Open songjmcn opened 7 years ago
In order to detect the small object, I using the deconvolution layer upsamping the hyper layer output,so I must re define the proposal layer. But I find my parameter to work.My prototxt like this: name: "PVANET-lite"
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layer { name: "input-data" type: "DummyData" top: "data" top: "im_info" dummy_data_param { shape { dim: 1 dim: 3 dim: 960 dim: 1280 } shape { dim: 1 dim: 6 } } }
################################################################################ layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 4 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv1/bn" type: "BatchNorm" bottom: "conv1" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv1/scale" type: "Scale" bottom: "conv1" top: "conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" }
################################################################################ layer { name: "conv2" type: "Convolution" bottom: "conv1" top: "conv2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 48 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv2/bn" type: "BatchNorm" bottom: "conv2" top: "conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv2/scale" type: "Scale" bottom: "conv2" top: "conv2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" }
################################################################################ layer { name: "conv3" type: "Convolution" bottom: "conv2" top: "conv3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv3/bn" type: "BatchNorm" bottom: "conv3" top: "conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv3/scale" type: "Scale" bottom: "conv3" top: "conv3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" }
################################################################################ layer { name: "inc3a/pool1" type: "Pooling" bottom: "conv3" top: "inc3a/pool1" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "inc3a/conv1" type: "Convolution" bottom: "inc3a/pool1" top: "inc3a/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv1/bn" type: "BatchNorm" bottom: "inc3a/conv1" top: "inc3a/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv1/scale" type: "Scale" bottom: "inc3a/conv1" top: "inc3a/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu1" type: "ReLU" bottom: "inc3a/conv1" top: "inc3a/conv1" } layer { name: "inc3a/conv3_1" type: "Convolution" bottom: "conv3" top: "inc3a/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv3_1/bn" type: "BatchNorm" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv3_1/scale" type: "Scale" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu3_1" type: "ReLU" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" } layer { name: "inc3a/conv3_2" type: "Convolution" bottom: "inc3a/conv3_1" top: "inc3a/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv3_2/bn" type: "BatchNorm" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv3_2/scale" type: "Scale" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu3_2" type: "ReLU" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" } layer { name: "inc3a/conv5_1" type: "Convolution" bottom: "conv3" top: "inc3a/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_1/bn" type: "BatchNorm" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_1/scale" type: "Scale" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_1" type: "ReLU" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" } layer { name: "inc3a/conv5_2" type: "Convolution" bottom: "inc3a/conv5_1" top: "inc3a/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_2/bn" type: "BatchNorm" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_2/scale" type: "Scale" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_2" type: "ReLU" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" } layer { name: "inc3a/conv5_3" type: "Convolution" bottom: "inc3a/conv5_2" top: "inc3a/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_3/bn" type: "BatchNorm" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_3/scale" type: "Scale" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_3" type: "ReLU" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" } layer { name: "inc3a" type: "Concat" bottom: "inc3a/conv1" bottom: "inc3a/conv3_2" bottom: "inc3a/conv5_3" top: "inc3a" }
################################################################################ layer { name: "inc3b/conv1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv1/bn" type: "BatchNorm" bottom: "inc3b/conv1" top: "inc3b/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv1/scale" type: "Scale" bottom: "inc3b/conv1" top: "inc3b/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu1" type: "ReLU" bottom: "inc3b/conv1" top: "inc3b/conv1" } layer { name: "inc3b/conv3_1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv3_1/bn" type: "BatchNorm" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv3_1/scale" type: "Scale" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu3_1" type: "ReLU" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" } layer { name: "inc3b/conv3_2" type: "Convolution" bottom: "inc3b/conv3_1" top: "inc3b/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv3_2/bn" type: "BatchNorm" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv3_2/scale" type: "Scale" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu3_2" type: "ReLU" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" } layer { name: "inc3b/conv5_1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_1/bn" type: "BatchNorm" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_1/scale" type: "Scale" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_1" type: "ReLU" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" } layer { name: "inc3b/conv5_2" type: "Convolution" bottom: "inc3b/conv5_1" top: "inc3b/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_2/bn" type: "BatchNorm" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_2/scale" type: "Scale" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_2" type: "ReLU" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" } layer { name: "inc3b/conv5_3" type: "Convolution" bottom: "inc3b/conv5_2" top: "inc3b/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_3/bn" type: "BatchNorm" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_3/scale" type: "Scale" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_3" type: "ReLU" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" } layer { name: "inc3b" type: "Concat" bottom: "inc3b/conv1" bottom: "inc3b/conv3_2" bottom: "inc3b/conv5_3" top: "inc3b" }
################################################################################ layer { name: "inc3c/conv1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv1/bn" type: "BatchNorm" bottom: "inc3c/conv1" top: "inc3c/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv1/scale" type: "Scale" bottom: "inc3c/conv1" top: "inc3c/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu1" type: "ReLU" bottom: "inc3c/conv1" top: "inc3c/conv1" } layer { name: "inc3c/conv3_1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv3_1/bn" type: "BatchNorm" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv3_1/scale" type: "Scale" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu3_1" type: "ReLU" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" } layer { name: "inc3c/conv3_2" type: "Convolution" bottom: "inc3c/conv3_1" top: "inc3c/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv3_2/bn" type: "BatchNorm" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv3_2/scale" type: "Scale" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu3_2" type: "ReLU" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" } layer { name: "inc3c/conv5_1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_1/bn" type: "BatchNorm" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_1/scale" type: "Scale" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_1" type: "ReLU" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" } layer { name: "inc3c/conv5_2" type: "Convolution" bottom: "inc3c/conv5_1" top: "inc3c/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_2/bn" type: "BatchNorm" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_2/scale" type: "Scale" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_2" type: "ReLU" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" } layer { name: "inc3c/conv5_3" type: "Convolution" bottom: "inc3c/conv5_2" top: "inc3c/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_3/bn" type: "BatchNorm" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_3/scale" type: "Scale" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_3" type: "ReLU" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" } layer { name: "inc3c" type: "Concat" bottom: "inc3c/conv1" bottom: "inc3c/conv3_2" bottom: "inc3c/conv5_3" top: "inc3c" }
################################################################################ layer { name: "inc3d/conv1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv1/bn" type: "BatchNorm" bottom: "inc3d/conv1" top: "inc3d/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv1/scale" type: "Scale" bottom: "inc3d/conv1" top: "inc3d/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu1" type: "ReLU" bottom: "inc3d/conv1" top: "inc3d/conv1" } layer { name: "inc3d/conv3_1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv3_1/bn" type: "BatchNorm" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv3_1/scale" type: "Scale" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu3_1" type: "ReLU" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" } layer { name: "inc3d/conv3_2" type: "Convolution" bottom: "inc3d/conv3_1" top: "inc3d/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv3_2/bn" type: "BatchNorm" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv3_2/scale" type: "Scale" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu3_2" type: "ReLU" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" } layer { name: "inc3d/conv5_1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_1/bn" type: "BatchNorm" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_1/scale" type: "Scale" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_1" type: "ReLU" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" } layer { name: "inc3d/conv5_2" type: "Convolution" bottom: "inc3d/conv5_1" top: "inc3d/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_2/bn" type: "BatchNorm" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_2/scale" type: "Scale" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_2" type: "ReLU" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" } layer { name: "inc3d/conv5_3" type: "Convolution" bottom: "inc3d/conv5_2" top: "inc3d/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_3/bn" type: "BatchNorm" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_3/scale" type: "Scale" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_3" type: "ReLU" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" } layer { name: "inc3d" type: "Concat" bottom: "inc3d/conv1" bottom: "inc3d/conv3_2" bottom: "inc3d/conv5_3" top: "inc3d" }
################################################################################ layer { name: "inc3e/conv1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv1/bn" type: "BatchNorm" bottom: "inc3e/conv1" top: "inc3e/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv1/scale" type: "Scale" bottom: "inc3e/conv1" top: "inc3e/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu1" type: "ReLU" bottom: "inc3e/conv1" top: "inc3e/conv1" } layer { name: "inc3e/conv3_1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv3_1/bn" type: "BatchNorm" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv3_1/scale" type: "Scale" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu3_1" type: "ReLU" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" } layer { name: "inc3e/conv3_2" type: "Convolution" bottom: "inc3e/conv3_1" top: "inc3e/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv3_2/bn" type: "BatchNorm" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv3_2/scale" type: "Scale" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu3_2" type: "ReLU" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" } layer { name: "inc3e/conv5_1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_1/bn" type: "BatchNorm" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_1/scale" type: "Scale" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_1" type: "ReLU" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" } layer { name: "inc3e/conv5_2" type: "Convolution" bottom: "inc3e/conv5_1" top: "inc3e/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_2/bn" type: "BatchNorm" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_2/scale" type: "Scale" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_2" type: "ReLU" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" } layer { name: "inc3e/conv5_3" type: "Convolution" bottom: "inc3e/conv5_2" top: "inc3e/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_3/bn" type: "BatchNorm" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_3/scale" type: "Scale" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_3" type: "ReLU" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" } layer { name: "inc3e" type: "Concat" bottom: "inc3e/conv1" bottom: "inc3e/conv3_2" bottom: "inc3e/conv5_3" top: "inc3e" }
################################################################################ layer { name: "inc4a/pool1" type: "Pooling" bottom: "inc3e" top: "inc4a/pool1" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "inc4a/conv1" type: "Convolution" bottom: "inc4a/pool1" top: "inc4a/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv1/bn" type: "BatchNorm" bottom: "inc4a/conv1" top: "inc4a/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv1/scale" type: "Scale" bottom: "inc4a/conv1" top: "inc4a/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu1" type: "ReLU" bottom: "inc4a/conv1" top: "inc4a/conv1" } layer { name: "inc4a/conv3_1" type: "Convolution" bottom: "inc3e" top: "inc4a/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv3_1/bn" type: "BatchNorm" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv3_1/scale" type: "Scale" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu3_1" type: "ReLU" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" } layer { name: "inc4a/conv3_2" type: "Convolution" bottom: "inc4a/conv3_1" top: "inc4a/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv3_2/bn" type: "BatchNorm" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv3_2/scale" type: "Scale" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu3_2" type: "ReLU" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" } layer { name: "inc4a/conv5_1" type: "Convolution" bottom: "inc3e" top: "inc4a/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_1/bn" type: "BatchNorm" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_1/scale" type: "Scale" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_1" type: "ReLU" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" } layer { name: "inc4a/conv5_2" type: "Convolution" bottom: "inc4a/conv5_1" top: "inc4a/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_2/bn" type: "BatchNorm" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_2/scale" type: "Scale" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_2" type: "ReLU" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" } layer { name: "inc4a/conv5_3" type: "Convolution" bottom: "inc4a/conv5_2" top: "inc4a/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_3/bn" type: "BatchNorm" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_3/scale" type: "Scale" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_3" type: "ReLU" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" } layer { name: "inc4a" type: "Concat" bottom: "inc4a/conv1" bottom: "inc4a/conv3_2" bottom: "inc4a/conv5_3" top: "inc4a" }
################################################################################ layer { name: "inc4b/conv1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv1/bn" type: "BatchNorm" bottom: "inc4b/conv1" top: "inc4b/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv1/scale" type: "Scale" bottom: "inc4b/conv1" top: "inc4b/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu1" type: "ReLU" bottom: "inc4b/conv1" top: "inc4b/conv1" } layer { name: "inc4b/conv3_1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv3_1/bn" type: "BatchNorm" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv3_1/scale" type: "Scale" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu3_1" type: "ReLU" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" } layer { name: "inc4b/conv3_2" type: "Convolution" bottom: "inc4b/conv3_1" top: "inc4b/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv3_2/bn" type: "BatchNorm" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv3_2/scale" type: "Scale" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu3_2" type: "ReLU" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" } layer { name: "inc4b/conv5_1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_1/bn" type: "BatchNorm" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_1/scale" type: "Scale" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_1" type: "ReLU" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" } layer { name: "inc4b/conv5_2" type: "Convolution" bottom: "inc4b/conv5_1" top: "inc4b/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_2/bn" type: "BatchNorm" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_2/scale" type: "Scale" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_2" type: "ReLU" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" } layer { name: "inc4b/conv5_3" type: "Convolution" bottom: "inc4b/conv5_2" top: "inc4b/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_3/bn" type: "BatchNorm" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_3/scale" type: "Scale" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_3" type: "ReLU" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" } layer { name: "inc4b" type: "Concat" bottom: "inc4b/conv1" bottom: "inc4b/conv3_2" bottom: "inc4b/conv5_3" top: "inc4b" }
################################################################################ layer { name: "inc4c/conv1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv1/bn" type: "BatchNorm" bottom: "inc4c/conv1" top: "inc4c/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv1/scale" type: "Scale" bottom: "inc4c/conv1" top: "inc4c/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu1" type: "ReLU" bottom: "inc4c/conv1" top: "inc4c/conv1" } layer { name: "inc4c/conv3_1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv3_1/bn" type: "BatchNorm" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv3_1/scale" type: "Scale" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu3_1" type: "ReLU" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" } layer { name: "inc4c/conv3_2" type: "Convolution" bottom: "inc4c/conv3_1" top: "inc4c/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv3_2/bn" type: "BatchNorm" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv3_2/scale" type: "Scale" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu3_2" type: "ReLU" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" } layer { name: "inc4c/conv5_1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_1/bn" type: "BatchNorm" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_1/scale" type: "Scale" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_1" type: "ReLU" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" } layer { name: "inc4c/conv5_2" type: "Convolution" bottom: "inc4c/conv5_1" top: "inc4c/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_2/bn" type: "BatchNorm" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_2/scale" type: "Scale" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_2" type: "ReLU" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" } layer { name: "inc4c/conv5_3" type: "Convolution" bottom: "inc4c/conv5_2" top: "inc4c/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_3/bn" type: "BatchNorm" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_3/scale" type: "Scale" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_3" type: "ReLU" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" } layer { name: "inc4c" type: "Concat" bottom: "inc4c/conv1" bottom: "inc4c/conv3_2" bottom: "inc4c/conv5_3" top: "inc4c" }
################################################################################ layer { name: "inc4d/conv1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv1/bn" type: "BatchNorm" bottom: "inc4d/conv1" top: "inc4d/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv1/scale" type: "Scale" bottom: "inc4d/conv1" top: "inc4d/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu1" type: "ReLU" bottom: "inc4d/conv1" top: "inc4d/conv1" } layer { name: "inc4d/conv3_1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv3_1/bn" type: "BatchNorm" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv3_1/scale" type: "Scale" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu3_1" type: "ReLU" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" } layer { name: "inc4d/conv3_2" type: "Convolution" bottom: "inc4d/conv3_1" top: "inc4d/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv3_2/bn" type: "BatchNorm" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv3_2/scale" type: "Scale" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu3_2" type: "ReLU" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" } layer { name: "inc4d/conv5_1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_1/bn" type: "BatchNorm" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_1/scale" type: "Scale" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_1" type: "ReLU" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" } layer { name: "inc4d/conv5_2" type: "Convolution" bottom: "inc4d/conv5_1" top: "inc4d/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_2/bn" type: "BatchNorm" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_2/scale" type: "Scale" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_2" type: "ReLU" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" } layer { name: "inc4d/conv5_3" type: "Convolution" bottom: "inc4d/conv5_2" top: "inc4d/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_3/bn" type: "BatchNorm" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_3/scale" type: "Scale" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_3" type: "ReLU" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" } layer { name: "inc4d" type: "Concat" bottom: "inc4d/conv1" bottom: "inc4d/conv3_2" bottom: "inc4d/conv5_3" top: "inc4d" }
################################################################################ layer { name: "inc4e/conv1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv1/bn" type: "BatchNorm" bottom: "inc4e/conv1" top: "inc4e/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv1/scale" type: "Scale" bottom: "inc4e/conv1" top: "inc4e/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu1" type: "ReLU" bottom: "inc4e/conv1" top: "inc4e/conv1" } layer { name: "inc4e/conv3_1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv3_1/bn" type: "BatchNorm" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv3_1/scale" type: "Scale" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu3_1" type: "ReLU" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" } layer { name: "inc4e/conv3_2" type: "Convolution" bottom: "inc4e/conv3_1" top: "inc4e/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv3_2/bn" type: "BatchNorm" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv3_2/scale" type: "Scale" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu3_2" type: "ReLU" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" } layer { name: "inc4e/conv5_1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_1/bn" type: "BatchNorm" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_1/scale" type: "Scale" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_1" type: "ReLU" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" } layer { name: "inc4e/conv5_2" type: "Convolution" bottom: "inc4e/conv5_1" top: "inc4e/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_2/bn" type: "BatchNorm" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_2/scale" type: "Scale" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_2" type: "ReLU" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" } layer { name: "inc4e/conv5_3" type: "Convolution" bottom: "inc4e/conv5_2" top: "inc4e/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_3/bn" type: "BatchNorm" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_3/scale" type: "Scale" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_3" type: "ReLU" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" } layer { name: "inc4e" type: "Concat" bottom: "inc4e/conv1" bottom: "inc4e/conv3_2" bottom: "inc4e/conv5_3" top: "inc4e" }
################################################################################ layer { name: "downsample" type: "Pooling" bottom: "conv3" top: "downsample" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "upsample" type: "Deconvolution" bottom: "inc4e" top: "upsample" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 4 stride: 2 pad: 1 group: 256 weight_filler: { type: "bilinear" } bias_term: false } } layer { name: "concat" type: "Concat" bottom: "downsample" bottom: "inc3e" bottom: "upsample" top: "concat" concat_param { axis: 1 } } layer { name: "convf" type: "Convolution" bottom: "concat" top: "convf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "reluf" type: "ReLU" bottom: "convf" top: "convf" } layer { name: "upsample-2x" type: "Deconvolution" bottom: "convf" top: "upsample-2x" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 4 stride: 2 pad: 1 group: 256 weight_filler: { type: "bilinear" } bias_term: false } }
layer { name: "rpn_conv1" type: "Convolution" bottom: "upsample-2x" top: "rpn_conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu1" type: "ReLU" bottom: "rpn_conv1" top: "rpn_conv1" }
layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn_conv1" top: "rpn_cls_score" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 50 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1" top: "rpn_bbox_pred" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 100 kernel_size: 1 stride: 1 pad: 0 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_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: 50 dim: -1 dim: 0 } } }
layer { name: 'proposal' type: 'Proposal' bottom: 'rpn_cls_prob_reshape' bottom: 'rpn_bbox_pred' bottom: 'im_info' top: 'rois' top: 'scores'
proposal_param { ratio: 0.5 ratio: 0.667 ratio: 1.0 ratio: 1.5 ratio: 2.0 scale: 3 scale: 6 scale: 9 scale: 16 scale: 32 base_size: 8 feat_stride: 8 pre_nms_topn: 6000 post_nms_topn: 400 nms_thresh: 0.3 min_size: 8 } }
################################################################################ layer { name: "roi_pool_conv5" type: "ROIPooling" bottom: "upsample-2x" bottom: "rois" top: "roi_pool_conv5" roi_pooling_param { pooled_w: 6 pooled_h: 6 spatial_scale: 0.125# 1/16 } } layer { name: "fc6_L" type: "InnerProduct" bottom: "roi_pool_conv5" top: "fc6_L" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 512 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "fc6_U" type: "InnerProduct" bottom: "fc6_L" top: "fc6_U" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "relu6" type: "ReLU" bottom: "fc6_U" top: "fc6_U" }
################################################################################ layer { name: "fc7_L" type: "InnerProduct" bottom: "fc6_U" top: "fc7_L" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 128 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "fc7_U" type: "InnerProduct" bottom: "fc7_L" top: "fc7_U" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "relu7" type: "ReLU" bottom: "fc7_U" top: "fc7_U" }
################################################################################ layer { name: "cls_score" type: "InnerProduct" bottom: "fc7_U" top: "cls_score" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 21 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "bbox_pred" type: "InnerProduct" bottom: "fc7_U" top: "bbox_pred" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 84 weight_filler { type: "gaussian" std: 0.001 } bias_filler { type: "constant" value: 0 } } } layer { name: "cls_prob" type: "Softmax" bottom: "cls_score" top: "cls_prob" loss_param { ignore_label: -1 normalize: true } } What's the probolem about my propotxt
In order to detect the small object, I using the deconvolution layer upsamping the hyper layer output,so I must re define the proposal layer. But I find my parameter to work.My prototxt like this: name: "PVANET-lite"
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Input
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layer { name: "input-data" type: "DummyData" top: "data" top: "im_info" dummy_data_param { shape { dim: 1 dim: 3 dim: 960 dim: 1280 } shape { dim: 1 dim: 6 } } }
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Conv 1
################################################################################ layer { name: "conv1" type: "Convolution" bottom: "data" top: "conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 4 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv1/bn" type: "BatchNorm" bottom: "conv1" top: "conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv1/scale" type: "Scale" bottom: "conv1" top: "conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu1" type: "ReLU" bottom: "conv1" top: "conv1" }
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Conv 2
################################################################################ layer { name: "conv2" type: "Convolution" bottom: "conv1" top: "conv2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 48 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv2/bn" type: "BatchNorm" bottom: "conv2" top: "conv2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv2/scale" type: "Scale" bottom: "conv2" top: "conv2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu2" type: "ReLU" bottom: "conv2" top: "conv2" }
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Conv 3
################################################################################ layer { name: "conv3" type: "Convolution" bottom: "conv2" top: "conv3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "conv3/bn" type: "BatchNorm" bottom: "conv3" top: "conv3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "conv3/scale" type: "Scale" bottom: "conv3" top: "conv3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "relu3" type: "ReLU" bottom: "conv3" top: "conv3" }
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Inception 3a
################################################################################ layer { name: "inc3a/pool1" type: "Pooling" bottom: "conv3" top: "inc3a/pool1" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "inc3a/conv1" type: "Convolution" bottom: "inc3a/pool1" top: "inc3a/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv1/bn" type: "BatchNorm" bottom: "inc3a/conv1" top: "inc3a/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv1/scale" type: "Scale" bottom: "inc3a/conv1" top: "inc3a/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu1" type: "ReLU" bottom: "inc3a/conv1" top: "inc3a/conv1" } layer { name: "inc3a/conv3_1" type: "Convolution" bottom: "conv3" top: "inc3a/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv3_1/bn" type: "BatchNorm" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv3_1/scale" type: "Scale" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu3_1" type: "ReLU" bottom: "inc3a/conv3_1" top: "inc3a/conv3_1" } layer { name: "inc3a/conv3_2" type: "Convolution" bottom: "inc3a/conv3_1" top: "inc3a/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv3_2/bn" type: "BatchNorm" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv3_2/scale" type: "Scale" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu3_2" type: "ReLU" bottom: "inc3a/conv3_2" top: "inc3a/conv3_2" } layer { name: "inc3a/conv5_1" type: "Convolution" bottom: "conv3" top: "inc3a/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_1/bn" type: "BatchNorm" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_1/scale" type: "Scale" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_1" type: "ReLU" bottom: "inc3a/conv5_1" top: "inc3a/conv5_1" } layer { name: "inc3a/conv5_2" type: "Convolution" bottom: "inc3a/conv5_1" top: "inc3a/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_2/bn" type: "BatchNorm" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_2/scale" type: "Scale" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_2" type: "ReLU" bottom: "inc3a/conv5_2" top: "inc3a/conv5_2" } layer { name: "inc3a/conv5_3" type: "Convolution" bottom: "inc3a/conv5_2" top: "inc3a/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3a/conv5_3/bn" type: "BatchNorm" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3a/conv5_3/scale" type: "Scale" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3a/relu5_3" type: "ReLU" bottom: "inc3a/conv5_3" top: "inc3a/conv5_3" } layer { name: "inc3a" type: "Concat" bottom: "inc3a/conv1" bottom: "inc3a/conv3_2" bottom: "inc3a/conv5_3" top: "inc3a" }
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Inception 3b
################################################################################ layer { name: "inc3b/conv1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv1/bn" type: "BatchNorm" bottom: "inc3b/conv1" top: "inc3b/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv1/scale" type: "Scale" bottom: "inc3b/conv1" top: "inc3b/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu1" type: "ReLU" bottom: "inc3b/conv1" top: "inc3b/conv1" } layer { name: "inc3b/conv3_1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv3_1/bn" type: "BatchNorm" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv3_1/scale" type: "Scale" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu3_1" type: "ReLU" bottom: "inc3b/conv3_1" top: "inc3b/conv3_1" } layer { name: "inc3b/conv3_2" type: "Convolution" bottom: "inc3b/conv3_1" top: "inc3b/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv3_2/bn" type: "BatchNorm" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv3_2/scale" type: "Scale" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu3_2" type: "ReLU" bottom: "inc3b/conv3_2" top: "inc3b/conv3_2" } layer { name: "inc3b/conv5_1" type: "Convolution" bottom: "inc3a" top: "inc3b/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_1/bn" type: "BatchNorm" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_1/scale" type: "Scale" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_1" type: "ReLU" bottom: "inc3b/conv5_1" top: "inc3b/conv5_1" } layer { name: "inc3b/conv5_2" type: "Convolution" bottom: "inc3b/conv5_1" top: "inc3b/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_2/bn" type: "BatchNorm" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_2/scale" type: "Scale" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_2" type: "ReLU" bottom: "inc3b/conv5_2" top: "inc3b/conv5_2" } layer { name: "inc3b/conv5_3" type: "Convolution" bottom: "inc3b/conv5_2" top: "inc3b/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3b/conv5_3/bn" type: "BatchNorm" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3b/conv5_3/scale" type: "Scale" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3b/relu5_3" type: "ReLU" bottom: "inc3b/conv5_3" top: "inc3b/conv5_3" } layer { name: "inc3b" type: "Concat" bottom: "inc3b/conv1" bottom: "inc3b/conv3_2" bottom: "inc3b/conv5_3" top: "inc3b" }
################################################################################
Inception 3c
################################################################################ layer { name: "inc3c/conv1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv1/bn" type: "BatchNorm" bottom: "inc3c/conv1" top: "inc3c/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv1/scale" type: "Scale" bottom: "inc3c/conv1" top: "inc3c/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu1" type: "ReLU" bottom: "inc3c/conv1" top: "inc3c/conv1" } layer { name: "inc3c/conv3_1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv3_1/bn" type: "BatchNorm" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv3_1/scale" type: "Scale" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu3_1" type: "ReLU" bottom: "inc3c/conv3_1" top: "inc3c/conv3_1" } layer { name: "inc3c/conv3_2" type: "Convolution" bottom: "inc3c/conv3_1" top: "inc3c/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv3_2/bn" type: "BatchNorm" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv3_2/scale" type: "Scale" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu3_2" type: "ReLU" bottom: "inc3c/conv3_2" top: "inc3c/conv3_2" } layer { name: "inc3c/conv5_1" type: "Convolution" bottom: "inc3b" top: "inc3c/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_1/bn" type: "BatchNorm" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_1/scale" type: "Scale" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_1" type: "ReLU" bottom: "inc3c/conv5_1" top: "inc3c/conv5_1" } layer { name: "inc3c/conv5_2" type: "Convolution" bottom: "inc3c/conv5_1" top: "inc3c/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_2/bn" type: "BatchNorm" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_2/scale" type: "Scale" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_2" type: "ReLU" bottom: "inc3c/conv5_2" top: "inc3c/conv5_2" } layer { name: "inc3c/conv5_3" type: "Convolution" bottom: "inc3c/conv5_2" top: "inc3c/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3c/conv5_3/bn" type: "BatchNorm" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3c/conv5_3/scale" type: "Scale" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3c/relu5_3" type: "ReLU" bottom: "inc3c/conv5_3" top: "inc3c/conv5_3" } layer { name: "inc3c" type: "Concat" bottom: "inc3c/conv1" bottom: "inc3c/conv3_2" bottom: "inc3c/conv5_3" top: "inc3c" }
################################################################################
Inception 3d
################################################################################ layer { name: "inc3d/conv1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv1/bn" type: "BatchNorm" bottom: "inc3d/conv1" top: "inc3d/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv1/scale" type: "Scale" bottom: "inc3d/conv1" top: "inc3d/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu1" type: "ReLU" bottom: "inc3d/conv1" top: "inc3d/conv1" } layer { name: "inc3d/conv3_1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv3_1/bn" type: "BatchNorm" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv3_1/scale" type: "Scale" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu3_1" type: "ReLU" bottom: "inc3d/conv3_1" top: "inc3d/conv3_1" } layer { name: "inc3d/conv3_2" type: "Convolution" bottom: "inc3d/conv3_1" top: "inc3d/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv3_2/bn" type: "BatchNorm" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv3_2/scale" type: "Scale" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu3_2" type: "ReLU" bottom: "inc3d/conv3_2" top: "inc3d/conv3_2" } layer { name: "inc3d/conv5_1" type: "Convolution" bottom: "inc3c" top: "inc3d/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_1/bn" type: "BatchNorm" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_1/scale" type: "Scale" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_1" type: "ReLU" bottom: "inc3d/conv5_1" top: "inc3d/conv5_1" } layer { name: "inc3d/conv5_2" type: "Convolution" bottom: "inc3d/conv5_1" top: "inc3d/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_2/bn" type: "BatchNorm" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_2/scale" type: "Scale" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_2" type: "ReLU" bottom: "inc3d/conv5_2" top: "inc3d/conv5_2" } layer { name: "inc3d/conv5_3" type: "Convolution" bottom: "inc3d/conv5_2" top: "inc3d/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3d/conv5_3/bn" type: "BatchNorm" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3d/conv5_3/scale" type: "Scale" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3d/relu5_3" type: "ReLU" bottom: "inc3d/conv5_3" top: "inc3d/conv5_3" } layer { name: "inc3d" type: "Concat" bottom: "inc3d/conv1" bottom: "inc3d/conv3_2" bottom: "inc3d/conv5_3" top: "inc3d" }
################################################################################
Inception 3e
################################################################################ layer { name: "inc3e/conv1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv1/bn" type: "BatchNorm" bottom: "inc3e/conv1" top: "inc3e/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv1/scale" type: "Scale" bottom: "inc3e/conv1" top: "inc3e/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu1" type: "ReLU" bottom: "inc3e/conv1" top: "inc3e/conv1" } layer { name: "inc3e/conv3_1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv3_1/bn" type: "BatchNorm" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv3_1/scale" type: "Scale" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu3_1" type: "ReLU" bottom: "inc3e/conv3_1" top: "inc3e/conv3_1" } layer { name: "inc3e/conv3_2" type: "Convolution" bottom: "inc3e/conv3_1" top: "inc3e/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 64 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv3_2/bn" type: "BatchNorm" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv3_2/scale" type: "Scale" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu3_2" type: "ReLU" bottom: "inc3e/conv3_2" top: "inc3e/conv3_2" } layer { name: "inc3e/conv5_1" type: "Convolution" bottom: "inc3d" top: "inc3e/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_1/bn" type: "BatchNorm" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_1/scale" type: "Scale" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_1" type: "ReLU" bottom: "inc3e/conv5_1" top: "inc3e/conv5_1" } layer { name: "inc3e/conv5_2" type: "Convolution" bottom: "inc3e/conv5_1" top: "inc3e/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_2/bn" type: "BatchNorm" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_2/scale" type: "Scale" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_2" type: "ReLU" bottom: "inc3e/conv5_2" top: "inc3e/conv5_2" } layer { name: "inc3e/conv5_3" type: "Convolution" bottom: "inc3e/conv5_2" top: "inc3e/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc3e/conv5_3/bn" type: "BatchNorm" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc3e/conv5_3/scale" type: "Scale" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc3e/relu5_3" type: "ReLU" bottom: "inc3e/conv5_3" top: "inc3e/conv5_3" } layer { name: "inc3e" type: "Concat" bottom: "inc3e/conv1" bottom: "inc3e/conv3_2" bottom: "inc3e/conv5_3" top: "inc3e" }
################################################################################
Inception 4a
################################################################################ layer { name: "inc4a/pool1" type: "Pooling" bottom: "inc3e" top: "inc4a/pool1" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "inc4a/conv1" type: "Convolution" bottom: "inc4a/pool1" top: "inc4a/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv1/bn" type: "BatchNorm" bottom: "inc4a/conv1" top: "inc4a/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv1/scale" type: "Scale" bottom: "inc4a/conv1" top: "inc4a/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu1" type: "ReLU" bottom: "inc4a/conv1" top: "inc4a/conv1" } layer { name: "inc4a/conv3_1" type: "Convolution" bottom: "inc3e" top: "inc4a/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv3_1/bn" type: "BatchNorm" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv3_1/scale" type: "Scale" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu3_1" type: "ReLU" bottom: "inc4a/conv3_1" top: "inc4a/conv3_1" } layer { name: "inc4a/conv3_2" type: "Convolution" bottom: "inc4a/conv3_1" top: "inc4a/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv3_2/bn" type: "BatchNorm" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv3_2/scale" type: "Scale" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu3_2" type: "ReLU" bottom: "inc4a/conv3_2" top: "inc4a/conv3_2" } layer { name: "inc4a/conv5_1" type: "Convolution" bottom: "inc3e" top: "inc4a/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_1/bn" type: "BatchNorm" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_1/scale" type: "Scale" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_1" type: "ReLU" bottom: "inc4a/conv5_1" top: "inc4a/conv5_1" } layer { name: "inc4a/conv5_2" type: "Convolution" bottom: "inc4a/conv5_1" top: "inc4a/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_2/bn" type: "BatchNorm" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_2/scale" type: "Scale" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_2" type: "ReLU" bottom: "inc4a/conv5_2" top: "inc4a/conv5_2" } layer { name: "inc4a/conv5_3" type: "Convolution" bottom: "inc4a/conv5_2" top: "inc4a/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 stride: 2 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4a/conv5_3/bn" type: "BatchNorm" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4a/conv5_3/scale" type: "Scale" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4a/relu5_3" type: "ReLU" bottom: "inc4a/conv5_3" top: "inc4a/conv5_3" } layer { name: "inc4a" type: "Concat" bottom: "inc4a/conv1" bottom: "inc4a/conv3_2" bottom: "inc4a/conv5_3" top: "inc4a" }
################################################################################
Inception 4b
################################################################################ layer { name: "inc4b/conv1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv1/bn" type: "BatchNorm" bottom: "inc4b/conv1" top: "inc4b/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv1/scale" type: "Scale" bottom: "inc4b/conv1" top: "inc4b/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu1" type: "ReLU" bottom: "inc4b/conv1" top: "inc4b/conv1" } layer { name: "inc4b/conv3_1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv3_1/bn" type: "BatchNorm" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv3_1/scale" type: "Scale" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu3_1" type: "ReLU" bottom: "inc4b/conv3_1" top: "inc4b/conv3_1" } layer { name: "inc4b/conv3_2" type: "Convolution" bottom: "inc4b/conv3_1" top: "inc4b/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv3_2/bn" type: "BatchNorm" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv3_2/scale" type: "Scale" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu3_2" type: "ReLU" bottom: "inc4b/conv3_2" top: "inc4b/conv3_2" } layer { name: "inc4b/conv5_1" type: "Convolution" bottom: "inc4a" top: "inc4b/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_1/bn" type: "BatchNorm" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_1/scale" type: "Scale" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_1" type: "ReLU" bottom: "inc4b/conv5_1" top: "inc4b/conv5_1" } layer { name: "inc4b/conv5_2" type: "Convolution" bottom: "inc4b/conv5_1" top: "inc4b/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_2/bn" type: "BatchNorm" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_2/scale" type: "Scale" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_2" type: "ReLU" bottom: "inc4b/conv5_2" top: "inc4b/conv5_2" } layer { name: "inc4b/conv5_3" type: "Convolution" bottom: "inc4b/conv5_2" top: "inc4b/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4b/conv5_3/bn" type: "BatchNorm" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4b/conv5_3/scale" type: "Scale" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4b/relu5_3" type: "ReLU" bottom: "inc4b/conv5_3" top: "inc4b/conv5_3" } layer { name: "inc4b" type: "Concat" bottom: "inc4b/conv1" bottom: "inc4b/conv3_2" bottom: "inc4b/conv5_3" top: "inc4b" }
################################################################################
Inception 4c
################################################################################ layer { name: "inc4c/conv1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv1/bn" type: "BatchNorm" bottom: "inc4c/conv1" top: "inc4c/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv1/scale" type: "Scale" bottom: "inc4c/conv1" top: "inc4c/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu1" type: "ReLU" bottom: "inc4c/conv1" top: "inc4c/conv1" } layer { name: "inc4c/conv3_1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv3_1/bn" type: "BatchNorm" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv3_1/scale" type: "Scale" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu3_1" type: "ReLU" bottom: "inc4c/conv3_1" top: "inc4c/conv3_1" } layer { name: "inc4c/conv3_2" type: "Convolution" bottom: "inc4c/conv3_1" top: "inc4c/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv3_2/bn" type: "BatchNorm" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv3_2/scale" type: "Scale" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu3_2" type: "ReLU" bottom: "inc4c/conv3_2" top: "inc4c/conv3_2" } layer { name: "inc4c/conv5_1" type: "Convolution" bottom: "inc4b" top: "inc4c/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_1/bn" type: "BatchNorm" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_1/scale" type: "Scale" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_1" type: "ReLU" bottom: "inc4c/conv5_1" top: "inc4c/conv5_1" } layer { name: "inc4c/conv5_2" type: "Convolution" bottom: "inc4c/conv5_1" top: "inc4c/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_2/bn" type: "BatchNorm" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_2/scale" type: "Scale" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_2" type: "ReLU" bottom: "inc4c/conv5_2" top: "inc4c/conv5_2" } layer { name: "inc4c/conv5_3" type: "Convolution" bottom: "inc4c/conv5_2" top: "inc4c/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4c/conv5_3/bn" type: "BatchNorm" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4c/conv5_3/scale" type: "Scale" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4c/relu5_3" type: "ReLU" bottom: "inc4c/conv5_3" top: "inc4c/conv5_3" } layer { name: "inc4c" type: "Concat" bottom: "inc4c/conv1" bottom: "inc4c/conv3_2" bottom: "inc4c/conv5_3" top: "inc4c" }
################################################################################
Inception 4d
################################################################################ layer { name: "inc4d/conv1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv1/bn" type: "BatchNorm" bottom: "inc4d/conv1" top: "inc4d/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv1/scale" type: "Scale" bottom: "inc4d/conv1" top: "inc4d/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu1" type: "ReLU" bottom: "inc4d/conv1" top: "inc4d/conv1" } layer { name: "inc4d/conv3_1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv3_1/bn" type: "BatchNorm" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv3_1/scale" type: "Scale" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu3_1" type: "ReLU" bottom: "inc4d/conv3_1" top: "inc4d/conv3_1" } layer { name: "inc4d/conv3_2" type: "Convolution" bottom: "inc4d/conv3_1" top: "inc4d/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv3_2/bn" type: "BatchNorm" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv3_2/scale" type: "Scale" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu3_2" type: "ReLU" bottom: "inc4d/conv3_2" top: "inc4d/conv3_2" } layer { name: "inc4d/conv5_1" type: "Convolution" bottom: "inc4c" top: "inc4d/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_1/bn" type: "BatchNorm" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_1/scale" type: "Scale" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_1" type: "ReLU" bottom: "inc4d/conv5_1" top: "inc4d/conv5_1" } layer { name: "inc4d/conv5_2" type: "Convolution" bottom: "inc4d/conv5_1" top: "inc4d/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_2/bn" type: "BatchNorm" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_2/scale" type: "Scale" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_2" type: "ReLU" bottom: "inc4d/conv5_2" top: "inc4d/conv5_2" } layer { name: "inc4d/conv5_3" type: "Convolution" bottom: "inc4d/conv5_2" top: "inc4d/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4d/conv5_3/bn" type: "BatchNorm" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4d/conv5_3/scale" type: "Scale" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4d/relu5_3" type: "ReLU" bottom: "inc4d/conv5_3" top: "inc4d/conv5_3" } layer { name: "inc4d" type: "Concat" bottom: "inc4d/conv1" bottom: "inc4d/conv3_2" bottom: "inc4d/conv5_3" top: "inc4d" }
################################################################################
Inception 4e
################################################################################ layer { name: "inc4e/conv1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 128 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv1/bn" type: "BatchNorm" bottom: "inc4e/conv1" top: "inc4e/conv1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv1/scale" type: "Scale" bottom: "inc4e/conv1" top: "inc4e/conv1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu1" type: "ReLU" bottom: "inc4e/conv1" top: "inc4e/conv1" } layer { name: "inc4e/conv3_1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv3_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv3_1/bn" type: "BatchNorm" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv3_1/scale" type: "Scale" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu3_1" type: "ReLU" bottom: "inc4e/conv3_1" top: "inc4e/conv3_1" } layer { name: "inc4e/conv3_2" type: "Convolution" bottom: "inc4e/conv3_1" top: "inc4e/conv3_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 96 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv3_2/bn" type: "BatchNorm" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv3_2/scale" type: "Scale" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu3_2" type: "ReLU" bottom: "inc4e/conv3_2" top: "inc4e/conv3_2" } layer { name: "inc4e/conv5_1" type: "Convolution" bottom: "inc4d" top: "inc4e/conv5_1" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 16 kernel_size: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_1/bn" type: "BatchNorm" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_1/scale" type: "Scale" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_1" type: "ReLU" bottom: "inc4e/conv5_1" top: "inc4e/conv5_1" } layer { name: "inc4e/conv5_2" type: "Convolution" bottom: "inc4e/conv5_1" top: "inc4e/conv5_2" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_2/bn" type: "BatchNorm" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_2/scale" type: "Scale" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_2" type: "ReLU" bottom: "inc4e/conv5_2" top: "inc4e/conv5_2" } layer { name: "inc4e/conv5_3" type: "Convolution" bottom: "inc4e/conv5_2" top: "inc4e/conv5_3" param { lr_mult: 0.1 decay_mult: 0.1 } param { lr_mult: 0.2 decay_mult: 0 } convolution_param { num_output: 32 kernel_size: 3 pad: 1 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "inc4e/conv5_3/bn" type: "BatchNorm" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } param { lr_mult: 0 decay_mult: 0 } batch_norm_param { use_global_stats: true } } layer { name: "inc4e/conv5_3/scale" type: "Scale" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" param { lr_mult: 0.1 decay_mult: 0 } param { lr_mult: 0.1 decay_mult: 0 } scale_param { bias_term: true } } layer { name: "inc4e/relu5_3" type: "ReLU" bottom: "inc4e/conv5_3" top: "inc4e/conv5_3" } layer { name: "inc4e" type: "Concat" bottom: "inc4e/conv1" bottom: "inc4e/conv3_2" bottom: "inc4e/conv5_3" top: "inc4e" }
################################################################################
hyper feature
################################################################################ layer { name: "downsample" type: "Pooling" bottom: "conv3" top: "downsample" pooling_param { kernel_size: 3 stride: 2 pad: 0 pool: MAX } } layer { name: "upsample" type: "Deconvolution" bottom: "inc4e" top: "upsample" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 4 stride: 2 pad: 1 group: 256 weight_filler: { type: "bilinear" } bias_term: false } } layer { name: "concat" type: "Concat" bottom: "downsample" bottom: "inc3e" bottom: "upsample" top: "concat" concat_param { axis: 1 } } layer { name: "convf" type: "Convolution" bottom: "concat" top: "convf" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "xavier" std: 0.1 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "reluf" type: "ReLU" bottom: "convf" top: "convf" } layer { name: "upsample-2x" type: "Deconvolution" bottom: "convf" top: "upsample-2x" param { lr_mult: 0 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 4 stride: 2 pad: 1 group: 256 weight_filler: { type: "bilinear" } bias_term: false } }
################################################################################
RPN
################################################################################
RPN
layer { name: "rpn_conv1" type: "Convolution" bottom: "upsample-2x" top: "rpn_conv1" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 256 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_relu1" type: "ReLU" bottom: "rpn_conv1" top: "rpn_conv1" }
layer { name: "rpn_cls_score" type: "Convolution" bottom: "rpn_conv1" top: "rpn_cls_score" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 50 kernel_size: 1 stride: 1 pad: 0 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "rpn_bbox_pred" type: "Convolution" bottom: "rpn_conv1" top: "rpn_bbox_pred" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } convolution_param { num_output: 100 kernel_size: 1 stride: 1 pad: 0 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 } } }
################################################################################
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: 50 dim: -1 dim: 0 } } }
layer { name: 'proposal' type: 'Proposal' bottom: 'rpn_cls_prob_reshape' bottom: 'rpn_bbox_pred' bottom: 'im_info' top: 'rois' top: 'scores'
include { phase: TEST }
proposal_param { ratio: 0.5 ratio: 0.667 ratio: 1.0 ratio: 1.5 ratio: 2.0 scale: 3 scale: 6 scale: 9 scale: 16 scale: 32 base_size: 8 feat_stride: 8 pre_nms_topn: 6000 post_nms_topn: 400 nms_thresh: 0.3 min_size: 8 } }
################################################################################
RCNN
################################################################################ layer { name: "roi_pool_conv5" type: "ROIPooling" bottom: "upsample-2x" bottom: "rois" top: "roi_pool_conv5" roi_pooling_param { pooled_w: 6 pooled_h: 6 spatial_scale: 0.125# 1/16 } } layer { name: "fc6_L" type: "InnerProduct" bottom: "roi_pool_conv5" top: "fc6_L" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 512 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "fc6_U" type: "InnerProduct" bottom: "fc6_L" top: "fc6_U" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "relu6" type: "ReLU" bottom: "fc6_U" top: "fc6_U" }
################################################################################
fc 7
################################################################################ layer { name: "fc7_L" type: "InnerProduct" bottom: "fc6_U" top: "fc7_L" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 128 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "fc7_U" type: "InnerProduct" bottom: "fc7_L" top: "fc7_U" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 4096 weight_filler { type: "xavier" std: 0.005 } bias_filler { type: "constant" value: 0.1 } } } layer { name: "relu7" type: "ReLU" bottom: "fc7_U" top: "fc7_U" }
################################################################################
output
################################################################################ layer { name: "cls_score" type: "InnerProduct" bottom: "fc7_U" top: "cls_score" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 21 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } } layer { name: "bbox_pred" type: "InnerProduct" bottom: "fc7_U" top: "bbox_pred" param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0 } inner_product_param { num_output: 84 weight_filler { type: "gaussian" std: 0.001 } bias_filler { type: "constant" value: 0 } } } layer { name: "cls_prob" type: "Softmax" bottom: "cls_score" top: "cls_prob" loss_param { ignore_label: -1 normalize: true } } What's the probolem about my propotxt