tensorflow/tflite-support

What it solves

It simplifies the process of deploying machine learning models to mobile devices by providing tools for model metadata management, automatic API generation, and pre/post-processing utilities to ensure consistency between training and inference.

How it works

The toolkit consists of four main components:

• TFLite Support Library: A cross-platform library for mobile deployment.
• TFLite Model Metadata: Tools to add and extract human- and machine-readable information about a model's purpose and usage.
• TFLite Support Codegen Tool: An executable that automatically generates model wrappers and ready-to-use APIs based on the model's metadata.
• TFLite Support Task Library: A set of optimized, ready-to-use libraries for common ML tasks like classification and detection.

Who it’s for

Developers who need to deploy TFLite models to mobile platforms (Java, C++, and Swift) and want options ranging from quick onboarding via automatic code generation to fully customizable inference pipelines.

Highlights

• Cross-platform support: Works across Java, C++ (WIP), and Swift (WIP).
• Automatic API generation: Uses a codegen tool to create model interfaces based on metadata.
• Optimized task libraries: Provides high-performance interfaces for popular ML tasks.
• Consistent processing: Includes utility methods for pre/post-processing that match TensorFlow modules like TF.Image and TF.text.