EN|简中
Generated from Tensorflow v2.3.1
A complete overview of Tensorflow Lite Micro.
Searching through the web, I found there are not that much information about it. So I create this project as a definitive place to go to research Tensorflow Lite Micro. This project is under active development. If you want to contribute please submit pull request or contact me directly at wniu(at)connect(dot)ust(dot)hk.
| OS/Platform | Makefile | Cmake | Qt | Visual Studio | Visual Studio code | Waf |
|---|---|---|---|---|---|---|
| Windows 10 | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ❌ |
| Linux(Ubuntu 18.04) | ✔️ | ✔️ | ✔️ | ❌ | ✔️ | ✔️ |
| Linux(Centos 7) | ✔️ | ✔️ | ✔️ | ❌ | ✔️ | ✔️ |
Build instruction for supported system can be found HERE.
- .vscode: Visual studio code project files
- doc: Documents
- LICENSE: license agreement
- Makefile: Make project file
- Makefile.plank: yet another Make project file
- Readme.md: Main document in English
- Readme_zh.md: Main document in Mandarin
- tensorflow: Tensorflow lite Micro source code
- core
- lite
- TFLM.pro: QT project file
- third_party:
- flatbuffer: flatbuffer header
- gemmlowp: gemmlowp header
- ruy
- VS2019:
- TFLM: Visual Studio Project files
- CMakeLists.txt: Cmake project file
- wscript: Waf project file
- Isolating TFLM source from Tensorflow v2.3.1
- Makefile
- Cmake
- QT
- Visual Studio
- Visual Studio Code
- WAF
- Documentation
- General Documentation on Tensorflow Lite Micro (WIP)
- UML Class diagram for Tensorflow Lite Flatbuffer model
- UML Class diagram for Tensorflow Lite Micro
- UML Sequence diagram for Tensorflow Lite Micro
- Provide helper script for file operation only depend on python
- Provide pre-trained models and trainning code
- MNIST Classification
- Visual Wake Word
- Cifar-10 Classification with resnet-10
- Anomaly detection with auto-encoder
- Keyword spotting with
-
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems Arxiv
-
TensorFlow Lite for Microcontrollers official documents
-
Tensorflow Github
-
Tensorflow Lite Micro Github
Im ninn55. A grads student from HKUST you can find me here.