input: "data" input_dim: 1 input_dim: 3 input_dim: 160 input_dim: 40 layer { name: "conv0" type: "Convolution" bottom: "data" top: "conv0" convolution_param { num_output: 32 bias_term: true pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "bn0" type: "BatchNorm" bottom: "conv0" top: "bn0" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "bn0_scale" type: "Scale" bottom: "bn0" top: "bn0" scale_param { bias_term: true } } layer { name: "relu0" type: "ReLU" bottom: "bn0" top: "bn0" } layer { name: "pool0" type: "Pooling" bottom: "bn0" top: "pool0" pooling_param { pool: MAX kernel_h: 2 kernel_w: 2 stride_h: 2 stride_w: 2 pad_h: 0 pad_w: 0 } } layer { name: "conv1" type: "Convolution" bottom: "pool0" top: "conv1" convolution_param { num_output: 64 bias_term: true pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "bn1" type: "BatchNorm" bottom: "conv1" top: "bn1" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "bn1_scale" type: "Scale" bottom: "bn1" top: "bn1" scale_param { bias_term: true } } layer { name: "relu1" type: "ReLU" bottom: "bn1" top: "bn1" } layer { name: "pool1" type: "Pooling" bottom: "bn1" top: "pool1" pooling_param { pool: MAX kernel_h: 2 kernel_w: 2 stride_h: 2 stride_w: 2 pad_h: 0 pad_w: 0 } } layer { name: "conv2" type: "Convolution" bottom: "pool1" top: "conv2" convolution_param { num_output: 128 bias_term: true pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "bn2" type: "BatchNorm" bottom: "conv2" top: "bn2" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "bn2_scale" type: "Scale" bottom: "bn2" top: "bn2" scale_param { bias_term: true } } layer { name: "relu2" type: "ReLU" bottom: "bn2" top: "bn2" } layer { name: "pool2" type: "Pooling" bottom: "bn2" top: "pool2" pooling_param { pool: MAX kernel_h: 2 kernel_w: 2 stride_h: 2 stride_w: 2 pad_h: 0 pad_w: 0 } } layer { name: "conv_512_15" type: "Convolution" bottom: "pool2" top: "conv_512_15" convolution_param { num_output: 512 bias_term: true pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 5 stride_h: 1 stride_w: 1 } } layer { name: "batch_normalization_1" type: "BatchNorm" bottom: "conv_512_15" top: "batch_normalization_1" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "batch_normalization_1_scale" type: "Scale" bottom: "batch_normalization_1" top: "batch_normalization_1" scale_param { bias_term: true } } layer { name: "activation_1" type: "ReLU" bottom: "batch_normalization_1" top: "batch_normalization_1" } layer { name: "conv_512_51" type: "Convolution" bottom: "batch_normalization_1" top: "conv_512_51" convolution_param { num_output: 512 bias_term: true pad_h: 0 pad_w: 0 kernel_h: 5 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "batch_normalization_2" type: "BatchNorm" bottom: "conv_512_51" top: "batch_normalization_2" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "batch_normalization_2_scale" type: "Scale" bottom: "batch_normalization_2" top: "batch_normalization_2" scale_param { bias_term: true } } layer { name: "activation_2" type: "ReLU" bottom: "batch_normalization_2" top: "batch_normalization_2" } layer { name: "conv_1024_11" type: "Convolution" bottom: "batch_normalization_2" top: "conv_1024_11" convolution_param { num_output: 1024 bias_term: true pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "batch_normalization_3" type: "BatchNorm" bottom: "conv_1024_11" top: "batch_normalization_3" batch_norm_param { moving_average_fraction: 0.99 eps: 0.001 } } layer { name: "batch_normalization_3_scale" type: "Scale" bottom: "batch_normalization_3" top: "batch_normalization_3" scale_param { bias_term: true } } layer { name: "activation_3" type: "ReLU" bottom: "batch_normalization_3" top: "batch_normalization_3" } layer { name: "conv_class_11" type: "Convolution" bottom: "batch_normalization_3" top: "conv_class_11" convolution_param { num_output: 84 bias_term: true pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "prob" type: "Softmax" bottom: "conv_class_11" top: "prob" }