一、参数解析
[net]
batch=64 # number of images pushed with a forward pass through the network
subdivisions=8 # 源码中的图片数量int imgs = net.batch * net.subdivisions * ngpus,按subdivisions大小分批进行训练
height=416 # height of input image
width=416 # width of input image
channels=3 # channel of input image
momentum=0.9 # CNN-梯度下降法中一种常用的加速技术
decay=0.0005 # CNN-防止过拟合
# 对于每次迭代训练,YOLOv2会基于角度(angle),饱和度(saturation),曝光(exposure),色调(hue)产生新的训练图片
angle=0 # 图片角度变化,单位为度,假如angle=5,就是生成新图片的时候随机旋转-5~5度
saturation = 1.5 # 饱和度变化大小,1到1.5倍
exposure = 1.5 # 曝光变化大小,1到1.5倍
hue=.1 # 色调变化范围,-0.1到0.1
learning_rate=0.0001 # 学习率
max_batches = 45000 # 最大迭代次数
policy=steps # 调整学习率的policy:CONSTANT, STEP, EXP, POLY,STEPS, SIG, RANDOM
steps=100,25000,35000 # 根据batch_num调整学习率,若steps=100,25000,35000,则在迭代100次,25000次,35000次时学习率发生变化,该参数与policy中的steps对应
scales=10,.1,.1 # 相对于当前学习率的变化比率,累计相乘,与steps中的参数个数保持一致
[convolutional]
batch_normalize=1 # 是否做BN-batch_normalize
filters=32
size=3
stride=1
pad=1
activation=leaky
# 激活函数-activation 包括logistic,loggy,relu,elu,relie,plse,hardtan,lhtan,linear,ramp,leaky,tanh,stair.
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
#######
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
# the route layer is to bring finer grained features in from earlier in the network
[route]
layers=-9
# the reorg layer is to make these features match the feature map size at the later layer;
# The end feature map is 13x13, the feature map from earlier is 26x26x512.
# The reorg layer maps the 26x26x512 feature map onto a 13x13x2048 feature map so that it can be concate_nated with the feature maps at 13x13 resolution.
[reorg]
stride=2
[route]
layers=-1,-3
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=125
activation=linear
[region]
# anchors: 预测框的初始宽高,第一个是w,第二个是h,总数量是num*2.
# YOLOv2作者说anchors是使用K-MEANS获得,其实就是计算出哪种类型的框比较多,可以增加收敛速度,如果不设置anchors,默认是0.5.
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1 # 如果为1,计算best iou时,预测宽高强制与anchors一致
classes=20 # 类别数量
coords=4 # BoundingBox的tx,ty,tw,th,tx与ty是相对于左上角的grid,同时是当前grid的比例,tw与th是宽度与高度取对数
num=5 # 每个grid预测的BoundingBox个数
softmax=1 # 如果为1,使用softmax
jitter=.2 # 利用数据抖动产生更多数据抑制过拟合.YOLOv2中使用的是crop,filp,以及net层的angle,flip是随机的,crop就是jitter的参数,tiny-yolo-voc.cfg中jitter=.2,就是在0~0.2中进行crop.
rescore=1 # 决定使用哪种方式计算IOU的误差,为1时,使用当前best iou计算,为0时,使用1计算
# *_scale是YOLOv1论文中cost function的权重,哪一个更大,每一次更新权重的时候,对应方面的权重更新相对比重更大
object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1
absolute=1
thresh = .6 # 决定是否需要计算IOU误差的参数,大于thresh,IOU误差不会夹在cost function中
random=0 # 如果为1每次迭代图片大小随机从320到608,步长为32,如果为0,每次训练大小与输入大小一致
二、训练log中各参数的意义
Region Avg IOU:平均的IOU,代表预测的bounding box和ground truth的交集与并集之比,期望该值趋近于1。
Class:是标注物体的概率,期望该值趋近于1.
Obj:期望该值趋近于1.
No Obj:期望该值越来越小但不为零.
Avg Recall:期望该值趋近1
avg:平均损失,期望该值趋近于0
mAP定义及相关概念
- mAP: mean Average Precision, 即各类别AP的平均值
- AP: PR曲线下面积,后文会详细讲解
- PR曲线: Precision-Recall曲线
- Precision: TP / (TP + FP)
- Recall: TP / (TP + FN)
- TP: IoU>0.5的检测框数量(同一Ground Truth只计算一次)
- FP: IoU<=0.5的检测框,或者是检测到同一个GT的多余检测框的数量
- FN: 没有检测到的GT的数量