IntptrMax.YoloSharp 1.1.5

YoloSharp

Train Yolo model in C# with TorchSharp. </br> With the help of this project you won't have to transform .pt model to onnx, and can train your own model in C#.

Feature

• Written in C# only.
• Train and predict your own model.
• Support Yolov5, Yolov5u, Yolov8, Yolov11 and Yolov12 now.
• Support Predict and Segment now.
• Support n/s/m/l/x size.
• Support LetterBox and Mosaic4 method for preprocessing images.
• Support NMS with GPU.
• Support Load PreTrained models from ultralytics/yolov5/yolov8/yolo11 and yolov12(converted).
• Support .Net6 or higher.

Models

You can download yolov5/yolov8 pre-trained models here.

<details> <summary>Prediction Checkpoints</summary>

</details>

<details> <summary>Segmention Checkpoints</summary>

</details>

How to use

You can download the code or add it from nuget.

dotnet add package IntptrMax.YoloSharp

[!NOTE] Please add one of libtorch-cpu, libtorch-cuda-12.1, libtorch-cuda-12.1-win-x64 or libtorch-cuda-12.1-linux-x64 version 2.5.1.0 to execute.

In your code you can use it as below.

Predict

You can use it with the code below:

MagickImage predictImage = new MagickImage(predictImagePath);

// Create predictor
Predictor predictor = new Predictor(sortCount, yoloType: yoloType, deviceType: deviceType, yoloSize: yoloSize, dtype: dtype);

// Train model
predictor.LoadModel(preTrainedModelPath, skipNcNotEqualLayers: true);
predictor.Train(trainDataPath, valDataPath, outputPath: outputPath, batchSize: batchSize, epochs: epochs, useMosaic: true);

//ImagePredict image
predictor.LoadModel(Path.Combine(outputPath, "best.bin"));
List<Predictor.PredictResult> predictResult = predictor.ImagePredict(predictImage, predictThreshold, iouThreshold);

Use yolov5n pre-trained model to detect.

Segment

You can use it with the code below:

MagickImage predictImage = new MagickImage(predictImagePath);

// Create segmenter
Segmenter segmenter = new Segmenter(sortCount, yoloType: yoloType, deviceType: deviceType, yoloSize: yoloSize, dtype: dtype);
segmenter.LoadModel(preTrainedModelPath, skipNcNotEqualLayers: true);

// Train model
segmenter.Train(trainDataPath, valDataPath, outputPath: outputPath, batchSize: batchSize, epochs: epochs, useMosaic: false);
segmenter.LoadModel(Path.Combine(outputPath, "best.bin"));

// ImagePredict image
var (predictResult, resultImage) = segmenter.ImagePredict(predictImage, predictThreshold, iouThreshold);

Use yolov8n-seg pre-trained model to detect.