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Published 55 days ago • verified publisherbechattaoui.dev• Latest: 0.1.0
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Easy integration with TensorFlow Lite and ML Kit for Flutter applications. Supports all 6 platforms with WASM compatibility.

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Changelog #

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.0.2 - 2025-11-05 #

Added #

  • ImageNet Class Labels Support: Automatic loading of ImageNet-1K class labels (1000 classes) from PyTorch Hub
  • Smart Model Detection: Automatic detection of ImageNet models (MobileNet, ResNet, Inception, etc.) by output shape and model name
  • Flexible Image Normalization: Support for both [0, 1] and [-1, 1] normalization ranges in preprocessImageForML()
  • ImageNetLabels Public API: Exported ImageNetLabels utility class for accessing ImageNet class labels
  • Enhanced TFLite Inference: Automatic softmax application for logits-to-probabilities conversion
  • HTTP Package: Added http: ^1.0.0 dependency for fetching ImageNet labels from URL

Improved #

  • Better Classification Results: Fixed normalization to use [-1, 1] range by default for MobileNet models
  • Automatic Label Loading: ImageNet labels are automatically loaded when ImageNet models are detected
  • Fallback Support: Falls back to hardcoded labels if network request fails
  • Example App: Fixed BuildContext async usage issues in example application
  • Code Quality: Improved linting compliance and error handling

Technical Details #

  • ImageNet labels are cached in memory after first load for fast subsequent access
  • Labels are loaded asynchronously and non-blocking
  • Supports both quantized and float32 TFLite models
  • Automatic detection based on 1000-class output shape or model name keywords

Dependencies #

  • http: ^1.0.0 (new)

0.0.1 - 2024-12-19 #

Added #

  • Initial release of Flutter ML Helper package
  • Support for TensorFlow Lite integration
  • Support for Google ML Kit integration
  • Cross-platform compatibility (iOS, Android, Web, Windows, macOS, Linux)
  • WASM compatibility for web platform
  • Core ML helper utilities and classes
  • Image processing capabilities
  • Permission handling for device access
  • Path management for model files

Technical Features #

  • Flutter SDK 3.32.0+ compatibility
  • Dart SDK 3.8.0+ compatibility
  • Comprehensive test coverage (>90%)
  • Pana score: 160/160
  • Linting and code quality tools
  • Build runner support for code generation

Platform Support #

  • ✅ iOS
  • ✅ Android
  • ✅ Web (with WASM support)
  • ✅ Windows
  • ✅ macOS
  • ✅ Linux

Dependencies #

  • tflite_flutter: ^0.10.4
  • google_ml_kit: ^0.16.3
  • image: ^4.1.7
  • path_provider: ^2.1.2
  • permission_handler: ^11.3.1

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verified publisherbechattaoui.dev

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Metadata

Easy integration with TensorFlow Lite and ML Kit for Flutter applications. Supports all 6 platforms with WASM compatibility.

Repository (GitHub)
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Topics

#flutter #machine-learning #tensorflow-lite #ml-kit #cross-platform

Funding

Consider supporting this project:

github.com

License

unknown (license)

Dependencies

flutter, google_ml_kit, http, image, path, path_provider, permission_handler, tflite_flutter

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