yolov8系列--YoloDotNet is a C# .NET 8.0 implementation of Yo.zip

# YoloDotNet ### YoloDotNet is a C# .NET 8.0 implementation of Yolov8 and ONNX runtime with CUDA YoloDotNet is a .NET 8 implementation of Yolov8 for detecting objects in images and videos using ML.NET and ONNX runtime with GPU acceleration using CUDA. YoloDotNet currently supports `Classification`, `Object Detection` and `Segmentation` in both images and videos. | Classification | Object Dectection | Segmentation | |:---:|:---:|:---:| | Categorize an image | Detect multiple objects in a single image | Separate detected objects by pixel maps | | [image from pexels.com](https://www.pexels.com/photo/hummingbird-drinking-nectar-from-blooming-flower-in-garden-5344570/) | [image from pexels.com](https://www.pexels.com/photo/men-s-brown-coat-842912/) | [image from pexels.com](https://www.pexels.com/photo/man-riding-a-black-touring-motorcycle-903972/) | YoloDotNet with GPU-acceleration requires CUDA and cuDNN. :information_source: Before installing CUDA and cuDNN, make sure to verify the ONNX runtime's [current compatibility with specific versions](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html#requirements). - Download and install [CUDA](https://developer.nvidia.com/cuda-downloads) - Download [cuDNN](https://developer.nvidia.com/cudnn) and follow the [installation instructions](https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html#install-windows) - Yolov8 model [exported to ONNX format](https://docs.ultralytics.com/modes/export/#usage-examples)<br> Currently, YoloDotNet supports `Classification`, `Object Detection` and `Segmentation` in both images and videos ## Verify your model ```csharp using YoloDotNet; // Instantiate a new Yolo object with your ONNX-model using var yolo = new Yolo(@"path\to\model.onnx"); Console.WriteLine(yolo.OnnxModel.ModelType); // Output if valid: Classification or ObjectDetection ``` > [!NOTE] > For Video, you need FFmpeg and FFProbe > - Download [FFMPEG](https://ffmpeg.org/download.html) > - Add FFmpeg and ffprobe to the Path-variable in your Environment Variables # Nuget ``` > dotnet add package YoloDotNet ``` # Example - Image ```csharp using SixLabors.ImageSharp; using SixLabors.ImageSharp.PixelFormats; using YoloDotNet; using YoloDotNet.Extensions; // Instantiate a new Yolo object with your ONNX-model and CUDA (default) using var yolo = new Yolo(@"path\to\your_model.onnx"); // Load image using var image = Image.Load<Rgba32>(@"path\to\image.jpg"); // Run var results = yolo.RunClassification(image, 5); // Top 5 classes //var results = yolo.RunObjectDetection(image, 0.25); //var results = yolo.RunSegmentation(image, 0.25); image.Draw(results); image.Save(@"path\to\save\image.jpg"); ``` # Example - Video ```csharp using SixLabors.ImageSharp; using SixLabors.ImageSharp.PixelFormats; using YoloDotNet; using YoloDotNet.Extensions; // Instantiate a new Yolo object with your ONNX-model and CUDA using var yolo = new Yolo(@"path\to\your_model.onnx"); // Video options var options = new VideoOptions { VideoFile = @"path\to\video.mp4", OutputDir = @"path\to\output\folder", //GenerateVideo = true, //DrawLabels = true, //FPS = 30, //Width = 1280, //Height = 720, //DrawConfidence = true, //KeepAudio = true, //KeepFrames = false }; // Run var results = yolo.RunClassification(options, 5); // Top 5 classes //var results = yolo.RunObjectDetection(options, 0.25); //var results = yolo.RunSegmentation(options, 0.25); // Do further processing with results if needed... ``` # GPU Object detection with GPU and GPU-Id = 0 is enabled by default ```csharp // Default setup. GPU with GPU-Id 0 using var yolo = new Yolo(@"path\to\model.onnx"); ``` With a specific GPU-Id ```csharp // GPU with a user defined GPU-Id using var yolo = new Yolo(@"path\to\model.onnx", true, 1); ``` # CPU YoloDotNet detection with CPU ```csharp // With CPU using var yolo = new Yolo(@"path\to\model.onnx", false); ``` # Access ONNX metadata and labels The internal ONNX metadata such as ***input & output parameters, version, author, description, date*** along with the labels can be accessed via the `yolo.OnnxModel` property. Example: ```csharp using var yolo = new Yolo(@"path\to\model.onnx"); // ONNX metadata and labels resides inside yolo.OnnxModel Console.WriteLine(yolo.OnnxModel); ``` Example: ```csharp // Instantiate a new object using var yolo = new Yolo(@"path\to\model.onnx"); // Display metadata foreach (var property in yolo.OnnxModel.GetType().GetProperties()) { var value = property.GetValue(yolo.OnnxModel); Console.WriteLine($"{property.Name,-20}{value!}"); if (property.Name == nameof(yolo.OnnxModel.CustomMetaData)) foreach (var data in (Dictionary<string, string>)value!) Console.WriteLine($"{"",-20}{data.Key,-20}{data.Value}"); } // Get ONNX labels var labels = yolo.OnnxModel.Labels; Console.WriteLine(); Console.WriteLine($"Labels ({labels.Length}):"); Console.WriteLine(new string('-', 58)); // Display for (var i = 0; i < labels.Length; i++) Console.WriteLine($"index: {i,-8} label: {labels[i].Name,20} color: {labels[i].Color}"); // Output: // ModelType ObjectDetection // InputName images // OutputName output0 // CustomMetaData System.Collections.Generic.Dictionary`2[System.String,System.String] // date 2023-11-07T13:33:33.565196 // description Ultralytics YOLOv8n model trained on coco.yaml // author Ultralytics // task detect // license AGPL-3.0 https://ultralytics.com/license // version 8.0.202 // stride 32 // batch 1 // imgsz [640, 640] // names {0: 'person', 1: 'bicycle', 2: 'car' ... } // ImageSize Size [ Width=640, Height=640 ] // Input Input { BatchSize = 1, Channels = 3, Width = 640, Height = 640 } // Output ObjectDetectionShape { BatchSize = 1, Elements = 84, Channels = 8400 } // Labels YoloDotNet.Models.LabelModel[] // // Labels (80): // --------------------------------------------------------- // index: 0 label: person color: #5d8aa8 // index: 1 label: bicycle color: #f0f8ff // index: 2 label: car color: #e32636 // index: 3 label: motorcycle color: #efdecd // ... ``` # Donate [https://paypal.me/nickswardh](https://paypal.me/nickswardh?country.x=SE&locale.x=en_US) # References & Acknowledgements https://github.com/ultralytics/ultralytics https://github.com/sstainba/Yolov8.Net https://github.com/mentalstack/yolov5-net