using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using Microsoft.ML;
using Microsoft.ML.Data;

namespace ImageClassification
{
    class Program
    {
        static void Main(string[] args)
        {
            // 设置训练数据和模型保存的路径
            var trainDataPath = Path.Combine(Environment.CurrentDirectory, "train");
            var modelPath = Path.Combine(Environment.CurrentDirectory, "model.zip");

            // 初始化ML.NET环境
            var context = new MLContext();

            // 加载训练数据
            var trainData = context.Data.LoadFromImageFolder<ImageData>(trainDataPath, true);

            // 数据预处理
            var pipeline = context.Transforms.Conversion.MapValueToKey("Label", "Label")
                .Append(context.Transforms.ResizeImages("input", 224, 224))
                .Append(context.Transforms.ExtractPixels("input", interleavePixelColors: true))
                .Append(context.Transforms.Normalize("input", new[] { 
                    new NormalizingEstimator.MinMaxColumn("input", 
                        new[] { 0.0f, 255.0f })
                }))
                .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel", "PredictedLabel"));

            // 定义模型训练管道
            var options = new ImageClassificationTrainer.Options()
            {
                FeatureColumnName = "input",
                LabelColumnName = "Label",
                Arch = ImageClassificationTrainer.Architecture.ResnetV250,
                Epoch = 100,
                BatchSize = 10,
                LearningRate = 0.01f,
                MetricsCallback = (metrics) => Console.WriteLine(metrics),
            };

            var trainer = context.MulticlassClassification.Trainers.ImageClassification(options)
                .Append(context.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

            var trainingPipeline = pipeline.Append(trainer);

            // 训练模型
            var model = trainingPipeline.Fit(trainData);

            // 保存模型
            context.Model.Save(model, trainData.Schema, modelPath);

            Console.WriteLine("Training completed!");
        }
    }

    public class ImageData
    {
        [ImageType(224, 224)]
        public BitmapImage Image { get; set; }
        public string Label { get; set; }
    }

    public class ImagePrediction
    {
        [ColumnName("PredictedLabel")]
        public string Prediction { get; set; }
    }
}

此示例代码使用ResNetv250模型，对训练集进行100个Epochs的训练，并将训练后的模型保存到本地。你可以根据需要进行修改。注意，此示例中使用的BitmapImage类型需要在Microsoft.ML.Data命名空间下引用。