Home

Awesome

OnnxSlim

<p align="center"> <a href="https://pypi.org/project/onnxslim"> <img src="https://badgen.net/pypi/v/onnxslim?color=blue" /> </a> <a href="https://pypi.org/project/onnxslim"> <img src="https://static.pepy.tech/badge/onnxslim/week" /> </a> <a href="https://pypi.org/project/onnxslim"> <img src="https://static.pepy.tech/badge/onnxslim/month" /> </a> <a href="https://pypi.org/project/onnxslim"> <img src="https://static.pepy.tech/badge/onnxslim" /> </a> <a href="https://github.com/inisis/onnxslim/actions/workflows/ci.yaml"> <img src="https://github.com/inisis/onnxslim/actions/workflows/ci.yml/badge.svg" /> </a> </p>

OnnxSlim can help you slim your onnx model, with less operators, but same accuracy, better inference speed.

Installation

Using Prebuilt

pip install onnxslim

Install From Source

pip install git+https://github.com/inisis/OnnxSlim@main

Install From Local

git clone https://github.com/inisis/OnnxSlim && cd OnnxSlim/
pip install .

How to use

onnxslim your_onnx_model slimmed_onnx_model
<div align=left><img src="https://raw.githubusercontent.com/inisis/onnxslim/main/images/onnxslim.gif"></div>

For more usage, see onnxslim -h or refer to our examples

References

Contact

Discord: https://discord.gg/nRw2Fd3VUS QQ Group: 873569894