🔥 Good news! Our work has been accepted by IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2024.

This is a PyTorch/GPU re-implementation of the paper Recurrent Generic Contour-based Instance Segmentation with Progressive Learning

Any questions or discussions are welcomed!

Please see INSTALL.md.

1. Download the pretrained model from Google Drive or Baidu Cloud, and put it to $ROOT/data/model/rcnn_snake/cityscapes/.
2. Test:

   # use coco evaluator
   python run.py --type evaluate --cfg_file configs/city_snake.yaml
   # use the cityscapes official evaluator
   python run.py --type evaluate --cfg_file configs/city_snake.yaml test.dataset CityscapesVal
   

3. Speed:

   python run.py --type network --cfg_file configs/city_snake.yaml
   

1. Download the pretrained model from Google Drive or Baidu Cloud, and put it to $ROOT/data/model/snake/kins/.
2. Test:

   python run.py --type evaluate --cfg_file configs/kins_snake.yaml test.dataset KinsVal
   

3. Speed:

   python run.py --type network --cfg_file configs/kins_snake.yaml test.dataset KinsVal
   

1. Download the pretrained model from Google Drive or Baidu Cloud, and put it to $ROOT/data/model/snake/sbd/.
2. Test:

   python run.py --type evaluate --cfg_file configs/sbd_snake.yaml test.dataset SbdVal
   

3. Speed:

   python run.py --type network --cfg_file configs/sbd_snake.yaml test.dataset SbdVal
   

1. Download the pretrained model from Google Drive or Baidu Cloud, and put it to $ROOT/data/model/snake/coco/.
2. Test:

   python run.py --type evaluate --cfg_file configs/coco_snake.yaml test.dataset CocoVal
   

3. Speed:

   python run.py --type network --cfg_file configs/coco_snake.yaml test.dataset CocoVal
   

1. Download the pretrained model from Google Drive, and put it to $ROOT/data/model/snake/ade20k/.
2. Test:

   python run.py --type evaluate --cfg_file configs/ade20k.yaml test.dataset ade20kVal
   

3. Speed:

   python run.py --type network --cfg_file configs/ade20k.yaml test.dataset ade20kVal
   

We support demo for image and image folder using python run.py --type demo --cfg_file /path/to/yaml_file demo_path /path/to/image ct_score 0.3.

For example:

python run.py --type demo --cfg_file configs/sbd_snake.yaml demo_path demo_images ct_score 0.3
# or
python run.py --type demo --cfg_file configs/city_snake.yaml demo_path demo_images/munster_000048_000019_leftImg8bit.png ct_score 0.3

If setup correctly, the output will be saved at $ROOT/demo_out/ and look like

The training parameters can be found in project_structure.md.

python train_net.py --cfg_file configs/city_snake.yaml model rcnn_snake det_model rcnn_det

python train_net.py --cfg_file configs/kins_snake.yaml model kins_snake

python train_net.py --cfg_file configs/sbd_snake.yaml model sbd_snake

python train_net.py --cfg_file configs/coco_snake.yaml model coco_snake

python train_net.py --cfg_file configs/ade20k.yaml model ade20k_snake

If you find this code useful for your research, please use the following BibTeX entry.

@inproceedings{PolySnake2023,
  title={Recurrent Generic Contour-based Instance Segmentation with Progressive Learning},
  author={Feng, Hao and Zhou Keyi and Zhou, Wengang and Yin, Yufei and Deng, Jiajun and Sun, Qi and Li, Houqiang},
  booktitle={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2024}
}

Our work benefits a lot from DeepSnake and E2EC. Thanks for their wonderful works.