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the prototxt file is shown as fallows: name: "ResNet-50" layer { name: 'input-data' type: 'Python' top: 'data' top: 'im_info' top: 'gt_boxes' python_param { module: 'roi_data_layer.layer' layer: 'RoIDataLayer' param_str: "'num_classes': 2" } }
layer { bottom: "data" top: "conv1" name: "conv1" type: "Convolution" convolution_param { num_output: 64 kernel_size: 7 pad: 3 stride: 2 } param { lr_mult: 0.0 } param { lr_mult: 0.0 }
}
layer { bottom: "conv1" top: "conv1" name: "bn_conv1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "conv1" top: "conv1" name: "scale_conv1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "conv1" top: "conv1" name: "conv1_relu" type: "ReLU" }
layer { bottom: "conv1" top: "pool1" name: "pool1" type: "Pooling" pooling_param { kernel_size: 3 stride: 2 pool: MAX } }
layer { bottom: "pool1" top: "res2a_branch1" name: "res2a_branch1" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "bn2a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch1" top: "res2a_branch1" name: "scale2a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "pool1" top: "res2a_branch2a" name: "res2a_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "bn2a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "scale2a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch2a" top: "res2a_branch2a" name: "res2a_branch2a_relu" type: "ReLU" }
layer { bottom: "res2a_branch2a" top: "res2a_branch2b" name: "res2a_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "bn2a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "scale2a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch2b" top: "res2a_branch2b" name: "res2a_branch2b_relu" type: "ReLU" }
layer { bottom: "res2a_branch2b" top: "res2a_branch2c" name: "res2a_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2a_branch2c" top: "res2a_branch2c" name: "bn2a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch2c" top: "res2a_branch2c" name: "scale2a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a_branch1" bottom: "res2a_branch2c" top: "res2a" name: "res2a" type: "Eltwise" }
layer { bottom: "res2a" top: "res2a" name: "res2a_relu" type: "ReLU" }
layer { bottom: "res2a" top: "res2b_branch2a" name: "res2b_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "bn2b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "scale2b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b_branch2a" top: "res2b_branch2a" name: "res2b_branch2a_relu" type: "ReLU" }
layer { bottom: "res2b_branch2a" top: "res2b_branch2b" name: "res2b_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "bn2b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "scale2b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b_branch2b" top: "res2b_branch2b" name: "res2b_branch2b_relu" type: "ReLU" }
layer { bottom: "res2b_branch2b" top: "res2b_branch2c" name: "res2b_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2b_branch2c" top: "res2b_branch2c" name: "bn2b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b_branch2c" top: "res2b_branch2c" name: "scale2b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2a" bottom: "res2b_branch2c" top: "res2b" name: "res2b" type: "Eltwise" }
layer { bottom: "res2b" top: "res2b" name: "res2b_relu" type: "ReLU" }
layer { bottom: "res2b" top: "res2c_branch2a" name: "res2c_branch2a" type: "Convolution" convolution_param { num_output: 64 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "bn2c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "scale2c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c_branch2a" top: "res2c_branch2a" name: "res2c_branch2a_relu" type: "ReLU" }
layer { bottom: "res2c_branch2a" top: "res2c_branch2b" name: "res2c_branch2b" type: "Convolution" convolution_param { num_output: 64 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "bn2c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "scale2c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c_branch2b" top: "res2c_branch2b" name: "res2c_branch2b_relu" type: "ReLU" }
layer { bottom: "res2c_branch2b" top: "res2c_branch2c" name: "res2c_branch2c" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 0.0 } }
layer { bottom: "res2c_branch2c" top: "res2c_branch2c" name: "bn2c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c_branch2c" top: "res2c_branch2c" name: "scale2c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2b" bottom: "res2c_branch2c" top: "res2c" name: "res2c" type: "Eltwise" }
layer { bottom: "res2c" top: "res2c" name: "res2c_relu" type: "ReLU" }
layer { bottom: "res2c" top: "res3a_branch1" name: "res3a_branch1" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3a_branch1" top: "res3a_branch1" name: "bn3a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch1" top: "res3a_branch1" name: "scale3a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res2c" top: "res3a_branch2a" name: "res3a_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "bn3a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "scale3a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch2a" top: "res3a_branch2a" name: "res3a_branch2a_relu" type: "ReLU" }
layer { bottom: "res3a_branch2a" top: "res3a_branch2b" name: "res3a_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "bn3a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "scale3a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch2b" top: "res3a_branch2b" name: "res3a_branch2b_relu" type: "ReLU" }
layer { bottom: "res3a_branch2b" top: "res3a_branch2c" name: "res3a_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3a_branch2c" top: "res3a_branch2c" name: "bn3a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch2c" top: "res3a_branch2c" name: "scale3a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a_branch1" bottom: "res3a_branch2c" top: "res3a" name: "res3a" type: "Eltwise" }
layer { bottom: "res3a" top: "res3a" name: "res3a_relu" type: "ReLU" }
layer { bottom: "res3a" top: "res3b_branch2a" name: "res3b_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "bn3b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "scale3b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b_branch2a" top: "res3b_branch2a" name: "res3b_branch2a_relu" type: "ReLU" }
layer { bottom: "res3b_branch2a" top: "res3b_branch2b" name: "res3b_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "bn3b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "scale3b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b_branch2b" top: "res3b_branch2b" name: "res3b_branch2b_relu" type: "ReLU" }
layer { bottom: "res3b_branch2b" top: "res3b_branch2c" name: "res3b_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3b_branch2c" top: "res3b_branch2c" name: "bn3b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b_branch2c" top: "res3b_branch2c" name: "scale3b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3a" bottom: "res3b_branch2c" top: "res3b" name: "res3b" type: "Eltwise" }
layer { bottom: "res3b" top: "res3b" name: "res3b_relu" type: "ReLU" }
layer { bottom: "res3b" top: "res3c_branch2a" name: "res3c_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "bn3c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "scale3c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c_branch2a" top: "res3c_branch2a" name: "res3c_branch2a_relu" type: "ReLU" }
layer { bottom: "res3c_branch2a" top: "res3c_branch2b" name: "res3c_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "bn3c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "scale3c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c_branch2b" top: "res3c_branch2b" name: "res3c_branch2b_relu" type: "ReLU" }
layer { bottom: "res3c_branch2b" top: "res3c_branch2c" name: "res3c_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3c_branch2c" top: "res3c_branch2c" name: "bn3c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c_branch2c" top: "res3c_branch2c" name: "scale3c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3b" bottom: "res3c_branch2c" top: "res3c" name: "res3c" type: "Eltwise" }
layer { bottom: "res3c" top: "res3c" name: "res3c_relu" type: "ReLU" }
layer { bottom: "res3c" top: "res3d_branch2a" name: "res3d_branch2a" type: "Convolution" convolution_param { num_output: 128 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "bn3d_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "scale3d_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d_branch2a" top: "res3d_branch2a" name: "res3d_branch2a_relu" type: "ReLU" }
layer { bottom: "res3d_branch2a" top: "res3d_branch2b" name: "res3d_branch2b" type: "Convolution" convolution_param { num_output: 128 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "bn3d_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "scale3d_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d_branch2b" top: "res3d_branch2b" name: "res3d_branch2b_relu" type: "ReLU" }
layer { bottom: "res3d_branch2b" top: "res3d_branch2c" name: "res3d_branch2c" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res3d_branch2c" top: "res3d_branch2c" name: "bn3d_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d_branch2c" top: "res3d_branch2c" name: "scale3d_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3c" bottom: "res3d_branch2c" top: "res3d" name: "res3d" type: "Eltwise" }
layer { bottom: "res3d" top: "res3d" name: "res3d_relu" type: "ReLU" }
layer { bottom: "res3d" top: "res4a_branch1" name: "res4a_branch1" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4a_branch1" top: "res4a_branch1" name: "bn4a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch1" top: "res4a_branch1" name: "scale4a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res3d" top: "res4a_branch2a" name: "res4a_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 2 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "bn4a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "scale4a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch2a" top: "res4a_branch2a" name: "res4a_branch2a_relu" type: "ReLU" }
layer { bottom: "res4a_branch2a" top: "res4a_branch2b" name: "res4a_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "bn4a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "scale4a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch2b" top: "res4a_branch2b" name: "res4a_branch2b_relu" type: "ReLU" }
layer { bottom: "res4a_branch2b" top: "res4a_branch2c" name: "res4a_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4a_branch2c" top: "res4a_branch2c" name: "bn4a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch2c" top: "res4a_branch2c" name: "scale4a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a_branch1" bottom: "res4a_branch2c" top: "res4a" name: "res4a" type: "Eltwise" }
layer { bottom: "res4a" top: "res4a" name: "res4a_relu" type: "ReLU" }
layer { bottom: "res4a" top: "res4b_branch2a" name: "res4b_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "bn4b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "scale4b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b_branch2a" top: "res4b_branch2a" name: "res4b_branch2a_relu" type: "ReLU" }
layer { bottom: "res4b_branch2a" top: "res4b_branch2b" name: "res4b_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "bn4b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "scale4b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b_branch2b" top: "res4b_branch2b" name: "res4b_branch2b_relu" type: "ReLU" }
layer { bottom: "res4b_branch2b" top: "res4b_branch2c" name: "res4b_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4b_branch2c" top: "res4b_branch2c" name: "bn4b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b_branch2c" top: "res4b_branch2c" name: "scale4b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4a" bottom: "res4b_branch2c" top: "res4b" name: "res4b" type: "Eltwise" }
layer { bottom: "res4b" top: "res4b" name: "res4b_relu" type: "ReLU" }
layer { bottom: "res4b" top: "res4c_branch2a" name: "res4c_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "bn4c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "scale4c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c_branch2a" top: "res4c_branch2a" name: "res4c_branch2a_relu" type: "ReLU" }
layer { bottom: "res4c_branch2a" top: "res4c_branch2b" name: "res4c_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "bn4c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "scale4c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c_branch2b" top: "res4c_branch2b" name: "res4c_branch2b_relu" type: "ReLU" }
layer { bottom: "res4c_branch2b" top: "res4c_branch2c" name: "res4c_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4c_branch2c" top: "res4c_branch2c" name: "bn4c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c_branch2c" top: "res4c_branch2c" name: "scale4c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4b" bottom: "res4c_branch2c" top: "res4c" name: "res4c" type: "Eltwise" }
layer { bottom: "res4c" top: "res4c" name: "res4c_relu" type: "ReLU" }
layer { bottom: "res4c" top: "res4d_branch2a" name: "res4d_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "bn4d_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "scale4d_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d_branch2a" top: "res4d_branch2a" name: "res4d_branch2a_relu" type: "ReLU" }
layer { bottom: "res4d_branch2a" top: "res4d_branch2b" name: "res4d_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "bn4d_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "scale4d_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d_branch2b" top: "res4d_branch2b" name: "res4d_branch2b_relu" type: "ReLU" }
layer { bottom: "res4d_branch2b" top: "res4d_branch2c" name: "res4d_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4d_branch2c" top: "res4d_branch2c" name: "bn4d_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d_branch2c" top: "res4d_branch2c" name: "scale4d_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4c" bottom: "res4d_branch2c" top: "res4d" name: "res4d" type: "Eltwise" }
layer { bottom: "res4d" top: "res4d" name: "res4d_relu" type: "ReLU" }
layer { bottom: "res4d" top: "res4e_branch2a" name: "res4e_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "bn4e_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "scale4e_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e_branch2a" top: "res4e_branch2a" name: "res4e_branch2a_relu" type: "ReLU" }
layer { bottom: "res4e_branch2a" top: "res4e_branch2b" name: "res4e_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "bn4e_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "scale4e_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e_branch2b" top: "res4e_branch2b" name: "res4e_branch2b_relu" type: "ReLU" }
layer { bottom: "res4e_branch2b" top: "res4e_branch2c" name: "res4e_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4e_branch2c" top: "res4e_branch2c" name: "bn4e_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e_branch2c" top: "res4e_branch2c" name: "scale4e_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4d" bottom: "res4e_branch2c" top: "res4e" name: "res4e" type: "Eltwise" }
layer { bottom: "res4e" top: "res4e" name: "res4e_relu" type: "ReLU" }
layer { bottom: "res4e" top: "res4f_branch2a" name: "res4f_branch2a" type: "Convolution" convolution_param { num_output: 256 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "bn4f_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "scale4f_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f_branch2a" top: "res4f_branch2a" name: "res4f_branch2a_relu" type: "ReLU" }
layer { bottom: "res4f_branch2a" top: "res4f_branch2b" name: "res4f_branch2b" type: "Convolution" convolution_param { num_output: 256 kernel_size: 3 pad: 1 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "bn4f_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "scale4f_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f_branch2b" top: "res4f_branch2b" name: "res4f_branch2b_relu" type: "ReLU" }
layer { bottom: "res4f_branch2b" top: "res4f_branch2c" name: "res4f_branch2c" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res4f_branch2c" top: "res4f_branch2c" name: "bn4f_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f_branch2c" top: "res4f_branch2c" name: "scale4f_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4e" bottom: "res4f_branch2c" top: "res4f" name: "res4f" type: "Eltwise" }
layer { bottom: "res4f" top: "res4f" name: "res4f_relu" type: "ReLU" }
layer { bottom: "res4f" top: "res5a_branch1" name: "res5a_branch1" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5a_branch1" top: "res5a_branch1" name: "bn5a_branch1" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch1" top: "res5a_branch1" name: "scale5a_branch1" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res4f" top: "res5a_branch2a" name: "res5a_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "bn5a_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "scale5a_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch2a" top: "res5a_branch2a" name: "res5a_branch2a_relu" type: "ReLU" }
layer { bottom: "res5a_branch2a" top: "res5a_branch2b" name: "res5a_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "bn5a_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "scale5a_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch2b" top: "res5a_branch2b" name: "res5a_branch2b_relu" type: "ReLU" }
layer { bottom: "res5a_branch2b" top: "res5a_branch2c" name: "res5a_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5a_branch2c" top: "res5a_branch2c" name: "bn5a_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch2c" top: "res5a_branch2c" name: "scale5a_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a_branch1" bottom: "res5a_branch2c" top: "res5a" name: "res5a" type: "Eltwise" }
layer { bottom: "res5a" top: "res5a" name: "res5a_relu" type: "ReLU" }
layer { bottom: "res5a" top: "res5b_branch2a" name: "res5b_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "bn5b_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "scale5b_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b_branch2a" top: "res5b_branch2a" name: "res5b_branch2a_relu" type: "ReLU" }
layer { bottom: "res5b_branch2a" top: "res5b_branch2b" name: "res5b_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "bn5b_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "scale5b_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b_branch2b" top: "res5b_branch2b" name: "res5b_branch2b_relu" type: "ReLU" }
layer { bottom: "res5b_branch2b" top: "res5b_branch2c" name: "res5b_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5b_branch2c" top: "res5b_branch2c" name: "bn5b_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b_branch2c" top: "res5b_branch2c" name: "scale5b_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5a" bottom: "res5b_branch2c" top: "res5b" name: "res5b" type: "Eltwise" }
layer { bottom: "res5b" top: "res5b" name: "res5b_relu" type: "ReLU" }
layer { bottom: "res5b" top: "res5c_branch2a" name: "res5c_branch2a" type: "Convolution" convolution_param { num_output: 512 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "bn5c_branch2a" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "scale5c_branch2a" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5c_branch2a" top: "res5c_branch2a" name: "res5c_branch2a_relu" type: "ReLU" }
layer { bottom: "res5c_branch2a" top: "res5c_branch2b" name: "res5c_branch2b" type: "Convolution" convolution_param { num_output: 512 kernel_size: 3 dilation: 2 pad: 2 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "bn5c_branch2b" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "scale5c_branch2b" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5c_branch2b" top: "res5c_branch2b" name: "res5c_branch2b_relu" type: "ReLU" }
layer { bottom: "res5c_branch2b" top: "res5c_branch2c" name: "res5c_branch2c" type: "Convolution" convolution_param { num_output: 2048 kernel_size: 1 pad: 0 stride: 1 bias_term: false } param { lr_mult: 1.0 } }
layer { bottom: "res5c_branch2c" top: "res5c_branch2c" name: "bn5c_branch2c" type: "BatchNorm" batch_norm_param { use_global_stats: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5c_branch2c" top: "res5c_branch2c" name: "scale5c_branch2c" type: "Scale" scale_param { bias_term: true } param { lr_mult: 0.0 decay_mult: 0.0 } param { lr_mult: 0.0 decay_mult: 0.0 } }
layer { bottom: "res5b" bottom: "res5c_branch2c" top: "res5c" name: "res5c" type: "Eltwise" }
layer { bottom: "res5c" top: "res5c" name: "res5c_relu" type: "ReLU" }
#========= RPN ============
layer { name: "rpn_conv/3x3" type: "Convolution" bottom: "res4f" top: "rpn/output" param { lr_mult: 1.0 } param { lr_mult: 2.0 } convolution_param { num_output: 512 kernel_size: 3 pad: 1 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu/3x3" type: "ReLU" bottom: "rpn/output" top: "rpn/output" }
layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn/output" top: "rpn_cls_score" param { lr_mult: 1.0 } param { lr_mult: 2.0 } convolution_param { num_output: 18 # 2(bg/fg) * 9(anchors) kernel_size: 1 pad: 0 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn/output" top: "rpn_bbox_pred" param { lr_mult: 1.0 } param { lr_mult: 2.0 } convolution_param { num_output: 36 # 4 * 9(anchors) kernel_size: 1 pad: 0 stride: 1 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } }
layer { bottom: "rpn_cls_score" top: "rpn_cls_score_reshape" name: "rpn_cls_score_reshape" type: "Reshape" reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } } }
layer { name: 'rpn-data' type: 'Python' bottom: 'rpn_cls_score' bottom: 'gt_boxes' bottom: 'im_info' bottom: 'data' top: 'rpn_labels' top: 'rpn_bbox_targets' top: 'rpn_bbox_inside_weights' top: 'rpn_bbox_outside_weights' python_param { module: 'rpn.anchor_target_layer' layer: 'AnchorTargetLayer' param_str: "'feat_stride': 16" } }
layer { name: "rpn_loss_cls" type: "SoftmaxWithLoss" bottom: "rpn_cls_score_reshape" bottom: "rpn_labels" propagate_down: 1 propagate_down: 0 top: "rpn_cls_loss" loss_weight: 1 loss_param { ignore_label: -1 normalize: true } }
layer { name: "rpn_loss_bbox" type: "SmoothL1Loss" bottom: "rpn_bbox_pred" bottom: "rpn_bbox_targets" bottom: 'rpn_bbox_inside_weights' bottom: 'rpn_bbox_outside_weights' top: "rpn_loss_bbox" loss_weight: 1 smooth_l1_loss_param { sigma: 3.0 } }
#========= RoI Proposal ============
layer { name: "rpn_cls_prob" type: "Softmax" bottom: "rpn_cls_score_reshape" top: "rpn_cls_prob" }
layer { name: 'rpn_cls_prob_reshape' type: 'Reshape' bottom: 'rpn_cls_prob' top: 'rpn_cls_prob_reshape' reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } } }
layer { name: 'proposal' type: 'Python' bottom: 'rpn_cls_prob_reshape' bottom: 'rpn_bbox_pred' bottom: 'im_info' top: 'rpn_rois'
python_param { module: 'rpn.proposal_layer' layer: 'ProposalLayer' param_str: "'feat_stride': 16" } }
#layer {
#}
layer { name: 'roi-data' type: 'Python' bottom: 'rpn_rois' bottom: 'gt_boxes' top: 'rois' top: 'labels' top: 'bbox_targets' top: 'bbox_inside_weights' top: 'bbox_outside_weights' python_param { module: 'rpn.proposal_target_layer' layer: 'ProposalTargetLayer' param_str: "'num_classes': 2" } }
#----------------------new conv layer------------------ layer { bottom: "res5c" top: "conv_new_1" name: "conv_new_1" type: "Convolution" convolution_param { num_output: 1024 kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } }
layer { bottom: "conv_new_1" top: "conv_new_1" name: "conv_new_1_relu" type: "ReLU" }
layer { bottom: "conv_new_1" top: "rfcn_cls" name: "rfcn_cls" type: "Convolution" convolution_param { num_output: 98 #21*(7^2) cls_num*(score_maps_size^2) kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } } layer { bottom: "conv_new_1" top: "rfcn_bbox" name: "rfcn_bbox" type: "Convolution" convolution_param { num_output: 392 #24(7^2) (bg/fg)(dx, dy, dw, dh)(score_maps_size^2) kernel_size: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } param { lr_mult: 1.0 } param { lr_mult: 2.0 } }
#--------------position sensitive RoI pooling-------------- layer { bottom: "rfcn_cls" bottom: "rois" top: "psroipooled_cls_rois" name: "psroipooled_cls_rois" type: "PSROIPooling" psroi_pooling_param { spatial_scale: 0.0625 output_dim: 2 group_size: 7 } }
layer { bottom: "psroipooled_cls_rois" top: "cls_score" name: "ave_cls_score_rois" type: "Pooling" pooling_param { pool: AVE kernel_size: 7 stride: 7 } }
layer { bottom: "rfcn_bbox" bottom: "rois" top: "psroipooled_loc_rois" name: "psroipooled_loc_rois" type: "PSROIPooling" psroi_pooling_param { spatial_scale: 0.0625 output_dim: 8 group_size: 7 } }
layer { bottom: "psroipooled_loc_rois" top: "bbox_pred" name: "ave_bbox_pred_rois" type: "Pooling" pooling_param { pool: AVE kernel_size: 7 stride: 7 } }
#--------------online hard example mining-------------- layer { name: "per_roi_loss_cls" type: "SoftmaxWithLossOHEM" bottom: "cls_score" bottom: "labels" top: "temp_loss_cls" top: "temp_prob_cls" top: "per_roi_loss_cls" loss_weight: 0 loss_weight: 0 loss_weight: 0 propagate_down: false propagate_down: false }
layer { name: "per_roi_loss_bbox" type: "SmoothL1LossOHEM" bottom: "bbox_pred" bottom: "bbox_targets" bottom: "bbox_inside_weights" top: "temp_loss_bbox" top: "per_roi_loss_bbox" loss_weight: 0 loss_weight: 0 propagate_down: false propagate_down: false propagate_down: false }
layer { name: "per_roi_loss" type: "Eltwise" bottom: "per_roi_loss_cls" bottom: "per_roi_loss_bbox" top: "per_roi_loss" propagate_down: false propagate_down: false }
layer { bottom: "rois" bottom: "per_roi_loss" bottom: "labels" bottom: "bbox_inside_weights" top: "labels_ohem" top: "bbox_loss_weights_ohem" name: "annotator_detector" type: "BoxAnnotatorOHEM" box_annotator_ohem_param { roi_per_img: 128 ignore_label: -1 } propagate_down: false propagate_down: false propagate_down: false propagate_down: false }
layer { name: "silence" type: "Silence" bottom: "bbox_outside_weights" bottom: "temp_loss_cls" bottom: "temp_prob_cls" bottom: "temp_loss_bbox" }
#-----------------------output------------------------ layer { name: "loss" type: "SoftmaxWithLoss" bottom: "cls_score" bottom: "labels_ohem" top: "loss_cls" loss_weight: 1 loss_param { ignore_label: -1 } propagate_down: true propagate_down: false }
layer { name: "accuarcy" type: "Accuracy" bottom: "cls_score" bottom: "labels_ohem" top: "accuarcy" #include: { phase: TEST } accuracy_param { ignore_label: -1 } propagate_down: false propagate_down: false }
layer { name: "loss_bbox" type: "SmoothL1LossOHEM" bottom: "bbox_pred" bottom: "bbox_targets" bottom: "bbox_loss_weights_ohem" top: "loss_bbox" loss_weight: 1 loss_param { normalization: PRE_FIXED pre_fixed_normalizer: 128 } propagate_down: true propagate_down: false propagate_down: false }
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the prototxt file is shown as fallows:
name: "ResNet-50"
layer {
name: 'input-data'
type: 'Python'
top: 'data'
top: 'im_info'
top: 'gt_boxes'
python_param {
module: 'roi_data_layer.layer'
layer: 'RoIDataLayer'
param_str: "'num_classes': 2"
}
}
------------------------ conv1 -----------------------------
layer {
bottom: "data"
top: "conv1"
name: "conv1"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
stride: 2
}
param {
lr_mult: 0.0
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "bn_conv1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "scale_conv1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_relu"
type: "ReLU"
}
layer {
bottom: "conv1"
top: "pool1"
name: "pool1"
type: "Pooling"
pooling_param {
kernel_size: 3
stride: 2
pool: MAX
}
}
layer {
bottom: "pool1"
top: "res2a_branch1"
name: "res2a_branch1"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "bn2a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch1"
top: "res2a_branch1"
name: "scale2a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "pool1"
top: "res2a_branch2a"
name: "res2a_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "bn2a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "scale2a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2a"
name: "res2a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2a"
top: "res2a_branch2b"
name: "res2a_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "bn2a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "scale2a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2b"
name: "res2a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2a_branch2b"
top: "res2a_branch2c"
name: "res2a_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "bn2a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch2c"
top: "res2a_branch2c"
name: "scale2a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a_branch1"
bottom: "res2a_branch2c"
top: "res2a"
name: "res2a"
type: "Eltwise"
}
layer {
bottom: "res2a"
top: "res2a"
name: "res2a_relu"
type: "ReLU"
}
layer {
bottom: "res2a"
top: "res2b_branch2a"
name: "res2b_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "bn2b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "scale2b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2a"
name: "res2b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2b_branch2a"
top: "res2b_branch2b"
name: "res2b_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "bn2b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "scale2b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2b"
name: "res2b_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2b_branch2b"
top: "res2b_branch2c"
name: "res2b_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "bn2b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b_branch2c"
top: "res2b_branch2c"
name: "scale2b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2a"
bottom: "res2b_branch2c"
top: "res2b"
name: "res2b"
type: "Eltwise"
}
layer {
bottom: "res2b"
top: "res2b"
name: "res2b_relu"
type: "ReLU"
}
layer {
bottom: "res2b"
top: "res2c_branch2a"
name: "res2c_branch2a"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "bn2c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "scale2c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c_branch2a"
top: "res2c_branch2a"
name: "res2c_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res2c_branch2a"
top: "res2c_branch2b"
name: "res2c_branch2b"
type: "Convolution"
convolution_param {
num_output: 64
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "bn2c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "scale2c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c_branch2b"
top: "res2c_branch2b"
name: "res2c_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res2c_branch2b"
top: "res2c_branch2c"
name: "res2c_branch2c"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 0.0
}
}
layer {
bottom: "res2c_branch2c"
top: "res2c_branch2c"
name: "bn2c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c_branch2c"
top: "res2c_branch2c"
name: "scale2c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2b"
bottom: "res2c_branch2c"
top: "res2c"
name: "res2c"
type: "Eltwise"
}
layer {
bottom: "res2c"
top: "res2c"
name: "res2c_relu"
type: "ReLU"
}
layer {
bottom: "res2c"
top: "res3a_branch1"
name: "res3a_branch1"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "bn3a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch1"
top: "res3a_branch1"
name: "scale3a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res2c"
top: "res3a_branch2a"
name: "res3a_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "bn3a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "scale3a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2a"
name: "res3a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3a_branch2a"
top: "res3a_branch2b"
name: "res3a_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "bn3a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "scale3a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2b"
name: "res3a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res3a_branch2b"
top: "res3a_branch2c"
name: "res3a_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3a_branch2c"
top: "res3a_branch2c"
name: "bn3a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch2c"
top: "res3a_branch2c"
name: "scale3a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a_branch1"
bottom: "res3a_branch2c"
top: "res3a"
name: "res3a"
type: "Eltwise"
}
layer {
bottom: "res3a"
top: "res3a"
name: "res3a_relu"
type: "ReLU"
}
layer {
bottom: "res3a"
top: "res3b_branch2a"
name: "res3b_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "bn3b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "scale3b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2a"
name: "res3b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3b_branch2a"
top: "res3b_branch2b"
name: "res3b_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "bn3b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "scale3b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2b"
name: "res3b_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res3b_branch2b"
top: "res3b_branch2c"
name: "res3b_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3b_branch2c"
top: "res3b_branch2c"
name: "bn3b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b_branch2c"
top: "res3b_branch2c"
name: "scale3b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3a"
bottom: "res3b_branch2c"
top: "res3b"
name: "res3b"
type: "Eltwise"
}
layer {
bottom: "res3b"
top: "res3b"
name: "res3b_relu"
type: "ReLU"
}
layer {
bottom: "res3b"
top: "res3c_branch2a"
name: "res3c_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "bn3c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "scale3c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c_branch2a"
top: "res3c_branch2a"
name: "res3c_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3c_branch2a"
top: "res3c_branch2b"
name: "res3c_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "bn3c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "scale3c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c_branch2b"
top: "res3c_branch2b"
name: "res3c_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res3c_branch2b"
top: "res3c_branch2c"
name: "res3c_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3c_branch2c"
top: "res3c_branch2c"
name: "bn3c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c_branch2c"
top: "res3c_branch2c"
name: "scale3c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3b"
bottom: "res3c_branch2c"
top: "res3c"
name: "res3c"
type: "Eltwise"
}
layer {
bottom: "res3c"
top: "res3c"
name: "res3c_relu"
type: "ReLU"
}
layer {
bottom: "res3c"
top: "res3d_branch2a"
name: "res3d_branch2a"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "bn3d_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "scale3d_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d_branch2a"
top: "res3d_branch2a"
name: "res3d_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res3d_branch2a"
top: "res3d_branch2b"
name: "res3d_branch2b"
type: "Convolution"
convolution_param {
num_output: 128
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "bn3d_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "scale3d_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d_branch2b"
top: "res3d_branch2b"
name: "res3d_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res3d_branch2b"
top: "res3d_branch2c"
name: "res3d_branch2c"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res3d_branch2c"
top: "res3d_branch2c"
name: "bn3d_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d_branch2c"
top: "res3d_branch2c"
name: "scale3d_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3c"
bottom: "res3d_branch2c"
top: "res3d"
name: "res3d"
type: "Eltwise"
}
layer {
bottom: "res3d"
top: "res3d"
name: "res3d_relu"
type: "ReLU"
}
layer {
bottom: "res3d"
top: "res4a_branch1"
name: "res4a_branch1"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "bn4a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch1"
top: "res4a_branch1"
name: "scale4a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res3d"
top: "res4a_branch2a"
name: "res4a_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 2
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "bn4a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "scale4a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2a"
name: "res4a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4a_branch2a"
top: "res4a_branch2b"
name: "res4a_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "bn4a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "scale4a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2b"
name: "res4a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4a_branch2b"
top: "res4a_branch2c"
name: "res4a_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4a_branch2c"
top: "res4a_branch2c"
name: "bn4a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch2c"
top: "res4a_branch2c"
name: "scale4a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a_branch1"
bottom: "res4a_branch2c"
top: "res4a"
name: "res4a"
type: "Eltwise"
}
layer {
bottom: "res4a"
top: "res4a"
name: "res4a_relu"
type: "ReLU"
}
layer {
bottom: "res4a"
top: "res4b_branch2a"
name: "res4b_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "bn4b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "scale4b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2a"
name: "res4b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4b_branch2a"
top: "res4b_branch2b"
name: "res4b_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "bn4b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "scale4b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2b"
name: "res4b_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4b_branch2b"
top: "res4b_branch2c"
name: "res4b_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4b_branch2c"
top: "res4b_branch2c"
name: "bn4b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b_branch2c"
top: "res4b_branch2c"
name: "scale4b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4a"
bottom: "res4b_branch2c"
top: "res4b"
name: "res4b"
type: "Eltwise"
}
layer {
bottom: "res4b"
top: "res4b"
name: "res4b_relu"
type: "ReLU"
}
layer {
bottom: "res4b"
top: "res4c_branch2a"
name: "res4c_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "bn4c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "scale4c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c_branch2a"
top: "res4c_branch2a"
name: "res4c_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4c_branch2a"
top: "res4c_branch2b"
name: "res4c_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "bn4c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "scale4c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c_branch2b"
top: "res4c_branch2b"
name: "res4c_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4c_branch2b"
top: "res4c_branch2c"
name: "res4c_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4c_branch2c"
top: "res4c_branch2c"
name: "bn4c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c_branch2c"
top: "res4c_branch2c"
name: "scale4c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4b"
bottom: "res4c_branch2c"
top: "res4c"
name: "res4c"
type: "Eltwise"
}
layer {
bottom: "res4c"
top: "res4c"
name: "res4c_relu"
type: "ReLU"
}
layer {
bottom: "res4c"
top: "res4d_branch2a"
name: "res4d_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "bn4d_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "scale4d_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d_branch2a"
top: "res4d_branch2a"
name: "res4d_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4d_branch2a"
top: "res4d_branch2b"
name: "res4d_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "bn4d_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "scale4d_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d_branch2b"
top: "res4d_branch2b"
name: "res4d_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4d_branch2b"
top: "res4d_branch2c"
name: "res4d_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4d_branch2c"
top: "res4d_branch2c"
name: "bn4d_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d_branch2c"
top: "res4d_branch2c"
name: "scale4d_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4c"
bottom: "res4d_branch2c"
top: "res4d"
name: "res4d"
type: "Eltwise"
}
layer {
bottom: "res4d"
top: "res4d"
name: "res4d_relu"
type: "ReLU"
}
layer {
bottom: "res4d"
top: "res4e_branch2a"
name: "res4e_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "bn4e_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "scale4e_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e_branch2a"
top: "res4e_branch2a"
name: "res4e_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4e_branch2a"
top: "res4e_branch2b"
name: "res4e_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "bn4e_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "scale4e_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e_branch2b"
top: "res4e_branch2b"
name: "res4e_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4e_branch2b"
top: "res4e_branch2c"
name: "res4e_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4e_branch2c"
top: "res4e_branch2c"
name: "bn4e_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e_branch2c"
top: "res4e_branch2c"
name: "scale4e_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4d"
bottom: "res4e_branch2c"
top: "res4e"
name: "res4e"
type: "Eltwise"
}
layer {
bottom: "res4e"
top: "res4e"
name: "res4e_relu"
type: "ReLU"
}
layer {
bottom: "res4e"
top: "res4f_branch2a"
name: "res4f_branch2a"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "bn4f_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "scale4f_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f_branch2a"
top: "res4f_branch2a"
name: "res4f_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res4f_branch2a"
top: "res4f_branch2b"
name: "res4f_branch2b"
type: "Convolution"
convolution_param {
num_output: 256
kernel_size: 3
pad: 1
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "bn4f_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "scale4f_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f_branch2b"
top: "res4f_branch2b"
name: "res4f_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res4f_branch2b"
top: "res4f_branch2c"
name: "res4f_branch2c"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res4f_branch2c"
top: "res4f_branch2c"
name: "bn4f_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f_branch2c"
top: "res4f_branch2c"
name: "scale4f_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4e"
bottom: "res4f_branch2c"
top: "res4f"
name: "res4f"
type: "Eltwise"
}
layer {
bottom: "res4f"
top: "res4f"
name: "res4f_relu"
type: "ReLU"
}
layer {
bottom: "res4f"
top: "res5a_branch1"
name: "res5a_branch1"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "bn5a_branch1"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch1"
top: "res5a_branch1"
name: "scale5a_branch1"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res4f"
top: "res5a_branch2a"
name: "res5a_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "bn5a_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "scale5a_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2a"
name: "res5a_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res5a_branch2a"
top: "res5a_branch2b"
name: "res5a_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "bn5a_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "scale5a_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2b"
name: "res5a_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res5a_branch2b"
top: "res5a_branch2c"
name: "res5a_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5a_branch2c"
top: "res5a_branch2c"
name: "bn5a_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch2c"
top: "res5a_branch2c"
name: "scale5a_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a_branch1"
bottom: "res5a_branch2c"
top: "res5a"
name: "res5a"
type: "Eltwise"
}
layer {
bottom: "res5a"
top: "res5a"
name: "res5a_relu"
type: "ReLU"
}
layer {
bottom: "res5a"
top: "res5b_branch2a"
name: "res5b_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "bn5b_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "scale5b_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2a"
name: "res5b_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res5b_branch2a"
top: "res5b_branch2b"
name: "res5b_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "bn5b_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "scale5b_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2b"
name: "res5b_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res5b_branch2b"
top: "res5b_branch2c"
name: "res5b_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5b_branch2c"
top: "res5b_branch2c"
name: "bn5b_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b_branch2c"
top: "res5b_branch2c"
name: "scale5b_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5a"
bottom: "res5b_branch2c"
top: "res5b"
name: "res5b"
type: "Eltwise"
}
layer {
bottom: "res5b"
top: "res5b"
name: "res5b_relu"
type: "ReLU"
}
layer {
bottom: "res5b"
top: "res5c_branch2a"
name: "res5c_branch2a"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "bn5c_branch2a"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "scale5c_branch2a"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5c_branch2a"
top: "res5c_branch2a"
name: "res5c_branch2a_relu"
type: "ReLU"
}
layer {
bottom: "res5c_branch2a"
top: "res5c_branch2b"
name: "res5c_branch2b"
type: "Convolution"
convolution_param {
num_output: 512
kernel_size: 3
dilation: 2
pad: 2
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "bn5c_branch2b"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "scale5c_branch2b"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5c_branch2b"
top: "res5c_branch2b"
name: "res5c_branch2b_relu"
type: "ReLU"
}
layer {
bottom: "res5c_branch2b"
top: "res5c_branch2c"
name: "res5c_branch2c"
type: "Convolution"
convolution_param {
num_output: 2048
kernel_size: 1
pad: 0
stride: 1
bias_term: false
}
param {
lr_mult: 1.0
}
}
layer {
bottom: "res5c_branch2c"
top: "res5c_branch2c"
name: "bn5c_branch2c"
type: "BatchNorm"
batch_norm_param {
use_global_stats: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5c_branch2c"
top: "res5c_branch2c"
name: "scale5c_branch2c"
type: "Scale"
scale_param {
bias_term: true
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
param {
lr_mult: 0.0
decay_mult: 0.0
}
}
layer {
bottom: "res5b"
bottom: "res5c_branch2c"
top: "res5c"
name: "res5c"
type: "Eltwise"
}
layer {
bottom: "res5c"
top: "res5c"
name: "res5c_relu"
type: "ReLU"
}
#========= RPN ============
layer {
name: "rpn_conv/3x3"
type: "Convolution"
bottom: "res4f"
top: "rpn/output"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 512
kernel_size: 3 pad: 1 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_relu/3x3"
type: "ReLU"
bottom: "rpn/output"
top: "rpn/output"
}
layer {
name: "rpn_cls_score"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_cls_score"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 18 # 2(bg/fg) * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
name: "rpn_bbox_pred"
type: "Convolution"
bottom: "rpn/output"
top: "rpn_bbox_pred"
param { lr_mult: 1.0 }
param { lr_mult: 2.0 }
convolution_param {
num_output: 36 # 4 * 9(anchors)
kernel_size: 1 pad: 0 stride: 1
weight_filler { type: "gaussian" std: 0.01 }
bias_filler { type: "constant" value: 0 }
}
}
layer {
bottom: "rpn_cls_score"
top: "rpn_cls_score_reshape"
name: "rpn_cls_score_reshape"
type: "Reshape"
reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
name: 'rpn-data'
type: 'Python'
bottom: 'rpn_cls_score'
bottom: 'gt_boxes'
bottom: 'im_info'
bottom: 'data'
top: 'rpn_labels'
top: 'rpn_bbox_targets'
top: 'rpn_bbox_inside_weights'
top: 'rpn_bbox_outside_weights'
python_param {
module: 'rpn.anchor_target_layer'
layer: 'AnchorTargetLayer'
param_str: "'feat_stride': 16"
}
}
layer {
name: "rpn_loss_cls"
type: "SoftmaxWithLoss"
bottom: "rpn_cls_score_reshape"
bottom: "rpn_labels"
propagate_down: 1
propagate_down: 0
top: "rpn_cls_loss"
loss_weight: 1
loss_param {
ignore_label: -1
normalize: true
}
}
layer {
name: "rpn_loss_bbox"
type: "SmoothL1Loss"
bottom: "rpn_bbox_pred"
bottom: "rpn_bbox_targets"
bottom: 'rpn_bbox_inside_weights'
bottom: 'rpn_bbox_outside_weights'
top: "rpn_loss_bbox"
loss_weight: 1
smooth_l1_loss_param { sigma: 3.0 }
}
#========= RoI Proposal ============
layer {
name: "rpn_cls_prob"
type: "Softmax"
bottom: "rpn_cls_score_reshape"
top: "rpn_cls_prob"
}
layer {
name: 'rpn_cls_prob_reshape'
type: 'Reshape'
bottom: 'rpn_cls_prob'
top: 'rpn_cls_prob_reshape'
reshape_param { shape { dim: 0 dim: 18 dim: -1 dim: 0 } }
}
layer {
name: 'proposal'
type: 'Python'
bottom: 'rpn_cls_prob_reshape'
bottom: 'rpn_bbox_pred'
bottom: 'im_info'
top: 'rpn_rois'
top: 'rpn_scores'
python_param {
module: 'rpn.proposal_layer'
layer: 'ProposalLayer'
param_str: "'feat_stride': 16"
}
}
#layer {
name: 'debug-data'
type: 'Python'
bottom: 'data'
bottom: 'rpn_rois'
bottom: 'rpn_scores'
python_param {
module: 'rpn.debug_layer'
layer: 'RPNDebugLayer'
}
#}
layer {
name: 'roi-data'
type: 'Python'
bottom: 'rpn_rois'
bottom: 'gt_boxes'
top: 'rois'
top: 'labels'
top: 'bbox_targets'
top: 'bbox_inside_weights'
top: 'bbox_outside_weights'
python_param {
module: 'rpn.proposal_target_layer'
layer: 'ProposalTargetLayer'
param_str: "'num_classes': 2"
}
}
#----------------------new conv layer------------------
layer {
bottom: "res5c"
top: "conv_new_1"
name: "conv_new_1"
type: "Convolution"
convolution_param {
num_output: 1024
kernel_size: 1
pad: 0
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
}
layer {
bottom: "conv_new_1"
top: "conv_new_1"
name: "conv_new_1_relu"
type: "ReLU"
}
layer {
bottom: "conv_new_1"
top: "rfcn_cls"
name: "rfcn_cls"
type: "Convolution"
convolution_param {
num_output: 98 #21*(7^2) cls_num*(score_maps_size^2)
kernel_size: 1
pad: 0
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
}
layer {
bottom: "conv_new_1"
top: "rfcn_bbox"
name: "rfcn_bbox"
type: "Convolution"
convolution_param {
num_output: 392 #24(7^2) (bg/fg)(dx, dy, dw, dh)(score_maps_size^2)
kernel_size: 1
pad: 0
weight_filler {
type: "gaussian"
std: 0.01
}
bias_filler {
type: "constant"
value: 0
}
}
param {
lr_mult: 1.0
}
param {
lr_mult: 2.0
}
}
#--------------position sensitive RoI pooling--------------
layer {
bottom: "rfcn_cls"
bottom: "rois"
top: "psroipooled_cls_rois"
name: "psroipooled_cls_rois"
type: "PSROIPooling"
psroi_pooling_param {
spatial_scale: 0.0625
output_dim: 2
group_size: 7
}
}
layer {
bottom: "psroipooled_cls_rois"
top: "cls_score"
name: "ave_cls_score_rois"
type: "Pooling"
pooling_param {
pool: AVE
kernel_size: 7
stride: 7
}
}
layer {
bottom: "rfcn_bbox"
bottom: "rois"
top: "psroipooled_loc_rois"
name: "psroipooled_loc_rois"
type: "PSROIPooling"
psroi_pooling_param {
spatial_scale: 0.0625
output_dim: 8
group_size: 7
}
}
layer {
bottom: "psroipooled_loc_rois"
top: "bbox_pred"
name: "ave_bbox_pred_rois"
type: "Pooling"
pooling_param {
pool: AVE
kernel_size: 7
stride: 7
}
}
#--------------online hard example mining--------------
layer {
name: "per_roi_loss_cls"
type: "SoftmaxWithLossOHEM"
bottom: "cls_score"
bottom: "labels"
top: "temp_loss_cls"
top: "temp_prob_cls"
top: "per_roi_loss_cls"
loss_weight: 0
loss_weight: 0
loss_weight: 0
propagate_down: false
propagate_down: false
}
layer {
name: "per_roi_loss_bbox"
type: "SmoothL1LossOHEM"
bottom: "bbox_pred"
bottom: "bbox_targets"
bottom: "bbox_inside_weights"
top: "temp_loss_bbox"
top: "per_roi_loss_bbox"
loss_weight: 0
loss_weight: 0
propagate_down: false
propagate_down: false
propagate_down: false
}
layer {
name: "per_roi_loss"
type: "Eltwise"
bottom: "per_roi_loss_cls"
bottom: "per_roi_loss_bbox"
top: "per_roi_loss"
propagate_down: false
propagate_down: false
}
layer {
bottom: "rois"
bottom: "per_roi_loss"
bottom: "labels"
bottom: "bbox_inside_weights"
top: "labels_ohem"
top: "bbox_loss_weights_ohem"
name: "annotator_detector"
type: "BoxAnnotatorOHEM"
box_annotator_ohem_param {
roi_per_img: 128
ignore_label: -1
}
propagate_down: false
propagate_down: false
propagate_down: false
propagate_down: false
}
layer {
name: "silence"
type: "Silence"
bottom: "bbox_outside_weights"
bottom: "temp_loss_cls"
bottom: "temp_prob_cls"
bottom: "temp_loss_bbox"
}
#-----------------------output------------------------
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "cls_score"
bottom: "labels_ohem"
top: "loss_cls"
loss_weight: 1
loss_param {
ignore_label: -1
}
propagate_down: true
propagate_down: false
}
layer {
name: "accuarcy"
type: "Accuracy"
bottom: "cls_score"
bottom: "labels_ohem"
top: "accuarcy"
#include: { phase: TEST }
accuracy_param {
ignore_label: -1
}
propagate_down: false
propagate_down: false
}
layer {
name: "loss_bbox"
type: "SmoothL1LossOHEM"
bottom: "bbox_pred"
bottom: "bbox_targets"
bottom: "bbox_loss_weights_ohem"
top: "loss_bbox"
loss_weight: 1
loss_param {
normalization: PRE_FIXED
pre_fixed_normalizer: 128
}
propagate_down: true
propagate_down: false
propagate_down: false
}
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