🔥 TensorFlow Lite Plus #

📋 Table of Contents #

• ✨ Features
• 🚀 Quick Start
• 📦 Installation
• ⚙️ Platform Setup
• 📚 Available Functions
• 🎯 Usage Examples
• 📊 Parameter Tables
• 🔧 Advanced Configuration
• ⚡ Performance Tips
• 🛠️ Troubleshooting
• 🧪 Complete Examples
• 🤝 Contributing
• 💬 Support
• 📄 License

✨ Features #

🚀 Quick Start #

import 'package:tflite_plus/tflite_plus.dart';

// 1. Load your model
await TflitePlus.loadModel(
  model: 'assets/models/mobilenet.tflite',
  labels: 'assets/models/labels.txt',
);

// 2. Run inference
final results = await TflitePlus.runModelOnImage(
  path: imagePath,
  numResults: 5,
  threshold: 0.1,
);

// 3. Use results
print('Prediction: ${results?[0]['label']}');
print('Confidence: ${results?[0]['confidence']}');

📦 Installation #

1. Add Dependency #

dependencies:
  tflite_plus: ^1.0.0

2. Install #

flutter pub get

3. Import #

import 'package:tflite_plus/tflite_plus.dart';

⚙️ Platform Setup #

Android Configuration #

Add to android/app/build.gradle:

android {
    defaultConfig {
        minSdkVersion 21
    }
}

iOS Configuration #

Add to ios/Runner/Info.plist:

<key>NSCameraUsageDescription</key>
<string>This app needs camera access for ML inference.</string>
<key>NSPhotoLibraryUsageDescription</key>
<string>This app needs photo library access for ML inference.</string>

Update ios/Podfile:

platform :ios, '12.0'

📚 Available Functions #

Core Functions #

Utility Functions #

🎯 Usage Examples #

1. Image Classification #

// Load classification model
await TflitePlus.loadModel(
  model: 'assets/models/mobilenet_v1_1.0_224.tflite',
  labels: 'assets/models/mobilenet_v1_1.0_224_labels.txt',
  numThreads: 1,
  useGpuDelegate: true,
);

// Classify image
final results = await TflitePlus.runModelOnImage(
  path: imagePath,
  numResults: 5,
  threshold: 0.1,
  imageMean: 117.0,
  imageStd: 1.0,
);

// Process results
for (var result in results ?? []) {
  print('${result['label']}: ${result['confidence']}');
}

2. Object Detection #

// Load detection model
await TflitePlus.loadModel(
  model: 'assets/models/ssd_mobilenet.tflite',
  labels: 'assets/models/ssd_mobilenet_labels.txt',
  useGpuDelegate: true,
);

// Detect objects
final detections = await TflitePlus.detectObjectOnImage(
  path: imagePath,
  numResultsPerClass: 5,
  threshold: 0.3,
  imageMean: 127.5,
  imageStd: 127.5,
);

// Process detections
for (var detection in detections ?? []) {
  final rect = detection['rect'];
  print('Found ${detection['label']} at (${rect['x']}, ${rect['y']})');
}

3. Pose Estimation #

// Load pose model
await TflitePlus.loadModel(
  model: 'assets/models/posenet.tflite',
  useGpuDelegate: true,
);

// Detect poses
final poses = await TflitePlus.runPoseNetOnImage(
  path: imagePath,
  numResults: 5,
  threshold: 0.1,
  imageMean: 127.5,
  imageStd: 127.5,
);

// Process keypoints
for (var pose in poses ?? []) {
  for (var keypoint in pose['keypoints']) {
    print('${keypoint['part']}: (${keypoint['x']}, ${keypoint['y']})');
  }
}

📊 Parameter Tables #

loadModel Parameters #

runModelOnImage Parameters #

detectObjectOnImage Parameters #

runPoseNetOnImage Parameters #

🔧 Advanced Configuration #

GPU Acceleration #

// Enable GPU acceleration
await TflitePlus.loadModel(
  model: 'assets/models/model.tflite',
  useGpuDelegate: true,  // Android: GPU, iOS: Metal
  numThreads: 1,
);

// Check GPU availability
final delegates = await TflitePlus.getAvailableDelegates();
final hasGpu = delegates?.contains('GPU') ?? false;

NNAPI/CoreML Acceleration #

// Enable NNAPI (Android) / CoreML (iOS)
await TflitePlus.loadModel(
  model: 'assets/models/model.tflite',
  useNnApiDelegate: true,
  numThreads: 1,
);

Thread Configuration #

// Optimize for different devices
final numCores = Platform.numberOfProcessors;
await TflitePlus.loadModel(
  model: 'assets/models/model.tflite',
  numThreads: math.min(numCores, 4), // Use up to 4 threads
);

Binary Data Processing #

// Process image bytes directly
final imageBytes = await file.readAsBytes();
final results = await TflitePlus.runModelOnBinary(
  bytesList: imageBytes,
  imageHeight: 224,
  imageWidth: 224,
  numResults: 5,
  threshold: 0.1,
);

⚡ Performance Tips #

🎯 Model Optimization #

# Optimize your TensorFlow Lite model
import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model('model')
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.target_spec.supported_types = [tf.float16]
tflite_model = converter.convert()

📱 Best Practices #

1. Use GPU Acceleration: 2-4x faster inference on supported devices
2. Quantize Models: Reduce size and improve speed
3. Batch Processing: Process multiple images together
4. Image Preprocessing: Resize to model input size
5. Resource Management: Always call close() when done

⚙️ Performance Benchmarks #

🛠️ Troubleshooting #

Common Issues & Solutions #

Model Loading Fails

// ❌ Problem: Model not found
// ✅ Solution: Check assets configuration
flutter:
  assets:
    - assets/models/

GPU Delegate Not Available

// ❌ Problem: GPU acceleration fails
// ✅ Solution: Check device compatibility
final delegates = await TflitePlus.getAvailableDelegates();
if (delegates?.contains('GPU') == true) {
  // GPU available
} else {
  // Use CPU fallback
}

Memory Issues

// ❌ Problem: Out of memory
// ✅ Solution: Resource management
await TflitePlus.close(); // Always clean up

// Process smaller batches
// Use quantized models
// Reduce image resolution

Inference Too Slow

// ❌ Problem: Slow inference
// ✅ Solution: Optimization strategies
await TflitePlus.loadModel(
  model: 'assets/models/model_quantized.tflite', // Use quantized model
  useGpuDelegate: true,      // Enable GPU
  numThreads: 4,            // Use multiple threads
);

Error Codes #

🧪 Complete Examples #

Real-time Camera Classification #

class CameraClassifier extends StatefulWidget {
  @override
  _CameraClassifierState createState() => _CameraClassifierState();
}

class _CameraClassifierState extends State<CameraClassifier> {
  CameraController? _controller;
  List<dynamic>? _results;
  bool _isDetecting = false;

  @override
  void initState() {
    super.initState();
    _initializeCamera();
    _loadModel();
  }

  Future<void> _loadModel() async {
    await TflitePlus.loadModel(
      model: 'assets/models/mobilenet.tflite',
      labels: 'assets/models/labels.txt',
      useGpuDelegate: true,
    );
  }

  Future<void> _initializeCamera() async {
    final cameras = await availableCameras();
    _controller = CameraController(cameras[0], ResolutionPreset.medium);
    await _controller!.initialize();
    
    _controller!.startImageStream((image) {
      if (!_isDetecting) {
        _isDetecting = true;
        _runInference(image);
      }
    });
    
    setState(() {});
  }

  Future<void> _runInference(CameraImage image) async {
    // Convert CameraImage to file or bytes
    final results = await TflitePlus.runModelOnBinary(
      bytesList: _imageToByteList(image),
      imageHeight: image.height,
      imageWidth: image.width,
    );
    
    setState(() {
      _results = results;
      _isDetecting = false;
    });
  }

  @override
  Widget build(BuildContext context) {
    if (_controller?.value.isInitialized != true) {
      return CircularProgressIndicator();
    }

    return Scaffold(
      body: Stack(
        children: [
          CameraPreview(_controller!),
          Positioned(
            bottom: 100,
            left: 20,
            right: 20,
            child: Container(
              padding: EdgeInsets.all(16),
              decoration: BoxDecoration(
                color: Colors.black.withOpacity(0.7),
                borderRadius: BorderRadius.circular(8),
              ),
              child: Column(
                children: _results?.map((result) => Text(
                  '${result['label']}: ${(result['confidence'] * 100).toInt()}%',
                  style: TextStyle(color: Colors.white, fontSize: 16),
                )).toList() ?? [Text('No results', style: TextStyle(color: Colors.white))],
              ),
            ),
          ),
        ],
      ),
    );
  }

  @override
  void dispose() {
    _controller?.dispose();
    TflitePlus.close();
    super.dispose();
  }
}

Batch Image Processing #

class BatchProcessor {
  static Future<List<Map<String, dynamic>>> processImages(
    List<String> imagePaths,
  ) async {
    await TflitePlus.loadModel(
      model: 'assets/models/classifier.tflite',
      labels: 'assets/models/labels.txt',
      useGpuDelegate: true,
    );

    final results = <Map<String, dynamic>>[];
    
    for (int i = 0; i < imagePaths.length; i++) {
      try {
        final result = await TflitePlus.runModelOnImage(
          path: imagePaths[i],
          numResults: 1,
          threshold: 0.1,
        );
        
        results.add({
          'path': imagePaths[i],
          'predictions': result,
          'status': 'success',
        });
        
        // Progress callback
        print('Processed ${i + 1}/${imagePaths.length} images');
        
      } catch (e) {
        results.add({
          'path': imagePaths[i],
          'error': e.toString(),
          'status': 'error',
        });
      }
    }
    
    await TflitePlus.close();
    return results;
  }
}

🤝 Contributing #

We welcome contributions from the community! 🎉

Contributors #

Shakil Ahmed 🚀 Creator & Maintainer

Want to see your profile here? Contribute to the project!

How to Contribute #

🚀 Quick Start

1. Fork & Clone

   git clone https://github.com/yourusername/tflite_plus.git
   cd tflite_plus
   

2. Create Branch

   git checkout -b feature/amazing-feature
   

3. Make Changes

   • Add your awesome code
   • Write tests
   • Update documentation

4. Test Your Changes

   flutter test
   flutter analyze
   

5. Submit PR

   git push origin feature/amazing-feature
   

🎯 Contribution Types

📋 Contribution Guidelines

• Code Style: Follow Dart Style Guide
• Testing: Add tests for new features
• Documentation: Update README and code comments
• Commits: Use Conventional Commits

🏆 Recognition

Contributors get:

• 🌟 Profile picture in README
• 🎖️ Contributor badge on GitHub
• 📢 Mention in release notes
• 🎁 Special Discord role (coming soon)

💬 Support #

📞 Support Channels #

🆘 Before Asking for Help #

1. Check Documentation: Read this README thoroughly
2. Search Issues: Look for existing solutions
3. Provide Details: Include code, error messages, device info
4. Minimal Example: Create a minimal reproducible example

📄 License #

MIT License

Copyright (c) 2024 CodeBumble

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.