SOTR: Segmenting Objects with Transformers [ICCV 2021]
By Ruohao Guo, Dantong Niu, Liao Qu, Zhenbo Li
Introduction
This is the official implementation of SOTR.
Models
COCO Instance Segmentation Baselines with SOTR
Name | mask AP | APS | APM | APL | download |
---|---|---|---|---|---|
SOTR_R101 | 40.2 | 10.2 | 59.0 | 73.1 | model |
SOTR_R101_DCN | 42.0 | 11.4 | 60.7 | 74.5 | model |
Installation & Quick start
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First install Detectron2 following the official guide: INSTALL.md.
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Then build SOTR with:
https://github.com/easton-cau/SOTR
cd SOTR
python setup.py build develop
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Then follow datasets/README.md to set up the datasets (e.g., MS-COCO).
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Evaluating
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Download the trained models for COCO.
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Run the following command
python tools/train_net.py \ --config-file configs/SOTR/R101.yaml \ --eval-only \ --num-gpus 4 \ MODEL.WEIGHTS work_dir/SOTR_R101/SOTR_R101.pth
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Training
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Run the following command
python tools/train_net.py \ --config-file configs/SOTR/R101.yaml \ --num-gpus 4 \
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Acknowledgement
Thanks Detectron2 and AdelaiDet contribution to the community!
The work is supported by National Key R&D Program of China (2020YFD0900204) and Key-Area Research and Development Program of Guangdong Province China (2020B0202010009).
FAQ
If you want to improve the usability or any piece of advice, please feel free to contant directly ([email protected]).
Citation
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follow.
@misc{guo2021sotr,
title={SOTR: Segmenting Objects with Transformers},
author={Ruohao Guo and Dantong Niu and Liao Qu and Zhenbo Li},
year={2021},
eprint={2108.06747},
archivePrefix={arXiv},
primaryClass={cs.CV}
}