This repository refactors and rewrites all the TensorFlow Lite examples included in the TensorFlow official website. For more details, please refer to:
https://www.tensorflow.org/lite/examples
The target of this repository is to provide you a much simpler way to use TensorFlow Lite on Android. It helps you easily understand how does a captured Bitmap, a small buffer of raw audio data, or a piece of text convert run in inference and how to represent those results on the user interface.
The first step of this repository is almost accomplished. I have cleaned up the source codes from the official examples and simply refactor them to remove duplicated and low-performance parts. The next step is to refactor the code deeply, extract more reusable parts, and create a support library.
Here is an overview of the progress of each standalone example:
| Examples | Status | Inference Performance |
|---|---|---|
| Image Classification | CLEANED UP | 25 ms per frame |
| Object Detection | CLEANED UP | 20 ms per frame |
| Pose Estimation | CLEANED UP | 75 ms per frame |
| Speech Recognition | CLEANED UP | 40 ms per 1.6K audio data |
| Gesture Recognition | NOT WORK WELL | 10 ms per 1.6K audio data |
| Smart Reply | CLEANED UP | 25 ms per sentence |
| Image Segmentation | CLEANED UP | 70 ms per frame |
| Style Transfer | CLEANED UP | 150 ms per frame |
| Digit Classifier | CLEANED UP | 6 ms per frame |
| Text Classification | CLEANED UP | 10 ms per frame |
| Q&A (BERT) | CLEANED UP | 280 ms per frame (Include pre-process time) |
The results above are tested on Oneplus 7 (Snapdragon 855 + 128G RAM) with TensorFlow Lite Nightly Build library.
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