【YOLO】YOLOv8实操：环境配置/自定义数据集准备/模型训练/预测(yolov5m)

【YOLO】YOLOv8实操：环境配置/自定义数据集准备/模型训练/预测 YOLOv8实操：环境配置/自定义数据集准备/模型训练/预测引言1 环境配置2 数据集准备3 模型训练4 模型预测引言

推荐整理分享【YOLO】YOLOv8实操：环境配置/自定义数据集准备/模型训练/预测(yolov5m)，希望有所帮助，仅作参考，欢迎阅读内容。

文章相关热门搜索词:yolov5l,yolo v3详解,yolo v3详解,yolo实战视频教程,yolo实战视频教程,yolo v3详解,yolo yolov2 yolov3,yolo v3详解,内容如对您有帮助，希望把文章链接给更多的朋友！

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/ultralyticscd ultralyticspip 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, classifymode: 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.ptdata: # 设置数据，支持多数类型 data.yaml, data_folder, dataset_nameepochs: 300 # 需要训练的epoch数patience: 50 # epochs to wait for no observable improvement for early stopping of trainingbatch: 16 # Dataloader的batch大小imgsz: 640 # Dataloader中图像数据的大小save: True # save train checkpoints and predict resultssave_period: -1 # Save checkpoint every x epochs (disabled if < 1)cache: True # True/ram, disk or False. Use cache for data loadingdevice: # device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpuworkers: 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 namename: yolov8 # experiment nameexist_ok: False # whether to overwrite existing experimentpretrained: False # whether to use a pretrained modeloptimizer: SGD # 支持的优化器：Adam, SGD， RMSPropverbose: True # whether to print verbose outputseed: 0 # random seed for reproducibilitydeterministic: True # whether to enable deterministic modesingle_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 epochsresume: False # resume training from last checkpointmin_memory: False # minimize memory footprint loss function, choices=[False, True, <roll_out_thr>]# Segmentationoverlap_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# Classificationdropout: 0.0 # 分类:训练时使用dropout# Val/Test settings ----------------------------------------------------------------------------------------------------val: True # validate/test during trainingsplit: val # dataset split to use for validation, i.e. 'val', 'test' or 'train'save_json: False # save results to JSON filesave_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 NMSmax_det: 300 # maximum number of detections per imagehalf: 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 scoressave_crop: False # save cropped images with resultshide_labels: False # hide labelshide_conf: False # hide confidence scoresvid_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 sourcesagnostic_nms: False # class-agnostic NMSclasses: # 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 tokeras: False # use Kerasoptimize: False # TorchScript: optimize for mobileint8: False # CoreML/TF INT8 quantizationdynamic: False # ONNX/TF/TensorRT: dynamic axessimplify: False # ONNX: simplify modelopset: # 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beta1weight_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lrbox: 7.5 # box loss gaincls: 0.5 # cls loss gain (scale with pixels)dfl: 1.5 # dfl loss gainfl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)label_smoothing: 0.0 # label smoothing (fraction)nbs: 64 # nominal batch sizehsv_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 0-0.001flipud: 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 dataloader4 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