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| name: "segnet" | |
| layer { | |
| name: "data" | |
| type: "DenseImageData" | |
| top: "data" | |
| top: "label" | |
| dense_image_data_param { | |
| source: "/SegNet/CamVid/train.txt" # Change this to the absolute path to your data file | |
| batch_size: 4 # Change this number to a batch size that will fit on your GPU | |
| shuffle: true | |
| } | |
| } | |
| layer { | |
| name: "norm" | |
| type: "LRN" | |
| bottom: "data" | |
| top: "norm" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "norm" | |
| top: "conv1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv1" | |
| top: "conv1" | |
| name: "conv1_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| top: "pool1_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "pool1" | |
| top: "conv2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv2" | |
| top: "conv2" | |
| name: "conv2_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| top: "pool2_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "pool2" | |
| top: "conv3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv3" | |
| top: "conv3" | |
| name: "conv3_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3" | |
| top: "pool3" | |
| top: "pool3_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "pool3" | |
| top: "conv4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv4" | |
| top: "conv4" | |
| name: "conv4_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4" | |
| top: "pool4" | |
| top: "pool4_mask" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "upsample4" | |
| type: "Upsample" | |
| bottom: "pool4" | |
| bottom: "pool4_mask" | |
| top: "upsample4" | |
| upsample_param { | |
| scale: 2 | |
| pad_out_h: true | |
| } | |
| } | |
| layer { | |
| name: "conv_decode4" | |
| type: "Convolution" | |
| bottom: "upsample4" | |
| top: "conv_decode4" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode4" | |
| top: "conv_decode4" | |
| name: "conv_decode4_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample3" | |
| type: "Upsample" | |
| bottom: "conv_decode4" | |
| bottom: "pool3_mask" | |
| top: "upsample3" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode3" | |
| type: "Convolution" | |
| bottom: "upsample3" | |
| top: "conv_decode3" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode3" | |
| top: "conv_decode3" | |
| name: "conv_decode3_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample2" | |
| type: "Upsample" | |
| bottom: "conv_decode3" | |
| bottom: "pool2_mask" | |
| top: "upsample2" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode2" | |
| type: "Convolution" | |
| bottom: "upsample2" | |
| top: "conv_decode2" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode2" | |
| top: "conv_decode2" | |
| name: "conv_decode2_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "upsample1" | |
| type: "Upsample" | |
| bottom: "conv_decode2" | |
| bottom: "pool1_mask" | |
| top: "upsample1" | |
| upsample_param { | |
| scale: 2 | |
| } | |
| } | |
| layer { | |
| name: "conv_decode1" | |
| type: "Convolution" | |
| bottom: "upsample1" | |
| top: "conv_decode1" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 7 | |
| pad: 3 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| bottom: "conv_decode1" | |
| top: "conv_decode1" | |
| name: "conv_decode1_bn" | |
| type: "BN" | |
| bn_param { | |
| scale_filler { | |
| type: "constant" | |
| value: 1 | |
| } | |
| shift_filler { | |
| type: "constant" | |
| value: 0.001 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv_classifier" | |
| type: "Convolution" | |
| bottom: "conv_decode1" | |
| top: "conv_classifier" | |
| param { | |
| lr_mult: 1 | |
| decay_mult: 1 | |
| } | |
| param { | |
| lr_mult: 2 | |
| decay_mult: 0 | |
| } | |
| convolution_param { | |
| num_output: 11 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "msra" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "loss" | |
| type: "SoftmaxWithLoss" | |
| bottom: "conv_classifier" | |
| bottom: "label" | |
| top: "loss" | |
| softmax_param {engine: CAFFE} | |
| loss_param: { | |
| weight_by_label_freqs: true | |
| ignore_label: 11 | |
| class_weighting: 0.2595 | |
| class_weighting: 0.1826 | |
| class_weighting: 4.5640 | |
| class_weighting: 0.1417 | |
| class_weighting: 0.9051 | |
| class_weighting: 0.3826 | |
| class_weighting: 9.6446 | |
| class_weighting: 1.8418 | |
| class_weighting: 0.6823 | |
| class_weighting: 6.2478 | |
| class_weighting: 7.3614 | |
| } | |
| } | |
| layer { | |
| name: "accuracy" | |
| type: "Accuracy" | |
| bottom: "conv_classifier" | |
| bottom: "label" | |
| top: "accuracy" | |
| top: "per_class_accuracy" | |
| } |
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