ultralytics_yolo 0.4.1

Flutter plugin for Ultralytics YOLO computer vision models.

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🚀 YOLO Flutter - Ultralytics Official Plugin

Ultralytics YOLO Flutter is the official plugin for running YOLO models in Flutter apps on iOS and Android. It supports detection, instance segmentation, semantic segmentation, classification, pose, and OBB with two simple entry points:

• YOLO for single-image inference
• YOLOView for real-time camera inference

The main goal is simple integration: use an official model ID, or drop in your own exported model and let the plugin resolve task metadata for you.

✨ Why This Plugin

• Official Ultralytics plugin for Flutter
• One Dart API for Android and iOS
• Metadata-first model loading with official model download and caching
• Real-time camera inference and single-image inference
• Production-ready controls for thresholds, GPU use, and streaming
• YOLO26 and YOLO11 model families supported

Feature	Android	iOS	Details
Object Detection	✅	✅	Bounding boxes, labels, and confidence scores
Instance Segmentation	✅	✅	Instance masks with boxes and classes
Semantic Segmentation	✅	✅	Dense class masks for every pixel
Image Classification	✅	✅	Top class predictions and scores
Pose Estimation	✅	✅	Keypoints with boxes and confidence scores
Oriented Bounding Box (OBB) Detection	✅	✅	Rotated boxes and polygon corners
Real-Time Camera Inference	✅	✅	YOLOView for live camera workflows
Single-Image Inference	✅	✅	YOLO for image bytes
Official Models	✅	✅	Discovery, download, and caching for packaged model IDs
Custom Models	✅	✅	LiteRT (TFLite) on Android, Core ML on iOS, metadata-first tasks

⚡ Quick Start

Install the package:

Package: https://pub.dev/packages/ultralytics_yolo

dependencies:
  ultralytics_yolo: ^0.4.0

flutter pub get

Start with the default official model:

import 'package:ultralytics_yolo/ultralytics_yolo.dart';

final modelId = YOLO.defaultOfficialModel() ?? 'yolo26n';

YOLOView(
  modelPath: modelId,
  onResult: (results) {
    for (final r in results) {
      debugPrint('${r.className}: ${r.confidence}');
    }
  },
)

For single-image inference:

final yolo = YOLO(modelPath: 'yolo26n');
await yolo.loadModel();
final results = await yolo.predict(imageBytes);

▶️ Example App | 📖 Installation Guide | ⚡ Quick Start Guide

📦 Model Loading

The plugin supports three model flows.

1. Official model IDs

Use the default official model or a specific official ID and let the plugin handle download and caching:

final yolo = YOLO(modelPath: YOLO.defaultOfficialModel() ?? 'yolo26n');

Call YOLO.officialModels() to see which official IDs are available on the current platform. Official assets are downloaded on first use and cached in app storage, so the app package does not carry large model files.

Official assets are maintained as GitHub release assets:

Platform	Runtime asset	Release
Android	TFLite int8 .tflite	yolo-flutter-app v0.3.5
iOS/macOS	Core ML int8 .mlpackage.zip	yolo-ios-app v8.3.0

The Flutter resolver uses the TFLite release for Android and the Core ML release for Apple platforms. These release tags are intentionally pinned for reproducible first-use downloads. See the model guide for the official export matrix, URL patterns, and model properties.

2. Your own exported model

Pass your own exported YOLO model as a local path or Flutter asset path:

final yolo = YOLO(modelPath: 'assets/models/my-finetuned-model.tflite');

If the exported model includes embedded metadata, the plugin infers task and class labels automatically — it reads Ultralytics' appended-ZIP metadata, with a standard TFLite (FlatBuffers) metadata fallback — so drag-and-drop custom models auto-detect. If metadata is missing, pass task explicitly.

final yolo = YOLO(
  modelPath: 'assets/models/my-finetuned-model.tflite',
  task: YOLOTask.detect,
);

3. Remote model URL

Pass an http or https URL and the plugin will download it into app storage before loading it.

🧭 Official vs. Custom

Use case	Recommended path
Fastest first integration	Official model ID like yolo26n
You trained or exported your own model	Custom asset or local file
You ship different models per customer or environment	Remote URL
You need the plugin to infer task automatically	Any export with metadata
You have an older or stripped export without metadata	Custom model plus explicit task

For official models, start with YOLO.defaultOfficialModel() or YOLO.officialModels(). For custom models, start with the exported file you actually plan to ship.

📥 Drop Your Own Model Into an App

For custom models, keep the app-side setup minimal.

• Android native assets: place .tflite files in android/app/src/main/assets
• Flutter assets on Android: place .tflite files in assets/models/
• iOS bundle: drag .mlpackage or .mlmodel into ios/Runner.xcworkspace
• Flutter assets on iOS: place .mlpackage.zip files in assets/models/

Then point modelPath at that file or asset path.

Official asset maintenance

The Android TFLite release assets are generated by scripts/export-tflite-models.py. The script defines the official YOLO26 task/size matrix, int8 export settings, Ultralytics task-specific calibration data, optional one-shot TFLite inference verification, and optional GitHub release upload. By default it reads ultralytics.cfg.TASK2CALIBRATIONDATA so each task uses the same canonical calibration dataset as the Ultralytics exporter.

Run it in a Linux Python 3.13 environment:

uv venv --python 3.13 .venv
uv pip install --index-url https://download.pytorch.org/whl/cpu torch torchvision
uv pip install -e "../ultralytics" "tensorflow>2.19.0" "onnx>=1.20.0" "onnxslim>=0.1.82" \
  "tf_keras>2.19.0" "sng4onnx>=1.0.1" "onnx_graphsurgeon>=0.3.26" \
  "ai-edge-litert>=1.2.0" "onnxruntime" "protobuf>=6.31.1,<7.0.0" \
  --extra-index-url https://pypi.ngc.nvidia.com --index-strategy unsafe-best-match
uv pip uninstall opencv-python
uv pip install opencv-python-headless
uv pip install --no-deps "onnx2tf>=2.3.0,<2.3.16"
uv run python scripts/export-tflite-models.py --verify

Use --upload --repo ultralytics/yolo-flutter-app --tag v0.3.5 to publish generated .tflite assets to the canonical Android release. The matching Core ML assets are generated by ../yolo-ios-app/scripts/export-models.py and hosted on the iOS v8.3.0 release.

Android inference runs on LiteRT 2.x through an automatic GPU -> CPU accelerator ladder. int8 assets are the official download artifacts for size, but int8 GPU coverage depends on the device driver and graph; unsupported graphs or ops may fall back to CPU. fp16 non-end-to-end TFLite exports can still be useful for GPU benchmarking on devices whose delegate supports the graph:

from ultralytics import YOLO

YOLO("yolo26n.pt").export(format="tflite", half=True, nms=False, end2end=False, imgsz=640)

🎯 Choose The Right API

Use YOLO when you already have image bytes and want one prediction at a time:

final yolo = YOLO(modelPath: 'yolo26n');
await yolo.loadModel();
final results = await yolo.predict(imageBytes);

Use YOLOView when you want live camera inference:

final controller = YOLOViewController();

YOLOView(
  modelPath: 'yolo26n',
  controller: controller,
  onResult: (results) {},
)

await controller.switchModel('assets/models/custom.tflite', YOLOTask.detect);

Use YOLOShowcase when you want the complete Ultralytics camera UI:

YOLOShowcase(
  modelPath: 'yolo26n',
  onCapture: (bytes) {},
)

🔄 Migrating From 0.3.x UI APIs

Version 0.4.0 removes the old Dart-side overlay/control layer. Camera detections are rendered natively by YOLOView; Flutter now owns only the surrounding app controls.

Removed 0.3.x API	0.4.0 replacement
YOLOOverlay, YOLOOverlayTheme	Remove these widgets. Use native YOLOView overlays, or consume onResult/YOLO.predict() data.
YOLOControls	Use YOLOShowcase for the full UI, or compose the exported Material widgets directly.
YOLOView.showNativeUI	Use YOLOShowcase for built-in controls; use bare YOLOView when building your own UI.
YOLOView.showOverlays, YOLOView.overlayTheme	No constructor replacement. Camera overlay drawing is native and not themed from Dart.
YOLOViewController.setShowUIControls()	Show/hide your own Flutter controls around YOLOView.
YOLOViewController.setShowOverlays()	No controller replacement. capturePhoto(withOverlays: false) only affects captured JPEG output.

🧩 Recommended Patterns

App type	Model loading pattern
Live camera app	YOLOView(modelPath: 'yolo26n')
Photo picker or gallery workflow	YOLO(modelPath: 'yolo26n')
App with your own bundled model	YOLO(modelPath: 'assets/models/custom.tflite')
Cross-platform Core ML + TFLite app	Use platform-appropriate exported assets and let metadata drive task
App that changes models at runtime	YOLOViewController.switchModel(...)

📚 Documentation

Guide	Description
Installation Guide	Requirements and platform setup
Quick Start	Minimal setup for the first working app
Model Guide	Official models, custom models, export flow
Usage Guide	Common app patterns and examples
API Reference	Full API surface
Performance Guide	Tuning and performance controls
Performance Record	On-device benchmark record
Troubleshooting	Common problems and fixes

🤝 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 for real-time detection, instance segmentation, semantic segmentation, classification, pose estimation, and OBB detection with 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!