🚀 YOLO Flutter - Ultralytics Official Plugin

Welcome to the Ultralytics YOLO Flutter plugin! Integrate cutting-edge Ultralytics YOLO computer vision models seamlessly into your Flutter mobile applications. This plugin at https://pub.dev/packages/ultralytics_yolo supports both Android and iOS platforms, offering APIs for object detection, image classification, instance segmentation, pose estimation, and oriented bounding box detection.

✨ Why Choose YOLO Flutter?

Feature	Android	iOS
Detection	✅	✅
Classification	✅	✅
Segmentation	✅	✅
Pose Estimation	✅	✅
OBB Detection	✅	✅

• Official Ultralytics Plugin - Direct from YOLO creators
• Real-time Performance - Up to 30 FPS on modern devices
• 5 AI Tasks - Detection, Segmentation, Classification, Pose, OBB
• Cross-platform - iOS & Android with single codebase
• Production Ready - Performance controls & optimization built-in
• Dynamic Model Loading - Switch models on-the-fly without restarting camera
• Frame Capture - Capture frames with detection overlays for sharing or saving

⚡ Quick Start (2 minutes)

import 'package:ultralytics_yolo/ultralytics_yolo.dart';

// Add this widget and you're detecting objects!
YOLOView(
  modelPath: 'yolo11n',
  task: YOLOTask.detect,
  onResult: (results) {
    print('Found ${results.length} objects!');
    for (final result in results) {
      print('${result.className}: ${result.confidence}');
    }
  },
)

▶️ Try the Live Demo | 📖 Full Setup Guide

🎯 What You Can Build

Task	Description	Use Cases	Performance
Detection	Find objects & their locations	Security, Inventory, Shopping	25-30 FPS
Segmentation	Pixel-perfect object masks	Photo editing,	15-25 FPS
Classification	Identify image categories	Content moderation, Tagging	30+ FPS
Pose Estimation	Human pose & keypoints	Fitness apps, Motion capture	20-30 FPS
OBB Detection	Rotated bounding boxes	Aerial imagery	20-25 FPS

📱 See Examples → | ⚡ Performance Guide →

🚀 Installation

1. Add to pubspec.yaml

dependencies:
  ultralytics_yolo: ^0.1.26

2. Install dependencies

flutter pub get

3. Add a model

You can get the model in one of the following ways:

1. Download from the release assets of this repository

2. Get it from Ultralytics HUB

3. Export it from Ultralytics/ultralytics (CoreML/TFLite)

Export Models for iOS

# Detection REQUIRES nms=True
YOLO("yolo11n.pt").export(format="coreml", nms=True)

# All other tasks use nms=False (default)
YOLO("yolo11n-seg.pt").export(format="coreml")

**[📥 Download Models](doc/install.md#models)** |

Bundle the model with your app using the following method.

For iOS: Drag and drop mlpackage/mlmodel directly into **ios/Runner.xcworkspace** and set target to Runner.

For Android: Create a folder called **android/app/src/main/assets** and place tflite files in it.

### 4. Platform-Specific Setup

**[🔧 Setup Guide](doc/install.md)**

## 🏆 Trusted by Developers

- ✅ **Official Ultralytics Plugin** - Maintained by YOLO creators
- ✅ **Production Tested** - Used in apps with many users
- ✅ **Active Development** - Regular updates & feature additions
- ✅ **Community Driven** - Open source with responsive support

**Performance**: Up to 30 FPS on modern devices | **Model Size**: Optimized from 6MB | **Platforms**: iOS 13.0+ & Android API 21+

## 📚 Documentation

| Guide                                              | Description                       | For             |
| -------------------------------------------------- | --------------------------------- | --------------- |
| **[Installation Guide](doc/install.md)**           | Installation, setup, requirements | New users       |
| **[Quick Start](doc/quickstart.md)**               | 2-minute setup guide              | New users       |
| **[Usage Guide](doc/usage.md)**                    | Common use cases & code samples   | All users       |
| **[Performance Optimization](doc/performance.md)** | Inference control & tuning        | Production apps |
| **[API Reference](doc/api.md)**                    | Complete technical reference      | Developers      |
| **[Troubleshooting](doc/troubleshooting.md)**      | Common issues & solutions         | All users       |

## 🤝 Community & Support

[![Ultralytics Discord](https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue)](https://discord.com/invite/ultralytics) [![Ultralytics Forums](https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue)](https://community.ultralytics.com/) [![Ultralytics Reddit](https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue)](https://www.reddit.com/r/ultralytics/)

- **💬 Questions?** [Discord](https://discord.com/invite/ultralytics) | [Forums](https://community.ultralytics.com/) | [GitHub Issues](https://github.com/ultralytics/yolo-flutter-app/issues)
- **🐛 Found a bug?** [Report it here](https://github.com/ultralytics/yolo-flutter-app/issues/new)
- **💡 Feature request?** [Let us know](https://github.com/ultralytics/yolo-flutter-app/discussions)

## 💡 Contribute

Ultralytics thrives on community collaboration, and we deeply value your contributions! Whether it's bug fixes, feature enhancements, or documentation improvements, your involvement is crucial. Please review our [Contributing Guide](https://docs.ultralytics.com/help/contributing/) for detailed insights on how to participate. We also encourage you to share your feedback through our [Survey](https://www.ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey). A heartfelt thank you 🙏 goes out to all our contributors!

[![Ultralytics open-source contributors](https://raw.githubusercontent.com/ultralytics/assets/main/im/image-contributors.png)](https://github.com/ultralytics/ultralytics/graphs/contributors)

## 📄 License

Ultralytics offers two licensing options to accommodate diverse needs:

- **AGPL-3.0 License**: Ideal for students, researchers, and enthusiasts passionate about open-source collaboration. This [OSI-approved](https://opensource.org/license/agpl-v3) license promotes knowledge sharing and open contribution. See the [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
- **Enterprise License**: Designed for commercial applications, this license permits seamless integration of Ultralytics software and AI models into commercial products and services, bypassing the open-source requirements of AGPL-3.0. For commercial use cases, please inquire about an [Enterprise License](https://www.ultralytics.com/license).

## 🔗 Related Resources

### Native iOS Development

If you're interested in using YOLO models directly in iOS applications with Swift (without Flutter), check out our dedicated iOS repository:

👉 **[Ultralytics YOLO iOS App](https://github.com/ultralytics/yolo-ios-app)** - A native iOS application demonstrating real-time object detection, segmentation, classification, and pose estimation using Ultralytics YOLO models.

This repository provides:

- Pure Swift implementation for iOS
- Direct Core ML integration
- Native iOS UI components
- Example code for various YOLO tasks
- Optimized for iOS performance

## 📮 Contact

Encountering issues or have feature requests related to Ultralytics YOLO? Please report them via [GitHub Issues](https://github.com/ultralytics/yolo-flutter-app/issues). For broader discussions, questions, and community support, join our [Discord](https://discord.com/invite/ultralytics) server!

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