | |
| bottom: "res4_3" | |
| top: "fc5" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| inner_product_param { | |
| num_output: 512 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0 | |
| } | |
| } | |
| } | |
| ############### A-Softmax Loss ############## | |
| layer { | |
| name: "fc6" | |
| type: "MarginInnerProduct" | |
| bottom: "fc5" | |
| bottom: "label" | |
| top: "fc6" | |
| top: "lambda" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| margin_inner_product_param { | |
| num_output: 10572 | |
| type: QUADRUPLE | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| base: 1000 | |
| gamma: 0.12 | |
| power: 1 | |
| lambda_min: 5 | |
| iteration: 0 | |
| } | |
| } | |
| layer { | |
| name: "softmax_loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "fc6" | |
| bottom: "label" | |
| top: "softmax_loss" | |
| } |
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