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Jiaqi Wang b225960587 | 3 years ago | |
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README.md | 3 years ago | |
ga_fast_r50_caffe_fpn_1x_coco.py | 3 years ago | |
ga_faster_r50_caffe_fpn_1x_coco.py | 4 years ago | |
ga_faster_r50_fpn_1x_coco.py | 4 years ago | |
ga_faster_r101_caffe_fpn_1x_coco.py | 3 years ago | |
ga_faster_x101_32x4d_fpn_1x_coco.py | 4 years ago | |
ga_faster_x101_64x4d_fpn_1x_coco.py | 4 years ago | |
ga_retinanet_r50_caffe_fpn_1x_coco.py | 4 years ago | |
ga_retinanet_r50_fpn_1x_coco.py | 4 years ago | |
ga_retinanet_r101_caffe_fpn_1x_coco.py | 3 years ago | |
ga_retinanet_r101_caffe_fpn_mstrain_2x.py | 3 years ago | |
ga_retinanet_x101_32x4d_fpn_1x_coco.py | 4 years ago | |
ga_retinanet_x101_64x4d_fpn_1x_coco.py | 4 years ago | |
ga_rpn_r50_caffe_fpn_1x_coco.py | 4 years ago | |
ga_rpn_r50_fpn_1x_coco.py | 4 years ago | |
ga_rpn_r101_caffe_fpn_1x_coco.py | 3 years ago | |
ga_rpn_x101_32x4d_fpn_1x_coco.py | 4 years ago | |
ga_rpn_x101_64x4d_fpn_1x_coco.py | 4 years ago |
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}
}
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 | Download |
---|---|---|---|---|---|---|---|
GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | model | log |
GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | model | log |
GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | model | log |
GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | model | log |
Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download |
---|---|---|---|---|---|---|---|
GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | 39.6 | model | log | |
GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | 41.5 | model | log | |
GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | model | log |
GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | model | log |
GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | model | log |
GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | model | log |
GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | model | log |
GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | 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 |
检测开源算法工具包
Python Markdown other
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