TensorFlow Lite Task Library contains a set of powerful and easy-to-use task-specific libraries for app developers to create ML experiences with TFLite. It provides optimized out-of-box model interfaces for popular machine learning tasks, such as image classification, question and answer, etc. The model interfaces are specifically designed for each task to achieve the best performance and usability. Task Library works cross-platform and is supported on Java, C++, and Swift.
What to expect from the Task Library
Clean and well-defined APIs usable by non-ML-experts
Inference can be done within just 5 lines of code. Use the powerful and easy-to-use APIs in the Task library as building blocks to help you easily develop ML with TFLite on mobile devices.
Complex but common data processing
Supports common vision and natural language processing logic to convert between your data and the data format required by the model. Provides the same, shareable processing logic for training and inference.
High performance gain
Data processing would take no more than a few milliseconds, ensuring the fast inference experience using TensorFlow Lite.
Extensibility and customization
You can leverage all benefits the Task Library infrastructure provides and easily build your own Android/iOS inference APIs.
Below is the list of the supported task types. The list is expected to grow as we continue enabling more and more use cases.