acuity

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

• 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