This is an official implementation of IEMask R-CNN in our IEEE Transactions on Big Data paper " IEMask R-CNN: Information-enhanced Mask R-CNN"
Our code is based on the Detectron2 and BMask R-CNN implementation.
python tools/train_net.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml --num-gpus 1
specify a config file and test with trained model
python train_net.py --config-file configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml --num-gpus 1 --eval-only MODEL.WEIGHTS ./output/model_final.pth
If you use IEMask R-CNN in your research, please cite our IEEE Transactions on Big Data paper.
@ARTICLE{9811396,
author={Bi, Xiuli and Hu, Jinwu and Xiao, Bin and Li, Weisheng and Gao, Xinbo},
journal={IEEE Transactions on Big Data},
title={IEMask R-CNN: Information-Enhanced Mask R-CNN},
year={2023},
volume={9},
number={2},
pages={688-700},
doi={10.1109/TBDATA.2022.3187413}}
The code (V1) is uploaded (Ongoing updates).