[convolutional] # conv + [bn] + [activation]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky

[shortcut]
from=-3  # 前三层
activation=linear  

[upsample] # 双线性上采样
stride=2

[route] # 它将输出由该值索引的图层的特征图
layers = -4  #该层将从Route层向后从第4层输出特征图。

[route]
layers = -1, 61  # concatenated along the depth dimension. from -1 and 61 layer

[yolo]  # 输出
mask = 0,1,2
anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1

[net]  # 网络训练信息，在推理时不用
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=16
width= 320
height = 320
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1