layer { name: "input_1" type: "Input" top: "input_1" input_param { shape { dim: 1 dim: 3 dim: 272 dim: 480 } } } layer { name: "conv1" type: "Convolution" bottom: "input_1" top: "conv1" convolution_param { num_output: 32 pad_h: 3 pad_w: 3 kernel_h: 7 kernel_w: 7 stride_h: 2 stride_w: 2 } } layer { name: "bn_conv1" type: "Scale" bottom: "conv1" top: "bn_conv1" scale_param { axis: 1 bias_term: true } } layer { name: "activation_1/Relu" type: "ReLU" bottom: "bn_conv1" top: "activation_1/Relu" } layer { name: "block_1a_conv_1" type: "Convolution" bottom: "activation_1/Relu" top: "block_1a_conv_1" convolution_param { num_output: 64 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 2 stride_w: 2 } } layer { name: "block_1a_conv_shortcut" type: "Convolution" bottom: "activation_1/Relu" top: "block_1a_conv_shortcut" convolution_param { num_output: 64 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 2 stride_w: 2 } } layer { name: "block_1a_bn_1" type: "Scale" bottom: "block_1a_conv_1" top: "block_1a_bn_1" scale_param { axis: 1 bias_term: true } } layer { name: "block_1a_bn_shortcut" type: "Scale" bottom: "block_1a_conv_shortcut" top: "block_1a_bn_shortcut" scale_param { axis: 1 bias_term: true } } layer { name: "activation_2/Relu" type: "ReLU" bottom: "block_1a_bn_1" top: "activation_2/Relu" } layer { name: "block_1a_conv_2" type: "Convolution" bottom: "activation_2/Relu" top: "block_1a_conv_2" convolution_param { num_output: 64 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "block_1a_bn_2" type: "Scale" bottom: "block_1a_conv_2" top: "block_1a_bn_2" scale_param { axis: 1 bias_term: true } } layer { name: "add_1" type: "Eltwise" bottom: "block_1a_bn_2" bottom: "block_1a_bn_shortcut" top: "add_1" eltwise_param { operation: SUM } } layer { name: "activation_3/Relu" type: "ReLU" bottom: "add_1" top: "activation_3/Relu" } layer { name: "block_2a_conv_1" type: "Convolution" bottom: "activation_3/Relu" top: "block_2a_conv_1" convolution_param { num_output: 128 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 2 stride_w: 2 } } layer { name: "block_2a_conv_shortcut" type: "Convolution" bottom: "activation_3/Relu" top: "block_2a_conv_shortcut" convolution_param { num_output: 128 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 2 stride_w: 2 } } layer { name: "block_2a_bn_1" type: "Scale" bottom: "block_2a_conv_1" top: "block_2a_bn_1" scale_param { axis: 1 bias_term: true } } layer { name: "block_2a_bn_shortcut" type: "Scale" bottom: "block_2a_conv_shortcut" top: "block_2a_bn_shortcut" scale_param { axis: 1 bias_term: true } } layer { name: "activation_4/Relu" type: "ReLU" bottom: "block_2a_bn_1" top: "activation_4/Relu" } layer { name: "block_2a_conv_2" type: "Convolution" bottom: "activation_4/Relu" top: "block_2a_conv_2" convolution_param { num_output: 128 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "block_2a_bn_2" type: "Scale" bottom: "block_2a_conv_2" top: "block_2a_bn_2" scale_param { axis: 1 bias_term: true } } layer { name: "add_2" type: "Eltwise" bottom: "block_2a_bn_2" bottom: "block_2a_bn_shortcut" top: "add_2" eltwise_param { operation: SUM } } layer { name: "activation_5/Relu" type: "ReLU" bottom: "add_2" top: "activation_5/Relu" } layer { name: "block_3a_conv_1" type: "Convolution" bottom: "activation_5/Relu" top: "block_3a_conv_1" convolution_param { num_output: 232 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 2 stride_w: 2 } } layer { name: "block_3a_conv_shortcut" type: "Convolution" bottom: "activation_5/Relu" top: "block_3a_conv_shortcut" convolution_param { num_output: 200 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 2 stride_w: 2 } } layer { name: "block_3a_bn_1" type: "Scale" bottom: "block_3a_conv_1" top: "block_3a_bn_1" scale_param { axis: 1 bias_term: true } } layer { name: "block_3a_bn_shortcut" type: "Scale" bottom: "block_3a_conv_shortcut" top: "block_3a_bn_shortcut" scale_param { axis: 1 bias_term: true } } layer { name: "activation_6/Relu" type: "ReLU" bottom: "block_3a_bn_1" top: "activation_6/Relu" } layer { name: "block_3a_conv_2" type: "Convolution" bottom: "activation_6/Relu" top: "block_3a_conv_2" convolution_param { num_output: 200 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "block_3a_bn_2" type: "Scale" bottom: "block_3a_conv_2" top: "block_3a_bn_2" scale_param { axis: 1 bias_term: true } } layer { name: "add_3" type: "Eltwise" bottom: "block_3a_bn_2" bottom: "block_3a_bn_shortcut" top: "add_3" eltwise_param { operation: SUM } } layer { name: "activation_7/Relu" type: "ReLU" bottom: "add_3" top: "activation_7/Relu" } layer { name: "block_4a_conv_1" type: "Convolution" bottom: "activation_7/Relu" top: "block_4a_conv_1" convolution_param { num_output: 152 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "block_4a_conv_shortcut" type: "Convolution" bottom: "activation_7/Relu" top: "block_4a_conv_shortcut" convolution_param { num_output: 176 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "block_4a_bn_1" type: "Scale" bottom: "block_4a_conv_1" top: "block_4a_bn_1" scale_param { axis: 1 bias_term: true } } layer { name: "block_4a_bn_shortcut" type: "Scale" bottom: "block_4a_conv_shortcut" top: "block_4a_bn_shortcut" scale_param { axis: 1 bias_term: true } } layer { name: "activation_8/Relu" type: "ReLU" bottom: "block_4a_bn_1" top: "activation_8/Relu" } layer { name: "block_4a_conv_2" type: "Convolution" bottom: "activation_8/Relu" top: "block_4a_conv_2" convolution_param { num_output: 176 pad_h: 1 pad_w: 1 kernel_h: 3 kernel_w: 3 stride_h: 1 stride_w: 1 } } layer { name: "block_4a_bn_2" type: "Scale" bottom: "block_4a_conv_2" top: "block_4a_bn_2" scale_param { axis: 1 bias_term: true } } layer { name: "add_4" type: "Eltwise" bottom: "block_4a_bn_2" bottom: "block_4a_bn_shortcut" top: "add_4" eltwise_param { operation: SUM } } layer { name: "activation_9/Relu" type: "ReLU" bottom: "add_4" top: "activation_9/Relu" } layer { name: "conv2d_bbox" type: "Convolution" bottom: "activation_9/Relu" top: "conv2d_bbox" convolution_param { num_output: 16 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "conv2d_cov" type: "Convolution" bottom: "activation_9/Relu" top: "conv2d_cov" convolution_param { num_output: 4 pad_h: 0 pad_w: 0 kernel_h: 1 kernel_w: 1 stride_h: 1 stride_w: 1 } } layer { name: "conv2d_cov/Sigmoid" type: "Sigmoid" bottom: "conv2d_cov" top: "conv2d_cov/Sigmoid" }