| Architecture | Softmax, RPN, Balanced L1 Loss, Balanced Feature Pyramid, Convolution, FPN, RoIPool, IoU-Balanced Sampling, ResNet |
|---|---|
| lr sched | 1x |
| SHOW MORE |
| Architecture | Softmax, RPN, Balanced L1 Loss, Balanced Feature Pyramid, Convolution, FPN, RoIPool, IoU-Balanced Sampling, ResNet |
|---|---|
| lr sched | 1x |
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| Architecture | Softmax, RPN, ResNeXt, Balanced L1 Loss, Balanced Feature Pyramid, Convolution, FPN, RoIPool, IoU-Balanced Sampling |
|---|---|
| lr sched | 1x |
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| Architecture | Balanced L1 Loss, Balanced Feature Pyramid, FPN, IoU-Balanced Sampling, ResNet, Focal Loss |
|---|---|
| lr sched | 1x |
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[ALGORITHM]
We provide config files to reproduce the results in the CVPR 2019 paper Libra R-CNN.
@inproceedings{pang2019libra,
title={Libra R-CNN: Towards Balanced Learning for Object Detection},
author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
year={2019}
}
The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download |
|---|---|---|---|---|---|---|---|---|
| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | config | model | log |
| Fast R-CNN | R-50-FPN | pytorch | 1x | |||||
| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | config | model | log |
| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | config | model | log |
| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | config | model | log |
Object Detection on COCO minival
| MODEL | BOX AP |
|---|---|
| Faster R-CNN Libra R-CNN (X-101-64x4d-FPN, 1x, pytorch) | 42.7 |
| Faster R-CNN Libra R-CNN (R-101-FPN, 1x, pytorch) | 40.1 |
| Faster R-CNN Libra R-CNN (R-50-FPN, 1x, pytorch) | 38.3 |
| RetinaNet Libra R-CNN (R-50-FPN, 1x, pytorch) | 37.6 |
