# YoloSharp
**Repository Path**: chuangchu/YoloSharp
## Basic Information
- **Project Name**: YoloSharp
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-12
- **Last Updated**: 2026-04-12
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# YoloSharp
Train and run YOLO models in pure C# with TorchSharp.
No Python required — from training to inference, everything stays in .NET.
## Features
- **100% C# implementation** – No Python environment needed.
- **Full pipeline support** – Train, validate, and predict with your own models.
- **Multiple YOLO versions** – Supports YOLOv5, YOLOv5u, YOLOv8, YOLOv11, and YOLOv12.
- **Various task types** – Object detection, segmentation, oriented bounding boxes (OBB), pose estimation (keypoints), and classification.
- **Model sizes** – n/s/m/l/x variants available.
- **Advanced preprocessing** – LetterBox and Mosaic4 data augmentation.
- **GPU-accelerated NMS** – Non-maximum suppression runs on GPU.
- **Pretrained model support** – Load models from Ultralytics YOLO (v5/v8/v11) and converted YOLOv12.
- **Cross-platform** – Compatible with .NET 6 and later.
## 🔥Important News
**2026/03/26**
🚀 Add metrics curves for training.
**2026/03/06**
🚀 Add config for training and predict.
🚀 Add more metrics for val.
**2026/02/03**
🚀 Add **Early Stop**.
🚀 Add **HSV transform**.
🚀 Add **Train Logs**.
**2026/01/20**
🚀 YoloSharp support **Mixed Precision Trainer** (simple amp)
🚀 **Tqdm** supported.
🚀 Add BF16 Precision.
## Models
You can download yolo pre-trained models here.
Prediction Checkpoints
| model | n| s | m | l | x |
| --- | ----------- | ----------- | ----------- | ----------- | ----------- |
| yolov5 | [yolov5n](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5n.bin) | [yolov5s](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5s.bin) | [yolov5m](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5m.bin) | [yolov5l](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5l.bin) | [yolov5x](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5x.bin) |
| yolov5 | [yolov5nu](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5nu.bin) | [yolov5su](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5su.bin) | [yolov5mu](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5mu.bin) | [yolov5lu](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5lu.bin) | [yolov5xu](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov5xu.bin) |
| yolov8 | [yolov8n](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8n.bin) | [yolov8s](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8s.bin) | [yolov8m](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8m.bin) | [yolov8l](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8l.bin) | [yolov8x](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8x.bin) |
| yolov11 | [yolov11n](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11n.bin) | [yolov11s](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11s.bin) | [yolov11m](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/yolov11m.bin) | [yolov11l](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11l.bin) | [yolov11x](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11x.bin) |
Segmention Checkpoints
| model | n| s | m | l | x |
| --- | ----------- | ----------- | ----------- | ----------- | ----------- |
| yolov8 | [yolov8n](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8n-seg.bin) | [yolov8s](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8s-seg.bin) | [yolov8m](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8m-seg.bin) | [yolov8l](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8l-seg.bin) | [yolov8x](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov8x-seg.bin) |
| yolov11 | [yolov11n](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11n-seg.bin) | [yolov11s](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11s-seg.bin) | [yolov11m](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11m-seg.bin) | [yolov11l](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11l-seg.bin) | [yolov11x](https://github.com/IntptrMax/YoloSharp/releases/download/1.0.6/Yolov11x-seg.bin) |
## 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 and OpenCvSharp4.runtime to execute.
You can use it with the code below:
### Yolo Task
```CSharp
string preTrainedModelPath = @"..\..\..\Assets\PreTrainedModels\yolov8n-obb.bin"; // Pretrained model path.
string predictImagePath = @"..\..\..\Assets\TestImage\trucks.jpg";
Mat predictImage = Cv2.ImRead(predictImagePath);
// Create a Yolo config
Config config = new Config
{
DeviceType = DeviceType.CUDA,
ScalarType = ScalarType.Float16,
RootPath = @"..\..\..\Assets\DataSets\dotav1",
TrainDataPath = "train.txt",
ValDataPath = "val.txt",
YoloType = YoloType.Yolov8,
YoloSize = YoloSize.n,
TaskType = TaskType.Obb,
ImageProcessType = ImageProcessType.Mosiac,
ImageSize = 640,
BatchSize = 16,
NumberClass = 15,
PredictThreshold = 0.3f,
IouThreshold = 0.7f,
Workers = 4,
Epochs = 100,
};
// Create a yolo task.
YoloTask yoloTask = new YoloTask(config);
// Load pre-trained model. If you don't want to use pre-trained model, skip the step.
yoloTask.LoadModel(preTrainedModelPath, skipNcNotEqualLayers: true);
// Train model
yoloTask.Train();
// Predict image, if the model is not trained or loaded, it will use random weight to predict.
List predictResult = yoloTask.ImagePredict(predictImage);
```
Use yolov8n pre-trained model to detect.
Use yolov8n-seg pre-trained model to detect.
Use yolov8n-obb pre-trained model to detect.
Use yolov8n-pose pre-trained model to detect.
