semvec/pypi-packages huggingface dataset:
bcuitylite 6.0.0
An end-to-end neural-network deployment tool for embedded systems

# A brief guide to Acuity Lite

Acuity Lite is an end-to-end neural-network deployment tool for embedded systems.<br/>
Acuity Lite support converting caffe model to Tim-vx case.
In addition, Acuity Lite support asysmetric uint8 qutization.

### System Requirement
Processor: Intel Core i5-6500 CPU @ 3.20GHz x 4 (need to support the avx instruction set)
GPU: Nvidia GTX-1060 or above preferred
Memory: 4GB RAM (at least)
OS:  Ubuntu Linux 16.04 LTS 64-bit (Known Issue with Ubuntu 16.10 which has a broken version of lxml, using of 16.10 or other versions is discouraged)
Other OS: Windows7 and MacOS are known to work but not supported

### Framework Support
- Importer Framework:
    [Caffe](https://github.com/BVLC/caffe), 
    [TFLite](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/lite)
- Exporter Framework:  [TIM-VX](https://github.com/VeriSilicon/TIM-VX)

### Document
    Reference: docs/lite/build/index.html

### How to run Tim-vx case
The exported Tim-vx case supports both make and cmake.<br/>
Please set environment for build and run case:<br/>
- TIM_VX_DIR=/path/to/tim-vx/build
- 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](https://github.com/VeriSilicon/TIM-VX) to build Tim-vx.

### Support
create issue on github or email to ML_Support@verisilicon.com