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
转自：
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作者：_John_Tian_ yolo-voc.2.0.cfg 参数解析
来源：CSDN
原文：https://blog.csdn.net/tianzhaixing2013/article/details/79269275
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