ML.net cant find input column, out of range exception when training algorithm

I'm new with the ML.NET and have a problem which i cannot resolve for few days. I can train my model, but when i try to CreateEnginePrediciton, i have an error: "Feature column 'Feature' not found" Here is my code:

 var context = new MLContext(seed: 0);

        // Create a DataView containing the image paths and labels
        var input = LoadLabeledImagesFromPath(_imagePath);
        var data = context.Data.LoadFromEnumerable(input);
        data = context.Data.ShuffleRows(data);

        // Load the images and convert the labels to keys to serve as categorical values
        var images = context.Transforms.Conversion.MapValueToKey(inputColumnName: nameof(Input.Label), outputColumnName: _keyColumnName)
            .Append(context.Transforms.LoadRawImageBytes(inputColumnName: nameof(Input.ImagePath), outputColumnName: nameof(Input.Image), imageFolder: _imagePath));
        var dataPrepModel = images.Fit(data);
        var dataPrepDataView = dataPrepModel.Transform(data);

        // Split the dataset for training and testing
        var trainTestData = context.Data.TrainTestSplit(dataPrepDataView, testFraction: 0.2, seed: 1);
        var trainData = trainTestData.TrainSet;
        var testData = trainTestData.TestSet;

        // Create an image-classification pipeline and train the model
        var options = new ImageClassificationTrainer.Options()
        {
            FeatureColumnName = nameof(Input.Image),
            LabelColumnName = _keyColumnName,
            ValidationSet = testData,
            Arch = ImageClassificationTrainer.Architecture.ResnetV2101, // Pretrained DNN
            MetricsCallback = (metrics) => Console.WriteLine(metrics),
            TestOnTrainSet = false
        };

        var pipeline = context.MulticlassClassification.Trainers.ImageClassification(options)
            .Append(context.Transforms.Conversion.MapKeyToValue(_predictedLabelColumnName));

        Console.WriteLine("Training the model...");
        var model = pipeline.Fit(trainData);

        // Evaluate the model and show the results
        var predictions = model.Transform(trainData);
        var metrics = context.MulticlassClassification.Evaluate(predictions, labelColumnName: _keyColumnName, predictedLabelColumnName: _predictedLabelColumnName);

        Console.WriteLine();
        Console.WriteLine($"Macro accuracy = {metrics.MacroAccuracy:P2}");
        Console.WriteLine($"Micro accuracy = {metrics.MicroAccuracy:P2}");
        Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
        Console.WriteLine();

        // Save the model
        Console.WriteLine();
        Console.WriteLine("Saving the model...");
        context.Model.Save(model, trainData.Schema, _savePath);
    }

    private static List<Input> LoadLabeledImagesFromPath(string path)
    {
        var images = new List<Input>();
        var directories = Directory.EnumerateDirectories(path);

        foreach (var directory in directories)
        {
            var files = Directory.EnumerateFiles(directory);

            images.AddRange(files.Select(x => new Input
            {
                ImagePath = Path.GetFullPath(x),
                Label = Path.GetFileName(directory)
            }));
        }

        return images;
    

And my classess:

public class Input
{
    public byte[] Image;
    public string ImagePath;
    public string Label;
}

public class Output
{
    public float[] Score;
    public string PredictedLabel;
}

And code where i try to create prediciton engine:

 public static PredictionEngine<ModelInput, ModelOutput> CreatePredictionEngine()
    {
        // Create new MLContext
        MLContext mlContext = new MLContext();

        // Load model & create prediction engine
        ITransformer mlModel = mlContext.Model.Load(MLNetModelPath, out var modelInputSchema);

        var predEngine = mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(mlModel);

        return predEngine;
    }

Could you help me? Code is mostly from ML.NET tutorials and i can train it but cannot use it :-(