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# TensorFlow Lite Support
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite
models onto mobile devices. It works cross-Platform and is supported on Java,
C++ (WIP), and Swift (WIP). The TFLite Support project consists of the following
major components:
* **TFLite Support Library**: a cross-platform library that helps to
deploy TFLite models onto mobile devices.
* **TFLite Model Metadata**: (metadata populator and metadata extractor
library): includes both human and machine readable information about what a
model does and how to use the model.
* **TFLite Support Codegen Tool**: an executable that generates model wrapper
automatically based on the Support Library and the metadata.
* **TFLite Support Task Library**: a flexible and ready-to-use library for
common machine learning model types, such as classification and detection,
client can also build their own native/Android/iOS inference API on Task
Library infra.
TFLite Support library serves different tiers of deployment requirements from
easy onboarding to fully customizable. There are three major use cases that
TFLite Support targets at:
* **Provide ready-to-use APIs for users to interact with the model**. \
This is achieved by the TFLite Support Codegen tool, where users can get the
model interface (contains ready-to-use APIs) simply by passing the model to
the codegen tool. The automatic codegen strategy is designed based on the
TFLite metadata.
* **Provide optimized model interface for popular ML tasks**. \
The model interfaces provided by the TFLite Support Task Library are
specifically optimized compared to the codegen version in terms of both
usability and performance. Users can also swap their own custom models with
the default models in each task.
* **Provide the flexibility to customize model interface and build inference
pipelines**. \
The TFLite Support Util Library contains varieties of util methods and data
structures to perform pre/post processing and data conversion. It is also
designed to match the behavior of TensorFlow modules, such as TF.Image and
TF.text, ensuring consistency from training to inferencing.
See the
[documentation on tensorflow.org](https://www.tensorflow.org/lite/inference_with_metadata/overview)
for more instruction and examples.
## Build Instructions
We use Bazel to build the project. When you're building the Java (Android)
Utils, you need to set up following env variables correctly:
* `ANDROID_NDK_HOME`
* `ANDROID_SDK_HOME`
* `ANDROID_NDK_API_LEVEL`
* `ANDROID_SDK_API_LEVEL`
* `ANDROID_BUILD_TOOLS_VERSION`
## Contact us
Let us know what you think about TFLite Support by creating a
[new Github issue](https://github.com/tensorflow/tflite-support/issues/new), or
email us at tflite-support-team@google.com.
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