【YOLOV5-6.1.x 源码注释】hyp.scratch-low.yaml

前言

源码： YOLOv5源码.
链接: 【YOLOV5-6.1 源码注释】整体项目文件导航.
注释版全部项目文件已上传至GitHub: yolov5-6.1-annotations.

配置文件

# YOLOv5  by Ultralytics, GPL-3.0 license
# Hyperparameters for low-augmentation COCO training from scratch
# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials

lr0: 0.01  # initial learning rate (SGD=1E-2, Adam=1E-3)
lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
momentum: 0.937  # SGD momentum/Adam beta1
weight_decay: 0.0005  # optimizer weight decay 5e-4
warmup_epochs: 3.0  # warmup epochs (fractions ok)
warmup_momentum: 0.8  # warmup initial momentum
warmup_bias_lr: 0.1  # warmup initial bias lr
box: 0.05  # box loss gain
cls: 0.5  # cls loss gain
cls_pw: 1.0  # cls BCELoss positive_weight
obj: 1.0  # obj loss gain (scale with pixels)
obj_pw: 1.0  # obj BCELoss positive_weight
iou_t: 0.20  # IoU training threshold
anchor_t: 4.0  # anchor-multiple threshold
# anchors: 3  # anchors per output layer (0 to ignore)
fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
hsv_h: 0.015  # image HSV-Hue augmentation (fraction)
hsv_s: 0.7  # image HSV-Saturation augmentation (fraction)
hsv_v: 0.4  # image HSV-Value augmentation (fraction)
degrees: 0.0  # image rotation (+/- deg)
translate: 0.1  # image translation (+/- fraction)
scale: 0.5  # image scale (+/- gain)
shear: 0.0  # image shear (+/- deg)
perspective: 0.0  # image perspective (+/- fraction), range 0-0.001
flipud: 0.0  # image flip up-down (probability)
fliplr: 0.5  # image flip left-right (probability)
mosaic: 1.0  # image mosaic (probability)
mixup: 0.0  # image mixup (probability)
copy_paste: 0.0  # segment copy-paste (probability)

注释

# ================================== 训练相关参数 ================================== #
# 优化器相关
lr0: 0.01  # 初始学习率(SGD=1E-2, Adam=1E-3)
lrf: 0.01  # 最终学习率, 以one_cycle形式或者线性从lr0衰减至lr0 * lrf
momentum: 0.937  # SGD momentum/Adam beta1
weight_decay: 0.0005  # optimizer 权重衰减系数 5e-4
# Warmup 相关
warmup_epochs: 3.0  # 前3个epoch进行warmup
warmup_momentum: 0.8  # warmup 初始化动量
warmup_bias_lr: 0.1  # warmup 初始bias学习率

# ================================== 损失函数相关参数 ================================== #
box: 0.05  # box iou 损失系数
cls: 0.5  # cls 损失系数
cls_pw: 1.0  # cls BCELoss 正样本权重
obj: 1.0  # obj 损失系数 (scale with pixels)
obj_pw: 1.0  # obj BCELoss 正样本权重
fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)

# ================================== 其他参数 ================================== #
iou_t: 0.20  # IoU training threshold # 应该是很久以前使用的一个参数，因为那个时候筛选正负样本是靠iou来筛选的，这个iou_t应该就是筛选正负样本用的iou阈值，现在都用宽高比来筛选了，所以这个参数现在也没什么用了
anchor_t: 4.0  # anchor-multiple threshold #  anchor的长宽比阈值（长:宽 = 4:1） 用于k-means中计算 bpr和aat
# anchors: 3  # anchors per output layer (0 to ignore)  # 每个输出层的anchors数量 (0 to ignore)

# ================================== 数据增强 ================================== #
# 色彩转换
hsv_h: 0.015  # hsv 增强系数 色调
hsv_s: 0.7  # hsv 增强系数 饱和度
hsv_v: 0.4  # hsv 增强系数 亮度
# 旋转、平移、扭曲等
degrees: 0.0  # random_perspective 增强系数 旋转角度  (+/- deg)
translate: 0.1  # random_perspective 增强系数 平移     (+/- fraction)
scale: 0.5  # random_perspective 增强系数 图像缩放 (+/- gain)
shear: 0.0  # random_perspective 增强系数 图像剪切 (+/- deg)
perspective: 0.0  # image perspective 增强系数 透明度 (+/- fraction), range 0-0.001
# 翻转
flipud: 0.0  # 上下翻转数据增强 (probability)
fliplr: 0.5  # 左右翻转数据增强 (probability)
# mosaic、mixup、copy_paste
mosaic: 1.0  # mosaic 数据增强 (probability)
mixup: 0.0  # mixup 数据增强 (probability)
copy_paste: 0.0  # segment copy-paste 数据增强 (probability)

参考

CSDN: https://blog.csdn.net/qq_38253797/article/details/119759746
CSDN: https://blog.csdn.net/irving512/article/details/116238499