图像识别-ResNet-18网络结构图示及解读

一、介绍

ResNet系列网络，图像分类领域的知名算法，经久不衰，历久弥新，直到今天依旧具有广泛的研究意义和应用场景。被业界各种改进，经常用于图像识别任务。

今天主要介绍一下ResNet-18网络结构，其他深层次网络，可以依次类推。

ResNet-18，数字代表的是网络的深度，也就是说ResNet18 网络就是18层的吗？实则不然，其实这里的18指定的是带有权重的 18层，包括卷积层和全连接层，不包括池化层和BN层。

二、网络结构

本文主要基于caffe框架，解读Resnet-18网络结构~

1. 网络参数

2. 网络图示

网络结构图，由caffe train.prototxt文件内容绘制：

caffe train.prototxt文件内容：

name: "ResNet-18"

layer {
  name: "data"
    type: "Data"
    top: "data"
    top: "label"
    include {
    phase: TRAIN
      }
  transform_param {
    mirror: true
      crop_size: 224
      mean_file: "/home/vgenty/git/caffe/build/tools/ub_seven_class_train_mean.binary"
      }
  data_param {
    source: "/home/vgenty/git/caffe/build/tools/ub_seven_class_train.db"
      batch_size: 8
      backend: LMDB
      }

}

layer {
  name: "data"
    type: "Data"
    top: "data"
    top: "label"
    include {
    phase: TEST
      }
  transform_param {
    mirror: false
      crop_size: 224
      mean_file: "/home/vgenty/git/caffe/build/tools/ub_seven_class_valid_mean.binary"
      }
  data_param {
    source: "/home/vgenty/git/caffe/build/tools/ub_seven_class_valid.db"
      batch_size:8
      backend: LMDB
      }
}

layer {
  bottom: "data"
    top: "conv1"
    name: "conv1"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 7
      pad: 3
      stride: 2
      }
}

layer {
  bottom: "conv1"
    top: "conv1"
    name: "bn_conv1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "conv1"
    top: "conv1"
    name: "scale_conv1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "conv1"
    top: "conv1"
    name: "conv1_relu"
    type: "ReLU"
    }

layer {
  bottom: "conv1"
    top: "pool1"
    name: "pool1"
    type: "Pooling"
    pooling_param {
    kernel_size: 3
      stride: 2
      pool: MAX
      }
}
##########################
######first shortcut######
##########################
layer {
  bottom: "pool1"
    top: "res2a_branch1"
    name: "res2a_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 1
      pad: 0
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "bn2a_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2a_branch1"
    top: "res2a_branch1"
    name: "scale2a_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "pool1"
    top: "res2a_branch2a"
    name: "res2a_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "bn2a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "scale2a_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res2a_branch2a"
    top: "res2a_branch2a"
    name: "res2a_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res2a_branch2a"
    top: "res2a_branch2b"
    name: "res2a_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "bn2a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2a_branch2b"
    top: "res2a_branch2b"
    name: "scale2a_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
    bottom: "res2a_branch1"
    bottom: "res2a_branch2b"
    top: "res2a"
    name: "res2a"
    type: "Eltwise"
    }

layer {
  bottom: "res2a"
    top: "res2a"
    name: "res2a_relu"
    type: "ReLU"
    }

##########################
######first-2 shortcut####
##########################

layer {
  bottom: "res2a"
    top: "res2b_branch1"
    name: "res2b_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 1
      pad: 0
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2b_branch1"
    top: "res2b_branch1"
    name: "bn2b_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2b_branch1"
    top: "res2b_branch1"
    name: "scale2b_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}


layer {
    bottom: "res2a"
    top: "res2b_branch2a"
    name: "res2b_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "bn2b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "scale2b_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res2b_branch2a"
    top: "res2b_branch2a"
    name: "res2b_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res2b_branch2a"
    top: "res2b_branch2b"
    name: "res2b_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 64
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "bn2b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res2b_branch2b"
    top: "res2b_branch2b"
    name: "scale2b_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
    bottom: "res2b_branch1"
    bottom: "res2b_branch2b"
    top: "res2b"
    name: "res2b"
    type: "Eltwise"
    }

layer {
  bottom: "res2b"
    top: "res2b"
    name: "res2b_relu"
    type: "ReLU"
    }


##########################
######second shortcut#####
##########################

layer {
  bottom: "res2b"
    top: "res3a_branch1"
    name: "res3a_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 1
      pad: 0
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "bn3a_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3a_branch1"
    top: "res3a_branch1"
    name: "scale3a_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res2b"
    top: "res3a_branch2a"
    name: "res3a_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 3
      pad: 1
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "bn3a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "scale3a_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res3a_branch2a"
    top: "res3a_branch2a"
    name: "res3a_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res3a_branch2a"
    top: "res3a_branch2b"
    name: "res3a_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "bn3a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3a_branch2b"
    top: "res3a_branch2b"
    name: "scale3a_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res3a_branch1"
    bottom: "res3a_branch2b"
    top: "res3a"
    name: "res3a"
    type: "Eltwise"
    }

layer {
  bottom: "res3a"
    top: "res3a"
    name: "res3a_relu"
    type: "ReLU"
    }


##########################
######second-2 shortcut#####
##########################

layer {
  bottom: "res3a"
    top: "res3b_branch1"
    name: "res3b_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 1
      pad: 0
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res3b_branch1"
    top: "res3b_branch1"
    name: "bn3b_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3b_branch1"
    top: "res3b_branch1"
    name: "scale3b_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}


layer {
  bottom: "res3a"
    top: "res3b_branch2a"
    name: "res3b_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res3b_branch2a"
    top: "res3b_branch2a"
    name: "bn3b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3b_branch2a"
    top: "res3b_branch2a"
    name: "scale3b_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res3b_branch2a"
    top: "res3b_branch2a"
    name: "res3b_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res3b_branch2a"
    top: "res3b_branch2b"
    name: "res3b_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 128
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res3b_branch2b"
    top: "res3b_branch2b"
    name: "bn3b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res3b_branch2b"
    top: "res3b_branch2b"
    name: "scale3b_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res3b_branch1"
    bottom: "res3b_branch2b"
    top: "res3b"
    name: "res3b"
    type: "Eltwise"
    }

layer {
  bottom: "res3b"
    top: "res3b"
    name: "res3b_relu"
    type: "ReLU"
    }

##########################
######third shortcut#####
##########################

layer {
  bottom: "res3b"
    top: "res4a_branch1"
    name: "res4a_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 1
      pad: 0
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "bn4a_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4a_branch1"
    top: "res4a_branch1"
    name: "scale4a_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res3b"
    top: "res4a_branch2a"
    name: "res4a_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 3
      pad: 1
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "bn4a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "scale4a_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res4a_branch2a"
    top: "res4a_branch2a"
    name: "res4a_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res4a_branch2a"
    top: "res4a_branch2b"
    name: "res4a_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "bn4a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4a_branch2b"
    top: "res4a_branch2b"
    name: "scale4a_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res4a_branch1"
    bottom: "res4a_branch2b"
    top: "res4a"
    name: "res4a"
    type: "Eltwise"
    }

layer {
  bottom: "res4a"
    top: "res4a"
    name: "res4a_relu"
    type: "ReLU"
    }



###########################
######third-2 shortcut#####
##########################

layer {
  bottom: "res4a"
    top: "res4b_branch1"
    name: "res4b_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 1
      pad: 0
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res4b_branch1"
    top: "res4b_branch1"
    name: "bn4b_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4b_branch1"
    top: "res4b_branch1"
    name: "scale4b_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}


layer {
  bottom: "res4a"
    top: "res4b_branch2a"
    name: "res4b_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res4b_branch2a"
    top: "res4b_branch2a"
    name: "bn4b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4b_branch2a"
    top: "res4b_branch2a"
    name: "scale4b_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res4b_branch2a"
    top: "res4b_branch2a"
    name: "res4b_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res4b_branch2a"
    top: "res4b_branch2b"
    name: "res4b_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 256
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res4b_branch2b"
    top: "res4b_branch2b"
    name: "bn4b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res4b_branch2b"
    top: "res4b_branch2b"
    name: "scale4b_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res4b_branch1"
    bottom: "res4b_branch2b"
    top: "res4b"
    name: "res4b"
    type: "Eltwise"
    }

layer {
  bottom: "res4b"
    top: "res4b"
    name: "res4b_relu"
    type: "ReLU"
    }
##########################
######forth shortcut#####
##########################

layer {
  bottom: "res4b"
    top: "res5a_branch1"
    name: "res5a_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 1
      pad: 0
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "bn5a_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5a_branch1"
    top: "res5a_branch1"
    name: "scale5a_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res4b"
    top: "res5a_branch2a"
    name: "res5a_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 3
      pad: 1
      stride: 2
      bias_term: false
      }
}

layer {
  bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "bn5a_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "scale5a_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res5a_branch2a"
    top: "res5a_branch2a"
    name: "res5a_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res5a_branch2a"
    top: "res5a_branch2b"
    name: "res5a_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "bn5a_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5a_branch2b"
    top: "res5a_branch2b"
    name: "scale5a_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}


layer {
  bottom: "res5a_branch1"
    bottom: "res5a_branch2b"
    top: "res5a"
    name: "res5a"
    type: "Eltwise"
    }

layer {
  bottom: "res5a"
    top: "res5a"
    name: "res5a_relu"
    type: "ReLU"
    }


##########################
######forth-2 shortcut#####
##########################

layer {
  bottom: "res5a"
    top: "res5b_branch1"
    name: "res5b_branch1"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 1
      pad: 0
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res5b_branch1"
    top: "res5b_branch1"
    name: "bn5b_branch1"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5b_branch1"
    top: "res5b_branch1"
    name: "scale5b_branch1"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}


layer {
  bottom: "res5a"
    top: "res5b_branch2a"
    name: "res5b_branch2a"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "bn5b_branch2a"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "scale5b_branch2a"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res5b_branch2a"
    top: "res5b_branch2a"
    name: "res5b_branch2a_relu"
    type: "ReLU"
    }

layer {
  bottom: "res5b_branch2a"
    top: "res5b_branch2b"
    name: "res5b_branch2b"
    type: "Convolution"
    convolution_param {
    num_output: 512
      kernel_size: 3
      pad: 1
      stride: 1
      bias_term: false
      }
}

layer {
  bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "bn5b_branch2b"
    type: "BatchNorm"
    batch_norm_param {
    use_global_stats: true
      }
}

layer {
  bottom: "res5b_branch2b"
    top: "res5b_branch2b"
    name: "scale5b_branch2b"
    type: "Scale"
    scale_param {
    bias_term: true
      }
}

layer {
  bottom: "res5b_branch1"
    bottom: "res5b_branch2b"
    top: "res5b"
    name: "res5b"
    type: "Eltwise"
    }

layer {
  bottom: "res5b"
    top: "res5b"
    name: "res5b_relu"
    type: "ReLU"
    }

layer {
  bottom: "res5b"
    top: "pool5"
    name: "pool5"
    type: "Pooling"
    pooling_param {
    kernel_size: 7
      stride: 1
      pool: AVE
      }
}

layer {
  bottom: "pool5"
    top: "fc7"
    name: "fc7"
    type: "InnerProduct"
    inner_product_param {
    num_output: 7
      }
}

#layer {
#  bottom: "fc7"
#    top: "prob"
#    name: "prob"
#    type: "Softmax"
#    }

  layer {
    name: "loss"
      type: "SoftmaxWithLoss"
      bottom: "fc7"
      bottom: "label"
      top: "loss"
      }

layer {
  name: "accuracy"
    type: "Accuracy"
    bottom: "fc7"
    bottom: "label"
    top: "accuracy"
    include {
    phase: TEST
      }
}

详细参数设置，可查看以上文件内容定义。

三、总结

ResNet及其变体网路系列，对于一般的图像识别任务表现优异，具体场景的算法应用，可以结合实际情况，进行具体网络结构改进，如网路裁剪，网络加深或其它策略，可以进行实践改进。

码字不易，如有不对，欢迎留言交流~

您的支持，是我不断创作的最大动力~

欢迎点赞，关注，留言交流~

深度学习，乐此不疲~

个人微信公众号，欢迎关注~