Yufei ccac9cf800 | 2 years ago | |
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README.md | 2 years ago | |
gfl_r50_fpn_1x_coco.py | 2 years ago | |
gfl_r50_fpn_mstrain_2x_coco.py | 2 years ago | |
gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py | 2 years ago | |
gfl_r101_fpn_mstrain_2x_coco.py | 2 years ago | |
gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py | 2 years ago | |
gfl_x101_32x4d_fpn_mstrain_2x_coco.py | 2 years ago | |
metafile.yml | 2 years ago |
We provide config files to reproduce the object detection results in the paper Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection
@article{li2020generalized,
title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection},
author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
journal={arXiv preprint arXiv:2006.04388},
year={2020}
}
Backbone | Style | Lr schd | Multi-scale Training | Inf time (fps) | box AP | Config | Download |
---|---|---|---|---|---|---|---|
R-50 | pytorch | 1x | No | 19.5 | 40.2 | config | model | log |
R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | config | model | log |
R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | config | model | log |
R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | config | model | log |
X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | config | model | log |
X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | config | model | log |
[1] 1x and 2x mean the model is trained for 90K and 180K iterations, respectively.
[2] All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..
[3] dcnv2
denotes deformable convolutional networks v2.
[4] FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.
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