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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) Inf time (fps) AR 1000 Config Download
GA-RPN R-50-FPN caffe 1x 5.3 15.8 68.4 config model | log
GA-RPN R-101-FPN caffe 1x 7.3 13.0 69.5 config model | log
GA-RPN X-101-32x4d-FPN pytorch 1x 8.5 10.0 70.6 config model | log
GA-RPN X-101-64x4d-FPN pytorch 1x 7.1 7.5 71.2 config model | log
Method Backbone Style Lr schd Mem (GB) Inf time (fps) box AP Config Download
GA-Faster RCNN R-50-FPN caffe 1x 5.5 39.6 config model | log
GA-Faster RCNN R-101-FPN caffe 1x 7.5 41.5 config model | log
GA-Faster RCNN X-101-32x4d-FPN pytorch 1x 8.7 9.7 43.0 config model | log
GA-Faster RCNN X-101-64x4d-FPN pytorch 1x 11.8 7.3 43.9 config model | log
GA-RetinaNet R-50-FPN caffe 1x 3.5 16.8 36.9 config model | log
GA-RetinaNet R-101-FPN caffe 1x 5.5 12.9 39.0 config model | log
GA-RetinaNet X-101-32x4d-FPN pytorch 1x 6.9 10.6 40.5 config model | log
GA-RetinaNet X-101-64x4d-FPN pytorch 1x 9.9 7.7 41.3 config model | log
  • 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.

  • Performance on COCO test-dev benchmark are shown as follows.

Method Backbone Style Lr schd Aug Train Score thr AP AP_50 AP_75 AP_small AP_medium AP_large Download
GA-Faster RCNN R-101-FPN caffe 1x F 0.05
GA-Faster RCNN R-101-FPN caffe 1x F 0.001
GA-RetinaNet R-101-FPN caffe 1x F 0.05
GA-RetinaNet R-101-FPN caffe 2x T 0.05