The native search is capable of searching and locating AEM assets and performing text search on various commonly used document formats such as plain-text files, Microsoft Office documents, and PDF documents. Java is a registered trademark of Oracle and/or its affiliates.Adobe Experience Manager provides a user interface to search and locate various assets stored in AEM. For details, see the Google Developers Site Policies. Unsupported on-device training, however it is on our Roadmap.Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. Microcontrollers and DSPs that contain only a few kilobytes of memory.Īll TensorFlow models cannot be converted into TensorFlow Lite TensorFlow Lite for Microcontrollers library for You can refer to the following guides based on your target device:Īndroid and iOS: Explore the Android quickstartĮmbedded Linux: Explore the Python quickstart for embedded Support for new hardware accelerators, you can Hexagon Delegate (on older devices) and on NNAPI Delegate (for newer devices) or the GPU Delegate, on android you can either use the On Android and iOS devices, you can improve performance using hardwareĪcceleration. Supported only on Java (Android) while Swift (iOS) and C++ is work in On android devices, users can automatically generate code wrappers using the Or build custom inference pipelines with the Models with metadata: You can either leverage the out-of-box APIs Platforms and languages such as Java, Swift, C++, Objective-C and Python. Inference refers to the process of executing a TensorFlow Lite model on-device Size and latency with minimal or no loss in accuracy. TensorFlow Lite Converter to convert a TensorFlow By default, all models containĬonvert a TensorFlow model into a TensorFlow Lite model: Use the TensorFlow Lite Examples to pick an existing model. Use an existing TensorFlow Lite model: Refer to You can generate a TensorFlow Lite model in the following ways: Generation of pre- and post-processing pipelines during on-device inference. Human-readable model description and machine-readable data for automatic Parsing/unpacking step) that enables TensorFlow Lite to execute efficiently onĭevices with limited compute and memory resources.Ī TensorFlow Lite model can optionally include metadata that has Over TensorFlow's protocol buffer model format such as reduced size (small codeįootprint) and faster inference (data is directly accessed without an extra Generate a TensorFlow Lite modelĪ TensorFlow Lite model is represented in a special efficient portable format Guide for an ideal balance of performance, model size, and accuracy. To further instructions: Note: Refer to the performance best practices The following guide walks through each step of the workflow and provides links Operators needed for supporting the common image classification models Key Point: The TensorFlow Lite binary is ~1MB when all 125+ supported operatorsĪre linked (for 32-bit ARM builds), and less than 300KB when using only the
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