Windows.ai.machinelearning | 95% PLUS |

mldata.exe model.onnx /namespace MyApp.ML /output ModelCode.cs

// Prepare input tensor (example: image 224x224 RGB) var inputData = new float[1 * 3 * 224 * 224]; // fill with your image data var inputTensor = TensorFloat.CreateFromArray(new long[] 1, 3, 224, 224 , inputData); binding.Bind("input", inputTensor); windows.ai.machinelearning

// 4. Bind & evaluate var session = new LearningModelSession(model); var binding = new LearningModelBinding(session); binding.Bind("data", tensor); mldata

using Microsoft.ML.OnnxRuntime; using Microsoft.AI.MachineLearning; // Load model var file = await StorageFile.GetFileFromApplicationUriAsync( new Uri("ms-appx:///Assets/model.onnx")); var model = await LearningModel.LoadFromStorageFileAsync(file); // Create session var session = new LearningModelSession(model, new LearningModelDevice(LearningModelDeviceKind.Default)); // Create binding var binding = new LearningModelBinding(session); var binding = new LearningModelBinding(session)

var info = LearningModelDevice.FindAllDevices(); foreach (var d in info) Console.WriteLine(d.AdapterId); | Model Type | Input Shape | Output Shape | |------------|-------------|---------------| | Image classification | [1,3,224,224] | [1,1000] | | Object detection (YOLO) | [1,3,640,640] | [1,84,8400] | | BERT text | [1,128] (ids) + [1,128] (mask) | [1,2] (logits) | 7. Debugging & Performance Enable diagnostics:

var result = await session.EvaluateAsync(binding, ""); var classId = result.Outputs["softmaxout"] as TensorFloat;

// Run inference var results = await session.EvaluateAsync(binding, "runId");