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