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August 31, 2016 22:48
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SpaceNet DIGITS examples
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| name: "DetectNet" | |
| layer { | |
| name: "train_data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "train_label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "val_data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| data_param { | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "val_label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| data_param { | |
| batch_size: 2 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "deploy_data" | |
| type: "Input" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| not_stage: "val" | |
| } | |
| input_param { | |
| shape { | |
| dim: 1 | |
| dim: 3 | |
| dim: 1280 | |
| dim: 1280 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "train_transform" | |
| type: "DetectNetTransformation" | |
| bottom: "data" | |
| bottom: "label" | |
| top: "transformed_data" | |
| top: "transformed_label" | |
| include { | |
| phase: TRAIN | |
| } | |
| transform_param { | |
| mean_value: 127.0 | |
| } | |
| detectnet_groundtruth_param { | |
| stride: 16 | |
| scale_cvg: 0.4 | |
| gridbox_type: GRIDBOX_MIN | |
| min_cvg_len: 20 | |
| coverage_type: RECTANGULAR | |
| image_size_x: 512 | |
| image_size_y: 512 | |
| obj_norm: true | |
| crop_bboxes: false | |
| } | |
| detectnet_augmentation_param { | |
| crop_prob: 1.0 | |
| shift_x: 32 | |
| shift_y: 32 | |
| scale_prob: 0.4 | |
| scale_min: 0.8 | |
| scale_max: 1.2 | |
| flip_prob: 0.5 | |
| rotation_prob: 0.0 | |
| max_rotate_degree: 5.0 | |
| hue_rotation_prob: 0.8 | |
| hue_rotation: 30.0 | |
| desaturation_prob: 0.8 | |
| desaturation_max: 0.8 | |
| } | |
| } | |
| layer { | |
| name: "val_transform" | |
| type: "DetectNetTransformation" | |
| bottom: "data" | |
| bottom: "label" | |
| top: "transformed_data" | |
| top: "transformed_label" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| transform_param { | |
| mean_value: 127.0 | |
| } | |
| detectnet_groundtruth_param { | |
| stride: 16 | |
| scale_cvg: 0.4 | |
| gridbox_type: GRIDBOX_MIN | |
| min_cvg_len: 20 | |
| coverage_type: RECTANGULAR | |
| image_size_x: 1280 | |
| image_size_y: 1280 | |
| obj_norm: true | |
| crop_bboxes: false | |
| } | |
| } | |
| layer { | |
| name: "deploy_transform" | |
| type: "Power" | |
| bottom: "data" | |
| top: "transformed_data" | |
| include { | |
| phase: TEST | |
| not_stage: "val" | |
| } | |
| power_param { | |
| shift: -127.0 | |
| } | |
| } | |
| layer { | |
| name: "slice-label" | |
| type: "Slice" | |
| bottom: "transformed_label" | |
| top: "foreground-label" | |
| top: "bbox-label" | |
| top: "size-label" | |
| top: "obj-label" | |
| top: "coverage-label" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| slice_param { | |
| slice_dim: 1 | |
| slice_point: 1 | |
| slice_point: 5 | |
| slice_point: 7 | |
| slice_point: 8 | |
| } | |
| } | |
| layer { | |
| name: "coverage-block" | |
| type: "Concat" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| bottom: "foreground-label" | |
| top: "coverage-block" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| } | |
| layer { | |
| name: "size-block" | |
| type: "Concat" | |
| bottom: "size-label" | |
| bottom: "size-label" | |
| top: "size-block" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| } | |
| layer { | |
| name: "obj-block" | |
| type: "Concat" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| bottom: "obj-label" | |
| top: "obj-block" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| concat_param { | |
| concat_dim: 1 | |
| } | |
| } | |
| layer { | |
| name: "bb-label-norm" | |
| type: "Eltwise" | |
| bottom: "bbox-label" | |
| bottom: "size-block" | |
| top: "bbox-label-norm" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "bb-obj-norm" | |
| type: "Eltwise" | |
| bottom: "bbox-label-norm" | |
| bottom: "obj-block" | |
| top: "bbox-obj-label-norm" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "conv1/7x7_s2" | |
| type: "Convolution" | |
| bottom: "transformed_data" | |
| top: "conv1/7x7_s2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 3 | |
| kernel_size: 7 | |
| stride: 2 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1/relu_7x7" | |
| type: "ReLU" | |
| bottom: "conv1/7x7_s2" | |
| top: "conv1/7x7_s2" | |
| } | |
| layer { | |
| name: "pool1/3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv1/7x7_s2" | |
| top: "pool1/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "pool1/norm1" | |
| type: "LRN" | |
| bottom: "pool1/3x3_s2" | |
| top: "pool1/norm1" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "conv2/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool1/norm1" | |
| top: "conv2/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "conv2/3x3_reduce" | |
| top: "conv2/3x3_reduce" | |
| } | |
| layer { | |
| name: "conv2/3x3" | |
| type: "Convolution" | |
| bottom: "conv2/3x3_reduce" | |
| top: "conv2/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2/relu_3x3" | |
| type: "ReLU" | |
| bottom: "conv2/3x3" | |
| top: "conv2/3x3" | |
| } | |
| layer { | |
| name: "conv2/norm2" | |
| type: "LRN" | |
| bottom: "conv2/3x3" | |
| top: "conv2/norm2" | |
| lrn_param { | |
| local_size: 5 | |
| alpha: 0.0001 | |
| beta: 0.75 | |
| } | |
| } | |
| layer { | |
| name: "pool2/3x3_s2" | |
| type: "Pooling" | |
| bottom: "conv2/norm2" | |
| top: "pool2/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/1x1" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_3a/1x1" | |
| top: "inception_3a/1x1" | |
| } | |
| layer { | |
| name: "inception_3a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3_reduce" | |
| top: "inception_3a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_3a/3x3_reduce" | |
| top: "inception_3a/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_3a/3x3" | |
| top: "inception_3a/3x3" | |
| } | |
| layer { | |
| name: "inception_3a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5_reduce" | |
| top: "inception_3a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_3a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_3a/5x5_reduce" | |
| top: "inception_3a/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_3a/5x5" | |
| top: "inception_3a/5x5" | |
| } | |
| layer { | |
| name: "inception_3a/pool" | |
| type: "Pooling" | |
| bottom: "pool2/3x3_s2" | |
| top: "inception_3a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_3a/pool" | |
| top: "inception_3a/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3a/output" | |
| type: "Concat" | |
| bottom: "inception_3a/1x1" | |
| bottom: "inception_3a/3x3" | |
| bottom: "inception_3a/5x5" | |
| bottom: "inception_3a/pool_proj" | |
| top: "inception_3a/output" | |
| } | |
| layer { | |
| name: "inception_3b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_3b/1x1" | |
| top: "inception_3b/1x1" | |
| } | |
| layer { | |
| name: "inception_3b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3_reduce" | |
| top: "inception_3b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_3b/3x3_reduce" | |
| top: "inception_3b/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_3b/3x3" | |
| top: "inception_3b/3x3" | |
| } | |
| layer { | |
| name: "inception_3b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5_reduce" | |
| top: "inception_3b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_3b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_3b/5x5_reduce" | |
| top: "inception_3b/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_3b/5x5" | |
| top: "inception_3b/5x5" | |
| } | |
| layer { | |
| name: "inception_3b/pool" | |
| type: "Pooling" | |
| bottom: "inception_3a/output" | |
| top: "inception_3b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_3b/pool" | |
| top: "inception_3b/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_3b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_3b/output" | |
| type: "Concat" | |
| bottom: "inception_3b/1x1" | |
| bottom: "inception_3b/3x3" | |
| bottom: "inception_3b/5x5" | |
| bottom: "inception_3b/pool_proj" | |
| top: "inception_3b/output" | |
| } | |
| layer { | |
| name: "pool3/3x3_s2" | |
| type: "Pooling" | |
| bottom: "inception_3b/output" | |
| top: "pool3/3x3_s2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/1x1" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4a/1x1" | |
| top: "inception_4a/1x1" | |
| } | |
| layer { | |
| name: "inception_4a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 96 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3_reduce" | |
| top: "inception_4a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4a/3x3_reduce" | |
| top: "inception_4a/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 208 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4a/3x3" | |
| top: "inception_4a/3x3" | |
| } | |
| layer { | |
| name: "inception_4a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 16 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5_reduce" | |
| top: "inception_4a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4a/5x5_reduce" | |
| top: "inception_4a/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4a/5x5" | |
| top: "inception_4a/5x5" | |
| } | |
| layer { | |
| name: "inception_4a/pool" | |
| type: "Pooling" | |
| bottom: "pool3/3x3_s2" | |
| top: "inception_4a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4a/pool" | |
| top: "inception_4a/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4a/output" | |
| type: "Concat" | |
| bottom: "inception_4a/1x1" | |
| bottom: "inception_4a/3x3" | |
| bottom: "inception_4a/5x5" | |
| bottom: "inception_4a/pool_proj" | |
| top: "inception_4a/output" | |
| } | |
| layer { | |
| name: "inception_4b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4b/1x1" | |
| top: "inception_4b/1x1" | |
| } | |
| layer { | |
| name: "inception_4b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3_reduce" | |
| top: "inception_4b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4b/3x3_reduce" | |
| top: "inception_4b/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 224 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4b/3x3" | |
| top: "inception_4b/3x3" | |
| } | |
| layer { | |
| name: "inception_4b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5_reduce" | |
| top: "inception_4b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4b/5x5_reduce" | |
| top: "inception_4b/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4b/5x5" | |
| top: "inception_4b/5x5" | |
| } | |
| layer { | |
| name: "inception_4b/pool" | |
| type: "Pooling" | |
| bottom: "inception_4a/output" | |
| top: "inception_4b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4b/pool" | |
| top: "inception_4b/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4b/output" | |
| type: "Concat" | |
| bottom: "inception_4b/1x1" | |
| bottom: "inception_4b/3x3" | |
| bottom: "inception_4b/5x5" | |
| bottom: "inception_4b/pool_proj" | |
| top: "inception_4b/output" | |
| } | |
| layer { | |
| name: "inception_4c/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4c/1x1" | |
| top: "inception_4c/1x1" | |
| } | |
| layer { | |
| name: "inception_4c/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3_reduce" | |
| top: "inception_4c/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4c/3x3_reduce" | |
| top: "inception_4c/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4c/3x3" | |
| top: "inception_4c/3x3" | |
| } | |
| layer { | |
| name: "inception_4c/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 24 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5_reduce" | |
| top: "inception_4c/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4c/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4c/5x5_reduce" | |
| top: "inception_4c/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4c/5x5" | |
| top: "inception_4c/5x5" | |
| } | |
| layer { | |
| name: "inception_4c/pool" | |
| type: "Pooling" | |
| bottom: "inception_4b/output" | |
| top: "inception_4c/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4c/pool" | |
| top: "inception_4c/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4c/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4c/output" | |
| type: "Concat" | |
| bottom: "inception_4c/1x1" | |
| bottom: "inception_4c/3x3" | |
| bottom: "inception_4c/5x5" | |
| bottom: "inception_4c/pool_proj" | |
| top: "inception_4c/output" | |
| } | |
| layer { | |
| name: "inception_4d/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 112 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4d/1x1" | |
| top: "inception_4d/1x1" | |
| } | |
| layer { | |
| name: "inception_4d/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 144 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3_reduce" | |
| top: "inception_4d/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4d/3x3_reduce" | |
| top: "inception_4d/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 288 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4d/3x3" | |
| top: "inception_4d/3x3" | |
| } | |
| layer { | |
| name: "inception_4d/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5_reduce" | |
| top: "inception_4d/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4d/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4d/5x5_reduce" | |
| top: "inception_4d/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4d/5x5" | |
| top: "inception_4d/5x5" | |
| } | |
| layer { | |
| name: "inception_4d/pool" | |
| type: "Pooling" | |
| bottom: "inception_4c/output" | |
| top: "inception_4d/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4d/pool" | |
| top: "inception_4d/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 64 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4d/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4d/output" | |
| type: "Concat" | |
| bottom: "inception_4d/1x1" | |
| bottom: "inception_4d/3x3" | |
| bottom: "inception_4d/5x5" | |
| bottom: "inception_4d/pool_proj" | |
| top: "inception_4d/output" | |
| } | |
| layer { | |
| name: "inception_4e/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_4e/1x1" | |
| top: "inception_4e/1x1" | |
| } | |
| layer { | |
| name: "inception_4e/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3_reduce" | |
| top: "inception_4e/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/3x3" | |
| type: "Convolution" | |
| bottom: "inception_4e/3x3_reduce" | |
| top: "inception_4e/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_4e/3x3" | |
| top: "inception_4e/3x3" | |
| } | |
| layer { | |
| name: "inception_4e/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5_reduce" | |
| top: "inception_4e/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_4e/5x5" | |
| type: "Convolution" | |
| bottom: "inception_4e/5x5_reduce" | |
| top: "inception_4e/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_4e/5x5" | |
| top: "inception_4e/5x5" | |
| } | |
| layer { | |
| name: "inception_4e/pool" | |
| type: "Pooling" | |
| bottom: "inception_4d/output" | |
| top: "inception_4e/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_4e/pool" | |
| top: "inception_4e/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_4e/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/pool_proj" | |
| } | |
| layer { | |
| name: "inception_4e/output" | |
| type: "Concat" | |
| bottom: "inception_4e/1x1" | |
| bottom: "inception_4e/3x3" | |
| bottom: "inception_4e/5x5" | |
| bottom: "inception_4e/pool_proj" | |
| top: "inception_4e/output" | |
| } | |
| layer { | |
| name: "inception_5a/1x1" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 256 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_5a/1x1" | |
| top: "inception_5a/1x1" | |
| } | |
| layer { | |
| name: "inception_5a/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 160 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.09 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3_reduce" | |
| top: "inception_5a/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/3x3" | |
| type: "Convolution" | |
| bottom: "inception_5a/3x3_reduce" | |
| top: "inception_5a/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 320 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_5a/3x3" | |
| top: "inception_5a/3x3" | |
| } | |
| layer { | |
| name: "inception_5a/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 32 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.2 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5_reduce" | |
| top: "inception_5a/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_5a/5x5" | |
| type: "Convolution" | |
| bottom: "inception_5a/5x5_reduce" | |
| top: "inception_5a/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_5a/5x5" | |
| top: "inception_5a/5x5" | |
| } | |
| layer { | |
| name: "inception_5a/pool" | |
| type: "Pooling" | |
| bottom: "inception_4e/output" | |
| top: "inception_5a/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_5a/pool" | |
| top: "inception_5a/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5a/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5a/output" | |
| type: "Concat" | |
| bottom: "inception_5a/1x1" | |
| bottom: "inception_5a/3x3" | |
| bottom: "inception_5a/5x5" | |
| bottom: "inception_5a/pool_proj" | |
| top: "inception_5a/output" | |
| } | |
| layer { | |
| name: "inception_5b/1x1" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/1x1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_1x1" | |
| type: "ReLU" | |
| bottom: "inception_5b/1x1" | |
| top: "inception_5b/1x1" | |
| } | |
| layer { | |
| name: "inception_5b/3x3_reduce" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/3x3_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 192 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3_reduce" | |
| top: "inception_5b/3x3_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/3x3" | |
| type: "Convolution" | |
| bottom: "inception_5b/3x3_reduce" | |
| top: "inception_5b/3x3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 384 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_3x3" | |
| type: "ReLU" | |
| bottom: "inception_5b/3x3" | |
| top: "inception_5b/3x3" | |
| } | |
| layer { | |
| name: "inception_5b/5x5_reduce" | |
| type: "Convolution" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/5x5_reduce" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 48 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5_reduce" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5_reduce" | |
| top: "inception_5b/5x5_reduce" | |
| } | |
| layer { | |
| name: "inception_5b/5x5" | |
| type: "Convolution" | |
| bottom: "inception_5b/5x5_reduce" | |
| top: "inception_5b/5x5" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| pad: 2 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_5x5" | |
| type: "ReLU" | |
| bottom: "inception_5b/5x5" | |
| top: "inception_5b/5x5" | |
| } | |
| layer { | |
| name: "inception_5b/pool" | |
| type: "Pooling" | |
| bottom: "inception_5a/output" | |
| top: "inception_5b/pool" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 1 | |
| pad: 1 | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/pool_proj" | |
| type: "Convolution" | |
| bottom: "inception_5b/pool" | |
| top: "inception_5b/pool_proj" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 128 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.1 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.2 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "inception_5b/relu_pool_proj" | |
| type: "ReLU" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/pool_proj" | |
| } | |
| layer { | |
| name: "inception_5b/output" | |
| type: "Concat" | |
| bottom: "inception_5b/1x1" | |
| bottom: "inception_5b/3x3" | |
| bottom: "inception_5b/5x5" | |
| bottom: "inception_5b/pool_proj" | |
| top: "inception_5b/output" | |
| } | |
| layer { | |
| name: "pool5/drop_s1" | |
| type: "Dropout" | |
| bottom: "inception_5b/output" | |
| top: "pool5/drop_s1" | |
| dropout_param { | |
| dropout_ratio: 0.4 | |
| } | |
| } | |
| layer { | |
| name: "cvg/classifier" | |
| type: "Convolution" | |
| bottom: "pool5/drop_s1" | |
| top: "cvg/classifier" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 1 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "coverage/sig" | |
| type: "Sigmoid" | |
| bottom: "cvg/classifier" | |
| top: "coverage" | |
| } | |
| layer { | |
| name: "bbox/regressor" | |
| type: "Convolution" | |
| bottom: "pool5/drop_s1" | |
| top: "bboxes" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 2.0 | |
| decay_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 4 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| std: 0.03 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "bbox_mask" | |
| type: "Eltwise" | |
| bottom: "bboxes" | |
| bottom: "coverage-block" | |
| top: "bboxes-masked" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "bbox-norm" | |
| type: "Eltwise" | |
| bottom: "bboxes-masked" | |
| bottom: "size-block" | |
| top: "bboxes-masked-norm" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "bbox-obj-norm" | |
| type: "Eltwise" | |
| bottom: "bboxes-masked-norm" | |
| bottom: "obj-block" | |
| top: "bboxes-obj-masked-norm" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| eltwise_param { | |
| operation: PROD | |
| } | |
| } | |
| layer { | |
| name: "bbox_loss" | |
| type: "L1Loss" | |
| bottom: "bboxes-obj-masked-norm" | |
| bottom: "bbox-obj-label-norm" | |
| top: "loss_bbox" | |
| loss_weight: 2.0 | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| } | |
| layer { | |
| name: "coverage_loss" | |
| type: "EuclideanLoss" | |
| bottom: "coverage" | |
| bottom: "coverage-label" | |
| top: "loss_coverage" | |
| include { | |
| phase: TRAIN | |
| } | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| } | |
| layer { | |
| name: "cluster" | |
| type: "Python" | |
| bottom: "coverage" | |
| bottom: "bboxes" | |
| top: "bbox-list" | |
| include { | |
| phase: TEST | |
| } | |
| python_param { | |
| module: "caffe.layers.detectnet.clustering" | |
| layer: "ClusterDetections" | |
| param_str: "1280, 1280, 16, 0.06, 3, 0.02, 10,1" | |
| } | |
| } | |
| layer { | |
| name: "cluster_gt" | |
| type: "Python" | |
| bottom: "coverage-label" | |
| bottom: "bbox-label" | |
| top: "bbox-list-label" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| python_param { | |
| module: "caffe.layers.detectnet.clustering" | |
| layer: "ClusterGroundtruth" | |
| param_str: "1280, 1280, 16" | |
| } | |
| } | |
| layer { | |
| name: "score" | |
| type: "Python" | |
| bottom: "bbox-list-label" | |
| bottom: "bbox-list" | |
| top: "bbox-list-scored" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| python_param { | |
| module: "caffe.layers.detectnet.mean_ap" | |
| layer: "ScoreDetections" | |
| } | |
| } | |
| layer { | |
| name: "mAP" | |
| type: "Python" | |
| bottom: "bbox-list-scored" | |
| top: "mAP" | |
| top: "precision" | |
| top: "recall" | |
| include { | |
| phase: TEST | |
| stage: "val" | |
| } | |
| python_param { | |
| module: "caffe.layers.detectnet.mean_ap" | |
| layer: "mAP" | |
| param_str: "1280, 1280, 16" | |
| } | |
| } |
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| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 17 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TRAIN | |
| } | |
| data_param { | |
| batch_size: 17 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "data" | |
| type: "Data" | |
| top: "data" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "label" | |
| type: "Data" | |
| top: "label" | |
| include { | |
| phase: TEST | |
| } | |
| data_param { | |
| batch_size: 1 | |
| backend: LMDB | |
| } | |
| } | |
| layer { | |
| name: "bn0" | |
| type: "BatchNorm" | |
| bottom: "data" | |
| top: "bn0" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv1" | |
| type: "Convolution" | |
| bottom: "bn0" | |
| top: "conv1" | |
| convolution_param { | |
| num_output: 50 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu1" | |
| type: "ReLU" | |
| bottom: "conv1" | |
| top: "conv1" | |
| } | |
| layer { | |
| name: "pool1" | |
| type: "Pooling" | |
| bottom: "conv1" | |
| top: "pool1" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "bn1" | |
| type: "BatchNorm" | |
| bottom: "pool1" | |
| top: "bn1" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv2" | |
| type: "Convolution" | |
| bottom: "bn1" | |
| top: "conv2" | |
| convolution_param { | |
| num_output: 70 | |
| kernel_size: 5 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu2" | |
| type: "ReLU" | |
| bottom: "conv2" | |
| top: "conv2" | |
| } | |
| layer { | |
| name: "pool2" | |
| type: "Pooling" | |
| bottom: "conv2" | |
| top: "pool2" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 2 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "bn2" | |
| type: "BatchNorm" | |
| bottom: "pool2" | |
| top: "bn2" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv3" | |
| type: "Convolution" | |
| bottom: "bn2" | |
| top: "conv3" | |
| convolution_param { | |
| num_output: 100 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu3" | |
| type: "ReLU" | |
| bottom: "conv3" | |
| top: "conv3" | |
| } | |
| layer { | |
| name: "pool3" | |
| type: "Pooling" | |
| bottom: "conv3" | |
| top: "pool3" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "bn3" | |
| type: "BatchNorm" | |
| bottom: "pool3" | |
| top: "bn3" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv4" | |
| type: "Convolution" | |
| bottom: "bn3" | |
| top: "conv4" | |
| convolution_param { | |
| num_output: 150 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu4" | |
| type: "ReLU" | |
| bottom: "conv4" | |
| top: "conv4" | |
| } | |
| layer { | |
| name: "pool4" | |
| type: "Pooling" | |
| bottom: "conv4" | |
| top: "pool4" | |
| pooling_param { | |
| pool: MAX | |
| kernel_size: 3 | |
| stride: 2 | |
| } | |
| } | |
| layer { | |
| name: "bn4" | |
| type: "BatchNorm" | |
| bottom: "pool4" | |
| top: "bn4" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv5" | |
| type: "Convolution" | |
| bottom: "bn4" | |
| top: "conv5" | |
| convolution_param { | |
| num_output: 150 | |
| kernel_size: 1 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu5" | |
| type: "ReLU" | |
| bottom: "conv5" | |
| top: "conv5" | |
| } | |
| layer { | |
| name: "conv6" | |
| type: "Convolution" | |
| bottom: "bn4" | |
| top: "conv6" | |
| convolution_param { | |
| num_output: 150 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu6" | |
| type: "ReLU" | |
| bottom: "conv6" | |
| top: "conv6" | |
| } | |
| layer { | |
| name: "concat_1" | |
| type: "Concat" | |
| bottom: "conv5" | |
| bottom: "conv6" | |
| top: "concat_1" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv7" | |
| type: "Convolution" | |
| bottom: "concat_1" | |
| top: "conv7" | |
| convolution_param { | |
| num_output: 100 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu7" | |
| type: "ReLU" | |
| bottom: "conv7" | |
| top: "conv7" | |
| } | |
| layer { | |
| name: "drop7" | |
| type: "Dropout" | |
| bottom: "conv7" | |
| top: "conv7" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "bn7" | |
| type: "BatchNorm" | |
| bottom: "conv7" | |
| top: "bn7" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "deconv_1" | |
| type: "Deconvolution" | |
| bottom: "bn7" | |
| top: "deconv_1" | |
| param { | |
| lr_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 100 | |
| bias_term: false | |
| kernel_size: 5 | |
| group: 100 | |
| stride: 2 | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv8" | |
| type: "Convolution" | |
| bottom: "bn3" | |
| top: "conv8" | |
| convolution_param { | |
| num_output: 100 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu8" | |
| type: "ReLU" | |
| bottom: "conv8" | |
| top: "conv8" | |
| } | |
| layer { | |
| name: "concat_2" | |
| type: "Concat" | |
| bottom: "deconv_1" | |
| bottom: "conv8" | |
| top: "concat_2" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv9" | |
| type: "Convolution" | |
| bottom: "concat_2" | |
| top: "conv9" | |
| convolution_param { | |
| num_output: 70 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu9" | |
| type: "ReLU" | |
| bottom: "conv9" | |
| top: "conv9" | |
| } | |
| layer { | |
| name: "drop9" | |
| type: "Dropout" | |
| bottom: "conv9" | |
| top: "conv9" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "bn9" | |
| type: "BatchNorm" | |
| bottom: "conv9" | |
| top: "bn9" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "deconv_2" | |
| type: "Deconvolution" | |
| bottom: "bn9" | |
| top: "deconv_2" | |
| param { | |
| lr_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 70 | |
| bias_term: false | |
| kernel_size: 5 | |
| group: 70 | |
| stride: 2 | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv10" | |
| type: "Convolution" | |
| bottom: "bn2" | |
| top: "conv10" | |
| convolution_param { | |
| num_output: 70 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu10" | |
| type: "ReLU" | |
| bottom: "conv10" | |
| top: "conv10" | |
| } | |
| layer { | |
| name: "concat_3" | |
| type: "Concat" | |
| bottom: "deconv_2" | |
| bottom: "conv10" | |
| top: "concat_3" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv11" | |
| type: "Convolution" | |
| bottom: "concat_3" | |
| top: "conv11" | |
| convolution_param { | |
| num_output: 50 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu11" | |
| type: "ReLU" | |
| bottom: "conv11" | |
| top: "conv11" | |
| } | |
| layer { | |
| name: "drop11" | |
| type: "Dropout" | |
| bottom: "conv11" | |
| top: "conv11" | |
| dropout_param { | |
| dropout_ratio: 0.5 | |
| } | |
| } | |
| layer { | |
| name: "bn11" | |
| type: "BatchNorm" | |
| bottom: "conv11" | |
| top: "bn11" | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 1.0 | |
| decay_mult: 1.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| param { | |
| lr_mult: 0.0 | |
| decay_mult: 0.0 | |
| } | |
| batch_norm_param { | |
| use_global_stats: false | |
| moving_average_fraction: 0.98 | |
| eps: 0.0001 | |
| scale_filler { | |
| type: "constant" | |
| value: 1.0 | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "deconv_3" | |
| type: "Deconvolution" | |
| bottom: "bn11" | |
| top: "deconv_3" | |
| param { | |
| lr_mult: 0.0 | |
| } | |
| convolution_param { | |
| num_output: 50 | |
| bias_term: false | |
| kernel_size: 6 | |
| group: 50 | |
| stride: 2 | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "conv12" | |
| type: "Convolution" | |
| bottom: "bn1" | |
| top: "conv12" | |
| convolution_param { | |
| num_output: 50 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu12" | |
| type: "ReLU" | |
| bottom: "conv12" | |
| top: "conv12" | |
| } | |
| layer { | |
| name: "concat_4" | |
| type: "Concat" | |
| bottom: "deconv_3" | |
| bottom: "conv12" | |
| top: "concat_4" | |
| concat_param { | |
| axis: 1 | |
| } | |
| } | |
| layer { | |
| name: "conv13" | |
| type: "Convolution" | |
| bottom: "concat_4" | |
| top: "conv13" | |
| convolution_param { | |
| num_output: 1 | |
| pad: 1 | |
| kernel_size: 3 | |
| weight_filler { | |
| type: "xavier" | |
| } | |
| bias_filler { | |
| type: "constant" | |
| value: 0.0 | |
| } | |
| } | |
| } | |
| layer { | |
| name: "relu13" | |
| type: "ReLU" | |
| bottom: "conv13" | |
| top: "conv13" | |
| } | |
| layer { | |
| name: "score" | |
| type: "Deconvolution" | |
| bottom: "conv13" | |
| top: "score" | |
| convolution_param { | |
| num_output: 1 | |
| bias_term: false | |
| kernel_size: 6 | |
| stride: 2 | |
| weight_filler { | |
| type: "bilinear" | |
| } | |
| } | |
| } | |
| layer { | |
| name: "class_loss" | |
| type: "EuclideanLoss" | |
| bottom: "score" | |
| bottom: "label" | |
| top: "class_loss" | |
| include { | |
| phase: TRAIN | |
| } | |
| exclude { | |
| stage: "deploy" | |
| } | |
| loss_param { | |
| normalize: false | |
| } | |
| } | |
| layer { | |
| name: "val_loss" | |
| type: "EuclideanLoss" | |
| bottom: "score" | |
| bottom: "label" | |
| top: "val_loss" | |
| include { | |
| stage: "val" | |
| } | |
| loss_param { | |
| normalize: false | |
| } | |
| } |
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