faster rcnn解析之stage1_rpn_solver60k80k.pt

train_net: "/py-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage1_rpn_train.pt"
base_lr: 0.001
lr_policy: "step"
gamma: 0.1
stepsize: 60000
display: 20
average_loss: 100
momentum: 0.9
weight_decay: 0.0005

# We disable standard caffe solver snapshotting and implement our own snapshot
# function
snapshot: 0
# We still use the snapshot prefix, though

snapshot_prefix: "vgg16_rpn"

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train_net: "/py-faster-rcnn/models/pascal_voc/VGG16/faster_rcnn_alt_opt/stage1_rpn_train.pt"是指用的网络模型，仅截取VGG之后的部分

base_lr: 0.001
lr_policy: "step"    #设置为step,则返回 base_lr * gamma ^ (floor(iter / stepsize)),其中iter表示当前的迭代次数
gamma: 0.1            #这个参数就是和learning rate相关的
stepsize: 60000     #This parameter indicates how often (at some iteration count) that we should move onto the next “step” of training. This value is a positive integer.

display: 20                                       #每训练100次，在屏幕上显示一次。如果设置为0，则不显示。

average_loss:100                          #取100次foward的loss作平均，进行显示输出

momentum:0.9                                #上一次梯度更新的权重

weight_decay: 0.0005                    #权重衰减项，用于防止过拟合

snapshot: 0                                      #训练快照，确定多久保存一次model和solverstate

snapshot_prefix:"vgg16_rpn"      #snapshot的前缀，就是model和solverstate的命名前缀，也代表路径