thangvubk / Cascade Rpn
Licence: apache-2.0
Code for NeurIPS 2019 paper: "Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution"
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Cascade RPN
We provide the code for reproducing experiment results of Cascade RPN
- NeurIPS 2019 spotlight paper: pdf.
- This project is based on mmdetection framework. Main code for Cascade RPN can be traced in commit 6b9866.
- If this work is helpful for your research, please cite Cascade RPN and mmdetection
@inproceedings{vu2019cascade,
title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution},
author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D},
booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
year={2019}
}
@article{mmdetection,
title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark},
author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and
Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and
Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and
Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and
Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong
and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua},
journal = {arXiv preprint arXiv:1906.07155},
year = {2019}
}
Benchmark
Region proposal performance
| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 | Download |
|---|---|---|---|---|---|---|---|
| RPN | R-50-FPN | caffe | - | - | - | 58.3 | model |
| CRPN | R-50-FPN | caffe | - | - | - | 71.7 | model |
Detection performance
| Method | Proposal | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
|---|---|---|---|---|---|---|---|---|---|
| Fast R-CNN | RPN | R-50-FPN | caffe | 1x | 3.5 | 0.250 | 16.5 | 36.9 | - |
| Fast R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | 3.5 | 0.250 | 16.5 | 40.0 | model |
| Faster R-CNN | RPN | R-50-FPN | caffe | 1x | 3.8 | 0.353 | 13.6 | 37.0 | - |
| Faster R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | 4.6 | 0.561 | 11.1 | 40.5 | model |
| Cascade R-CNN | RPN | R-50-FPN | pytorch | 1x | 4.1 | 0.455 | 11.9 | 40.8 | - |
| Cascade R-CNN | Cascade RPN | R-50-FPN | pytorch | 1x | 5.2 | 0.650 | 9.6 | 41.6 | model |
Setup
Please follow official installation and getting_started guides.
Testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
Note:
- Config files of Cascade RPN and accompanied detectors are located in cascade_rpn
- Eval metrics are
proposal_fastandbboxfor region proposal and detection, respectively
Example of cascade rpn eval on 8 gpus:
./tools/dish_test.sh configs/cascade_rpn/cascade_rpn_r50_fpn_1x.py \
checkpoint/cascade_rpn_r50_fpn_1x_20191008-d3e01c91.pth 8 --out \
results/results.pkl --eval proposal_fast
Training
./tools/dist_train.sh ${CONFIG_FILE} ${GPU_NUM} [--validate] [other_optional_args]
Note: We train Cascade RPN and accompanied detectors with 8 GPUs and 2 img/GPU. If your configuration is different, please follow the Linear Scaling Rule.
TODO
- [x] Release Cascade RPN code base
- [x] Release baseline models
- [ ] Release more models
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