VisionCatalog.ImageClassification Method
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
Overloads
| ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ImageClassificationTrainer+Options) |
Create ImageClassificationTrainer using advanced options, which trains a Deep Neural Network(DNN) to classify images. |
| ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, IDataView) |
Create ImageClassificationTrainer, which trains a Deep Neural Network(DNN) to classify images. |
ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ImageClassificationTrainer+Options)
Create ImageClassificationTrainer using advanced options, which trains a Deep Neural Network(DNN) to classify images.
public static Microsoft.ML.Vision.ImageClassificationTrainer ImageClassification (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, Microsoft.ML.Vision.ImageClassificationTrainer.Options options);static member ImageClassification : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * Microsoft.ML.Vision.ImageClassificationTrainer.Options -> Microsoft.ML.Vision.ImageClassificationTrainer<Extension()>
Public Function ImageClassification (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, options As ImageClassificationTrainer.Options) As ImageClassificationTrainerParameters
Catalog
An ImageClassificationTrainer.Options object specifying advanced options for ImageClassificationTrainer.
Returns
Examples
using System;
using System.Collections.Generic;
using System.IO;
using System.IO.Compression;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.ML;
using Microsoft.ML.Data;
using Microsoft.ML.Vision;
using static Microsoft.ML.DataOperationsCatalog;
namespace Samples.Dynamic
{
public class ResnetV2101TransferLearningTrainTestSplit
{
public static void Example()
{
// Set the path for input images.
string assetsRelativePath = @"../../../assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
string imagesDownloadFolderPath = Path.Combine(assetsPath, "inputs",
"images");
//Download the image set and unzip, set the path to image folder.
string finalImagesFolderName = DownloadImageSet(
imagesDownloadFolderPath);
string fullImagesetFolderPath = Path.Combine(
imagesDownloadFolderPath, finalImagesFolderName);
MLContext mlContext = new MLContext(seed: 1);
// Load all the original images info.
IEnumerable<ImageData> images = LoadImagesFromDirectory(
folder: fullImagesetFolderPath, useFolderNameAsLabel: true);
// Shuffle images.
IDataView shuffledFullImagesDataset = mlContext.Data.ShuffleRows(
mlContext.Data.LoadFromEnumerable(images));
// Apply transforms to the input dataset:
// MapValueToKey : map 'string' type labels to keys
// LoadImages : load raw images to "Image" column
shuffledFullImagesDataset = mlContext.Transforms.Conversion
.MapValueToKey("Label", keyOrdinality: Microsoft.ML.Transforms
.ValueToKeyMappingEstimator.KeyOrdinality.ByValue)
.Append(mlContext.Transforms.LoadRawImageBytes("Image",
fullImagesetFolderPath, "ImagePath"))
.Fit(shuffledFullImagesDataset)
.Transform(shuffledFullImagesDataset);
// Split the data 90:10 into train and test sets.
TrainTestData trainTestData = mlContext.Data.TrainTestSplit(
shuffledFullImagesDataset, testFraction: 0.1, seed: 1);
IDataView trainDataset = trainTestData.TrainSet;
IDataView testDataset = trainTestData.TestSet;
// Set the options for ImageClassification.
var options = new ImageClassificationTrainer.Options()
{
FeatureColumnName = "Image",
LabelColumnName = "Label",
// Just by changing/selecting InceptionV3/MobilenetV2/ResnetV250
// here instead of ResnetV2101 you can try a different
// architecture/ pre-trained model.
Arch = ImageClassificationTrainer.Architecture.ResnetV2101,
Epoch = 50,
BatchSize = 10,
LearningRate = 0.01f,
MetricsCallback = (metrics) => Console.WriteLine(metrics),
ValidationSet = testDataset,
// Disable EarlyStopping to run to specified number of epochs.
EarlyStoppingCriteria = null
};
// Create the ImageClassification pipeline.
var pipeline = mlContext.MulticlassClassification.Trainers.
ImageClassification(options)
.Append(mlContext.Transforms.Conversion.MapKeyToValue(
outputColumnName: "PredictedLabel",
inputColumnName: "PredictedLabel"));
Console.WriteLine("*** Training the image classification model " +
"with DNN Transfer Learning on top of the selected " +
"pre-trained model/architecture ***");
// Train the model
// This involves calculating the bottleneck values, and then
// training the final layer. Sample output is:
// Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1
// Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2
// ...
// Phase: Training, Dataset used: Train, Batch Processed Count: 18, Learning Rate: 0.01 Epoch: 0, Accuracy: 0.9166667, Cross-Entropy: 0.4787029
// ...
// Phase: Training, Dataset used: Train, Batch Processed Count: 18, Learning Rate: 0.002265001 Epoch: 49, Accuracy: 1, Cross-Entropy: 0.03529362
// Phase: Training, Dataset used: Validation, Batch Processed Count: 3, Epoch: 49, Accuracy: 0.8238096
var trainedModel = pipeline.Fit(trainDataset);
Console.WriteLine("Training with transfer learning finished.");
// Save the trained model.
mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema,
"model.zip");
// Load the trained and saved model for prediction.
ITransformer loadedModel;
DataViewSchema schema;
using (var file = File.OpenRead("model.zip"))
loadedModel = mlContext.Model.Load(file, out schema);
// Evaluate the model on the test dataset.
// Sample output:
// Making bulk predictions and evaluating model's quality...
// Micro - accuracy: 0.851851851851852,macro - accuracy = 0.85
EvaluateModel(mlContext, testDataset, loadedModel);
// Predict on a single image class using an in-memory image.
// Sample output:
// Scores : [0.9305387,0.005769793,0.0001719091,0.06005093,0.003468738], Predicted Label : daisy
TrySinglePrediction(fullImagesetFolderPath, mlContext, loadedModel);
Console.WriteLine("Prediction on a single image finished.");
Console.WriteLine("Press any key to finish");
Console.ReadKey();
}
// Predict on a single image.
private static void TrySinglePrediction(string imagesForPredictions,
MLContext mlContext, ITransformer trainedModel)
{
// Create prediction function to try one prediction.
var predictionEngine = mlContext.Model
.CreatePredictionEngine<InMemoryImageData,
ImagePrediction>(trainedModel);
// Load test images.
IEnumerable<InMemoryImageData> testImages =
LoadInMemoryImagesFromDirectory(imagesForPredictions, false);
// Create an in-memory image object from the first image in the test data.
InMemoryImageData imageToPredict = new InMemoryImageData
{
Image = testImages.First().Image
};
// Predict on the single image.
var prediction = predictionEngine.Predict(imageToPredict);
Console.WriteLine($"Scores : [{string.Join(",", prediction.Score)}], " +
$"Predicted Label : {prediction.PredictedLabel}");
}
// Evaluate the trained model on the passed test dataset.
private static void EvaluateModel(MLContext mlContext,
IDataView testDataset, ITransformer trainedModel)
{
Console.WriteLine("Making bulk predictions and evaluating model's " +
"quality...");
// Evaluate the model on the test data and get the evaluation metrics.
IDataView predictions = trainedModel.Transform(testDataset);
var metrics = mlContext.MulticlassClassification.Evaluate(predictions);
Console.WriteLine($"Micro-accuracy: {metrics.MicroAccuracy}," +
$"macro-accuracy = {metrics.MacroAccuracy}");
Console.WriteLine("Predicting and Evaluation complete.");
}
//Load the Image Data from input directory.
public static IEnumerable<ImageData> LoadImagesFromDirectory(string folder,
bool useFolderNameAsLabel = true)
{
var files = Directory.GetFiles(folder, "*",
searchOption: SearchOption.AllDirectories);
foreach (var file in files)
{
if (Path.GetExtension(file) != ".jpg")
continue;
var label = Path.GetFileName(file);
if (useFolderNameAsLabel)
label = Directory.GetParent(file).Name;
else
{
for (int index = 0; index < label.Length; index++)
{
if (!char.IsLetter(label[index]))
{
label = label.Substring(0, index);
break;
}
}
}
yield return new ImageData()
{
ImagePath = file,
Label = label
};
}
}
// Load In memory raw images from directory.
public static IEnumerable<InMemoryImageData>
LoadInMemoryImagesFromDirectory(string folder,
bool useFolderNameAsLabel = true)
{
var files = Directory.GetFiles(folder, "*",
searchOption: SearchOption.AllDirectories);
foreach (var file in files)
{
if (Path.GetExtension(file) != ".jpg")
continue;
var label = Path.GetFileName(file);
if (useFolderNameAsLabel)
label = Directory.GetParent(file).Name;
else
{
for (int index = 0; index < label.Length; index++)
{
if (!char.IsLetter(label[index]))
{
label = label.Substring(0, index);
break;
}
}
}
yield return new InMemoryImageData()
{
Image = File.ReadAllBytes(file),
Label = label
};
}
}
// Download and unzip the image dataset.
public static string DownloadImageSet(string imagesDownloadFolder)
{
// get a set of images to teach the network about the new classes
//SINGLE SMALL FLOWERS IMAGESET (200 files)
string fileName = "flower_photos_small_set.zip";
string url = $"https://aka.ms/mlnet-resources/datasets/flower_photos_small_set.zip";
Download(url, imagesDownloadFolder, fileName).Wait();
UnZip(Path.Combine(imagesDownloadFolder, fileName), imagesDownloadFolder);
return Path.GetFileNameWithoutExtension(fileName);
}
// Download file to destination directory from input URL.
public static async Task<bool> Download(string url, string destDir, string destFileName)
{
if (destFileName == null)
destFileName = url.Split(Path.DirectorySeparatorChar).Last();
Directory.CreateDirectory(destDir);
string relativeFilePath = Path.Combine(destDir, destFileName);
if (File.Exists(relativeFilePath))
{
Console.WriteLine($"{relativeFilePath} already exists.");
return false;
}
Console.WriteLine($"Downloading {relativeFilePath}");
using (HttpClient client = new HttpClient())
{
var response = await client.GetStreamAsync(new Uri($"{url}")).ConfigureAwait(false);
using (var fs = new FileStream(relativeFilePath, FileMode.CreateNew))
{
await response.CopyToAsync(fs);
}
}
Console.WriteLine("");
Console.WriteLine($"Downloaded {relativeFilePath}");
return true;
}
// Unzip the file to destination folder.
public static void UnZip(String gzArchiveName, String destFolder)
{
var flag = gzArchiveName.Split(Path.DirectorySeparatorChar)
.Last()
.Split('.')
.First() + ".bin";
if (File.Exists(Path.Combine(destFolder, flag))) return;
Console.WriteLine($"Extracting.");
var task = Task.Run(() =>
{
ZipFile.ExtractToDirectory(gzArchiveName, destFolder);
});
while (!task.IsCompleted)
{
Thread.Sleep(200);
Console.Write(".");
}
File.Create(Path.Combine(destFolder, flag));
Console.WriteLine("");
Console.WriteLine("Extracting is completed.");
}
// Get absolute path from relative path.
public static string GetAbsolutePath(string relativePath)
{
FileInfo _dataRoot = new FileInfo(typeof(
ResnetV2101TransferLearningTrainTestSplit).Assembly.Location);
string assemblyFolderPath = _dataRoot.Directory.FullName;
string fullPath = Path.Combine(assemblyFolderPath, relativePath);
return fullPath;
}
// InMemoryImageData class holding the raw image byte array and label.
public class InMemoryImageData
{
[LoadColumn(0)]
public byte[] Image;
[LoadColumn(1)]
public string Label;
}
// ImageData class holding the image path and label.
public class ImageData
{
[LoadColumn(0)]
public string ImagePath;
[LoadColumn(1)]
public string Label;
}
// ImagePrediction class holding the score and predicted label metrics.
public class ImagePrediction
{
[ColumnName("Score")]
public float[] Score;
[ColumnName("PredictedLabel")]
public string PredictedLabel;
}
}
}
Applies to
ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, IDataView)
Create ImageClassificationTrainer, which trains a Deep Neural Network(DNN) to classify images.
public static Microsoft.ML.Vision.ImageClassificationTrainer ImageClassification (this Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers catalog, string labelColumnName = "Label", string featureColumnName = "Features", string scoreColumnName = "Score", string predictedLabelColumnName = "PredictedLabel", Microsoft.ML.IDataView validationSet = default);static member ImageClassification : Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers * string * string * string * string * Microsoft.ML.IDataView -> Microsoft.ML.Vision.ImageClassificationTrainer<Extension()>
Public Function ImageClassification (catalog As MulticlassClassificationCatalog.MulticlassClassificationTrainers, Optional labelColumnName As String = "Label", Optional featureColumnName As String = "Features", Optional scoreColumnName As String = "Score", Optional predictedLabelColumnName As String = "PredictedLabel", Optional validationSet As IDataView = Nothing) As ImageClassificationTrainerParameters
Catalog
- labelColumnName
- String
The name of the labels column. The default for this parameter is "label".
- featureColumnName
- String
The name of the input features column. The default for this parameter is "Features".
- scoreColumnName
- String
The name of the output score column. The default for this parameter is "Score"
- predictedLabelColumnName
- String
The name of the output predicted label columns. The default for this parameter is "PredictedLabel"
- validationSet
- IDataView
The validation set used while training to improve model quality. The default for this parameter is null.
Returns
Examples
using System;
using System.Collections.Generic;
using System.IO;
using System.IO.Compression;
using System.Linq;
using System.Net;
using System.Net.Http;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.ML;
using Microsoft.ML.Data;
using static Microsoft.ML.DataOperationsCatalog;
namespace Samples.Dynamic
{
public class ImageClassificationDefault
{
public static void Example()
{
// Set the path for input images.
string assetsRelativePath = @"../../../assets";
string assetsPath = GetAbsolutePath(assetsRelativePath);
string imagesDownloadFolderPath = Path.Combine(assetsPath, "inputs",
"images");
//Download the image set and unzip, set the path to image folder.
string finalImagesFolderName = DownloadImageSet(
imagesDownloadFolderPath);
string fullImagesetFolderPath = Path.Combine(
imagesDownloadFolderPath, finalImagesFolderName);
MLContext mlContext = new MLContext(seed: 1);
mlContext.Log += MlContext_Log;
// Load all the original images info
IEnumerable<ImageData> images = LoadImagesFromDirectory(
folder: fullImagesetFolderPath, useFolderNameAsLabel: true);
// Shuffle images.
IDataView shuffledFullImagesDataset = mlContext.Data.ShuffleRows(
mlContext.Data.LoadFromEnumerable(images));
// Apply transforms to the input dataset:
// MapValueToKey : map 'string' type labels to keys
// LoadImages : load raw images to "Image" column
shuffledFullImagesDataset = mlContext.Transforms.Conversion
.MapValueToKey("Label", keyOrdinality: Microsoft.ML.Transforms
.ValueToKeyMappingEstimator.KeyOrdinality.ByValue)
.Append(mlContext.Transforms.LoadRawImageBytes("Image",
fullImagesetFolderPath, "ImagePath"))
.Fit(shuffledFullImagesDataset)
.Transform(shuffledFullImagesDataset);
// Split the data 90:10 into train and test sets.
TrainTestData trainTestData = mlContext.Data.TrainTestSplit(
shuffledFullImagesDataset, testFraction: 0.1, seed: 1);
IDataView trainDataset = trainTestData.TrainSet;
IDataView testDataset = trainTestData.TestSet;
// Create the ImageClassification pipeline by just passing the
// input feature and label column name.
var pipeline = mlContext.MulticlassClassification.Trainers
.ImageClassification(featureColumnName: "Image")
.Append(mlContext.Transforms.Conversion.MapKeyToValue(
outputColumnName: "PredictedLabel",
inputColumnName: "PredictedLabel"));
Console.WriteLine("*** Training the image classification model " +
"with DNN Transfer Learning on top of the selected " +
"pre-trained model/architecture ***");
// Train the model.
// This involves calculating the bottleneck values, and then
// training the final layerSample output is:
// [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Bottleneck Computation, Dataset used: Train, Image Index: 1
// [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Bottleneck Computation, Dataset used: Train, Image Index: 2
// ...
// [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used: Train, Batch Processed Count: 18, Learning Rate: 0.01 Epoch: 0, Accuracy: 0.9, Cross-Entropy: 0.481340
// ...
// [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used: Train, Batch Processed Count: 18, Learning Rate: 0.004759203 Epoch: 25, Accuracy: 1, Cross-Entropy: 0.04848097
// [Source=ImageClassificationTrainer; ImageClassificationTrainer, Kind=Trace] Phase: Training, Dataset used: Train, Batch Processed Count: 18, Learning Rate: 0.004473651 Epoch: 26, Accuracy: 1, Cross-Entropy: 0.04930306
var trainedModel = pipeline.Fit(trainDataset);
Console.WriteLine("Training with transfer learning finished.");
// Save the trained model.
mlContext.Model.Save(trainedModel, shuffledFullImagesDataset.Schema,
"model.zip");
// Load the trained and saved model for prediction.
ITransformer loadedModel;
DataViewSchema schema;
using (var file = File.OpenRead("model.zip"))
loadedModel = mlContext.Model.Load(file, out schema);
// Evaluate the model on the test dataset.
// Sample output:
// Making bulk predictions and evaluating model's quality...
// Micro-accuracy: 0.925925925925926,macro-accuracy = 0.933333333333333
EvaluateModel(mlContext, testDataset, loadedModel);
// Predict on a single image class using an in-memory image.
// Sample output:
// Scores : [0.8657553,0.006911285,1.46484E-05,0.1266835,0.0006352618], Predicted Label : daisy
TrySinglePrediction(fullImagesetFolderPath, mlContext, loadedModel);
Console.WriteLine("Prediction on a single image finished.");
Console.WriteLine("Press any key to finish");
Console.ReadKey();
}
private static void MlContext_Log(object sender, LoggingEventArgs e)
{
if (e.Message.StartsWith("[Source=ImageClassificationTrainer;"))
{
Console.WriteLine(e.Message);
}
}
// Predict on a single image.
private static void TrySinglePrediction(string imagesForPredictions,
MLContext mlContext, ITransformer trainedModel)
{
// Create prediction function to try one prediction.
var predictionEngine = mlContext.Model
.CreatePredictionEngine<InMemoryImageData,
ImagePrediction>(trainedModel);
// Load test images.
IEnumerable<InMemoryImageData> testImages =
LoadInMemoryImagesFromDirectory(imagesForPredictions, false);
// Create an in-memory image object from the first image in the test data.
InMemoryImageData imageToPredict = new InMemoryImageData
{
Image = testImages.First().Image
};
// Predict on the single image.
var prediction = predictionEngine.Predict(imageToPredict);
Console.WriteLine($"Scores : [{string.Join(",", prediction.Score)}], " +
$"Predicted Label : {prediction.PredictedLabel}");
}
// Evaluate the trained model on the passed test dataset.
private static void EvaluateModel(MLContext mlContext,
IDataView testDataset, ITransformer trainedModel)
{
Console.WriteLine("Making bulk predictions and evaluating model's " +
"quality...");
// Evaluate the model on the test data and get the evaluation metrics.
IDataView predictions = trainedModel.Transform(testDataset);
var metrics = mlContext.MulticlassClassification.Evaluate(predictions);
Console.WriteLine($"Micro-accuracy: {metrics.MicroAccuracy}," +
$"macro-accuracy = {metrics.MacroAccuracy}");
Console.WriteLine("Predicting and Evaluation complete.");
}
//Load the Image Data from input directory.
public static IEnumerable<ImageData> LoadImagesFromDirectory(string folder,
bool useFolderNameAsLabel = true)
{
var files = Directory.GetFiles(folder, "*",
searchOption: SearchOption.AllDirectories);
foreach (var file in files)
{
if (Path.GetExtension(file) != ".jpg")
continue;
var label = Path.GetFileName(file);
if (useFolderNameAsLabel)
label = Directory.GetParent(file).Name;
else
{
for (int index = 0; index < label.Length; index++)
{
if (!char.IsLetter(label[index]))
{
label = label.Substring(0, index);
break;
}
}
}
yield return new ImageData()
{
ImagePath = file,
Label = label
};
}
}
// Load In memory raw images from directory.
public static IEnumerable<InMemoryImageData>
LoadInMemoryImagesFromDirectory(string folder,
bool useFolderNameAsLabel = true)
{
var files = Directory.GetFiles(folder, "*",
searchOption: SearchOption.AllDirectories);
foreach (var file in files)
{
if (Path.GetExtension(file) != ".jpg")
continue;
var label = Path.GetFileName(file);
if (useFolderNameAsLabel)
label = Directory.GetParent(file).Name;
else
{
for (int index = 0; index < label.Length; index++)
{
if (!char.IsLetter(label[index]))
{
label = label.Substring(0, index);
break;
}
}
}
yield return new InMemoryImageData()
{
Image = File.ReadAllBytes(file),
Label = label
};
}
}
// Download and unzip the image dataset.
public static string DownloadImageSet(string imagesDownloadFolder)
{
// get a set of images to teach the network about the new classes
//SINGLE SMALL FLOWERS IMAGESET (200 files)
string fileName = "flower_photos_small_set.zip";
string url = $"https://aka.ms/mlnet-resources/datasets/flower_photos_small_set.zip";
Download(url, imagesDownloadFolder, fileName).Wait();
UnZip(Path.Combine(imagesDownloadFolder, fileName), imagesDownloadFolder);
return Path.GetFileNameWithoutExtension(fileName);
}
// Download file to destination directory from input URL.
public static async Task<bool> Download(string url, string destDir, string destFileName)
{
if (destFileName == null)
destFileName = url.Split(Path.DirectorySeparatorChar).Last();
Directory.CreateDirectory(destDir);
string relativeFilePath = Path.Combine(destDir, destFileName);
if (File.Exists(relativeFilePath))
{
Console.WriteLine($"{relativeFilePath} already exists.");
return false;
}
Console.WriteLine($"Downloading {relativeFilePath}");
using (HttpClient client = new HttpClient())
{
var response = await client.GetStreamAsync(new Uri($"{url}")).ConfigureAwait(false);
using (var fs = new FileStream(relativeFilePath, FileMode.CreateNew))
{
await response.CopyToAsync(fs);
}
}
Console.WriteLine("");
Console.WriteLine($"Downloaded {relativeFilePath}");
return true;
}
// Unzip the file to destination folder.
public static void UnZip(String gzArchiveName, String destFolder)
{
var flag = gzArchiveName.Split(Path.DirectorySeparatorChar)
.Last()
.Split('.')
.First() + ".bin";
if (File.Exists(Path.Combine(destFolder, flag))) return;
Console.WriteLine($"Extracting.");
ZipFile.ExtractToDirectory(gzArchiveName, destFolder);
File.Create(Path.Combine(destFolder, flag));
Console.WriteLine("");
Console.WriteLine("Extracting is completed.");
}
// Get absolute path from relative path.
public static string GetAbsolutePath(string relativePath)
{
FileInfo _dataRoot = new FileInfo(typeof(
ImageClassificationDefault).Assembly.Location);
string assemblyFolderPath = _dataRoot.Directory.FullName;
string fullPath = Path.Combine(assemblyFolderPath, relativePath);
return fullPath;
}
// InMemoryImageData class holding the raw image byte array and label.
public class InMemoryImageData
{
[LoadColumn(0)]
public byte[] Image;
[LoadColumn(1)]
public string Label;
}
// ImageData class holding the imagepath and label.
public class ImageData
{
[LoadColumn(0)]
public string ImagePath;
[LoadColumn(1)]
public string Label;
}
// ImagePrediction class holding the score and predicted label metrics.
public class ImagePrediction
{
[ColumnName("Score")]
public float[] Score;
[ColumnName("PredictedLabel")]
public string PredictedLabel;
}
}
}