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.

Attention: We have introduced some important changes and updated the APIs that are not compatible with the version before Acuitylite6.20.0(include). Please read the document and demos carefully.

System Requirement

OS:
Ubuntu Linux 20.04 LTS 64-bit(python3.8)
Ubuntu Linux 22.04 LTS 64-bit(python3.10)

Install

1. build the recommended docker image and run a container
2. pip install acuitylite --no-deps

Document

Reference: https://verisilicon.github.io/acuitylite

Framework Support

Importer: Caffe, Darknet, Onnx, Tensorflow, TFLite
Exporter: TFLite, TIM-VX

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