flutter_yolo_open_kit 1.1.0

A commercial-friendly YOLO detector for Flutter, powered by YOLOX and PP-YOLOE+ (Apache 2.0).

Flutter YOLO Open Kit

A commercial-friendly YOLO-like detector for Flutter, powered by YOLOX and PP-YOLOE+ (Apache 2.0).

A high-performance Flutter plugin for real-time object detection using YOLO models with ONNX Runtime.

Demo

Features

• Multiple YOLO Models: Support for YOLOX and PP-YOLOE+ models
• Real-time Detection: Optimized for camera stream processing
• Cross-platform: iOS, Android, and Linux support
• Hardware Acceleration:
  • iOS: Core ML integration
  • Android: NNAPI support
• Flexible Input: Detect from image files, BGRA buffers, or YUV camera frames
• Isolate Support: Run detection in separate isolate to avoid UI blocking

Supported Models

Model	Size	Description
PP-YOLOE+ S	30MB	Small, fastest
PP-YOLOE+ M	90MB	Medium, balanced
PP-YOLOE+ L	200MB	Large, most accurate
YOLOX-M	97MB	Medium
YOLOX-L	207MB	Large

Installation

Add to your pubspec.yaml:

dependencies:
  flutter_yolo_open_kit:
    git:
      url: https://github.com/robert008/flutter_yolo_open_kit.git

Quick Start

Basic Usage

import 'package:flutter_yolo_open_kit/flutter_yolo_open_kit.dart';

// Get singleton instance
final yolo = FlutterYoloOpenKit.instance;

// Initialize with model path
final success = yolo.init('/path/to/model.onnx');

// Detect from image file
final result = yolo.detectFromPath(
  '/path/to/image.jpg',
  confThreshold: 0.5,
  iouThreshold: 0.45,
);

// Process results
for (final detection in result.detections) {
  print('${detection.className}: ${detection.confidence}');
  print('Box: (${detection.x1}, ${detection.y1}) - (${detection.x2}, ${detection.y2})');
}

// Release when done
yolo.release();

Real-time Camera Detection

For real-time camera processing, use the isolate-based YoloService:

import 'package:flutter_yolo_open_kit/flutter_yolo_open_kit.dart';

final yoloService = YoloService.instance;

// Initialize in isolate
await yoloService.init(modelPath);

// Detect from camera frame (iOS - BGRA)
final result = await yoloService.detectFromBuffer(
  imageData: plane.bytes,
  width: image.width,
  height: image.height,
  stride: plane.bytesPerRow,
);

// Detect from camera frame (Android - YUV420)
final result = await yoloService.detectFromYuv(
  yData: yPlane.bytes,
  uData: uPlane.bytes,
  vData: vPlane.bytes,
  width: image.width,
  height: image.height,
  yRowStride: yPlane.bytesPerRow,
  uvRowStride: uPlane.bytesPerRow,
  uvPixelStride: uPlane.bytesPerPixel ?? 1,
  rotation: sensorOrientation,
);

API Reference

FlutterYoloOpenKit

Method	Description
init(String modelPath)	Initialize detector with ONNX model
detectFromPath(String imagePath, {double confThreshold, double iouThreshold})	Detect from image file
detectFromBuffer(Pointer<Uint8> imageData, int width, int height, int stride, {...})	Detect from BGRA buffer
detectFromYUV(...)	Detect from YUV420 buffer
setClassNames(List<String> classNames)	Set custom class names
release()	Release resources
isInitialized	Check if detector is ready
version	Get library version

YoloResult

Property	Type	Description
detections	List<YoloDetection>	List of detected objects
count	int	Number of detections
inferenceTimeMs	int	Inference time in milliseconds
imageWidth	int	Input image width
imageHeight	int	Input image height
error	String?	Error message if any

YoloDetection

Property	Type	Description
classId	int	Class ID (0-79 for COCO)
className	String	Class name
confidence	double	Detection confidence (0-1)
x1, y1, x2, y2	double	Bounding box coordinates

Platform Setup

iOS

The plugin uses pre-built static libraries. No additional setup required.

Minimum iOS version: 12.0

Android

The plugin includes pre-built native libraries for arm64-v8a and armeabi-v7a.

Minimum SDK version: 24

Linux

Install OpenCV development libraries:

# Ubuntu/Debian
sudo apt install libopencv-dev

# Fedora
sudo dnf install opencv-devel

# Then download ONNX Runtime
cd linux
./download_libs.sh

Supported architectures: x86_64, aarch64 (Raspberry Pi, Jetson)

Building from Source

iOS Static Library

cd ios
./build_static_lib.sh

Android Native Library

The Android library is built automatically by Gradle using CMake.

Linux

cd linux
./download_libs.sh
flutter build linux

Model & Library Downloads

Models and pre-built native libraries are available in GitHub Releases.

Release Assets

Asset	Description
ppyoloe_plus_s.onnx	PP-YOLOE+ Small model (30MB)
ppyoloe_plus_m.onnx	PP-YOLOE+ Medium model (90MB)
ppyoloe_plus_l.onnx	PP-YOLOE+ Large model (200MB)
yolox_m.onnx	YOLOX Medium model (97MB)
yolox_l.onnx	YOLOX Large model (207MB)
ios-frameworks.zip	iOS static libraries & frameworks
android-jniLibs.zip	Android native libraries (.so)

Native Libraries (Auto-Download)

Native libraries are automatically downloaded from GitHub Releases on first build:

• iOS: Downloads during pod install
• Android: Downloads during Gradle build

You can also manually trigger the download:

# iOS
cd ios && ./download_frameworks.sh

# Android
cd android && ./gradlew downloadNativeLibraries

Models Setup

1. Download models from GitHub Releases
2. Place in your app's assets/ folder
3. Models will be copied to documents directory at runtime

Performance Tips

1. Use Isolate: For real-time processing, use YoloService to run detection in a separate isolate
2. Choose Right Model: PP-YOLOE+ S is fastest, L is most accurate
3. Adjust Thresholds: Higher confThreshold reduces false positives but may miss objects
4. Resolution: Lower camera resolution = faster processing

Related Projects

• flutter_document_capture - Document capture & preprocessing
• flutter_ocr_kit - OCR + Layout Detection

Author

Robert Chuang

• Email: [e-mail]
• LinkedIn: https://www.linkedin.com/in/robert-chuang-88090932b

License

Apache License 2.0 - see LICENSE for details.

Acknowledgments

• ONNX Runtime - High-performance inference engine
• OpenCV - Image processing
• PaddleDetection - PP-YOLOE+ models
• YOLOX - YOLOX models