| name: "CaffeNet" |
| layer { |
| name: "data" |
| type: "Data" |
| top: "data" |
| top: "label" |
| include { |
| phase: TRAIN |
| } |
| transform_param { |
| mirror: true |
| crop_size: 227 |
| mean_file: "data/ilsvrc12/imagenet_mean.binaryproto" |
| } |
| # mean pixel / channel-wise mean instead of mean image |
| # transform_param { |
| # crop_size: 227 |
| # mean_value: 104 |
| # mean_value: 117 |
| # mean_value: 123 |
| # mirror: true |
| # } |
| data_param { |
| source: "examples/imagenet/ilsvrc12_train_lmdb" |
| batch_size: 256 |
| backend: LMDB |
| } |
| } |
| layer { |
| name: "data" |
| type: "Data" |
| top: "data" |
| top: "label" |
| include { |
| phase: TEST |
| } |
| transform_param { |
| mirror: false |
| crop_size: 227 |
| mean_file: "data/ilsvrc12/imagenet_mean.binaryproto" |
| } |
| # mean pixel / channel-wise mean instead of mean image |
| # transform_param { |
| # crop_size: 227 |
| # mean_value: 104 |
| # mean_value: 117 |
| # mean_value: 123 |
| # mirror: false |
| # } |
| data_param { |
| source: "examples/imagenet/ilsvrc12_val_lmdb" |
| batch_size: 50 |
| backend: LMDB |
| } |
| } |
| layer { |
| name: "conv1" |
| type: "Convolution" |
| bottom: "data" |
| top: "conv1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 96 |
| kernel_size: 11 |
| stride: 4 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu1" |
| type: "ReLU" |
| bottom: "conv1" |
| top: "conv1" |
| } |
| layer { |
| name: "pool1" |
| type: "Pooling" |
| bottom: "conv1" |
| top: "pool1" |
| pooling_param { |
| pool: MAX |
| kernel_size: 3 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "norm1" |
| type: "LRN" |
| bottom: "pool1" |
| top: "norm1" |
| lrn_param { |
| local_size: 5 |
| alpha: 0.0001 |
| beta: 0.75 |
| } |
| } |
| layer { |
| name: "conv2" |
| type: "Convolution" |
| bottom: "norm1" |
| top: "conv2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 256 |
| pad: 2 |
| kernel_size: 5 |
| group: 2 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 1 |
| } |
| } |
| } |
| layer { |
| name: "relu2" |
| type: "ReLU" |
| bottom: "conv2" |
| top: "conv2" |
| } |
| layer { |
| name: "pool2" |
| type: "Pooling" |
| bottom: "conv2" |
| top: "pool2" |
| pooling_param { |
| pool: MAX |
| kernel_size: 3 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "norm2" |
| type: "LRN" |
| bottom: "pool2" |
| top: "norm2" |
| lrn_param { |
| local_size: 5 |
| alpha: 0.0001 |
| beta: 0.75 |
| } |
| } |
| layer { |
| name: "conv3" |
| type: "Convolution" |
| bottom: "norm2" |
| top: "conv3" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 384 |
| pad: 1 |
| kernel_size: 3 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu3" |
| type: "ReLU" |
| bottom: "conv3" |
| top: "conv3" |
| } |
| layer { |
| name: "conv4" |
| type: "Convolution" |
| bottom: "conv3" |
| top: "conv4" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 384 |
| pad: 1 |
| kernel_size: 3 |
| group: 2 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 1 |
| } |
| } |
| } |
| layer { |
| name: "relu4" |
| type: "ReLU" |
| bottom: "conv4" |
| top: "conv4" |
| } |
| layer { |
| name: "conv5" |
| type: "Convolution" |
| bottom: "conv4" |
| top: "conv5" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 256 |
| pad: 1 |
| kernel_size: 3 |
| group: 2 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 1 |
| } |
| } |
| } |
| layer { |
| name: "relu5" |
| type: "ReLU" |
| bottom: "conv5" |
| top: "conv5" |
| } |
| layer { |
| name: "pool5" |
| type: "Pooling" |
| bottom: "conv5" |
| top: "pool5" |
| pooling_param { |
| pool: MAX |
| kernel_size: 3 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "fc6" |
| type: "InnerProduct" |
| bottom: "pool5" |
| top: "fc6" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| inner_product_param { |
| num_output: 4096 |
| weight_filler { |
| type: "gaussian" |
| std: 0.005 |
| } |
| bias_filler { |
| type: "constant" |
| value: 1 |
| } |
| } |
| } |
| layer { |
| name: "relu6" |
| type: "ReLU" |
| bottom: "fc6" |
| top: "fc6" |
| } |
| layer { |
| name: "drop6" |
| type: "Dropout" |
| bottom: "fc6" |
| top: "fc6" |
| dropout_param { |
| dropout_ratio: 0.5 |
| } |
| } |
| layer { |
| name: "fc7" |
| type: "InnerProduct" |
| bottom: "fc6" |
| top: "fc7" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| inner_product_param { |
| num_output: 4096 |
| weight_filler { |
| type: "gaussian" |
| std: 0.005 |
| } |
| bias_filler { |
| type: "constant" |
| value: 1 |
| } |
| } |
| } |
| layer { |
| name: "relu7" |
| type: "ReLU" |
| bottom: "fc7" |
| top: "fc7" |
| } |
| layer { |
| name: "drop7" |
| type: "Dropout" |
| bottom: "fc7" |
| top: "fc7" |
| dropout_param { |
| dropout_ratio: 0.5 |
| } |
| } |
| 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 { |
| 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 { |
| phase: TEST |
| } |
| } |
| layer { |
| name: "loss" |
| type: "SoftmaxWithLoss" |
| bottom: "fc8" |
| bottom: "label" |
| top: "loss" |
| } |
|
|
