C# for Image Recognition and Classification

Image recognition and classification in C# often involve the use of machine learning models. In this example, we'll introduce the concept with a basic code snippet for image classification using ML.NET, a machine learning framework for .NET. A complete image classification solution would use more complex models and datasets.

Sample C# Code for Image Classification

Here's a basic example of C# code for image classification using ML.NET:

using System;
using System.Drawing;
using System.IO;
using Microsoft.ML;
using Microsoft.ML.Data;
class ImageData
public string ImagePath;
public string Label;
class ImagePrediction
public string Label;
class Program
static void Main()
var context = new MLContext();
var data = context.Data.LoadFromTextFile<ImageData>("image_data.txt", separatorChar: ',');
var pipeline = context.Transforms.Conversion.MapValueToKey("Label")
.Append(context.Transforms.LoadImages("Image", "ImagePath"))
.Append(context.Transforms.ResizeImages("Image", 224, 224))
.Append(context.Transforms.ApplyOnnxModel("Label", "Model.onnx"))
var model = pipeline.Fit(data);
var predictionEngine = context.Model.CreatePredictionEngine<ImageData, ImagePrediction>(model);
var imageFilePath = "sample_image.jpg";
var imageData = new ImageData { ImagePath = imageFilePath };
var prediction = predictionEngine.Predict(imageData);
Console.WriteLine("Image Classification Result:");
Console.WriteLine($"Image: {imageData.ImagePath}");
Console.WriteLine($"Predicted Label: {prediction.Label}");

This code uses a pre-trained ONNX model for image classification. You need to replace "image_data.txt," "Model.onnx," and "sample_image.jpg" with your own dataset, model, and image. ML.NET simplifies the process of working with machine learning models in C#.

Sample HTML Image Preview

Below is a simple HTML element to preview the image:

Sample Image