引言

源码链接：https://github.com/ultralytics/ultralytics
yolov8和yolov5是同一作者，相比yolov5，yolov8的集成性更好了，更加面向用户了
YOLO命令行界面(command line interface, CLI) 方便在各种任务和版本上训练、验证或推断模型。CLI不需要定制或代码，可以使用yolo命令从终端运行所有任务。

如果想了解yolo系列的更新迭代，以及yolov8的模型结构，推荐下面的链接：
YOLOv8详解 【网络结构+代码+实操】
笔者直接从实操入手

1 环境配置

安装pytorch、torchvision和其他依赖库

环境配置部分可以参考笔者的博客
【YOLO】YOLOv5-6.0环境搭建（不定时更新）

安装ultralytics

git clone https://github.com/ultralytics/ultralytics
cd ultralytics
pip install -e .

2 数据集准备

针对检测的数据集准备可以参考笔者的博客，这里不再赘述了
【YOLO】训练自己的数据集

3 模型训练

比起YOLOv5，YOLOv8的训练封装性更好了，有利有弊吧，参数默认值修改比较麻烦

训练指令如下：

yolo task=detect mode=train model=yolov8s.pt data=/media/ll/L/llr/DATASET/subwayDatasets/coco.yaml device=0 cache=True epochs=300 project=/media/ll/L/llr/mode name=yolov8

除了上述笔者使用的参数，其他参数说明

task: detect  # 可选择：detect, segment, classify
mode: train  #可选择： train, val, predict
# Train settings -------------------------------------------------------------------------------------------------------
model:  # 设置模型。格式因任务类型而异。支持model_name, model.yaml，model.pt
data:  # 设置数据，支持多数类型 data.yaml, data_folder, dataset_name
epochs: 300  # 需要训练的epoch数
patience: 50  # epochs to wait for no observable improvement for early stopping of training
batch: 16  # Dataloader的batch大小
imgsz: 640  # Dataloader中图像数据的大小
save: True  # save train checkpoints and predict results
save_period: -1 # Save checkpoint every x epochs (disabled if < 1)
cache: True  # 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  # 每个进程使用的cpu worker数。使用DDP自动伸缩
project: /media/ll/L/llr/model # project name
name: yolov8 # experiment name
exist_ok: False  # whether to overwrite existing experiment
pretrained: False  # whether to use a pretrained model
optimizer: SGD  # 支持的优化器：Adam, SGD， RMSProp
verbose: True  # whether to print verbose output
seed: 0  # random seed for reproducibility
deterministic: True  # whether to enable deterministic mode
single_cls: False  # 将多类数据作为单类进行训练
image_weights: False  # 使用加权图像选择进行训练
rect: False  # 启用矩形训练
cos_lr: False  # 使用cosine LR调度器
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  # 分割:在训练中使用掩码重叠
mask_ratio: 4  # 分割:设置掩码下采样
# Classification
dropout: 0.0  # 分类:训练时使用dropout
# Val/Test settings ----------------------------------------------------------------------------------------------------
val: True  # validate/test during training
split: val  # dataset split to use for validation, i.e. 'val', 'test' or 'train'
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  # 使用OpenCV DNN进行ONNX推断
plots: True  # 在验证时保存图像
# Prediction settings --------------------------------------------------------------------------------------------------
source:  # 输入源。支持图片、文件夹、视频、网址
show: False  # 查看预测图片
save_txt: False  # 保存结果到txt文件中
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  # 输入视频帧率步长
line_thickness: 3  # bounding box thickness (pixels)
visualize: False  # 可视化模型特征
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  #分割:高分辨率掩模
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  # 初始化学习率
lrf: 0.01  # 最终的OneCycleLR学习率
momentum: 0.937  # 作为SGD的momentum和Adam的beta1
weight_decay: 0.0005  # 优化器权重衰减
warmup_epochs: 3.0  # Warmup的epoch数，支持分数)
warmup_momentum: 0.8  # warmup的初始动量
warmup_bias_lr: 0.1  # Warmup的初始偏差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 模型预测

weight_path = "best.pt"  # 自训练的模型
imgdir = r'/media/ll/L/llr/DATASET/subwayDatasets/bjdt/images' 
img_path = r'/media/ll/L/llr/DATASET/subwayDatasets/bjdt/images/L_0000018.jpg'
model = YOLO(weight_path)
results = model(img_path,show=False,save=False)  # 是否显示和保存结果数据

预测一张图片，results如下图所示：

预测文件夹目录，results如图所示：

无论是一张图片还是图片目录，返回的results都是list

要对预测结果进行处理需要索引进去，如下图所示

结果参数说明：

 boxes：各种形式的检测框信息（xyxy、xywh、归一化的）、类别索引、置信度等
 names：类别字典
 orig_img：原图数组
 orig_shape：原图尺寸
 plots：在验证时保存图像（预测时一般为None）
 speed：处理速度

基于上述模型提供的检测结果进行后处理算法等

上述即为yolov8的快速使用