YoloV8 1.6.0
See the version list below for details.
dotnet add package YoloV8 --version 1.6.0
NuGet\Install-Package YoloV8 -Version 1.6.0
<PackageReference Include="YoloV8" Version="1.6.0" />
paket add YoloV8 --version 1.6.0
#r "nuget: YoloV8, 1.6.0"
// Install YoloV8 as a Cake Addin
#addin nuget:?package=YoloV8&version=1.6.0
// Install YoloV8 as a Cake Tool
#tool nuget:?package=YoloV8&version=1.6.0
YOLOv8
Use YOLOv8 in real-time for object detection, instance segmentation, pose estimation and image classification, via ONNX Runtime
Install
The YoloV8
project is available in two versions of nuget packages: YoloV8 and YoloV8.Gpu, if you use with CPU add the YoloV8 package reference to your project (contains reference to Microsoft.ML.OnnxRuntime package)
dotnet add package YoloV8 --version 1.6.0
If you use with GPU you need to add the YoloV8.Gpu package reference (contains reference to Microsoft.ML.OnnxRuntime.Gpu package)
dotnet add package YoloV8.Gpu --version 1.6.0
Use
Export the model from PyTorch to ONNX format:
Run the following python code to export the model to ONNX format:
from ultralytics import YOLO
# Load a model
model = YOLO('path/to/best')
# export the model to ONNX format
model.export(format='onnx')
Use in exported model with C#:
using Compunet.YoloV8;
using SixLabors.ImageSharp;
using var predictor = new YoloV8(model);
var result = predictor.Detect("path/to/image");
// or
var result = await predictor.DetectAsync("path/to/image");
Console.WriteLine(result);
Plotting
You can to plot the input image for preview the model prediction results, this code demonstrates how to perform a prediction with the model and then plot the prediction results on the input image and save to file:
using Compunet.YoloV8;
using Compunet.YoloV8.Plotting;
using SixLabors.ImageSharp;
var imagePath = "path/to/image";
using var predictor = new YoloV8("path/to/model");
var result = predictor.Pose(imagePath);
using var image = Image.Load(imagePath);
using var ploted = result.PlotImage(image);
ploted.Save("./pose_demo.jpg")
Demo Images:
Detection:
Pose:
Segmentation:
License
MIT License
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net6.0 is compatible. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 is compatible. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
-
net6.0
- Microsoft.ML.OnnxRuntime (>= 1.15.1)
- SixLabors.ImageSharp (>= 3.0.2)
- SixLabors.ImageSharp.Drawing (>= 2.0.0)
-
net7.0
- Microsoft.ML.OnnxRuntime (>= 1.15.1)
- SixLabors.ImageSharp (>= 3.0.2)
- SixLabors.ImageSharp.Drawing (>= 2.0.0)
NuGet packages
This package is not used by any NuGet packages.
GitHub repositories (1)
Showing the top 1 popular GitHub repositories that depend on YoloV8:
Repository | Stars |
---|---|
babalae/better-genshin-impact
📦BetterGI · 更好的原神 - 自动拾取 | 自动剧情 | 全自动钓鱼(AI) | 全自动七圣召唤 | 自动伐木 | 自动刷本 - UI Automation Testing Tools For Genshin Impact
|
Version | Downloads | Last updated |
---|---|---|
4.1.5 | 678 | 4/14/2024 |
4.1.4 | 91 | 4/14/2024 |
4.0.0 | 824 | 3/6/2024 |
3.1.1 | 456 | 2/4/2024 |
3.1.0 | 154 | 1/29/2024 |
3.0.0 | 1,269 | 11/27/2023 |
2.0.1 | 1,427 | 10/10/2023 |
2.0.0 | 285 | 9/27/2023 |
1.6.0 | 321 | 9/21/2023 |
1.5.0 | 231 | 9/15/2023 |
1.4.0 | 295 | 9/8/2023 |
1.3.0 | 1,078 | 8/29/2023 |
1.2.0 | 200 | 8/21/2023 |
1.0.1 | 192 | 8/16/2023 |
1.0.0 | 336 | 7/23/2023 |