YOLO系列模型在目标检测领域有着十分重要的地位，随着版本不停的迭代，模型的性能在不断地提升，源码提供的功能也越来越多，那么如何使用源码就显得十分的重要，接下来通过文章带大家手把手去了解Yolov8（最新版本）的每一个参数的含义，并且通过具体的图片例子让大家明白每个参数改动将会给网络带来哪些影响。

这篇文章讲解的是 关于 YOLOv8最新版本 的配置解析🚀

包含 训练｜验证｜推理 部分汇总

YOLOv7 模型 手把手调参系列🚀

• 训练部分：手把手调参最新 YOLOv7 模型 训练部分🔗

• 推理部分：手把手调参最新 YOLOv7 模型 推理部分🔗

YOLOv8 模型 手把手调参系列🚀

• 配置部分：手把手调参 YOLOv8 模型之 训练｜验证｜推理配置-详解🔗

1. 代码获取方式🌟

官方YOLOv8 项目地址：https://github.com/ultralytics/ultralytics

进入仓库 可以查看项目目前提供的最新版本

选择的代码是main分支版本

2. 准备项目环境✨

在配置Conda环境后就可以进入项目了，在终端中键入如下指令：

pip install ultralytics

3. YOLOv8 💡

YOLOv8 可以直接在命令行界面（CLI）中使用 yolo 命令运行：

yolo predict model=yolov8n.pt source="https://ultralytics.com/images/bus.jpg"

4. default.yaml

default.yaml配置文件如下

# Ultralytics YOLO 🚀, GPL-3.0 license
# Default training settings and hyperparameters for medium-augmentation COCO training

task: detect  # inference task, i.e. detect, segment, classify
mode: train  # YOLO mode, i.e. train, val, predict, export

# Train settings -------------------------------------------------------------------------------------------------------
model:  # path to model file, i.e. yolov8n.pt, yolov8n.yaml
data:  # path to data file, i.e. i.e. coco128.yaml
epochs: 100  # number of epochs to train for
patience: 50  # epochs to wait for no observable improvement for early stopping of training
batch: 16  # number of images per batch (-1 for AutoBatch)
imgsz: 640  # size of input images as integer or w,h
save: True  # save train checkpoints and predict results
cache: False  # True/ram, disk or False. Use cache for data loading
device:  # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu
workers: 8  # number of worker threads for data loading (per RANK if DDP)
project:  # project name
name:  # experiment name
exist_ok: False  # whether to overwrite existing experiment
pretrained: False  # whether to use a pretrained model
optimizer: SGD  # optimizer to use, choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
verbose: True  # whether to print verbose output
seed: 0  # random seed for reproducibility
deterministic: True  # whether to enable deterministic mode
single_cls: False  # train multi-class data as single-class
image_weights: False  # use weighted image selection for training
rect: False  # support rectangular training if mode='train', support rectangular evaluation if mode='val'
cos_lr: False  # use cosine learning rate scheduler
close_mosaic: 10  # disable mosaic augmentation for final 10 epochs
resume: False  # resume training from last checkpoint
min_memory: False  # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>]
# Segmentation
overlap_mask: True  # masks should overlap during training (segment train only)
mask_ratio: 4  # mask downsample ratio (segment train only)
# Classification
dropout: 0.0  # use dropout regularization (classify train only)

# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True  # validate/test during training
save_json: False  # save results to JSON file
save_hybrid: False  # save hybrid version of labels (labels + additional predictions)
conf:  # object confidence threshold for detection (default 0.25 predict, 0.001 val)
iou: 0.7  # intersection over union (IoU) threshold for NMS
max_det: 300  # maximum number of detections per image
half: False  # use half precision (FP16)
dnn: False  # use OpenCV DNN for ONNX inference
plots: True  # save plots during train/val

# Prediction settings --------------------------------------------------------------------------------------------------
source:  # source directory for images or videos
show: False  # show results if possible
save_txt: False  # save results as .txt file
save_conf: False  # save results with confidence scores
save_crop: False  # save cropped images with results
hide_labels: False  # hide labels
hide_conf: False  # hide confidence scores
vid_stride: 1  # video frame-rate stride
line_thickness: 3  # bounding box thickness (pixels)
visualize: False  # visualize model features
augment: False  # apply image augmentation to prediction sources
agnostic_nms: False  # class-agnostic NMS
classes:  # filter results by class, i.e. class=0, or class=[0,2,3]
retina_masks: False  # use high-resolution segmentation masks
boxes: True # Show boxes in segmentation predictions

# Export settings ------------------------------------------------------------------------------------------------------
format: torchscript  # format to export to
keras: False  # use Keras
optimize: False  # TorchScript: optimize for mobile
int8: False  # CoreML/TF INT8 quantization
dynamic: False  # ONNX/TF/TensorRT: dynamic axes
simplify: False  # ONNX: simplify model
opset:  # ONNX: opset version (optional)
workspace: 4  # TensorRT: workspace size (GB)
nms: False  # CoreML: add NMS

# Hyperparameters ------------------------------------------------------------------------------------------------------
lr0: 0.01  # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
lrf: 0.01  # final 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: 7.5  # box loss gain
cls: 0.5  # cls loss gain (scale with pixels)
dfl: 1.5  # dfl loss gain
fl_gamma: 0.0  # focal loss gamma (efficientDet default gamma=1.5)
label_smoothing: 0.0  # label smoothing (fraction)
nbs: 64  # nominal batch size
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)

# Custom config.yaml ---------------------------------------------------------------------------------------------------
cfg:  # for overriding defaults.yaml

# Debug, do not modify -------------------------------------------------------------------------------------------------
v5loader: False  # use legacy YOLOv5 dataloader

4.1 YOLOv8 网络模型结构图

图源: MMYOLO

4.2 Predict参数详解🚀

在default.yaml文件中，就是参数配置，其中

4.2.1 “source”

source:  

这个就是 图片或视频的源目录

---

4.2.2 “show: False”

show: False

是否显示结果

---

4.2.3 “save_txt: False”

save_txt: False

这个意思就是将结果保存为 .txt 文件

---

4.2.4 “save_conf: False”

save_conf: False

这个就是保存带有置信度分数的结果

---

4.2.5 “save_crop: False”

save_crop: False

这个参数就是保存裁剪后的图像和结果

4.2.6 “hide_conf: False”

hide_conf: False

这个参数意思就是隐藏标签。

---

4.2.7 “vid_stride: 1”

vid_stride: 1

表示视频帧率步幅。

---

4.2.8 “line_thickness: 3”

line_thickness: 3

这个参数意思就是检测的时候边界框粗细（像素）

---

4.2.9 “visualize: False”

visualize: False

这个参数的意思就是：是否使用可视化模型特征

---

4.2.10 “augment: False”

augment: False

这个参数的意思就是将图像增强应用于预测

---

4.2.11 “agnostic_nms: False”

agnostic_nms: False

类别不可知的 NMS

---

4.2.12 “classes:”

classes:

按类别过滤结果，即 class=0 或 class=[0,2,3]

这个的意思就是我们可以给变量指定多个赋值，也就是说我们可以把“0”赋值给“classes”，也可以把“0”“2”“4”“6”都赋值给“classes”

接下来说classes参数，这里看一下coco128.yaml的配置文件就明白了，比如说我这里给classes指定“0”，那么意思就是只检测人这个类别。

---

4.2.13 “retina_masks: False”

retina_masks: False

这个参数表示使用高分辨率分割掩码

---

4.2.14 “boxes: True”

boxes: True

在分割预测中显示框

---

Predict参数配置一览

model.predict接受控制预测操作的多个参数。这些参数可以直接传递给model.predict：
model.predict(source, save=True, imgsz=320, conf=0.5)

All supported arguments:

Key	Value	Description
source	'ultralytics/assets'	图片或视频的源目录
conf	0.25	用于检测的对象置信度阈值
iou	0.7	NMS 的联合交集 (IoU) 阈值
half	False	使用半精度 (FP16)
device	None	要运行的设备，即 cuda device=0/1/2/3 或 device=cpu
show	False	尽可能显示结果
save	False	保存图像和结果
save_txt	False	将结果保存为 .txt 文件
save_conf	False	保存带有置信度分数的结果
save_crop	False	保存裁剪后的图像和结果
hide_labels	False	隐藏标签
hide_conf	False	隐藏置信度分数
max_det	300	每张图像的最大检测数
vid_stride	False	视频帧率步幅
line_thickness	3	边界框大小（像素）
visualize	False	可视化模型特征
augment	False	将图像增强应用于预测源
agnostic_nms	False	类别不可知的 NMS
retina_masks	False	使用高分辨率分割蒙版
classes	None	按类过滤结果，即class=0，或class=[0,2,3]
boxes	True	在分割预测中显示框

train 参数配置

YOLO 模型的训练设置是指用于在数据集上训练模型的各种超参数和配置。

Key	Value	Description
model	None	模型文件路径，即yolov8n.pt、yolov8n.yaml
data	None	数据文件的路径，即 coco128.yaml
epochs	100	要训​​练的时期数
patience	50	等待早期停止训练没有明显改善的时代
batch	16	每批次的图像数量（AutoBatch 为 -1）
imgsz	640	输入图像的大小为整数或 w,h
save	True	保存火车检查站并预测结果
save_period	-1	每 x 个时期保存检查点（如果 < 1 则禁用）
cache	False	True/ram、disk 或 False。使用缓存进行数据加载
device	None	要运行的设备，即 cuda device=0 或 device=0,1,2,3 或 device=cpu
workers	8	用于数据加载的工作线程数（如果是 DDP，则为每个 RANK）
project	None	项目名(eg:mg)
name	None	实验名称
exist_ok	False	是否覆盖现有实验
pretrained	False	是否使用预训练模型
optimizer	'SGD'	要使用的优化器，choices=[‘SGD’, ‘Adam’, ‘AdamW’, ‘RMSProp’]
verbose	False	是否打印详细输出
seed	0	可重复性的随机种子
deterministic	True	是否启用确定性模式
single_cls	False	将多类数据训练为单类
image_weights	False	使用加权图像选择进行训练
rect	False	为最小填充整理每批次的矩形训练
cos_lr	False	使用余弦学习率调度器
close_mosaic	0	(int) 禁用最后时期的马赛克增强
resume	False	从上一个检查点恢复训练
amp	True	自动混合精度 (AMP) 训练，选择=[True, False]
lr0	0.01	初始学习率（即SGD=1E-2，Adam=1E-3）
lrf	0.01	最终学习率 (lr0 * lrf)
momentum	0.937	SGD momentum/亚当 beta1
weight_decay	0.0005	优化器权重衰减 5e-4
warmup_epochs	3.0	热身时期（分数确定）
warmup_momentum	0.8	热身初始动量
warmup_bias_lr	0.1	预热初始偏置 lr
box	7.5	框丢失增益
cls	0.5	cls 损失增益（按像素缩放）
dfl	1.5	dfl 损失增益
pose	12.0	姿势损失增益（仅限姿pose）
kobj	2.0	关键点对象损失增益（仅限关键点）
label_smoothing	0.0	标签平滑（分数）
nbs	64	标称批量
overlap_mask	True	训练期间掩码应该重叠（仅限分段训练）
mask_ratio	4	掩码下采样率（仅分段训练）
dropout	0.0	使用 dropout 正则化（仅分类训练）
val	True	在培训期间验证/测试

val参数配置

YOLO 模型的验证设置是指用于验证的各种超参数和配置
评估模型在验证数据集上的性能。

Key	Value	Description
data	None	数据文件的路径，即coco128.yaml
imgsz	640	图像大作为标量或 (h, w) 列表，即 (640, 480)
batch	16	每次批次的图像数量（AutoBatch为-1）
save_json	False	将结果保存到 JSON 文件
save_hybrid	False	保存标签的混合版本（标签+附加预测）
conf	0.001	用于检测的对象设置信度值
iou	0.6	NMS 的联合交易集 (IoU) 阈值
max_det	300	每个张图的最大检测数
half	True	使用半精度 (FP16)
device	None	要运行的设备，即cuda device=0/1/2/3 或者device=cpu
dnn	False	使用 OpenCV DNN 进行 ONNX 推理
plots	False	在训练期间显示图
rect	False	为最小填充整顿每次批次的形状值
split	val	数据集拆分用于验证，即“val”、“test”或“train”

5. COCO数据集训练配置

# Ultralytics YOLO 🚀, GPL-3.0 license
# COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
# Example usage: python train.py --data coco8.yaml
# parent
# ├── yolov5
# └── datasets
#     └── coco8  ← downloads here (1 MB)


# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco8  # dataset root dir
train: images/train  # train images (relative to 'path') 4 images
val: images/val  # val images (relative to 'path') 4 images
test:  # test images (optional)

# Classes
names:
  0: person
  1: bicycle
  2: car
  3: motorcycle
  4: airplane
  5: bus
  6: train
  7: truck
  8: boat
  9: traffic light
  10: fire hydrant
  11: stop sign
  12: parking meter
  13: bench
  14: bird
  15: cat
  16: dog
  17: horse
  18: sheep
  19: cow
  20: elephant
  21: bear
  22: zebra
  23: giraffe
  24: backpack
  25: umbrella
  26: handbag
  27: tie
  28: suitcase
  29: frisbee
  30: skis
  31: snowboard
  32: sports ball
  33: kite
  34: baseball bat
  35: baseball glove
  36: skateboard
  37: surfboard
  38: tennis racket
  39: bottle
  40: wine glass
  41: cup
  42: fork
  43: knife
  44: spoon
  45: bowl
  46: banana
  47: apple
  48: sandwich
  49: orange
  50: broccoli
  51: carrot
  52: hot dog
  53: pizza
  54: donut
  55: cake
  56: chair
  57: couch
  58: potted plant
  59: bed
  60: dining table
  61: toilet
  62: tv
  63: laptop
  64: mouse
  65: remote
  66: keyboard
  67: cell phone
  68: microwave
  69: oven
  70: toaster
  71: sink
  72: refrigerator
  73: book
  74: clock
  75: vase
  76: scissors
  77: teddy bear
  78: hair drier
  79: toothbrush


# Download script/URL (optional)
download: https://ultralytics.com/assets/coco8.zip

6. YOLOv8 网络配置

# Ultralytics YOLO 🚀, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # scales module repeats
width_multiple: 0.50  # scales convolution channels

# YOLOv8.0s backbone
backbone:
  # [from, repeats, module, args]
  - [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2
  - [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4
  - [-1, 3, C2f, [128, True]]
  - [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8
  - [-1, 6, C2f, [256, True]]
  - [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16
  - [-1, 6, C2f, [512, True]]
  - [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [1024, True]]
  - [-1, 1, SPPF, [1024, 5]]  # 9

# YOLOv8.0s head
head:
  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 6], 1, Concat, [1]]  # cat backbone P4
  - [-1, 3, C2f, [512]]  # 13

  - [-1, 1, nn.Upsample, [None, 2, 'nearest']]
  - [[-1, 4], 1, Concat, [1]]  # cat backbone P3
  - [-1, 3, C2f, [256]]  # 17 (P3/8-small)

  - [-1, 1, Conv, [256, 3, 2]]
  - [[-1, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 20 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 23 (P5/32-large)

  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

其他的后续补充

参考链接: YOLOv8官方仓库 https://github.com/ultralytics/ultralytics