No project description provided
Project description
A brief guide to Acuitylite
Acuitylite is an end-to-end neural-network deployment tool for embedded systems.
Acuitylite support converting caffe/darknet/onnx/tensorflow/tflite models to TIM-VX/TFLite cases.
In addition, Acuitylite support asymmetric uint8 and symmetric int8 quantization.
System Requirement
- OS: Ubuntu Linux 20.04 LTS 64-bit (recommend)
- Python Version: python3.8 (needed)
Install
pip install acuitylite
Document
Reference: https://verisilicon.github.io/acuitylite
Framework Support
Tips: You can export a TFLite app and using tflite-vx-delegate to run on TIM-VX if the exported TIM-VX app does not meet your requirements.
How to run TIM-VX case
The exported TIM-VX case supports both make and cmake.
Please set environment for build and run case:
- TIM_VX_DIR=/path/to/tim-vx/build/install
- VIVANTE_SDK_DIR=/path/to/tim-vx/prebuilt-sdk/x86_64_linux
- LD_LIBRARY_PATH=$TIM_VX_DIR/lib:$VIVANTE_SDK_DIR/lib
Attention: The TIM_VX_DIR path should include lib and header files of TIM-VX. You can refer TIM-VX to build TIM-VX.
Support
Create issue on github or email to ML_Support@verisilicon.com
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distribution
Hashes for acuitypro-6.18.0-py3-none-manylinux2010_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 45d693899b145da8b3e41e4ffd81a51274b0107dc16f9e7791faa0ee5357770f |
|
MD5 | 27bbc39d2314fc0577e9fcf770df45e2 |
|
BLAKE2b-256 | bf3760ed49710883467504a61e1bab2db138c8e53c83b7c461e7473ab29b0b33 |