machine-learning

On-device machine learning in Flutter works by integrating TensorFlow Lite plugins and Firebase ML Kit. The system loads model files from assets to execute inference directly on mobile hardware, supporting both offline and online workflows for image and text processing.

Adding image classification and object detection to Flutter is done by integrating TensorFlow Lite with Firebase ML Kit. This combination supports loading custom model files from assets to process images locally, handling real-time inference and result management across mobile platforms.

Firebase ML Kit supports text recognition and barcode scanning offline by deploying on-device models. Loading model files locally from app assets allows inference to function without an internet connection, handling results directly on the mobile platform.

Loading a custom TFLite model in Flutter requires adding TensorFlow Lite dependencies and placing model files in the assets/models directory. The provided classifier classes then load the model and execute inference, managing results for real-time or batch processing.

Flutter on-device ML supports cloud model updates through Firebase ML. While TensorFlow Lite handles local inference using asset files, Firebase ML Kit provides optional cloud-based options to download and update models dynamically.