🦖detrex: Benchmarking Detection Transformers

Introduction

detrex is an open-source toolbox that provides state-of-the-art Transformer-based detection algorithms. It is built on top of Detectron2 and its module design is partially borrowed from MMDetection and DETR. Many thanks for their nicely organized code. The main branch works with Pytorch 1.10+ or higher (we recommend Pytorch 1.12).

Fun Facts

The repo name detrex has several interpretations:

• detr-ex : We take our hats off to DETR and regard this repo as an extension of Transformer-based detection algorithms.

• det-rex : rex literally means 'king' in Latin. We hope this repo can help advance the state of the art on object detection by providing the best Transformer-based detection algorithms from the research community.

• de-t.rex : de means 'the' in Dutch. T.rex, also called Tyrannosaurus Rex, means 'king of the tyrant lizards' and connects to our research work 'DINO', which is short for Dinosaur.

What's New

v0.5.0 was released on 16/07/2023:

• Support Focus-DETR (ICCV'2023).
• Support SQR-DETR (CVPR'2023), credits to Fangyi Chen
• Support Align-DETR (ArXiv'2023), credits to Zhi Cai
• Support EVA-01 (CVPR'2023 Highlight) and EVA-02 (ArXiv'2023) backbones, please check DINO-EVA for more benchmarking results.

Please see changelog.md for details and release history.

Installation

Please refer to Installation Instructions for the details of installation.

Getting Started

Please refer to Getting Started with detrex for the basic usage of detrex. We also provides other tutorials for:

• Learn about the config system of detrex
• How to convert the pretrained weights from original detr repo into detrex format
• Visualize your training data and testing results on COCO dataset
• Analyze the model under detrex
• Download and initialize with the pretrained backbone weights
• Frequently asked questions
• A simple onnx convert tutorial provided by powermano
• Simple training techniques: Model-EMA, Mixed Precision Training, Activation Checkpoint
• Simple tutorial about custom dataset training

Although some of the tutorials are currently presented with relatively simple content, we will constantly improve our documentation to help users achieve a better user experience.

Documentation

Please see documentation for full API documentation and tutorials.

Model Zoo

Results and models are available in model zoo.

License

This project is released under the Apache 2.0 license.

Acknowledgement

• detrex is an open-source toolbox for Transformer-based detection algorithms created by researchers of IDEACVR. We appreciate all contributions to detrex!
• detrex is built based on Detectron2 and part of its module design is borrowed from MMDetection, DETR, and Deformable-DETR.

Citation

If you use this toolbox in your research or wish to refer to the baseline results published here, please use the following BibTeX entries:

• Citing detrex:

@misc{ren2023detrex,
      title={detrex: Benchmarking Detection Transformers}, 
      author={Tianhe Ren and Shilong Liu and Feng Li and Hao Zhang and Ailing Zeng and Jie Yang and Xingyu Liao and Ding Jia and Hongyang Li and He Cao and Jianan Wang and Zhaoyang Zeng and Xianbiao Qi and Yuhui Yuan and Jianwei Yang and Lei Zhang},
      year={2023},
      eprint={2306.07265},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}