Fetching metadata from the HF Docker repository... aryrk
[init]
2bb76b3 | layer { |
| name: "data" |
| type: "Input" |
| top: "data" |
| input_param { |
| shape { |
| dim: 1 |
| dim: 3 |
| dim: 500 |
| dim: 500 |
| } |
| } |
| } |
| layer { |
| name: "conv1_1" |
| type: "Convolution" |
| bottom: "data" |
| top: "conv1_1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 64 |
| pad: 100 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu1_1" |
| type: "ReLU" |
| bottom: "conv1_1" |
| top: "conv1_1" |
| } |
| layer { |
| name: "conv1_2" |
| type: "Convolution" |
| bottom: "conv1_1" |
| top: "conv1_2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 64 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu1_2" |
| type: "ReLU" |
| bottom: "conv1_2" |
| top: "conv1_2" |
| } |
| layer { |
| name: "pool1" |
| type: "Pooling" |
| bottom: "conv1_2" |
| top: "pool1" |
| pooling_param { |
| pool: MAX |
| kernel_size: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "conv2_1" |
| type: "Convolution" |
| bottom: "pool1" |
| top: "conv2_1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 128 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu2_1" |
| type: "ReLU" |
| bottom: "conv2_1" |
| top: "conv2_1" |
| } |
| layer { |
| name: "conv2_2" |
| type: "Convolution" |
| bottom: "conv2_1" |
| top: "conv2_2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 128 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu2_2" |
| type: "ReLU" |
| bottom: "conv2_2" |
| top: "conv2_2" |
| } |
| layer { |
| name: "pool2" |
| type: "Pooling" |
| bottom: "conv2_2" |
| top: "pool2" |
| pooling_param { |
| pool: MAX |
| kernel_size: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "conv3_1" |
| type: "Convolution" |
| bottom: "pool2" |
| top: "conv3_1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 256 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu3_1" |
| type: "ReLU" |
| bottom: "conv3_1" |
| top: "conv3_1" |
| } |
| layer { |
| name: "conv3_2" |
| type: "Convolution" |
| bottom: "conv3_1" |
| top: "conv3_2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 256 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu3_2" |
| type: "ReLU" |
| bottom: "conv3_2" |
| top: "conv3_2" |
| } |
| layer { |
| name: "conv3_3" |
| type: "Convolution" |
| bottom: "conv3_2" |
| top: "conv3_3" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 256 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu3_3" |
| type: "ReLU" |
| bottom: "conv3_3" |
| top: "conv3_3" |
| } |
| layer { |
| name: "pool3" |
| type: "Pooling" |
| bottom: "conv3_3" |
| top: "pool3" |
| pooling_param { |
| pool: MAX |
| kernel_size: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "conv4_1" |
| type: "Convolution" |
| bottom: "pool3" |
| top: "conv4_1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu4_1" |
| type: "ReLU" |
| bottom: "conv4_1" |
| top: "conv4_1" |
| } |
| layer { |
| name: "conv4_2" |
| type: "Convolution" |
| bottom: "conv4_1" |
| top: "conv4_2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu4_2" |
| type: "ReLU" |
| bottom: "conv4_2" |
| top: "conv4_2" |
| } |
| layer { |
| name: "conv4_3" |
| type: "Convolution" |
| bottom: "conv4_2" |
| top: "conv4_3" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu4_3" |
| type: "ReLU" |
| bottom: "conv4_3" |
| top: "conv4_3" |
| } |
| layer { |
| name: "pool4" |
| type: "Pooling" |
| bottom: "conv4_3" |
| top: "pool4" |
| pooling_param { |
| pool: MAX |
| kernel_size: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "conv5_1" |
| type: "Convolution" |
| bottom: "pool4" |
| top: "conv5_1" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu5_1" |
| type: "ReLU" |
| bottom: "conv5_1" |
| top: "conv5_1" |
| } |
| layer { |
| name: "conv5_2" |
| type: "Convolution" |
| bottom: "conv5_1" |
| top: "conv5_2" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu5_2" |
| type: "ReLU" |
| bottom: "conv5_2" |
| top: "conv5_2" |
| } |
| layer { |
| name: "conv5_3" |
| type: "Convolution" |
| bottom: "conv5_2" |
| top: "conv5_3" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 512 |
| pad: 1 |
| kernel_size: 3 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu5_3" |
| type: "ReLU" |
| bottom: "conv5_3" |
| top: "conv5_3" |
| } |
| layer { |
| name: "pool5" |
| type: "Pooling" |
| bottom: "conv5_3" |
| top: "pool5" |
| pooling_param { |
| pool: MAX |
| kernel_size: 2 |
| stride: 2 |
| } |
| } |
| layer { |
| name: "fc6_cs" |
| type: "Convolution" |
| bottom: "pool5" |
| top: "fc6_cs" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 4096 |
| pad: 0 |
| kernel_size: 7 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu6_cs" |
| type: "ReLU" |
| bottom: "fc6_cs" |
| top: "fc6_cs" |
| } |
| layer { |
| name: "fc7_cs" |
| type: "Convolution" |
| bottom: "fc6_cs" |
| top: "fc7_cs" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 4096 |
| pad: 0 |
| kernel_size: 1 |
| stride: 1 |
| weight_filler { |
| type: "gaussian" |
| std: 0.01 |
| } |
| bias_filler { |
| type: "constant" |
| value: 0 |
| } |
| } |
| } |
| layer { |
| name: "relu7_cs" |
| type: "ReLU" |
| bottom: "fc7_cs" |
| top: "fc7_cs" |
| } |
| layer { |
| name: "score_fr" |
| type: "Convolution" |
| bottom: "fc7_cs" |
| top: "score_fr" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 20 |
| pad: 0 |
| kernel_size: 1 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "upscore2" |
| type: "Deconvolution" |
| bottom: "score_fr" |
| top: "upscore2" |
| param { |
| lr_mult: 1 |
| } |
| convolution_param { |
| num_output: 20 |
| bias_term: false |
| kernel_size: 4 |
| stride: 2 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "score_pool4" |
| type: "Convolution" |
| bottom: "pool4" |
| top: "score_pool4" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 20 |
| pad: 0 |
| kernel_size: 1 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "score_pool4c" |
| type: "Crop" |
| bottom: "score_pool4" |
| bottom: "upscore2" |
| top: "score_pool4c" |
| crop_param { |
| axis: 2 |
| offset: 5 |
| } |
| } |
| layer { |
| name: "fuse_pool4" |
| type: "Eltwise" |
| bottom: "upscore2" |
| bottom: "score_pool4c" |
| top: "fuse_pool4" |
| eltwise_param { |
| operation: SUM |
| } |
| } |
| layer { |
| name: "upscore_pool4" |
| type: "Deconvolution" |
| bottom: "fuse_pool4" |
| top: "upscore_pool4" |
| param { |
| lr_mult: 1 |
| } |
| convolution_param { |
| num_output: 20 |
| bias_term: false |
| kernel_size: 4 |
| stride: 2 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "score_pool3" |
| type: "Convolution" |
| bottom: "pool3" |
| top: "score_pool3" |
| param { |
| lr_mult: 1 |
| decay_mult: 1 |
| } |
| param { |
| lr_mult: 2 |
| decay_mult: 0 |
| } |
| convolution_param { |
| num_output: 20 |
| pad: 0 |
| kernel_size: 1 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "score_pool3c" |
| type: "Crop" |
| bottom: "score_pool3" |
| bottom: "upscore_pool4" |
| top: "score_pool3c" |
| crop_param { |
| axis: 2 |
| offset: 9 |
| } |
| } |
| layer { |
| name: "fuse_pool3" |
| type: "Eltwise" |
| bottom: "upscore_pool4" |
| bottom: "score_pool3c" |
| top: "fuse_pool3" |
| eltwise_param { |
| operation: SUM |
| } |
| } |
| layer { |
| name: "upscore8" |
| type: "Deconvolution" |
| bottom: "fuse_pool3" |
| top: "upscore8" |
| param { |
| lr_mult: 1 |
| } |
| convolution_param { |
| num_output: 20 |
| bias_term: false |
| kernel_size: 16 |
| stride: 8 |
| weight_filler { |
| type: "xavier" |
| } |
| bias_filler { |
| type: "constant" |
| } |
| } |
| } |
| layer { |
| name: "score" |
| type: "Crop" |
| bottom: "upscore8" |
| bottom: "data" |
| top: "score" |
| crop_param { |
| axis: 2 |
| offset: 31 |
| } |
| } |
|
|
