Good news! Snake algorithms exhibit state-of-the-art performances on COCO dataset: DANCE

Deep Snake for Real-Time Instance Segmentation
Sida Peng, Wen Jiang, Huaijin Pi, Xiuli Li, Hujun Bao, Xiaowei Zhou
CVPR 2020 oral

Any questions or discussions are welcomed!

Please see INSTALL.md.

1. Download the pretrained model here and put it to $ROOT/data/model/rcnn_snake/long_rcnn/197.pth.
2. Test:

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

3. Speed:

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

1. Download the pretrained model here and put it to $ROOT/data/model/snake/kins/149.pth.
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 here and put it to $ROOT/data/model/snake/sbd/149.pth.
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 here and put it to $ROOT/data/model/rcnn_snake/long_rcnn/197.pth.
2. Visualize:

   # Visualize Cityscapes test set
   python run.py --type visualize --cfg_file configs/city_rcnn_snake.yaml test.dataset CityscapesTest ct_score 0.3
   # Visualize Cityscapes val set
   python run.py --type visualize --cfg_file configs/city_rcnn_snake.yaml test.dataset CityscapesVal ct_score 0.3
   

If setup correctly, the output will look like

1. Download the pretrained model here and put it to $ROOT/data/model/snake/kins/149.pth.
2. Visualize:

   python run.py --type visualize --cfg_file configs/kins_snake.yaml test.dataset KinsVal ct_score 0.3
   

1. Download the pretrained model here and put it to $ROOT/data/model/snake/sbd/149.pth.
2. Visualize:

   python run.py --type visualize --cfg_file configs/sbd_snake.yaml test.dataset SbdVal ct_score 0.3
   

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/sbd_snake.yaml demo_path demo_images/2009_000871.jpg ct_score 0.3

If setup correctly, the output will look like

The training parameters can be found in project_structure.md.

Two-stage training:

1. Train the detector:

   python train_net.py --cfg_file configs/city_ct_rcnn.yaml model rcnn_det
   

2. Train the detector and snake together:

   python train_net.py --cfg_file configs/city_rcnn_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

We provide tensorboard for seeing the training status:

# for the rcnn_snake task
tensorboard --logdir data/record/rcnn_snake
# for the snake task
tensorboard --logdir data/record/snake

If setup correctly, the output will look like

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

@inproceedings{peng2020deep,
  title={Deep Snake for Real-Time Instance Segmentation},
  author={Peng, Sida and Jiang, Wen and Pi, Huaijin and Li, Xiuli and Bao, Hujun and Zhou, Xiaowei},
  booktitle={CVPR},
  year={2020}
}