Cascade R-CNN: Delving into High Quality Object Detection

by Zhaowei Cai and Nuno Vasconcelos

This repository is written by Zhaowei Cai at UC San Diego, on the base of Detectron @ e8942c8.

Introduction

This repository re-implements Cascade R-CNN on the base of Detectron. Very consistent gains are available for all tested models, regardless of baseline strength.

Please follow Detectron on how to install and use Detectron-Cascade-RCNN.

It is also recommended to use our original implementation, cascade-rcnn based on Caffe, and the third-party implementation, mmdetection based on PyTorch and tensorpack based on TensorFlow.

Citation

If you use our code/model/data, please cite our paper:

@inproceedings{cai18cascadercnn,
  author = {Zhaowei Cai and Nuno Vasconcelos},
  Title = {Cascade R-CNN: Delving into High Quality Object Detection},
  booktitle = {CVPR},
  Year  = {2018}
}

or its extension:

@article{cai2019cascadercnn,
  author = {Zhaowei Cai and Nuno Vasconcelos},
  title = {Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
  journal = {arXiv preprint arXiv:1906.09756},
  year = {2019}
}

and Detectron:

@misc{Detectron2018,
  author =       {Ross Girshick and Ilija Radosavovic and Georgia Gkioxari and
                  Piotr Doll\'{a}r and Kaiming He},
  title =        {Detectron},
  howpublished = {\url{https://github.com/facebookresearch/detectron}},
  year =         {2018}
}

Benchmarking

End-to-End Faster & Mask R-CNN Baselines

Mask R-CNN with Bells & Whistles

Faster & Mask R-CNN with GN