Android Support Library
A toolkit for developing and deploying TensorFlow Lite models on mobile and IoT devices with cross-platform support.
What is Android Support Library?
TensorFlow Lite Support is a toolkit that helps developers deploy TensorFlow Lite machine learning models onto mobile and IoT devices. It provides libraries, tools, and utilities to simplify model integration, preprocessing, and inference, ensuring consistency across platforms. The toolkit addresses the challenges of deploying ML models in resource-constrained environments like smartphones and embedded devices.
Mobile and IoT developers working with TensorFlow Lite models, particularly those building applications for Android, iOS, or cross-platform environments who need efficient model deployment and inference pipelines.
Developers choose TFLite Support for its cross-platform compatibility, ready-to-use APIs for common ML tasks, and flexibility to customize inference pipelines. Its integration with TensorFlow modules ensures consistency from training to deployment, reducing development time and complexity.
Overview
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile / ioT devices.
Use Cases
Best For
- Deploying TensorFlow Lite models to Android applications
- Building cross-platform mobile apps with ML inference capabilities
- Automating model wrapper code generation for TFLite models
- Implementing common ML tasks like image classification or object detection on mobile devices
- Customizing pre- and post-processing pipelines for TFLite models
- Ensuring consistency between TensorFlow training and TFLite inference workflows
Not Ideal For
- Projects not using TensorFlow Lite models, as it's tightly coupled with the TFLite ecosystem and won't work with other frameworks like PyTorch Mobile.
- Server-side or desktop applications, since it's optimized for resource-constrained mobile and IoT devices with specific deployment challenges.
- Teams needing immediate, stable support for C++ or Swift, as the README notes these are work in progress and may lack full functionality.
- Developers seeking a no-build, plug-and-play solution, due to the reliance on Bazel and manual environment variable setup for Android builds.
Pros & Cons
Pros
The Codegen Tool automatically generates model wrapper code based on metadata, reducing manual effort and errors in creating inference interfaces.
Task Library provides ready-to-use, performance-optimized APIs for common ML tasks like classification and detection, with support for swapping in custom models.
Util Library matches TensorFlow modules like TF.Image and TF.text, ensuring preprocessing behavior is consistent from training to inference, reducing integration bugs.
Supports Java for Android and has work-in-progress C++ and Swift libraries, facilitating deployment across multiple mobile platforms.
Cons
Requires Bazel and specific environment variables like ANDROID_NDK_HOME, which can be a barrier for developers unfamiliar with these tools or seeking quick prototyping.
C++ and Swift support is labeled as work in progress, limiting immediate use for cross-platform apps requiring stable native implementations.
Heavily dependent on the TensorFlow ecosystem, making it less flexible for projects that might need to switch ML frameworks or avoid Google-specific tooling.
Frequently Asked Questions
Related Projects
Hexagon delegateAn Open Source Machine Learning Framework for Everyone
Model MakerA transfer learning library that simplifies the process of training, evaluation and deployment for TensorFlow Lite models (support: Image Classification, Object Detection, Text Classification, BERT Question Answer, Audio Classification, Recommendation etc.; API reference)
On-device trainingIt is finally here! Currently limited to transfer learning for image classification only but it's a great start. See the official Android sample code and another one from the community (Blog | Android)
Announcement of the new converterMLIR-based and enables conversion of new classes of models such as Mask R-CNN and Mobile BERT etc., supports functional control flow and better error handling during conversion. Enabled by default in the nightly builds
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