在这个AI的时代我们也得与时俱进，给大家带来使用YOLO最新库来进行图像识别，标注、训练、验证、应用

yolo detect train data=ai_card_config.yaml model=yolov8n.yaml epochs=100 imgsz=640

# -*- coding: utf-8 -*-
from ultralytics import YOLO

# 加载训练后的模型权重文件
model = YOLO('best.pt')

# 设置要进行推理的图像路径
image_path = '3.jpg'

# 进行推理
results = model(image_path)

# 输出结果
results.print()  # 打印结果
results.save('output/')  # 保存带有预测结果的图像

# 查看预测的标签和置信度
for result in results:
    print(f'Label: {result.labels}, Confidence: {result.conf}')

# Load a model
model = YOLO("best.pt")  # load a custom trained model

# Export the model
model.export(format="onnx")

/// <summary>
/// yolov8 检测操作数据
/// </summary>
public class YoloDetectData
{
    /// <summary>
    /// 基础数据
    /// </summary>
    public class Basics
    {
        /// <summary>
        /// 模型路径
        /// </summary>
        public string ModelPath { get; set; }
    }
}


 /// <summary>
 /// yolov8 检测操作
 /// </summary>
 public class YoloDetectOperate : CoreAbstract<YoloDetectOperate, YoloDetectData.Basics>, IGetStatus, IDisposable
 {
     /// <summary>
     /// 有参构造函数
     /// </summary>
     /// <param name="basics">基础数据</param>
     public YoloDetectOperate(YoloDetectData.Basics basics) : base(basics)
     {
         yolo = new Yolo(new YoloOptions
         {
             OnnxModel = basics.ModelPath,      // Your Yolov8 or Yolov10 model in onnx format
             ModelType = ModelType.ObjectDetection,  // Model type
             Cuda = false,                           // Use CPU or CUDA for GPU accelerated inference. Default = true
             GpuId = 0,                             // Select Gpu by id. Default = 0
             PrimeGpu = false,                       // Pre-allocate GPU before first. Default = false
         });
     }

     /// <summary>
     /// 检测服务
     /// </summary>
     private Yolo yolo;

     /// <summary>
     /// 运行检测
     /// </summary>
     /// <param name="image">图片源</param>
     /// <returns>操作结果</returns>
     public OperateResult Run(SKImage image)
     {
         //检测
         List<ObjectDetection> results = yolo.RunObjectDetection(image);
         //results 返回的结果,自行修改此处代码
         return OperateResult.CreateSuccessResult("");
     }

     /// <summary>
     /// 运行检测
     /// </summary>
     /// <param name="imagePath">图片路径</param>
     /// <returns>操作结果</returns>
     public OperateResult Run(string imagePath) => Run(SKImage.FromEncodedData(imagePath));




     /// #<inheritdoc/>
     public OperateResult GetStatus()
     {
         if (yolo == null)
         {
             return OperateResult.CreateFailureResult("模型未加载");
         }
         return OperateResult.CreateSuccessResult("模型已加载");
     }

     public async Task<OperateResult> GetStatusAsync(CancellationTokenSource? token = null) => await Task.Run(() => GetStatus(), token?.Token ?? CancellationToken.None);
 }