daanelson/yolox

High performance and lightweight object detection models

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  • 14.8K runs

Run time and cost

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 7 seconds. The predict time for this model varies significantly based on the inputs.

Readme

High performance and lightweight object detection models

About the demo

The available weights are downloaded from the link at the Github repository of this project

Introduction

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our report on Arxiv.

Cite YOLOX

If you use YOLOX in your research, please cite our work by using the following BibTeX entry:

 @article{yolox2021,
  title={YOLOX: Exceeding YOLO Series in 2021},
  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2107.08430},
  year={2021}
}

In memory of Dr. Jian Sun

Without the guidance of Dr. Sun Jian, YOLOX would not have been released and open sourced to the community. The passing away of Dr. Sun Jian is a great loss to the Computer Vision field. We have added this section here to express our remembrance and condolences to our captain Dr. Sun. It is hoped that every AI practitioner in the world will stick to the concept of “continuous innovation to expand cognitive boundaries, and extraordinary technology to achieve product value” and move forward all the way.

没有孙剑博士的指导,YOLOX也不会问世并开源给社区使用。 孙剑博士的离去是CV领域的一大损失,我们在此特别添加了这个部分来表达对我们的“船长”孙老师的纪念和哀思。 希望世界上的每个AI从业者秉持着“持续创新拓展认知边界,非凡科技成就产品价值”的观念,一路向前。