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Region Proposal by Guided Anchoring

Introduction

We provide config files to reproduce the results in the CVPR 2019 paper for Region Proposal by Guided Anchoring.

@inproceedings{wang2019region,
    title={Region Proposal by Guided Anchoring},
    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
    year={2019}
}

Results and Models

The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).

Method Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) AR 1000 Download
GA-RPN R-50-FPN caffe 1x 5.0 0.55 13.3 68.5 model
GA-RPN R-101-FPN caffe 1x 7.1 0.66 9.8 69.6 model
GA-RPN X-101-32x4d-FPN pytorch 1x 8.5 0.88 8.5 70.0 model
GA-RPN X-101-64x4d-FPN pytorch 1x 11.4 1.24 6.5 70.5 model
Method Backbone Style Lr schd Mem (GB) Train time (s/iter) Inf time (fps) box AP Download
GA-Fast RCNN R-50-FPN caffe 1x 3.3 0.23 14.9 39.5 model
GA-Faster RCNN R-50-FPN caffe 1x 5.1 0.64 9.6 39.9 model
GA-Faster RCNN R-101-FPN caffe 1x 7.3 0.75 8.0 41.5 model
GA-Faster RCNN X-101-32x4d-FPN pytorch 1x 8.7 0.97 7.1 42.9 model
GA-Faster RCNN X-101-64x4d-FPN pytorch 1x 11.6 1.33 5.7 43.9 model
GA-RetinaNet R-50-FPN caffe 1x 3.2 0.50 10.7 37.0 model
GA-RetinaNet R-101-FPN caffe 1x 5.3 0.63 8.5 38.9 model
GA-RetinaNet X-101-32x4d-FPN pytorch 1x 6.7 0.87 7.5 40.3 model
GA-RetinaNet X-101-64x4d-FPN pytorch 1x 9.6 1.22 5.8 40.8 model
  • In the Guided Anchoring paper, score_thr is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.