ML.NET Schema mismatch: expected VarVector<Byte> got Vector<Byte>

I figured out how to make this work. The issue here was that the underlying types were correct but the schema was wrong. I was able to solve my issue with a CustomMapping and some attribute magic.See CustomMapping documentation for how the CustomMapping works.

The corrected code snippet is below.

    // SOMEWHERE OUTSIDE OF THE FUNCTION DEFINE:
    // Define a class to represent the input column type for the custom transform
    class InputData 
    {
        [VectorType(1)] // attribute specifies vector type of known length
        public VBuffer<Byte> Image1; // the VBuffer<> type actually represents the data
    }

    // Define a class to represent the output column type for the custom transform
    class OutputData 
    {
        // THE MAGICAL FIX: attribute specifies vector type of unknown length (i.e. VarVector)
        [VectorType()]
        public VBuffer<Byte> Image; // the VBuffer<> type actually represents the data
    }

    // ------------------------------------------------------------------
    // INSIDE THE FUNCTION THAT WILL DO THE TRAINING
    // ------------------------------------------------------------------

    // Fill an IDataView with the Label and ImagePath information:
    // The ImageData class is defined for this purpose and it is just:
    // 
    //  public class ImageData {
    //       public string ImagePath { get; set; }
    //       public string Label { get; set; }
    //  }
    // 
    // Create a bunch of image names and their labels into a list of ImageData objects.
    IEnumerable<ImageData> images = LoadImagesFromDirectory(folder: assetsRelativePath, 
                                                            useFolderNameAsLabel: true);
    IDataView imageData = mlContext.Data.LoadFromEnumerable(images);

    // Define the preprocessing pipeline to:
    // 1. Map the Label to a unique key
    // 2. Load the image data using the ImagePath column
    // 3. Reduce the resolution of the image data
    // 4. Crop the reduced resolution image data
    // 5. Transform the image data from type "Image" to a byte array.
    var preprocessingPipeline = mlContext.Transforms.Conversion.MapValueToKey(
                    inputColumnName: "Label",
                    outputColumnName: "LabelAsKey")
                .Append(mlContext.Transforms.LoadImages(
                    inputColumnName: "ImagePath",
                    outputColumnName: "RawImage",
                    imageFolder: assetsRelativePath))
                .Append(mlContext.Transforms.ResizeImages(
                    inputColumnName: "RawImage",
                    outputColumnName: "ResReduxImage",
                    imageWidth: 512,
                    imageHeight: 459,
                    resizing: Microsoft.ML.Transforms.Image.ImageResizingEstimator.ResizingKind.Fill))
                .Append(mlContext.Transforms.ResizeImages(
                    inputColumnName: "ResReduxImage",
                    outputColumnName: "CroppedImage",
                    imageWidth: 512,
                    imageHeight: 459,
                    resizing: Microsoft.ML.Transforms.Image.ImageResizingEstimator.ResizingKind.IsoCrop,
                    cropAnchor: Microsoft.ML.Transforms.Image.ImageResizingEstimator.Anchor.Center))
                .Append(mlContext.Transforms.ExtractPixels(
                    inputColumnName: "CroppedImage",
                    outputColumnName: "Image1",
                    outputAsFloatArray: false,
                    colorsToExtract: ImagePixelExtractingEstimator.ColorBits.Red));

     // Transform the raw ImageData into the TransformedData that the Model will train on
     IDataView preProcessedData = 
           preprocessingPipeline.Fit(imageData).Transform(imageData);

     // Create an action representing the custom transform...
     // The data itself does not need to be changed at all, so this is just an 
     // identity transform
     Action<InputData, OutputData> convertVecType 
               = (input, output) => output.Image = input.Image1;
 
     var convertTypePipeline = mlContext.Transforms.CustomMapping(convertVecType, 
                                                                 "convertVecType");
     preProcessedData = 
              convertTypePipeline.Fit(preProcessedData).Transform(preProcessedData);

     // Partition the data set for training and validation
     TrainTestData trainSplit = mlContext.Data.TrainTestSplit(data: preProcessedData, testFraction: 0.4);
     TrainTestData validationTestSplit = mlContext.Data.TrainTestSplit(trainSplit.TestSet);

     IDataView trainSet = trainSplit.TrainSet;
     IDataView validationSet = validationTestSplit.TrainSet;
     IDataView testSet = validationTestSplit.TestSet;

     var classifierOptions = new ImageClassificationTrainer.Options()
     {
          FeatureColumnName = "Image",
          LabelColumnName = "LabelAsKey",
          ValidationSet = validationSet,
          Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
          MetricsCallback = (metrics) => Console.WriteLine(metrics),
          TestOnTrainSet = false,
          ReuseTrainSetBottleneckCachedValues = true,
          ReuseValidationSetBottleneckCachedValues = true,
          WorkspacePath=workspaceRelativePath
     };

     var trainingPipeline = 
           mlContext.MulticlassClassification.Trainers.ImageClassification(classifierOptions)
           .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"));

     // When I call Fit() in the next line of code, the following runtime error occurs: 
     // System.ArgumentOutOfRangeException: 'Schema mismatch for feature column 'Image': 
     // expected VarVector<Byte>, got Vector<Byte> Parameter name: inputSchema'
     ITransformer trainedModel = trainingPipeline.Fit(trainSet);