🚀 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:

Download from the release assets of this repository
Get it from Ultralytics HUB
Export it from Ultralytics/ultralytics (CoreML/TFLite)

For YOLO26, use the same steps and grab the yolo26* artifacts from the v0.2.0 release (e.g., yolo26n.tflite / yolo26n.mlpackage).

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

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

🏆 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	Installation, setup, requirements	New users
Quick Start	2-minute setup guide	New users
Usage Guide	Common use cases & code samples	All users
Performance Optimization	Inference control & tuning	Production apps
API Reference	Complete technical reference	Developers
Troubleshooting	Common issues & solutions	All users

🤝 Community & Support

💬 Questions? Discord | Forums | GitHub Issues
🐛 Found a bug? Report it here
💡 Feature request? Let us know

💡 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 for detailed insights on how to participate. We also encourage you to share your feedback through our Survey. A heartfelt thank you 🙏 goes out to all our 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 license promotes knowledge sharing and open contribution. See the 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.

🔗 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 - 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. For broader discussions, questions, and community support, join our Discord server!