longer-is-better / featurized-queryrcnn

Featurized Query R-CNN

Featurized Query R-CNN

Wenqiang Zhang*, Tianheng Cheng*, Xinggang Wang^†, Shaoyu Chen, Qian Zhang, Wenyu Liu
(*: equal contribution, †: corresponding author)

[arXiv]

News

• 14 June, 2022: Code and models of Featurized Query R-CNN have been released!

Introduction

The query mechanism introduced in the DETR method is changing the paradigm of object detection and recently there are many query-based methods have obtained strong object detection performance. However, the current query-based detection pipelines suffer from the following two issues. Firstly, multi-stage decoders are required to optimize the randomly initialized object queries, incurring a large computation burden. Secondly, the queries are fixed after training, leading to unsatisfying generalization capability. To remedy the above issues, we present featurized object queries predicted by a query generation network in the well-established Faster R-CNN framework and develop a Featurized Query R-CNN. Extensive experiments on the COCO dataset show that our Featurized Query R-CNN obtains the best speed-accuracy trade-off among all R-CNN detectors, including the recent state-of-the-art Sparse R-CNN detector.

Installation and Training

Our methods are based on detectron2, please refer to here for more details.

Install the detectron2:

git clone https://github.com/facebookresearch/detectron2.git

python setup.py build develop

For training, run:

python train_net.py --config-file <config-file> --num-gpus <num-gpus>

Main Results

Model	Backbone	Epoch	AP	FPS	Weights
Featurized QR-CNN (100 Queries)	ResNet-50	36	41.3	26	Google Drive
Cascade Featurized QR-CNN (100 Queries)	ResNet-50	36	43.0	24	Google Drive
Cascade Featurized QR-CNN (300 Queries)	ResNet-50	36	44.6	24	Google Drive
Cascade Featurized QR-CNN (100 Queries)	ResNet-101	36	43.9	18	Google Drive
Cascade Featurized QR-CNN (300 Queries)	ResNet-101	36	45.8	17	Google Drive

• The speed is tested on a single RTX 2080Ti GPU on COCO val set.
• If you have trouble accessing the models in Google Drive, we also provide the models in BaiduPan for you (Password: n91e).

Acknowledgements

Our implementation is based on detectron2 and Sparse R-CNN, we thank for their open-source code.

License

Featurized Query R-CNN is released under the MIT Licence.

Citation

If you find Featurized Query R-CNN is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.

@article{FeaturizedQR-CNN,
  title={Featurized Query R-CNN},
  author={Zhang, Wenqiang and Cheng, Tianheng and Wang, Xinggang and Chen, Shaoyu and Zhang, Qian and Liu, Wenyu},
  journal={arXiv preprint arXiv:2206.04584},
  year={2022}
}