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This paper proposes a novel method for high-quality image segmentation of both objects and scenes.
Inspired by the dilation and erosion operations in morphological image processing techniques, the pixel-level image segmentation problems are treated as squeezing object boundaries.
# Or, to install it from a local clone:
git clone https://github.com/lxtGH/BSSeg
cd BSSeg
pip install -r requirements.txt
python setup.py build develop
# Preprare data path
ln -s /path/to/your/coco/dataset datasets/coco
# Enter a specific experiment dir cd playground/detection/coco/bs_mask/boundary_refine_mask_rcnn_r50_ms_1x_3_subgt_warpping_dice_erode_dilate_gn
# Train
pods_train --num-gpus 8
# Test
pods_test --num-gpus 8 \
MODEL.WEIGHTS /path/to/your/save_dir/ckpt.pth # optional
OUTPUT_DIR /path/to/your/save_dir # optional# Multi node training## sudo apt install net-tools ifconfig
pods_train --num-gpus 8 --num-machines N --machine-rank 0/1/.../N-1 --dist-url "tcp://MASTER_IP:port"
If you find this codebase is useful to your research, plese consider cite the paper and original codebase.
@misc{he2021boundarysqueeze,
title={BoundarySqueeze: Image Segmentation as Boundary Squeezing},
author={Hao He and Xiangtai Li and Guangliang Cheng and Yunhai Tong and Lubin Weng},
year={2021},
eprint={2105.11668},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
@misc{zhu2020cvpods,
title={cvpods: All-in-one Toolbox for Computer Vision Research},
author={Zhu*, Benjin and Wang*, Feng and Wang, Jianfeng and Yang, Siwei and Chen, Jianhu and Li, Zeming},
year={2020}
}
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BoundarySqueeze: Image Segmentation as Boundary Squeezing