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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}
}

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

        backbone         type lr
schd
im/
gpu
box
AP
box
AP50
box
AP75
mask
AP
mask
AP50
mask
AP75
download
links
R-50-FPN-baseline Faster 1x 2 36.7 58.4 39.6 - - - model | boxes
R-50-FPN-cascade Faster 1x 2 40.9 59.0 44.6 - - - model | boxes
R-101-FPN-baseline Faster 1x 2 39.4 61.2 43.4 - - - model | boxes
R-101-FPN-cascade Faster 1x 2 42.8 61.4 46.1 - - - model | boxes
X-101-64x4d-FPN-baseline Faster 1x 1 41.5 63.8 44.9 - - - model | boxes
X-101-64x4d-FPN-cascade Faster 1x 1 45.4 64.0 49.8 - - - model | boxes
X-101-32x8d-FPN-baseline Faster 1x 1 41.3 63.7 44.7 - - - model | boxes
X-101-32x8d-FPN-cascade Faster 1x 1 44.7 63.7 48.8 - - - model | boxes
R-50-FPN-baseline Mask 1x 2 37.7 59.2 40.9 33.9 55.8 35.8 model | boxes | masks
R-50-FPN-cascade Mask 1x 2 41.3 59.6 44.9 35.4 56.2 37.8 model | boxes | masks
R-101-FPN-baseline Mask 1x 2 40.0 61.8 43.7 35.9 58.3 38.0 model | boxes | masks
R-101-FPN-cascade Mask 1x 2 43.3 61.7 47.2 37.1 58.6 39.8 model | boxes | masks
X-101-64x4d-FPN-baseline Mask 1x 1 42.4 64.3 46.4 37.5 60.6 39.9 model | boxes | masks
X-101-64x4d-FPN-cascade Mask 1x 1 45.9 64.4 50.2 38.8 61.3 41.6 model | boxes | masks
X-101-32x8d-FPN-baseline Mask 1x 1 42.1 64.1 45.9 37.3 60.3 39.5 model | boxes | masks
X-101-32x8d-FPN-cascade Mask 1x 1 45.8 64.1 50.3 38.6 60.6 41.5 model | boxes | masks

Mask R-CNN with Bells & Whistles

        backbone         type lr
schd
im/
gpu
box
AP
box
AP50
box
AP75
mask
AP
mask
AP50
mask
AP75
download
links
X-152-32x8d-FPN-IN5k-baseline Mask s1x 1 48.1 68.3 52.9 41.5 65.1 44.7 model | boxes | masks
[above without test-time aug.] 45.2 66.9 49.7 39.7 63.5 42.4
X-152-32x8d-FPN-IN5k-cascade Mask s1x 1 50.2 68.2 55.0 42.3 65.4 45.8 model | boxes | masks
[above without test-time aug.] 48.1 66.7 52.6 40.7 63.7 43.8

Faster & Mask R-CNN with GN

        backbone         type lr
schd
im/
gpu
box
AP
box
AP50
box
AP75
mask
AP
mask
AP50
mask
AP75
download
links
R-50-FPN-GN-baseline Faster 1x 2 38.4 59.9 41.7 - - - model | boxes
R-50-FPN-GN-cascade Faster 1x 2 42.2 60.6 45.8 - - - model | boxes
R-101-FPN-GN-baseline Faster 1x 2 39.9 61.3 43.3 - - - model | boxes
R-101-FPN-GN-cascade Faster 1x 1 43.8 62.2 47.6 - - - model | boxes
R-50-FPN-GN-baseline Mask 1x 2 39.2 60.5 42.9 34.9 57.1 36.9 model | boxes
R-50-FPN-GN-cascade Mask 1x 1 42.9 60.7 46.6 36.6 57.7 39.2 model | boxes | masks
R-101-FPN-GN-baseline Mask 1x 2 41.1 62.1 45.1 36.3 58.9 38.5 model | boxes | masks
R-101-FPN-GN-cascade Mask 1x 1 44.8 62.8 48.8 38.0 59.8 40.8 model | boxes | masks