VisionCatalog.ImageClassification Метод
Определение
Важно!
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Перегрузки
| ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ImageClassificationTrainer+Options) |
Создание ImageClassificationTrainer с помощью дополнительных параметров, которые обучают глубокой нейронной сети (DNN) для классификации изображений. |
| ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, IDataView) |
Создание ImageClassificationTrainer, которое обучает глубокую нейронную сеть (DNN) для классификации изображений. |
ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ImageClassificationTrainer+Options)
Создание ImageClassificationTrainer с помощью дополнительных параметров, которые обучают глубокой нейронной сети (DNN) для классификации изображений.
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 ImageClassificationTrainerПараметры
Каталог
Объект, указывающий ImageClassificationTrainer.Options дополнительные параметры для ImageClassificationTrainer.
Возвращаемое значение
Примеры
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;
}
}
}
Применяется к
ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, IDataView)
Создание ImageClassificationTrainer, которое обучает глубокую нейронную сеть (DNN) для классификации изображений.
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 ImageClassificationTrainerПараметры
Каталог
- labelColumnName
- String
Имя столбца меток. Значение по умолчанию для этого параметра — label.
- featureColumnName
- String
Имя столбца входных признаков. Значение по умолчанию для этого параметра — Features.
- scoreColumnName
- String
Имя столбца оценки выходных данных. Значение по умолчанию для этого параметра — Score (Оценка)
- predictedLabelColumnName
- String
Имя выходных прогнозируемых столбцов меток. Значение по умолчанию для этого параметра — "PredictedLabel"
- validationSet
- IDataView
Набор проверки, используемый при обучении для повышения качества модели. Значение по умолчанию для этого параметра равно NULL.
Возвращаемое значение
Примеры
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;
}
}
}