BoxSnake: Polygonal Instance Segmentation with Box Supervision

Rui Yang, Lin Song, Yixiao Ge, Xiu Li

BoxSnake is an end-to-end training technique to achieve effective polygonal instance segmentation using only box annotations. It consists of two loss functions: (1) a point-based unary loss that constrains the bounding box of predicted polygons to achieve coarse-grained segmentation; and (2) a distance-aware pairwise loss that encourages the predicted polygons to fit the object boundaries.

Arxiv Paper | [Video Demo]

Installation

To install Detectron2, torch 1.9.0+ for BoxSnake:

pip install -r requirements.txt

BoxSnake also uses the deformable attention modules introduced in Deformable-DETR and the differentiable rasterizer introduced in BoundaryFormer. Please build them on your system:

bash scripts/auto_build.sh

cd ./modeling/layers/deform_attn
sh ./make.sh
cd ./modeling/layers/diff_ras
python setup.py build install

Model Zoo

COCO

Arch	Backbone	lr sched	mask AP	mask AP	Download
RCNN	R50-FPN	1X	31.1	config	weights
RCNN	R50-FPN	2X	31.6	config	weights
RCNN	R101-FPN	1X	31.6	config	weights
RCNN	R101-FPN	2X	32.1	config	weights
RCNN	Swin-B-FPN	1X	38.3	config	weights
RCNN	Swin-L-FPN	1X	38.9	config	weights

mask AP is the result on validation set.

Cityscapes

Arch	Backbone	lr sched	mask AP	config	Download
RCNN	R50-FPN	24K iter	26.3	config	weights

Getting Start

We use the COCO dataset and Cityscapes dataset. Please following here to prepare them.

If you would like to use swin transformer backbone, please download swin weights from here and convert them to pkl format:

python tools/convert-pretrained-model-to-d2.py ${your_swin_pretrained.pth} ${yout_swin_pretrained.pkl}

Training

To train on COCO dataset using the R50 backbone at a 1X schedule:

# 8 gpus
python train_net.py --num-gpus 8 --config-file configs/COCO-InstanceSegmentation/BoxSnake_RCNN/boxsnake_rcnn_R_50_FPN_1x.yaml

You can also run below code:

bash scripts/auto_run.sh $CONFIG  # your config

Inference

To inference on COCO validation set using trained weights:

# 8 gpus
python train_net.py --num-gpus 8 --config-file configs/COCO-InstanceSegmentation/BoxSnake_RCNN/boxsnake_rcnn_R_50_FPN_1x.yaml
 --eval-only MODEL.WEIGHTS ${your/checkpoints/boxsnake_rcnn_R_50_FPN_coco_1x.pth}

Inference on a single image using trained weights:

python demo/demo.py --config-file configs/COCO-InstanceSegmentation/BoxSnake_RCNN/boxsnake_rcnn_R_50_FPN_1x.yaml --input demo/demo.jpg --output ${/your/visualized/dir} --confidence-threshold 0.5 --opts MODEL.WEIGHTS ${your/checkpoints/boxsnake_rcnn_R_50_FPN_coco_1x.pth}

Others

BoxSnake is inspired by traditional levelset (including boxlevelset) and GVF methods, and you can check below links to learn them:

Some geometric knowledge may help readers to understand the BoxSnake better:

Points in Polygon (PIP): https://wrfranklin.org/Research/Short_Notes/pnpoly.html#The%20Method
PIP: https://towardsdatascience.com/is-the-point-inside-the-polygon-574b86472119
PIP numpy: https://github.com/Dan-Patterson/numpy_geometry/blob/dbc1a00baaf86d8ae437236659a45cfb57f4f35e/arcpro_npg/npg/npg/npg_pip.py
Distance from a point to a line: http://paulbourke.net/geometry/pointlineplane
Distance from a point to a polygon: https://stackoverflow.com/questions/10983872/distance-from-a-point-to-a-polygon

Acknowledgement

If you find BoxSnake helpful, please cite:

@misc{BoxSnake,
      title={BoxSnake: Polygonal Instance Segmentation with Box Supervision}, 
      author={Rui Yang and Lin Song and Yixiao Ge and Xiu Li},
      year={2023},
      eprint={2303.11630},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Yangr116/BoxSnake